Note: Where available, the PDF/Word icon below is provided to view the complete and fully formatted document
Australian Institute of Health and Welfare—Report—Australia’s health 2020 data insights


Download PDF Download PDF

Australia’s health 2020: data insights

Australian Institute of Health and Welfare

Australia’s health 2020: data insights presents an overview of health data in Australia and explores selected health topics—including—in 10 original articles.

Australia’s health 2020 is the 17th biennial health report of the Australian Institute of Health and Welfare. This edition has a new format and expanded product suite:

Australia’s health

data insights 2020

• Australia’s health 2020: data insights

• Australia’s health snapshots

• Australia’s health 2020: in brief.

Australia’s health

data insights 2020

The Australian Institute of Health and Welfare is a major national agency whose purpose is to create authoritative and accessible information and statistics that inform decisions and improve the health and wellbeing of all Australians.

© Australian Institute of Health and Welfare 2020

This product, excluding the AIHW logo, Commonwealth Coat of Arms and any material owned by a third party or protected by a trademark, has been released under a Creative Commons BY 3.0 (CC BY 3.0) licence. Excluded material owned by third parties may include, for example, design and layout, images obtained under licence from third parties and signatures. We have made all reasonable efforts to identify and label material owned by third parties.

You may distribute, remix and build upon this work. However, you must attribute the AIHW as the copyright holder of the work in compliance with our attribution policy available at www.aihw.gov.au/copyright/. The full terms and conditions of this licence are available at http://creativecommons.org/licenses/by/3.0/au/.

This publication is part of the Australian Institute of Health and Welfare’s Australia’s health series. A complete list of the Institute’s publications is available from the Institute’s website www.aihw.gov.au.

ISSN 2651-978X (Online) ISSN 1032-6138 (Print)

ISBN 978-1-76054-689-2 (Online) ISBN 978-1-76054-690-8 (Print)

DOI 10.25816/5f05371c539f3

Suggested citation

Australian Institute of Health and Welfare 2020. Australia’s health 2020 data insights. Australia’s health series no. 17. Cat. no. AUS 231. Canberra: AIHW.

Australian Institute of Health and Welfare

Board Chair Chief Executive Officer

Mrs Louise Markus Mr Barry Sandison

Any enquiries relating to copyright or comments on this publication should be directed to:

Australian Institute of Health and Welfare GPO Box 570 Canberra ACT 2601

Tel: (02) 6244 1000 Email: info@aihw.gov.au

Published by the Australian Institute of Health and Welfare.

Please note that there is the potential for minor revisions of data in this report.

Please check the online version at www.aihw.gov.au.

iii

1 Thynne Street, Bruce ACT 2617 www.aihw.gov.au

GPO Box 570, Canberra ACT 2601

+61 2 6244 1000

@aihw info@aihw.gov.au

The Hon Greg Hunt MP Minister for Health Parliament House Canberra ACT 2600

Dear Minister

On behalf of the Board of the Australian Institute of Health and Welfare, I am pleased to present to you Australia’s health 2020, as required under Subsection 31(1A) of the Australian Institute of Health and Welfare Act 1987.

This edition continues the AIHW tradition of delivering high quality evidence and value-added analysis on health in Australia, and it continues the multi-format report introduced in Australia’s welfare 2019. The report provides comprehensive coverage of topics in statistical snapshots (online) and explores new insights into topical issues, in narrative articles (print and online). This report discusses health data in Australia and includes an article on what is known about COVID-19 in Australia four months on from the first confirmed case. This report also explores how more timely data could better meet the needs of policy makers, service providers, researchers and the public.

I commend this report to you as a significant contribution to national information on health-related issues and to the development and evaluation of health systems and programs in Australia. The relevance of this report is heightened by the fact that we are in a time when it is acknowledged that availability of data and evidence is more important than ever.

Yours sincerely,

Mrs Louise Markus

Chair

AIHW Board

9 June 2020

iv

About Australia’s health 2020

About Australia’s health 2020

This edition of the AIHW’s biennial flagship report on health introduces a new format and an expanded product suite:

Australia’s health 2020: data insights This is a collection of topical, in-depth articles on selected health issues, including a picture of health data in Australia. It is available online and as a print report.

Australia’s health snapshots These are web pages that present key information and data on the health system, health of Australians and factors that can influence our health. The 71 snapshots are available online in HTML and as a PDF.

Australia’s health 2020: in brief This is a short, visual report summarising key findings and concepts from the snapshots to provide a holistic picture of health in Australia. It is available online and as a print report.

All products can be viewed or downloaded at www.aihw.gov.au/australias-health

v

Contents

Preface ................................................................................................................................vi

Introduction ..................................................................................................................... ix

List of Australia’s health snapshots .......................................................................xxii

Chapter 1: Health data in Australia .............................................................................. 1

Chapter 2: Four months in: what we know about the new coronavirus disease in Australia ................................................................................... 19

Chapter 3: Social determinants of health in Australia ............................................. 77

Chapter 4: Housing conditions and key challenges in Indigenous health .......... 107

Chapter 5: Potentially preventable hospitalisations—an opportunity for greater exploration of health inequity ................................................. 133

Chapter 6: Funding health care in Australia ............................................................ 159

Chapter 7: Changes in people’s health service use around the time of entering permanent residential aged care .......................................... 181

Chapter 8: Dementia data in Australia—understanding gaps and opportunities ........................................................................................... 209

Chapter 9: Improving suicide and intentional self-harm monitoring in Australia ............................................................................................... 231

Chapter 10: Longer lives, healthier lives? .................................................................. 257

Acknowledgements .................................................................................................... 272

Abbreviations ............................................................................................................... 272

Symbols .......................................................................................................................... 274

Glossary .......................................................................................................................... 275

vi

Preface

Australia’s health 2020 is the 17th biennial flagship report on health released by the Australian Institute of Health and Welfare (AIHW) since it was established in 1987.

The AIHW’s flagship reports, Australia’s health and Australia’s welfare, are highly regarded by policy makers, service providers, researchers and the public as sources of independent, authoritative and accessible information. They are compiled from multiple data sources and explore different perspectives on topical and ongoing issues. By exploring how we are faring as a nation, they also serve as ‘report cards’ on the health and welfare of Australians.

Australia’s health 2020 continues the trend of providing independent and trusted information to the wide range of Australians who use it. It reports on our health status and health system, and takes an in-depth look at a number of topical health issues— including the links between the environment and health, and the complex role that socioeconomic factors play in our health.

Global and national events over the past year have placed health at the forefront of our minds—for individuals, families, communities, and nations. In particular, the novel coronavirus (COVID-19) continues to pose a great potential threat to health and to Australia’s health system. Australian governments and the Australian community have responded well to this crisis and, as a result, it appears that Australia may have avoided some of the large adverse impacts that have been seen in some other countries. Nevertheless, COVID-19 has changed most aspects of our lives, including social, cultural and economic activities. More than any other event in recent history, this pandemic has illustrated how integral our health is to the effective functioning of society and of its support systems, including the health system.

The AIHW’s core purpose—to produce authoritative and accessible information and statistics—is now more relevant than ever. Every day, data on the number of new cases and on the number of deaths related to COVID-19 have been reported in the media, and governments have needed up-to-date, timely and reliable data on health-system capacity and on the potential indirect effects of COVID-19, such as on employment, mental health and family violence. The AIHW has helped meet this immediate need by seconding staff to assist the Department of Health with its response to the crisis and by helping compile timely data for governments on Australians’ use of a range of health services. (To read more on how the AIHW is assisting governments in responding to the COVID-19 crisis, see the AIHW website at https://www.aihw.gov.au/news-media/ news/2020-1/march/covid-19).

vii

Because of the availability of data at the time of writing, much of Australia’s health 2020 reflects ‘pre-COVID Australia’. However, to present what we do know about the disease in Australia, this report includes a special article on COVID-19 prepared by the AIHW in collaboration with Associate Professor Sanjaya Senanayake, an infectious disease specialist at the Australian National University. This article draws on data and information from the 4 months since Australia’s first reported case. In future publications, the AIHW will continue to incorporate information about the impact of COVID-19 on relevant health and welfare issues.

The broad scope of Australia’s response measures—and their swift implementation to suppress COVID-19—have required unprecedented cooperation and data-sharing between Australian, state and territory governments. The pandemic has also emphasised the need for the AIHW, the Australian Bureau of Statistics (ABS) and other government agencies to consider how well the Australian statistical system supports the planning and delivery of health services.

Many current developments and opportunities for improving our data evidence base are explored in Australia’s health 2020. Lessons learnt from the timely provision of data in a crisis can help improve the breadth, depth and timeliness of existing data collection and analysis. This is of particular importance when considering acute, time-sensitive issues such as mental health, suicide and intentional self-harm.

In recent years, the AIHW has also dedicated itself to improving the accessibility of its information and is continuing to move from large hard-copy publications towards more diverse and accessible formats. Australia’s health 2020 builds on the new multi-product format, first introduced in Australia’s welfare 2019.

The Australia’s health 2020 product suite comprises: online snapshots (statistical and contextual information); Australia’s health 2020: in brief report (key findings from the snapshots); and this report, Australia’s health 2020: data insights (a collection of articles on timely issues). In addition, updates to the Australian Health Performance Framework (AHPF) indicators provide the latest trends in health. This new format is consistent with global moves away from large print publications towards more diverse and accessible formats.

The new print publication—Australia’s health 2020: data insights—contains original articles on selected health issues and presents an overview of health data in Australia. The common theme across all the articles is the importance of data, and of building the evidence base for achieving long-term, sustainable improvements in heath and health care for all Australians. Australia’s health 2020: data insights presents information on how to fill data gaps and build the evidence for addressing these inequalities.

viii

Australia’s health includes 71 online snapshots, presenting statistical, easily digestible and interactive information on health status, determinants of health, health systems, health of population groups and the health of Aboriginal and Torres Strait Islander people. The statistical and contextual information presented in snapshots are from a number of sources—reflecting the many organisations involved in collecting and producing health data in Australia.

Australia’s health snapshots are accompanied by Australia’s health 2020: in brief, a print and online product that summarises the key concepts and findings from the snapshots. Australia’s health 2020: in brief is accessible and visually appealing, and is for all audiences to gain an understanding of the holistic picture of health in Australia.

The AIHW manages a number of national health information assets, and works with state and territory governments, the ABS, other independent bodies and the non-government sector, to ensure the data included in Australia’s health 2020 are comprehensive, accurate and informative.

The new format and expanded suite of products for Australia’s health 2020 showcases the AIHW’s commitment to its 5 strategic goals: to be leaders in health and welfare data; drivers of data improvements; expert sources of value-added analysis; champions of open and accessible data and information; and trusted strategic partners.

I would like to thank everyone involved in producing this report and to acknowledge the valuable advice provided by the many experts who reviewed draft material. We are committed to improving the usefulness and relevance of our flagship reports and we would welcome your feedback via flagships@aihw.gov.au.

Barry Sandison

CEO

ix

Introduction

Health is ‘a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity’ (WHO 1946).

Health influences, and is influenced by, how we feel and how we interact with the world around us. Health is broader than just the presence or absence of disease, it reflects the complex interactions of an individual’s genetics, lifestyle and environment. Generally, a person’s health depends on determinants (factors that influence health) and on interventions (actions taken to improve health, and the resources required for those interventions). These determinants can strengthen or undermine the health of individuals and communities.

Health outcomes and experiences of health are not the same for everyone and are often shaped by the distribution of wealth and resources at national and local levels (WHO 2020). Income, education, conditions of employment and social support (often known as ‘social determinants’ of health) are known contributors to health inequalities between population groups.

Compared with their more advantaged counterparts, some population groups within the community—such as Aboriginal and Torres Strait Islander people; people with disability; and people from rural and remote Australia—may experience poorer health and/or have difficulty accessing health care. These inequalities are a major focus for research and are important for monitoring population health risks and outcomes. (For example, 34% of the gap in health between Indigenous and non-Indigenous Australians is due to social determinants.)

Health systems play a crucial role in health and can help to reduce the burden that ill health places on the community. Australia’s health system is considered one of the best in the world, with many services funded and delivered by Australian, state and territory governments. Australia’s health system includes public and private hospitals; primary health care services (such as general practitioners and allied health services); and referred medical services (including many specialists).

In the past year, Australia has faced several major public health crises that have required large-scale government intervention—crises that have further highlighted how important health is to our quality of life and overall wellbeing.

x

The coronavirus disease (COVID-19) pandemic is a major health threat; it is highly infectious and has a higher death rate than many other infectious diseases. Since the World Health Organization (WHO) classified COVID-19 as a pandemic in March 2020, the Australian community has implemented many changes to reduce the spread of the disease. The health benefits of ‘social distancing’ measures are clear, and have resulted in a slowed spread of infection and reduced pressure on health services—but the long-term impacts of the pandemic are not yet known.

Australia has dealt with the potential threat of COVID-19 comparatively well and as a result, discussions are now focusing more on other critical aspects of overall health, including mental health. Isolation from family, friends and other support networks can negatively affect mental health and may also lead to a reduction in physical activity or to increased use of alcohol and other drugs (FARE 2020). Large-scale loss of employment, broad economic downturn and general uncertainty add to these stressors (Frasquilho et al. 2015). The social and economic impacts of COVID-19 may have a range of flow-on effects—for example, an increased incidence of family, domestic and sexual violence and a greater burden of mental health issues. In May 2020, to address some of these concerns, the Australian Government appointed the first Deputy Chief Medical Officer for Mental Health to focus of strengthening the coordinated medical and mental health response, including delivery of system reforms.

Natural disasters (such as bushfires) are known contributors to post-traumatic stress disorder, other mental health conditions and other longer-term health outcomes—adding to the immediate effects of death and trauma from the fire (Clemens et al. 2013). The unprecedented 2019-20 Australian bushfires, for example, saw intense smoke and air pollution hit areas of Australia. While the immediate threat to life has passed, the long-term impacts on our health are not yet fully known.

In the aftermath of the 2019-20 bushfires, and for the ongoing management of COVID-19, governments and policymakers need accurate, relevant and timely data to develop and implement evidence-based policies. The articles in Australia’s health 2020: data insights illustrate how health data are crucial to improving the health of Australians and ensuring that health systems respond effectively to current and changing needs.

As a health and welfare statistical agency, the AIHW recognises that health data are crucially important for improving health for individuals and populations, as well as for monitoring trends and planning for future health needs. To understand health needs at individual and population levels, we need to be able to measure health status and to collect health data; to understand people’s interactions with multiple parts of the system—and with multiple systems—we also need to be able to link relevant data.

Box 1 summarises how Australia was faring across a range of measures before the emergence of COVID-19. While the full impact of COVID-19 on the health of Australians will not be known for some time, it is expected that COVID-19 will affect many of the statistics in Box 1, particularly elective surgery wait times and emergency department presentations.

xi

Box 1: Measuring health performance: how are we faring?

In general, Australians enjoy good health and have an effective health system. How do we know this? We use the Australian Health Performance Framework (the Framework) to describe and assess the health of our population and health system and to compare Australia with other Organisation for Economic Co-operation and Development (OECD) countries.

The Framework includes an initial set of health indicators that describe specific aspects of our health and our health system’s performance. It also compares data for different population groups and different degrees of remoteness from essential services.

International comparisons

Comparing Australia with other OECD countries on a range of health measures, we find that:

• Australian males have the ninth highest life expectancy at birth, and females have the seventh highest

• Australia has lower rates of deaths due to coronary heart disease than the average for OECD countries

• the obesity rate in Australia remains higher than most other OECD member countries―Australia has the fifth highest rate of obesity among the OECD countries

• Australians consumed 9.4 litres of pure alcohol per year for each person aged 15 and over. This is higher than the OECD average of 8.9 litres per person.

International comparison for selected health indicators are summarised in ‘International comparisons of health data’ https://www.aihw.gov.au/reports/ australias-health/international-comparisons-of-health-data

Australian Health Performance Framework

Based on the health indicators in the Framework, Australians are improving on many aspects of their health:

• People are living longer. In 2015-2017, life expectancy at birth (for males and females combined) was 82.5 years. This is up from 81.9 years in 2009-2011.

• Infant and child mortality rates are down. In 2018, the infant (aged under 1) mortality rate was 3.1 deaths per 1,000 live births—down from 4.2 deaths per 1,000 in 2009—and the child (aged 0-4) mortality rate was 72.9 deaths per

100,000 population—down from 104.6 deaths per 100,000 in 2009.

• Decrease in potentially avoidable deaths. In 2015-2017, there were 104 potentially avoidable deaths per 100,000 population (age-standardised rate). (These were deaths among people aged under 75 that were potentially preventable through individualised care and/or treatable through existing primary or hospital care.) This was down from 116 deaths per 100,000 in 2009-2011.

continued:

xii

Box 1: (continued) Measuring health performance: how are we faring?

• Smoking rates are down. In 2017-18, 13.8% of people aged 18 and over were daily smokers. This was down from 18.9% in 2007-08, but has remained relatively stable since 2014-15, at around 14%.

• Fewer children are exposed to tobacco smoke in the home. In 2019, 2.1% of households with children aged 14 and under had someone who smoked inside the home. This is down from 19.7% in 2001 and 2.8% in 2016.

• Fewer adults are drinking alcohol at risky levels. In 2017-18, 16.1% of people aged 18 and over consumed (on average) more than 2 standard drinks per day—exceeding the lifetime risk guideline. This is down from 20.9% in 2007-08.

A few things warrant attention:

• More people are overweight and obese. In 2017-18, 66.4% of people aged 18 and over were overweight or obese (age-standardised rate). This is up from 61.1% in 2007-08.

• Elective surgery waiting times are increasing. In 2018-19, before COVID-19, 50% of patients waited at least 41 days for admission from elective surgery waiting lists. This is up from 33 days in 2008-09.

• Fewer emergency department presentations seen on time. In 2018-19, before COVID-19, 71% of emergency department presentations were ‘seen on time’. This is down from 75% in 2013-14.

Health indicators in the Framework also show notable patterns for:

• Immunisation. In 2018-19, 94.2% of children aged 1, 91.4% of children aged 2 and 94.8% of children aged 5 had received all the scheduled vaccinations appropriate for their age.

• Cancer survival. In 2012-2016, 5-year relative survival for all cancers combined was 69%. (This means that people diagnosed with cancer had a 69% chance of surviving for at least 5 years, compared with their counterparts in the general population.) This was an increase from a 5-year survival rate of 51% in 1987-1991.

• Heart attacks. In 2017, there were 324.9 acute coronary events in the form of a heart attack or unstable angina per 100,000 people aged 25 and over (age-standardised rate). This compares with 379.2 such events per 100,000 in 2013.

• Diabetes. In 2017-18, 4.8% of people aged 18 and over had diabetes (age-standardised rate). This is similar to the rate in 2014-15.

• Suicide. In 2018, there were 12.1 deaths by suicide per 100,000 population (age-standardised rate). This compares with 10.7 deaths per 100,000 in 2009.

See https://www.aihw.gov.au/reports-data/australias-health-performance/ australias-health-performance-framework to explore data for all indicators.

xiii

Current focus in Australia’s health: Australia’s health 2020: data insights examines issues related to health and health systems. It underscores both the importance of data, and of building the evidence base, in achieving long-term, sustainable improvements in health and health care for all Australians. The 10 chapters that follow present focused discussions, analyses and evidence on current issues in health data and evidence.

The report begins with Chapter 1 ‘Health data in Australia’, an overview of what the health data landscape currently looks like, and key issues and challenges faced. Robust and consistent health data practices—in terms of the availability, collection, collation and analysis of health data—are important for the planning and delivery of appropriate health care and for assessing the health system as a whole.

We have opportunities to improve the ways in which data are collected, accessed and analysed to inform how we respond to many existing or emerging challenges—including the health impacts of the recent bushfires across Australia; the emergence of COVID-19; and existing issues such as the rate of suicide in Australia. Increased use of data linkage methods may provide opportunities to better understand and address these challenges. The article also discusses data gaps and limitations; developments in health data; the AIHW’s work; and the future of health data.

During crises, there is a strong need to obtain data as quickly as possible to allow for an informed, immediate response to manage the situation. Timely data, and innovative uses of data, has been vital in informing Australia’s response to the challenges posed by COVID-19. Chapter 2 ‘Four months in: what we know about the new coronavirus disease in Australia’, is a point in time article that summarises what is known about the epidemic in Australia so far. While we remain in the middle of an evolving situation, with many facets of the epidemic not yet fully understood, it is apparent that Australia, at least so far, has been able to contain the epidemic when compared with many other countries. As at 7 June 2020, Australia had recorded 7,277 cases and 102 deaths. Analysis within the chapter reveals that, if Australia had experienced the same COVID-19 case and death rates as Canada, Sweden or the UK, it is estimated Australia would have had between 8 and 14 times the number of cases and around 5,000 to 14,000 extra deaths.

There are a number of potential indirect effects from changes within the health system and changes in wider society due to interventions put in place to manage the spread and impact of COVID-19. For example, the need for as many people to stay at home as possible to increase physical distancing meant that many people were isolated from family, friends and other support networks. The widespread interventions have a number of longer-term potential adverse health and welfare effects, although interventions can be put in place to reduce the risk of these. The large-scale loss of employment and the general economic downturn is a large challenge, and the longer-term effects will need to be monitored into the future.

xiv

Understanding the broad contextual factors that influence our health is important

because our health is not immune to social and environmental influences. Chapter 3

‘Social determinants of health in Australia’ looks at how health is affected by social

and economic conditions of everyday life, such as family circumstances, housing,

working conditions, livelihood and education. The connections between these social

factors and health outcomes are complex and occur over many years. There is now

a strong evidence base to help us understand the social determinants of health and

the relationships between social determinants and biological mechanisms. In many

cases, it is the social determinants that contribute to inequalities in health between

population groups.

There is no particular level of poverty that indicates poorer health (though absolute

poverty remains important). Instead, social factors affect population health across all

levels of society: the relationship between socioeconomic position and health follows

a ‘gradient’, with health status improving as socioeconomic circumstances rise. Social

determinants can increase or decrease a person’s risk of subsequent health outcomes:

not everyone from a low-income family will necessarily have poor health, but their risk

will be higher than others.

Further, those with multiple unfavourable social determinants over a lifetime will be

at even higher risk—and will be most vulnerable when another life challenge occurs.

Social influences on an individual’s health and wellbeing occur in combination and

cumulatively across their lives and the impacts from earlier in life are apparent over

many years, and potentially for generations. (Disadvantage in early childhood, for

example, can reduce social and health opportunities in the future, and this pattern

can continue and accumulate over an individual’s life.) Further, the length of time in

disadvantage increases the risk of ill health.

Despite substantial improvements in the health of Aboriginal and Torres Strait Islander

people over the past 30 years, there are disparities in health outcomes between

Indigenous Australians and non-Indigenous Australians. The reasons for these

disparities are complex, and include the lasting impact of colonisation and separation

from Country. It is also recognised that, for Indigenous Australians, social determinants

of health result in differences in risks, exposures, access to services and outcomes

throughout life.

xv

Chapter 4 ‘Housing conditions and key challenges in Indigenous health’ examines

social determinants that have a substantial impact on Indigenous health. Diseases

including chronic kidney disease, rheumatic heart disease, and certain eye and ear

diseases, disproportionately affect Indigenous Australians. Some of the common

factors underlying these health conditions are housing, living conditions and access to

services. For example, Indigenous Australians have among the highest recorded rates

of acute rheumatic fever and rheumatic heart disease in the world. These diseases are

preventable and treatable, and common in low- and middle-income countries—both

linked to overcrowding, socioeconomic deprivation, and inadequate access to health

hardware and health resources. Lack of access to health services also affects health

outcomes for Indigenous Australians, as access to primary health care services is critical

for timely management of acute and relatively minor illnesses such as infections.

Two critical factors connecting housing conditions to health are the state of domestic

health hardware (the physical equipment and infrastructure needed to support good

health) and the impact of overcrowding. Housing not only provides shelter and safety,

but also supports family, culture and cultural practices—while the lack of available and

adequate health hardware can lead to illness or injury. Compared with non-Indigenous

Australians, Indigenous Australians have less access to adequate, affordable or

secure housing and are more likely to live in overcrowded conditions or to experience

homelessness. Dwellings that are inadequate for the number of residents, including

long-term visitors, may result in premature failure of health hardware and lead to poor

health outcomes.

Meeting these health challenges requires a multi-sectoral approach that addresses the

basic needs of adequate housing and access to health services, including maintaining

and fixing health hardware. Data about housing adequacy; service-access issues; and

the incidence and prevalence of various health conditions; and evidence for what

achieves improvement, are key to reducing the disparities.

The issues of health inequity and preventive measures are further explored in

Chapter 5 ‘Potentially preventable hospitalisations—an opportunity for greater

exploration of health inequity’. The concept of ‘equity’ in health is that, ideally,

everyone should have a fair opportunity to attain their full health potential and that

disadvantage should not prevent them from achieving this potential. Understanding

health equity is a core component of the Australian Health Performance Framework,

and potentially preventable hospitalisation (PPH) statistics provide a useful measure

for examining this issue. In Australia, and other countries, PPH data are used to assess

timely, effective and appropriate primary health care.

xvi

PPH are grouped into 3 categories: vaccine-preventable conditions; acute conditions;

and chronic conditions. In 2017-18, 1 in every 15 (6.6%) hospitalisations were classified

as potentially preventable. New analyses quantifying the economic costs of PPH to

the hospital sector found that expenditure varied by PPH condition, patient age and

sex—but that, overall, PPH conditions cost the hospital sector $4.5 billion in 2015-16.

Three of the most common chronic PPH conditions—Congestive cardiac failure,

Chronic obstructive pulmonary disease (COPD) and Diabetes complications—had the

highest expenditure, with more than $1.5 billion spent on these hospitalisations in

2015-16.

Interpretation of PPH statistics over time is complex, because PPH is influenced by

many factors, including individual circumstances, patient characteristics and

health-system factors. However, PPH statistics remain a valuable tool for exploring

health disparities between different populations. For example, a case study on PPH

for diabetes complications explores who is most at risk, comparing trends in PPH for

specific groups within the Australian population—Indigenous Australians; people

living in remote and socioeconomically disadvantaged areas; the very young; and

older Australians.

The article discusses the importance of exploring and understanding patient-care

pathways that result in PPH. A number of studies using linked patient data

are underway across Australia to determine the true preventability of these

hospitalisations, and for example, to explore broader social factors influencing

PPH in Indigenous children. The future use of health data linkage presents

opportunities for a more nuanced understanding of patient-care pathways that

result in, and follow on from, PPH.

Data relating to health expenditure and funding are shaped both by health system

activity and by health funding mechanisms. Australia’s health system is a complex

mix of government and privately funded services delivered in a variety of settings.

Chapter 6 ‘Funding health care in Australia’ provides an overview of the funding

arrangements in place to fund Australia’s health system and provides some

comparisons with other OECD countries.

There have been changes to the funding of the Australian health system over time,

including to the relative contribution of different funders across different areas of

health expenditure. Across the OECD there are also differences in how countries

fund their health systems. The Australian health system is financed through a hybrid

model—a mix of both welfare state and market models—in which governments

provide universal public insurance for access to health services but individuals can

choose to pay for private health insurance in addition to their public insurance.

xvii

Managing the rising costs of health care is a challenge facing many OECD countries.

Over the past 2 decades health expenditure in Australia has grown faster than

either inflation or population growth. In the 20-year period to 2017-18, total health

expenditure in Australia increased from $77.5 billion to $185.4 billion in real terms,

and spending per person increased from $4,189 to $7,485. As a proportion of Gross

Domestic Product (GDP), health expenditure increased from 7.6% in 1997-98 to 10%

in 2017-18 (current prices). In the context of increasing health expenditure, different

approaches to health care financing are being explored, including value-based health

care models, capitation-based funding, and bundled and blended payments. Finding

alternative mechanisms is especially important for increasingly complex and long-term

health care needs.

Meeting complex and long-term health care needs is critical, especially with Australia’s

ageing population, and necessitates even greater interaction between the health and

welfare systems. An example of this intersection is found in the aged-care sector,

where an individual’s need for a higher level of care can be the result of various

factors including chronic and complex health issues or cognitive and/or physical

decline. As people’s abilities decline, everyday self-care activities become increasingly

difficult to manage and, for some older people, this can mean moving into residential

aged-care facilities.

Not only is this a transition in living situation, but it can also prompt a transition in

the use of selected health services and medications. Chapter 7 ‘Changes in people’s

health service use around the time of entering permanent residential care’ explores

people’s use of health services in the 6 months before and after entry into permanent

residential aged care. Using original analysis, it provides insights into the nature of

health-service use during this transition, which may help us to understand the context

for change. The analysis focused on 3 groups of people who first entered permanent

residential aged care in a selected 3-month period in 2014, 2015 or 2016. Using

linked administrative data from aged care, Medicare Benefits Schedule claims and

Pharmaceutical Benefits Scheme dispensing data, the analysis focuses on general

practitioner (GP) and specialist attendances and on prescriptions dispensed for

selected medicines.

Access to care and services is influenced by the interactions aged-care services have

with health care services; the availability of health care professionals in the local area;

the workforce available within residential aged care; and prescribing practices within

facilities. GPs play a central role in prescribing medicines for older people in residential

aged care and access to medicines can be relatively straightforward.

xviii

Medicines that act on the central nervous system have been of particular interest due

to their effects on older people, and many are prescribed at high rates in residential

aged care. Through the proceedings of the recent Royal Commission into Aged Care

Quality and Safety, there has been a focus on how people living in such facilities are

able to access health care services and how medicines are used within residential

aged care. The interim report of the Royal Commission highlighted workforce issues,

and potentially problematic use of certain medicines (particularly antipsychotics), and

recommended immediate action to reduce their use as a chemical restraint.

People living with dementia also experience the intersection of health services and

aged-care services. The Royal Commission found systemic issues in the aged-care

sector and has called for fundamental reforms to address failures in providing

appropriate care for older people—including the growing number with dementia.

Over half of those in residential aged-care facilities have dementia, and a large

proportion of people with dementia are living at home.

Dementia is a major health issue in Australia, causing substantial illness, high levels of

dependency, and death. The number of Australians living with dementia is estimated to

be between 400,000 and 459,000 in 2020, but the exact number is unknown. In 2018,

dementia was the second leading cause of death in Australia and the leading cause of

death for women. It has also been a leading contributor to the burden of disease and

injury, requiring $428 million in direct health expenditure in 2015-16.

Without a significant breakthrough in treatment, the number of people with dementia

is expected to double by 2050, placing a greater demand on Australia’s health and

aged-care systems. Chapter 8 ‘Dementia data in Australia—understanding the gaps

and opportunities’ examines current issues and gaps in Australia’s dementia data and

how this affects our understanding of—and response to—dementia in Australia. It also

looks at opportunities for data development to ensure Australia has sufficient data to

inform dementia policy and service planning.

Monitoring dementia—and its impact on individuals and their carers and on Australia’s

health and aged-care systems—is essential for the development of evidence-based

health, aged-care and social policy and for associated service planning. Gaps in

the data include a lack of dementia diagnosis in GP and other specialist care data;

inconsistent reporting of dementia across different datasets and over time; and

ad-hoc and limited data on groups of interest and across different health care types.

The progression of dementia is also complex and each person with dementia has

different needs and experiences.

xix

However, national monitoring of dementia has been irregular and inconsistent, limiting its ability to inform policy development and service planning. To address some of these gaps and data limitations, the Australian Government has committed to improve national dementia data assets and capabilities. The AIHW recently used linked administrative data to understand the health service use pathway of people with dementia in their last year of life, finding that people with dementia used fewer health services than people without dementia.

Another area with a national public focus—one for which timely data, monitoring, and evidence-based information are crucial—is suicide and intentional self-harm. Suicide prevention in Australia is a complex area of policy, with governments, policymakers and service providers all having a role in reducing suicides and cases of intentional self-harm. The reasons for suicide are also complex and are different for each individual, and the prevalence, characteristics and methods of suicidal behaviour vary between different communities, demographic groups and over time. Effective suicide prevention t hus requires a multi-sector approach, including health, education; employment; welfare and law-enforcement agencies; housing providers; and non-government organisations.

Australian governments have also agreed to take a national approach to mental health planning and service delivery, including improving the quality and timeliness of data collection on suicide, suicide attempts and intentional self-harm in Australia. A National Suicide Prevention Adviser was recently appointed and a National Suicide Prevention Taskforce has been established to coordinate activities between government agencies and across different levels of government.

Chapter 9 ‘Improving suicide and intentional self-harm monitoring in Australia’ provides an overview of the policy context for intentional self-harm and suicide monitoring and examines existing national sources of data currently used for this purpose. It discusses the limitations of these data sources, current data gaps and potential new sources of data that may strengthen the evidence base. There is also a particular focus on Indigenous Australians and on serving and ex-serving Australian Defence Force personnel.

Collection of data on suicide and intentional self-harm is essential to establish the extent of the problem; to highlight trends and emerging areas of concern; and to identify vulnerable populations. Data underpins the appropriate targeting of prevention strategies or research, and it is therefore important that monitoring of both suicide and intentional self-harm is as comprehensive and informative as possible. The National Suicide and Self-harm Monitoring System has been established to collate and coordinate data and information on suicide and intentional self-harm in Australia to improve their coherence, accessibility, quality and timeliness. This will better inform the development of suicide and intentional self-harm prevention policies and service planning. The AIHW will receive funding of $5 million per year for 3 years (2019-20 to 2021-22) to deliver the monitoring system.

xx

Picking up on some of the threads in the other articles—ageing population, health

status, inequality/health inequity—the report ends with a fundamental question about

our understanding of health and wellbeing, and how to measure them. Life expectancy

is often used as a key indicator of the health of a population and of overall progress in

health and wellbeing over time. In Australia, life expectancy has increased substantially

over many decades, and in 2018 it was 80.7 years for males and 84.9 years for females.

But what does our increasing life expectancy mean for individuals, and for the health

system, in a country like Australia? Our longer lives have implications, not only for the

quality of life of individuals, but also for health care planning, demand and need for

health and welfare services, as well as health-system costs. Does living a longer life

mean that people are also living healthier lives—or are we enduring poor health for

longer at the end of our lives? (In 2015, Australians aged 65 and over represented 15%

of the population—but experienced one-third (33%) of the burden of ill health.)

Chapter ‘10 Longer lives, healthier lives?’ looks at this important distinction—between

years lived in full or in ill health—in the years of life we have gained.

There is ongoing debate about whether there has been an increase in the amount of

ill health experienced by older Australians. Burden of disease analyses—particularly

health-adjusted life expectancy (HALE) which combines the health-related quality of life

and life expectancy into a single measure—assists health planning and the assessment

of health in Australia. To highlight trends in the health of Australia’s ageing population,

the article focuses on HALE at age 65, and also explores differences in HALE for

Australians from different socioeconomic areas.

At a national level for people aged 65—while life expectancy continues to increase—the

proportion of their lifetime spent in ill health has remained constant. However, this

does not apply to all population groups. There is a clear gradient: life expectancy and

years lived in full health increase as socioeconomic status increases.

The 10 articles in this report discuss issues covering health inequity; health indicators

and measures; data linkage; data gaps and limitations; interactions between different

parts of the health system and the welfare sector; and the external influences on our

health. Data can facilitate greater understanding about how differences in personal

circumstances and behaviours may lead to different health outcomes over time—and

can be used to provide an overview of the functioning of the health care sector and the

health of Australians.

What is evident from these articles is the vital importance of data in supporting better

understanding and planning for current and future health needs.

xxi

References Clemens SL, Berry HL, McDermott DM & Harper CM 2013. Summer of sorrow: measuring exposure to and impacts of trauma after Queensland’s natural disasters of 2010-2011. The Medical Journal of Australia 199(8):552-555. https://doi.org/10.5694/mja13.10307

FARE (Foundation for Alcohol Research and Education) 2020. An alcohol ad every 35 seconds: A snapshot of how the alcohol industry is using a global pandemic as a marketing opportunity. Viewed 25 May 2020, https://fare.org.au/wp-content/uploads/2020-05-08-CCWA-FARE-An-alcohol-ad-every-35-seconds-A-snapshot-final.pdf

Frasquilho D, Matos M, Salonna F, Guerreiro D, Storti C, Gaspar T et al. 2015. Mental health outcomes in times of economic recession: a systematic literature review. BMC Public Health 16:115. https://doi.org/10.1186/s12889-016-2720-y

WHO (World Health Organization) 1946. Preamble of the Constitution of the World Health Organization as adopted by the International Health Conference, New York, 19-22 June 1946. New York: WHO.

WHO 2020. About social determinants of health. Geneva: WHO. Viewed 25 May 2020, https://www.who.int/social_determinants/sdh_definition/en/

xxii

List of Australia’s health snapshots Australia’s health snapshots are web pages that present

key information and data on the health system, health of

Australians and factors that can influence our health.

The full list of snapshots is provided here and can be viewed

at www.aihw.gov.au/australias-health/snapshots.

Health status

Bone and joint health

Burden of disease

Cancer

Causes of death

Chronic conditions and multimorbidity

Chronic kidney disease

Chronic respiratory conditions

Coronary heart disease

Dementia

Diabetes

Health impacts of family,

domestic and sexual violence

Health of people experiencing

homelessness

How healthy are Australians?

Infectious and communicable diseases

Injury

International comparisons of

health data

Mental health

Physical health of people with

mental illness

Stroke

Suicide and intentional self-harm

What is health?

Determinants of health

Alcohol risk and harm

Biomedical risk factors

Built environment and health

Diet

Health literacy

Illicit drug use

Insufficient physical activity

Natural environment and health

Overweight and obesity

Social determinants of health

Stress and trauma

Tobacco smoking

Health system

Alcohol and other drug

treatment services

Allied health and dental services

Cancer screening and treatment

Clinical quality registries

Digital health

Health and medical research

Health expenditure

Health promotion

Health system overview

xxiii

Health workforce

Hospital care

Immunisation and vaccination

Medicines in the health system

Mental health services

Palliative care services

Patient experience of health care

Primary health care

Private health insurance

Safety and quality of health care

Specialist, pathology and other

diagnostic services

Workers’ compensation

Health of population groups

Health across socioeconomic groups

Health of children

Health of mothers and babies

Health of older people

Health of people with disability

Health of prisoners

Health of veterans

Health of young people

Rural and remote health

Indigenous health

Culturally safe health care for

Indigenous Australians

Health risk factors among Indigenous

Australians

Indigenous Australians’ use of

health services

Indigenous health and wellbeing

Indigenous hearing health

Indigenous life expectancy and deaths

Profile of Indigenous Australians

Social determinants and

Indigenous health

1 Australia’s health 2020: data insights

1

Health data in Australia

2 Australia’s health 2020: data insights

Chapter

1

‘Health’ is not simply the presence or absence of disease or injury but should

be considered as a state of wellbeing (WHO 1946). As the nation’s health and

welfare statistics agency, the AIHW knows that decisions that can improve the

health of Australians require good health data. Policymakers, service providers

and researchers—and the Australian community—also have high expectations

that data will be available to inform them.

Health can influence, and be influenced by, the world around us, as events over the past

year have shown. The novel coronavirus disease (COVID-19) pandemic—characterised

as a ‘human, economic and social crisis’ by the United Nations (UN 2020)—continues

to pose a very large potential threat to health and to the health system. Fortunately,

early responses to the pandemic in Australia have been positive and thus far Australia’s

health services have been able to manage the challenges posed by the virus well.

While the long-term health effects of COVID-19 are largely unknown at present,

health data—in particular, linked data—will be critical to understanding its impact

on health, society and the economy.

Data have been central to the COVID-19 response because governments have

needed immediate data to make swift, evidence-based decisions. They also have

a need for data to quantify the impact of COVID-19 on various other matters, for

example, employment, mental health, and family violence. Data from Australia and

overseas have featured heavily in media reporting of the pandemic, highlighting

the community’s appetite for current and accessible data and the important role of

data in coordinating measures to slow the spread of disease. The AIHW is among the

many government departments and agencies that provided practical assistance and

expertise to assist the government with its immediate data needs. For example, in

addition to seconding staff to the Department of Health to assist with responding to

the COVID-19 crisis, the AIHW compiled data on the use of hospital, mental health,

and homelessness services, as well as data from various crisis help lines

Beyond such public health crises, health data are crucial to the planning, delivery,

responsiveness and effectiveness of health care services and the health system as a

whole. This article provides an overview of health data in Australia and discusses data

gaps and limitations; recent and emerging developments in health data; and the future

of health data in Australia.

3 Australia’s health 2020: data insights

Chapter

1

The health data landscape The Australian health data landscape includes a range of information about the health

of Australians and the functioning of the health system, including:

• the determinants of health: the links between a person’s behaviours and

circumstances and their lifetime risks and health outcomes

• the health status of a person—their health conditions, functioning ability and

general wellbeing

• the health system, including information to support health-service provision, funding

and planning: the system’s effectiveness, efficiency and appropriateness; its safety

and accessibility; and the sustainability of health care

• the broader area of societal impacts (contextual information)—the changing

demographics of the Australian population, the advancements in research, economic

circumstances impacting workforce and infrastructure, and the expansion and

improvements in the collection of data (AIHW 2020b).

Measuring health status at a population level involves analysis of trends and patterns

in risk factors; disease frequency and impact; and health-service use. Data used in

population health monitoring in Australia include surveys, disease-specific registries

and disease-surveillance systems.

Australia has well established national health reporting systems, which enable

identification of emerging health issues. One of these national reporting networks,

the Communicable Diseases Network Australia (CDNA), delivers state and territory

notifiable diseases data into the National Notifiable Diseases Surveillance System

(NNDSS). The work of the CDNA has been essential to Australia’s national response to

the COVID-19 pandemic. See Chapter 2 ‘Four months in: what we know about the new

coronavirus disease in Australia’ for more information.

Population health monitoring is supported by a variety of data sources, including

clinical trials and other research; cross-sectoral data from, for example, mental

health, disability and aged care services; new consumer sources (such as banking and

supermarket data); and emerging data sets (genomic data, electronic health records

and enduring multi-source linked datasets).

4 Australia’s health 2020: data insights

Chapter

1

Health-services data are commonly used in population health monitoring, and

measures of population health may act as indicators of health-system efficacy.

Health system data provide information on the equity, efficiency and effectiveness of

a range of health services in delivery of health care in Australia. This includes data on

the health workforce; health services; safety and quality in health care settings; and

electronic health records. Health-services data are collected from episodes of service

use, such as hospital admissions, pharmaceutical dispensing and general practitioner

visits. These data are used in health system planning and administration, including in

activity-based hospital funding arrangements and Medicare Benefits Schedule (MBS)

claims for doctor visits.

Health data are also integral to support and prioritise effective health research.

The work of the National Health and Medical Research Council and the Medical

Research Future Fund (MRFF), in addition to countless research institutes and

universities across Australia is underpinned by the evidence base that arises from

these varied data sources. They depend on the proper management and curation of

data as well as the synthesis of health information to inform decision making about

projects and their interpretation of findings.

Using data to monitor health outcomes and services and inform responses Robust and accessible health data can inform decisions and policies, service

planning and resource allocation—which is particularly important in areas where

there are disparities in health status or outcomes, or in health-service access.

Data also inform responses during crises, as seen during the COVID-19 pandemic

and the 2019-20 Australian bushfires. Box 1 describes the role of data in responding

to, and understanding the health impacts of, natural disasters—such as the

widespread 2019-20 bushfires.

5 Australia’s health 2020: data insights

Chapter

1

Box 1: How we can better use data to understand the impact of natural disasters

Extreme weather events and natural disasters such as heat waves, drought,

bushfires and floods can affect health. Data can assist with monitoring health

impacts and planning responses—including action to minimise the effect

of future natural disasters, such as, targeting vulnerable populations with

precautionary measures or improving warning systems. For example, during the

2019-20 Australian bushfires, up to date low-level geographical data enabled

effective fire management through critical responses, such as text messaging,

to order residents to evacuate at-risk areas. Frequently updated air quality data

and forecasts also enabled individuals to manage their exposure to hazardous

environmental conditions.

The AIHW is currently using a variety of data sources—including bushfire burn-area

mapping and air-quality, pharmaceutical, Medicare and hospital emergency

department data—to assess the impact of the 2019-20 bushfires on health and

on the health system in some affected areas. See A burning issue: The short-term

health impacts of the 2019-20 Australian bushfires (AIHW forthcoming 2020).

While timely data are important, there are likely to be a range of long-term

health effects from the 2019—20 bushfires that will not be evident for some

time. Studies of firefighters after a fire season show reduced lung function can

return to baseline over a long follow-up period, however cumulative and repeat

effects are unknown (Black et al. 2017).

Long-term mental health can be affected by natural disasters. For example,

for those with any exposure to bushfires in the 2009 Victorian ‘Black Saturday’

bushfires, levels of post-traumatic stress disorder (PTSD) were markedly higher

than for the general population. The same longitudinal study showed that,

while the majority of people affected showed great resilience in the face of

the disaster experience and its aftermath, those who suffered bereavement

or severe property loss have later shown signs of impaired resilience and of

deteriorating mental health. These types of data are critical in focusing recovery

efforts (Bryant et al. 2018).

The MRFF is funding a large-scale research project to look at the medium-term

health impacts of smoke and ash exposure, including mental health, for frontline

responders and affected communities (Department of Health 2020).

6 Australia’s health 2020: data insights

Chapter

1

Measures which summarise information can be used to inform decision making and may

be structured and analysed using a framework. For example, Australia’s Health Ministers

have agreed to the Australian Health Performance Framework (AHPF) (NHIPPC 2017),

which includes domains for the determinants of health; health systems; health status;

and the health system context, with consideration of equity. More information can be

found at https://www.aihw.gov.au/reports-data/australias-health-performance.

Other national examples of frameworks include the National Aboriginal and Torres

Strait Islander Health Performance Framework and the National Strategic Framework

for Chronic Conditions. Typically, indicator frameworks:

• allow different population groups, regions and countries to be compared over time

and with each other

• provide information on the effect of changes to policies, practices and programs

• support accountability and transparency of service provision

• support service improvement activities.

Data gaps and limitations

There are parts of the health system, and aspects of health of Australians, where

information is not adequate for population and system monitoring or reporting purposes.

Data gaps can exist where data are not collected or recorded; where the data are

collected but are not in a suitable format for easy collation, processing or reporting; or

where the data are collected in isolated systems that are either not easily accessible

or not comparable. In addition to data gaps, analysis gaps exist where data may be

available but are not currently brought together efficiently.

Some notable gaps in Australian health data and analysis—relating to health status; patient

pathways and health service use; and health system activity and performance—are:

• incidence and prevalence data for some conditions, such as dementia

• data on the contribution of some health determinants

• links between public health interventions and health outcomes

• information on some population groups, including people with disability;

culturally and linguistically diverse populations; refugees; and lesbian, gay,

bisexual, transgender, queer and intersex populations

• data for smaller geographical areas to identify variations in health status and

care by location

7 Australia’s health 2020: data insights

Chapter

1

• environmental data for understanding links between the natural and built

environments and human health

• person-centred data including social and economic factors that affect health and

patient pathways through the health system, across jurisdictional boundaries and

between sectors

• measures of health system efficiency and cost-effectiveness

• national, comparable and reportable data on primary health care activity and outcomes

• indicators of health system safety and quality, including outcomes of interventions

and patient rated outcome and experience measures (all of which are available only

for a limited range of health services).

Developments in health data Australian health data are undergoing rapid change. Increasing digitisation of health

information means more data are being collected at a more detailed level, and this

expands the possibilities for analysing and reporting. There is increasing demand

for accurate and secure health information that is available in real time and at small

geographic levels for service planning and delivery; easily accessible, flexible and

interactive for a variety of uses and users; comparable at national and sub-national

levels; and which maintains privacy and confidentiality.

These requirements present challenges and opportunities for using health data to

improve the health of Australians.

Digital health

One of the most substantial drivers of change in health data is the rise of digital

technology in health care (ADHA 2017). Digital health is the use of technology by

individuals (through digital access to health services, wearable devices) and by

clinicians and administrators (through clinical information systems and patient

administration systems) to collect and share a person’s health information.

Digital health technology has the potential to:

• remove barriers to service access, for example through the use of telemedicine to

provide specialist care to remote or isolated communities

• improve continuity in patient care through the use of electronic health records

(such as My Health Record)

• enhance clinical decision making and system-wide responses with real-time access

to health information between services, sectors and jurisdictions.

8 Australia’s health 2020: data insights

Chapter

1

For individuals, digital health technology can enable people to understand and take

control of their own health and health information. For clinicians, this technology

can support improved interactions with patients, continuity of care and improved

effectiveness, efficiency and delivery of health services. Secondary use of data from

digital health information can also improve understanding of health-service use and

patient pathways through the health system. See ‘Digital health’ https://www.aihw.gov.

au/reports/australias-health/digital-health for further information.

The scope and use of digital health technologies are growing and changing rapidly in

Australia, enabling real-time information to be available to patients and health care

providers. However, the rate of this change is not consistent or coordinated across

the health system (ADHA 2017). There can also be a disconnect between information

collected in digital information systems and the need to have data available for

statistical reporting systems. The lack of systematic, standardised collection of data

from primary care data, relative to other areas of health, poses a particular challenge.

This also affects our ability to share information within and between systems

and sectors to inform good patient care. There is a need to ensure that enabling

infrastructure is put in place for the capture, analysis and reporting of these data,

including governance arrangements to maintain the privacy and security of individuals.

Secondary use of data from digital health information also allows understanding of

health service use and patient pathways through the health system.

Connecting different parts of the health system (for example primary care to

allied health and hospitals) through interoperable technology and complementary

governance arrangements, will be an important underpinning for an integrated health

system, for patient journey analysis and for supporting the continuity and quality of

patient-centred care.

Person-centred data

A significant proportion of data on the health system in Australia are organised around

individual services. While these data are useful for managing individual parts of the

system, they are not ideally placed to help us understand how people interact with

a range of services and they do not always provide useful information on health and

other outcomes. By linking data across the health system (while preserving privacy),

and with other data including data from surveys, it is possible to gain a much richer

understanding of how people interact with services and their health outcomes.

9 Australia’s health 2020: data insights

Chapter

1

The importance of ‘person-centred data’ has emerged due to a number of factors.

These include:

• the link between health and wellbeing

• developments in personalised medicine

• the importance of continuity and coordinated health care for positive outcomes

and the potential for value-based rather than activity-based management of health

service provision.

Following a cohort of individuals from diagnosis, through interactions with the

health system, to recovery, deterioration or death improves our ability to analyse

the development and trajectory of disease; the interaction of determinants and

interventions; and the role and performance of the health system in managing, treating

and preventing disease. This is achieved in 2 ways: through longitudinal analysis of a

single data set, or through the integration (linkage) of two or more datasets. The most

noteworthy developments in recent times are in relation to building and managing

large-scale data linkage.

Data linkage

Data linkage, also known as data integration, is a process that brings together

information from more than one source. Linked datasets can provide more detailed

information than could be gained from each individual dataset, by matching disparate

pieces of information together. This can fill gaps in our knowledge on specific diseases,

service use, specific population groups and across the health and welfare sectors.

An example is the use of different types of data to understand participation in

cervical cancer screening. Combining health services data (cervical screening program

participation, human papillomavirus (HPV) vaccination data) and population health

data (cancer incidence, deaths data) has shown the links the between HPV vaccination,

increased participation in cervical cancer screening, and decreased incidence of high

grade cervical abnormalities (AIHW 2019a).

In Australia, our health and welfare sectors and their associated evidence bases are

largely disconnected. Recent developments in person-centred data have included

cross-sector data linkage and analyses, such as a study on the interface of aged care

and health (see Chapter 7 ‘Changes in people’s health service use around the time of

entering permanent residential aged care’).

10 Australia’s health 2020: data insights

Chapter

1

Multi-source Enduring Linked Data Assets (MELDAs)—such as the National Integrated Health Services Information Analysis Asset (NIHSI AA or the Asset) developed by the AIHW—are a standout example of this development in health data. The Asset is deidentified and links multiple health data sources (including hospital admitted, non-admitted and emergency department care; residential aged care; mortality; the MBS and the Pharmaceutical Benefits Scheme) from multiple jurisdictions, as an enduring asset, for the first time. While the Asset is currently available for analysis by Australian Government and state and territory health authorities, the AIHW continues to work with stakeholders to support access for other potential users of data from the Asset. At least 27 analysis projects are currently approved to use this national health information resource. Examples of NIHSI projects include exploring patterns of service use in the last year of life, and the quality of care and outcomes following hospitalisation for hip fracture.

Another example of a MELDA is the Multi-Agency Data Integration Project (MADIP) developed by the Australian Bureau of Statistics (ABS). This project is a partnership among Australian Government agencies to develop a secure and enduring approach for combining information on topics including health care, education, government payments, personal income tax, and population demographics (including the Census of Population and Housing) to create a comprehensive picture of conditions in Australia over time. More than 260 government and academic users are drawing from 10 available MADIP datasets for a large number of research projects to help inform future government policies and services (ABS 2019a, 2019b).

Developments in the broader data context

These MELDAs are examples of the improved use of the significant data assets held by governments across Australia. The AIHW is a national Accredited Integrating Authority authorised to perform data linkage within and between Australian Government, state and territory data collections.

This work is part of a broader reform agenda for the use of data in Australia. The Office of the National Data Commissioner (ONDC) has recently been established and is currently developing a simpler data sharing framework for public sector data in Australia. The ONDC is responsible for progressing new legislation—to be known as the Data Availability and Transparency Act (DATA)—to support better sharing of government-held data. This proposed legislative framework will help overcome barriers which prevent efficient use and reuse of public sector data, while maintaining the strong security and privacy protections that the community expects (ONDC 2019).

The safekeeping of data assets and individual privacy, with data access and availability for a wide range of uses, is critical to ongoing development and innovation in the use of health data. This is particularly relevant in building and maintaining public trust in relation to the use of person-centred and digital data.

11 Australia’s health 2020: data insights

Chapter

1

On the horizon

Substantial gains have already been made to address some of the longstanding gaps in

health data and to overcome the disconnectedness of health information. Wellbeing,

digital transformation and genomics are areas in which health data are undergoing

change, and are providing challenges for data collection, analysis and reporting.

Wellbeing

‘Wellbeing’ is a term describing quality of life and living conditions. It combines the

more commonly reported domains of health and welfare (education and skills;

housing; employment; income and finance; social support; justice and safety), and

includes contextual factors (environment, community engagement) and subjective

measures (life satisfaction and work-life balance) (AIHW 2019b; OECD 2013).

In Australia and internationally, the concept of wellbeing as an indicator (or set of

indicators) is increasingly being used as a more holistic measure of, and benchmark

for, economic and social development. Examples are the Organisation for Economic

Co-operation and Development’s (OECD) Better Life Initiative (OECD 2017), the New

Zealand Government’s Wellbeing Budget (NZ Government 2019) and the Australian

Capital Territory Government’s planned ACT Wellbeing Framework (ACT Government 2019).

Digital transformation

Substantial developments in digital technologies for health are not being holistically

or consistently adopted and integrated into the Australian health system (ADHA 2017).

However, there are emerging developments in the systems for defining, classifying,

storing, transmitting and analysing health information that could, if adopted, lead to

true interoperability and integration between acute, primary and allied care systems

and their data.

For example:

• The Australian Digital Health Agency’s Framework for Action outlines priority activities

and opportunities, including the development of the National Health Interoperability

Framework, to overcome barriers to sharing clinical information between services

and systems (AHDA 2018).

• The digitally enabled health classification system, the International Classification of

Diseases 11th Revision (ICD-11) (WHO 2019), represents an opportunity to bridge

the digital divide between clinical systems and statistical systems for the acute and

primary care sectors, and to facilitate data availability for statistical reporting in

services such as ambulance and community health (AIHW 2020a).

12 Australia’s health 2020: data insights

Chapter

1

Genomics

Genomics is a data-rich field of research and a rapidly developing area with potential

for improving risk detection, diagnosis, treatment and patient outcomes. Australia has

agreed on a national approach to genomic policy, data collection, storage, analysis

and clinical application, laid out in the National Health Genomics Policy Framework

(Department of Health 2017) and its implementation plan (Department of Health 2018).

Incorporating genomic information—and the precision and personalised medicine it

facilitates—into population and health system data is an emerging and challenging

area for Australian health data.

AIHW and the future of health data The AIHW produces independent and authoritative health and welfare information and

statistics. Nationally and internationally, the AIHW works with other organisations to:

• maintain and enhance the health evidence base

• facilitate approved access to health data

• provide leadership, partnership and advice in relation to improving data quality

and availability

• provide data governance, technology and analysis capability.

These roles, and the work of other agencies in the health data landscape, are

fundamental to ensuring stronger evidence for better decisions in relation to health.

Responding to the changing landscape

The changing shape of health data defines the environment in which AIHW operates.

The AIHW is engaged in a variety of activities to help it to better understand and meet

Australian health information needs; to support the further digitisation of health

information; to improve data utility and accessibility; and to create the enabling

infrastructure to meet future health data needs. The COVID-19 pandemic has

demonstrated the AIHW’s responsiveness to changing data needs and will be a

focus for future data development and reporting (Box 2).

13 Australia’s health 2020: data insights

Chapter

1

Box 2: Data opportunities resulting from the COVID-19 response

More than any other recent event COVID-19 has highlighted the need for timely

data. The AIHW provided immediate assistance to help the Department of Health

meet their data needs, such as compiling up to date data on mental health

services and crisis help line use to support the COVID-19 response. While many

AIHW data collections and associated releases have established schedules for

collection, analysis and reporting, AIHW activity associated with the COVID-19

response used a flexible approach to data collection and analysis, giving decision

makers access to comparable, credible and up-to-date data for monitoring

change. It is expected there will be increased demand for near real time data as

a result of COVID-19, and the AIHW’s future planning will consider its capacity to

deliver information more quickly, while maintaining quality and accuracy.

Australia’s COVID-19 response has resulted in new data sources and opportunities

for data improvements to, or integration with, existing sources to enable more

nuanced information. These new sources and linkage opportunities will enable

analysis of longer-term outcomes associated with COVID-19. For example, at time

of writing, the AIHW has been assisting the Department of Health by compiling

data on mental health on a weekly basis. It has also worked with the Australian

National University to develop a survey (conducted in May 2020), using the Life in

Australia Panel (a national probability-based online panel). This survey had a focus

on mental health, loneliness, housing and alcohol consumption.

The AIHW’s future reporting will incorporate information about the impact of

COVID-19 on health and welfare issues relevant to Australians.

The AIHW continues to play a valued role in data linkage by providing researchers

with access to deidentified data in secure environments. The AIHW is also building

enduring assets such as the NIHSI AA and is working closely with the ABS and states and

territories to facilitate more efficient and effective data linkage while preserving privacy.

Integration of digital health data with existing data sources will improve the

cohesiveness of the Australian health information system. The AIHW, in partnership

with the Australian Digital Health Agency and the Department of Health, is exploring

the interoperability of digital and other health data standards, governance and

reporting, and developing the governance and analysis capability for the secondary use

of My Health Record data. As discussed above, this work has overlaps with a national

review to inform decision making on the implementation of the ICD-11 as a potential

replacement for ICD-10 and ICD-10-AM in our health and vital statistics systems.

14 Australia’s health 2020: data insights

Chapter

1

Closing data gaps

The AIHW, in collaboration with other organisations, engages in initiatives to fill identified information gaps.

Projects underway to fill longstanding disease and sector-specific gaps are covered in Chapter 8 ‘Dementia data in Australia—understanding gaps and opportunities’ and Chapter 9 ‘Improving suicide and intentional self-harm monitoring in Australia’. To improve understanding of inter-sectoral pathways, Chapter 7 ‘Changes in people’s health service use around the time of entering permanent residential aged care’, covers the interfaces between the aged care and health systems. Further work is underway to build a new disability data asset. Much of this work relies on the use of MELDAs and other linked data.

The AIHW is working to establish the National Suicide and Self-harm Monitoring System in collaboration with the National Mental Health Commission and the Department of Health. An important role for the system will be to provide more timely data on suspected deaths by suicide and to improve data on risk factors. The AIHW has been compiling data on suspected deaths by suicide that are already available from suicide registers in some jurisdictions. These data have been extremely valuable to governments in monitoring the impact of the pandemic.

The AIHW also has an integral role in improving data governance for health data in Australia, including by:

• exploring options to improve the efficiency of data linkage and the handling of our large integrated datasets through improved data architecture and high performance computing capabilities

• playing a leadership role in national health data governance in Australia, supported by legislation and by the AIHW Ethics Committee; a longstanding role as custodian of national health datasets; and expanding and emerging roles as custodians of multi-source and multi-use integrated data assets and of secondary use of My Health Record data

• continuing management of health metadata on behalf of Australian Health Ministers’ Advisory Council (AHMAC)

• management of national health metadata in METeOR (the AIHW’s Metadata Online Registry)

• investing in partnerships with governments, non-government bodies and research agencies, in Australia and internationally—recognising that developments in the health data landscape to date, and those on the horizon, are built on collaboration between agencies, researchers and sectors.

15 Australia’s health 2020: data insights

Chapter

1

Improved access to and value of data

The AIHW is working to improve access to our data holdings, and to the analysis and reports we produce—for example, by publishing more regional data and by making data available in a more interactive form. We are also developing processes and improving systems to allow quicker secure access to AIHW data, by approved researchers.

The AIHW is continuing to make more of its data available using interactive displays online. Compared with static data displays, which illustrate specific findings, interactive displays are flexible and enable users to answer their own questions of the data, which in turn supports data-driven decision making. For example, users can focus on specific parts of the Australian population, for a given time period, or in some cases select relevant levels of geography—all in much greater detail than has been available previously.

The AIHW also brings together, and regularly updates, diverse data on a single topic for easy access and use. The AIHW web report Alcohol, tobacco & other drugs in Australia is an example. The Australian Health Performance Framework ‘national front door’ also provides a high-level overview of health indicators.

As noted earlier, the COVID-19 pandemic required a rapid and innovative response from AIHW researchers to help meet the health information needs of Australian governments in formulating a response the COVID-19 crisis. Some administrative collections were used to report to policy and decision makers on the changing situation at a much higher cadence than previously (sometimes daily) while ensuring that the highest quality research standards were maintained. Collaboration and coordination between data providers, governments and other stakeholders were key to establish the effects of the COVID-19 pandemic on the Australian population.

Innovative presentation techniques were needed so that decision makers could assimilate detailed information from diverse data sources quickly. Extensive use was made of business intelligence software for rapid analysis and trend identification; key insights were presented in easy to understand data visualisations.

Going forward the AIHW will harness these learnings to improve the timeliness, accessibility and presentation of its health and welfare statistics to better inform policy and service delivery decisions.

Taking a strategic approach to future data needs

There is a need to develop data to better measure the health impacts of recent global and local events on the health of Australians, immediately and in the long term. This has implications for national approaches to collecting, managing and using health data.

The AIHW is working to use data to better understand the links between the natural and built environments on health—building on and complementing the work of experts in environmental, respiratory and mental health, other research bodies, and Australian Government, state and territory agencies.

16 Australia’s health 2020: data insights

Chapter

1

Improvements to health outcomes for all Australians—and to the efficiency and effectiveness of the health system—will require a strategic approach to managing health system data. Recently, AHMAC has agreed that an independent expert panel will develop a National Health Information Strategy, in consultation with stakeholders, for consideration in 2021. The AIHW supports the work of this panel. The Strategy will provide a framework for ongoing improvement of national health information resources for the next 10-15 years, and provide a basis for shorter investment plans including specific improvement activities for 3-4 year periods. The framework may incorporate principles to shape the Strategy and associated activities, and to outline a vision for the future.

The Strategy may form the basis for a revised National Health Information Agreement (the current agreement was formed in 2013), setting out national arrangements governing national health data assets in a contemporary health data environment. Another example of the AIHW’s work in addressing emerging health data needs is its work with the World Health Organization to develop appropriate data coding for COVID-19. This will assist with research into the impact of the pandemic. Addressing existing gaps and limitations, enhancing data assets and planning for developments in health data collection, analysis and reporting, builds our capacity and capability to respond to the health information needs of the Australian population. This includes

those arising during, and following, future health crises.

References ABS (Australian Bureau of Statistics) 2019a. Case study: expanding MADIP. Canberra: ABS. Viewed 11 May 2020, https://www.abs.gov.au/websitedbs/D3310114.nsf/home/ Statistical+Data+Integration+-+Case+Study:+Expanding+MADIP.

ABS 2019b. Multi-Agency Data Integration Project (MADIP). Viewed 11 May 2020, https://www.abs.

gov.au/websitedbs/D3310114.nsf/home/Statistical+Data+Integration+-+MADIP.

ACT Government 2019. ACT Wellbeing Project. Canberra: ACT Government. Viewed 25 February 2020, https://www.yoursay.act.gov.au/wellbeing.

ADHA (Australian Digital Health Agency) 2017. National Digital Health Strategy. Sydney: ADHA. Viewed 28 February 2020, https://conversation.digitalhealth.gov.au/australias-national-digital-health-strategy.

ADHA 2018. National Digital Health Strategy: framework for action. Sydney: ADHA. Viewed 28 February 2020, https://conversation.digitalhealth.gov.au/framework-for-action.

AIHW (Australian Institute of Health and Welfare) 2019a. Analysis of cervical cancer and abnormality outcomes in an era of cervical screening and HPV vaccination in Australia. Cat. no. CAN 129. Canberra: AIHW. Viewed 12 May 2020, https://www.aihw.gov.au/reports/cancer-screening/analysis-of-cervical-cancer-and-abnormality/contents/table-of-contents.

17 Australia’s health 2020: data insights

Chapter

1

AIHW 2019b. Australia’s welfare 2019: data insights. Cat. no. AUS 226. Canberra: AIHW.

AIHW 2020a. ICD-11 review stakeholder consultation report. Cat. no. HWI 31. Canberra: AIHW.

AIHW 2020b. Learn more about the framework. Viewed 12 May 2020, https://www.aihw.gov.au/ reports-data/australias-health-performance/learn-more-about-the-framework.

Black C, Tesfaigzi Y, Bassein JA & Miller LA 2017. Wildfire smoke exposure and human health: significant gaps in research for a growing public health issue. Environmental Toxicology and Pharmacology 55:186-95.

Bryant RA, Gibbs L, Gallagher HC, Pattison P, Lusher D, MacDougall C et al. 2018. Longitudinal study of changing psychological outcomes following the Victorian Black Saturday bushfires. Australian & New Zealand Journal of Psychiatry 52(6):542-51.

Department of Health 2017. National Health Genomics Policy Framework 2018-2021. Canberra: Department of Health.

Department of Health 2018. Implementation plan—National Health Genomics Policy Framework. Canberra: Department of Health.

Department of Health 2020. $5 million for research into bushfire impact on Australian communities. Viewed 12 May 2020, https://www.health.gov.au/ministers/the-hon-greg-hunt-mp/ media/5-million-for-research-into-bushfire-impact-on-australian-communities.

NHIPPC (National Health Information and Performance Principal Committee) 2017. The Australian Health Performance Framework. Canberra: NHIPPC.

NZ Government (New Zealand Government) 2019. The wellbeing budget 2019. Wellington: New Zealand Government. Viewed 5 February 2020, https://treasury.govt.nz/publications/ wellbeing-budget/wellbeing-budget-2019.

OECD (Organisation for Economic Co-operation and Development) 2013. OECD guidelines on measuring subjective well-being. Paris: OECD.

OECD 2017. How’s life? 2017: measuring well-being. Paris: OECD. Viewed 5 February 2020, https://doi.org/10.1787/how_life-2017-en.

ONDC (Office of the National Data Commissioner) 2019. New legislation. Canberra: Department of the Prime Minister and Cabinet. Viewed 13 May 2020, https://www.datacommissioner.gov.au/ data-sharing/legislation.

UN (United Nations) 2020. The social impact of COVID-19. New York: United Nations. Viewed 21 May 2020, https://www.un.org/development/desa/dspd/2020/04/social-impact-of-covid-19/.

WHO (World Health Organization) 1946. Preamble to the Constitution of the World Health Organization as adopted by the International Health Conference, New York, 19-22 June, 1946. Geneva: WHO.

WHO 2019. ICD-11 International Classification of Diseases for Mortality and Morbidity Statistics, 11th revision, reference guide. Geneva: WHO. Viewed 28 February 2020, https://icd.who.int/ icd11refguide/en/index.html.

19 Australia’s health 2020: data insights

2

Four months in: what we know about the new coronavirus disease in Australia

20 Australia’s health 2020: data insights

Chapter

2

The information and data provided in this chapter were accurate at the time

of writing. However, given the dynamic nature of the pandemic there may

be changes or revisions to the underlying data and/or information as more

is learnt about the disease. This chapter focuses on the first 4 months of the

disease in Australia, covering 25 January 2020 (when the first Australian cases

were confirmed) to the end of May. Analyses in this chapter are also preliminary

and in some instances less complex than would normally be the case, reflecting

the limited amount of detailed data available at this stage of the epidemic.

COVID-19 is a disease caused by the new coronavirus SARS-CoV-2. It is a major health

threat and international crisis, which has led to substantial disruption to almost all

parts of society worldwide. The outbreak first came to international notice through a

cluster of unexplained pneumonia cases in Wuhan, China, in late December 2019. The

COVID-19 epidemic was declared a pandemic (the worldwide spread of a new infectious

disease) by the World Health Organization (WHO) on 11 March, and by 31 May there

had been over 5.9 million confirmed cases and over 360,000 deaths worldwide

(WHO 2020g).

There are several reasons why COVID-19 has become such a major crisis. Briefly, being

caused by a virus not previously seen in humans, there is no immunity in the population

and currently no vaccine or specific treatments. It is also highly infectious and affects

some people severely. It was therefore important to protect the health of vulnerable

people and prevent the health system from being overwhelmed with many severe

cases presenting to hospital at once. The only practical way to contain its spread at

this stage is by travel bans, strong physical distancing policies and practices (such as

through closure of non-essential services and keeping a minimum distance from others)

and personal hygiene. These restrictions have had a serious impact on economies and

societies across the world, with travel, trade and people’s ability to work, attend school

and socialise, all affected.

Most countries have not had recent experience with similar epidemics, making the

adjustment to new ways of living challenging. However, the threat of a pandemic was

recognised internationally prior to the emergence of the virus (Ziegler et al. 2018) and

Australia had its own well-developed system of public health response to communicable

diseases (WHO 2018). The Australian Government developed an emergency response

plan specifically for COVID-19, which was released on 27 February (Department of

Health 2020a).

21 Australia’s health 2020: data insights

Chapter

2

The Communicable Diseases Network Australia (CDNA) coordinates communicable

disease surveillance and investigation across jurisdictions. Part of their work is to

bring together the data collected by states and territories on notifiable diseases into

a de-identified national dataset—the National Notifiable Diseases Surveillance System

(NNDSS). These data are an important component of this chapter, and are described

further below. In addition, the Public Health Laboratory Network is a collaborative

group of laboratory representatives that contributes laboratory-level expertise to the

response to infections of public health importance (Department of Health 2020j).

There are also Centres of Research Excellence that can provide valuable real-time

clinical research which contributes to both the national and international efforts to

combat the pandemic (Doherty Institute 2020).

To date, Australia has fortunately avoided the severe health impacts seen in many

other countries, where there have been large numbers of severe cases and deaths,

putting a huge strain on the health system (MacIntyre & Heslop 2020). While we

do not yet have detailed knowledge of which specific factors may have contributed

to the favourable situation in Australia, the early implementation of international

travel restrictions and physical distancing measures in combination with one of the

highest testing rates in the world have played a key role (Cheng & Williamson 2020).

It is difficult, at least at this stage, to know definitively what may have happened in

Australia without these measures. Also, as with all prevention, it can be challenging

for the community to fully appreciate the value of the preventive actions undertaken

(Hemenway 2010). It is not possible to predict what may happen in the future, and the

infectious nature of the virus means there could still be further outbreaks in Australia.

We remain in the middle of an evolving situation, with many facets of the epidemic

not yet fully understood, though research continues to become available to fill some

of the gaps in knowledge. Similarly, due to the rapid development and applied nature

of the data collections currently available, the completeness and accuracy of the data

may improve over time; therefore, data in this chapter are preliminary. There are also a

number of areas where national data are not yet available.

This chapter is a point in time article that reviews the first 4 months of the epidemic in

Australia using currently available data. The areas covered are outlined in Box 2.1.

22 Australia’s health 2020: data insights

Chapter

2

Box 2.1: Chapter focus and outline

This chapter takes a broad ‘monitoring’ approach to provide an overview of the

epidemic so far in Australia. This is in contrast to ‘surveillance’ data collection

and analyses done by the federal, state and territory governments specifically

to take action to manage the epidemic (see the glossary for relevant definitions

of terms used in this chapter).

The main focus of this chapter is on the short-term situation given the stage of

the epidemic we are at. Sections of the chapter cover:

• key characteristics of the disease, and its prevention, control and treatment

• available information on the number of cases and deaths in Australia—including

variation across the country and population

• data and analysis on age at death and severity of the disease

• a focus on particular at-risk populations

• comparison to previous epidemics

• comparison to the situation in other countries

• an overview of the indirect effects, including impact on the health system and

broader health and welfare

• discussion of the use of data in epidemics and how the current epidemic has

extended these.

About the disease This section is intended to give broad background on the key characteristics of COVID-19,

and the current prevention and treatment available. It does not aim to provide detail on

these topics, but rather is provided as background for the sections that follow.

Disease characteristics

COVID-19 is predominantly a disease of the respiratory system, particularly in the early

stages of the illness, caused by the coronavirus SARS-CoV-2 (Box 2.2). Common early

symptoms are similar to other respiratory illnesses such as fever, cough, sore throat,

runny nose and shortness of breath. However, the infection can have a wide variety

of manifestations, including diarrhoea, loss of smell and loss of taste (CDNA 2020a).

In some people the infection can progress to become a more severe disease, with the

immune system overreacting, resulting in inflammation and lack of oxygen to many

23 Australia’s health 2020: data insights

Chapter

2

parts of the body. This can lead to multiple organ failure and death. Severe symptoms

tend to develop in the second week of the disease.

Box 2.2: What are coronaviruses?

Coronaviruses are RNA viruses that are mainly found in animals. Under an

electron microscope, they give the appearance of the corona of the sun; hence,

the name ‘coronavirus’. Seven coronaviruses have occurred in the human

population. Four of these (OC43, HKU1, NL63, 229E) have been circulating for

many years, and account for about 20% of the cases of common cold. The

remaining 3 coronaviruses cause more serious illnesses, namely Middle East

Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS) and

now the Coronavirus Disease 2019 (COVID-19). SARS-CoV-2 is the virus that

causes COVID-19. This virus is 96% genetically similar to a bat virus. It is therefore

likely that SARS-CoV-2 originated in bats before moving to humans through

an intermediate animal host (Andersen et al. 2020). The pangolin has been

nominated as a possible intermediate host since its own coronavirus is

very similar to SARS-CoV-2 (Zhang et al. 2020).

In the Northern Hemisphere, clusters of an unusual condition have occurred in

children, the majority of whom have had positive antibody tests for SARS-CoV-2. This

has been called a multisystem inflammatory syndrome. Its association with COVID-19

is still unclear and is being investigated (WHO 2020j). Despite this, COVID-19 appears to

be an uncommon infection in children (WHO 2020n) but research is not yet definitive

on whether the low rates of confirmed cases in children are driven by lower chances

of children catching the disease or lower rates of symptoms and therefore less

testing (Vogel & Couzin-Frankel 2020). In addition, children do not appear to transmit

COVID-19 easily (NCIRS 2020). This is quite unusual as children are often major sources

of community transmission of respiratory infections, such as influenza.

Another emerging unusual feature of the COVID-19 illness is a propensity to form

blood clots. This appears to be more common in critically ill patients, can involve both

the arteries and veins, and lead to life-threatening complications such as stroke and

pulmonary embolism (Willyard 2020).

COVID-19 is a highly infectious disease with a wide spectrum of severity. Many people

experience mild to moderate disease, but unfortunately some develop very serious

illnesses and it has a higher death rate than many common infectious diseases.

The severity spectrum ranges from asymptomatic (no symptoms), to mild/moderate

disease (symptoms confined to the upper respiratory system, or flu-like symptoms

serious enough to keep someone off work), to severe (with pneumonia, respiratory

24 Australia’s health 2020: data insights

Chapter

2

failure, septic shock, organ failure and potentially death). Early in the epidemic using data

on cases in China, it was estimated that 81% of cases had relatively mild to moderate

disease and 14% severe disease requiring hospitalisation. At the most severe end,

around 5% of cases required intensive care unit (ICU) admission (Wu & McGoogan 2020).

Current estimates of the proportion of infections that are asymptomatic range from

18-43% (Gudbjartsson et al. 2020; Mizumoto & Chowell 2020; Nishiura et al. 2020a).

An estimate of case-fatality (the percentage of known cases that are fatal) from the

early epidemic was 1.4%, based on data from cases in China, adjusted for demographic

factors and potential missed cases (Verity et al. 2020). However, the rates differ

substantially across age groups, from less than 0.3% for all age groups under 50 years,

then steadily increasing with age to 13.4% for those over 80 years. Accurate case-fatality

rates require full counts of both those dying from the disease (the numerator) and the

number of cases (the denominator), otherwise adjustments need to be made for

under-counting.

South Korea had an earlier epidemic than many other countries, and had good levels

of testing, making it likely that the denominator included a high proportion of cases.

The crude case-fatality in South Korea up to 10 March was 0.7% (KSID & KCDCP 2020).

The age pattern was similar to the Chinese estimates but with lower rates: 0.1% or

less for all age groups under 50 years, then steadily increasing to 6.8% for those

over 80 years. Given the strong age effect, case-fatality rates are highly influenced

by the age profile of people contracting the disease within a population. High-quality

care contributes to lower case-fatality rates, and thus an important aim of epidemic

management is to contain the spread of the disease to ensure intensive care units

(ICUs) are not overwhelmed with too many cases at once, which would compromise

their ability to provide care to everyone who could benefit.

As well as age, there are other factors that increase the risk of severe disease. At this

stage, these appear to include smoking (WHO 2020o), obesity (Simonnet et al. 2020),

and having chronic conditions such as heart or respiratory disease, diabetes or cancer

(Department of Health 2020c). Even more at risk are those with multiple comorbidities

or who are immunocompromised due to disease or therapy (Liang et al. 2020).

Disadvantaged groups are at increased risk for a range of reasons, including their

higher rates of these risk factors and overcrowded housing (PHE 2020).

The primary reasons COVID-19 has become a worldwide crisis are its severity in

combination with high transmission rates. These high transmission rates are driven

by a number of factors: it is a new virus and thus there was no immunity in the

population; there is currently no vaccine; and it can be transmitted by people who

are not very ill (such as those with no or very mild symptoms), allowing it to spread

throughout the community ‘under the radar’ (MacIntyre & Heslop 2020). In addition,

25 Australia’s health 2020: data insights

Chapter

2

peak infectiousness appears to occur prior to or just after symptoms develop (He et al.

2020). This contrasts to the SARS outbreak of 2003, where cases only became infectious

after they became unwell, and where the peak infectiousness occurred later in the

illness. In other words, it was easier for SARS cases to be identified and isolated as soon

as they developed symptoms, reducing the risk of further transmission significantly.

COVID-19 also spread very quickly around the world due to high levels of international

travel prior to travel bans.

The measures used for estimating and monitoring the spread of the virus are outlined

in Box 2.3. The median incubation period of 5-6 days (though ranging from 1-14 days;

WHO 2020e) and high transmission rates results in rapid growth in the spread of the

infection if measures are not in place to stop the chains of transmission.

Box 2.3: Measuring spread of the disease

The basic reproduction number (R0) quantifies, at the start of the epidemic (when there are no public health interventions and no immunity), the average

number of people each case infects (see diagram below showing an R0 of 2; Delamater et al. 2019). For SARS-CoV-2, the estimated R0 is around 2.5 (WHO 2020n). As the epidemic continues, and the impact of the public health interventions are

seen, the effective reproduction number (R e) can be estimated. It is expected to fall as a result of these interventions, and when under 1 for a sustained period of

time, the epidemic is in decay. However, unless the Re is close to 0, any change to the public health measures in place mean it could quickly increase again to over

1 (Pan et al. 2020).

(continued)

Estimate Uncertainty

3

2

1

Re

Time

Effective reproduction number (Re)

If Re is greater than 1, cases will increase, but if it is less than 1, the outbreak will spread more slowly and eventually end.

Basic reproduction number (Ro)

etc

etc

etc

etc

R o = 2

26 Australia’s health 2020: data insights

Chapter

2

Box 2.3: (continued) Measuring spread of the disease

An R0 of 2.5 with an average incubation period of 5 days would result in 1 case leading to 406 new infections within 30 days. With 50% less exposure to other people following public health interventions (an Re of 1.25), 1 person would infect 15 people within 30 days. With 75% less exposure, only 2.5 other people would be infected within 30 days.

Four factors determine the value of R: the duration of infectiousness, the number of opportunities for transmission (for example, how many close contacts the infectious person has), how likely the virus is to be transmitted when the opportunity arises, and the susceptibility of the population (Kucharski 2020). Addressing any of these factors has the potential to reduce the value of R. For example, physical distancing measures reduce the opportunities for transmission, and a vaccine would decrease the proportion of the population that were susceptible.

Another measure of spread of infection is the ‘serial interval’. This measures the time between a case becoming unwell and someone they have infected becoming unwell. For COVID-19, the median serial interval has been estimated at 4.6 days—shorter than the average incubation period—which is evidence for transmission of infection before cases become ill (Nishiura et al. 2020b).

Some infections lead to lifelong immunity—they can never be contracted again.

However, this is not the case for all infections. It is currently unclear into which

category COVID-19 falls. Some people with COVID-19 have been shown to develop a

strong immune response to the virus, suggesting that they will be immune to further

infection (Thevarajan et al. 2020); however, the duration of that immunity is unclear.

Further research will be needed to answer this question (Senanayake 2020).

27 Australia’s health 2020: data insights

Chapter

2

Prevention, control and treatment

Public health response

The aim of public health interventions is to stop or slow transmission of the virus.

Unlike some other infectious diseases in Australia, there is currently no vaccine for the

SARS-CoV-2 virus. There are a number in development but they are not expected to be

available before 2021 (Graham 2020). This means that more traditional public health

interventions are the focus of prevention measures.

There are 3 major groups of prevention actions instigated or undertaken by the public

health workforce: policies aimed at the population level; actions that can be taken

by individuals; and case isolation and contact tracing/quarantine. In the absence of

a vaccine, these measures are vital and effective in reducing the spread of disease

(Chang et al. 2020).

Population-level actions

A range of population-wide interventions are possible, aiming to stop the chain of

transmission of the virus. These focus on reducing the number of interactions between

individuals and ensuring physical distancing measures are used when interactions are

unavoidable. These interventions can be mandated using laws or fines, or advisory

notices. Examples of interventions Australia has used include travel bans, bans on

social gatherings of a certain size, closing pubs and clubs, and encouraging people to

work or educate from home. These measures, some of which are expected to be in

place for a long period, have a substantial impact on people’s lives. Governments have

needed to mitigate the income, employment and social isolation effects with a range of

substantial policies and programs.

The concept of ‘flattening the curve’ has been used extensively during the pandemic.

It is explained in Box 2.4.

28 Australia’s health 2020: data insights

Chapter

2

Box 2.4: Flattening the curve

During the early stages of the epidemic, community discussion occurred on the concept of ‘flattening the curve’. This refers to the epidemic curve, which shows the number of new infections over time. Without any vaccine or public health interventions, the expected shape is quite steep (line A in the figure below). In an epidemic like the current one where a proportion of cases will be severe enough to require hospitalisation, this large peak can result in the health system being overwhelmed and the health workforce being put at significant risk of infection and death themselves. The concept of flattening the curve is to use various public health interventions to push this distribution of cases down, so that the peak is much lower, though the total period of the epidemic is extended (line B). As well as flattening the curve, prevention activities can also reduce the total number of infections (Churches & Jorm 2020).

By controlling the number of cases requiring hospital treatment at any point in time to within the capacity of the health system to manage them, the chances of better outcomes are increased for patients, which is likely to save lives. If the curve is not flattened, the risk is there will not be enough resources (for example, personal protective equipment (PPE) for health workers; ventilators in ICU) to treat everyone safely and effectively, as was the case in countries such as Italy, Spain, the UK and the US (Ranney et al. 2020).

Additionally, if the number of cases is not controlled, the impact on the health system itself would be large. The likelihood of many more infections amongst the health workforce is increased, as demonstrated in Spain where 20% of infections were amongst health care workers (ECDC 2020a). A health system overwhelmed by COVID-19 is also less likely to serve other functions and treat other patients.

Flattening of the curve also buys time: for the health system to better prepare for the extra cases needing treatment and to develop systems for prevention of transmission through contact tracing and other public health measures; for treatments of cases to be refined as research evidence accumulates on

effectiveness; and potentially for a vaccine to be developed.

Daily number of cases

No intervention

Interventions implemented

Health care capacity

Number of days since first case

A

B

29 Australia’s health 2020: data insights

Chapter

2

Individual-level actions

As well as encouraging individuals to follow the population-wide measures outlined

above, there are other behaviours individuals can follow to reduce their risk of

contracting or spreading the virus. These focus on regular handwashing or sanitising,

not touching the face, good respiratory hygiene, staying home when unwell and

getting tested for SARS-CoV-2 (Department of Health 2020h). In addition, individuals

are encouraged to follow physical distancing measures including working from home

when possible. During this pandemic, the issue of ‘presenteeism’ has also come to the

forefront, which refers to people coming into work when they are unwell. They might

do this for a variety of reasons, such as concerns over letting their colleagues down by

not turning up, fear of losing their job, or worries about not being paid. However, given

the risk of an infected worker introducing COVID-19 into the workplace, presenteeism

is now being actively discouraged. The various measures are communicated to the

public in a variety of ways, including large-scale information campaigns through the

main media platforms.

Isolation of cases, and contact tracing and quarantine

Alongside prevention measures, a vital component of the public health response is

isolation of cases and quarantine of cases’ contacts or others at high risk, to stop

transmission of the virus. The first step aims to find as many cases as possible, which

relies on high levels of testing of suspected COVID-19 cases, and to then isolate them

from other people until they are considered to be no longer infectious. The second

step is contact tracing which aims to identify all people who had close interactions with

the cases while they were infectious. Quarantine is then required for those considered

to have been potentially exposed to a case, including returning travellers, and they

are monitored to see if they develop symptoms. The detailed requirements for each

of these steps are updated in the National Guidelines, as new information becomes

available (CDNA 2020b).

Diagnosis and treatment

Acute COVID-19 is currently diagnosed by taking a swab of the nose/throat or of

sputum (mucus from the respiratory tract). A blood test can identify those with

an immune response from past infection, which may have been asymptomatic or

undiagnosed, but has no current role in diagnosing acute infections (CDNA 2020b).

Unlike other viral diseases, which may benefit from treatments such as antivirals,

there is currently no specific pharmaceutical treatment for COVID-19. A number

of trials are underway, which may identify drugs that reduce severity and possibly

infectiousness (Davis et al. 2020). In the absence of specific treatments, supportive

30 Australia’s health 2020: data insights

Chapter

2

care is provided to keep the body functioning as well as possible while it fights the

infection. For mild-moderate cases, this is likely to include common symptom relievers

such as paracetamol. For more severe cases, treatment in hospital and supplemental

oxygen therapy may be required. For critical cases, this supportive care would require

admission to ICUs, often with various advanced technology treatments such as

mechanical ventilation.

Vaccine development

A vaccine is the best way to rapidly build immunity against the virus and protect

the population from developing disease. Scientists across the world are working on

developing and testing candidates for vaccines at a rapid rate using a number of

different technologies. However, vaccine development is a lengthy and costly process,

with many challenges to overcome. The development of SARS and MERS vaccines

raised concerns about adverse reactions (such as worsening of lung disease) so

rigorous testing in animal models and safety monitoring in clinical trials in humans will

be important (Luri et al. 2020).

The epidemic in Australia so far When this chapter was finalised, it had been around 4 months since the first case of

COVID-19 was diagnosed in Australia. This section outlines what is known so far about

how the disease has affected the health of the Australian population. While relying on

early data, which may not be as comprehensive or refined as data that will become

available later, the need for data to be available quickly to manage the epidemic has meant

that enough information was available to paint a picture of the key, short-term impacts.

Data for this section primarily come from the NNDSS which contains de-identified,

official notification data from each of the states and territories. The NNDSS was

established in 1990 and contains national surveillance data for more than 60

communicable diseases or disease groups (Department of Health 2015). NNDSS data

presented here cover the period to the end of May and early June, sourced from

published reports containing NNDSS data, and from data supplied directly from the

NNDSS. Due to the dynamic nature of the NNDSS, data in this extract are subject to

retrospective revision and may vary from data in published NNDSS reports and reports

of notification data by states and territories. Note that ‘confirmed cases’ in this section

essentially refer to laboratory-confirmed cases of COVID-19 notified to the NNDSS;

it may also include a small number of probable cases.

31 Australia’s health 2020: data insights

Chapter

2

The deaths data provided here from the NNDSS may differ from counts of deaths that

will be available from death certificate data in coming months. This issue is discussed

further in the ‘Use of data in epidemics and pandemics’ section below.

Confirmed cases

Australia’s first cases were diagnosed on 25 January amongst a group that had

travelled from Wuhan, China. There were then sporadic cases, with either zero or

small numbers of cases diagnosed each day until early March, when the numbers

diagnosed started to accelerate and clusters of cases started to emerge. Particular

groups at risk were those who returned from overseas, lived with a person who had

caught the virus overseas, or those in a residential care facility. The 100th case was

diagnosed on 10 March, the 200th on 15 March, the 400th on 18 March and the

800th on 21 March (ECDC 2020b). This shows that cases were doubling every 3-4 days

in these early days of the epidemic. The peak (to date) was reached on 23 March,

when 611 cases were diagnosed in one day, after which the rate of growth started to

slow substantially.

In terms of date of illness onset, the peak day was 20 March with 468 new illnesses

(Figure 2.1). The large drop in daily cases at the end of March and into April coincided

with the various mitigation measures introduced (national actions are summarised

in Figure 2.1).

By 7 June, there had been 7,277 laboratory confirmed cases in Australia, and 102 of

these people had died (COVID-19 NIRST 2020e). During the epidemic, cumulative case

counts have also been provided by states and territories daily for national reporting.

Those data indicate that, by 9 June, the vast majority of cases had recovered and only

6% were still active cases (Department of Health 2020d).

32 Australia’s health 2020: data insights

Chapter

2

Figure 2.1: Number of confirmed cases of COVID-19 in Australia, by date of illness onset and source of infection

Notes 1. Data to 31 May 2020. 2. Where date of illness onset was not available, the earliest of the specimen collection date, the notification creation date, or the notification received date has been used.

Source: NNDSS, Australian Government Department of Health.

0

50

100

150

200

250

300

350

400

450

500

13/01 20/01 27/01 3/02 10/02 17/02 24/02 2/03 9/03 16/03 23/03 30/03 6/04 13/04 20/04 27/04 4/05 11/05 18/05 25/05

Number of new cases

Locally acquired

Under Investigation

Overseas

Travel bans imposed

Borders closed

'Physical distancing' began

Strictest measures in place

Start of easing of restrictions

At the start of the epidemic, a substantial number of infections were acquired overseas.

This included visitors to Australia until travel bans began, as well as Australians

returning home during the pandemic. This continued to a lesser extent during April and

May as further Australians returned home. The largest proportion of cases in Australia

were in people who were infected overseas (Figure 2.1), and this remained the case to

early June with 62.2% of cases being acquired overseas (Department of Health 2020e).

Without these overseas-acquired cases, the transmission within Australia has been

relatively small, with the peak day being 21 March with 128 new illnesses on that day.

Even within the locally-acquired infections, nearly three-quarters were among people

who were a close contact of a case, and thus only 10% of all cases were in people

without a known contact (Department of Health 2020e).

33 Australia’s health 2020: data insights

Chapter

2

High rates of testing for the virus are needed within a population to ensure that

cases and contacts are not missed, and to enable isolation and quarantine to reduce

the chance of transmission within the community. High testing rates are also required

for notification data to be accurate. In the early days of the epidemic in Australia, only

certain groups were eligible for testing. At the end of March, the eligible groups were

those who had returned from overseas or had been in close contact with a confirmed

case in the last 14 days; had severe, unexplained pneumonia; or were a health care

worker or from certain other high-risk groups and had symptoms consistent with

COVID-19 (ABC News 2020c; MacIntyre 2020). The groups eligible for testing were

expanded over time, and by 12 May, anyone with symptoms of respiratory illness was

eligible for testing in many states and territories, and were being actively encouraged

to be tested (Department of Health 2020l; NSW Health 2020a; Queensland Government

2020; Victoria State Government 2020).

Australia has had high levels of testing, as reflected in the number of tests per capita

(34 per 1,000) and a low percentage of tests found to be positive (0.5%) (Department

of Health 2020d). Studies also suggest Australia is unlikely to be undercounting cases

(Russell et al. 2020), reflecting the high testing rates.

The Re in Australia was estimated to have been between 1.5 and 2.0 in the first week

of March (Figure 2.2). The initial high values reflect infections in Australians returning

from overseas, rather than high levels of transmission within Australia. The Re then

fell sharply over the next 10 days, and was estimated to be under 1.0 in the last week

of March. These trends reflect the travel bans and physical distancing measures

implemented during this period. In the most recent period, it remained around 1.0

due to contained outbreaks in New South Wales and Victoria. Note that the Re

becomes increasingly unstable when the number of cases becomes very low, which

is the current situation in Australia (Golding et al. 2020).

34 Australia’s health 2020: data insights

Chapter

2

Figure 2.2: Time-varying estimate of the effective reproduction number (Re) for COVID-19, Australia, 4 March to 21 May 2020

Note: Light ribbon = 90% credible interval; dark ribbon = 50% credible interval.

Source: Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine; https://epiforecasts.io/covid/posts/national/australia/.

0.0

0.5

1.0

1.5

2.0

2.5

04 Mar 11 Mar 18 Mar 25 Mar 01 Apr 08 Apr 15 Apr 22 Apr 29 Apr 06 May 13 May 20 May

Date

Re

Variation across the country

Using the WHO definition of stages of the epidemic, Australia continued with ‘clusters

of cases’ (WHO 2020d) until the end of May (WHO 2020h). These clusters resulted in

variation in the number of cases across the country, although there were cases in

every state and territory (Table 2.1). New South Wales had the most cases, with 3,117

diagnosed by 24 May, followed by Victoria (1,616) and Queensland (1,058). Taking

into account the size of the population, Tasmania had the highest rate by that date,

followed by New South Wales.

35 Australia’s health 2020: data insights

Chapter

2

Table 2.1: Total confirmed cases of COVID-19, by state and territory

Number of cases Rate (per 100,000)

NSW 3,117 38.5

Vic 1,616 24.5

Qld 1,058 20.8

WA 541 20.6

SA 439 25.1

Tas 228 42.7

NT 29 11.8

ACT 107 25.1

Australia 7,135 28.1

Note: Data to 24 May 2020.

Source: COVID-19 NIRST 2020d.

Variation by age and sex

By the end of May, there was little difference in the total number of cases between

males and females. However, there was variation in the number of cases in each age

group (Figure 2.3a). The 20-29 age group accounted for the highest number of cases,

with age groups up to 70 years having lower numbers. The highest rates for females

were in the 20-29 and 60-69 age groups, while for males the highest rates were for

the 60-69 and 70-79 age groups.

In contrast, there was more variation by age and sex for reported deaths (Figure 2.3b).

The majority of deaths were in the older age groups, with the 80-89 age group having

the most deaths. There are steep increases in death rates across the age groups and

higher rates for males than females, particularly in the oldest age groups. Similar

patterns in the distribution of deaths have been observed in other countries

(ONS 2020; Salje et al. 2020).

36 Australia’s health 2020: data insights

Chapter

2

Figure 2.3: Number and rate of confirmed cases of COVID-19 and associated

deaths in Australia, by age and sex

Note: Data to 31 May 2020.

Source: NNDSS, Australian Government Department of Health; Department of Health 2020e.

0

10

20

30

40

50

60

0

100

200

300

400

500

600

700

800

900

0 – 9 10 – 19 20 – 29 30 – 39 40 – 49 50 – 59 60 – 69 70 – 79 80 – 89 90+

Age group

Males Females

Males–rate Females–rate

Number of cases Rate (per 100,000) Median age: 47 yrs

0

2

4

6

8

10

12

14

0

2

4

6

8

10

12

14

16

18

20

Number of deaths Rate (per 100,000) Median age: 80 yrs

0 – 9 10 – 19 20 – 29 30 – 39 40 – 49 50 – 59 60 – 69 70 – 79 80 – 89 90+

Age group

Males Females

Males–rate Females–rate

a) Confirmed cases

b) Deaths

37 Australia’s health 2020: data insights

Chapter

2

Age at death

The median age at death for COVID-19 was 80 years, which is slightly lower than that

for all causes of death in 2018 (81 years). It is also somewhat lower than many other

leading causes of death that commonly occur in older age. Compared with the top 7

leading causes of death occurring in 2018, as well as suicide (12th), pneumonia (14th)

and influenza (90th) (Table S2.1), the median age at death for COVID-19 was:

• lower than the 3 leading causes of death—coronary heart disease (CHD) (84 years),

dementia (88) and stroke (86)—and pneumonia (89) and influenza (82)

• similar to diabetes (81) and chronic obstructive pulmonary disease (COPD) (80)

• higher than bowel cancer (77), lung cancer (73) and suicide (44).

Another way to examine the impact of age at death is to measure years of life lost

(YLL), which counts the number of years between the age at death and life expectancy

at that age. There has been speculation that YLL are low for COVID-19, which would

indicate that some people dying from the disease did not have a long life expectancy

prior to developing COVID-19, largely due to being older or having comorbidities that

put them at higher risk of the severe effects of disease. A study using Italian and UK

data shed some light on this, showing that average YLL per person was 14 for men

and 12 for women (Hanlon et al. 2020). The authors also produced modelled estimates

adjusting for comorbidities, which showed that the presence of comorbidities did not

greatly decrease the estimates, reducing average YLL to 13 for men and 11 for women.

Preliminary calculations for Australia (not adjusted for comorbidity) using similar

methods to the European paper shows average YLL per person was 17 years for men

and 14 for women (including deaths up to 31 May). These higher estimates in Australia

indicate a lower proportion of deaths in older people, possibly due to fewer outbreaks

in aged care facilities than have occurred in other countries (COVID-19 NIRSTd). Using

methods similar to Australia’s usual approach for calculating YLL (which uses a different

reference life table) results in preliminary estimates of average YLL per person of 14

and 11 for males and females respectively. Corresponding estimates for 2015 for the

5 leading causes of death in Australia are: CHD (14 for males, 8 for females), dementia

(9 and 7), stroke (11 and 8), lung cancer (17 and 18) and COPD (13 and 12) (AIHW 2019).

This shows that those dying from COVID-19 lost more years of their expected life span

than most other major causes of death. This suggests there is a strong possibility

that the COVID-19 deaths were among people that, on average, would not have been

expected to die soon, particularly when taken alongside the Hanlon et al. (2020) finding

that comorbidity did not greatly reduce YLL.

38 Australia’s health 2020: data insights

Chapter

2

Severity

The vast majority of cases were mild-moderate in severity and were managed at home.

However, a small proportion of people developed more severe disease. By 24 May,

13% of diagnosed cases had been admitted to hospital (Table 2.2). Hospitalisation

usually indicates more severe disease, though in the early stages of the epidemic in

Australia, some mild cases were admitted to hospital to enhance isolation procedures

in order to minimise the chance of further transmission. The median age for

hospitalised cases was older than for all cases (61 years compared with 47 years).

Table 2.2: Characteristics of confirmed cases, hospitalisations and deaths

All cases Hospitalisations Deaths

Number 7,135 947 102

Per cent of all cases 100 13 1.4

Median age (years) 47 61 80

Age interquartile range (years) 29-62 42-72 74-86

Note: Data to 24 May 2020.

Source: COVID-19 NIRST 2020d.

The more severe cases were admitted to ICU—202 cases, which is 2.8% of all diagnosed

cases up to 24 May (COVID-19 NIRST 2020d). Over one-quarter (28%) of those in ICU

received mechanical ventilation.

In terms of the crude case-fatality rates, 1.4% of cases had died by 24 May—102

deaths. As noted above, these mostly occurred in the older age groups, and the

median age at death was 80 years (COVID-19 NIRST 2020d). As observed in other

countries the case-fatality rates in Australia increase with age, being 0.1% or lower

up to age 50, then 0.4% for those aged 50-64, 3.1% for the 65-79 age group and 22.7%

for those 80 and over. The case-fatality rate for males (1.6%) was higher than for

females (1.3%). Possible reasons for this difference include more chronic conditions

in older men and a stronger immune system in women (Lawton 2020).

At-risk populations

There are a number of population groups that are at increased risk of infection or

severe disease if infected. This section focusses on 4 important high-risk population

groups: health care workers, people in aged care, Aboriginal and Torres Strait Islander

people and cruise ship passengers. People who live in shared residential settings, such

as correctional facilities, military bases, and residential disability care facilities are also

at increased risk of infection from outbreaks in these settings.

39 Australia’s health 2020: data insights

Chapter

2

Health care workers

Members of the health workforce are at higher risk of catching COVID-19 as they

may be treating (and therefore in close contact with) people with the disease.

This is why the availability of PPE and being competent in both putting on (“donning”)

and removing (“doffing”) the PPE is so important.

Outbreaks in the Alfred Hospital in Melbourne and the North West Regional and North

West Private hospitals in Tasmania demonstrate how hospitals can become focal

points for outbreaks. The outbreak of COVID-19 in hospitals in northwest Tasmania

began in late March 2020. Cases occurred among health care workers, patients and

household contacts. As of 27 April 2020, there were 125 persons associated with the

outbreak, including 78 staff members (COVID-19 NIRST 2020a). Outbreaks in aged

care facilities (see the next section) also demonstrate the risk for health care workers

in those settings. Ongoing monitoring of health care workers will be an important part

of the response to COVID-19.

The importance of PPE in protecting the health workforce from infection has been an

ongoing worldwide challenge in the management of COVID-19 due to supply chain

issues (WHO 2020m).

People in aged care

Given that people aged 60 and over are at greater risk of poorer outcomes due to

COVID-19 than people aged less than 60 years (WHO 2020k), and that aged care

residents often live in close proximity to each other, the aged care sector is a high risk

setting. Residential aged care facilities often deal with infectious disease outbreaks,

such as influenza and gastrointestinal illness (Kirk et al. 2010), and have procedures in

place to respond to and manage them (CDNA 2017). As of 24 May, there had been 129

confirmed cases of COVID-19 in residential aged care facilities in Australia (66 residents

and 63 staff), with 27 associated deaths (and 72 recovered cases). In addition, there

were 42 cases in ‘in home care’ settings, 31 of which occurred in care recipients and

11 in care staff (with 37 recoveries), and 3 deaths (COVID-19 NIRST 2020d). A large

outbreak in an aged care facility in New South Wales, resulting in 16 associated deaths

by 6 May, was challenging to contain and highlights the risk to aged care residents

(Aged Care Quality and Safety Commission 2020).

In the early stages of the epidemic, the Australian and state and territory governments

put restrictions in place to protect older Australians in residential aged care facilities,

including limiting the number of visitors to 2 people per resident and not permitting

children aged 16 and under to visit (Department of Health 2020g). The CDNA released

the National Guidelines for the Prevention, Control and Public Health Management of

COVID-19 Outbreaks in Residential Care Facilities in Australia (CDNA 2020c).

40 Australia’s health 2020: data insights

Chapter

2

Aboriginal and Torres Strait Islander people and communities

Aboriginal and Torres Strait Islander people and their communities are at high risk

of COVID-19 outbreaks and severe outcomes for a number of reasons. They are a

mobile population and remote communities have frequent visitors (including fly-in

fly-out health care workers), increasing the chances of disease importation. They

often have reduced access to health services either due to physical distance for those

in remote areas or due to other barriers related to institutional racism, and mistrust

of mainstream health services (CDNA 2020b). In addition, Indigenous Australians

experience a burden of disease 2.3 times the rate of non-Indigenous Australians, with

64% of the burden due to chronic diseases such as diabetes and CHD (AIHW 2016).

Overcrowding in homes and lack of infrastructure to support personal hygiene in

remote areas can promote disease transmission and make physical distancing and

efforts to self-quarantine challenging.

To protect remote communities from COVID-19, governments working in

collaboration with Aboriginal and Torres Strait Islander organisations and

communities began restricting the movement of people in and out of remote areas

and began setting up respiratory clinics to support Indigenous Australians

(Hunt & Wyatt 2020). The National Management Plan for Aboriginal and Torres Strait

Islander Peoples has been developed by the Aboriginal and Torres Strait Islander

Advisory Group on COVID-19 and endorsed by the Australian Health Protection

Principal Committee, and was released on 30 March 2020.

As of 24 May, less than 1% of notified cases had been reported in Indigenous

Australians (59 cases; with 95% Indigenous identification completeness for notified

cases), who represent 3.3% of the Australian population. Ten per cent of these cases

were acquired in Outer Regional areas, and none in Remote or Very Remote areas;

47% were acquired overseas (COVID-19 NIRST 2020d).

While the number of cases in Australian Indigenous communities has been low,

there have been outbreaks in Indigenous populations in Brazil and in Navajo Native

Americans in the USA (SBS 2020, The Guardian 2020).

41 Australia’s health 2020: data insights

Chapter

2

Cruise ship passengers

The number of people travelling on cruise ships globally has increased in recent years.

An estimated 30 million passengers travelled on cruise ships in 2019, an increase from

17.8 million in 2009 (Cruise Lines International Association 2019), and 1.35 million

Australians took a cruise in 2018 (Cruise Lines International Association Australasia

2020). Disease outbreaks can occur on cruise ships due to the large numbers of people

confined in close proximity on board (Kak 2015). Large outbreaks of COVID-19 on cruise

ships have been a feature of the early part of the pandemic. On one cruise ship, the

virus seemed to be so transmissible that the R0 onboard was calculated to be as high as 11—4 times the basic R0 of COVID-19 (Mizumoto et al. 2020). In early February 2020, the largest cluster of COVID-19 cases outside Mainland China occurred on the Diamond

Princess cruise ship docked in Yokohama Japan with 2,666 passengers (including 223

Australians) and 1,045 crew on board (Moriarty et al. 2020). The ship was quarantined

on 5 February with passengers confined to their cabins, but the crew continued to

work throughout the ship. By the end of quarantine, there were approximately 700

confirmed cases of COVID-19 among passengers and crew (Kakimoto et al. 2020). On 20

February, 164 Australians who were COVID-19 negative and not displaying symptoms

were repatriated by air to Darwin to undergo further quarantine (Department of Health

2020k). A small number subsequently developed symptoms and tested positive for

SARS-CoV-2.

A number of other cruise ships around the world have recorded COVID-19 cases,

including some in Australian waters. On 19 March, around 2,600 passengers

disembarked from the Ruby Princess cruise ship in Sydney and either returned to

their homes across Australia, or returned to their home countries. On 20 March, 3

passengers and 1 crew member tested positive for SARS-CoV-2 (NSW Health 2020c).

Subsequently, a number of cases and deaths across Australia were linked to the Ruby

Princess (ABC News 2020a).

As at 17 May, of those cases with place of acquisition recorded, 1,126 were acquired

at sea on a cruise ship, representing around 18% of these cases, and there were 26

associated deaths (COVID-19 NIRST 2020c).

42 Australia’s health 2020: data insights

Chapter

2

Comparison to previous epidemics

SARS, MERS and swine flu

It is possible to compare COVID-19 to other recent epidemics, including SARS and MERS which were also coronaviruses, and the last pandemic influenza (commonly referred to as swine flu). Some key characteristics are outlined in Table 2.3.

Table 2.3: Comparison of the characteristics of COVID-19, SARS, MERS and ‘swine flu’ epidemics

COVID-19 SARS MERS

Influenza A(H1N1) pdm09 (‘swine flu’)

Median incubation period 5-6 days 4-5 days 5 days 3-4 days

Modes of transmission

Respiratory droplet, close contact, fomites

Respiratory droplet, close contact, fomites

Respiratory droplet, close contact, possibly fomites

Respiratory droplet, close contact, fomites

Pandemic Yes (2020) No No Yes (2009)

Year(s) 2019 to present 2003-2004 2012-2020

sporadic outbreaks

Emerged in 2009, with seasonal outbreaks each year

Regions affected Global (ongoing pandemic) Mainland China, Hong Kong SAR,

Taiwan, Canada, Singapore

Saudi Arabia (2012-current)

Republic of Korea (2015)

Global (Seasonal outbreaks)

Number of global cases (during pandemic period)

5.93 million(a)

(by 31 May 2020)

8,098 2,494 491,382(a)

(laboratory-confirmed April 2009-Aug 2010))

Number of global deaths (during pandemic period)

367,200(a)

(by 31 May 2020)

774 858 18,631(a)

in laboratory-confirmed cases

Estimated case- fatality rate (based on latest data)

1.38%(c)

0.7%(d)

9.6% 34% 0.03%(b)

Basic reproduction number (R0) 2-2.5 (initial estimate

based on data from China)

2-4 (initial estimates) <1 1.7

(initial estimate)

Vaccine now available

No (candidates being tested) No No (candidate

being trialled) Yes

Transmission by pre-symptomatic/ asymptomatic cases

Yes No No Yes

(a) Laboratory-confirmed cases/deaths reported by WHO during the epidemic—likely to be an underestimate of true numbers.

(b) Estimate by Donaldson et al. 2009 using data from England.

(c) Data from China adjusted for censoring, demography, and under-ascertainment by Verity et al. 2020.

(d) Estimate from South Korea (KSID & KCDCP 2020).

43 Australia’s health 2020: data insights

Chapter

2

By 31 May, the number of COVID-19 cases and deaths worldwide had surpassed all

of these other epidemics, with over 5.9 million cases and more than 367,000 deaths.

Swine flu had the next highest number of cases (nearly 500,000) and deaths (over

18,000) during the epidemic period. It is important to note that the number of cases

and deaths reported during epidemic periods is often an underestimate of the true

number in the community. For example, a modelling study carried out after the swine

flu pandemic estimated there were 123,000-203,000 pandemic respiratory deaths

worldwide, substantially higher than the estimate available at the time of the pandemic

(Simonsen et al. 2013). As a result, case-fatality rates cannot be calculated directly from

the estimates in Table 2.3.

Comparing with the other coronaviruses first, the R 0 for COVID-19 is in the same range as SARS but higher than it was for MERS. However, the case-fatality rate for COVID-19 is

much lower than either, reflecting the higher proportion of people with mild-moderate

disease. This also increases the spread of the disease, as people with milder disease

do not require treatment in hospital—they can still go about their daily lives making

it more likely they could spread the infection to others. Further, COVID-19 can also be

spread prior to symptoms developing, and perhaps when no symptoms are present

(Arons et al. 2020).

Of the 3 epidemics, swine flu is the only other one with locally-acquired cases in

Australia. Compared with the most recent pandemic of swine flu, COVID-19 has a much

higher case-fatality rate and higher transmissibility (R0). In other words, it is much more likely to spread and is also a much more severe disease for many people. The swine flu

pandemic was able to be controlled following the development of the vaccine, however

the strain still exists and is responsible for seasonal outbreaks each year.

1918-1919 pneumonic influenza

Looking even further back takes us to the very large epidemic of pneumonic influenza

in 1918-1919, also known as the ‘Spanish flu’ (as it was first widely reported in Spain,

rather than originating there). The most detailed data are the deaths data. Information

from death registrations is used in this section, which is different to the data above on

death notifications made through the infectious disease process (see discussion in the

‘Use of data in epidemics and pandemics’ section below).

The Spanish flu caused approximately 12,000 deaths in Australia in 1919 making it the

most common cause of death in Australia that year (Cumpston 1989). In the previous 5

years, there were an average of 400 influenza deaths each year (AIHW 2020b). The 1919

figure corresponds to a crude rate of 220 deaths per 100,000 people, which is much

higher than the current death rate for COVID-19 of approximately 0.4 per 100,000.

44 Australia’s health 2020: data insights

Chapter

2

The Spanish flu deaths led to a large spike in influenza death rates, as well as the

broader respiratory death rate (Figure 2.4b and c). Despite this, it did not have a large

impact on overall death rates (Figure 2.4a). Figure 2.4d also shows the relative impact

of influenza compared with other respiratory diseases. Australia’s death rate was one

of the lowest in the world, though in some Aboriginal communities, mortality rates of

50% were recorded (National Museum of Australia 2020).

It is estimated that the Spanish flu pandemic killed at least 20 million people

worldwide, although some estimates range to 50 and 100 million (Hobbins 2019).

Similar to COVID-19, higher death rates were recorded for older people. However,

unusually for respiratory epidemics, younger, healthy adults also had high death rates

(CDC 2020b; Cumpston 1989). A vaccine was developed in Australia, which was later

evaluated to have been partially effective (National Museum of Australia 2020).

There are many reasons why the world is in a better position to control viral epidemics

today than 100 years ago. There is much more knowledge on all aspects of viruses

now, including how they spread. Health care has advanced enormously, enabling

advanced supportive care and antibiotics for secondary infections, even in the absence

of specific treatments or vaccines for a particular virus. Scientific methods have

developed substantially—for instance, the invention of the electronic microscope in the

late 1930s enabled viruses to be visualised and their structure understood. The health

of the population has improved, and communication methods mean that international

cooperation is much more possible now. Nevertheless, at this stage, successful control

of COVID-19 spread still relies on similar public health approaches to those used in

1919 such as quarantine, isolation and physical distancing.

45 Australia’s health 2020: data insights

Chapter

2

Figure 2.4: Deaths due to all causes (a), respiratory diseases (b), influenza (c)

and respiratory diseases by more detailed cause (d), Australia, 1907-2018

Source: AIHW National Mortality Database.

0

500

1,000

1,500

2,000

2,500

3,000

Males Females

Deaths per 100,000 population

0

100

200

300

400

500

600 Males Females

Deaths per 100,000 population

'Spanish flu'

0

50

100

150

200

250

300

350

Males Females

Deaths per 100,000 population

'Spanish flu'

0

5 0

1 0 0

1 5 0

2 0 0

2 5 0

3 0 0

3 5 0

4 0 0

4 5 0

5 0 0

Other respiratory

COPD

Pneumonia

Influenza

Deaths per 100,000 population

'Spanish flu'

(a)

(b)

(c)

(d)

46 Australia’s health 2020: data insights

Chapter

2

Comparison with other countries The first confirmed cases of COVID-19 outside China were in Thailand, Japan and South

Korea, which all had a small number of identified cases by 20 January (WHO 2020a).

At the start of February, the virus had already spread to 23 countries other than China,

including Australia, with the highest numbers of cases of COVID-19 in Thailand, Japan

and Singapore (WHO 2020b). By March it was in 58 countries (WHO 2020c), by April in

175 (WHO 2020d), and 182 by the start of May (WHO 2020f), indicating that very few

countries had no detected cases. At this stage, Australia has been able to manage the

epidemic very well compared with many other countries.

It is always challenging to obtain comparable data across countries to enable these

types of assessments, and even more so in the midst of a crisis. Nevertheless, due to the

importance of data in managing the pandemic, countries have been reporting on 2 main

aspects: the number of confirmed cases and the number of deaths among that group.

While there have been specifications produced to enhance comparability (WHO 2020h),

some differences remain. These are outlined in the relevant sections below.

Data compiled by the European Centre for Disease Prevention and Control (ECDC)

are used in this section. This dataset was chosen because it contained consistent

information on cases and deaths, was updated daily, and was available in an easily

accessible machine-readable format. Box 2.5 outlines some concepts for interpreting

the figures presented below containing international data.

47 Australia’s health 2020: data insights

Chapter

2

Box 2.5: Interpreting the international data in figures 2.5 and 2.6

To compare the situation in various countries, a particular presentation of the

data is used in figures 2.5 and 2.6. The following are important in interpreting

these graphs:

• The data are presented on a log scale. This is particularly useful when data are

following an exponential rather than a linear path (see Figure A below). When

the number of cases or deaths is increasing exponentially, it quickly becomes

difficult to examine the patterns in the data, as the rate of increase is so steep.

By using a log scale, the exponential curve becomes linear (see Figure B below)

with increments in multiples of 10s (1, 10, 100, 1,000 etc.) rather than 1s (1, 2,

3, 4 etc.). The steeper the line, the more quickly the numbers are increasing.

The log scale also helps comparisons between countries with very different

population sizes.

• 7-day moving averages are used to reduce the volatility in the trend lines.

This makes it clearer to see the underlying trends, rather than them being

dominated by unusual daily counts (for example, from different testing or

reporting rates over weekends or holidays). It is also very useful when the

numbers are small in some countries (small numbers are often associated

with much more volatility in the data).

• The trends are presented as the number of weeks since a certain threshold in

the number of cases or deaths was reached. Thus, comparisons are made across

countries over the trajectory of the epidemic rather than by calendar day/week,

as the epidemic took off at different times across the world. The threshold is also

used so that the focus is on when the epidemic was well established, as there

was variation in how quickly initial cases where found through testing, or spread.

Figure A Figure B

exponential

linear

Cases

exponential

linear

Cases (log)

Time Time

48 Australia’s health 2020: data insights

Chapter

2

Number of cases

It is important to recognise that the completeness of testing for SARS-CoV-2 in each

country—both in terms of the testing rates and the scope of testing (who is being

tested)—can have a large impact on the absolute number of cases. As mentioned

above, the testing rates varied substantially across countries (Russell et al. 2020),

and therefore care needs to be taken when comparing the number of cases in each

country. However, it is likely that the trends within a country are still fairly reliable

unless there are major changes in testing regimes within a country.

Despite the challenges in comparing the situation across countries, some broad

patterns can be seen. The number of cases over the course of the epidemic for

selected countries is shown in Figure 2.5, over the weeks since they experienced

an average of 30 daily cases.

For nearly all these selected countries, there was a similar trajectory in the initial days

after 30 cases had occurred. There was then variation around the peaks in the number

of cases per day (based on the 7-day moving averages) and subsequent declines where

they have occurred. One group had peaks around 500 cases per day or less (Australia,

New Zealand and South Korea), and another around 5,000 per day (such as China,

Italy and the UK). The US has had the highest peak so far at over 30,000 per day,

and have since plateaued at that level rather than declining. Other countries that have

not yet commenced a clear decline include Canada, Singapore and Sweden, and it is

notable that most countries are experiencing a very gradual decline. Brazil continues

to have very large increases, currently with over 20,000 cases per day on average.

Some countries also had further upturns (such as China, South Korea and Iran).

49 Australia’s health 2020: data insights

Chapter

2

Figure 2.5: Number of confirmed cases of COVID-19 per day in selected

countries from date when an average of 30 cases reported daily

Note: Data shown are for 7-day moving averages using data to 31 May.

Source: ECDC.

Australia

Brazil

Canada

China

Iran

Italy

New Zealand

Singapore

South Korea

Sweden

UK

USA

1

1 0

1 0 0

1 , 0 0 0

1 0 , 0 0 0

1 0 0 , 0 0 0

0 5 1 0 1 5

Cases (log)

Weeks since an average of 30 daily cases

So far, Australia has kept case numbers down to the level of other countries that

have managed to contain their epidemics and prevent the health system from being

overwhelmed. Across the countries compared here, those with the lowest rate of

confirmed cases (per 100,000 population) by 31 May were Australia (29), New Zealand

(24), South Korea (22), Japan (13) and China (6) (Table S2.2). Countries with much higher

rates (>300) were Italy, Singapore, Sweden, the UK and the US.

A country with an even lower rate of cases is Taiwan, where there is only a slightly

smaller population size than Australia. By 31 May they had 442 confirmed cases in

the country (based on ECDC data) which translates to a rate of 1.9 per 100,000. They

also did not meet the threshold of daily cases to be included in Figure 2.5. Some of the

reasons cited as to why Taiwan have been so successful in containing the virus include

experience from the 2003 SARS epidemic, acting very early (in early January they were

screening flights from Wuhan), widespread testing and contact tracing, and the use of

linked data to assist in finding suspected cases (Le Thu 2020; Wang et al. 2020).

50 Australia’s health 2020: data insights

Chapter

2

Number of deaths

Differences in detection rates across countries are compounded further in the

deaths data. Some countries focussed on deaths in hospital particularly in the earlier

phases of the pandemic, while others also included deaths in nursing homes and

in the community (Caul 2020; CDC 2020a). In addition, a higher proportion of older

people and those with particular chronic diseases in the population are important risk

factors for higher death rates. Despite these differences, it is still useful to compare the

trajectories across countries, and the number of deaths may be a better indicator of

the size of the epidemic in a country where testing rates are lower.

Figure 2.6 shows the number of daily deaths for a selected group of countries that

had at least 3 deaths per day on average. Australia and South Korea had notably lower

numbers of deaths and have been able to maintain these levels. Countries with the

highest numbers included Italy, the UK and the US, while the number of deaths in

Brazil continues to rapidly increase. It is notable that some countries are experiencing

a plateauing of the number of daily deaths, rather than a decline. Singapore and

New Zealand are not shown here as they have been able to keep their daily deaths

lower than the cut-off of 3 deaths per day on average.

51 Australia’s health 2020: data insights

Chapter

2

Figure 2.6: Number of deaths among confirmed cases of COVID-19 per day in

selected countries from date when an average of 3 daily deaths was reported

Notes

Notes

1. A large number of deaths in China were reported on 17 April that reflected reclassification of deaths from the preceding period. These deaths have been redistributed to earlier dates in the same proportion as existing deaths.

2. The y-axis commences at 1 rather than 0, to aid in presenting the key components of the trends. This means that once the average number of deaths per day goes below 1, the trends are not shown on this figure (the case for Australia, South Korea and China).

3. Data shown are for 7-day moving averages using data to 31 May.

Source: ECDC.

Australia

Brazil

Canada

China

Iran

Italy

South Korea

Sweden

UK

USA

1

10

100

1,000

10,000

0 5 10 15

Weeks since an average of 3 daily deaths

Deaths (log)

When these numbers of deaths are expressed as a rate to account for differences

in population size, Australia was in the group of countries with lower rates. Australia

(0.41), New Zealand (0.45), and all the Asian countries in the group being compared had

crude death rates lower than 1 per 100,000 (Table S2.2). Notably, while Singapore had

a relatively high case rate as indicated above, the death rate there has remained very

low (0.41 per 100,000). Italy, Sweden and the UK all had death rates over 40 per 100,000.

Taiwan had an exceptionally low rate of 0.03 per 100,000 (from 7 deaths). Box 2.6

examines the scenarios if Australia had experienced the same crude death rates as

3 comparable countries who have had larger epidemics than Australia: Canada,

Sweden and the UK. Under these scenarios, Australia would have had between 4,800

and 14,400 deaths.

52 Australia’s health 2020: data insights

Chapter

2

Box 2.6: ‘What-if’ scenarios: if Australia had not fared as well over the first 4 months

It is not possible to precisely estimate what the situation in Australia would have

been if the epidemic had not been as well contained, as we do not yet know all the

factors that influence the number of cases and deaths. However, it is of interest to

look at what did happen in other countries and then estimate what the situation

would have been in Australia if the same rates (which account for the different

sizes of populations) had applied. The simple scenarios provide some broad

context, at this early stage, of the order of magnitude of what might have been.

It is expected that more detailed research and analysis will be undertaken in the

future, taking into account more factors than is possible at this time.

A small set of countries have been chosen for this comparison—Canada, Sweden

and the UK. These countries did apply some level of travel bans or physical

distancing, though to varying degrees. However, the estimates provided here

are not an analysis of the impact of these interventions—a much more detailed

analysis would be required for that. The 3 countries are all comparable to

Australia in ways relevant to the analysis: they have similar proportions of people

over 65 (which will partly account for different population age structures), similar

health as summarised by life expectancy at birth, and similar health systems and

expenditure on health care (Table S2.2).

By the end of the first 4 months, Canada, Sweden and the UK had increasingly

higher case and death rates compared with Australia (Table S2.2):

• case rates (per 100,000) were 243 in Canada, 364 in Sweden and 410 in the UK,

while in Australia it was 29

• death rates (per 100,000) were 19, 43 and 58 in Canada, Sweden and the UK

respectively; in Australia it was 0.4.

When the rates for the other 3 countries are applied to the Australian population,

it can clearly be seen how fortunate Australia has been (Table 2.4). If those rates

had applied, Australia would have had between 8 and 14 times the number of

cases. The number of deaths under these scenarios would have also been much

higher—from around 4,800 to 14,400 deaths. These volumes of cases, and in

particular severe cases as indicated by the number of deaths, would have put

substantial pressure on the health system. Until a vaccine or specific treatment

is developed, rates in this order could still happen in Australia if exhaustive

testing, contact tracing and isolation of new cases, and the carefully considered

application of physical distancing measures do not continue (Grattan 2020).

(continued)

53 Australia’s health 2020: data insights

Chapter

2

Box 2.6: (continued) ‘What-if’ scenarios: if Australia had not fared as well over the first 4 months

Table 2.4: Scenarios in Australia if rates in Canada, Sweden and the UK had applied

Country Confirmed cases Deaths

Number Extra cases

Ratio

scenario: observed Number Extra deaths

Ratio

scenario: observed

Australia (observed)

7,277 102

Canada rates

60,816 53,539 8.4 4,770 4,668 46.8

Sweden rates

91,086 83,809 12.5 10,787 10,685 105.8

UK rates 102,552 95,275 14.1 14,425 14,323 141.4

Note: Australian data to 7 June 2020, rates from other countries to 31 May 2020.

Source: Table S2.2.

If any of these country’s death rates had applied in Australia, the deaths from

COVID-19 would have been similar in magnitude to the leading causes of death

in Australia in 2018 (Table S2.1). The Swedish and UK death rates would have

resulted in more deaths than from CHD—the leading cause of death in Australia

in 2018. Using a more direct comparison of the current situation in the UK, the

age-standardised death rate from COVID-19 in April 2020 was nearly 3 times as

high as the next cause of death, dementia, based on all deaths that occurred in

England and Wales in that month (ONS 2020).

54 Australia’s health 2020: data insights

Chapter

2

One way to manage the problem of some deaths not being classified as due to

COVID—leading to potential undercounts of deaths in some countries—is to undertake

analysis of ‘excess deaths’ (Leon et al. 2020). This compares the counts of all deaths

observed in the country to the expected counts based on patterns from previous

non-pandemic years. As an illustration, the EuroMOMO (European mortality

monitoring) network has been monitoring excess deaths for 24 participating

European countries and has observed an increase in weekly excess deaths since

week 12 of 2020, compared with 2018 and 2019, for people of all ages (Figure 2.7).

The number of excess deaths declined back towards the baseline by week 20.

Similar analysis for Australia is not likely to show a significant impact at this stage

due to the small number of deaths, though there may be interest in whether or how

the measures put in place in response to the epidemic have affected mortality rates.

Figure 2.7: Weekly excess deaths for the EuroMOMO network of countries,

all ages, 2018-2020

Note: Participating countries include: Austria, Belgium, Denmark, Estonia, Finland, France, Germany (Berlin), Germany (Hesse), Greece, Hungary, Ireland, Italy, Luxembourg, Malta, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, UK (England), UK (Northern Ireland), UK (Scotland), UK (Wales).

Source: EuroMOMO.

– 2,500

2,500

7,500

12,500

17,500

22,500

27,500

32,500

37,500

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52

Week

2018 2019 2020

Excess deaths

Baseline

55 Australia’s health 2020: data insights

Chapter

2

Indirect effects The information above on the epidemic so far in Australia covers the direct,

short-term effects of COVID-19. However, there are also a number of potential

indirect effects from changes within the health system and changes in wider society

due to interventions put in place (Douglas et al. 2020). This section seeks to outline

some of these effects, but not to provide full detail.

Impacts on the health system

Key aims of the measures taken to control the COVID-19 epidemic were to protect all

parts of the health system from being overwhelmed and to protect health care workers

from infection as much as possible. Areas of particular concern were the public health

sector, general practitioners (GPs), ICUs and other parts of the hospital system. In

particular, overseas experience had shown the importance of controlling the number

of people requiring ICU care by reducing the number of infections (Remuzzi & Remuzzi,

2020). Overseas experience had also shown the vulnerability of health care workers to

being infected by the virus (ECDC 2020a). Due to the success in delaying and containing

the virus in Australia, the impact on hospitals and health care workers has been

managed and preparations made for any future increase in cases.

A number of changes have been made to the Australian health system during the

epidemic. Some were to treat the initial COVID-19 cases and to prevent transmission

of the virus to other people, including new types of health care settings such as

respiratory clinics and drive-through testing clinics. Other changes aimed to prepare

for a potential surge in the number of COVID-19 patients, such as sourcing many more

ventilators and establishing agreements with private hospitals for support if required.

While the number of COVID-19 cases requiring health care has remained manageable

to date, including in ICUs, if the need increases, it is not known whether this will reduce

available resources for the care of people with other diseases. There were also other

considerations in the changes made, such as reducing the chance of exposing people

to infection from the virus through elective surgery.

There have been changes in people’s use of other types of health care. Initially,

lower priority elective surgery was cancelled, and it is not yet known what the impact

of the consequential delay will be for the health of the population. There has also

been concern that people may delay their usual care (such as management of chronic

conditions (WHO 2020l) to avoid exposure to the virus. It is encouraging that, for the

period 1 January to 30 April, total GP visits (including telehealth services) were in fact

higher than the same period last year: they had increased by 3.9% (AIHW analysis

of Medicare Benefits Schedule (MBS) data on 3 June, adjusted for working days).

56 Australia’s health 2020: data insights

Chapter

2

This is larger than the annual population increase to the end of September 2019

of 1.5% (ABS 2019) While this indicates there has not been a decline for this key

component of primary care provision—though there may have been variation across

the population or parts of the country—there were declines in elective surgery due

to the enforced bans and it is possible that other parts of the health system have had

declines in use as well.

The changes made to increase the availability of telehealth services (PM&C 2020),

aimed to protect patients and health care workers from potential infection. These

services have been used in large numbers to date. During March and April 2020,

1 in 5 (20%) MBS-subsidised GP visits used telehealth methods compared with less

than 1% of GP visits in the same period in 2019 (AIHW analysis of MBS data on 3 June).

Broader effects on health and welfare

The large scale changes to society that were required to contain this virus also

have a number of potential adverse health and welfare effects, although there are

interventions that can be put in place to reduce the risk of these. At this stage we are

still likely to be experiencing the earlier effects of the COVID-19 epidemic, but clearly

these longer-term effects will need to be monitored into the future.

Loneliness and mental health effects

The need for as many people to stay at home as possible to increase physical

distancing meant that many people were isolated from family, friends and other

support networks. By mid-April, based on self-reported information in the Australian

Bureau of Statistics (ABS) household impact of COVID-19 survey, one-third of Australian

adults had reduced the frequency of contact with family and friends since the start

of the COVID-19 epidemic, and the most commonly reported personal stressor was

loneliness—reported by 22% of people (ABS 2020c). A longitudinal survey showed that,

in April 2020, 41% of male and 50% of female respondents felt lonely at some time, but

those percentages decreased to 31% and 40% respectively in May (Biddle et al. 2020b).

The initial impacts of the epidemic in Australia appear to have increased levels of

psychological distress. This was particularly the case for those in age groups in the

range 18-44 years, where there were statistically significant increases in levels of

distress using the Kessler (K6) scale when comparing April 2020 estimates with those

from February 2017 (Biddle et al. 2020b).

To provide more information on the broader effects of the COVID-19 epidemic, the

AIHW is compiling data on the use of mental health services and from the various

crisis help lines, as well as data on the use of homelessness services.

57 Australia’s health 2020: data insights

Chapter

2

Changes to health risk factors

The large changes in society may have other effects such as changes to diet, a

reduction in incidental physical activity or increases in alcohol use. During the period

April and early May, based on self-reported information, 22% of adults increased their

intake of unhealthy snack foods and 20% decreased physical activity (although 25%

increased it) (ABS 2020c). Around 1 in 5 reported they had increased their alcohol

consumption since the spread of COVID-19 (17.9% of males, 22.8% of females), and a

little more than 1 in 4 said they had decreased their consumption (27.5% for males,

26.7% for females) (Biddle et al. 2020a). For those who increased their consumption,

45.8% said the increase was 1-2 standard drinks per week, and 27.8% reported an

increase of 3-4 standard drinks per week.

Labour force and income changes

The large-scale loss of employment and the general economic downturn added to the

challenges mentioned above, and we know that these and other social determinants

are important for an individual’s health and wellbeing.

By April, 31% of Australian adults’ household finances had worsened due to COVID-19,

based on self-reported data (ABS 2020b). Further, longitudinal data show that per

person after-tax income decreased by 8.2% between February and May. However,

this decline occurred prior to May as there was an increase in after-tax income of 1%

between April and May (Biddle et al. 2020c).

Behind these numbers are dramatic changes in the labour market. During April,

employment fell by 594,300 people which was nearly 5% of total employment (ABS

2020e). In addition, average hours worked for those employed fell by 9.2%. Together,

these figures meant that around 20% of those employed in March either left their

employment or had their work hours reduced in April.

A number of government programs were put in place aiming to reduce the impact of

these labour market changes. Two of the largest were the doubling of the JobSeeker

(unemployment) payment and the introduction of the JobKeeper program, which

provides wage subsidies to eligible businesses for payment to their employees.

Overall, between 28 February and 22 May 2020, the number of recipients receiving

unemployment payments (including JobSeeker, Bereavement Allowance, Sickness

Allowance and Youth Allowance (Other)) doubled—an increase of nearly 825,000

recipients over this period (Commonwealth of Australia 2020; Services Australia 2020).

As at 20 May 2020, around 2.9 million employees from nearly 760,000 businesses

had received benefits under the JobKeeper program, totalling $8.7 billion in approved

payments (The Treasury 2020).

58 Australia’s health 2020: data insights

Chapter

2

As mentioned earlier, evidence from other countries shows marked inequalities

in direct COVID-19 impacts (PHE 2020) and it is expected that there will also be

inequalities in the indirect effects which will not be apparent for some time.

However, in the early stages of the epidemic in Australia, people whose incomes

were lower prior to the epidemic generally experienced an increase in income due

to government support measures. On the other hand, people whose incomes were

higher before the epidemic experienced a fall in income (Biddle et al. 2020c).

Children and families

All parts of society have been affected by the short-term impacts of the response to

COVID-19, including children and families. A large proportion of children were schooled

or cared for at home—76% of Australians with children in their household kept them

at home from school or childcare (ABS 2020d)—potentially putting pressure on families

and their workplaces to accommodate this, and on children academically and socially.

In times of major crisis, such as natural disasters and disease epidemics, the risk of

family and domestic violence can also increase (Peterman et al. 2020; van Gelder

et al. 2020). Early evidence of increases in family and domestic violence in Australia

are mixed and subject to the complexities in detecting these forms of violence.

For example, the number of domestic violence assaults reported to or detected by

NSW Police in March and April 2020 was similar (March), or lower (April) than the

corresponding months in 2019 (Freeman 2020a; Freeman 2020b). However, Freeman

(2020a) notes ‘it is possible that an increase has been masked by isolation strategies

affecting victim willingness or ability to seek assistance from police’. International

literature suggests that children are at increased risk of abuse and neglect during the

COVID-19 crisis (UNICEF 2020). As for domestic violence, it may be difficult to detect

and respond to such abuse in the short term, particularly in light of the fact that school

personnel are the second most likely profession to draw suspected child abuse to the

attention of authorities (behind police) (AIHW 2020a). Finally, the number of calls to

the Men’s Referral Service (a family violence telephone counselling, information and

referral service) increased by 37% in the last week of April compared with the same

period in 2019 (ABC News 2020d), which may indicate an underlying issue not yet

apparent in other data.

Evidence on the early impact of the COVID-19 epidemic on older adult victims

specifically is limited. However, as COVID-19 has had substantial economic impact and

people who commit elder abuse are more likely to be financially dependent on the

older victim, an increased risk of elder abuse has been noted (Storey & Rogers 2020).

59 Australia’s health 2020: data insights

Chapter

2

Potential positive effects

Despite the challenges outlined above, the changes to society during the epidemic

may have some positive health effects. An early example is the reduction in influenza

cases. In April, the number of laboratory-confirmed cases of influenza was 98.4% lower

than in April 2019, and 85% lower than in April 2018 (Department of Health 2020i).

It is plausible that the dramatic reduction in influenza cases reflects measures taken to

address COVID-19, such as physical distancing and the closure of schools (as children

are major drivers of influenza transmission in the community). These findings are also

supported by broader tracking of respiratory illnesses in New South Wales where low

positive rates for influenza testing, decreased pneumonia presentations at hospital

and a decrease in flu-like symptoms for this time of year were linked to the community

restrictions and physical distancing in place (NSW Health 2020b). It is also possible

that the increased uptake of influenza immunisation has played a role—although it

may be too early to see the beneficial effect of increased vaccination uptake—or that

there was reduced influenza testing during part of the period. Other potential positive

health effects include reduction in mortality from traffic accidents and air pollution,

particularly CHD and stroke deaths (Chen et al. 2020; Shilling & Waetjen 2020;

Toffolutti & Suhrcke 2014). It is very complex to weigh the positive and negative

effects against each other (Holden & Preston 2020).

Use of data in epidemics and pandemics Different types of data are being used in a number of new ways during the response

to COVID-19 in order to gain insight and an understanding of how the SARS-CoV-2 virus

is spreading across the world and the impact it is having on populations. During this

and other crises, there is a strong need to obtain data as quickly as possible. These

data may not be perfect but they are needed immediately to be able to manage

the situation.

Current data systems expanded

When the first cases of COVID-19 were confirmed in Australia, there was rapid

development and rollout of enhanced data fields for COVID-19 in the NNDSS data

supplied daily by the states and territories for collation into the national dataset.

This shows adaption of the passive surveillance dataset in the current critical situation.

CDNA set up a COVID-19 Working Group who developed a COVID-19 National

Surveillance Plan to guide surveillance activities and provide critical evidence to

inform public health responses (Department of Health 2020b).

60 Australia’s health 2020: data insights

Chapter

2

A range of existing complementary infectious disease surveillance systems have also

been expanded or used differently during this crisis. Many of these were part of the

National Influenza Surveillance Scheme, which monitors and reports on aspects of

influenza severity, incidence and virology (Sullivan et al. 2020). An example is the

Influenza Complications Alert Network (FluCAN-PAEDS), which is a sentinel hospital

surveillance system for people with confirmed influenza who require hospitalisation.

It has been expanded to also capture information on hospitalised cases of COVID-19,

and has increased its coverage of participating hospitals. Similarly, the Australian

Sentinel Practices Research Network, a network of sentinel general practitioners that

collects information on influenza-like illness (ILI) presentations in general practice,

including test positivity, was also expanded to include COVID-19 cases. In addition,

FluTracking, an online health surveillance system which collects information on ILI

in the community during the influenza season, began its survey early and expanded

its list of questions to capture the impact of COVID-19 and provide early warning of

increased respiratory illness in the community.

Other data collections were established in response to COVID-19. Some of these

related to the urgent need for timely data on hospital capacity and activity to assess

health system capacity to respond to the pandemic. For example, the AIHW has

worked with the states and territories to collect daily data on emergency departments,

admitted patients and elective surgery, as well as data from the newly created Critical

Health Resource Information System (CHRIS). The CHRIS was developed in response

to COVID-19 by the Australian and New Zealand Intensive Care Society, Ambulance

Victoria and Telstra Purple and covers ICU capacity and activity (Hunt 2020).

Developments and innovations since previous epidemics

There has been a notable increase in the amount of data provided to the public

during this epidemic compared with previous ones. The Australian, state and territory

governments have all used tools to keep the public informed of the situation in

near real time. For example, many have used some type of summary infographic or

dashboard to communicate the daily situation (DHHS Victoria 2020; NSW Health 2020d)

and to engage the public in the efforts to contain the virus. There has also been more

use of innovative data presentations by the media and research institutions (ABC News

2020b; Dong et al. 2020; Financial Times 2020). Sophisticated modelling of the epidemic

and potential future progression have been important for informing governments and

the public (Costantino et al. 2020; Moss et al. 2020).

61 Australia’s health 2020: data insights

Chapter

2

Genomic sequencing data

Recent developments in scientific knowledge have supported enhanced management

of this epidemic. Very soon after the cluster of novel coronavirus cases was reported to

the WHO, China shared the genomic sequence (WHO 2020k), and the virus was found

to be closely related to 2 bat-derived SARS-like coronaviruses (Lu et al. 2020). Genomic

sequencing is being used more routinely in disease outbreak investigations, as it is now

cheaper and easier to perform. During the COVID-19 pandemic an open source online

resource called NextStrain (www.nextstrain.org) is tracking SARS-CoV-2 genomes in

real-time as they are released. The information provided by sequence data can assist

in determining the origins of a viral outbreak and allows monitoring of virus mutations.

Keeping track of how a virus changes can help public health officials contain the spread

and can also assist with drug and treatment development. Understanding where

mutations occur in the virus is also important to inform the development of vaccines.

Mobile phone data

Innovative use of mobile phone data has also been a feature of this pandemic.

Most countries have introduced measures to reduce movement within and between

countries and interaction between people (‘physical distancing’) to reduce the spread

of the SARS-CoV-2 virus. Some countries have put these measures in rapidly and strictly

to substantially reduce transmission (for example, mainland China and New Zealand),

while others have not implemented them as strictly (for example, Japan and Sweden).

Data from mobile phones have been used to track the movement of populations in

response to public health interventions, for instance in Wuhan, China, to assess the

effectiveness of quarantine measures (Jia et al. 2020).

The large technology companies Google and Apple have publically released aggregated,

anonymised mobility data from their mapping products to provide insight into

movement trends over time in response to public health measures introduced during

the COVID-19 pandemic. The mobility data released by Apple for Australia shows

a substantial decrease in mobility in the categories of driving, walking and public

transport from early March as public health measures were introduced to reduce

transmission of the virus in the community (Figure 2.8).

62 Australia’s health 2020: data insights

Chapter

2

Figure 2.8: Apple mobility index, Australia, 13 Jan to 30 May 2020

Note: The data represent daily changes in requests for directions in Apple Maps by transportation type (driving, walking or public transport), compared to a baseline volume on 13 January 2020. The baseline is shown as a red line at 100. No data were available for 11 and 12 May.

Source: Apple Mobility Trends; https://www.apple.com/covid19/mobility.

0

20

40

60

80

100

120

140

160

Date

Mobility index

Driving Walking Public transport

13/01 20/01 27/01 3/02 10/02 17/02 24/02 2/03 9/03 18/03 23/03 30/03 6/04 27/04 13/04 20/04 4/05 11/05 18/05 25/05

A number of countries have introduced mobile phone applications to assist in the

contract tracing work needed as part of this epidemic, including South Korea, Singapore

and the UK. Australia introduced its own voluntary application, COVIDSafe, in late April

(Department of Health 2020f). By 17 May, there had been around 5.7 million downloads

of the application (COVID-19 NIRST 2020c). These applications use various features

of people’s phones so that their interactions with other people can be traced if either

they, or one of the people they have been in contact with, contract the virus. This

can complement an individual’s recall of contacts (they may not remember or know

all people they were in contact with) and also speed up the notification of potential

exposure to these contacts.

Linked data

Some countries have been able to use linked administrative data sets to assist with their

response to COVID-19 (Park et al. 2020). Taiwan, for example, integrated their national

health insurance database with immigration and customs data to develop real-time

alerts to aid case identification (Wang et al. 2020).

63 Australia’s health 2020: data insights

Chapter

2

There is the potential to use de-identified linked administrative data in Australia to

enhance disease surveillance, monitoring and research. This could also include linkage

between health and welfare (social) data sets, providing information on risk factors,

vulnerable populations, outcomes and treatment/vaccination efficacy. For example,

linked data (such as Medicare and pharmaceutical data) could allow analysis of

longer-term patient outcomes after they have recovered from COVID-19 and their use

of the health system.

This could provide important evidence to inform future planning, particularly if

there are further waves of disease and if a vaccine is developed and administered to

protect the population. The use of data linkage has been increasing in Australia and is

recognised as a cost-effective method for filling data gaps and enhancing the value of

health data (Rowe et al. 2019).

Data during crisis may differ to final data

During a crisis, having data quickly is important. This can mean some usual quality

checks and processes cannot be completed. For example, deaths data used in this

chapter may differ compared with the final death registration data, which will be

available in the future for official cause of death reporting. The deaths data reported

here are those notified as part of the NNDSS. While the NNDSS is currently receiving

reliable information on COVID-19 associated deaths, it may not capture instances

where COVID-19 is a contributing factor to the death that occurs after the case has

recovered from their initial infection. This is because cases are discharged from public

health monitoring once they have recovered. It is also not possible from these data

to determine whether COVID-19 was the main (underlying) or an associated cause of

death (the terminology used in the official deaths data).

In contrast, deaths data collected through the death registration process takes time to

prepare as deaths must be certified by a doctor, registered and processed to ensure

the data are as accurate as possible. These data cover all deaths occurring in Australia,

and include causes of death information coded using the International Classification

of Diseases (ICD) 10th Revision (ABS 2020a). There have been specific emergency ICD

codes developed by the WHO for COVID-19, which allows a death to be coded as a

confirmed or suspected COVID-19 case (WHO 2020i). Notably, from these data it will

be possible to determine whether COVID-19 was the underlying cause of death or an

associated cause.

64 Australia’s health 2020: data insights

Chapter

2

In Australia, the full registration based cause of death dataset is typically released by

the ABS approximately 9 months after the end of a particular reference period. In 2020,

the ABS has brought forward coding of data (using ICD codes) to enable the release of

provisional mortality data on a monthly basis. These interim reports are designed to

enable early detection of changes in patterns of mortality by key causes of death and

bring forward the reporting of deaths substantially. Data contained in these reports

can still lag by several weeks, reflecting both the legislative requirements around death

registration in Australia and the need to enable meaningful comparison with historic data.

The first publication of the provisional deaths data (excluding coroner-certified deaths)

was released on 24 June (ABS 2020f). There had been 89 death registrations received

by the ABS by the end of May—less than the number reported through the NNDSS

(102)—though further registrations are still expected for this period. The release also

included provisional data for the period 1 January to 31 March on all deaths (to enable

excess mortality analysis) and selected causes of death. More detailed analysis will be

important as further data become available, enhancing the evidence base on the direct

and indirect effects of the epidemic

Next steps As a continually evolving situation, there are many things we still do not fully

understand about COVID-19. Data are continuing to be collected and will be analysed

to provide further clarity in the coming months.

As well as continuing to monitor the various aspects outlined in this chapter, there are

many other aspects of the COVID-19 epidemic that will still need further analysis.

Some of the unknown characteristics of the virus and disease are outlined in Box 2.7.

65 Australia’s health 2020: data insights

Chapter

2

Box 2.7 Some remaining questions about COVID-19

• What proportion of cases are truly asymptomatic throughout the course of their infection?

• Are asymptomatic cases more likely, less likely or just as likely to generate secondary cases compared with cases who are unwell?

• Why do children appear to be less susceptible to contract and transmit COVID-19 compared with other respiratory tract infections, such as influenza?

• Is the multisystem inflammatory disorder seen in children in the Northern Hemisphere caused by SARS-CoV-2?

• Can a person get COVID-19 twice? Is immunity developed and how long does it last?

• Will clinical trials show that certain medications or antibody-derived therapy improve the outcome of COVID-19?

• Will a vaccine be developed for SARS-CoV-2?

• Will there be a second wave of infections?

As well as health data mentioned throughout this chapter, there are other datasets

that will help us understand the broader health and social impacts. These include data

on homelessness, mental health, employment and Centrelink payments. The AIHW are

planning to produce further reports using relevant new data as they become available.

66 Australia’s health 2020: data insights

Chapter

2

References ABC News 2020a. Australia’s coronavirus death toll rises after 81yo Ruby Princess passenger becomes latest fatality. Viewed 13 May 2020, https://www.abc.net.au/news/2020-05-13/ australia-coronavirus-death-toll-rises-ruby-princess-fatality/12239626.

ABC News 2020b. Charting the COVID-19 spread in Australia. Digital Story Innovation Team. Published 17 Mar 2020; Updated 8 Jun 2020. Viewed 10 June 2020, https://www.

abc.net.au/news/2020-03-17/coronavirus-cases-data-reveals-how-covid-19-spreads-in-australia/12060704?nw=0.

ABC News 2020c. How to get a coronavirus test: who is eligible, how many have been done and how are they carried out. Viewed 10 June 2020, https://www.abc.net.au/news/2020-03-27/ coronavirus-covid-19-testing-criteria-eligibility/12097990.

ABC News 2020d. Inside the Men’s Referral Service, a call centre dealing with Australia’s abusive men and domestic violence. Viewed 12 June 2020, https://www.abc.net.au/news/2020-05-03/ mens-referral-service-family-violence-coronavirus/12207558.

ABS (Australian Bureau of Statistics) 2019. Australian Demographic Statistics, Sep 2019. ABS cat. no. 3101.0. Canberra: ABS.

ABS 2020a. Guidance for Certifying Deaths due to COVID-19. 25 March 2020. Viewed 12 May 2020, https://www.abs.gov.au/ausstats/abs@.nsf/mf/1205.0.55.001?OpenDocument.

ABS 2020b. Household Impacts of COVID-19 Survey, 14-17 Apr 2020. ABS cat. No. 4940.0. Canberra: ABS.

ABS 2020c. Household Impacts of COVID-19 Survey, 29 Apr-4 May 2020. ABS cat. No. 4940.0. Canberra: ABS.

ABS 2020d. Household Impacts of COVID-19 Survey, 12-15 May 2020. ABS cat. No. 4940.0. Canberra: ABS.

ABS 2020e. Labour Force, Australia, Apr 2020. ABS cat. no. 6202.0. Canberra: ABS.

ABS 2020f. Provisional Mortality Statistics, Jan-Mar 2020. ABS cat. no. 3303.0.55.004. Canberra: ABS.

Aged Care Quality and Safety Commission 2020. The Commission’s regulatory actions in response to Newmarch House outbreak. Media release by the Aged Care Quality and Safety Commissioner. 6 May. Canberra. Viewed 12 May 2020, https://www.agedcarequality.gov.au/ sites/default/files/media/20200506%20Commission%27s%20regulatory%20actions%20in%20 response%20to%20Newmarch%20House%20outbreak.pdf.

Andersen KG, Rambaut A, Lipkin WI, Holmes EC & Garry RF. The proximal origin of SARS-CoV-2. Nature Medicine. 2020;26(4):450-452. doi:10.1038/s41591-020-0820-9.

Arons M, Hatfield KM, Reddy SC, Kimball A, James A et al. 2020. Presymptomatic SARS-CoV-2 infections and transmission in a skilled nursing facility. New England Journal of Medicine. April 24. DOI: 10.1056/NEJMoa2008457.

AIHW (Australian Institute of Health and Welfare) 2016. Australian Burden of Disease Study: impact and causes of illness and death in Aboriginal and Torres Strait Islander people 2011. Cat. no. BOD 7. Canberra: AIHW.

AIHW 2019. Australian Burden of Disease Study: impact and causes of illness and death in Australia 2015. Australian Burden of Disease series no. 19. Cat. no. BOD 22. Canberra: AIHW.

67 Australia’s health 2020: data insights

Chapter

2

AIHW 2020a. Child protection Australia 2018-19. Child welfare series no. 72. Cat. no. CWS 74. Canberra: AIHW.

AIHW 2020b. General Record of Incidence of Mortality (GRIM) data. Cat. no. PHE 229. Canberra: AIHW.

Biddle N, Edwards B, Gray M & Sollis K 2020a. Alcohol consumption during the COVID-19 period: May 2020. ANU Centre for Social Research and Methods. Canberra: ANU.

Biddle N, Edwards B, Gray M & Sollis K 2020b. Initial impacts of COVID-19 on mental health in Australia. ANU Centre for Social Research and Methods. Canberra: ANU.

Biddle N, Edwards B, Gray M & Sollis K 2020c. Tracking outcomes during the COVID-19 pandemic (May 2020) - Job and income losses halted and confidence rising. ANU Centre for Social Research and Methods. Canberra: ANU.

Caul S 2020. Counting deaths involving the coronavirus (COVID-19). Viewed April 3 2020, https://blog.ons.gov.uk/2020/03/31/counting-deaths-involving-the-coronavirus-covid-19/.

CDC (Centers for Disease Control and Prevention) 2020a. FAQ: COVID-19 data and surveillance. Viewed 26 May 2020, https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html.

CDC 2020b. History of 1918 flu pandemic. Viewed 13 May 2020, https://www.cdc.gov/flu/ pandemic-resources/1918-commemoration/1918-pandemic-history.htm.

CDNA (Communicable Diseases Network Australia) 2017. A practical Guide to assist in the Prevention and Management of Influenza Outbreaks in Residential Care Facilities in Australia. https://www1.health.gov.au/internet/main/publishing.nsf/Content/cdna-flu-guidelines.htm .

CDNA 2020a. Coronavirus Disease 2019 (COVID-19) CDNA National Guidelines for Public Health Units. Version 2.5 published 6 April 2020. https://www1.health.gov.au/internet/main/publishing.

nsf/Content/cdna-song-novel-coronavirus.htm.

CDNA 2020b. Coronavirus Disease 2019 (COVID-19) CDNA National Guidelines for Public Health Units. Version 3.0 published 28 May 2020.

CDNA 2020c. National Guidelines for the Prevention, Control and Public Health Management of COVID-19 Outbreaks in Residential Care Facilities in Australia. https://www.health.gov.au/sites/ default/files/documents/2020/03/coronavirus-covid-19-guidelines-for-outbreaks-in-residential-care-facilities.pdf.

Chang SL, Harding N, Zachreson C, Cliff OM & Prokopenko M 2020. Modelling transmission and control of the COVID-19 pandemic in Australia. Viewed 9 June 2020, https://arxiv.org/ abs/2003.10218.

Chen K, Wang M, Huang C, Kinney PL & Anastas PT 2020. Air pollution reduction and mortality benefit during the COVID-19 outbreak in China. The Lancet. Planetary health, 10.1016/S2542-5196(20)30107-8. Advance online publication. https://doi.org/10.1016/S2542-5196(20).

Cheng AC & Williamson DA 2020. An outbreak of COVID-19 caused by a new coronavirus: what we know so far. The Medical journal of Australia, 212(9), 393-394.e1. https://doi.org/10.5694/ mja2.50530.

Churches T & Jorm L 2020. We can “shrink” the COVID-19 curve, rather than just flatten it. Viewed 15 May 2020, https://newsroom.unsw.edu.au/news/health/we-can-shrink-covid-19-curve-rather-just-flatten-it .

68 Australia’s health 2020: data insights

Chapter

2

Commonwealth of Australia 2020. Senate Select Committee on COVID-19 Proof Committee Hansard, Tuesday 2 June 2020. Viewed 11 June 2020, https://parlinfo.aph.gov.au/ parlInfo/download/committees/commsen/b913893e-51b9-402e-ab95-3de8f4bb58b5/ toc_pdf/Senate%20Select%20Committee%20on%20COVID-19_2020_06_02_7747.

pdf;fileType=application%2Fpdf#search=%22committees/commsen/b913893e-51b9-402e-ab95-3de8f4bb58b5/0000%22.

Costantino V, Heslop DJ & MacIntyre CR 2020. The effectiveness of full and partial travel bans against COVID-19 spread in Australia for travellers from China during and after the epidemic peak in China. Journal of travel medicine, taaa081. Advance online publication.

COVID-19 NIRST (National Incident Room Surveillance Team) 2020a. COVID-19, Australia: Epidemiology Report 13: Reporting week ending 26 April 2020. Communicable Diseases Intelligence Volume 44.

COVID-19 NIRST 2020b. COVID-19, Australia: Epidemiology Report 14: Reporting week ending 3 May 2020. Communicable Diseases Intelligence Volume 44.

COVID-19 NIRST 2020c. COVID-19, Australia: Epidemiology Report 16: Reporting week ending 17 May 2020. Communicable Diseases Intelligence Volume 44.

COVID-19 NIRST 2020d. COVID-19, Australia: Epidemiology Report 17: Fortnightly reporting period ending 24 May 2020. Communicable Diseases Intelligence Volume 44.

COVID-19 NIRST 2020e. COVID-19, Australia: Epidemiology Report 18: Fortnightly reporting period ending 7 June 2020. Communicable Diseases Intelligence Volume 44.

Cruise Lines International Association 2019. 2019 cruise trends & industry outlook. Washington, DC: Cruise Line International Association. https://cruising.org/news-and-research/-/media/CLIA/ Research/CLIA-2019-State-of-the-Industry.pdf.

Cruise Lines International Association Australasia 2020. 2018 Australian ocean source market. Viewed 22 May 2020, https://cruising.org/-/media/research-updates/research/clia_2019-source-market-reports_australia.pdf.

Cumpston JHL 1989. Health and disease in Australia: a history (Lewis ML ed.) Canberra: AGPS.

Davis JS, Ferreira D, Denholm JT, Tong SYC 2020. Clinical trials for the prevention and treatment of coronavirus disease 2019 (COVID-19): the current state of play. Medical Journal of Australia. https://www.mja.com.au/journal/2020/clinical-trials-prevention-and-treatment-coronavirus-disease-2019-covid-19-current [Preprint, 27 April 2020].

Delamater PL, Street EJ, Leslie TF, Yang Y & Jacobsen KH 2019. Complexity of the basic reproduction number (R0). Emerging Infectious Diseases, 25(1), 1-4. https://dx.doi.org/10.3201/ eid2501.171901.

Department of Health 2015. Introduction to the National Notifiable Diseases Surveillance System. Viewed 12 May 2020, https://www1.health.gov.au/internet/main/Publishing.nsf/ Content/cda-surveil-nndss-nndssintro.htm.

Department of Health 2020a. Australian Health Sector Emergency Response Plan for Novel Coronavirus (COVID-19). Viewed 26 March 2020, https://www.health.gov.au/resources/ publications/australian-health-sector-emergency-response-plan-for-novel-coronavirus-covid-19.

Department of Health 2020b, Australian National Disease Surveillance Plan for COVID-19. Viewed 2 June 2020, https://www.health.gov.au/sites/default/files/documents/2020/05/ australian-national-disease-surveillance-plan-for-covid-19.pdf.

69 Australia’s health 2020: data insights

Chapter

2

Department of Health 2020c. Coronavirus (COVID-19) advice for people with chronic health conditions. Viewed 29 May 2020, https://www.health.gov.au/news/health-alerts/novel-coronavirus-2019-ncov-health-alert/advice-for-people-at-risk-of-coronavirus-covid-19/ coronavirus-covid-19-advice-for-people-with-chronic-health-conditions.

Department of Health 2020d. Coronavirus (COVID-19) at a glance infographic. 31 May 2020. Viewed 5 June 2020, https://www.health.gov.au/resources/collections/coronavirus-covid-19-at-a-glance-infographic-collection.

Department of Health 2020e. Coronavirus (COVID-19) at a glance infographic. 9 June 2020. Viewed 10 June 2020, https://www.health.gov.au/sites/default/files/documents/2020/06/ coronavirus-covid-19-at-a-glance-coronavirus-covid-19-at-a-glance-infographic_7.pdf.

Department of Health 2020f. COVIDSafe app. Viewed 11 May 2020, https://www.health.gov.au/ resources/apps-and-tools/covidsafe-app.

Department of Health 2020g. Fact Sheet Families and residents on restricted visits to residential aged care facilities. Viewed 8 April 2020, https://www.health.gov.au/sites/default/files/ documents/2020/04/coronavirus-covid-19-information-for-families-and-residents-on-restricted-visits-to-residential-aged-care-facilities.pdf.

Department of Health 2020h. Good hygiene for coronavirus (COVID-19). Viewed 5 June 2020. https://www.health.gov.au/news/health-alerts/novel-coronavirus-2019-ncov-health-alert/how-to-protect-yourself-and-others-from-coronavirus-covid-19/good-hygiene-for-coronavirus-covid-19.

Department of Health 2020i. National Notifiable Diseases Surveillance System online data report number 3. Viewed 27 May 2020, http://www9.health.gov.au/cda/source/rpt_3.cfm.

Department of Health 2020j. Public Health Laboratory Network. Viewed 27 May 2020, https://www1.health.gov.au/internet/main/publishing.nsf/Content/cda-cdna-phln-index.htm.

Department of Health 2020k. Two Diamond Princess passengers positive for COVID-19. Media release 21 Feb. https://www.health.gov.au/news/two-diamond-princess-passengers-positive-for-covid-19.

Department of Health 2020l. What you need to know about coronavirus (COVID-19). Viewed 12 May 2020, https://www.health.gov.au/news/health-alerts/novel-coronavirus-2019-ncov-health-alert/what-you-need-to-know-about-coronavirus-covid-19#testing.

DHSS (Department of Health and Human Services) Victoria 2020. Coronavirus (COVID-19). Viewed 10 June 2020, https://www.dhhs.vic.gov.au/coronavirus.

Doherty Institute 2020. Doherty Institute scientist first to grow and share 2019 novel coronavirus. Viewed 27 May 2020, https://www.doherty.edu.au/news-events/news/coronavirus.

Donaldson LJ, Rutter PD, Ellis BM, et al. 2009. Mortality from pandemic A/H1N1 2009 influenza in England: public health surveillance study. BMJ (Clinical research ed.), 339, b5213. https://doi.org/10.1136/bmj.b5213.

Dong E, Du H & Gardner L 2020. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infectious Diseases. 20(5):533-534. doi:10.1016/S1473-3099(20)30120-1.

Douglas M, Katikireddi SV, Taulbut M, McKee M, & McCartney G 2020. Mitigating the wider health effects of covid-19 pandemic response. BMJ (Clinical research ed.), 369, m1557. https://doi.org/10.1136/bmj.m1557.

70 Australia’s health 2020: data insights

Chapter

2

ECDC (European Centre for Disease Prevention and Control) 2020a. Coronavirus disease 2019 (COVID-19) in the EU/EEA and the UK—ninth update, 23 April 2020. Stockholm: ECDC. https:// www.ecdc.europa.eu/sites/default/files/documents/covid-19-rapid-risk-assessment-coronavirus-disease-2019-ninth-update-23-april-2020.pdf.

ECDC 2020b. Download today’s data on the geographic distribution of COVID-19 cases worldwide. Viewed 22 June 2020, https://www.ecdc.europa.eu/en/publications-data/ download-todays-data-geographic-distribution-covid-19-cases-worldwide.

Financial Times 2020. Coronavirus tracked: the latest figures as countries fight to contain the pandemic. Viewed 10 June 2020, https://www.ft.com/content/a26fbf7e-48f8-11ea-aeb3-955839e06441.

Freeman K 2020a. Has domestic violence increased in NSW in the wake of COVID-19 social distancing and isolation? Update to April 2020 (Bureau Brief No. 146). Sydney: NSW Bureau of Crime Statistics and Research.

Freeman K 2020b. Monitoring changes in domestic violence in the wake of COVID-19 social isolation measures. Bureau Brief No. 145. Sydney: NSW Bureau of Crime Statistics and Research.

Golding N, Shearer FM, Moss R, Dawson P, Gibbs L, Alisic E, et al. 2020. Estimating temporal variation in transmission of COVID-19 and adherence to social distancing measures in Australia. Technical Report Doherty Institute 15 May 2020. Viewed 4 June 2020, https://www.doherty.edu.

au/uploads/content_doc/Technical_report_15_Maypdf.pdf.

Graham B 2020. Rapid COVID-19 vaccine development. Science Published online 8 May 2020. DOI: 10.1126/science.abb8923.

Grattan M 2020. Politics with Michelle Grattan: Paul Kelly on the risk of a COVID-19 second-wave. The Conversation. May 12. Viewed 27 May 2020, https://theconversation.com/politics-with-michelle-grattan-paul-kelly-on-the-risk-of-a-covid-19-second-wave-138432.

Gudbjartsson DF, Helgason A, Jonsson H, Magnusson OT, Melsted P, Gudmundur L, et al. 2020. Spread of SARS-CoV-2 in the Icelandic population. New England Journal of Medicine. April 14 DOI: 10.1056/NEJMoa2006100.

Hanlon P, Chadwick F, Shah A, Wood R, Minton J, McCartney G, et al. 2020. COVID-19 - exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study (pre-print). Wellcome Open Research, 5:75 (https://doi.org/10.12688/ wellcomeopenres.15849.1)

He X, Lau E, Wu P, Deng X, Wang J, Hao X, et al. 2020. Temporal dynamics in viral shedding and transmissibility of COVID-19. Nature medicine, 26(5), 672-675.

Hemenway D 2010. Why we don’t spend enough on public health. The New England journal of medicine, 362(18), 1657-1658. https://doi.org/10.1056/NEJMp1001784.

Hobbins P 2019. 100 years later, why don’t we commemorate the victims and heroes of ‘Spanish flu’? The Conversation, January 21, 2019. Viewed 2 June 2020, https://theconversation.com/100-years-later-why-dont-we-commemorate-the-victims-and-heroes-of-spanish-flu-109885 .

Holden R & Preston B 2020. The costs of the shutdown are overestimated - they’re outweighed by its $1 trillion benefit. The Conversation. May 16, 2020. Viewed 4 June 2020, https://theconversation.com/the-costs-of-the-shutdown-are-overestimated-theyre-outweighed-by-its-1-trillion-benefit-138303 .

71 Australia’s health 2020: data insights

Chapter

2

Hunt, the Hon. G 2020. COVID-19: rapid response boost for Australia’s intensive care units. Media release by Minister for Health, 23 April. Canberra. Viewed 5 June 2020, https://www.

health.gov.au/ministers/the-hon-greg-hunt-mp/media/covid-19-rapid-response-boost-for-australias-intensive-care-units.

Hunt, the Hon. G & Wyatt, the Hon. K 2020. Government backs remote communities with COVID-19 support. Joint media release by Minister for Health and Minister for Indigenous Australians. 25 March. Canberra. Viewed 9 April 2020, https://www.naccho.org.au/wp-content/ uploads/Government-backs-remote-communities-with-COVID-19-support.pdf

Jia JS, Lu X, Yuan Y et al. 2020. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature https://doi.org/10.1038/s41586-020-2284-y.

Kak V. 2015. Infections on cruise ships. Microbiol Spectrum 3(4):IOL5-0007-2015. doi:10.1128/ microbiolspec.IOL5-0007-2015.

Kakimoto K, Kamiya H, Yamagishi T, Matsui T, Suzuki M & Wakita T 2020. Initial investigation of transmission of COVID-19 among crew members during quarantine of a cruise ship— Yokohama, Japan, February 2020. MMWR Morb Mortal Wkly Rep;69:312-313. DOI: http://dx.doi.

org/10.15585/mmwr.mm6911e2.

Kirk MD, Fullerton KE, Hall GV, Gregory J, Stafford R, Veitch MG, et al. 2010. Surveillance for outbreaks of gastroenteritis in long-term care facilities, Australia, 2002 - 2008. Clinical Infectious Diseases, 51(8):907-914.

KSID & KCDCP (Korean Society of Infectious Diseases and Korea Centers for Disease Control and Prevention) 2020. Analysis on 54 Mortality Cases of Coronavirus Disease 2019 in the Republic of Korea from January 19 to March 10, 2020. Journal of Korean Medical Science. 2020 Mar 30;35(12):e132 https://doi.org/10.3346/jkms.2020.35.e132 eISSN 1598-6357·pISSN 1011-8934.

Kucharski A 2020. The rules of contagion: why things spread - and why they stop. London: Profile Books Ltd.

Lawton G 2020. Why are men more likely to get worse symptoms and die from covid-19? New Scientist 16 April. Viewed 27 May 2020, https://www.newscientist.com/article/2240898-why-are-men-more-likely-to-get-worse-symptoms-and-die-from-covid-19/.

Le Thu H 2020. Why Singapore, Taiwan and Vietnam have been effective in fighting Covid-19. The Strategist. Viewed 19 May 2020, https://www.aspistrategist.org.au/why-singapore-taiwan-and-vietnam-have-been-effective-in-fighting-covid-19/ .

Leon DA, Shkolnikov VM, Smeeth L, Magnus P, Pechholdová M & Jarvis CI 2020. COVID-19: a need for real-time monitoring of weekly excess deaths. The Lancet. 22 April. https://doi.org/10.1016/ S0140-6736(20)30933-8.

Liang W, Guan W, Chen R, Wang W, Li J, Xu K, et al. (2020). Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China. The Lancet. Oncology, 21(3), 335-337. https://doi.

org/10.1016/S1470-2045(20)30096-6.

Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, et al. 2020. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. The Lancet. 395: 565-74. https://doi.org/10.1016/S0140-6736(20)30251-8.

Lurie N, Saville M, Hatchett R & Halton J 2020. Developing COVID-19 Vaccines at pandemic speed. The New England journal of medicine, 382(21): 1969-1973. https://doi.org/10.1056/ NEJMp2005630.

72 Australia’s health 2020: data insights

Chapter

2

MacIntyre CR 2020. To get on top of the coronavirus, we also need to test people without symptoms. The Conversation, March 26. Viewed 17 June 2020, https://theconversation.com/to-get-on-top-of-the-coronavirus-we-also-need-to-test-people-without-symptoms-134381.

MacIntyre CR & Heslop DJ 2020. Public health, health systems and palliation planning for COVID-19 on an exponential timeline. Medical Journal of Australia 212 (10): 440-442.e1.

Mizumoto K & Chowell G 2020. Transmission potential of the novel coronavirus (COVID-19) onboard the Diamond Princess Cruises Ship, 2020. Infectious Disease Modelling, 5: 264-270.

Mizumoto K, Kagaya K, Zarebski A & Chowell G 2020. Estimating the asymptomatic proportion of coronavirus disease 2019 (COVID-19) cases on board the Diamond Princess cruise ship, Yokohama, Japan, 2020. Eurosurveillance 25 (10).

Moriarty LF, Plucinski MM, Marston BJ, Kurbatova EV, Knust B, Murray EL, et al. 2020. Public health responses to COVID-19 outbreaks on cruise ships - worldwide, February-March 2020. MMWR. Morbidity and mortality weekly report, 69(12), 347-352. https://doi.org/10.15585/ mmwr.mm6912e3.

Moss R, Wood J, Brown D, Shearer F, Black AJ, Cheng A, et al. 2020. Modelling the impact of COVID-19 in Australia to inform transmission reducing measures and health system preparedness. Viewed 10 June 2020, https://www.doherty.edu.au/uploads/content_doc/ McVernon_Modelling_COVID-19_07Apr1_with_appendix.pdf.

National Museum of Australia 2020. Defining moments: influenza pandemic. Viewed 2 June 2020, https://www.nma.gov.au/defining-moments/resources/influenza-pandemic .

NCIRS (National Centre for Immunisation Research and Surveillance) 2020. COVID-19 in schools - the experience in NSW. Sydney: NCIRS and NSW Health. Viewed 27 May 2020, http://ncirs.org.au/sites/default/files/2020-04/NCIRS%20NSW%20Schools%20COVID_ Summary_FINAL%20public_26%20April%202020.pdf.

Nishiura H, Kobayashi T, Miyama T, Suzuki A, Jung S-m, Hayashi K, et al. 2020a. Estimation of the asymptomatic ratio of novel coronavirus infections (COVID-19). International Journal of Infectious Diseases. 94: 154-155.

Nishiura H, Linton NM & Akhmetzhanov AR 2020b. Serial interval of novel coronavirus (COVID-19) infections. International Journal of Infectious Diseases 93: 284-286.

NSW Health 2020a. COVID-19: Updated advice for health professionals as of 24 April 2020. Viewed 12 May 2020, https://www.health.nsw.gov.au/Infectious/covid-19/Pages/advice-for-professionals.aspx.

NSW Health 2020b. COVID-19 weekly surveillance in NSW. Week 23, ending 6 June 2020. Viewed 17 June 2020, https://www.health.nsw.gov.au/Infectious/covid-19/Documents/covid-19-surveillance-report-20200606.pdf.

NSW Health 2020c. Cruise ship passengers identified with COVID-19. Media release by NSW Health. 20 March. Viewed 9 April 2020, https://www.health.nsw.gov.au/news/ Pages/20200320_03.aspx.

NSW Health 2020d. NSW COVID-19 case statistics. Viewed 10 June 2020, https://www.health.nsw.

gov.au/Infectious/covid-19/Pages/stats-nsw.aspx.

OECD (Organisation for Economic Co-operation and Development) 2020. Elderly population (indicator). doi: 10.1787/8d805ea1-en (Accessed on 14 April 2020).

73 Australia’s health 2020: data insights

Chapter

2

ONS (Office for National Statistics) 2020. Deaths involving COVID-19, England and Wales: deaths occurring in April 2020. Viewed 21 May 2020, https://www.ons.gov.

uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/bulletins/ deathsinvolvingcovid19englandandwales/deathsoccurringinapril2020.

Pan A, Liu L, Wang C, Guo H, Hao X, Wang Q, et al. 2020. Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China. JAMA, 323(19), 1-9. Advance online publication.

Park S, Choi GJ & Ko H 2020. Information Technology-based tracing strategy in response to COVID-19 in South Korea-privacy controversies [published online ahead of print, 2020 Apr 23]. JAMA. 2020;10.1001/jama.2020.6602. doi:10.1001/jama.2020.6602.

Peterman A, Potts A, O’Donnell M, Thompson K, Shah N, Oertelt-Prigione S, et al. 2020. Pandemics and violence against women and children. Center for Global Development Working Paper (in press). Viewed 6 May 2020.

PHE (Public Health England) 2020. Disparities in the risk and outcomes of COVID-19. London: PHE.

PM&C (Department of the Prime Minister & Cabinet) 2020. Senate Select Committee on COVID-19. Whole-of-Government Submission. 12 May 2020. Canberra. Viewed 5 June 2020.

Queensland Government 2020. Testing and fever clinics—coronavirus (COVID-19). Viewed 12 May 2020, https://www.qld.gov.au/health/conditions/health-alerts/coronavirus-covid-19/stay-informed/testing-and-fever-clinics.

Ranney, ML, Griffeth, V, & Jha, AK 2020. Critical supply shortages - the need for ventilators and personal protective equipment during the Covid-19 pandemic. The New England Journal of Medicine, 382(18), e41. https://doi.org/10.1056/NEJMp2006141.

Remuzzi A & Remuzzi G 2020. COVID-19 and Italy: what next?. The Lancet, 395(10231), 1225-1228. https://doi.org/10.1016/S0140-6736(20)30627-9.

Rowe SL, Stephens N, Cowie BC, Nolan T, Leder K, & Cheng AC 2019. Use of data linkage to improve communicable disease surveillance and control in Australia: existing practices, barriers and enablers. Australian and New Zealand journal of public health, 43(1), 33-40.

Russell TW, Hellewell J, Abbott S, Golding N, Gibbs H, Jarvis C, et al. 2020. Using a delay-adjusted case fatality ratio to estimate under-reporting. Real-time report. Viewed 12 May 2020, https://cmmid.github.io/topics/covid19/global_cfr_estimates.html.

Salje H, Kiem CT, Lefrancq N, Courtejoie N, Bosetti P, Paireau J, et al. 2020. Estimating the burden of SARS-CoV-2 in France. Science 10.1126/science.abc3517.

SBS News 2020. Fears for Brazil’s indigenous people as coronavirus deaths spike. Viewed 5 June 2020, https://www.sbs.com.au/news/fears-for-brazil-s-indigenous-people-as-coronavirus-deaths-spike.

Senanayake S 2020. Can you get the COVID-19 coronavirus twice? The Conversation May 4. Viewed 27 May 2020, https://theconversation.com/can-you-get-the-covid-19-coronavirus-twice-137309.

Services Australia 2020. Services Australia administrative data provided to the Senate Select Committee on COVID-19 as answer to Question on Notice. Viewed 15 June 2020, https://www.

aph.gov.au/DocumentStore.ashx?id=fb15ce41-0693-4455-bff8-945efb4c0b45 .

74 Australia’s health 2020: data insights

Chapter

2

Shilling F & Waetjen D 2020. Special report (update): Impact of COVID-19 mitigation on numbers and costs of California traffic crashes. Road Ecology Center, UC Davis. Viewed 10 June 2020, https://roadecology.ucdavis.edu/files/content/projects/COVID_CHIPs_Impacts.pdfx .

Simonnet A, Chetboun M, Poissy J, et al. 2020. High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requiring invasive mechanical ventilation. Obesity 28(7): 1195-1199. https://doi.org/10.1002/oby.22831.

Simonsen L, Spreeuwenberg P, Lustig R, Taylor RJ, Fleming DM, Kroneman M, et al. 2013 Global mortality estimates for the 2009 influenza pandemic from the GLaMOR Project: a modeling Study. PLoS Med 10(11): e1001558. doi:10.1371/journal.pmed.1001558.

Storey JE & Rogers M 2020. Coronavirus lockdown measures may be putting older adults at greater risk of abuse. The Conversation, 11 May 2020. Viewed 16 June 2020, https://theconversation.com/coronavirus-lockdown-measures-may-be-putting-older-adults- at-greater-risk-of-abuse-137430.

Sullivan SG, Pennington K, Raupach J, Franklin LJ, Bareja C, de Kluyver R, et al. 2020. A Summary of Influenza Surveillance Systems in Australia, 2015. Viewed 5 June 2020, https://www1.health. gov.au/internet/main/publishing.nsf/Content/cda-surveil-ozflu-flucurr.htm/$File/Influenza-Surveillance-Systems-Paper.pdf.

The Guardian 2020. Navajo nation reels under weight of coronavirus - and history of broken promises. Viewed 5 June 2020, https://www.theguardian.com/world/2020/may/08/navajo-nation-coronavirus.

The Treasury 2020. JobKeeper update. The Treasury and Australian Tax Office joint media release. Viewed 11 June 2020, https://treasury.gov.au/media-release/jobkeeper-update.

Thevarajan I, Nguyen THO, Koutsakos M, Druce J, Caly L, van de Sandt CE, et al. 2020. Breadth of concomitant immune responses prior to patient recovery: a case report of non-severe COVID-19. Nature Medicine 26: 453-455.

Toffolutti V & Suhrcke M 2014. Assessing the short term health impact of the Great Recession in the European Union: a cross-country panel analysis. Preventive medicine, 64, 54-62. https://doi. org/10.1016/j.ypmed.2014.03.028.

UNICEF 2020. COVID-19: Children at heightened risk of abuse, neglect, exploitation and violence amidst intensifying containment measures, posted on 23 March 2020.

van Gelder N, Peterman A, Potts A, O’Donnell M, Thompson K, Shah N et al. 2020. COVID-19: Reducing the risk of infection might increase the risk of intimate partner violence. EClinicalMedicine 21. Viewed 6 May 2020.

Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, et al. 2020. Estimates of the severity of coronavirus disease 2019: a model-based analysis. The Lancet. Infectious diseases, 20(6), 669-677. https://doi.org/10.1016/S1473-3099(20)30243-7.

Victoria State Government 2020. 2019 Coronavirus disease (COVID-19). Viewed 12 May 2020, https://www2.health.vic.gov.au/about/news-and-events/healthalerts/2019-Coronavirus-disease--COVID-19.

Vogel G & Couzin-Frankel J 2020. Children’s role in pandemic is still a puzzle. Science (6491), 562-563. DOI: 10.1126/science.368.6491.562.

Wang CJ, Mg CY & Brook RH 2020. Response to COVID-19 in Taiwan. JAMA. Published Online: March 3, 2020. doi:10.1001/jama.2020.3151.

WHO (World Health Organization) 2018. Joint External Evaluation of IHR Core Capacities of Australia. Geneva: WHO.

75 Australia’s health 2020: data insights

Chapter

2

WHO 2020a. Coronavirus disease 2019 (COVID-19): Situation Report - 1. Geneva: WHO; 2020 21 January 2020.

WHO 2020b. Coronavirus disease 2019 (COVID-19): Situation Report - 12. Geneva: WHO; 2020 1 February 2020.

WHO 2020c. Coronavirus disease 2019 (COVID-19): Situation Report - 41. Geneva: WHO; 2020 1 March 2020.

WHO 2020d. Coronavirus disease 2019 (COVID-19): Situation Report - 72. Geneva: WHO; 2020 1 April 2020.

WHO 2020e. Coronavirus disease 2019 (COVID-19): Situation Report - 73. Geneva: WHO; 2020 2 April 2020.

WHO 2020f. Coronavirus disease 2019 (COVID-19): Situation Report - 102. Geneva: WHO; 2020 1 May 2020.

WHO 2020g. Coronavirus disease 2019 (COVID-19): Situation Report - 132. Geneva: WHO; 2020 31 May 2020.

WHO 2020h. Global surveillance for COVID-19 caused by human infection with COVID-19 virus Interim guidance 20 March 2020. Geneva: WHO.

WHO 2020i. International guidelines for certification and classification (coding) of COVID-19 as cause of death. 20 April 2020. Viewed 12 May 2020, https://www.who.int/classifications/icd/ Guidelines_Cause_of_Death_COVID-19-20200420-EN.pdf.

WHO 2020j. Multisystem inflammatory syndrome in children and adolescents temporally related to COVID-19: scientific brief. Viewed 27 May 2020, https://www.who.int/news-room/ commentaries/detail/multisystem-inflammatory-syndrome-in-children-and-adolescents-with-covid-19.

WHO 2020k. Novel Coronavirus - China. Disease outbreak news: Update.12 Jan 2020. Viewed 28 April 2020, https://www.who.int/csr/don/12-january-2020-novel-coronavirus-china/en/.

WHO 2020l. Rapid assessment of service delivery for NCDs during the COVID-19 pandemic. Viewed 10 June 2020, https://www.who.int/publications/m/item/rapid-assessment-of-service-delivery-for-ncds-during-the-covid-19-pandemic.

WHO 2020m. Rational use of personal protective equipment (PPE) for coronavirus disease (COVID-19). Interim guidance 19 March 2020. Viewed 12 may 2020, https://apps.who.int/iris/ bitstream/handle/10665/331498/WHO-2019-nCoV-IPCPPE_use-2020.2-eng.pdf.

WHO 2020n. Report of the WHO-China joint mission on coronavirus disease 2019 (COVID-19). [Internet.] Geneva: WHO; 2020. [Accessed 11 May 2020.] Available from: https://www.who.int/ docs/default-source/coronaviruse/ who-china-joint-mission-on-covid-19-final-report.pdf .

WHO 2020o. Smoking and COVID-19. Scientific brief, 26 May 2020. Geneva: WHO.

Willyard C 2020. Coronavirus blood-clot mystery intensifies. Nature News. Viewed 27 May 2020, https://www.nature.com/articles/d41586-020-01403-8.

Wu Z & McGoogan JM 2020. Characteristics of and important lessons from the Coronavirus Disease 2019 (COVID-19) outbreak in China. Journal of the American Medical Association 323(13): 1239-1242.

Zhang T, Wu Q & Zhang Z 2020. Probable Pangolin Origin of SARS-CoV-2 Associated with the COVID-19 Outbreak. Current Biology 30(7): 1346-1351.e2.

Ziegler T, Mamahit A & Cox NJ 2018. 65 years of influenza surveillance by a World Health Organization-coordinated global network. Influenza and other respiratory viruses, 12(5), 558-565. https://doi.org/10.1111/irv.12570.

77 Australia’s health 2020: data insights

3

Social determinants of health in Australia

78 Australia’s health 2020: data insights

Chapter

3

It is well known that certain biomedical factors and health behaviours are risk factors

for ill health. However, there are social features that influence these risk factors—

the ‘causes of the causes’—as well as impacting on health directly. Known as ‘social

determinants’ of health, these are the social and economic conditions of everyday

life that impact on health, such as family circumstances, housing, working conditions,

income and education (Lucyk 2017; Marmot & Wilkinson 1999).

The pathways and interactions between these social factors and health outcomes

are typically complex, involving mechanisms with uneven distribution (differential

exposure) and effect (differential vulnerability); with causal factors that vary by

socioeconomic position; and which may occur over many years (Diderichsen et al.

2019). The complexity of these relationships makes it unlikely that any one research

study would be able to fully demonstrate the links between social determinants and

health. However, there is now a very strong evidence base built up from many studies

showing the direct and indirect associations between social determinants and health,

the pathways between them and the biological mechanisms involved (Braveman

et al. 2011). This chapter uses this evidence to outline relationships between these

determinants and health and uses specific Australian data to illustrate the patterns

(Box 3.1).

79 Australia’s health 2020: data insights

Chapter

3

Box 3.1: Chapter focus and key issues

This chapter provides an overview of the important relationship between the

social and economic conditions of everyday life and health outcomes, based on

the current strong international evidence base. Australian data are provided to

illustrate these relationships, and how these factors should be monitored into the

future is outlined.

The key contemporary review of the international evidence around social

determinants of health is the final report of the World Health Organization’s

Commission on Social Determinants of Health (WHO CSDH) (Friel & Marmot 2011;

WHO CSDH 2008). The 3 years’ work of the Commission synthesised global

evidence on social determinants and their impact on health and health inequalities.

Some of the findings of the review are included in sections of this chapter.

The large body of research available, taken as a whole, provides strong evidence

of the link between social determinants and health outcomes. This chapter cites

just some of this evidence, largely drawn from large-scale reviews or various

longitudinal studies showing relationships between social factors and health

outcomes over time. It also uses analyses from the Australian Burden of Disease

Study (ABDS) that quantifies the health impact of a number of social determinants

at the population level, by bringing together high-quality research on the links

between specific risk factors and health outcomes (AIHW 2019b).

In addition, this chapter provides other Australian data that illustrate the

sometimes complex causal links between social determinants and individual or

population health outcomes. While these illustrations often show the relationship

between 2 factors only, the actual pathways may also involve other factors.

In many cases, social determinants contribute to inequalities in health

between population groups that have been defined according to criteria such

as socioeconomic position; gender; race or ethnicity; or location. Thus, these

inequalities are a major focus for research on social determinants of health

and for monitoring population health risks and outcomes. Socially determined

inequalities in health that are deemed to be remediable and unfair are referred

to as ‘health inequities’ (Lucyk 2017; Whitehead 1992; WHO 2019; Wilkinson &

Pickett 2009).

80 Australia’s health 2020: data insights

Chapter

3

What are social determinants of health? Evidence around the social determinants of health has increased dramatically in recent decades (Honjo 2004). However, the interest in social and related environmental causes of ill health has a much longer history. Some well-known examples are the seminal work of John Snow in the mid-1800s identifying the source of a cholera outbreak in London as a contaminated water source and William Farr’s ground-breaking work in the same period, using statistics to examine social inequalities in health in England and Wales (Whitehead 2000). Broader social causes of ill health started to receive much more attention towards the end of the 20th century. An example is the findings of the UK Whitehall studies, commenced in the 1970s and continuing today, which have examined the relationship between various occupational and social factors and health outcomes across employment grades in the relatively homogenous group of British civil servants (Marmot et al. 1984; Marmot et al. 1991; Marmot et al.1997). Much further research on social determinants of health has since been undertaken, and the WHO CSDH (described further below) brought these findings together in a major report in 2008, leading to the Rio Political Declaration on Social Determinants of Health in October 2011.

Social determinants have been represented in a number of ways, including through conceptual diagrams or as a list of various social and economic factors that have an impact on health (Lucyk 2017). Some conceptual diagrams illustrate the various factors that influence the health of an individual, from their own biology and behaviour, through to family, school and working conditions, to community and societal factors (Dahlgren & Whitehead 2006; Krieger 2008). The framework in Figure 3.1 depicts risk factors from ‘downstream’ behavioural and biomedical factors to ‘upstream’ risk factors (further away in the causal chain from the health outcome), which include broad features of society such as culture and affluence. It is the socioeconomic characteristics in the framework that are the main focus of this chapter—including education; employment; income; family circumstances and early childhood; housing; working conditions; and social support. While these social determinants have each been shown to affect health outcomes, there are overlaps between them: for example, people with higher education are more likely to earn higher incomes. Due to the long lead time between exposure to these risk factors and subsequent effects on health outcomes, it is often the downstream factors that receive the most attention—potentially missing opportunities to address the more fundamental, socioeconomic causes of population health and illness (Braveman et al. 2011).

To illustrate the magnitude of the association between 1 social determinant—education level—and health, Figure 3.2 shows life expectancy at age 25 across 3 broad education groups in Australia. For men aged 25, those with higher levels of education (diploma or degree) can expect to have around 59 years left to live, while those with lower levels of education (an attainment less than Year 12, Certificate I, or Certificate II) can expect to have nearly 53 more years—a gap of over 6 years. For women, the gap is nearly 4 years.

81 Australia’s health 2020: data insights

Chapter

3

Figure 3.1: Framework for determinants of health

Health behaviours Tobacco use Alcohol consumption Physical activity

Dietary behaviour Use of illicit drugs Sexual practices Vaccination

Psychological factors Stress Trauma, torture

Safety factors Risk taking, violence Occupational health and safety

Biological factors

Birthweight

Body weight

Blood pressure

Blood cholesterol

Glucose tolerance

Immune status

Individual and population health and wellbeing

Broad features of society

Culture Affluence Social cohesion Social inclusion Political structures Public policy decisions

Media Language

Environmental factors

Natural Built

Geographical location Remoteness Latitude

Socioeconomic characteristics

Education

Employment

Income and wealth

Family, neighbourhood

Housing

Access to services

Food security

 Knowledge, attitudes and beliefs

Health literacy

Individual physical and psychological make-up

Genetics, antenatal environment, gender, ageing, life course and intergenerational influences, migration and refugee status

82 Australia’s health 2020: data insights

Chapter

3

Figure 3.2: Life expectancy at age 25, by education level, 2011

4 0

4 5

5 0

5 5

6 0

6 5

M en Women

Sex

L o w M ediu m H igh

Life expectancy (years)

0

Education level

Note: Education levels at age 25: ‘Low’ = less than Year 12, Certificate I or II; ‘Medium’ = Year 11 or 12, Certificate III or IV; ‘High’ = diploma, degree or higher.

Source: Australian data included in Murtin et al. 2017.

How do social determinants affect population health?

The mechanisms and pathways between the various social determinants and health

outcomes are complex and typically take effect over a long period of time. Social

factors may affect health because they may reduce access to health care or increase

exposure to unhealthy living or working conditions. Stress is viewed as another

common pathway between social determinants and downstream risk factors and

health outcomes. The chronic anxiety and lack of control arising from unfavourable

family, work or other circumstances can have both biological and psychological

outcomes, through neuroendocrine, inflammatory, immune and vascular processes

(Braveman et al. 2011; Fisher & Baum 2010; Wilkinson & Marmot 2003). Chronic stress

can increase people’s dispositions to adopt unhealthy behaviours such as smoking,

over-eating or alcohol use as forms of relief-seeking (Krueger & Chang 2008).

83 Australia’s health 2020: data insights

Chapter

3

As noted earlier, social determinants have a direct influence on health outcomes

as well as indirect effects by influencing downstream behavioural and biomedical

risk factors. Various research studies have demonstrated the direct role of social

determinants by separating their impact from that of other determinants (Moor et al.

2017; Walker et al. 2015). An Australian example showed that 34% of the gap in health

between Aboriginal and Torres Strait Islander people and Other Australians could

be attributed to social determinants and another 19% to health risk factors (with

the remaining portion unexplained). These factors do not work in isolation, and an

estimated 11% of the gap was attributed to the combined effect of social determinants

and health risk factors. The remaining 47% of the gap—the unexplained portion—

may include factors for which measurement is more difficult, such as access to health

services; the cumulative effects of early life events; or the effect of marginalisation

(AIHW 2018a).

As with any risk factor, social determinants can increase or decrease a person’s risk

of subsequent health outcomes: in other words, not everyone from a low-income

family will necessarily have poor health, it is just that their risk is higher than others.

Further, those with multiple unfavourable social determinants over their life will

be at even higher risk and be most vulnerable when another life challenge occurs

(Braveman et al. 2011).

The relationship between socioeconomic position and health typically follows a

‘gradient’ pattern, with stepwise increases in health status across each successive

increase in socioeconomic circumstances (Lucyk 2017; WHO CSDH 2008; Wilkinson &

Marmot 2003). This shows that there is no particular level of poverty that necessarily

entails poorer health (though absolute poverty remains important)—but rather, on

average, social factors affect population health across all levels of society.

A comprehensive measure of population health is provided by burden of disease

analyses, which quantify the health loss from virtually all diseases and injuries in

the population using the disability-adjusted life year (DALY) measure. (See ‘Burden

of disease’ webpage https://www.aihw.gov.au/reports/australias-health/burden-of-disease). As reflected in Figure 3.3, analysis across socioeconomic groups (based

on the socioeconomic status of individuals’ area of usual residence) illustrates the

social gradient described above for many major diseases, with the burden of disease

decreasing as socioeconomic status increases.

The total inequality across all diseases is substantial: 20% of the disease burden in

2015 could have been avoided if there had been no difference in burden across the

5 socioeconomic groups analysed (AIHW 2019a).

84 Australia’s health 2020: data insights

Chapter

3

Figure 3.3: Total disease burden for selected diseases, by socioeconomic

area, 2015

0

5

1 0

1 5

2 0

Anxiety disorders Coronary heart

disease

Chronic kidney disease

COPD Dementia Depressive

disorders

Lung cancer

Stroke Suicide &

self-inflicted injuries

Type 2 diabetes

Disease

1 L o w est 2 3 4 5 H ighest

DALY per 1,000

Notes

1. DALY = Disability-adjusted life year.

2. COPD = Chronic obstructive pulmonary disease.

3. Socioeconomic areas are based on the ABS Index of Relative Socio-economic Disadvantage (IRSD). The 5 groups represent the most disadvantaged 20% of the population to the least disadvantaged 20%, based on the individual’s area of residence.

4. Rates were age-standardised to the 2001 Australian Standard population.

5. Prevalence estimates and deaths with insufficient geographic detail to align to a socioeconomic area are excluded from the analysis.

Source: AIHW ABDS 2015.

The next sections provide details on a number of particular social determinants and

illustrate the magnitude of the effects on population health in Australia. This list of

social determinants is not intended to be exhaustive and there are related sections

later in Australia’s health 2020 that provide further detail (Box 3.2).

85 Australia’s health 2020: data insights

Chapter

3

Box 3.2: Australia’s health 2020 content relevant to social determinants

Australia’s health 2020 includes significant content relevant to the social

determinants of health or health inequalities, as listed below.

Chapters in this report:

Chapter 4: Housing conditions and key challenges in Indigenous health

Chapter 5: Potentially preventable hospitalisations—an opportunity for greater

exploration of health inequity

Chapter 10: Longer lives, healthier lives?

Australia’s health snapshots include:

‘Social determinants of health’ webpage https://www.aihw.gov.au/reports/

australias-health/social-determinants-of-health

‘Health across socioeconomic groups’ webpage https://www.aihw.gov.au/reports/

australias-health/health-across-socioeconomic-groups

‘Built environment and health’ webpage https://www.aihw.gov.au/reports/

australias-health/built-environment-and-health

‘Social determinants and Indigenous health’ webpage https://auth.aihw.gov.au/

reports/australias-health/social-determinants-and-indigenous-health

Socioeconomic position The concept of socioeconomic position describes the social and economic

circumstances of an individual, household or area. It can be measured using either

1 specific indicator—such as levels of education, occupation or income—or a

composite measure (Dutton et al. 2005), such as the Australian Bureau of Statistics

(ABS) Socio-Economic Indexes for Areas (SEIFA), which brings data together on a range

of factors. When constrained by data availability, area-based measures (which reflect

the average socioeconomic circumstances of people living within a particular area)

are often used in routine reporting of inequalities in health outcomes in Australia.

This corresponding index is often used as a proxy for an individual’s socioeconomic

position, as people of similar circumstances often live in the same area (Dutton et al.

2005). It may also reflect aspects of the local area that impact on an individual’s health.

While the average level is used as a measure of the socioeconomic position of the

area, there is also variation in socioeconomic circumstances between individuals

within the area.

86 Australia’s health 2020: data insights

Chapter

3

In general, an individual’s socioeconomic position indicates their status relative to

others in the population, and it has been found that related aspects—such as control

over life choices and prestige—may have an impact on downstream risk factors

and health outcomes (Marmot et al. 2012). While health can also have an impact on

socioeconomic position (as being unwell may reduce earning capacity, for example),

it is accepted that the main direction of causation is from socioeconomic position

to health outcomes (Braveman et al. 2011). The 3 commonly used components of

socioeconomic position—education; employment and occupation; and income—are

examined in more detail in the following sections.

Education

The education level of an individual has a number of fairly direct influences on their

health. These include having the knowledge and skills to increase their material

resources and ultimately their general socioeconomic position, through higher

skilled jobs or higher income. More directly, higher levels of education can assist in

understanding and implementing health messages, and health literacy is higher in

those with higher education (WHO CSDH 2008). People with higher levels of education

are also more likely to participate in society—such as through the political process—

that can lead to improvements in living standards for that group, and they are more

likely to be able to adapt to changes in the labour market than groups with less

education (Mikkonen & Raphael 2010).

Mortality rates show a clear gradient across differing levels of education, with the

probability of dying in 2011 decreasing as education levels increase (Figure 3.4).

Relative gradients are steeper at younger ages. This possibly reflects the fact that

more non-preventable causes of death occur at older ages and that—for a fair

proportion of the population—education levels have increased across generations,

making disadvantage now more concentrated in the lower education groups

(Korda et al. 2019).

87 Australia’s health 2020: data insights

Chapter

3

Figure 3.4: Mortality rates by education level, by age and sex, 2011

0

5

10

15

20

Males Females

No post secondary + no year 12 No post secondary + year 12

Mortality rate (25-44 years)

0

10

20

30

40

50

60

70

Males Females

Other post-secondary + no year 12

Other post-secondary + year 12

Mortality rate (45-64 years)

0

100

200

300

400

Males Females

Bachelor degree or higher

Mortality rate (65-84 years)

Notes

1. Rates presented as deaths per 10,000 person years.

2. Y-axis scale differs for the 3 age groups, and these cannot be directly compared.

Source: Korda et al. 2019.

Employment and occupation

Whether someone is able to obtain a job—and the nature of that occupation—is an

important component of socioeconomic position. Unemployment leads to reduced

income, and potentially to social isolation, psychological stress and unhealthy

behaviours, and this can then lead to poor physical and mental health (Kasl & Jones

2000; Wilkinson & Marmot 2003). In Australia, the unemployment rate was around 5%

in 2019 (average annual 2019 rate) but the rate is much higher for younger people, at

around 10% (AIHW 2019d). Even for those who are employed, around 10% are classed

as ‘underemployed’, which means they are willing and able to work more hours than

they currently do.

The health of those in and out of the workforce is known to vary. In 2017-18, 64%

of those aged 18-64 years who were employed rated their health as ‘excellent’ or

‘very good’ (Figure 3.5). This percentage was lower for those who were ‘unemployed’

(45%) or ‘not in the labour force’ (44%) (a diverse group that includes students,

stay-at-home parents, carers, retirees and those who have given up looking for a job).

Further, there was substantial difference in reported health among the unemployed,

depending on their length of unemployment: 52% of those unemployed for less than

12 months rated their health as ‘excellent’ or ‘very good’, whereas only 30% of those

unemployed for over 12 months did so. While it is not clear from these data whether

the unemployment caused lower health or whether ill health contributed to being

unemployed, other studies have demonstrated that unemployment does increase the

risk of ill health (Montgomery et al. 1999; Wilkinson & Marmot 2003).

88 Australia’s health 2020: data insights

Chapter

3

Figure 3.5: Self-reported ‘Excellent’ or ‘Very good’ health among people aged

18-64, by labour force status, 2017-18

0 1 0 2 0 3 0 4 0 5 0 6 0 7 0

Employed

Unemployed less than 12 months

Unemployed more than 12 months

Not in the labour force

Per cent

Notes

1. Excludes those for whom labour force status is not applicable.

2. Percentage is age-standardised to the 2001 Australian Standard Population.

3. Bars represent 95% confidence intervals.

Source: ABS 2019.

For those who are able to find work when they want to, advantages include income

and other benefits such as a sense of purpose (Marmot & Friel 2008). As with education

levels, some occupations are higher in status than others, which has its own impact

on socioeconomic position (that is, occupational status is not dependent solely on the

income received).

Further information on occupational risks and job security are provided in the section

on working conditions below.

Income

The final key component of socioeconomic position considered here is income, which

most obviously impacts on the financial resources available to the individual or family,

directly influencing their standard of living. Higher income levels provide more choices

in relation to food availability and quality, housing, physical activity, social participation

and health care, which can lead to better health outcomes (Braveman et al. 2011).

In addition, higher levels of income are likely to result in less stress in meeting the

demands of everyday life. As with other social determinants, there is the potential for

reverse causality, with ill health leading to loss of income.

89 Australia’s health 2020: data insights

Chapter

3

In developed countries like Australia, important aspects in understanding the

relationship between income and health are whether an individual’s income can

provide the necessities of life, and their relative income level compared to others

(Mikkonen & Raphael 2010). Figure 3.6 illustrates the relationship between household

income levels and death rates from diabetes in 2011-12: death rates for those in the

lowest income bracket (less than $300 per week) were more than double those seen

in the highest income bracket ($1,500 or more per week) for males, while for females

in the lowest income bracket, the rate was nearly 60% higher than for females in the

highest bracket.

Figure 3.6: Diabetes mortality by equivalised household income, 2011-12

0

10

20

30

40

50

60

70

80

90

Males Females

<$300 per week $300–$599 per week $600–$999 per week

$1000–$1499 per week $1500 or more per week

Deaths per 100,000

Notes

1. Rates are age-standardised to the 2001 Australian population.

2. Includes diabetes as either an underlying or associated cause of death.

3. Includes persons living in occupied private dwelling only.

Source: AIHW 2019f.

90 Australia’s health 2020: data insights

Chapter

3

Family situation An individual’s family functioning and situation has a large influence on their health.

All members of an immediate family usually share the same social and economic

resources and the influence of those on their health. The influence of the family

on health—from parents, siblings, partners and other family members—continues

through childhood, young adulthood, adulthood and older age. As with other health

determinants, this follows a continuum from large potential benefit for those in

high-functioning, cohesive and supportive relationships, to substantial potential

disadvantage in families experiencing violence, abuse, interaction with the justice

system or other significant challenges (AIHW 2019g).

Two examples of situations which put substantial stress on families are child abuse

and neglect, and intimate partner violence. These are included as risk factors in the

ABDS, and therefore the impact of these risk factors on disease burden in Australia

can be estimated. These estimates take into account the subsequent increased risk of

developing and/or dying from diseases known to be linked to the risk factor, based on

high-quality research studies relevant to Australia.

Child abuse and neglect increases the risk of anxiety disorders, depressive disorders

and suicide/self-inflicted injuries and the effects occur both during childhood and later

in life (AIHW 2019b). In terms of the proportion of disease burden attributed to this

risk factor, the largest impacts in 2015 were during young adulthood (15-24 years;

8.0% of disease burden for females and 5.1% for males) and the earlier working years

(25-44 years; 6.5% for females and 4.7% for males) (Figure 3.7). These are large health

impacts: child abuse and neglect was the leading risk factor (that is, causing the most

disease burden) for all children aged 5-14, and for females aged 15-24 and 25-44,

and the third leading risk factor for males aged 15-24 and 25-44.

91 Australia’s health 2020: data insights

Chapter

3

Figure 3.7: Burden of disease attributed to child abuse and neglect, by sex

and age, 2015

0 1 2 3 4 5 6 7 8

65+

45 – 64

25 – 44

15 – 24

5 – 14

65+

45 – 64

25 – 44

15 – 24

5 – 14

Males Females

Anxiety disorders Suicide & self-inflicted injuries Depressive disorders

Per cent total burden (DALY)

Note: DALY = Disability-adjusted life year.

Source: AIHW ABDS 2015.

Similar data for intimate partner violence among women also shows the large impact

on health that results from partner violence, including emotional, physical or sexual

violence. As shown for child abuse and neglect, anxiety disorders, depressive disorders

and suicide/self-inflicted injuries were also linked to intimate partner violence, along

with 3 other factors: alcohol use disorders, homicide/violence, and early pregnancy

loss. Again, the impacts were large and occurred across all age groups (Figure 3.8).

Intimate partner violence accounted for 2.3% of burden in 15-24 year olds (ranked the

fourth leading risk factor for this age group), 4.1% for 25-44 year olds (ranked third)

and 2.3% for 35-64 year olds (ranked eighth).

92 Australia’s health 2020: data insights

Chapter

3

Figure 3.8: Burden of disease attributed to intimate partner violence for

females, by age, 2015

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

65+

45 -64

25 -44

15 -24

Depressive disorders Anxiety disorders Suicide & self-inflicted injuries

Homicide and violence (where females were the victim)

Alcohol use disorders Early pregnancy loss

Per cent total burden (DALY)

Note: DALY = Disability-adjusted life year.

Source: AIHW ABDS 2015.

Early childhood Early childhood circumstances have been shown to have a large impact on a child’s

current and future health. This life stage is often viewed as one of the best times

to intervene to reduce health inequities stemming from inequalities in family’s

socioeconomic circumstances (Marmot 2015). Early childhood development lays the

critical foundation for the child’s future health and wellbeing, and it has been shown that

brain development is highly sensitive to the early life situation (WHO CSDH 2008). This

development begins before birth, when the mother’s health and diet are particularly

important, and continues in the early years of life when material, emotional and social

circumstances are highly influential. The impacts may be through various pathways: for

example, having a lower readiness to learn when entering school can have an impact

on longer-term education outcomes, or more directly through unhealthy learned

behaviours. There is also the potential for cumulative effects: for example, the longer

a child lives under deprivation, the more likely they are to have health effects from it

(Aber et al. 2007).

93 Australia’s health 2020: data insights

Chapter

3

Early childhood and preschool education has been shown to provide substantial

advantages for child development outside the family (AIHW 2019c, 2019i; Elliott 2006;

WHO CSDH 2008). As an illustration of this, Australian data from developmental checks

on school entry show an association between preschool programs and development

(Figure 3.9). In this study (Goldfeld et al. 2016), ‘vulnerability’ is defined as being in the

bottom 10% of children in a particular domain, covering aspects such as language

and communication skills; emotional and social competence; and physical health and

wellbeing. In almost all cases, the risk of being at the vulnerable end of the spectrum

was lower for those children who attended preschool programs, compared with those

who attended other types of non-family care or were cared for by their parents only.

The effect was greatest in the domains of communication skills and general knowledge,

and of language and cognitive skills: children who attended preschool were around

60% less likely to be considered vulnerable in those domains, compared with children

who were in their parents’ care only (odds ratio = 0.4). It is also important to note

that this pattern was also found regardless of socioeconomic group: the advantages

obtained from preschool education were apparent for children from both well-off and

less well-off groups.

94 Australia’s health 2020: data insights

Chapter

3

Figure 3.9: Likelihood of vulnerability across developmental domains in first

year of schooling, by preschool attendance compared with parental care

only, 2009

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6

Physical health and wellbeing

Social competence

Emotional maturity

Language and cognitive skills

Communication skills and general knowledge

Early childhood developmental domain

Did not attend preschool but attended other non-parental care

Attended preschool

Odds ratio

Notes

1. All models are adjusted for gender; Language Backgrounds Other Than English; Aboriginal and Torres Strait Islander status; socioeconomic status of area of usual habitation; and state or territory.

2. ‘Developmental vulnerability’ is defined as being in the bottom 10% of children in a particular early childhood development domain.

3. Odds ratios represent the likelihood of a child being developmentally vulnerable, compared with children who received care from their parents only (reference group, indicated by the vertical line passing through 1). Compared with a child who received care from their parents only, an odds ratio of less than 1 means a child is less likely to be developmentally vulnerable in a developmental domain, and an odds ratio of more than 1 means a child is more likely to be developmentally vulnerable in a developmental domain.

4. Horizontal bars indicate 95% confidence intervals (CIs). CIs not overlapping with the reference group line usually indicate statistically significant differences. The exception is emotional maturity, where ‘attended pre-school group’ is not significantly different to the reference group.

Source: Goldfeld et al. 2016.

95 Australia’s health 2020: data insights

Chapter

3

Housing It has been shown that a number of aspects of housing can have an impact on health

outcomes (Mikkonen & Raphael 2010) and that housing improvements, when needed,

can lead to improved health (Thomson et al. 2013). There are direct, more physical

effects, such as the level of overcrowding (which can increase the spread of infectious

diseases); the quality of the infrastructure within the house; and the ability of the

structure to protect the inhabitants from excessive temperatures, storms, insects and

various types of pollution.

There are also a number of characteristics of housing that can have a less direct

impact on health. Insecure housing (where there is no guarantee of long-term

occupancy); unaffordable housing; or, in extreme cases, homelessness can result in

stress, substance abuse, or the need to relocate often, which can impact on schooling,

employment and family and social support (Sandel et al. 2018). Homelessness and

insecure housing can present barriers to accessing health care (ABS 2015), and

high housing costs also reduce the resources available for other health-promoting

purchases, including quality food. Health problems, including mental illness and

substance abuse, can in turn lead to housing challenges.

Home ownership is viewed as the most stable form of housing, whereas renting can

range from stable long-term arrangements to short-term, precarious arrangements.

Even with this variability in renting arrangements, a difference in health outcomes

is seen. For example, cardiovascular disease mortality rates are clearly higher for

those renting, compared with rates for home owners (Figure 3.10). The proportion

of the population who are renters has increased steadily over time, with 30% of the

population renting in 2017-18 (AIHW 2019e).

96 Australia’s health 2020: data insights

Chapter

3

Figure 3.10: Cardiovascular disease mortality, persons aged 25 and over,

by housing tenure and sex, 2011-12

0

50

100

150

200

250

300

350

400

Men Women

Rented Owned

Deaths per 100,000

Notes

1. Age-standardised to the 2001 Australian population.

2. Includes persons living in occupied private dwellings only.

3. Excludes ‘Tenure type not stated’ and ‘Tenure type not applicable’.

Source: AIHW 2019f.

Working conditions An individual’s workplace and employment conditions are another social determinant

of health, and again there are direct and less direct pathways between this social

determinant and health outcomes. Direct impacts include exposure to harmful

substances and injury risks, resulting in higher rates of a range of conditions including

some cancers (such as mesothelioma) (AIHW 2018b); certain respiratory diseases; back

pain; hearing loss; and a number of injuries. The impact of these occupationally-linked

diseases is quantified as part of the ABDS in a similar way to other risk factors described

above. It shows that these are important causes of disease burden for both males and

females, ranking in the top 5 leading risk factors for males aged 15-24 and 25-44 and

for females aged 15-24 (AIHW 2019b)—though the rate for males is considerably higher

than for females. The occupational disease burden also shows a strong social gradient,

with much higher attributed burden for the lower socioeconomic groups compared with

the higher ones (Figure 3.11).

97 Australia’s health 2020: data insights

Chapter

3

Figure 3.11: Burden of disease from occupational exposures and hazards,

by socioeconomic area and sex, 2015

0

1

2

3

4

5

6

7

8

Males Females

1 Lowest 2 3 4 5 Highest

DALY per 1,000

Notes

1. DALY = Disability-adjusted life year.

2. Rates are age-standardised and presented as DALY per 1,000 population.

3. Socioeconomic areas are based on the ABS Index of Relative Socio-economic Disadvantage (IRSD). The 5 groups represent the most disadvantaged 20% of the population to the least disadvantaged 20%, based on the individual’s area of residence.

Source: AIHW ABDS 2015.

There are also indirect adverse impacts related to employment conditions, including

being in more insecure work such as temporary or casual arrangements (WHO CSDH

2008) or being underemployed (where an individual can obtain some work but not as

much as they would like) (Milner & LaMontagne 2017). Working excessively long hours

or being in a job with high demands but little control have also been demonstrated

to be more stressful and to have subsequent adverse health effects, while social

connections at work have been shown to have health benefits (Braveman et al. 2011;

Stansfeld & Candy 2006).

Social support and participation Having strong social networks outside the family has been shown to be very beneficial

for physical and mental health (Holt-Lunstad et al. 2010; Wilkinson & Marmot 2003).

These networks are able to provide practical and emotional help and support, particularly

during challenging periods, and can encourage healthy lifestyles (Berkman & Glass 2000;

98 Australia’s health 2020: data insights

Chapter

3

Cockerham et al. 2007). Conversely, lack of support and loneliness are detrimental to an

individual’s health and have been shown to increase rates of various risk factors, stress,

depression and the risk of premature death (AIHW 2019h; Holden et al. 2015; Wilkinson

& Marmot 2003).

A meta-analysis which combined results from 148 research studies showed a 50%

survival benefit among those with stronger social relationships, which the authors note is

comparable to other well-established risk factors for mortality (Holt-Lunstad et al. 2010).

An Australian study using data on young women (aged from 22-27 to 35-39 years)

from the Australian Longitudinal Study on Women’s Health (Holden et al. 2015) showed

a strong gradient in general health scores across 5 levels of social support, after

adjusting for demographic and behavioural characteristics (Figure 3.12). This gradient

held for both current and previous social support levels (that is, when social support

was measured prior to the subsequent health score). Note that, in this figure, the taller

bars indicate better health outcomes.

Figure 3.12: General health scores, by social support levels in Australian

women aged 22-39, 2012

68

70

72

74

76

78

1 Lowest 2 3 4 5 Highest

Social support quintile

Current social support Previous social support

General health score

0

Notes

1. Scores are adjusted for psychological distress; area of residence; education; ability to manage on available income; alcohol consumption; smoking status; and physical activity.

2. Social support levels were measured using the 6-item Medical Outcomes Study Social Support Scale (MOS-SSS-6) (Holden et al. 2014).

Source: Holden et al. 2015.

99 Australia’s health 2020: data insights

Chapter

3

Social exclusion occurs when an individual or groups of the population do not have the

opportunity to participate in community life and decision making (Saunders et al. 2008).

Social exclusion may result from people being excluded from certain services such as

housing or education; geographically isolated from others; or unable to participate

in social and cultural activities due to lack of income (Mathieson et al. 2008). Some

groups are more likely to experience social exclusion, including those living in poverty;

the unemployed; older people; immigrants from non-English speaking countries;

Indigenous Australians; people with disability or a long-term health condition; and

single-person and lone-parent households (Brotherhood of St Laurence & MIAESR 2018;

Wilkinson & Marmot 2003). Social exclusion reduces opportunities for education and

employment, and adversely affects mental health, with rates of depression being shown

to be higher among those experiencing social exclusion (Mikkonen & Raphael 2010).

Life course and intergenerational impacts The sections above describe the role of the key individual social factors; however, these

factors do not occur in isolation. Social influences on an individual’s health and wellbeing

occur in combination and cumulatively across life, and the impacts from earlier life are

apparent over many years and potentially for generations (Singh-Manoux & Marmot

2005). As discussed earlier in the context of early childhood, disadvantage at that stage

of life can reduce social and health opportunities in the future, and this pattern can

continue and accumulate over an individual’s life (Braveman et al. 2011; Marmot 2012).

Further, the length of time in disadvantage increases the risk of ill health in later life

(Wilkinson & Marmot 2003).

There are many research studies and reviews that have demonstrated the role of

lifetime social and economic conditions on health. For example, in the context of early

life, accumulation of disadvantage during childhood was highlighted in an Australian

evidence review as having a negative impact on children’s development, health and

wellbeing, and also their health in later life (Moore et al. 2014). Another Australian

study demonstrated that those who had been in manual occupations for longer had

an increased risk of being a smoker in mid-life, compared with those who consistently

reported being in non-manual occupations (Tian et al. 2019).

Other examples drawn from studies in the UK include 2 from the Whitehall study of

civil servants. The first of these showed that men with the highest accumulation of

disadvantage had higher risk of coronary heart disease, poor physical functioning and

poor mental functioning, and for women this was found for coronary heart disease and

physical functioning (Singh-Manoux 2004). The second study showed that the likelihood

of adult overweight and obesity increased with accumulation of social disadvantage

(Heraclides & Brunner 2009).

100 Australia’s health 2020: data insights

Chapter

3

Wealth, as a measure of accumulated socioeconomic position over the life course,

was found to be strongly associated with all-cause mortality in an English longitudinal

study of adults aged 50 and over (Demakakos et al. 2016; Siegrist 2016). The study

used repeated measures of wealth and various risk factors, and wealth was found

to be more strongly associated with lower mortality than were other socioeconomic

variables.

As well as the impact over an individual’s lifetime, there are also impacts across

generations. Children of socially disadvantaged parents have a higher risk of being

socially disadvantaged themselves (Braveman et al. 2011; Cobb-Clark 2019). These

impacts start from conception and continue throughout childhood and into adulthood

(Aizer & Currie 2014; Assink et al. 2018; Bowers & Yehuda 2016; Elhakeem et al. 2016).

The impact of social determinants over a lifetime and across generations results in a

number of population groups that are particularly vulnerable to adverse health effects.

Social exclusion (discussed earlier) has been noted to have negative effects particularly

in combination with vulnerability and resilience (Marmot et al. 2012). Groups at high

risk include those with a high accumulation of social disadvantage, such as Indigenous

Australians, those with mental health illness or disabilities, and older people.

Need for ongoing monitoring The WHO CSDH highlighted the importance of ongoing monitoring of the

social determinants of health and health inequalities: the need to measure and

understand the problem (including routine monitoring) was 1 of the 3 overarching

recommendations of the Commission (WHO CSDH 2008), and other commentators

have echoed this call (Braveman et al. 2011; Donkin et al. 2017). More recently, the

United National Sustainable Development Goals and associated indicators include

many social determinants of health (UN 2020).

In the WHO report, measuring and monitoring was 1 of the 3 primary

recommendations, aiming to ensure routine monitoring of 2 aspects: the social

determinants themselves (such as income, education, housing) and the distribution

of health across population groups (inequality and inequity). The goal is to assess the

magnitude of the problem; who is most affected; and whether the situation is changing

over time (MEKN 2007). The need for more longitudinal research to track individuals’

experiences over time and across generations is also highlighted.

101 Australia’s health 2020: data insights

Chapter

3

In Australia’s health 2020, through 2 corresponding ‘snapshots’, we have established a

mechanism for core ongoing monitoring of the 2 aspects called for in the international

literature: one to monitor the social determinants themselves ‘Social determinants

of health’, and another to monitor health inequality ‘Health across socioeconomic

groups’. There is also potential for more detailed monitoring outside these core

components. The AIHW also continues to include related analysis in many of its other

reports on health and welfare, including detailed analysis at the small geographic

area level.

Further reading Social determinants of health are explored further in the 2 Australia’s health 2020

data insights chapters: Housing conditions and key challenges in Indigenous health and

Longer lives, healthier lives?

There are also a number of web pages included in the online components of Australia’s

health 2020 that provide data on the social determinants, including comparisons over

time and across population groups. These snapshots include ‘Social determinants

of health’, ‘Health across socioeconomic groups’, ‘Health of people experiencing

homelessness’, ‘Built environment and health’ and ‘Social determinants of Indigenous

health’, and are available at www.aihw.gov.au/australias-health/snapshots.

Equitable and safe access to health and social services are both important social

determinants of health and wellbeing. Further information on access to health services

can be found in snapshots relating to the health system in Australia’s health 2020,

including ‘Safety and quality of health care’, ‘Private health insurance’, and

‘Cancer screening and treatment’. Access to social services is addressed in Australia’s

welfare 2019 www.aihw.gov.au/reports-data/health-welfare-overview/australias-welfare/overview.

There are a number of reports that have followed the WHO’s Commission on the

Social Determinants which reviewed the situation in Europe, England and Australia and

potential approaches to continue to address the social determinants of health (Marmot

et al. 2010; Marmot et al. 2012; Senate Community Affairs Reference Committee 2013).

102 Australia’s health 2020: data insights

Chapter

3

References Aber JL, Bennett NG, Conley DC, Li J 1997. The effects of poverty on child health and development. Annual review of Public Health 18:463-83.

ABS (Australian Bureau of Statistics) 2015. General Social Survey: summary results, Australia, 2014. ABS cat no. 4159.0. Canberra: ABS.

ABS 2019. Microdata: National Health Survey, 2017-18. Findings based on Detailed Microdata analysis. ABS cat. no. 4324.0.55.001. Canberra: ABS.

AIHW (Australian Institute of Health and Welfare) 2018a. Australia’s health 2018. Canberra: AIHW.

AIHW 2018b. Australia’s health 2018: 3.5 Mesothelioma. Australia’s health series no. 16. AUS 221. Canberra: AIHW.

AIHW 2019a. Australian Burden of Disease Study: impact and causes of illness and death in Australia 2015. Australian Burden of Disease series no. 19. Cat. no. BOD 22. Canberra: AIHW.

AIHW 2019b. Australian Burden of Disease Study: methods and supplementary material 2015. Australian Burden of Disease Study no. 20. Cat. no. BOD 23. Canberra: AIHW.

AIHW 2019c. Childcare and early childhood education. Canberra: AIHW. Viewed 9 January 2020, https://www.aihw.gov.au/reports/australias-welfare/childcare-and-early-childhood-education.

AIHW 2019d. Employment trends. Canberra: AIHW. Viewed 10 March 2020, https://www.aihw. gov.au/reports/australias-welfare/employment-trends.

AIHW 2019e. Home ownership and housing tenure. Canberra: AIHW. Viewed 10 March 2020, https://www.aihw.gov.au/reports/australias-welfare/home-ownership-and-housing-tenure.

AIHW 2019f. Indicators of socioeconomic inequalities in cardiovascular disease, diabetes and chronic kidney disease. Cat. no. CDK 12. Canberra: AIHW.

AIHW 2019g. National framework for protecting Australia’s children indicators. Cat. no. CWS 62. Canberra: AIHW. Viewed 18 March 2020, https://www.aihw.gov.au/reports/child-protection/ nfpac/contents/national-framework-indicators/1-1-family-functioning.

AIHW 2019h. Social isolation and loneliness. Canberra: AIHW. Viewed 9 January 2020, https://www.aihw.gov.au/reports/australias-welfare/social-isolation-and-loneliness.

AIHW 2019i. Transition to primary school. Canberra: AIHW. Viewed 9 January 2020, https://www.aihw.gov.au/reports/australias-welfare/transition-to-primary-school.

Aizer A & Currie J 2014. The intergenerational transmission of inequality: maternal disadvantage and health at birth. Science 344(6186):856-61.

Assink M, Spruit A, Schuts M, Lindauer R, van der Put CE & Stams GJM 2018. The intergenerational transmission of child maltreatment: a three-level meta-analysis. Child Abuse & Neglect 84:131-45.

Berkman LF & Glass TA 2000. Social integration, social networks, social support, and health. In: Berkman LF & Kawachi I (eds). Social epidemiology. New York: Oxford University Press, 137-73.

Bowers ME & Yehuda R 2016. Intergenerational transmission of stress in humans. Neuropsychopharmacology Reviews 41:232-244;

Braveman P, Egerter S & Williams DR 2011. The social determinants of health: coming of age. Annual Review of Public Health 32:381-98.

Brotherhood of St Laurence & MIAESR (Melbourne Institute of Applied Economic and Social Research) 2018. Social exclusion monitor. Melbourne: Brotherhood of St Laurence.

Cobb-Clark DA 2019. Intergenerational transmission of disadvantage in Australia. In Australia’s welfare 2019: data insights. Canberra: AIHW.

103 Australia’s health 2020: data insights

Chapter

3

Cockerham WC, Harnby BW & Oates GR 2017. The social determinants of chronic disease. American Journal of Preventive Medicine 52(1S1):55-12.

Dahlgren G, Whitehead M & World Health Organization (WHO) 2006. European strategies for tackling social inequities in health: levelling up part 2. Copenhagen: WHO Regional Office for Europe.

Demakakos P, Biddulph JP, Bobak M & Marmot MG 2016. Wealth and mortality at older ages: a prospective cohort study. Journal of Epidemiology and Community Health 70(4):346-53.

Diderichsen F, Hallqvist J, Whitehead M 2019. Differential vulnerability and susceptibility: how to make use of recent development in our understanding of mediation and interaction to tackle health inequalities, International Journal of Epidemiology 48(1):268-274.

Donkin A, Goldblatt P, Allen J, Nathanson V & Marmot GM 2017. Global action on the social determinants of health. BMJ Global Health 3(Issue Supplement 1):e000603.

Dutton T, Turrell, G & Oldenburg B.F 2005. Measuring socioeconomic position in population health monitoring and health research. Health inequalities monitoring series number 3. Brisbane: Queensland University of Technology, School of Public Health.

Elhakeem A, Hardy R, Bann D, Caleyachetty R, Cosco TD, Hayhoe RPG,et al. 2017. Journal of Epidemiology and Community Health 71:673-680.

Elliott A 2006. Early childhood education: pathways to quality and equity for all children. Camberwell, Vic.: Australian Council for Educational Research.

Fisher M & Baum F 2010. The social determinants of mental health: implications for research and health promotion. Australian and New Zealand Journal of Psychiatry 44(12):1057-63.

Friel S & Marmot MG 2011. Action on the social determinants of health and health inequities goes global. Annual Review of Public Health 32:225-36.

Goldfeld S, O’Connor E, O’Connor M, Sayers M, Moore T, Kvalsvig A et al. 2016. The role of preschool in promoting children’s healthy development: evidence from an Australian population cohort. Early Childhood Research Quarterly 35:40-48.

Heraclides A & Brunner E 2009. Social mobility and social accumulation across the life course in relation to adult overweight and obesity: the Whitehall II study. Journal of Epidemiology and Community Health 64(8):714-19.

Holden L, Lee C, Hockey R, Ware RS & Dobson AJ 2014. Validation of the MOS Social Support Survey 6-item (MOS-SSS-6) measure with two large population-based samples of Australian women. Quality of Life Research 23(10):2849-53.

Holden L, Lee C, Hockey R, Ware RS & Dobson AJ 2015. Longitudinal analysis of relationships between social support and general health in an Australian population cohort of young women. Quality of Life Research 24(2):485-92.

Holt-Lunstad J, Smith TB & Layton JB 2010. Social relationships and mortality risk: a meta-analytic review. PLoS Medicine 7(7):e1000316.

Honjo K 2004. Social epidemiology: definition, history, and research examples. Environmental Health and Preventive Medicine 9(5):193-99.

Kasl SV & Jones BA 2000. The impact of job loss and retirement on health. In: Berkman LF & Kawachi I (eds). Social epidemiology. New York: Oxford University Press.

Korda RJ, Biddle N, Lynch J, Eynstone-Hinkins J, Soga K, Banks E et al. 2019. Education inequalities in adult all-cause mortality: first national data for Australia using linked Census and mortality data. International Journal of Epidemiology October 3 2019 [published online ahead of print]. pii:dyz191. https://academic.oup.com/ije/article/49/2/511/5579828.

104 Australia’s health 2020: data insights

Chapter

3

Krieger N 2008. Ladders, pyramids and champagne: the iconography of health inequities. Journal of Epidemiology and Community Health 62(12):1098-1104.

Krueger PM & Chang VW 2008. Being poor and coping with stress: health behaviors and the risk of death. American Journal of Public Health 98(5):889-96.

Lucyk K & McLaren L 2017. Taking stock of the social determinants of health: a scoping review. PLoS ONE 12(5): e0177306.

Marmot MG, Stansfeld S, Patel C, North F, Head J, I. White I et al. 1991. Health inequalities among British civil servants: The Whitehall II study. Epidemiology 337(8754):1387-1393.

Marmot MG 2015. The health gap: the challenge of an unequal world. Lancet 386(10011): 2442-44.

Marmot MG, Allen J, Bell R, Bloomer E, Goldblatt P, on behalf of the Consortium for the European Review of Social Determinants of Health and the Health Divide 2012. WHO European review of social determinants of health and the health divide. Lancet 380(9846):1011-29.

Marmot MG, Allen J, Goldblatt P, Boyce T, McNeish D, Grady M et al. 2010. Fair society, healthy lives: the Marmot Review: strategic review of health inequalities in England post-2010. London: The Marmot Review

Marmot MG, Bosma H, Hemingway H, Brunner E & Stansfeld S 1997. Contribution of job control and other risk factors to social variations in coronary heart disease incidence. Lancet 350(9073):235-9.

Marmot MG & Friel S 2008. Global health equity: evidence for action on the social determinants of health. Journal of Epidemiology and Community Health 62:1095-1097.

Marmot MG, Shipley MJ & Rose G 1984. Inequalities in death—specific explanations of a general pattern? Lancet 1(8384):1003-6.

Marmot MG, Stansfeld S, Patel C, North F, Head J, I. White I et al. 1991. Health inequalities among British civil servants: The Whitehall II study. Epidemiology 337(8754):1387-1393.

Marmot MG & Wilkinson RG 1999. Social determinants of health. Oxford: Oxford University Press.

Mathieson J, Popay J, Enoch E, Escorel S, Hernandez M, Johnston H et al. 2008. Social exclusion: meaning, measurement and experience and links to health inequalities: a review of literature. WHO Social Exclusion Knowledge Network Background Paper 1. Lancaster: Lancaster University.

MEKN (Measurement and Evidence Knowledge Network) 2007. The social determinants of health: developing an evidence base for political action. Final report to World Health Organization Commission on the Social Determinants of Health. Chile: Universidad de Desarrollo and United Kingdom: National Institute for Health and Clinical Excellence.

Mikkonen J & Raphael D 2010. Social determinants of health: the Canadian facts. Toronto: York University School of Health Policy and Management.

Milner A & LaMontagne AD 2017. Underemployment and mental health: comparing fixed-effects and random-effects regression approaches in an Australian working population cohort. Occupational and Environmental Medicine 74(5):344-350.

Montgomery SM, Cook DG, Bartley MJ & Wadsworth MEJ 1999. Unemployment pre-dates symptoms of depression and anxiety resulting in medical consultation in young men. International Journal of Epidemiology 28(1):95-100.

Moor I, Spallek J, Richter M 2017. Explaining socioeconomic inequalities in self-rated health: a systematic review of the relative contribution of material, psychosocial and behavioural factors. Journal of Epidemiology and Community Health 71(6):565-75.

Moore T, McDonald M & McHugh-Dillon H 2014. Early childhood development and the social determinants of health inequities: a review of the evidence. Carlton, Victoria: VicHealth.

105 Australia’s health 2020: data insights

Chapter

3

Murtin F, Mackenbach J, Jasilionis D & Mira d’Ercole M 2017. Inequalities in longevity by education in OECD countries: insights from new OECD estimates. OECD Statistics Working Papers 2017/02. Paris: OECD Publishing.

Sandel M, Sheward R, Ettinger de Cuba S, Coleman SM, Frank DA, Chilton M et al. 2018. Unstable housing and caregiver and child health in renter families. Pediatrics 41(2):e20172199.

Saunders P, Naidoo Y & Bedford M 2008. Towards new indicators of disadvantage: deprivation and social exclusion in Australia. The Australian Journal of Social Issues 43(2):175-194.

Senate Community Affairs Reference Committee 2013. Australia’s domestic response to the World Health Organization’s (WHO) Commission on Social Determinants of Health report ‘Closing the gap within a generation’. Canberra: SCARC.

Siegrist J 2016. Accumulation of disadvantage over the life course and mortality. Journal of Epidemiology and Community Health 70(5):423.

Singh-Manoux A & Marmot M 2005. Role of socialization in explaining social inequalities in health. Social Science & Medicine 60(9):2129-33.

Stansfeld S & Candy B 2006. Psychosocial work environment and mental health--a meta-analytic review. Scandinavian Journal of Work, Environment and Health 32(6):443-62.

Thomson H, Thomas S, Sellstrom E & Petticrew M 2013. Housing improvements for health and associated socio-economic outcomes. Cochrane Database of Systematic Reviews. Viewed 9 March 2020, https://www.cochranelibrary.com/cdsr/doi/10.1002/14651858.CD008657.pub2/ full?highlightAbstract=withdrawn%7Chealth%7Cdeterminants%7Cdetermin%7Csocial%7Cof.

Tian J, Gall S, Patterson K, Otahal P, Blizzard L, Patton G et al. 2019. Socioeconomic position over the life course from childhood and smoking status in mid-adulthood: results from a 25-year follow-up study. BMC Public Health 19:169.

UN (United Nations) 2020. Sustainable development goals. Viewed 18 March 2020, https://sustainabledevelopment.un.org/?menu=1300.

Walker RJ, Gebregziabher M, Martin-Harris B & Egede LE 2015. Quantifying direct effects of social determinants of health on glycemic control in adults with type 2 diabetes. Diabetes Technology & Therapuetics 17(2).

Wilkinson R & Marmot M (eds) 2003. Social determinants of health: the solid facts. 2nd edn. Copenhagen: WHO.

Whitehead M 1992. The concepts and principles of equity and health. International Journal of Health Services 22(3):429-45.

Whitehead M 2000. William Farr’s legacy to the study of inequalities in health. Bulletin of the World Health Organization 78(1):86-7.

WHO (World Health Organization) 2019. Health impact assessment (HIA): glossary of terms used. Geneva: WHO. Viewed 6 January 2020, https://www.who.int/hia/about/glos/en/index1.html.

WHO CSDH (World Health Organization Commission on Social Determinants of Health) 2008. Closing the gap in a generation: health equity through action on the social determinants of health. Final report of the Commission on Social Determinants of Health. Geneva: World Health Organization.

Wilkinson R & Pickett K 2009. The spirit level: Why more equal societies almost always do better. London: Penguin Books.

107 Australia’s health 2020: data insights

Housing conditions and key challenges in Indigenous health

4

‘Without fundamental things like access to water, the ability to wash, … the ability to get rid of waste, the ability to live in a house that is safe … to try and improve health is impossible.’

— Dr Lilon Bandler, Sydney Medical School. The importance of living conditions to health. www.housingforhealth.com.

108 Australia’s health 2020: data insights

Chapter

4

There have been substantial improvements in Aboriginal and Torres Strait Islander

health over the past 30 years, with decreases in cardiovascular death rates and infant

mortality, and increases in life expectancy and in the number of people accessing

preventive or health monitoring services (such as health checks, chronic disease

management plans, and antenatal care) (AHMAC 2017). However, Indigenous

Australians as a group still experience poorer health outcomes compared with

non-Indigenous Australians. The reasons for this disparity are complex, and key among

these is the impact of colonisation and separation from Country on the wellbeing of

Indigenous Australians (Osborne et al. 2013). It is also well recognised that disparities

in upstream factors—the social determinants of health—result in differences in risks,

exposures, access to services and in outcomes throughout life. One social determinant

having a substantial impact on Indigenous health is housing conditions.

This article considers common factors underlying a number of diseases prevalent

in the Indigenous Australian population, particularly those in remote areas, but

less commonly or even rarely seen among non-Indigenous Australians: chronic

kidney disease, rheumatic heart disease, and certain eye and ear diseases. Bringing

together the available information, and highlighting data gaps, can help us to

draw out critical issues and to identify potential points of intervention that would

produce benefits across multiple areas. Given that many of the social determinants,

including housing, lie outside of the health system, the efforts of a range of systems,

government departments and other organisations will be needed to support and drive

interventions. Establishing agreed indicators across the main domains of interest, and

regularly monitoring these, can also help to ensure that all parties, both within and

outside the health system, are able to gauge progress and to make sure their efforts

are having the desired impact.

Social determinants of health and Indigenous Australians The World Health Organization (WHO) describes social determinants of health as ‘the

structural determinants and conditions of daily life’—that is, the conditions of work or

leisure; people’s homes, communities and environments; and their access to education

and health care (WHO CSDH 2008). People’s opportunities and circumstances are

shaped by the distribution of power, income, goods and services, which are in turn

affected by policy choices, and are a major component of health inequities between

and within countries.

109 Australia’s health 2020: data insights

Chapter

4

Commonly recognised social determinants of health include housing, education,

employment, income, and social networks and connections. For Indigenous Australians

and other Indigenous peoples across the world, cultural factors—including connection

with land and waters, identity, and language, as well as the ongoing effects of

dispossession, marginalisation, racism, and discrimination—also play a key role in

influencing health outcomes (Figure 4.1).

Other important social determinants affecting health outcomes include:

• health literacy (the ability to obtain, read, understand and use health-related

information to make appropriate health decisions), and

• availability of health resources (the funds, equipment, facilities, personnel and other

items such as medicines and medical supplies) needed to provide health services.

The social determinants of health act through complex and multidirectional pathways,

and underlie a broad range of poor health and welfare outcomes. A combination

of factors may act at the community and the individual level to influence health.

For example, an individual’s level of education and household income may influence

their food choices, while the area in which they live may affect the availability and cost

of various foods.

Other articles in this report and the Australia’s health 2020 snapshots (for example,

‘Social determinants of health’ https://www.aihw.gov.au/reports/australias-health/

social-determinants-of-health and ‘Social determinants and Indigenous health’

https://www.aihw.gov.au/reports/australias-health/social-determinants-and-indigenous-health) detail the range of social determinants and how they relate

to health; their impact throughout life; and their contribution to the gap in health

outcomes between Indigenous and non-Indigenous Australians. This article examines

key health conditions disproportionately impacting Indigenous Australians, which are

affected by housing conditions and access to services.

Two of the critical factors connecting housing conditions to health are the impact of

overcrowding and the state of domestic health hardware. ‘Health hardware’ refers to

the physical equipment needed to support good health. This includes safe electrical

systems; access to water; working taps, showers, and sinks with plugs; toilets;

waste and wastewater removal systems; and facilities needed for the safe storage

and preparation of food. If any of these facilities are unavailable, not working, or

inadequate to support the number of residents, illness or injury can occur. Also implicit

in this is that local infrastructure should minimise environmental health risks, by

providing access to safe drinking water, and by supporting sanitation and waste

management services.

110 Australia’s health 2020: data insights

Chapter

4

Figure 4.1: Conceptual model for social determinants of Indigenous health and health inequities Source: Adapted from Osborne, Baum & Brown 2013.

Colonisation, separation from land, family, community, culture

Midstream factors Upstream factors Downstream factors

Individual agency and personal choice

Areas of public policy

Economic

Land/property distribution

Welfare

Health

Housing

Transport

Criminal justice

Socioeconomic factors

Educational attainment

Employment

Income

Housing

Connection to family, community, culture

Interaction with Government systems

Racism

Health behaviours

Diet/nutrition

Tobacco use

Alcohol and drug use

Addiction

Physical activity

Psychosocial factors

Social and emotional wellbeing

Grief and loss

Self-esteem

Coping

Control

Stress

Isolation

Trust

Hostility

Physiological systems/ biological reactions

E.g. effects on endocrine system

High blood pressure

Body weight

Suppressed immune function

Outcomes

Socioeconomic health inequities

Lower life expectancy

Chronic disease

Poorer health and wellbeing throughout the life course

111 Australia’s health 2020: data insights

Chapter

4

Key challenges in Indigenous health Although the social determinants of health—in particular the housing conditions in

which people live and their access to relevant health services—affect the incidence

and prevalence of many health conditions, there are several conditions which have

been identified by governments, health organisations and Aboriginal and Torres Strait

Islander communities as being key challenges in Indigenous health. These are kidney

disease; acute rheumatic fever (ARF) and rheumatic heart disease (RHD); eye health;

and hearing health. Each of these conditions cause considerable burden to individuals,

communities and health services, and can lead to hospitalisations that are potentially

preventable. ‘Roadmaps’ outlining a framework to deliver programs and services have

been, or are being, developed for each of these conditions (Hearing Health Sector

Committee 2019; Taylor et al. 2012) and were presented to the Council of Australian

Governments (COAG) Health Council in late 2019.

Kidney disease

Chronic kidney disease (CKD) is common among Indigenous Australians, with around

1 in 6 (18%) Indigenous adults showing signs of kidney problems in 2012-13—more

than twice the rate among non-Indigenous adults, after adjusting for age (ABS 2014).

If left undiagnosed or untreated, kidney problems can progress to end-stage kidney

disease (ESKD), requiring dialysis or kidney transplant for survival (see Box 4.1). CKD

may develop as a complication of diabetes or heart disease, or occur independently

of these conditions. Although making up only 3.3% of the total Australian population,

Indigenous Australians accounted for 12% of new patients beginning ESKD treatment

in Australia in 2017, and 8.9% of all Australians either on dialysis or who had a

functioning kidney transplant at the end of that year (ANZDATA Registry 2018).

112 Australia’s health 2020: data insights

Chapter

4

Box 4.1: Stages of chronic kidney disease

CKD is categorised into 5 stages, according to the level of kidney function

(measured as the estimated glomerular filtration rate, or eGFR), or evidence of

kidney damage (measured as the albumin-to-creatinine ratio, or ACR).

Early stages (1-2)

Tests show an eGFR ≥60mL/min/1.73m2 and/or ACR≥2.5mg/mmol for males or

ACR≥3.5mg/mmol for females. There are usually no symptoms.

Middle stages (3-4)

Tests show an eGFR of 15-59mL/min/1.73m2. Level of waste (urea and creatinine)

in the blood rises and kidney function slows down. The person may start to

feel unwell.

End stage (5)

Tests show an eGFR <15mL/min/1.73m2. The person requires dialysis or a kidney

transplant to stay alive.

For more information see ‘Chronic kidney disease’ https://www.aihw.gov.au/

reports/australias-health/chronic-kidney-disease.

Diabetic nephropathy is the most common primary disease among Indigenous

Australians receiving ESKD treatment (ANZDATA Registry 2018), and it is often assumed

that diabetes is the major cause of CKD among Indigenous Australians. However, a

review of kidney biopsy results showed that, although diabetes was more common

among Indigenous compared with non-Indigenous Australians with kidney disease,

diabetic changes were present in fewer than half of the Indigenous cases, and the

results varied considerably with remoteness (Hoy et al. 2012). CKD has also been

linked to infections, in particular post-streptococcal glomerulonephritis (PSGN), with

adolescents and younger adults with a history of PGSN being 3-4 times as likely as

those without to have signs of CKD (Hoy et al. 2012, 2015). PSGN is caused by infection

with certain strains of group A streptococcus (GAS) and is common in developing

countries and resource-poor settings in developed countries (Worthing et al. 2019).

Acute PSGN is notifiable in Western Australia and the Northern Territory, with

notification rates highest among Indigenous children aged under 15. Over the years

2009-2016 in the Northern Territory, the notification rate among Indigenous children

aged under 15 was almost 19 times that for non-Indigenous children of the same age

113 Australia’s health 2020: data insights

Chapter

4

(Chaturvedi et al. 2018). McMullen and others (2016) suggested that 75% of PSGN

in the Kimberley region of Western Australia was attributable to factors such as the

availability of clean water, laundry and bathroom facilities; and housing conditions

including overcrowding.

Acute rheumatic fever and rheumatic heart disease

ARF is an autoimmune response to infection of the throat by GAS bacteria. There is also

increasing evidence that GAS infection of the skin may lead to ARF. The first episode of

ARF typically occurs between 5 and 15 years of age. The risk of ARF recurrence is high

after an initial ARF episode, with repeated episodes increasing the chance of RHD

(long-term heart valve damage). Untreated RHD can cause heart failure, arrhythmias,

stroke, endocarditis and pregnancy complications, and may be fatal.

ARF and RHD are preventable and treatable diseases. Both ARF and RHD are

associated with overcrowding, socioeconomic deprivation, and inadequate access to

health hardware and health resources. They are common in low- and middle-income

countries, and in high-income countries they usually persist only in socioeconomically

disadvantaged populations. Aboriginal and Torres Strait Islander people have among

the highest recorded rates of ARF and RHD in the world. Maori and Pacific Islanders

and migrants from developing countries are also at high risk.

Clinical registers of people with ARF and RHD in Queensland, Western Australia,

South Australia and the Northern Territory are supported under the Australian

Government’s Rheumatic Fever Strategy. During the 5-year period from 2013 to 2017,

among Indigenous Australians across these 4 jurisdictions, there were almost 1,800

ARF diagnoses and more than 1,000 new RHD diagnoses—accounting for 94% and

83% of all reported cases, respectively (AIHW 2019b).

Eye health

Eye diseases and vision problems are the most common long-term health conditions

in Australia, with around 1 in 3 (33%) Indigenous Australians and 1 in 2 (54%)

non-Indigenous Australians affected (ABS 2013). While short- and long-sightedness are

the most commonly reported vision problems in Australia, preventable conditions such

as cataracts, macular degeneration and diabetic retinopathy also cause vision loss.

Most of the blindness and vision impairment experienced by Indigenous Australians is

caused by conditions that are preventable or can be treated.

114 Australia’s health 2020: data insights

Chapter

4

Indigenous Australians aged 40 and over have 3 times the rate of vision loss of other

Australians, with cataracts and diabetic retinopathy accounting for 1 in 4 cases (AIHW

2018b). Lack of access to and lower uptake of relevant health services are key factors

in this disparity (Taylor et al. 2012). In addition, a number of Indigenous communities

are affected by trachoma, an eye infection caused by Chlamydia trachomatis bacteria.

Repeated infection, especially during childhood, may lead to scarring and contraction

of the eyelid, causing the eyelashes to rub against the cornea. This is known as

trichiasis and, if uncorrected, results in gradual vision loss and blindness. Australia

is the only high-income country in the world to still have endemic trachoma (Vision

2020 Australia 2019). In 2017, across 130 remote Indigenous communities considered

‘at risk’, the prevalence of active trachoma among children aged 5-9 years was 3.8%,

with 60 communities having a prevalence of 5% or more in this age group (indicating

endemic levels) (Kirby Institute 2017). High rates of trachoma are associated with poor

access to clean water and health hardware; household overcrowding; and lack of

access to medical services.

Hearing health

Aboriginal and Torres Strait Islander people experience some of the highest rates of

middle ear disease in the world. Further, while non-Indigenous Australians tend to

develop hearing loss at older ages, Indigenous Australians with acquired hearing loss

tend to have developed it in childhood.

Otitis media (OM) (middle ear infection) is the most common ear disease among

Indigenous children. It is largely the result of socioeconomic factors including poverty,

crowded housing, lack of adequate health hardware, and limited access to primary

health care and treatment. While it affects children across Australia, with an overall

prevalence of 2.6% among Indigenous children aged 0-14 (ABS 2019b), the prevalence

of OM among young Indigenous children in remote communities is often considerably

higher, and has been reported to be as high as 90% in some studies (see, for example,

Leach et al. 2016).

Compared with non-Indigenous children, OM in Indigenous children tends to occur

earlier in life, to occur more often, to be of greater severity and to last longer. The

condition often results in perforation of the eardrum and chronic discharge of mucous

from the affected ear. Data from the Northern Territory Outreach Hearing Health

Program showed that 47% of Indigenous children and young people who received

services in 2018 had hearing loss, and 29% had hearing impairment (AIHW 2019c).

Among children under 15, those aged 3-5 were the most likely to have hearing loss.

115 Australia’s health 2020: data insights

Chapter

4

This affects a child’s ability to learn and to interact with others during the critical early developmental years. Children who experience multiple episodes of OM prior to the start of school are likely to have difficulties distinguishing, processing and remembering sounds, and in identifying sounds in words. These skills are critical for developing oral communication, literacy and numeracy. Hearing loss can contribute to poor school performance; absenteeism; dropping out of school and subsequent difficulties gaining employment; and increased interaction with the justice system (AIHW 2018a, Su et al. 2019). For more detail, see ‘Indigenous hearing health’ https://www.aihw.gov.au/reports/australias-health/indigenous-hearing-health.

Housing and living conditions Not all Indigenous Australians have benefited from the improvements in living conditions during the 20th century that resulted in the virtual elimination of diseases such as ARF and trachoma in the non-Indigenous population. Colonisation and its ongoing effects have had a significant impact on Indigenous housing conditions and homelessness. Indigenous Australians have less access to affordable or secure housing than other Australians, and are considerably more likely to live in overcrowded conditions, or to experience homelessness (including ‘sleeping rough’, and living in severely overcrowded dwellings or in other temporary or supported accommodation) (AIHW 2019a). The Australian Medical Association’s 2018 report card on Indigenous health lists ‘addressing environmental health and housing’ as 1 of 6 fundamental targets required to achieve the Closing the Gap health strategy (AMA 2018). This is also recognised in the National Aboriginal and Torres Strait Islander Health Plan 2013-2023 (Australian Government 2013).

Both overcrowding and inadequate health hardware (or lack of access to these facilities, as a result of homelessness) increase the risk of repeatedly contracting and spreading infection. These living conditions, combined with lack of access to services, means that infections may not be managed, resulting in higher disease transmission; increased risk of long-term complications; and greater disease burden on individuals and communities.

Skin infections are a common cause of morbidity in disadvantaged populations, especially among children. Two of the most prevalent are scabies and skin sores. Evidence from child health checks undertaken between July 2007 and June 2009 under the Northern Territory Emergency Response showed that 9.9% of the Indigenous children assessed had skin sores and 7.9% had scabies (AIHW & Department of Health and Ageing 2009). Scabies is endemic in remote northern Australia, affecting up to 35% of children and 25% of adults (Romani et al. 2015). It is spread by close physical contact or in some cases through sharing clothes, towels and bedding.

116 Australia’s health 2020: data insights

Chapter

4

Skin sores (impetigo) are caused by infection with bacteria, with GAS bacteria being

the most common cause of skin sores in northern Australia (Parks et al. 2012). It may

be spread by direct contact with sores or via contaminated clothing or linens. Remote

Aboriginal communities in northern Australia have the world’s highest prevalence of

skin sores (Romani et al. 2015). In a study of 320 children across 5 remote Aboriginal

communities, Kearns and others (2013) found that more than 80% had presented to

health services with skin sores by their first birthday. Children infested with scabies are

up to 12 times more likely to develop skin sores (Aung et al. 2018).

Overcrowding

Overcrowding is an important issue that has an impact on the health and wellbeing

of individuals and households. Data from the National Aboriginal and Torres Strait

Islander Health Survey (NATSIHS) suggested that 18% of Indigenous Australians

were living in an overcrowded dwelling in 2018-19 (based on the Canadian National

Occupancy Standard; see Box 4.2), with this being considerably more common in

remote areas (42% compared with 12% in non-remote areas). In some regions,

particularly in northern Australia, relatively large proportions of the Indigenous

population were living in overcrowded dwellings (Figure 4.2).

Box 4.2: The Canadian National Occupancy Standard

Various approaches are used to define and measure the extent of overcrowding.

This article uses the definition currently used by the ABS, which is based on the

Canadian National Occupancy Standard (CNOS). Using this definition, a dwelling

is overcrowded if it requires at least 1 additional bedroom to accommodate the

people who usually live there, given their ages, sex and relationships to each

other, as follows:

• There should be no more than 2 persons per bedroom

• Children less than 5 years of age of different sexes may reasonably share a

bedroom

• Children 5 years of age or older of opposite sex should have separate bedrooms

• Children less than 18 years of age and of the same sex may reasonably share

a bedroom

• Single household members 18 years or older should have a separate bedroom,

as should parents or couples.

117 Australia’s health 2020: data insights

Chapter

4

The concept of overcrowding can be a subjective one that is influenced by a number of

factors, including cultural and housing design considerations. Memmott and others (2012)

note, however, that it is generally the CNOS-type standards that underpin the design of

housing even in remote Australia, and that housing stock is usually inadequate to house

the large, extended and complex family structures typical of Indigenous communities.

Housing represents not only shelter and safety but is a place that supports family, culture

and cultural practices. Family visiting during celebrations, sporting or cultural events, for

Sorry Business, or at other times may increase the number of residents in Indigenous

households for days or weeks. This puts additional pressure on sleeping and living

capacity and on kitchen, bathroom and laundry facilities. Dwellings that are inadequate

for the number of residents (including long-term visitors) may lead to poor health

outcomes, or result in premature failure of health hardware (Healthabitat 2019b).

Figure 4.2: Proportion of Indigenous Australians living in overcrowded

dwellings, by National Indigenous Australians Agency region, 2018-19

Up to 10

>10 to 2 0

>20 to 4 0

>40 to 6 0

>60 to 8 0

Proportion(%)

Far North Queensland

Arnhem Land and Groote Eylandt

Top End and Tiwi Islands 42.1

75.3

Kimberley 27.9

Central Australia 45.7

Greater Western Australia 16.5

South Australia 14.8

41.3

Gulf and

North Queensland 20.8

South Queensland 9.9

Western

New South Wales 16.2

Victoria and Tasmania 10.3

Eastern

New South Wales

9.4

Note: An ‘overcrowded dwelling’ is one that requires at least 1 additional bedroom to accommodate the people who usually live there, as defined by the CNOS.

Source: ABS 2019a.

118 Australia’s health 2020: data insights

Chapter

4

Health hardware

The Indigenous-specific health and social surveys conducted by the ABS collect

self-reported information on defects and issues in the dwellings of respondents, from

which data on the proportion of households having working health hardware can be

derived. A dwelling with working health hardware is defined in this chapter as one

which has working facilities for washing people; for washing clothes and bedding; for

safely storing and preparing food; and for safely removing waste.

In 2018-19, around 1 in 7 (13%) Indigenous households were living in dwellings which

did not have working health hardware—equating to almost 47,000 households across

Australia. Households in Very remote areas were up to 4 times as likely as those in other

areas to be living in dwellings that did not have working health hardware (Figure 4.3).

Figure 4.3: Proportion of Indigenous households living in dwellings without

working health hardware, by remoteness area, 2018-19

0

5

10

15

20

25

30

35

40

45

Major cities Inner regional Outer regional Remote Very remote

Per cent

Note: A ‘dwelling without working health hardware’ is one where at least 1 of the following facilities is unavailable or not working: facilities for washing people; facilities for washing clothes and bedding; facilities for safely removing waste; facilities for safely storing and preparing food.

Source: ABS 2019a.

The most commonly reported issue related to preparing and storing food, where

27% of Very remote households and 7.8% of households in other areas reported not

having working facilities (Figure 4.4). Problems with facilities for washing clothes and

bedding were also common (20% of Very remote households and 3.4% of households in

other areas), while facilities for washing people were a problem for 11% of Very remote

households and 2.1% of households in other areas.

119 Australia’s health 2020: data insights

Chapter

4

Figure 4.4: Proportion of Indigenous households living in dwellings without

working facilities, Very remote areas compared with other remoteness

areas, 2018-19

0

5

1 0

1 5

2 0

2 5

3 0

Preparing food Washing clothes / bedding Washing people Sewerage

Per cent

Very remote areas Other areas

Source: ABS 2019a.

These estimates suggest that in 2018-19, more than 39,000 Indigenous Australians

were living in dwellings that did not have working facilities for washing clothes, and

over 27,000 were living in dwellings that did not have working facilities for washing

themselves.

In addition to people living in households without working facilities, or in overcrowded

conditions, people who are living in improvised dwellings, tents, or sleeping out (‘rough

sleepers’) may also have considerable difficulty in accessing facilities for washing

people and clothes. Rough sleepers and others experiencing homelessness may also

have difficulty accessing health services when they are needed, further increasing

the risk of poor health outcomes. On Census night in 2016, almost 2,200 Indigenous

Australians were sleeping rough, equivalent to a rate of 33 people per 10,000

population—14 times the rate among non-Indigenous Australians (AIHW 2019a).

Lack of access to health services

In the 2014-15 National Aboriginal and Torres Strait Islander Social Survey,

5.7% of respondents (7.4% in remote areas and 5.1% non-remote areas) reported

problems accessing doctors, equating to around 25,000 people aged 15 and over.

120 Australia’s health 2020: data insights

Chapter

4

The most commonly cited barriers to access included:

• services in the area were not available or inadequate

• transport or distance was an issue

• waiting times were too long or appointments were not available when required

(ABS 2016).

Barriers relating to service availability or travel were considerably more likely to be

reported by respondents in remote areas, while barriers relating to waiting time or

availability of appointments did not vary by remoteness.

Analyses by the AIHW have examined geographic variations in Indigenous Australians’

access to a range of different types of primary health services (both Indigenous-specific

and mainstream), hospitals and maternity services. They also examined the distribution

of the health workforce relative to the distribution of the Indigenous population.

These include general practitioners, nurses, pharmacists, optometrists and dentists

(AIHW 2014, 2015, 2016, 2017, 2018a).

Although primary health services are, in general, well-positioned in relation to the

Indigenous Australian population, with Indigenous-specific primary health care services

supplemented by Royal Flying Doctors Service clinics servicing many remote areas

(Figure 4.5), there are still areas where access is difficult. A ‘drive-time’ analysis revealed

there were 40 local areas—including several with populations of more than 600

Indigenous people—where a person seeking care has more than 1 hour’s drive to the

nearest Indigenous-specific primary health care service (AIHW 2015).

Lack of access to primary health care services, whether these are provided by

Indigenous-specific or mainstream health services, means that acute and relatively

minor illnesses such as infections may not be managed in a timely way, if at all. Timely

management is important not only in reducing the impact of illness on individuals, but

also in reducing both the risk of transmission and the risk of progression of an illness

(for example, from a throat infection to ARF). For those who have chronic diseases,

such as RHD and CKD, lack of access to ongoing and regular management increases

the risk of complications and worsening of the disease. This also applies to people

who have had ARF, for whom secondary prophylaxis to reduce the risk of RHD (or its

progression) needs to be delivered every 21 to 28 days for many years.

Even when there are well-positioned health services available, issues relating to

whether individuals perceive a service as being culturally safe may also affect uptake.

121 Australia’s health 2020: data insights

Chapter

4

Figure 4.5: Location of primary health care services in Australia, by service

type, 2017-2019

Primary Health Care Service Locations

Aboriginal Medical Service (2019)

Royal Flying Doctors Service —regular clinic (2018)

General practitioner (2017)

Primary Health Network (2017)

Note: Appearance on the map does not necessarily indicate that a site is a regular full-time practice.

Source: AIHW 2019b.

Addressing the problem The health problems outlined in this article are all related to infection, particularly in childhood. PSGN, ARF, OM, trachoma and skin infections are all most common among children, exposing them to the risk of long-term complications and ongoing health issues throughout their lifetime.

Addressing the basic underlying determinants of adequate housing and access to health services is a key step in meeting these health challenges. Strategies that address basic, (apparently) non-health factors such as living conditions as a means of improving health outcomes are referred to as ‘primordial prevention’.

122 Australia’s health 2020: data insights

Chapter

4

Reducing risk

Primordial prevention strategies are likely to be multi-sectoral, as responsibility in many cases will lie outside of the health system. The Marmot Review of health inequalities in England (Marmot et al. 2010) noted that action by health departments and health services alone would not reduce health inequalities; action on the social determinants of health needs to involve all central and local government departments as well as the private sector. In addition to multi-sectoral approaches, the interventions required to achieve change can be complex and slow to implement, creating additional challenges (Waters 2001).

The Australian Healthy Skin Consortium (2018) notes that although there is insufficient evidence to determine whether housing improvements directly affect the incidence of skin sores or scabies, there is evidence that housing improvements result in improved skin health in general, and that further research in this area should be a priority. They do state, however, that washing hands with soap is effective in treating and preventing skin sores in resource-limited settings, and therefore access to clean water is critical. According to May and others (2016), improvements in housing quality and access to health care are the top priorities in reducing inequalities in GAS-related outcomes, particularly for Indigenous Australians in remote areas.

In remote areas, access to services and tradespeople is often limited, and fixing problems with plumbing, electricity or appliances can be both slow and expensive. Aboriginal Environmental Health units operate within state and territory health departments, and work with (or within) public health units to improve the conditions in Aboriginal communities by addressing issues relating to utilities, pests, waste management and food safety. For example, in New South Wales, the Housing for Health program has been used to implement change (see Box 4.3).

Box 4.3: Housing for Health in New South Wales

The Housing for Health program has been delivered to Aboriginal communities

across New South Wales since 1997, with 118 community Housing for Health

projects delivered by the end of 2018. The program has been successful in

improving living conditions, with data for projects delivered during 2016-17 and

2017-18 showing a substantial increase in the proportion of dwellings supporting

the Healthy Living Practices (Box 4.4) (NSW Health 2019). An evaluation of the

program’s first 10 years showed a 40% reduction in hospital admissions for

infectious diseases among residents of houses within the program, compared with

Indigenous residents in other rural areas of New South Wales (NSW Health 2010).

123 Australia’s health 2020: data insights

Chapter

4

The Housing for Health program aims to improve health in Indigenous communities, particularly among young children, through improving living conditions. The program assesses and then repairs or replaces health hardware so that houses are safe and the occupants have the ability to carry out healthy living practices (see Box 4.4). A key aspect is engaging local community members in all aspects of the process so as to provide employment, build capacity and deliver skills that can be used to help maintain dwellings (Healthabitat 2019a).

Box 4.4: The 9 Healthy Living Practices

In the mid-1980s, an environmental health review was undertaken in the Anangu Pitjantjatjara (APY) Lands in the north-west of South Australia, through a cooperative initiative by the Nganampa Health Council, the South Australian Health Commission and the Aboriginal Health Organisation of South Australia (1987). The review, Uwankara Palyanyku Kanyintjaku (A Strategy for Well-being), identified health problems that could be reduced by changes in the living environment for Indigenous communities in remote Australia. As a result, a prioritised list of 9 ‘Healthy Living Practices’ that could help prevent the spread of infectious diseases was developed. These were adopted by the Australian Government within the National Indigenous Housing Guide (FaCSIA 2012), and underpin the Housing for Health program. The practices are largely dependent on environmental services and infrastructure such as drainage, water supply and waste management, along with adequate housing stock with working fixtures:

1. Washing people

2. Washing clothes and bedding

3. Removing waste water safely

4. Improving nutrition and the ability to store, prepare and cook food

5. Reducing the negative impacts of overcrowding

6. Reducing the negative effects of animals, insects and vermin

7. Reducing the health impacts of dust

8. Controlling the temperature of the living environment

9. Reducing hazards that cause trauma.

The first 4 of these are considered critical, as they are essential for people to be able to practice healthy living.

See more on the Healthabitat webpage: https://www.healthabitat.com/

what-we-do/safety-and-the-9-healthy-living-practices/.

124 Australia’s health 2020: data insights

Chapter

4

The Fixing Houses for Better Health program, which ran from 1999 to 2011 in all

jurisdictions except Tasmania and the Australian Capital Territory, used a similar

methodology to improve housing within Indigenous communities. However, while

the program resulted in improvements to the way houses supported healthy living

practices, no data were collected that could link these improvements to changes

in health outcomes in the communities involved (ANAO 2010). In 2008, the 10-year

National Partnership Agreement on Remote Indigenous Housing aimed to address

overcrowding, homelessness, poor housing conditions and severe housing shortages

in remote Indigenous communities across all jurisdictions except the Australian Capital

Territory. This was replaced in 2016 by a 2-year National Partnership Agreement on

Remote Housing, involving the Australian, Queensland, Western Australian, South

Australian and Northern Territory governments. This agreement focused more on new

housing, reducing barriers to home ownership and improving tenancy management

and rental housing stock. Again, while these programs resulted in improvements to

housing, reduced levels of overcrowding and generated local employment, little to no

data are available to link these improvements to changes in health outcomes (DPMC

2017). Australian Government funding for Indigenous housing has since been provided

to some jurisdictions through individual agreements, as well as more generally via the

National Housing and Homelessness Agreement, under which Indigenous Australians

are a priority cohort.

Improving detection by health services

Good quality housing and community facilities (such as childcare centres), with working

health hardware, are critical for reducing the transmission of pathogens that cause

diseases such as trachoma, OM, ARF, scabies, PSGN, and other infections. Access to

timely and responsive primary health care is also important, meaning not only that

services need to be available and acceptable to the community, but that health workers

need to be able to recognise, diagnose and treat conditions that are not commonly

seen in the non-Indigenous population.

There is evidence that conditions such as skin infections may be substantially

under-diagnosed even in endemic regions. Yeoh and others (2017) argue that scabies

and skin sores are ‘normalised’ by clinicians working in high prevalence areas, and so

cases in patients presenting for other reasons may go undiagnosed and untreated.

ARF may also be under-reported as it is complex to diagnose, with identification

relying on a combination of pathology, symptoms and exclusions (known as the Jones

criteria) rather than a simple diagnostic test (AIHW 2019b). CKD also is known to be

under-diagnosed as it may progress to quite significant loss of kidney function without

any symptoms that would lead a person to seek medical care.

125 Australia’s health 2020: data insights

Chapter

4

For ARF and skin infections especially, a factor that may contribute to under-diagnosis

is the comparative rarity of these conditions in the non-Indigenous population. Visual

aids—such as those developed for the East Arnhem Regional Healthy Skin Project

(Andrews et al. 2009) and used in the National Healthy Skin Guideline (Australian

Healthy Skin Consortium 2018)—and standard treatment protocols are valuable

resources for health workers and clinicians who may be less familiar with the clinical

presentation and recommended guidelines for these conditions. Ongoing professional

and community education and awareness-raising activities, such as those delivered

by RHD Australia and the Northern Territory Outreach Hearing Health program, also

provide important support for improving case detection and diagnosis.

Monitoring progress

Data about the extent of housing adequacy and service access issues; the number of

people affected by various health conditions; and evidence for what works to create

improvement, are key to reducing the disparities described in this article. Although data on

several of these aspects is available, there are gaps where improvements could be made.

Data on the prevalence of CKD; ARF and RHD; and vision and hearing problems among

Indigenous Australians are available from a range of national and jurisdictional data

collections. For example:

• measured data on kidney problems is available from the ABS National Aboriginal

and Torres Strait Islander Health Measurement Survey 2012-13, with self-reported

information available from the 2018-19 NATSIHS. The Australian and New Zealand

Dialysis and Transplant Registry provides annual data on people beginning or

continuing dialysis or living with a kidney transplant. Data on diagnoses of PSGN

are available from the Northern Territory and Western Australia, the only

jurisdictions in which this is currently a notifiable disease

• the AIHW National Rheumatic Heart Disease Data Collection includes information on

people diagnosed with ARF or RHD, sourced from clinical registers in Queensland,

Western Australia, South Australia and the Northern Territory. The New South Wales

register also provides data to the AIHW, which is included in annual reports (AIHW 2019b)

• the National Eye Health Survey (Foreman et al. 2018) provides data on the

prevalence and causes of vision loss among both Indigenous and non-Indigenous

Australians, while the trachoma control programs in several jurisdictions provide

data on trachoma and trichiasis (National Trachoma Surveillance and Reporting Unit

2018). These and other sources are used by the AIHW to report annually against the

Indigenous Eye Health measures (AIHW 2018b)

126 Australia’s health 2020: data insights

Chapter

4

• detailed data on OM, hearing loss and hearing impairment in Indigenous children

and young people is available from the Northern Territory Outreach Hearing Health

Program and the Queensland Deadly Ears program, but is not available from

other jurisdictions. Some self-reported data are also available from the NATSIHS.

A number of research projects and regional studies provide valuable information on

the prevalence of conditions such as skin sores, scabies and OM, and on the links

between these conditions and social determinants of health.

Detection of vision and hearing problems relies on regular screening and early

intervention. The 2018-19 NATSIHS, which included audiometry testing, provides the

first national data on hearing loss among Indigenous Australians, and is an important

baseline from which to develop policies and identify areas of need (see ‘Indigenous

hearing health’ https://www.aihw.gov.au/reports/australias-health/indigenous-

hearing-health). Repeating this survey in several years would provide follow-up

data to allow progress to be assessed.

May and others (2016) suggest that legislating for notification of GAS diseases that

disproportionately affect Indigenous Australians (such as ARF, PSGN and impetigo)

would facilitate accurate disease monitoring and directed public health responses.

They also note that school-based screening programs for sore throat and skin

infections exist in New Zealand, where similar issues affect Maori and Pacific

Islander peoples.

Although surveys such as the National Aboriginal and Torres Strait Islander Social

Survey and the National Social Housing Survey (AIHW 2019d) provide basic information

on overcrowding and health hardware, these are based on self-reported information

from a sample population. More detailed audits, such as those administered by

environmental health units across the states and territories, can provide a more

comprehensive picture of the current status of housing and living conditions for

Indigenous Australians, and help in identifying areas of need and the types of

intervention required. For completeness, both social housing stock and private

dwellings (owned or rented) should be included. Health information—both before and

after any interventions—is also important: as noted earlier, many of the programs

aimed at improving housing in Indigenous communities did not collect data which

could enable the assessment of whether the program improved health outcomes.

Such information would be highly valuable for both increasing our knowledge of the

relationship between housing and health, and for making decisions about the aims

and scope of future programs.

127 Australia’s health 2020: data insights

Chapter

4

Data from the National RHD data collection show cases of ARF occurring in both

remote and urban locations, though incidence tends to increase in more remote

areas (AIHW 2019b). The National Healthy Skin Guideline also notes that although

the burden of skin infections is greatest in remote Indigenous communities, there is

also a significant burden for urban Indigenous populations (Australian Healthy Skin

Consortium 2018). Although the proportion of people experiencing housing-related

problems and the prevalence of related health conditions are considerably greater in

remote areas, data from both urban and remote populations is required to ensure the

needs of all Indigenous Australians facing these key health challenges are met.

Conclusion Housing conditions are associated with several health problems that are prevalent

among Indigenous Australians, including CKD, ARF and RHD, eye disorders and hearing

problems. Infections, particularly those occurring in childhood, are an important factor

in the development of these problems. Good quality, regular and reliable data about

these diseases and their underlying determinants (in particular, housing and living

conditions)—along with information about access to relevant health services—are

critical if these key health challenges are to be met.

References ABS (Australian Bureau of Statistics) 2013. Australian Aboriginal and Torres Strait Islander Health Survey: first results, Australia, 2012-13. ABS cat. no. 4727.0.55.001. Canberra: ABS.

ABS 2014. Australian Aboriginal and Torres Strait Islander Health Survey: biomedical results, 2012-13. ABS cat. no. 4727.0.55.003. Canberra: ABS.

ABS 2016. Microdata: National Aboriginal and Torres Strait Islander Social Survey, 2014-15. ABS cat. no. 4720.0.55.002. Findings based on TableBuilder analysis. Canberra: ABS.

ABS 2019a. Microdata: National Aboriginal and Torres Strait Islander Health, 2018-19. ABS cat. no. 4715.0.55.001. Findings based on Detailed Microdata analysis. Canberra: ABS.

ABS 2019b. National Aboriginal and Torres Strait Islander Health Survey, 2018-19. ABS cat. no. 4715.0. Canberra: ABS.

AHMAC (Australian Health Ministers’ Advisory Council) 2017. Aboriginal and Torres Strait Islander Health Performance Framework 2017 report. Canberra: AHMAC.

AIHW (Australian Institute of Health and Welfare) 2014. Access to primary health care relative to need for Indigenous Australians. Cat. no. IHW 128. Canberra: AIHW.

AIHW 2015. Spatial variation in Aboriginal and Torres Strait Islander people’s access to primary health care. Cat. no. IHW 155. Canberra: AIHW.

AIHW 2016. Spatial distribution of the supply of the clinical health workforce 2014: relationship to the distribution of the Indigenous population. Cat. no. IHW 170. Canberra: AIHW.

128 Australia’s health 2020: data insights

Chapter

4

AIHW 2017. Spatial variation in Aboriginal and Torres Strait Islander women’s access to 4 types of maternal health services. Cat. no. IHW 187. Canberra: AIHW.

AIHW 2018a. Australia’s health 2018. Cat. no. AUS 221. Canberra: AIHW.

AIHW 2018b. Indigenous eye health measures 2017. Cat. no. IHW 192. Canberra: AIHW. Viewed 8 November 2019, http://www.aihw.gov.au/reports/indigenous-australians/indigenous-eye-health-measures-2017/contents/summary.

AIHW 2019a. Aboriginal and Torres Strait Islander people: a focus report on housing and homelessness. Cat. no. HOU 301. Canberra: AIHW.

AIHW 2019b. Acute rheumatic fever and rheumatic heart disease in Australia. Cat. no. CVD 86. Canberra: AIHW. Viewed 8 November 2019, http://www.aihw.gov.au/reports/indigenous-australians/acute-rheumatic-fever-rheumatic-heart-disease/contents/summary.

AIHW 2019c. Hearing health outreach services for Aboriginal and Torres Strait Islander children in the Northern Territory: July 2012 to December 2018. Cat. no. IHW 213. Canberra: AIHW. Viewed 11 November 2019, https://www.aihw.gov.au/reports/indigenous-australians/hearing-health-outreach-services/contents/table-of-contents.

AIHW 2019d. National Social Housing Survey 2018: Key results. Cat. no. HOU 311. Canberra: AIHW.

AIHW and Department of Health and Ageing 2009. Progress of the Northern Territory Emergency Response Child Health Check Initiative: update on results from the Child Health Check and follow-up data collections. Cat. no. IHW 28. Canberra: AIHW.

AMA (Australian Medical Association) 2018. 2018 report card on Indigenous health: rebuilding the Closing the Gap health strategy. Canberra: AMA. Viewed 11 November 2019, https://ama.

com.au/article/2018-ama-report-card-indigenous-health-rebuilding-closing-gap-health-strategy-and-review.

ANAO (Australian National Audit Office) 2010. Indigenous housing initiatives: the Fixing Houses for Better Health program. Audit report no. 21, 2010-11. Canberra: ANAO.

Andrews RM, Kearns T, Connors C, Parker C, Carville K, Currie BJ et al. 2009. A regional initiative to reduce skin infections amongst Aboriginal children living in remote communities of the Northern Territory, Australia. PLOS Neglected Tropical Diseases 3(11):e554. Viewed 8 November 2019, https://doi.org/10.1371/journal.pntd.0000554.

ANZDATA (Australia & New Zealand Dialysis & Transplant) Registry 2018. 41st Report, Chapter 10: End stage kidney disease in Indigenous peoples of Australia and Aotearoa/New Zealand. Adelaide: ANZDATA Registry.

Aung PTZ, Cunningham W, Hwang K, Andrews RM, Carapetis JR, Kearns T, et al. 2018. Scabies and risk of skin sores in remote Australian Aboriginal communities: a self-controlled case series study. PLOS Neglected Tropical Diseases 12(7):e0006668. Viewed 8 November 2019, https://doi.org/10.1371/journal.pntd.0006668.

Australian Government 2013. National Aboriginal and Torres Strait Islander health plan 2013-2023. Canberra: Australian Government.

Australian Healthy Skin Consortium 2018. National Healthy Skin Guideline for the prevention, treatment and public health control of impetigo, scabies, crusted scabies and tinea for Indigenous populations and communities in Australia. 1st edn. Perth: Telethon Kids Institute.

129 Australia’s health 2020: data insights

Chapter

4

Viewed 18 November 2019, https://infectiousdiseases.telethonkids.org.au/siteassets/media-images-wesfarmers-centre/national-healthy-skin-guideline-1st-ed.-2018.pdf.

Bandler L 2013. The importance of living conditions to health. Sydney: Simon J Forbes. Viewed 11 November 2019, http://www.housingforhealth.com/realworld/importance-living-conditions-health/.

Chaturvedi S, Boyd R & Krause V 2018. Acute post-streptococcal glomerulonephritis in the Northern Territory of Australia: a review of data from 2009 to 2016 and comparison with the literature. American Journal of Tropical Medicine and Hygiene 99(6):1643-8.

COAG (Council of Australian Governments) 2009. National Partnership Agreement on Remote Indigenous Housing. Canberra: COAG. Viewed 19 November 2019, http://www.

federalfinancialrelations.gov.au/content/npa/housing/national-partnership/past/remote_ indigenous_housing_NP.pdf.

Department of Health 2013. National Aboriginal and Torres Strait Islander Health Plan 2013-2023. Canberra: Department of Health.

DPMC (Department of the Prime Minister and Cabinet) 2017. Remote housing review: a review of the National Partnership Agreement on Remote Indigenous Housing and the Remote Housing Strategy (2008-2018). Canberra: DPMC.

FaCSIA (Department of Families, Community Services and Indigenous Affairs) 2012. National Indigenous Housing Guide: improving the living environment for safety, health and sustainability. Canberra: FaCSIA.

Foreman J, Keel S, Xie J, van Wijngaarden P, Crowston J, Taylor HR et al. 2018. The National Eye Health Survey 2016. Melbourne: Centre for Eye Research Australia and Vision 2020.

Healthabitat 2019a. Local employment. Sydney: Healthhabitat. Viewed 1 May 2020, https://www.

healthabitat.com/what-we-do/local-employment/.

Healthabitat 2019b. Reducing the negative impacts of crowding. Sydney: Healthabitat. Viewed 8 November 2019, http://www.housingforhealth.com/the-guide/health-housing/reducing-the-negative-impacts-of-crowding/.

Hearing Health Sector Committee 2019. Roadmap for hearing health. Canberra: Department of Health. Viewed 15 November 2019, https://www1.health.gov.au/internet/main/ publishing.nsf/Content/roadmap-for-hearing-health.

Hoy WE, Samuel T, Mott SA, Kincaid-Smith PS, Fogo AB, Dowling JP et al. 2012. Renal biopsy findings among Indigenous Australians: a nationwide review. Kidney International 82(12): 1321-31.

Hoy WE, White A, Tipiloura B, Singh G, Sharma S, Bloomfield H et al. 2015. The multideterminant model of renal disease in a remote Australian Aboriginal population in the context of early life risk factors. Clinical Nephrology 83(7 Supplement 1):75-81.

Kearns T, Clucas D, Connors C, Currie BJ, Carapetis JR & Andrews RM 2013. Clinic attendances during the first 12 months of life for Aboriginal children in five remote communities of northern Australia. PLOS ONE 8(3):e58231. Viewed 8 November 2019, https://doi.org/10.1371/journal.

pone.0058231.

Kirby Institute 2018. Australian trachoma surveillance report 2017. Sydney: Kirby Institute.

130 Australia’s health 2020: data insights

Chapter

4

Leach AJ, Wigger C, Beissbarth J, Woltring D, Andrews R, Chatfield MD et al. 2016. General health, otitis media, nasopharyngeal carriage and middle ear microbiology in Northern Territory Aboriginal children vaccinated during consecutive periods of 10-valent or 13-valent pneumococcal conjugate vaccines. International Journal of Pediatric Otorhinolaryngology 86:224-32.

Marmot M, Allen J, Goldblatt P, Boyce T, McNeish D, Grady M et al. I 2010. Fair society, healthy lives. London: Institute of Health Equity.

May PJ, Bowen AC & Carapetis JR 2016. The inequitable burden of group A streptococcal diseases in Indigenous Australians. Medical Journal of Australia 205(5):201-3.

McMullen C, Eastwood A & Ward J 2016. Environmental attributable fractions in remote Australia: the potential of a new approach for local public health action. Australian and New Zealand Journal of Public Health 40:174-80.

Memmott P, Birdsall-Janes C & Greenop K 2012. Australian Indigenous house crowding. AHURI final report no. 194. Melbourne: Australian Housing and Urban Research Institute.

Nganampa Health Council Inc., South Australian Health Commission and Aboriginal Health Organisation of South Australia 1987. Report of Uwankara Palyanyku Kanyintjaku: an environmental and public health review within the Anangu Pitjantjatjara Lands, Alice Springs. Adelaide: Committee of Review on Environmental and Public Health within the Anangu Pitjantjatjara Lands.

NSW Health 2010. Closing the gap: 10 years of Housing for Health in NSW: an evaluation of a healthy housing intervention. North Sydney: NSW Ministry of Health.

NSW Health 2019. Healthy living practices in Aboriginal houses, 2016-17 to 2017-18. HealthStats NSW. North Sydney: NSW Ministry of Health. Viewed 18 November 2019, http://www.healthstats.

nsw.gov.au/Indicator/env_hlp_stat/env_hlp_stat?&topic=Aboriginal health&topic1=topic_ aboriginal_health&code=atsi dqi hlp.

Osborne K, Baum F & Brown L 2013. What works? A review of actions addressing the social and economic determinants of Indigenous health. Issues paper no. 7. Produced for the Closing the Gap Clearinghouse. Cat. no. IHW 113. Canberra: AIHW and the Melbourne Institute of Family Studies.

Parks T, Smeesters PR & Steer AC 2012. Streptococcal skin infection and rheumatic heart disease. Current Opinion in Infectious Diseases 25(2):145-53.

Romani L, Steer AC, Whitfeld MJ & Kaldor JM 2015. Prevalence of scabies and impetigo worldwide: a systematic review. Lancet Infectious Diseases 15(8):960-7.

Su J-Y, He VY, Guthridge S, Howard D, Leach A & Silburn S 2019. The impact of hearing impairment on Aboriginal children’s school attendance in remote Northern Territory: a data linkage study. Australian and New Zealand Journal of Public Health 43(6):544-50.

Taylor HR, Anjou MD, Boudville AI, McNeil RJ 2012. The roadmap to close the gap for vision: full report. Melbourne: Indigenous Eye Health Unit, The University of Melbourne.

Vision 2020 Australia 2019. Strong eyes, strong communities: a five year plan for Aboriginal and Torres Strait Islander eye health and vision 2019-2024. Melbourne: Vision 2020 Australia. Viewed 8 November 2019, http://www.vision2020australia.org.au/resources/strong-eyes-strong-communities/.

131 Australia’s health 2020: data insights

Chapter

4

Waters A-M 2001. Do housing conditions impact on health inequalities between Australia’s rich and poor? AHURI positioning paper no. 2. Melbourne: AHURI (Australian Housing and Urban Research Institute).

WHO CSDH (World Health Organization Commission on the Social Determinants of Health) 2008. Closing the gap in a generation: health equity through action on the social determinants of health. Final report of the Commission on the Social Determinants of Health. Geneva: WHO.

Worthing KA, Lacey JA, Price DJ, McIntyre L, Steer AC, Tong SYC & Davies MR 2019. Systematic review of group A streptococcal emm types associated with acute post-streptococcal glomerulonephritis. The American Journal of Tropical Medicine and Hygiene 100(5):1066-70.

Yeoh DK, Anderson A, Cleland G & Bowen AC 2017. Are scabies and impetigo “normalised”? A cross-sectional comparative study of hospitalised children in northern Australia assessing clinical recognition and treatment of skin infections. PLOS Neglected Tropical Diseases 11(7): e0005726. Viewed 18 November 2019, https://doi.org/10.1371/journal.pntd.0005726.

133 Australia’s health 2020: data insights

Potentially preventable hospitalisations— an opportunity for greater exploration of health inequity

5

134 Australia’s health 2020: data insights

Chapter

5

The health of individuals is the product of complex interactions between biological,

social, cultural and economic factors. In ideal circumstances, preventive measures,

early intervention, and effective and appropriate primary and community health care

may prevent the onset and worsening of conditions that result in hospitalisations.

In the late 1980s and early 1990s, this reasoning led to the concept of a potentially

preventable hospitalisation (PPH) and its current use as an indicator of primary health

care effectiveness (Box 5.1).

Studies have shown a relationship between PPH and various measures of primary health

care access, such as physician supply or self-rated access to care (for example, Ansari et

al. 2006; Bindman et al. 1995; Laditka et al. 2005). However, many of these studies were

from the United States, where the health system is structured differently to Australia.

Australian research has shown that PPH are influenced by many factors—some of which

are beyond the control of the primary and community health care sector.

Patient characteristics affecting PPH can include age, sex, ethnicity, area of residence,

socioeconomic factors, social and family support, mental health, health literacy, health

behaviours and disease prevalence (Ansari et al. 2006; Berkman et al. 2011; Falster

et al. 2015; Longman et al. 2018; Mohanty et al. 2016; Tran et al. 2014). In some studies,

factors like these have been found to account for a greater amount of geographic

variation in PPH than general practitioner supply (Falster et al. 2015) or access to

primary care services (Mazumdar et al. 2019).

PPH rates are also affected by health system factors such as changes in clinical

classification standards, diagnostic practices and hospital admission policies (AIHW

2020a), which can make the interpretation of PPH statistics over time complex.

Despite these reporting and interpretation challenges, PPH remain a valuable tool for

exploring health disparities between different populations (ACSQHC & AIHW 2017;

Duckett & Griffiths 2016; Health Performance Council 2019; PHIDU 2018; Queensland

Health 2018; WAPHA 2017). Knowing who in the Australian community is more at

risk of PPH can assist policy makers and health service providers target the delivery

of preventive health measures to those most in need. As hospitalisation generally

involves higher costs to patients and the health system, preventing and managing

health conditions in the community can potentially generate substantial savings

in hospital expenditure, as well as resulting in better outcomes for patients

(Bellon et al. 2017; Duckett & Griffiths 2016; Hollingworth et al. 2017; Swerissen et al.

2016; Zhao et al. 2014).

135 Australia’s health 2020: data insights

Chapter

5

This article focuses on three aspects of PPH. First, it presents new data quantifying

the economic costs of PPH on the hospital sector and shows how expenditure varies

by PPH condition, patient age and sex. Secondly, the question of who is more at risk

of PPH is explored through a case study on PPH for Diabetes complications. The case

study demonstrates the use of recently available data that provide insights into trends

in PPH for specific groups within the Australian population, including Aboriginal and

Torres Strait Islander people, remote and socioeconomically disadvantaged areas, the

very young and older people. Thirdly, the article outlines how current developments in

health data linkage present opportunities for a more nuanced understanding of patient

care pathways resulting in, and following on from, PPH.

Box 5.1: Overview of potentially preventable hospitalisations (PPH)

History of PPH

The concept of PPH (also known as ambulatory care sensitive conditions or

potentially avoidable hospitalisations) originated in the United States as a tool

for examining socioeconomic and racial disparities in primary care access (Billings

et al. 1993). A number of countries—including Australia, the United Kingdom,

New Zealand and Canada—adapted the tool as a measure of access to timely,

effective and appropriate primary health care (Ansari 2002; Falster & Jorm 2017),

although differences between health care systems and indicator definitions limit

the use of international comparisons.

Types of PPH

In Australia, PPH are monitored using a set of conditions that are considered

indicators of access to timely, effective and appropriate primary health care. It is

important to note that the PPH conditions monitored are not an exhaustive set of

all potentially avoidable hospitalisations (Falster & Jorm 2017).

PPH are grouped into 3 main categories (AIHW 2019g):

• Vaccine-preventable conditions: hospitalisations due to diseases that can be

prevented by vaccination, such as influenza, measles and whooping cough.

• Acute conditions: these conditions usually have a quick onset and may not be

preventable, but theoretically would not result in hospitalisation if timely and

adequate care was received in the community. This category includes conditions

such as dental conditions, cellulitis, urinary tract infections and ear, nose and

throat infections.

(continued)

136 Australia’s health 2020: data insights

Chapter

5

Box 5.1: (continued) Overview of potentially preventable hospitalisations (PPH)

• Chronic conditions: these long-lasting conditions may be preventable through

lifestyle change but are also manageable in the community health care setting

to prevent worsening of symptoms and hospitalisation. This category includes

conditions such as diabetes complications, heart failure, chronic obstructive

pulmonary disease (COPD) and asthma.

Having a ‘potentially preventable hospitalisation’ does not mean that the patient

did not require hospitalisation at the time, but rather the hospitalisation may have

been avoided through improved prevention programs; better care in the primary

health care or community setting; and/or better coordination of care between

health services.

It is important to note that PPH are based on counts of hospital separations and

cannot be used to estimate the number of people with a given condition.

Use of PPH in Australia

In Australia, PPH are currently:

• a performance indicator for primary and community health services in the

Australian National Healthcare Agreement (COAG 2012)

• an indicator of health system effectiveness under the Australian Health

Performance Framework (AIHW 2019b), and the Aboriginal and Torres Strait

Islander Health Performance Framework (AIHW 2018)

• used by policy makers, health service managers and researchers as a marker of

variation to identify and investigate areas or populations of need.

How common are PPH?

In 2017-18, 1 in every 15 hospitalisations (748,000, or 6.6%) were classified as potentially

preventable; these accounted for nearly 3 million hospital bed days (9.8% of all bed days)

(AIHW 2020a).

Of the 3 PPH categories, Chronic conditions accounted for almost half of all PPH (46%),

Acute conditions accounted for 44% and Vaccine-preventable conditions accounted for 11%

(as more than 1 PPH condition may be reported for a hospital admission, the sum of

Vaccine-preventable, Acute and Chronic conditions does not equal the number of Total PPH).

Between 2012-13 and 2017-18, overall rates of PPH increased by 17%, largely driven

by hospitalisations for influenza.

137 Australia’s health 2020: data insights

Chapter

5

The cost of PPH PPH conditions involve a substantial cost to the health system and to patients and

their carers (see ‘Health expenditure’ https://www.aihw.gov.au/reports/australias-health/health-expenditure for more information). In the Australian context, it is

generally less expensive to prevent disease, or address conditions early and manage

these in the primary care setting than it is to treat these conditions, often in a more

severe form, in hospitals (Duckett & Griffiths 2016). Analysis of the most recent

disease expenditure data for PPH conditions found that they cost the hospital sector

$4.5 billion (or about $6,600 per PPH) in 2015-16, with Chronic conditions costing

$2.3 billion, Acute conditions costing $1.6 billion, and Vaccine-preventable conditions

costing $616.7 million (Table 5.1).

Three of the most common chronic PPH conditions—Congestive cardiac failure, COPD

and Diabetes complications—had the highest expenditure (Figure 5.1), with more than

$1.5 billion spent on hospitalisations for these three conditions combined in 2015-16.

These complex conditions also attract substantial expenditure outside of hospital care,

in primary care costs, medication and other management and mitigation (AIHW 2019d).

Some conditions had expenditure that was disproportionate to the number of PPHs

(Table 5.1). The highest hospital expenditure per PPH (PPH expenditure divided by

number of PPH) was for conditions representing more advanced stages of disease—

Rheumatic heart disease ($26,100 per PPH), followed by Gangrene ($24,300 per PPH)—

which reflects the need for more complex or longer-term hospital care for these patients.

The PPH conditions with the most same day admissions—Dental conditions, Iron

deficiency anaemia and Ear, nose and throat infections—had the lowest costs per PPH,

each at about $2,500 per PPH (Table 5.1).

138 Australia’s health 2020: data insights

Chapter

5

Table 5.1: Potentially preventable hospitalisations expenditure (average cost per hospitalisation and total cost), 2015-16

Potentially preventable hospitalisation (PPH) condition Number of PPH

Average cost per PPH ($) Total cost ($) 

Vaccine preventable conditions

Pneumonia and influenza (vaccine-preventable) 23,774 13,751 326,909,959

Other vaccine-preventable conditions 27,022 11,000 297,230,429

Total vaccine preventable conditions 50,559 12,199 616,748,806

Chronic conditions

Congestive cardiac failure 60,964 9,798 597,347,284

COPD 71,861 7,391 531,123,111

Diabetes complications 47,112 9,135 430,376,939

Type 1 Diabetes complications 14,615 6,602 96,481,969

Type 2 Diabetes complications 31,726 7,930 251,580,763

Angina 35,401 9,165 324,436,337

Iron deficiency anaemia 53,045 2,477 131,405,658

Rheumatic heart disease 3,874 26,135 101,248,835

Asthma 31,245 2,860 89,365,433

Bronchiectasis 7,119 9,717 69,173,737

Hypertension 9,990 3,021 30,182,634

Nutritional deficiencies 737 19,950 14,702,860

Total chronic conditions 321,340 7,217 2,319,115,130

Acute conditions

Urinary tract infections 75,617 4,864 367,816,297

Cellulitis 64,572 5,200 335,784,317

Gangrene 12,121 24,275 294,237,263

Convulsions and epilepsy 37,951 4,835 183,495,903

Dental conditions 67,266 2,468 165,990,584

Ear, nose and throat infections 41,624 2,526 105,139,702

Perforated/bleeding ulcer 5,859 14,365 84,162,169

Pneumonia (not vaccine-preventable) 3,497 13,591 47,526,612

Pelvic inflammatory disease 4,619 6,190 28,592,271

Eclampsia 79 7,394 584,148

Total acute conditions 312,803 5,139 1,607,620,257

Total potentially preventable hospitalisations 678,373 6,564 4,452,539,114

Note: As more than 1 PPH condition may be reported for a hospital admission, the sum of Vaccine-preventable, Acute and Chronic conditions does not equal the number of Total PPH.

Source: AIHW National Hospital Morbidity Database; AIHW Disease Expenditure Database.

139 Australia’s health 2020: data insights

Chapter

5

Figure 5.1: Potentially preventable hospitalisations expenditure relative to

number of hospitalisations, by condition, 2015-16

Source: AIHW National Hospital Morbidity Database, AIHW Disease Expenditure Database.

Hospital expenditure on PPH varied with patient sex and age: after a small peak in the

0-4 age group, expenditure generally rose with age—gradually from the teen years to

the 30s, and then more steeply to the 65 and over age group (Figure 5.2). Expenditure

was higher for men in middle age onwards until the 80-84 age group. These patterns

reflect overall patterns of PPH, with PPH increasingly common in the older age groups and

men typically having longer admissions (AIHW 2020b). From the age of 85, expenditure

was higher for women, as they make up a greater proportion of this age group and have

higher expenditure for Congestive cardiac failure and Urinary tract infections.

Number of PPH (per 1,000s)

Expenditure ($ million)

250

300

350

400

450

500

550

600

200

150

100

50

0

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75

Congestive heart failure

Eclampsia

Hypertension

Pneumonia (not vaccine-preventable)

Bronchiectasis

Asthma

Perforated/bleeding ulcer

Rheumatic heart disease Ear, nose and throat infections

Iron deficiency anaemia

Convulsions and epilepsy

Dental conditions

Gangrene

Other vaccine-preventable conditions

Pneumonia and influenza (vaccine-preventable) Angina Cellulitis

Urinary tract infection, including pyelonephritis

Diabetes complications

Chronic obstructive pulmonary disease

Nutritional deficiencies

Pelvic inflam- matory disease

140 Australia’s health 2020: data insights

Chapter

5

Figure 5.2: Potentially preventable hospitalisations expenditure, by age,

by sex, 2015-16

0

50

100

150

200

250

300

350

400

0-4

5-9

10-14

15-19

20-24

25-29

30-34

35-39

40-44

45-49

50-54

55-59

60-64

65-69

70-74

75-79

80-84

85+

PPH expenditure ($ million)

Males Females

Age group

Source: AIHW Disease Expenditure Database.

What can disparities in PPH reveal about health inequities in Australia?

The concept of equity in health is that, ideally, everyone should have a fair opportunity

to attain their full health potential and that no one should be hindered in achieving

this potential (WHO 2019). Understanding health equity is a core component of the

Australian Health Performance Framework (AIHW 2019b) and PPH statistics provide a

useful measure for examining this issue. This is because PPH focus on conditions that

should theoretically be preventable or manageable in the primary and community care

setting, and therefore can draw attention to population characteristics associated with

different care outcomes.

141 Australia’s health 2020: data insights

Chapter

5

Some populations in Australia experience a disproportionately high rate of PPH,

and, in recent years, the disparities for some PPH conditions have widened (AIHW

2020a). Demographic and socioeconomic factors can have an important influence

on the way in which people access primary and secondary care, and the ways in

which they manage their health (Turrell et al. 2006). These factors, and others such

as the health service environment, can affect the likelihood of a person developing

conditions that may be preventable; the capacity to access early assistance; and

the ability to understand and adhere to treatments that may avert hospitalisation

(ACSQHC 2014).

It is important that the patient groups most at risk of PPH (and therefore at risk of poor

management of their health generally) are identified in communities, and by health

policy makers, to ensure they can achieve equitable health outcomes.

In 2020, the AIHW released a large dataset containing information on how rates of

PPH have varied over time (AIHW 2020a). The data are interactive and can be explored

according to where people live and their circumstances—including their age, whether

they are male or female, Aboriginal and/or Torres Strait Islander, live in a lower

socioeconomic area, or live in a more remote part of Australia.

The following case study demonstrates how PPH data can be used to illustrate health

inequalities, understand demographic and socioeconomic patterns, and play a role in

developing targeted and equitable interventions.

Case study—PPH for diabetes complications At least 1.2 million Australians (or 4.9% of the total population) self-reported having

diabetes in 2017-18 (ABS 2018b). When someone has diabetes, their body can’t

maintain healthy levels of glucose in the blood (Box 5.2). Early diagnosis, optimal

treatment and effective ongoing support and management of all types of diabetes

are required to reduce the risk of comorbidities such as heart disease and stroke;

eye disease; kidney disease; amputation; and depression or anxiety (Department of

Health 2015; Diabetes Australia 2019).

142 Australia’s health 2020: data insights

Chapter

5

Box 5.2: Main types of diabetes

• Type 1 diabetes is an autoimmune disease that usually occurs in childhood or early

adulthood. It is a life-threatening condition and needs to be closely managed with

daily insulin injections and lifestyle modification. Daily monitoring of blood sugar

levels is required to prevent short-term and long-term complications.

• Type 2 diabetes is the most common form of diabetes, generally occurring in

adulthood. It is largely preventable and is often associated with lifestyle factors

such as insufficient physical activity, unhealthy diet, obesity and tobacco

smoking. Risk is also associated with genetic and family-related factors.

Ongoing maintenance is required to manage disease progression and to

prevent short-term and long-term complications.

• Gestational diabetes occurs during pregnancy and is not included in the current

PPH specification.

• Other types of diabetes, resulting from a range of different health conditions or

circumstances, are not included in the current PPH specification.

See ‘Diabetes’ https://www.aihw.gov.au/reports/australias-health/diabetes

for more information.

Diabetes accounts for a substantial burden of disease in Australia. In 2015, type

1 diabetes accounted for 14,700 disability-adjusted life years (DALY—that is, the

number of healthy years lost due to ill-health, disability or early death in the Australian

population at a point in time) while type 2 diabetes accounted for 103,000 DALY

(AIHW 2019a). This burden reduces quality of life and opportunities for those with the

condition and is costly to the health system. In 2015-16, there were 14,600 PPH for

type 1 Diabetes complications, and 31,700 PPH for type 2 Diabetes complications,

with a cost to hospitals of nearly $97 million ($6,600 per PPH) and $252 million

($7,900 per PPH), respectively (Table 5.1).

Hospitalisation data provides insights into one of the many aspects of disease burden

experienced by people with diabetes. Much can be learned from and about PPH for

Diabetes complications in terms of how well the health system may be functioning for

different sectors of the Australian population.

143 Australia’s health 2020: data insights

Chapter

5

As part of primary care performance monitoring, PPH for type 1 and type 2 diabetes

are often reported under the one condition, Diabetes complications. However, the age

of onset, biological mechanisms of development, and management of type 1 and type

2 diabetes vary substantially; and therefore, reporting them under one condition limits

our ability to observe differences in hospital admission patterns for these conditions.

To better understand which groups in the community are affected by PPH for Diabetes

complications, we have examined type 1 and type 2 Diabetes complications separately,

and explored patterns of PPH by age, sex, Indigenous status, and for areas of

socioeconomic disadvantage and remoteness.

Age profiles of PPH for diabetes complications

PPH for type 1 and type 2 Diabetes complications in 2017-18 showed different patterns

by patient age (Figure 5.3). Rates of PPH for type 1 Diabetes complications were

highest in the teen years, reflecting the usual diagnosis of disease in childhood and

adolescence (AIHW 2020c). One in 5 (21%) PPH for type 1 Diabetes complications were

in young people aged 10-14 and 15-19, and rates decreased with age. There was no

difference between males and females in the overall rate of PPH for type 1 Diabetes

complications (both 64 per 100,000) (AIHW 2020b). However, the pattern by age differed

slightly, with females having higher rates of PPH in earlier years and males having

higher rates from age 30 onwards.

Rates of PPH for type 2 Diabetes complications were highest in older age groups—more

than 60% were for people aged 65 and older (Figure 5.3), reflecting the association

of type 2 diabetes with lifestyle risk factors, the effects of which accumulate with

age—such as obesity, tobacco smoking and physical inactivity (AIHW 2019c).

Men aged 65 and over had more than double the PPH rate of women in this age group

(762 PPH per 100,000 compared with 360 PPH per 100,000, respectively) (AIHW 2020b).

144 Australia’s health 2020: data insights

Chapter

5

Figure 5.3: Rate of potentially preventable hospitalisations for type 1 and

type 2 diabetes complications, by age and sex, 2017-18

Source: AIHW National Hospital Morbidity Database.

Hospital expenditure for PPH for diabetes complications

The hospital expenditure for PPH for Diabetes complications (both type 1 and type 2)

differed across age groups and by sex (Figure 5.4). In 2015-16, expenditure for type 1

Diabetes complications was highest for females aged 10-14 and 15-19. By contrast,

PPH expenditure for type 2 Diabetes complications was substantially higher for men than

for women, increasing from ages 35-39 onwards and peaking in the 65-69 age group.

The hospital expenditure per PPH for type 1 Diabetes complications was similar for

males and females (about $6,600). However, expenditure per PPH for type 2 Diabetes

complications was higher for males than females ($8,200 compared with $7,400),

which is likely to reflect the longer average length of stay in hospital for males

(6.5 days compared with 5.8 days for females). This may be due to males having a

higher likelihood of comorbidities such as cardiovascular disease or chronic kidney

disease (AIHW 2014a), which require more complex care.

0

200

400

600

800

1,000

1,200

0 –4

5 –9

10 –14

15 –19

20 –24

25 –29

30 –34

35 –39

40 –44

45 –49

50 –54

55 –59

60 –64

65 –69

70 –74

75 –79

80 –84

85+

PPH per 100,000

Age group

Type 2 diabetes *males

Type 2 diabetes *females

Type 1 diabetes *males

Type 1 diabetes *females

145 Australia’s health 2020: data insights

Chapter

5

Figure 5.4. Potentially preventable hospitalisations expenditure for type 1

and type 2 diabetes complications, by age and sex, 2015-16

Source: AIHW Disease Expenditure Database.

Indigenous Australians have higher rates of PPH for diabetes complications

Diabetes (predominantly type 2) is one of the leading causes of disease burden for Indigenous Australians (AIHW 2016; Department of Health 2015). In 2018-19, the diabetes prevalence rate was 2.9 times as high among Indigenous Australians as non-Indigenous Australians, based on age-standardised self-reported data (ABS 2019).

Historically, incidence rates of type 1 diabetes have been lower for Indigenous Australians compared with non-Indigenous Australians, although rates were similar in 2017 (AIHW 2019c). Between 2012-13 and 2017-18, age-standardised rates of PPH for type 1 Diabetes complications increased by 64% for Indigenous Australians, compared with almost no change for non-Indigenous Australians (Figure 5.5). In 2017-18, the age-standardised rate of PPH for type 1 Diabetes complications among Indigenous Australians was double that of non-Indigenous Australians (128 per 100,000 compared with 61 per 100,000, respectively). Changes in clinical practice and/or coding of diabetes type among Indigenous Australians may have contributed to these data, including the challenges of coding intermediate phenotypes of diabetes reported in young Indigenous people (with elements of each of type 1 and type 2 diabetes) (Stone et al. 2013).

0

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

30,000,000

0 –4

5 –9

10 –14

15 –19

20 –24

25 –29

30 –34

35 –39

40 –44

45 –49

50 –54

55 –59

60 –64

65 –69

70 –74

75 –79

80 –84

85+

Expenditure ($)

Age group

Type 2 diabetes *males

Type 2 diabetes *females

Type 1 diabetes *males

Type 1 diabetes *females

146 Australia’s health 2020: data insights

Chapter

5

Figure 5.5. Age-standardised rate of potentially preventable hospitalisations

for type 1 and type 2 diabetes complications, by Indigenous status, 2012-13

to 2017-18

0

100

200

300

400

500

600

2012 –13

2013 –14

2014 –15

2015 –16

2016 –17

2017 –18

PPH per 100,000

Indigenous Australians *Type 2 diabetes

Indigenous Australians *Type 1 diabetes

Non-Indigenous Australians *Type 2 diabetes

Non-Indigenous Australians *Type 1 diabetes

Note: Hospitalisations where Indigenous status was Not stated were not included in the analysis.

Source: AIHW National Hospital Morbidity Database.

The age profile of PPH rates for type 1 Diabetes complications in 2017-18 shows

considerable differences between Indigenous and non-Indigenous Australians

(Figure 5.6). While Indigenous and non-Indigenous Australians had similar rates in

the first 10 years of life, rates of PPH among Indigenous Australians were substantially

higher from the 10-14 age group onwards. A peak in the 35-39 age group was seen in

2016-17 and 2017-18, but not in previous years (AIHW 2020b). Further analysis found

that this peak was unlikely to be solely due to data fluctuations, and correlated with

an increased proportion of admissions for sub-optimal glucose levels, particularly

in women. This age group warrants further scrutiny in coming years’ data in case

they represent an emerging issue. PPH rates for type 1 Diabetes complications for

non-Indigenous Australians peaked in the 15-19 age group and then decreased.

147 Australia’s health 2020: data insights

Chapter

5

Figure 5.6. Rate of potentially preventable hospitalisations for type 1

diabetes complications, by age and Indigenous status, 2017-18

Notes

1. Hospitalisations where Indigenous status was Not stated were not included in the analysis.

2. Data suppressed due to low numbers (ages 50-54) are excluded from the figure.

Source: AIHW National Hospital Morbidity Database.

Rates of PPH for type 2 Diabetes complications among Indigenous Australians have

been consistently high, and in 2017-18 were 4.7 times the rate for non-Indigenous

Australians (522 per 100,000 compared with 110 per 100,000, respectively) (Figure 5.5).

The age profile of PPH for type 2 Diabetes complications differs between Indigenous and

non-Indigenous Australians (Figure 5.7). In 2017-18, the age at which rates of PPH for

type 2 Diabetes complications began to rise was far earlier for Indigenous Australians

(at 25-29 years) than for non-Indigenous Australians (at 45-49 years). Rates of PPH

for type 2 Diabetes complications in Indigenous males were 1.2 times higher than

Indigenous females, while rates in non-Indigenous males were double those of

non-Indigenous females (AIHW 2020b).

These data suggest a complex interplay of factors influencing diagnosis, disease

management (including use of diabetes technologies) and hospitalisation for diabetes

among Indigenous Australians. It should also be noted that incomplete and inconsistent

reporting of Indigenous status might occur, which may result in an underestimate of the

differences in PPH between Indigenous and non-Indigenous Australians.

0

100

200

300

400

500

0 -4

5 -9

10 -14

15 -19

20 -24

25 -29

30 -34

35 -39

40 -44

45 -49

50 -54

55 -59

60 -64

65+

PPH per 100,000

Age group

Indigenous Australians

Non-Indigenous Australians

148 Australia’s health 2020: data insights

Chapter

5

Figure 5.7. Rate of potentially preventable hospitalisations for type 2

diabetes complications, by age and Indigenous status, 2017-18

Notes

1. Hospitalisations where Indigenous status was Not stated were not included in the analysis.

2. Data suppressed due to low numbers (ages 0-14) are excluded from the figure.

Source: AIHW National Hospital Morbidity Database.

Socioeconomic disadvantage—the health gap for diabetes complications has widened

The prevalence of diabetes, particularly type 2 diabetes, increases with increasing

socioeconomic disadvantage (AIHW 2019c), and there are large socioeconomic

inequalities in diabetes prevalence, hospitalisation and deaths (AIHW 2019e).

(See ‘Diabetes’ https://www.aihw.gov.au/reports/australias-health/diabetes

for more information).

In 2017-18, the rate of PPH for type 1 Diabetes complications for people living in

the lowest socioeconomic areas was more than double the rate in the highest

socioeconomic areas (93 per 100,000 compared with 42 per 100,000, respectively)

(Figure 5.8). The difference was even greater for type 2 Diabetes complications: people

living in the lowest socioeconomic areas had almost 3 times the rate of people living

in the highest socioeconomic areas (184 per 100,000 compared with 67 per 100,000,

respectively) (Figure 5.8).

Indigenous Australians

Non-Indigenous Australians

0

500

1,000

1,500

2,000

15 -19

20 -24

25 -29

30 -34

35 -39

40 -44

45 -49

50 -54

55 -59

60 -64

65+

PPH per 100,000

Age group

149 Australia’s health 2020: data insights

Chapter

5

This gap in PPH rates between the highest and lowest socioeconomic areas has

increased in recent years (Figure 5.8). People living in the lowest socioeconomic areas

had the largest increases in PPH rates for Diabetes complications between 2012-13 and

2017-18:

• an 18% increase in PPH rates for type 1 Diabetes complications (from 79 per 100,000

in 2012-13 to 93 per 100,000 in 2017-18), compared with no change, or a slight

decrease, for people living in other areas

• a 27% increase in PPH rates for type 2 Diabetes complications (from 145 per 100,000

to 184 per 100,000), compared with almost no change for people living in the highest

socioeconomic areas.

Figure 5.8. Age-standardised rate of potentially preventable hospitalisations

for type 1 and type 2 diabetes complications, by socioeconomic area,

2012-13 and 2017-18

Note: Socioeconomic areas are based on the ABS Index of Relative Socio-economic Disadvantage (IRSD). The 5 groups represent area-based socioeconomic disadvantage, from the least disadvantaged 20% of areas to the most disadvantaged 20%. Data from 2012-13 were calculated using 2011 IRSD scores; data from 2017-18 were calculated using 2016 IRSD scores.

Source: AIHW National Hospital Morbidity Database.

0

20

40

60

80

100

1

Lowest

2 3 4 5

Highest

PPH per 100,000

Socioeconomic area

Type 1 diabetes

2012 -13 2017 -18

0

40

80

120

160

200

1

Lowest

2 3 4 5

Highest

PPH per 100,000

Socioeconomic area

Type 2 diabetes

2012 -13 2017 -18

150 Australia’s health 2020: data insights

Chapter

5

PPH rates for type 2 diabetes complications increase with increasing remoteness

The relationship between remoteness and type 1 diabetes prevalence appears to be

complex, and is likely to be influenced by the lower capture of Indigenous Australians

and people living in Remote and very remote areas in the primary data sources of the

National (insulin-treated) Diabetes Register (AIHW 2020c). In 2017, the prevalence of

type 1 diabetes in children aged 0-14 was higher in Inner regional and Outer regional

areas (169 per 100,000 and 149 per 100,000, respectively) and lower in Remote and very

remote areas (86 per 100,000) and Major cities (134 per 100,000) (AIHW 2019c).

Similarly, in 2017-18, people living in Inner and Outer regional areas had the highest

rates of PPH for type 1 Diabetes complications (89 per 100,000 and 80 per 100,000,

respectively), and people living in Very remote areas and Major cities had the lowest

rates (52 per 100,000 and 56 per 100,000, respectively) (Figure 5.9). Unlike most areas,

where rates remained relatively stable, rates of PPH for type 1 Diabetes complications

for people living in Remote areas increased from 61 per 100,000 in 2012-13 to 73 per

100,000 in 2017-18.

In 2017-18, the prevalence of type 2 diabetes among adults (based on self-reported

data) was higher for people living in Outer regional and remote areas (6.0%) than for

people living in Inner regional areas (4.2%) and in Major cities (4.8%) (AIHW 2019c).

Correspondingly, rates of PPH for type 2 Diabetes complications increased with

increasing remoteness (Figure 5.9), and in 2017-18, people living in Very remote areas

had 3.7 times the rate of PPH for type 2 Diabetes complications of people living in

Major cities (418 per 100,000 and 113 per 100,000, respectively). Males and females

living in Very remote areas had similar rates of PPH for type 2 Diabetes complications,

but in all other areas, males had higher rates than females (AIHW 2020b).

151 Australia’s health 2020: data insights

Chapter

5

Figure 5.9. Age-standardised rate of potentially preventable hospitalisations

for type 1 and type 2 diabetes complications, by remoteness area, 2012-13

to 2017-18

Source: AIHW National Hospital Morbidity Database.

It is important to note that PPH statistics are determined based on where patients live, not where they go to hospital. It can be difficult to assess the implications of remoteness for health due to interactions between remoteness, low socioeconomic position and the higher proportion of Indigenous Australians in many of these areas compared with Major cities—for example, nearly half of all people living in Very remote areas are Indigenous (ABS 2018a; AIHW 2019i). The impact of remoteness and socioeconomic disadvantage on the likelihood of a PPH does appear to be stronger for Indigenous Australians than for non-Indigenous Australians (AIHW 2014b; Banham et al. 2017; Falster et al. 2016a; Harrold et al. 2014; Productivity Commission 2019).

In addition, a number of other factors may affect PPH rates for patients from regional and remote areas. For example, smaller regional hospitals acting as a substitute for primary health care services may represent an appropriate use of local resources (Falster et al. 2019). Higher rates of short-stay PPH at these hospitals (Falster et al. 2019) may be due to the admission of low-acuity patients for observation to avoid long travel times, or subsequent transfer of patients with more complex conditions to larger hospitals (which are counted as separate admissions) (ACSQHC & AIHW 2017; Falster et al. 2019).

0

20

40

60

80

100

2012-13

2013-14

2014-15

2015-16

2016-17

2017-18

PPH per 100,000

Type 1 diabetes

0

50

100

150

200

250

300

350

400

450

2012-13

2013-14

2014-15

2015-16

2016-17

2017-18

PPH per 100,000

Type 2 diabetes

Very remote Remote Outer regional Inner regional Major cities

152 Australia’s health 2020: data insights

Chapter

5

How can PPH be used to improve health care provision and health outcomes?

Many disparities in health outcomes in Australia, from disease prevalence to mortality

rates (for example, AIHW 2019e), raise questions about population health, risk factors

(including those outside the health sector such as housing and employment) and

how the health system works for different groups of people. Disparities in PPH are a

particularly useful measure to examine because, through their focus on conditions

that could be prevented, or looked after through improved care models, they highlight

those differences that influence how well peoples’ health is managed.

This article demonstrates how the recently available PPH data can guide investigations

concerning PPH. Knowing ‘who’ has high rates of PPH and how rates are changing can

assist policy makers and health service providers to target the delivery of preventive

health programs and effective health care to those most in need.

The PPH indicator provides scope to explore a wide range of conditions, however,

it should be noted that it is a representative, not comprehensive set of potentially

avoidable hospitalisations, and does not include all conditions for which there are

disparities in disease burden, such as chronic kidney disease or suicide (AIHW 2016).

The Second Australian Atlas of Healthcare Variation (ACSQHC & AIHW 2017) discusses

strategies for reducing PPH, particularly those for chronic diseases, with an emphasis

on disease prevention and coordinated, integrated multi-disciplinary care to manage

disease where it already exists. A number of community-based programs have

led to reductions in potentially avoidable hospitalisations for chronic conditions

(Erny-Albrecht et al. 2016). Strategies focusing on vulnerable populations include

increasing patient health literacy, making the health system easier to navigate and

health information easier to understand, and designing culturally safe models of care

in partnership with Indigenous communities (ACSQHC & AIHW 2017; Wakerman &

Shannon 2016).

Although access to preventive care and early intervention in the community is essential,

it is important not to assume that higher rates of PPH always indicate a less effective

primary care system. There are many reasons why an area or group of people may have

higher rates of PPH—including higher rates of disease, lifestyle factors and other risks.

Some PPH may not be avoidable, such as those for patients with complex illnesses.

Older people hospitalised for a PPH have reported that they did not consider their

admission to be preventable, due to a number of factors such as lack of social support;

mental health difficulties; poor health literacy and understanding of their condition;

153 Australia’s health 2020: data insights

Chapter

5

and capacity to adhere to treatment (Longman et al. 2018). Indeed, reductions in PPH

rates are not necessarily associated with improved clinical outcomes (Katterl et al.

2012) and rates of PPH might rise following improvements in disease screening or

health checks (AIHW 2019f, 2019h), or changes in hospital admission practices

(AIHW 2020a).

Exploring patient care pathways

There are likely to be a number of explanations for variation in PPH rates, and without

exploring and understanding patient care pathways that result in PPH, it is very difficult

to paint a complete picture. For example, with the current data, we cannot distinguish

the hospitalisation of a patient with a first diagnosis of type 1 diabetes—for whom

some time in hospital may be unavoidable—from that of a patient whose type 1

diabetes is not well controlled.

Studies using linked health data have found that people with PPH admissions tended to

have high levels of engagement with primary care services before their hospitalisation

(Falster et al. 2016b). This suggests that, in at least some cases, PPH may reflect an

appropriate use of hospital services in response to need. A number of other studies

using linked patient data are underway across Australia, for example, to determine the

true preventability of PPH (Passey et al. 2015), and to explore broader social factors

influencing PPH in Indigenous children (McNamara et al. 2018). Future analysis of PPH

using linked patient data has the potential to provide insights into the relationships

between different groups of people with PPH and disease prevalence; the use of

primary and community care; the use of medicines; and health outcomes. For example,

the AIHW’s National Integrated Health Services Information Analysis Asset (NIHSI AA)

will enable analysis of patient journeys associated with PPH. This would allow better

targeting of resources across the health and social services sectors to help achieve

health equity for all Australians.

For further information about PPH in Australia, please see the recently released report

Disparities in potentially preventable hospitalisations across Australia, 2012-13 to 2017-18

(AIHW 2020a) and the accompanying data tables and interactive graphs (AIHW 2020b).

Further reading The following AIHW publication relating to potentially preventable hospitalisations may

be of interest:

• AIHW 2018. A potentially preventable hospitalisation indicator for general practice:

consultation paper. Cat. no. HSE 214. Canberra: AIHW.

154 Australia’s health 2020: data insights

Chapter

5

References ABS (Australian Bureau of Statistics) 2018a. Estimates of Aboriginal and Torres Strait Islander Australians, June 2016. ABS cat. no. 3238.0.55.001. Canberra: ABS.

ABS 2018b. National Health Survey: first results, 2017-18. ABS cat. no. 4364.0.55.001. Canberra: ABS.

ABS 2019. National Aboriginal and Torres Strait Islander Health Survey, 2018-19. ABS cat. no. 4715.0. Canberra: ABS.

ACSQHC (Australian Commission on Safety and Quality in Health Care) 2014. Health literacy: taking action to improve safety and quality. Sydney: ACSQHC.

ACSQHC & AIHW (Australian Institute of Health and Welfare) 2017. The Second Australian Atlas of Healthcare Variation. Sydney: ACSQHC.

AIHW 2014a. Cardiovascular disease, diabetes and chronic kidney disease: Australian facts: morbidity—hospital care. Cardiovascular, diabetes and chronic kidney disease series no. 3. Cat. no. CDK 3. Canberra: AIHW.

AIHW 2014b. Cardiovascular disease, diabetes and chronic kidney disease—Australian facts: prevalence and incidence. Cardiovascular, diabetes and chronic kidney disease series no. 2. Cat. no. CDK 2. Canberra: AIHW.

AIHW 2016. Australian Burden of Disease Study: impact and causes of illness and death in Aboriginal and Torres Strait Islander people 2011. Australian Burden of Disease Study series no. 6. Cat. no. BOD 7. Canberra: AIHW.

AIHW 2018. Aboriginal and Torres Strait Islander Health Performance Framework (HPF) report 2017. Cat. no. IHW 194. Canberra: AIHW. Viewed 20 April 2020, https://www.aihw.gov.au/ reports/indigenous-australians/health-performance-framework

AIHW 2019a. Australian Burden of Disease Study: impact and causes of illness and death in Australia 2015. Australian Burden of Disease series no. 19. Cat. no. BOD 22. Canberra: AIHW.

AIHW 2019b. Australian Health Performance Framework. Cat. no. HPF 47. Canberra: AIHW.

AIHW 2019c. Diabetes. Cat. no. CVD 82. Canberra: AIHW. Viewed 18 December 2019, https://www.aihw.gov.au/reports/diabetes/diabetes.

AIHW 2019d. Disease expenditure in Australia. Cat. no. HWE 76. Canberra: AIHW. Viewed 10 January 2020, https://www.aihw.gov.au/reports/health-welfare-expenditure/disease-expenditure-australia.

AIHW 2019e. Indicators of socioeconomic inequalities in cardiovascular disease, diabetes and chronic kidney disease. Cat. no. CDK 12. Canberra: AIHW.

AIHW 2019f. Indigenous health checks and follow-ups. Cat. no. IHW 209. Canberra: AIHW. Viewed 11 November 2019, https://www.aihw.gov.au/reports/indigenous-australians/ indigenous-health-checks-follow-ups.

AIHW 2019g. National Healthcare Agreement: PI 18—Selected potentially preventable hospitalisations, 2019. Canberra: AIHW. Viewed 25 September 2019, https://meteor.aihw.gov.au/ content/index.phtml/itemId/698904.

155 Australia’s health 2020: data insights

Chapter

5

AIHW 2019h. Regional variation in uptake of Indigenous health checks and in preventable hospitalisations and deaths. Cat. no. IHW 216. Canberra: AIHW.

AIHW 2019i. Rural and remote health. Cat. no. PHE 255. Canberra: AIHW. Viewed 11 November 2019, https://www.aihw.gov.au/reports/rural-remote-australians/rural-remote-health.

AIHW 2020a. Disparities in potentially preventable hospitalisations across Australia, 2012-13 to 2017-18. Cat. no. HPF 50 Canberra: AIHW.

AIHW 2020b. Disparities in potentially preventable hospitalisations across Australia: exploring the data. Cat. no. HPF 51. Canberra: AIHW. Viewed 11 February 2020, https://www.aihw.gov.au/ reports/primary-health-care/disparities-in-potentially-preventable-hospitalisations-exploring-the-data.

AIHW 2020c. Incidence of insulin-treated diabetes in Australia. Cat. no. CDK 11. Canberra: AIHW. Viewed 20 April 2020, https://www.aihw.gov.au/reports/diabetes/incidence-of-insulin-treated-diabetes.

Ansari Z, Carson NJ, Serraglio A, Barbetti T & Cicuttini F 2002. The Victorian Ambulatory Care Sensitive Conditions Study: reducing demand on hospital services in Victoria. Australian Health Review 25(2):71-7.

Ansari Z, Laditka JN & Laditka, SB 2006. Access to health care and hospitalization for ambulatory care sensitive conditions. Medical Care Research and Review 63(6):719-41.

Banham D, Chen T, Karnon J, Brown A & Lynch J 2017. Sociodemographic variations in the amount, duration and cost of potentially preventable hospitalisation for chronic conditions among Aboriginal and non-Aboriginal Australians: a period prevalence study of linked public hospital data. BMJ Open 7(10). http://dx.doi.org/10.1136/bmjopen-2017-017331.

Bellon ML, Barton C, McCaffrey N, Parker D & Hutchinson C 2017. Seizure-related hospital admissions, readmissions and costs: comparisons with asthma and diabetes in South Australia. Seizure 50:73-9.

Berkman ND, Sheridan SL, Donahue KE, Halpern DJ, Viera A, Crotty K et al. 2011. Health literacy interventions and outcomes: an updated systematic review. Evidence report/technology assesment no. 199. AHRQ publication number 11-E006. Rockville, MD: Agency for Healthcare Research and Quality.

Billings J, Zeitel L, Lukomnik J, Carey TS, Blank AE & Newman L 1993. Impact of socioeconomic status on hospital use in New York City. Health Affairs, 12(1):162-73.

Bindman AB, Grumbach K, Osmond D, Komaromy M, Vranizan K, Lurie N, et al. 1995. Preventable hospitalizations and access to health care. JAMA 274(4):305-11.

COAG (Coalition of Australian Governments) 2012. Intergovernmental Agreement on Federal Financial Relations: Schedule F National Healthcare Agreement. Canberra: Commonwealth of Australia. Viewed 14 August 2019, http://www.federalfinancialrelations.gov.au/content/npa/ health/_archive/healthcare_national-agreement.pdf.

Department of Health 2015. Australian National Diabetes Strategy 2016-2020. Canberra: Department of Health.

Diabetes Australia 2019. Viewed 4 October 2019, www.diabetesaustralia.com.au/what-is-diabetes.

Duckett S & Griffiths K 2016. Perils of place: identifying hotspots of health inequalities. Melbourne: Grattan Institute.

156 Australia’s health 2020: data insights

Chapter

5

Erny-Albrecht K, Oliver-Baxter J & Bywood PT 2016. Primary health care-based programmes targeting potentially avoidable hospitalisations in vulnerable groups with chronic disease. PHCRIS Policy Issue Review. Adelaide: Primary Health Care Research & Information Service.

Falster K, Banks E, Lujic S, Falster M, Lynch J, Zwi K et al. 2016a. Inequalities in pediatric avoidable hospitalizations between Aboriginal and non-Aboriginal children in Australia: a population data linkage study. BMC Pediatrics 16(1):169-81.

Falster MO & Jorm LR 2017. A guide to the potentially preventable hospitalisations indicator in Australia. Sydney: Centre for Big Data Research in Health, University of New South Wales in consultation with Australian Commission on Safety and Quality in Health Care and Australian Institute of Health and Welfare.

Falster MO, Jorm LR, Douglas KA, Blyth FM, Elliott RF & Leyland A 2015. Sociodemographic and health characteristics, rather than primary care supply, are major drivers of geographic variation in preventable hospitalizations in Australia. Medical Care 53(5):436-45.

Falster MO, Jorm LR & Leyland AH 2016b. Visualising linked health data to explore health events around preventable hospitalisations in NSW Australia. BMJ open, 6(9):p.e012031.

Falster MO, Leyland AH & Jorm LR 2019. Do hospitals influence geographic variation in admission for preventable hospitalisation? A data linkage study in New South Wales, Australia. BMJ Open 9(2). http://dx.doi.org/10.1136/bmjopen-2018-027639.

Harrold TC, Randall DA, Falster MO, Lujic S & Jorm LR 2014. The contribution of geography to disparities in preventable hospitalisations between Indigenous and non-Indigenous Australians. PLOS ONE 9(5): e97892. https://doi.org/10.1371/journal.pone.0097892.

Health Performance Council (South Australia) 2019. Hotspots of potentially preventable hospital admissions: pinpointing potential health inequalities by analysis of South Australian public hospital admitted patient care data. Adelaide: Government of South Australia. Viewed 20 April 2020, www.hpcsa.com.au/statistics/hotspots-of-potentially-preventable-hospital-admissions-2019.

Hollingworth SA, Donald M, Zhang J, Vaikuntam BP, Russell A & Jackson C 2017. Impact of a general practitioner-led integrated model of care on the cost of potentially preventable diabetes-related hospitalisations. Primary Care Diabetes 11(4):344-7.

Katterl R, Anikeeva O, Butler C, Brown L, Smith B & Bywood P 2012. Potentially avoidable hospitalisations in Australia: causes for hospitalisations and primary health care interventions. Primary Health Care Research & Information Service (PHC RIS) Policy Issue Review. Adelaide: PHC RIS.

Laditka JN, Laditka SB & Probst JC 2005. More may be better: evidence of a negative relationship between physician supply and hospitalization for ambulatory care sensitive conditions. Health Services Research 40(4):1148-66.

Longman J, Rix E, Johnston J & Passey, M 2018. Ambulatory care sensitive chronic conditions: what can we learn from patients about the role of primary health care in preventing admissions? Australian Journal of Primary Health, 24(4):304-10.

Mazumdar S, Chon, S, Arnold L & Jalaludin B 2019. Spatial clusters of chronic preventable hospitalizations (ambulatory care sensitive conditions) and access to primary care. Journal of Public Health (Oxford, England). https://doi.org/10.1093/pubmed/fdz040.

157 Australia’s health 2020: data insights

Chapter

5

McNamara B, Gubhaju L, Jorm L, Preen D, Jones J, Joshy G et al. 2018. Exploring factors impacting early childhood health among Aboriginal and Torres Strait Islander families and communities: protocol for a population-based cohort study using data linkage (the ‘Defying the Odds’ study). BMJ Open 8(3):e021236. http://dx.doi.org/10.1136/bmjopen-2017-021236.

Mohanty I, Edvardsson M, Abello A & Eldridge D 2016. Child social exclusion risk and child health outcomes in Australia. PLOS ONE 11(5):e0154536.

Passey ME, Longman JM, Johnston JJ, Jorm L, Ewald D, Morgan GG et al. 2015. Diagnosing Potentially Preventable Hospitalisations (DaPPHne): protocol for a mixed-methods data-linkage study. BMJ Open 5(11):e009879.

Productivity Commission 2019. Report on Government Services 2019. Chapter 10: Primary and community health. Canberra: Productivity Commission. Viewed 14 August 2019, https:// www.pc.gov.au/research/ongoing/report-on-government-services/2019/health/primary-and-community-health.

PHIDU (Public Health Information Development Unit) 2018. Potentially preventable hospitalisations in Australia: variations by sociodemographic characteristics and geographic areas, with a focus on Aboriginal and Torres Strait Islander people, 2012/13 to 2014/15. Adelaide: PHIDU.

Queensland Health 2018. Queensland Clinical Senate meeting report: Dare to compare: reducing unwarranted variation for potentially preventable hospitalisations 30 November-1 December 2017. Viewed 4 December 2018, https://clinicalexcellence.qld.gov.au/priority-areas/ clinician-engagement/queensland-clinical-senate/meetings/dare-compare-reducing.

Stone M, Baker A & Maple Brown L 2013. Diabetes in young people in the Top End of the Northern Territory. Journal of Paediatrics and Child Health 49(11):976-9.

Swerissen H, Duckett S & Wright J 2016. Chronic failure in primary care. Melbourne: Grattan Institute.

Tran B, Falster M, Douglas K, Blyth F & Jorm L 2014. Health behaviours and potentially preventable hospitalisation: a prospective study of older Australian adults. PLOS ONE 9(4):e93111. https://doi.org/10.1371/journal.pone.0093111.

Turrell G, Stanley L, de Looper M & Oldenburg B 2006. Health inequalities in Australia: morbidity, health behaviours, risk factors and health service use. Health Inequalities Monitoring Series No. 2. AIHW Cat. no. PHE 72. Canberra: Queensland University of Technology and the Australian Institute of Health and Welfare.

WAPHA (WA Primary Health Alliance) 2017. Lessons of location: potentially preventable hospitalisation hotspots in Western Australia 2017. East Perth WA: WA Department of Health.

Wakerman J & Shannon C 2016. Strengthening primary health care to improve Indigenous health outcomes. Medical Journal of Australia 204(10):363-4.

WHO (World Health Organization) 2019. Health equity. Geneva: World Health Organization. Viewed 20 November 2019, https://www.who.int/topics/health_equity/en/.

Zhao Y, Thomas S, Guthridge S & Wakerman J 2014. Better health outcomes at lower costs: the benefits of primary care utilisation for chronic disease management in remote Indigenous communities in Australia’s Northern Territory. BMC Health Services Research 14:463-72. https://doi.org/10.1186/1472-6963-14-463.

159 Australia’s health 2020: data insights

Funding health care in Australia

6

160 Australia’s health 2020: data insights

Chapter

6

The Australian health system is complex, with a division of roles and responsibilities

in terms of both service delivery and funding (AIHW 2018a, 2020). In 2017-18,

an estimated $185.4 billion was spent on health goods and services in Australia

(AIHW 2019a). This expenditure was financed through a range of different funding

sources and through different administrative arrangements. The funders of the

Australian health system can be broadly categorised as either government or

non-government. Government funders include the Australian Government and state

and territory governments which jointly fund some areas of expenditure, such as

public hospitals. Non-government funders include individuals (who provide funding

through out-of-pocket payments), private health insurers (funded in turn by individuals’

premium outlays, net of the government subsidy) and other non-government funders

(for example, workers’ compensation schemes). An overview of the funders of the

Australian health system, and their relative contributions across different areas of

health care, is presented in Figure 6.1.

Over two-thirds (68.3% or $126.7 billion) of total health expenditure during 2017-18

was funded by governments (AIHW 2019a). The Australian Government contribution

to total health expenditure was 41.6% (or $77.1 billion) and the state and territory

contribution was 26.7% ($49.5 billion). About one-third (31.7% or $58.8 billion) of total

Australian health expenditure during 2017-18 was funded by non-government sources

(AIHW 2019a). The contribution of individuals’ out-of-pocket spending to total health

expenditure was 16.5% (or $30.6 billion); private health insurers 9.0% (or $16.6 billion);

and other non-government sources 6.2% (or $11.5 billion).

See ‘Health expenditure’ https://www.aihw.gov.au/reports/australias-health/

health-expenditure for more information.

Over the decade to 2017-18, health expenditure grew at an annual average rate of

3.9% in real terms (AIHW 2019a). There have been a range of changes to the funding of

the health system, including changes to the relative contribution of different funders

across different areas of health expenditure during this period.

The main purpose of this chapter is to provide an overview of the arrangements in

place to fund the different components of the Australian health system, and how this is

changing over time. The discussion centres around the data that is within the scope of

the Australian National Health Accounts (AIHW 2019a), and so excludes health-related

sectors classified as ‘welfare’ (including aged care and disability support services)

(AIHW 2019b).

161 Australia’s health 2020: data insights

Chapter

6

Figure 6.1: Funding by area of expenditure, source, and key transfer mechanisms, 2017-18

Notes

1. Figure 6.1 excludes the medical expenses tax rebate (equivalent to 0.01% of total health expenditure in 2017-18).

2. ‘Other’ funding sources include workers’ compensation schemes, compulsory third-party motor vehicle insurers, miscellaneous non-patient revenue that health care providers receive, private non-profit organisations, and other private funding.

Source: AIHW health expenditure database.

Funding source

Recurrent or capital Broad area of expenditure

Detailed area of expenditure Government

Health insurance providers Individuals Other

Recurrent expenditure Hospitals Public hospital

services

Private hospitals

Primary health care

Community health and other

Public health

Benefit-paid pharmaceuticals

All other medications

Dental services

Other health practitioners

Unreferred medical services

Referred medical services Referred medical services

Other services

Aids and appliances

Patient transport services

Administration

Research Research

Capital expenditure Capital expenditure

Capital expenditure

Expenditure 2017-18 ($m)

3

10,000

20,000

30,000

40,000

52,585

Key mechanisms

Activity-based + Block funding

Pharmaceutical Benefits Scheme payments

Medicare Benefits Schedule pricing

Grants

User fees for goods and services

Benefits for goods and services

Other (or not classified)

162 Australia’s health 2020: data insights

Chapter

6

Box 6.1: Health system financing

Models of health system financing

The financing of health systems varies between countries and can be broadly

categorised as either a state financed model, a privately financed model or a

hybrid model (that is, a mix of both state financed and private models) (Dixit &

Sambasivan 2018; Donaldson et al. 2005; Duckett & Willcox 2011).

There are differences between member countries in the Organisation for

Economic Co-operation and Development (OECD) in terms of the funding

arrangements that exist in their respective health systems. In some countries,

the main source of funds is through general taxation (for example, United

Kingdom) and for others it is via compulsory insurance contributions (for

example, France). A number of countries fund their health systems through

a combination of general taxation and compulsory insurance contributions.

Voluntary private health insurance is also key to funding some health

systems, either as the main source of funds (for example, the United States of

America, prior to the introduction of the Affordable Care Act, which mandated

enrolment) or in combination with other funding approaches (for example,

the Netherlands).

The Australian health system is a hybrid system where health care can be

funded through either taxation or privately, with a regulated voluntary health

insurance system being a key aspect. It should be noted that this system

relates to the source of the money used to pay for health goods and services,

rather than who necessarily provides those services. Tax revenue, for example,

is used by governments to purchase health goods and services from both

public providers (for example, public hospitals) and from private providers (for

example, general practitioners (GPs)). Similarly, individuals can choose to use

private funds to access services in public or private settings (for example, public

or private hospitals).

Government funding and compulsory contributory health insurance

across countries

Across the OECD, government and compulsory contributory health insurance

schemes generally account for the majority of countries’ health financing

(Figure 6.2). In 2017, their contribution ranged from a minimum of 51.5% in

Mexico to a maximum of 86.8% in Germany.

(continued)

163 Australia’s health 2020: data insights

Chapter

6

Box 6.1 (continued): Health system financing

In Australia, according to the System of Health Accounts (SHA) methodology

used by the OECD, government funding was estimated to contribute 68.6% of

total health expenditure (AIHW 2019a). This is similar to the level seen in Canada,

though the mechanics of funding mechanisms differ across the 2 countries

(Allin & Rudoler 2019). In Australia, government funding includes the private

health insurance premium rebate funded by the Australian Government.

Compulsory contributory health insurance differs from government financing

schemes in that coverage is generally instated by hypothecated taxation payments

or some other proactive action on an individual’s behalf; it differs from voluntary

private health insurance in that is compulsory (OECD 2019b).

In the United States (as reflected in Figure 6.2), most private health insurance was

classified as compulsory health insurance in 2017 by the OECD. This is because

the country’s introduction of the Affordable Care Act compelled individuals to

either purchase health insurance, or be charged a penalty (OECD 2019a). This

did not mean that everyone had private health insurance and, as of January

2019, this penalty no longer applies at the Federal level (United States Centers

for Medicare & Medicaid Services 2020). In France, the government manages a

statutory health insurance (SHI) scheme which provides universal and compulsory

coverage to residents. It is financed through a combination of taxation (including

payroll tax, income tax, taxes levied on voluntary health insurance companies)

and state subsidies (Durand-Zaleski et al. 2019), and the central government

shapes the mechanisms through which funds are transferred to providers. In

the Netherlands, SHI coverage is purchased from private insurers, and funded

through a contribution of income-related payments, government endowments

(for young people), and individual premiums (unrelated to health status)

(Wammes et al. 2019). In the Netherlands case, insurers negotiate payment

mechanisms with health care providers.

Voluntary health care payments across countries

The contribution of voluntary health care payments (including private health

insurance) to total health care expenditure varies considerably across the OECD,

from a minimum of 0.4% in Norway to 17% in Ireland (Figure 6.2). In Australia,

according to the OECD methodology, voluntary health insurance schemes

financed 9.8% of health care spending in 2017.

(continued)

164 Australia’s health 2020: data insights

Chapter

6

Box 6.1 (continued): Health system financing

The role of private health insurance differs across countries. Private health

insurance can be either complementary or supplementary to government-provided

coverage in Australia, in that it provides coverage for both additional health

services (such as dental care), as well as differentiated care (for example, faster

access to treatment). In some European countries, such as France, voluntary health

insurance is primarily complementary, as its benefits are directed toward meeting

the payments associated with other financing schemes (though some additional

services are also covered).

Out-of-pocket spending across countries

In 2017, the share of individuals’ out-of-pocket spending in total health care

expenditure ranged from 6.7% in France to 42.6% in Latvia (Figure 6.2).

In Australia, this share was 17.9%—higher than some other comparative

countries, such as Canada. However, it is noted that out-of-pocket spending

data might not be captured well in some OECD countries.

Many countries in the OECD have policies which cap or reduce individuals’

out-of-pocket payments for health care, though these work in different ways

across different countries. Australia implements several safety-net schemes,

including Medicare and pharmaceutical safety nets. These schemes provide

higher subsidies when individuals or families spend over particular thresholds on

certain health goods and services, with differential conditions for some segments

of the population (low-income households). In Germany, the government has

implemented a cap limiting an individual’s out-of-pocket spending at 2% of their

income; in Norway out-of-pocket spending is capped at a fixed dollar amount;

while in Denmark, a cap on out-of-pocket expenditure on medical goods is

targeted to the chronically ill (Commonwealth Fund 2011).

165 Australia’s health 2020: data insights

Chapter

6

Figure 6.2: Financing arrangements as a proportion of total health

expenditure, OECD countries, current prices and local currency, 2017

Notes

1. ‘Other’ refers to other financial contributions, some of which are from foreign countries.

2. Spending by long-term care facilities is excluded from health expenditure figures for all countries, to ensure comparability with Australia (where residential long-term care is classified as ‘welfare’ for expenditure purposes).

3. The 2011 SHA framework is used by the OECD to ensure consistency in analysing the consumption, provision and financing of health care across countries (OECD et al. 2017).

4. The proportions for Australia are estimates calculated by AIHW. Due to country-specific differences, caution should be taken when comparing between countries.

Sources: AIHW health expenditure database; OECD 2019b.

0

10

20

30

40

50

60

70

80

90

100

Germany

United States Norway France Luxembourg

Denmark Japan

United Kingdom Sweden

Czech Republic Slovak Republic New Zealand Iceland

Turkey

Netherlands Austria Estonia Belgium

Italy

Finland Ireland Slovenia Spain

Poland Canada Australia Hungary

Lithuania Portugal Switzerland Israel

Greece Chile Korea Latvia Mexico

Per cent

Government and compulsory health insurance Voluntary health care payments

Household out-of-pocket payments Other

166 Australia’s health 2020: data insights

Chapter

6

Main health service funding mechanisms There are various approaches to the funding of health services in the Australian

health system. These can be broadly categorised as either volume-based funding or

block funding.

This section will consider the funding arrangements for selected areas of funding.

Public hospitals

Throughout recent history (beginning when Medicare was established in the 1980s),

the Australian Government has entered into a series of national agreements with

states and territories to provide funding for public hospitals to support the provision

of fee-free treatment for public patients (Department of the Senate 2016). Up

until 2011, under these agreements, Australian Government funding for public

hospitals was primarily through ‘block funding’ transfers which were agreed largely

through negotiation but also included adjustments to reflect population growth and

demographic changes and health sector inflation.

In 2011, the National Health Reform Agreement (NHRA) saw a number of changes to

public hospital funding arrangements implemented from 2012-13. These included

a mixture of activity-based funding (ABF) and block funding for the Australian

Government contributions, with a preference for ABF where appropriate. The 2011

NHRA also involved the establishment of a ‘national funding pool’. This funding

pool includes dedicated accounts for each state and territory through which it was

agreed that all Australian Government and state and territory ABF payments to

public hospitals would be administered. States and territories determine how both

their own and the Australian Government contribution is spent through Service

Agreements with Local Hospital Networks.

ABF is designed to reflect the volume and case-mix of services provided by a

hospital. This is service (hospital separation) based rather than based on individual

patients (as a single patient may have multiple separations within a given hospital

stay). The level of funding provided for a given hospital separation reflects an

estimate of the ‘efficient cost’ (the National Efficient Price) of providing similar public

hospital services nationally (IHPA 2019). Currently, the types of hospital services

that are funded using an ABF approach include emergency department services,

admitted patient care (including mental health services), sub-acute and non-acute

care (for example, palliative care), and non-admitted care (for example, outpatient

care) (NHFB 2019).

167 Australia’s health 2020: data insights

Chapter

6

Block funding (an aggregated funding payment) is provided for those public hospital services that are not suitable to fund through ABF, either because of a lack of adequate data or the nature of the service. These include, for example, some specialist hospital services; teaching; training; research and services in areas with small volumes and large fixed costs, for example, in regional and rural communities (NHFB 2019). Block funding levels reflect the average cost of providing relevant services in similar settings (IHPA 2018).

Not all public hospital funding is managed through the ABF and block funding arrangements. This includes, for example, specific funding for highly specialised drugs; funding for blood and organ donation programs; and funding provided by the Department of Veterans’ Affairs (AIHW 2019a). These other public hospital funding arrangements are a mixture of both volume and block funding arrangements.

An insured person with hospital coverage can opt to be treated as a public patient in a public hospital or can elect to be treated as a private patient in a public hospital (Department of Health 2019b). In this case, funding is also sourced from health insurers and potentially through individual out-of-pocket payments. These patients are able to access certain benefits in hospital; for example, while public patients are treated by doctors nominated by the hospital, in many circumstances private patients are able to choose their doctor (Department of Health 2019e; Private Health 2019).

The estimates of expenditure reported in the Australian National Health Accounts include both NHRA-based payments as well as the other public hospital funding schemes (see AIHW 2019a:Table A11).

In 2017-18, public hospital expenditure in the National Health Accounts was $57.7 billion (AIHW 2019a). Funders included:

• state and territory governments (51.7% or $29.9 billion)

• the Australian Government (39.3% or $22.7 billion)

• individuals (2.9% or $1.7 billion)

• health insurers (2.2% or $1.2 billion)

• other non-government funders (3.8% or $2.2 billion).

Prior to the NHRA, there were several years where state and territory governments contributed an increasing share of public hospital funding, relative to the Australian Government. Since the NHRA was introduced, the Australian Government share has generally increased relative to states and territories (Figure 6.3). Overall, expenditure on public hospitals grew at an annual average rate of 3.9% in real terms over the decade to 2017-18 (AIHW 2019a). The growth rates in 2017-18 were affected by the previous year having included one-off capital expenditure on projects such as the new Royal Adelaide Hospital as well as a previous spike in Australian Government spending on new drugs to treat hepatitis C.

168 Australia’s health 2020: data insights

Chapter

6

Figure 6.3: Proportion of public hospital expenditure, by source of funds,

current prices, 2000-01 to 2017-18

Source: AIHW health expenditure database.

Private hospitals

In 2017-18 private hospital expenditure was $16.3 billion (AIHW 2019a). There was a range of funders including:

• health insurers (50.0% or $8.2 billion)

• the Australian Government (23.0% or $3.8 billion)

• individuals (13.4% or $2.2 billion)

• state and territory governments (6.0% or $1.0 billion)

• other non-government funders (7.6% or $1.2 billion).

To avoid double counting, these figures reflect spending on goods and services by the different funders and do not include the insurance premiums paid by individuals (insurance not being categorised as a health good or service). The health insurers’ amount, for example, reflects the amount spent by insurers on health care. Similarly, spending by individuals includes the payments paid directly to services, not the insurance premiums. An exception to this is the contribution from the Australian Government, provided in the form of a premium rebate. To ensure this amount is appropriately captured, it is treated as spending on private hospital services by the Australian Government, rather than spending on insurers. Total expenditure on private hospitals grew by an annual average of 5.1% in real terms over the decade to 2017-18 (AIHW 2019a).

0

10

20

30

40

50

60

Australian Government State and territory governments Individuals

Health insurers Other non-government funders

Per cent

2000-01

2017-18

2001-02

2002-03

2003-04

2004-05

2005-06

2006-07

2007-08

2008-09

2009-10

2010-11

2011-12

2012-13

2016-17

2015-16

2014-15

2013-14

169 Australia’s health 2020: data insights

Chapter

6

The scope of hospital insurance coverage for private patients varies across different

insurance products. Insurance products have been categorised into gold, silver, bronze

or basic tiers, with some products providing more than the standardised coverage

package in each category (Department of Health 2018).

Private hospital funding is also provided through the Medicare Benefits Schedule

(MBS-Medicare), and is captured as expenditure on referred medical services.

Private health insurance covers a minimum of 25% of the MBS schedule fee associated

with services provided in hospital, with Medicare covering 75%, but if a doctor charges

more than the MBS schedule fee (or more than that covered by the insurer plus

Medicare), patients may be required to pay an out-of-pocket gap payment

(Department of Health 2019e).

The relative contribution of different funders has changed over the decade to

2017-18. The proportion of spending on private hospitals by private health insurers

has increased slightly, while the Australian Government proportion has decreased,

particularly following the introduction of income testing for eligibility to the premium

rebate (Figure 6.4) (AIHW 2019a).

Figure 6.4: Proportion of private hospital expenditure, by source of funds,

current prices, 2000-01 to 2017-18

Note: Private hospital expenditure from State and territory governments has been collected since 2002-03.

Source: AIHW health expenditure database.

Australian Government State and territory governments Individuals

Health insurers Other non-government funders

2000-01

2017-18

2001-02

2002-03

2003-04

2004-05

2005-06

2006-07

2007-08

2008-09

2009-10

2010-11

2011-12

2012-13

2016-17

2015-16

2014-15

2013-14

0

10

20

30

40

50

60

Per cent

170 Australia’s health 2020: data insights

Chapter

6

Primary health care

There is currently no single definition of what constitutes primary health care. For the purposes of the health spending analysis and the Australian National Health Accounts, primary health care includes unreferred medical services (for example, general practice care); dental services, other health practitioners, community health, public health and medications.

See ‘Primary health care’ https://www.aihw.gov.au/reports/australias-health/ primary-health-care for more information.

In 2017-18, primary health care expenditure was $63.4 billion (AIHW 2019a). There was a range of funders including:

• the Australian Government (44.3% or $28.1 billion)

• individuals (31.7% or $20.1 billion)

• state and territory governments (15.8% or $10.0 billion)

• health insurers (4.7% or $2.9 billion)

• other non-government funders (3.5% or $2.2 billion).

Expenditure on primary health care grew by an annual average of 3.3% in real terms over the decade to 2017-18 (AIHW 2019a). The real annual average growth per capita was 1.6%. In considering this rate of growth, it should be noted that, between 2013 and mid-2019, the Australian Government maintained a ‘freeze’ on indexation of MBS fees as part of a budget savings plan.

Funding arrangements differ for the different service types classified as primary health care.

Some primary health goods and services are primarily funded by government through program-specific block grants. These include community health programs (largely funded by state and territory governments) and public health programs (funded jointly by Australian and state and territory governments) (AIHW 2019a).

Much of primary health care is funded through a fee-for-service approach. The MBS lists medical services subsidised by the Australian Government and their associated schedule fees, which provide a benchmark level for the public subsidy. The government reimburses 100% of the schedule fee for GP services and there are no out-of-pocket costs to an individual when a doctor bills Medicare directly—(a practice known as ‘bulk billing’) (Private Health 2019). However, when a doctor charges more than the schedule fee, the individual will be required to fund the gap payment. In 2016-17, 86% of GP consultations were bulk billed (AIHW 2018c). If, over the course of a year, an individual or family’s annual out-of-pocket medical expenses exceeds a certain threshold, higher subsidies become available through the operation of Medicare safety nets (Department of Human Services 2019a).

171 Australia’s health 2020: data insights

Chapter

6

By law, private health insurance funds do not cover out-of-hospital services provided

by medical practitioners, including consultations with GPs (Department of Health

2019e). Private health insurance coverage is available for ancillary goods and services

not covered by Medicare (that is, extras cover), such as dental services, physiotherapy,

chiropractic treatment, home nursing, and glasses and contact lenses (Department

of Health 2019e). The range of extras services covered differs across policies, and the

extent of coverage for a particular type of service is usually capped (for example, at

some dollar amount over the course of a year, or at a specified proportion of the total

spend). Consumers pay the provider price for health goods and services not covered

by Medicare—or a gap-fee in circumstances where an individual has private insurance

coverage—but the benefit under their policy does not completely cover the service

cost. Some insurers are directly engaged in the provision of some types of services

(such as dental care), and incentivise attendance at in-house providers by limiting

out-of-pocket costs for such attendances.

See ‘Private health insurance’ https://www.aihw.gov.au/reports/australias-health/

primary-health-care for more information.

Although governments do not provide universal coverage for dental services in

Australia, a number of Australian Government and state and territory government

schemes exist to subsidise access to dental services for vulnerable populations,

including young children (for example, the Australian Government’s Child Dental

Benefits Schedule; Department of Health 2019d) and people living in low-income

households. For the wider population, out-of-pocket spending on dental services is

20% of total out-of-pocket health expenditure (AIHW 2019a).

The Pharmaceutical Benefits Scheme (PBS) lists medicines subsidised by the Australian

Government. The listings are based on recommendations by the independent

Pharmaceutical Benefits Advisory Committee, based on a medicines’ health impact

(relative to its main alternative therapy) and cost-effectiveness. On listing a medicine

on the PBS, the Australian Government negotiates a price with the supplier. Individuals

(generally) contribute a co-payment on purchasing medicines listed on the PBS

(Department of Health 2019a), rather than paying the provider price for unlisted

medicines. The PBS provides higher subsidies for concession-card holders, and, through

the PBS Safety Net, provides higher subsidies when total annual contributions made by

individuals or their families exceed specified thresholds (Department of Human Services

2019b). Individuals fund the vast majority (92.1%) of expenditure on medication which

is not subsidised (for example, private prescriptions and over-the-counter medicines)

through out-of-pocket payments.

172 Australia’s health 2020: data insights

Chapter

6

Figure 6.5: Proportion of primary health care expenditure, by source of

funds, current prices, 2000-01 to 2017-18

Source: AIHW health expenditure database.

Referred medical services

For the purposes of the health spending analysis and the Australian National Health

Accounts, referred medical services are those where a person had been referred by

a GP or specialist for further medical care. This includes referrals for consultations

with medical specialists (such as obstetricians or oncologists) and with allied health

professionals (such as psychologists or podiatrists), and referrals to diagnostic services

such as (pathology and medical imaging providers).

In 2017-18, expenditure on referred medical services was $19.4 billion (AIHW 2019a).

There were a range of funders including:

• the Australian Government (74.5% or $14.4 billion)

• individuals (16.6% or $3.2 billion)

• health insurers (8.9% or $1.7 billion).

Expenditure on referred medical services grew by an annual average rate of 4.4% in

real terms over the decade to 2017-18 (AIHW 2019a). As seen for primary health care,

the ‘freeze’ on indexation of MBS fees should be considered for this growth.

Australian Government State and territory governments Individuals

Health insurers Other non-government funders

2000-01

2017-18

2001-02

2002-03

2003-04

2004-05

2005-06

2006-07

2007-08

2008-09

2009-10

2010-11

2011-12

2012-13

2016-17

2015-16

2014-15

2013-14

0

5

10

15

20

25

30

35

40

45

50

Per cent

173 Australia’s health 2020: data insights

Chapter

6

As with other areas of the health system, the Australian Government provides

subsidies for referred medical services that are listed on the MBS. Individuals also

contribute a proportion of funding through additional out-of-pocket payments.

Figure 6.6: Proportion of referred medical services expenditure, by source of

funds, current prices, 2000-01 to 2017-18

Source: AIHW health expenditure database.

Health financing in future

Managing growing costs

Recent trends suggest health care costs are likely to continue to rise into the future,

although this will be dependent on broader economic and social factors such as

wealth growth and policy decisions. Health care costs have increased substantially in

Australia over the past 2 decades, reflecting advances in the provision of health care,

as well as increased wealth within the community, population growth and population

ageing. During this period, health expenditure has grown faster than inflation and

population growth combined. Total health expenditure in Australia increased from

$77.5 billion to $185.4 billion in real terms (2017-18 dollars) over the twenty-year

period to 2017-18. Over the same period, spending per person increased from $4,189

to $7,485 (2017-18 dollars), implying average annual growth of 2.9%. As a proportion

of Australia’s gross domestic product (GDP), health expenditure increased from 7.6% in

1997-98 to 10.0% in 2017-18 (current prices; AIHW 2019a).

Australian Government Individuals Health insurers

2000-01

2017-18

2001-02

2002-03

2003-04

2004-05

2005-06

2006-07

2007-08

2008-09

2009-10

2010-11

2011-12

2012-13

2016-17

2015-16

2014-15

2013-14

0

10

20

30

40

50

60

70

80

90

Per cent

174 Australia’s health 2020: data insights

Chapter

6

This trend of growing health care costs is evident across all OECD countries, with

average health expenditure per person growing by an average annual rate of 2.4%

in real terms from 2000 to 2017, measured in 2010 prices (OECD 2019b). Over the

same period, the average OECD ratio of health expenditure to GDP increased from

6.9% to 8.1% (OECD 2019b). The OECD projects that health expenditure as a share of

GDP will continue to rise across its member countries in the coming decade to 2030

(OECD 2019a).

In the context of rising health care costs, and a growing prevalence of complex,

long-term chronic conditions, many countries are exploring ways to improve the

sustainability of health care provision and financing. As outlined in this chapter,

the majority of health services in Australia are activity-funded (as is common in other

countries). Other funding mechanisms, such as value-based health care models,

capitation-based funding and bundled pricing are being explored for their potential to

provide alternative incentive structures around the provision health care, particularly

in cases where health care needs are complex and long-term.

Value-based health care

Value-based health care, including pay-for-performance financing, is an approach

to service provision which emphasises ‘value over volume’ in financing health

services. It aims to incentivise the provision of the health care which most improves

the outcomes that patients value (EIU 2016). There are some widely acknowledged

challenges associated with implementing pay-for-performance funding models

(including around defining and measuring performance), and associated risks to

health care provision (including the risk of reducing incentives to care for patients with

health issues that are particularly challenging to overcome) (Kyeremanteng et al. 2019).

In Australia, the Australian Commission on Safety and Quality in Health Care has

supported a patient-centred approach to care through the development of the

Australian Hospital Patient Experience Question Set. This questionnaire is administered

at hospitals and health care services to collect information about patients’ experiences

of treatment and care, with results relayed back to practitioners (ACSQHC 2019).

Patient-reported outcome measures have also been collected and used elsewhere in

the Australian context, including as a benchmarking tool in the provision of palliative

care (through the Palliative Care Outcomes Collaboration) and of care for patients with

prostate cancer (through the Prostate Cancer Outcomes Registry) (AIHW 2018b).

Data relating to patient outcomes and experiences of health services, as well as

considered assessments of cost-effectiveness, are key to the effective provision of

any health funding scheme, including value-based health care. In some countries,

175 Australia’s health 2020: data insights

Chapter

6

health technology assessment organisations have been established to support the

development of this knowledge base. For example, the German government’s Institute

for Quality and Efficiency in Healthcare has prepared evidence-based reports on

various health services to support a transition to value-based health care (IQWiG

2019). Some agencies, such as the National Health Service in the United Kingdom,

publish reviews of health services to support transparency and better decision

making (EIU 2016). In the Netherlands, some hospitals are using metrics created by

the International Consortium for Health Outcomes Measurement to measure patient

outcomes (EIU 2016).

Capitation payments

Capitation-based funding involves remunerating providers based on the number

(and potentially the case-mix) of patients they have enrolled or registered, rather

than the volume (or type) of health care services provided (Biggs 2014). This funding

approach has the potential to encourage early interventions which reduce the

demand for health care over the longer-term, and to remove any incentive to provide

interventions with minimal benefit to patients.

In New Zealand, a capitation-based payment mechanism has been implemented to

fund GP services. Since different demographic groups require varying levels of care,

the payments reflect the demographic structure of patient cohorts (NZ MoH 2019).

The funding scheme also includes a mechanism to limit co-payments associated

with accessing services, enabling greater access to care for Indigenous Māori and

encouraging preventive visits (Thomson 2019).

A recent initiative centred around a capitation approach in Australia is the Voluntary

Patient Enrolment Program for GPs, which is expected to commence 1 July 2020.

Under this scheme, quarterly payments will be made to GPs, based on the number

of patients they have voluntarily enrolled with their practice. Though enrolment is

not mandatory and enrolled patients are allowed to see other GPs, this approach is

expected to formalise patient-doctor relationships, and support the provision of more

flexible, digitally enabled care (Department of Health 2019c).

The Indigenous Australians’ Health Programme Primary Health Care Funding Model

developed by the Australian Government is a combination of both capitation and

activity-based approaches. The capitation aspect of the funding model acknowledges that

services provided by Aboriginal Community Controlled Health Services are not all clinical

activities and cannot always be claimed by Medicare (Department of Health 2019b).

The efficacy and challenges associated with these approaches is largely unknown at

this point.

176 Australia’s health 2020: data insights

Chapter

6

Bundled payments

Bundled payment mechanisms and capitation mechanisms are closely related.

‘Bundled pricing’ refers to a financing arrangement where a single payment is made

to cover health services and care within a particular episode—to treat a patient’s

particular medical condition over a particular period of time and (potentially)

across a variety of settings (Porter & Kaplan 2016).

A recent investigation into the potential use of bundled payments for funding

maternity care in Australia highlighted several possible benefits of the

approach—including its potential to support innovation in the provision of

care (IHPA 2017). However, in this case, practical barriers were uncovered which

prevent implementation at the present time.

Blended payments

In general, any particular funding model might work well in some circumstances

and for some classes of patients, and less well in other circumstances and for other

patients. Blended funding models—combining aspects of fee-for-service remuneration,

capitation payments, pay-for-performance, and/or other funding models—may be

adopted to balance different incentives when providing health services. In practice,

much of the exploration of alternative approaches to health care funding (beyond

established ABF mechanisms) could be considered as falling into this category.

Health funding and data

Data relating to health expenditure and financing are shaped by both health

systems activity and health funding mechanisms. The reverse relationships also

hold: health system activity and funding mechanisms are affected by the availability,

and feasibility, of good quality data collection. As a result, changes in health funding

mechanisms in future are likely to prompt related changes in data collection and

reporting, and vice-versa.

In addition to influencing broad data categorisations (for example, the classification

of medicines by whether or not they are subsidised under the PBS), funding

mechanisms have the potential to affect the types of data collected, and the

resources available to support relevant data collection. In Australia, the maturation

of the ABF mechanism—which relies upon highly detailed hospital records for its

implementation—is recognised to have strengthened efforts to improve the quality

of activity-related data collections, which is beneficial for both the clinical and

administrative applications of these data (see, for example, Heslop 2019). Challenges

around defining appropriate metrics, and holistically monitoring improvements in

177 Australia’s health 2020: data insights

Chapter

6

health, come to the fore when data plays a central role in funding mechanisms.

The consistency of data classifications and methods across settings, and collaboration

among funders, providers and other stakeholders, is clearly important.

Although growing health care costs present a challenge to the sector, in Australia and

across the world, there have been efforts to further evolve funding mechanisms to

more efficiently and effectively support people’s long-term wellbeing. This experience

has shown the collection and use of accurate, timely and high quality data to be

increasingly central.

References ACSQHC (Australian Commission on Safety and Quality in Health Care) 2019. Australian Hospital Patient Experience Question Set. Sydney: ACSQHC. Viewed on 11 November 2019, https://www.

safetyandquality.gov.au/our-work/indicators-measurement-and-reporting/australian-hospital-patient-experience-question-set.

AIHW (Australian Institute of Health and Welfare) 2018a. Australia’s health 2018. 2.1 How does Australia’s health system work? Australia’s health series no. 16. Cat. no. AUS 221. Canberra: AIHW.

AIHW 2018b. Australia’s health 2018. 7.17 Patient-reported experience and outcome measures. Australia’s health series no. 16. Cat. no. AUS 221. Canberra: AIHW.

AIHW 2018c. Patients’ out-of-pocket spending on Medicare services 2016-17. Cat. no. HPF 35. Canberra: AIHW.

AIHW 2019a. Health expenditure Australia 2017-18. Health and welfare expenditure series no. 65. Cat. no. HWE 77. Canberra: AIHW.

AIHW 2019b. Australia’s welfare 2019. Australia’s welfare series no. 14. Cat. no. AUS 226. Canberra: AIHW.

AIHW 2020 (forthcoming). Australia’s health 2020. Health system overview. Canberra: AIHW.

Allin S & Rudoler D 2019. The Canadian health care system. New York: The Commonwealth Fund. Viewed 26 September 2019, https://international.commonwealthfund.org/countries/ canada/.

Biggs A 2014. Explainer: paying for GP services. Canberra: Parliament of Australia. Viewed 11 November 2019, https://www.aph.gov.au/About_Parliament/Parliamentary_Departments/ Parliamentary_Library/FlagPost/2014/March/Explainer_paying_for_GP_services.

Commonwealth Fund 2011. International profiles of health care systems, 2011. Commonwealth Fund pub. no. 1562. New York: The Commonwealth Fund.

Department of Health 2018. Private health insurance reforms: Gold/Silver/Bronze/Basic product tiers. Canberra: Department of Health. Viewed 10 February 2020, https://www1.health.gov.au/ internet/main/publishing.nsf/Content/private-health-insurance-reforms-fact-sheet-gold-gilver-bronze-basic-product-categories.

Department of Health 2019a. About the PBS. Canberra: Department of Health. Viewed on 11 November 2019, http://www.pbs.gov.au/info/about-the-pbs.

178 Australia’s health 2020: data insights

Chapter

6

Department of Health 2019b. Indigenous Australians’ Health Programme Primary Health Care Funding Model. Canberra: Department of Health. Viewed 5 February 2020, https://www1.

health.gov.au/internet/main/publishing.nsf/Content/indigenous-australians-health-programme-funding-model.

Department of Health 2019c. Statement from the Chair — MBS Review recommendations accepted by Government. Canberra: Department of Health. Viewed on 13 February 2020, https://www1.health.gov.au/internet/main/publishing.nsf/Content/statement-from-the-chair-mbs-review-recommendations-accepted-by-government.

Department of Health 2019d. The Child Dental Benefits Schedule. Canberra: Department of Health. Viewed 11 November 2019, https://www1.health.gov.au/internet/main/publishing.nsf/ Content/childdental.

Department of Health 2019e. What private health insurance covers. Canberra: Department of Health. Viewed 11 November 2019, https://www.health.gov.au/health-topics/private-health-insurance/what-private-health-insurance-covers.

Department of Human Services 2019a. Medicare Safety Nets. Canberra: Department of Human Services. Viewed 25 October 2019, https://www.humanservices.gov.au/individuals/services/ medicare/medicare-safety-nets.

Department of Human Services 2019b. When you spend a lot on PBS medicines. Canberra: Department of Human Services. Viewed 11 November 2019, https://www.humanservices.gov.

au/individuals/services/medicare/pharmaceutical-benefits-scheme/when-you-spend-lot-pbs-medicines.

Department of the Senate 2016. The final report Hospital funding cuts: the perfect storm: the demolition of Federal-State health relations 2014-16. Canberra: Department of the Senate. Viewed 25 October 2019, https://www.aph.gov.au/Parliamentary_Business/Committees/Senate/ Health/Health/Final_Report.

Dixit SK & Sambasivan M 2018. A review of the Australian healthcare system: a policy perspective. SAGE Open Medicine 6. https://doi.org/10.1177/2050312118769211.

Donaldson C, Gerard K, Jan S, Mitton C & Wiseman V 2005. Economics of health care financing: the visible hand. Palgrave, London.

Duckett SJ & Willcox S 2011. The Australian health care system. 4th edn. South Melbourne, Victoria: Oxford University Press.

Durand-Zaleski I, AP-HP & Université Paris-Est 2019. The French health care system. Paris: The commonwealth Fund. Viewed 2 October 2019, https://international.commonwealthfund.

org/countries/france/.

EIU (Economist Intelligence Unit) 2018. Value-based healthcare: a global assessment. London: The Economist.

Heslop L 2019. Activity-based funding for safety and quality: a policy discussion of issues and directions for nursing-focused health services outcomes research. International Journal of Nursing Practice 25(5):e12775. Viewed 14 February 2020, https://doi.org/10.1111/ijn.12775.

IHPA (Independent Hospital Pricing Authority) 2017. Bundled pricing for maternity care: final report of IHPA and the Bundled Pricing Advisory Group. Sydney: IHPA. Viewed 14 February 2020, https://www.ihpa.gov.au/sites/default/files/bundled_pricing_for_maternity_care_-_final_ report.pdf.

179 Australia’s health 2020: data insights

Chapter

6

IHPA 2018. Understanding the NEP and NEC 2018-19. Sydney: IHPA. Viewed 11 November 2019, https://www.ihpa.gov.au/sites/default/files/publications/understanding_the_nep_and_nec_2018-19.pdf.

IHPA 2019. Activity based funding. Sydney: IHPA. Viewed 11 November 2019, https://www.ihpa.

gov.au/what-we-do/activity-based-funding.

IQWiG (Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen, or Institute for Quality and Efficiency in Health Care) 2019. Medicine put to the test. Köln: IQWiG . Viewed 11 November 2019, https://www.iqwig.de/en/home.2724.html.

Kyeremanteng K, Robidoux R, D’Egidio G, Fernando SM & Neilipovitz D 2019. An analysis of pay-for-performance schemes and their potential impacts on health systems and outcomes for patients. Critical Care Research and Practice 2019(8943972). Viewed 12 February 2020, https://doi.org/10.1155/2019/8943972.

NHFB (National Health Funding Body) 2019. Funding types: National Health Reform funding. Canberra: National Health Funding Body. Viewed 12 September 2019, https://www.publichospitalfunding.gov.au/public-hospital-funding/funding-types.

NZ MoH (New Zealand Government Ministry of Health) 2019. Capitation funding. Wellington: NZ MoH. Viewed 12 February 2020, https://www.health.govt.nz/our-work/primary-health-care/ primary-health-care-subsidies-and-services/capitation-funding.

OECD (Organisation for Economic Co-operation and Development) 2019a. Health at a glance 2019: OECD indicators. Paris: OECD, Viewed 18 November 2019, https://www.oecd.org/health/ health-at-a-glance-19991312.htm.

OECD 2019b. OECD Health Statistics 2019. Paris: OECD. Viewed on 11 February 2019, http://www.oecd.org/els/health-systems/health-data.htm.

OECD, Eurostat (European Union Eurostat) & WHO (World Health Organization) 2017. A system of health accounts 2011: revised edition. Paris: OECD. Viewed 02 October 2019, http://dx.doi.org/10.1787/9789264270985-en.

Porter ME & Kaplan RS 2016. How to pay for health care. Harvard Business Review July-August 2016:88-100.

Private Health 2019. What is covered by Medicare? Canberra: Commonwealth Ombudsman: Private Health Insurance. Viewed 11 November 2019, https://www.privatehealth.gov.au/health_ insurance/what_is_covered/medicare.htm.

Thomson M 2019. Who had access to doctors before and after new universal capitated subsidies in New Zealand? Health Policy 123(8):756-64.

United States Centers for Medicare & Medicaid Services 2020. Baltimore MD: United States Centers for Medicare & Medicaid Services. Viewed 11 February 2020, https://www.healthcare.

gov/fees/fee-for-not-being-covered/.

Wammes J, Jeurissen P, Westert G & Tanke M 2019. The Dutch health care system. New York: The Commonwealth Fund. Viewed 2 October 2019, https://international.commonwealthfund.

org/countries/netherlands/.

181 Australia’s health 2020: data insights

Changes in people’s health service use around the time of entering permanent residential aged care

7

182 Australia’s health 2020: data insights

Chapter

7

Entering permanent residential aged care is a significant life transition. From an

individual perspective, life at home provides a degree of privacy and autonomy that

may be difficult to maintain in a residential aged care facility where resources are

shared, and nursing and personal care staff provide supervision and assistance

potentially round-the-clock: these facilities are often also called ‘nursing homes’.

On the other hand, moving into residential aged care can open up more forms of

support for a frail older person, such as increased opportunities for social interaction

and access to in-house services.

The need for a higher level of care can be triggered by different factors (for example,

chronic and complex health issues or everyday self-care activities can become

increasingly difficult to manage due to a decline in a person’s cognitive or physical

abilities). People may experience falls or other acute health events; there are

concerns for their safety; or their carer becomes unavailable, prompting a change in

living conditions.

Exploring how people use health services in the months before and after entry into

permanent residential aged care provides some insight into the nature of health

service use during this transition. Health services cover a broad range of medical care

provided by doctors, dentists, nurses, pharmacists and other allied health professionals

in various settings. Analysis in this chapter focuses on general practitioner (GP) and

specialist attendances and prescriptions dispensed for selected medicines of interest

using linked administrative data (Box 7.1).

Box 7.1: What are the data sources in this chapter?

Data on Medicare Benefits Schedule (MBS) claims and prescriptions dispensed

under the Pharmaceutical Benefits Scheme (PBS)/Repatriation Pharmaceutical

Benefits Scheme (RPBS) were linked with aged care program data, and with deaths

from the National Deaths Index, to create an integrated data set covering 5 years

from 2012-13 to 2016-17. As MBS and PBS data are administrative, only those

health care services and prescriptions that were processed by the respective

schedule/scheme are captured (Box 7.2). While data from other aged care

programs were included in the linkage, only residential aged care is reported here.

183 Australia’s health 2020: data insights

Chapter

7

Potential interactions between health service and medicine use in residential aged care People living in permanent residential aged care are often frail older people with

complex care needs who require not only basic assistance with mobility or eating,

but also nursing care through health care procedures and medicine management.

Many everyday living supports, nursing care and allied health services are expected

to be provided routinely in residential aged care. In addition, people often require

other health services, such as those provided by GPs and specialists. Use of multiple

medicines is also common (AIHW 2019; Elliott & Woodward 2011; Morin et al. 2016;

Poudel et al. 2015; Roughead et al. 2008) and use of certain medicines has been shown

to increase after people move into residential aged care (Harrison et al. 2020a, 2020b).

Access to care and services is influenced by the workforce available within residential

aged care; the interaction aged care has with health services; and the availability of

health care professionals in the local area, as well as health care provision, prescribing

practices and medicine regimes within facilities (Harrison et al. 2019; Hillen et al. 2016;

Somers et al. 2010; Westbury et al. 2018b). The change in living setting itself may be

associated with changes in how people access health services.

Residential aged care facilities have difficulty attracting and retaining health care

professionals (Eagar et al. 2019; RCACQS 2019a). The staffing profile in residential

aged care has changed in the last 12 years: the number of personal care attendants

increased by 169% between 2003 and 2016, while the number of registered nurses

increased by only 23%. Over the same time period, the number of people living in

permanent residential aged care increased by 25% (GEN 2020b; Mavromaras et al.

2017). Personal care attendants can provide some basic nursing care but are not

qualified nurses. While some facilities may employ other types of workers, such as

allied health staff, access to allied health and dental care in residential aged care is

often limited (Hearn & Slack-Smith 2014; Mavromaras et al. 2017).

Regular consultations with a GP can help people transition into residential aged care.

GPs assess people’s medical and functional needs comprehensively and plan for their

current and future needs, as well as providing a point of liaison between specialists,

allied health services and residential aged-care staff (RACGP 2020). But, in practice,

GPs may have limited time available to visit a facility or may only be able to do so at

less optimal times, such as after-hours (Gadzhanova & Reed 2007; Hillen et al. 2016;

Pearson et al. 2018). People can also leave a facility to attend a GP at a practice, but

frailty and medical complexity may make this difficult.

184 Australia’s health 2020: data insights

Chapter

7

Regular and timely access to GPs can improve not only the interactions health care

professionals have with each person and their representatives, and their ability

to fully assess people’s care needs, but also the interactions between various

health care professionals (Hillen et al. 2016; RACGP 2020). Whether this is

coordinated or happens by chance, collaboration is important to planning for

people’s care needs (Harrison et al. 2019; RACGP 2020)—particularly as direct access

to certain health care professionals, such as specialists, is relatively rare for people

living in permanent residential aged care (AIHW 2019).

GPs also play a central role in prescribing medicines for older people in residential aged

care and access to medicines can be relatively straightforward in these settings. For

example, where facilities use National Residential Medication Chart (NRMC)-compliant

systems to record the ordering and administration of medicines, pharmacies are able to

dispense the medicines directly from information on a person’s medicine chart without

the need for a traditional paper prescription. This also allows the pharmacy to make

streamlined PBS claims (ACSQHC 2014).

There has been considerable interest in how medicines are used within residential aged

care. Most recently, in delivering its October 2019 interim report, the Royal Commission

into Aged Care Quality and Safety (RCACQS) highlighted issues around the aged-care

workforce and potentially problematic use of certain medicines. It recommended

immediate action to reduce the use of antipsychotic medicines as a chemical restraint

(that is, the use of medicines to influence people’s behaviour, other than medicines

prescribed for relevant health conditions) (RCACQS 2019a, 2019b).

Specific legal requirements were already in place for providers regarding physical

and chemical restraint, as part of the quality standards for residential aged care

(ACQSC 2019; Quality of Care Principles 2014). From 1 July 2019, the Quality of

Care Amendment (Reviewing Restraints Principles) 2019 further amended the

Quality of Care Principles 2014 to state that chemical restraint should only be used

as a last resort.

In general, medicines that act on the central nervous system have been of particular

interest due to their effects on older people, and many are prescribed at high rates in

residential aged care (AIHW 2019; Harrison et al. 2019; Morin et al. 2016; Westbury et

al. 2018b). Medicines that act on the central nervous system are a broad group within

the Anatomical Therapeutic Chemical (ATC) Classification System, and this group covers

many different types of medicines that have an effect on the brain or spinal cord. These

medicines can be taken for different reasons, such as to reduce fever, suppress nausea

or relieve pain, or to manage particular health conditions and their symptoms (this group

includes many common treatments for mental health and neurological conditions).

185 Australia’s health 2020: data insights

Chapter

7

Certain medicines within this group are problematic for older people as the risks

of harm increase with increasing age, frailty and medical complexity (AGS 2019;

Elliott & Woodward 2011; O’Mahony et al. 2015; Box 7.3). In particular, anti-dementia,

antidepressant, antipsychotic, benzodiazepine and opioid medicines are all associated

with dizziness or drowsiness and this brings an increased risk of falls (AGS 2019;

Cox et al. 2016; Epstein et al. 2014; Fraser et al. 2015; O’Mahony et al. 2015). Partly as

a consequence of this, many of these medicines are also associated with other adverse

health outcomes, such as fractures and hospitalisations, as well as being associated

with an increased risk of death—they also commonly interact with other medicines

and health conditions (AGS 2019; O’Mahony et al. 2015; Shash et al. 2016).

At any time, around half of people living in permanent residential aged care have

diagnosed dementia (GEN 2020c), and many others live with other similar degenerative

illnesses or may have undiagnosed dementia. (See ‘Dementia’ https://www.aihw.

gov.au/reports/australias-health/dementia for more information.) The behavioural

and psychological symptoms of dementia (BPSD) are varied, but can include sleep

disturbances, depression, disruptive behaviours and agitation or aggression

(see glossary). Some degree of BPSD is experienced by most people with dementia

(RANZCP 2016). BPSD may reflect stress, unmet need or pain (and the inability to

communicate these clearly) or it may relate to the biological neurodegenerative

processes of the dementia itself (Arvanitakis et al. 2019; RACGP 2020).

Instead of pharmacological treatments such as antipsychotics, the recommended

primary approaches for addressing BPSD are one-on-one care; individualised

behavioural management; and occupational therapy strategies (ACSQHC 2018;

Arvanitakis et al. 2019; GAC 2016; Marx et al. 2017; RACGP 2020; RANZCP 2016;

Westaway et al. 2018). Targeted interventions that address the prescribing culture

within facilities, particularly through education and interdisciplinary involvement,

have been shown to reduce reliance on medicines and to improve care

(Harrison et al. 2019; McDerby et al. 2018; Poudel et al. 2015; Westbury et al. 2018a).

Considered against the background of frailty and medical complexity, the health care

provided to people living in permanent residential aged care and the prescribing

practices within it become increasingly crucial. This chapter examines access to GPs

and specialists through the Medicare scheme, and PBS-reimbursed prescriptions

dispensed for selected medicines in the 6 months before and after people first enter

permanent residential aged care. In addition to looking at these broad patterns, the

chapter also looks at people who were ‘new users’ of these medicines, to examine

when anti-dementia, antidepressant, antipsychotic, benzodiazepine and opioid

medicines were initiated.

186 Australia’s health 2020: data insights

Chapter

7

Characteristics of people entering care The people included in this study first entered permanent residential aged care in

the 3 months from 1 July to 30 September in 2014, 2015 and 2016; were aged 50 and

over at the start of the financial year; and had an Aged Care Funding Instrument (ACFI)

assessment while in care. In all, in these 3-month periods in these 3 years, there were

around 45,000 people who moved into permanent residential aged care and had never

used it before. These people are described as ‘new entrant’ groups.

The number of people entering permanent residential aged care fluctuates throughout

the year, influenced not only by people’s need for care, but also by whether a place

is available, whether people can afford the care and whether they can access other

support in the community. People also commonly use respite residential aged care

before entering permanent residential aged care. Respite care can be short, regular

and planned episodes over a long period of time, but it can also cater for unplanned

admissions; for people waiting for a place in permanent residential aged care to

become available; or for those trying out residential aged care ahead of a permanent

move. Respite use can be an indication of people experiencing an acute deterioration

in their health and needing care immediately. In 2014, 31% of the new entrant

cohort had used respite care in the 7 days before their first admission to permanent

residential aged care, it was 39% in 2015 and 41% in 2016.

Most new entrants are women

Permanent residential aged care is the ‘highest’ level of aged care, in that it provides

people with up to 24-hour nursing care and assistance. People who use permanent

residential aged care tend to be older, and they are also more likely to be women.

In each of the 3 groups, there was little difference in the median age on admission,

which was 85 years (86 for women and 84 for men). Overall, 3 in 5 (60%) people

entering permanent residential aged care for the first time were women, and this

proportion increased with age (Figure 7.1). This is similar to the distribution in the

older population more generally, reflecting women’s longer life expectancy.

187 Australia’s health 2020: data insights

Chapter

7

Figure 7.1: Sex distribution of the ‘new entrant’ groups, by age, 2014 to 2016

(all years)

Men

Women

0

20

40

60

80

100

65-74 75-84 85-94 95 and over

Per cent

Age group

Source: Linked aged care, MBS and PBS data.

Around half have diagnosed dementia

One of the most common health conditions among people in permanent residential

aged care is dementia. While this is an umbrella term for a number of different

conditions, they all affect people’s ability to reason, remember and move—and to live

independently. (See Chapter 8 ‘Dementia in Australia—understanding the gaps and

opportunities’). Around half (46%) of people in each of the 3 new entrant groups had a

diagnosis of dementia captured on their first ACFI assessment after admission (noting

that the ACFI is a funding instrument and its primary focus is on assessing the cost of

care). Diagnosis of dementia varied by age, with the youngest and oldest age groups

least likely to have dementia recorded (Figure 7.2).

In addition to information on diagnosed health conditions, ACFI assessments provide

some indication of people’s functional status across 3 domains (activities of daily living;

behaviour and cognition; and complex health care). Regardless of their exact health

conditions, people entering permanent residential aged care have broadly similar

needs around core activities (such as movement, self-care and communication),

and difficulties with 1 or more of these everyday activities contribute to their move

into residential aged care.

188 Australia’s health 2020: data insights

Chapter

7

Figure 7.2: Proportion of people in the ‘new entrant’ groups who had

diagnosed dementia at first ACFI assessment after admission, by age,

2014 to 2016 (all years)

0

10

20

30

40

50

60

50-64 65-74 75-84 85-94 95 and over

Per cent

Age group

Note: Data for this figure are available in Supplementary Table S7.1.

Source: Linked aged care, MBS and PBS data.

The proportions of people assessed on their first ACFI assessment after admission as

having ‘high’ need for care in each of the 3 domains were 17% in 2014; 21% in 2015;

and 20% in 2016. Put simply, this means that their ability to perform common activities

of daily living was impaired; they had behavioural or cognitive needs that affected

others; and they required regular complex health care.

Almost one-third die in the year they enter care

Many people enter permanent residential aged care towards the end of their lives.

Almost one-third (29%) of people in each of the 3 groups died in the same financial

year in which they entered permanent residential aged care (meaning that they died

within 12 months of their admission). People who entered permanent residential

aged care at an older age were more likely to die by the end of the financial year

(particularly older men) (Table 7.1).

189 Australia’s health 2020: data insights

Chapter

7

Table 7.1: Proportion of people in the ‘new entrant’ groups who died in the financial year of admission, by sex and age group, 2014 to 2016

Sex/age group 2014 2015 2016

%

Women

50-64 19.9 21.8 20.0

65-74 22.0 20.2 24.4

75-84 23.5 22.5 20.0

85-94 23.4 24.1 25.2

95+ 33.1 34.2 36.0

Total women 23.9 24.0 24.2

Men

50-64 29.8 25.6 29.2

65-74 30.1 27.6 25.9

75-84 34.7 33.3 32.1

85-94 40.3 38.4 39.1

95+ 43.2 53.0 48.0

Total men 36.6 35.3 35.0

Note: ‘Financial year’ refers to the period from 1 July to 30 June.

Source: Linked aged care, MBS and PBS data.

As the focus of this chapter is on the immediate period around admission, it does not

take into account all deaths for the 3 groups (that is, those occurring beyond the end

of the financial year). The patterns of health-service use shown in the next section

also capture only a part of some people’s time in permanent residential aged care.

People can continue to live a considerable number of years in aged care—for example,

25% of those who entered in the 3-month sample period in 2014 were still living in the

same residential aged care facility at 30 June 2017. The average length for an episode

of permanent residential aged care is around 2.5 years (noting that some people go

on to have more than 1 episode of care, for example due to moving between facilities)

(GEN 2020a).

190 Australia’s health 2020: data insights

Chapter

7

Use of GP and specialist services Health service use by people living in residential aged care may be influenced by a

number of factors. For example, some facilities have in-house health services, but the

services available to people can vary considerably between facilities (and these are not

captured in the available data). Issues that affect the aged care industry generally—

such as workforce availability and interactions with health services and prescribing

practices—can also affect individual facilities differently. Outside of the facility, it can

be difficult for people to attend appointments or to find appropriate services—and

where they do, this can be affected by the same constraints as health service use in the

broader population, with rural and remote areas generally having lower rates of use

(AIHW 2018b).

People are more likely to see a GP after admission and less likely to see a specialist

Most people see a GP both before and after entry to a residential aged care facility, but

the proportion of those seeing a GP and the rate of GP attendances were both higher

in the 6 months after entering permanent residential aged care, compared with the 6

months before entry. For example, where people had seen a GP at least once prior to

entry, they saw one around 3 times as often after entry (Table 7.2). The proportions of

people seeing a GP before and after entry to permanent residential care also increased

over the 3 years, as did the rate of GP attendances after entry to care.

On the other hand, the proportion of people who saw a specialist decreased after

entry, and, for those who had them, the number of specialist attendances was lower

(decreasing by around half after entry to permanent residential aged care) (Table 7.2).

This may not fully reflect the services people are able to access—some specialist care

can, for example, be provided through outreach services not captured in MBS data—

but nonetheless, this suggests that for many people, the patterns of health service use

do change in the 6-month period before and after entry into permanent residential

aged care.

191 Australia’s health 2020: data insights

Chapter

7

Table 7.2: Proportion of people in the ‘new entrant’ groups with GP/specialist attendances in the 6 months before/after entry into permanent residential aged care, 2014 to 2016

Attendance type

Admission year

2014 2015 2016 Total

Before After Before After Before After Before After

GP attendances

% with at least 1 80.4 90.0 81.7 91.2 82.7 92.3 81.7 91.3

Average number (per person) 5.5 13.9 5.5 14.5 5.5 15.1 5.5 14.6

Specialist attendances

% with at least 1 38.8 31.6 39.4 34.0 40.2 35.1 39.5 33.7

Average number (per person) 8.5 3.8 7.9 3.8 8.3 4.2 8.2 4.0

Note: Data for this table are available online in Supplementary tables S7.2 and S7.3.

Source: Linked aged care, MBS and PBS data.

The proportion of people with a GP or specialist attendance decreased with age.

Compared with younger age groups, a smaller proportion of people aged 85 and over

had seen a GP or specialist in the 6 months either before or after their entry

into permanent residential aged care. The proportions were lowest for men aged

95 and over: in the 6 months after entering care, 74% of men in this age group had

a GP attendance and just 20% had a specialist attendance. Similarly, the average

number of attendances decreased with age (tables 7.2 and 7.3). The reasons for

this are not clear but may be related to the fact that data are restricted to MBS claims

(Box 7.2).

192 Australia’s health 2020: data insights

Chapter

7

Box 7.2: MBS data scope

The MBS data analysed in this chapter relate only to those GP and specialist services that were subsidised by the scheme and does not present a full picture of health service use. For example, the data do not include admitted patient care or out-patient care in the public system, or services provided through programs such as the Dementia Behaviour Management Advisory Service or the Severe Behaviour Response Teams that operate in residential aged care. People may receive equivalent services outside of MBS—either because no MBS claim was made, or because the service was delivered through another funding arrangement or paid for privately.

Many people in permanent residential aged care are eligible for Department of Veterans’ Affairs (DVA) funding and may access GP and specialist services this way. At 30 June 2017, there were over 26,000 DVA clients in residential aged care, accounting for 14% of people in residential aged care (AIHW 2018a).

While there are limitations to what MBS data cover, the data are consistent across the time periods analysed here: any services that would be excluded because they were outside of MBS data scope are thus excluded for both the 6 months before

and 6 months after entry into permanent residential aged care.

The most common types of specialists seen in the 6 months before entry were consultant physicians in general medicine, geriatric medicine and rehabilitation medicine (accounting for 18%, 18% and 12% of specialist attendances, respectively). In the 6 months after entry, the most common types of specialists were consultant physicians in geriatric medicine and general medicine (15% and 13%, respectively), followed by ophthalmologists (9.0%)— while attendances with consultant physicians in rehabilitation medicine accounted for only 3.8% of all specialists attendances in the 6 months after entry.

These attendance patterns before and after entry may be influenced by different factors, such as what other health services people access, or the aged care services they use, and how well these meet their needs for health care. The patterns may also relate to the changes in people’s health and functional status that precipitated their entry into residential aged care. Further, this time-limited view of GP and specialist attendances does not fully reflect the overall patterns of use for those living in the community or in residential aged care. For example, in the lead-up to entering permanent residential aged care, specialists can be consulted more frequently to finalise paperwork for diagnoses and comprehensive assessments, as well as for initiating certain new medicines, whereas additional GP attendances may be required soon after entry to assess care needs and to review existing or prescribe new medicines.

193 Australia’s health 2020: data insights

Chapter

7

Around one-third have a medicine review after admission

Medicine reviews that involve both GPs and pharmacists are captured in MBS data.

These collaborative reviews are intended to ensure that people’s medicine regimes

are appropriate and to minimise possible risks of harm. Few people had an MBS claim

for a medicine review in the 6 months before their entry, but the proportion increased

considerably after entry to permanent residential aged care (from 1.8% to 32%).

This also varied by age group: around 1 in 3 (34%) people in the younger age groups

had their medicines reviewed after admission, while only 1 in 4 (26%) in the oldest age

group did (Figure 7.3).

Figure 7.3: Proportion of people in the ‘new entrant’ groups with a medicine

review in the 6 months before and after admission, 2014 to 2016 (all years)

Source: Linked aged care, MBS and PBS data.

People may also have their medicines reviewed as part of an in-patient stay in

hospital—either by the GP as part of a general consultation, or through a community

pharmacy alone—and this would not be visible in the MBS data.

0

5

10

15

20

25

30

35

40

Before After Before After Before After Before After Before After

50-64 65-74 75-84 85-94 95 and over

Per cent

Age group

194 Australia’s health 2020: data insights

Chapter

7

Use of selected prescription medicines Older people often have multiple health conditions and use a number of different

prescription medicines. For community-dwelling older people, the most common

type of prescription medicines used are cardiovascular medicines, while for people

living in permanent residential aged care, medicines that act on the central nervous

system are the most common (AIHW 2019). The medicines analysed here all belong to

this broad group. The 5 medicine types of interest are anti-dementia, antidepressant,

antipsychotic, benzodiazepine and opioid medicines (Box 7.3).

Box 7.3: Selected prescription medicines

The analysis in this chapter is focused only on prescriptions dispensed for selected

medicine types of interest, and does not include all prescriptions dispensed

through PBS/RPBS, or all medicines used, as some people may obtain medicines

outside of the PBS/RPBS, either privately, in hospital or bought over the counter

(meaning they do not attract a government subsidy).

The selected medicines of interest here were identified by ATC codes. They are

prescribed to older people relatively commonly, particularly to those living in

residential aged care (AIHW 2019), and they are used for various reasons:

• Anti-dementia medicines may relieve symptoms of dementia and are mostly

prescribed for people with mild-to-moderate Alzheimer’s disease (noting that

it can be difficult to identify the type of dementia accurately).

• Antidepressant medicines are commonly used to treat symptoms of depression

and anxiety, but some can also be prescribed for other mental health conditions

such as bipolar disorder or bulimia, as well as for diabetic neuropathy and

neuropathic pain.

• Antipsychotic medicines can be used to manage the symptoms of certain

mental health conditions, such as schizophrenia (where delusions,

hallucinations and paranoia are common symptoms), and some are used to

manage the behavioural symptoms of dementia.

• Benzodiazepine medicines can be used to manage the symptoms of certain

mental health conditions, such as anxiety disorders, and to treat insomnia,

seizures or muscle spasms.

• Opioid medicines can help to relieve pain and relax muscles; some may be

used in palliative care.

195 Australia’s health 2020: data insights

Chapter

7

While these medicines can be beneficial, they also present a risk of harm through

potential side effects such as sedation, confusion and dizziness (AGS 2019;

Lapane et al. 2015; O’Mahony et al. 2015). One of the most noteworthy issues with

antidepressant, antipsychotic, benzodiazepine and opioid medicines is their association

with an increased risk of falls and fractures in older people (AGS 2019; Cox et al.

2016; Epstein et al. 2014; Fraser et al. 2015; O’Mahony et al. 2015). The likelihood of

harm increases as a person ages, and many of these medicines should only be used

for particular indications and for restricted periods of time, and prescribed in lower

doses for older people.

They can also exacerbate issues that older people already commonly experience.

The potential side effects of these medicines can arise from the medicine itself, or from

how it interacts either with other medicines the person is prescribed, or with other

health conditions the person has, such as frailty, dementia or heart disease (AGS 2019;

O’Mahony et al. 2015). In addition, the way medicines contribute to these conditions

can be under-recognised or inappropriately attributed to a health condition, geriatric

syndromes or the ageing process. For example:

• antipsychotic medicines prescribed for a person with pre-existing swallowing

difficulties (for example due to dementia) may increase these swallowing difficulties,

and thus the likelihood of the person developing pneumonia and/or malnutrition.

Some antipsychotic medicines are also associated with adverse cardiovascular

effects such as arrhythmias and hypotension (AGS 2019; O’Mahony et al. 2015) and a

higher risk of mortality (Harrison et al. 2020a)

• benzodiazepine medicines are associated with memory problems and cognitive

impairment, and may worsen gait or other physical abilities, as well as exacerbate

existing dementia symptoms (AGS 2019; O’Mahony et al. 2015; Shash et al. 2016)

• opioid medicines can affect balance and have a number of other potential effects,

such as reduced respiration rates and increased constipation and cognitive

impairment (AGS 2019; Chokhavatia et al. 2016; O’Mahony et al. 2015).

A higher proportion of people have prescriptions dispensed after entry

Generally, for most of these selected medicines, people were more likely to have

prescriptions dispensed in the period following entry into permanent residential aged

care than before entry. The proportion of people who had a prescription dispensed for

anti-dementia medicines was similar in the 6 months before and after entry, but for

antidepressant, antipsychotic and opioid medicines, the proportion of people in the

196 Australia’s health 2020: data insights

Chapter

7

‘new entrant’ groups who were dispensed at least 1 prescription increased in the

6 months after entry (Table 7.3). For example, the proportion of people who had

a prescription dispensed for an antipsychotic medicine increased, from around

1 in 6 (15%-16%) people in the 6 months before entry, to 1 in 4 (24%-25%) people in

the 6 months after entry.

Table 7.3: Proportion of people in the ‘new entrant’ groups with prescription for selected medicine types in the 6 months before and after entry into permanent residential aged care, 2014 to 2016(a)

Medicine type

Admission year

2014 2015 2016 Total

Before After Before After Before After Before After

%

Anti-dementia 10.7 9.4 10.9 9.8 11.1 10.1 10.9 9.8

Antidepressant 36.0 42.0 36.1 42.2 37.5 43.1 36.6 42.5

Antipsychotic 15.3 24.8 15.9 24.6 16.4 24.0 15.9 24.5

Benzodiazepine 24.1 32.4 23.2 29.6 23.5 30.3 23.6 30.6

Opioid 32.9 45.8 33.2 46.1 34.1 46.4 33.4 46.1

(a) Proportion of people with at least 1 prescription dispensed in the 6 months before/after their admission date into permanent residential aged care.

Note: Data for this table are available in online Supplementary Table S7.4.

Source: Linked aged care, MBS and PBS data.

Some of these medicines are used for particular indications, and this may affect their patterns of use. For example, opioid medicines are commonly given as pain relief and to ease breathing towards the end of life (Lapane et al. 2015; Morin et al. 2016). The increasing pattern of use around entry into permanent residential aged care was less marked for this medicine type when subsequent deaths were taken into account— noting that the use of opioid medicines (regardless of why they are used) may also increase mortality (AGS 2019; O’Mahony et al. 2015). Among people who died in the same financial year as their admission into aged care, 2 in 5 (41%) were dispensed an opioid medicine in the 6 months before entry, and 2 in 3 (65%) were dispensed one in the 6 months after entry. For people who did not die in that time period, the respective proportions were 30% and 38%.

On the other hand, people who had a dementia diagnosis recorded on their first ACFI assessment were less likely to be dispensed opioid medicines, but the proportions nonetheless also increased following entry into permanent residential aged care: in the 6 months before entry, 25% were dispensed at least 1 opioid medicine, and 40%

197 Australia’s health 2020: data insights

Chapter

7

in the 6 months after entry (compared with 41% and 52%, respectively, among those who did not have a dementia diagnosis). People with dementia were also more likely to be dispensed antipsychotic medicines after entry: 24% of people with dementia were dispensed at least 1 prescription for this medicine type in the months before, and 36% in the months after entry (compared with 8.8% and 14%, respectively, among those who did not have a dementia diagnosis).

Prescriptions are commonly written by GPs

For each medicine of interest, the majority of prescriptions dispensed were written

by GPs, and these proportions increased after entry, reflecting the reduced access to

specialists following admission into permanent residential aged care. For prescriptions

for anti-dementia medicines dispensed in the 6 months before entry, 75% were written

by GPs (increasing to 89% after entry). Specialists accounted for the remainder of the

prescriptions. For the other 4 medicines, the pattern was less pronounced—92% of

prescriptions for antidepressant medicines, 87% of antipsychotic medicines, 93% of

benzodiazepine medicines and 92% of opioid medicines were written by GPs

(increasing to 96%, 96%, 98% and 97%, respectively, in the 6 months after entry).

Antipsychotic medicines may be dispensed at a higher volume after entry

Each prescription of medicine can be for a different quantity and amount of medicine

(meaning that the number of pills and the volume of active ingredient can vary

considerably within the same medicine type). Prescriptions can also be written as PRN

(pro re nata—that is, to be taken as required). In residential aged care, these may be

ordered and dispensed ahead of when they are required in order to have the medicine

available in case it is later required, rather than because it is currently being used.

Regularly prescribed medicines may also have additional directions to take more

as required, or to allow flexibility for existing use to be tapered up or down

(Stasinopoulos et al. 2018; Westbury 2018a, 2018b).

The directions for how the medicine is to be taken are not recorded in PBS data,

nor do the data capture compliance—how well those directions were followed.

With these limitations in mind, another way of estimating consumption is the defined

daily dose (DDD) (WHO 2003, 2019). PBS data record the amount and quantity of

medicine dispensed and, using this in combination with prescribing guidelines, it is

possible to estimate consumption. These calculations give an estimate of the number

of days for which the person may have used a medicine if it was used as indicated in

prescribing guidelines (Box 7.4).

198 Australia’s health 2020: data insights

Chapter

7

Box 7.4: Defined daily dose

The DDD is a World Health Organization (WHO) measure for estimating the

consumption of a medicine. The WHO determines the assumed average

maintenance dose per day for its main indication in adults, and this can be used to

estimate the volume of medicine use and the number of days on which a person

may have taken the medicine.

This assumed dose is often different to the dose prescribed or recommended to

the person: the DDD is an international measure based on a whole-of-population

approach and does not take into account local differences in prescribing practices

or best-practice prescribing for different sub-populations. For these analyses, the

Australian prescribing guidelines for the usual dose were used to determine the

assumed dose. To facilitate international comparisons, data for the WHO DDD are

also shown in Supplementary Table S7.5.

Risperidone has been identified as the most commonly dispensed distinct

antipsychotic medicine after entry into residential aged care (Harrison et al. 2020a,

2020b; Inacio et al. 2019), and this was the case for the ‘new entrant’ groups in this

study as well; risperidone accounted for 43% of the antipsychotic prescriptions

dispensed to new entrants in the 6 months before entry, and 47% of those dispensed

in the 6 months after entry. Its common indication is for management of schizophrenia

(and this is the indication for the WHO DDD calculation), but for older people with

dementia, it may also be prescribed for managing BPSD.

Using Australian prescribing guidelines from the Australian Medicines Handbook

(AMH)—which recommend a usual dose of 1mg per day for this purpose, although

doses up to 2mg can be used—the estimated median number of days of use differed

in the months before and after entry. For people with dementia who were dispensed

oral risperidone in the 6 months before entry into permanent residential aged care,

the estimated median days the medicine was used for was 2 months (60 days), and

half of these people may have used the medicine for between 1 and 4 months in the

6-month period (the interquartile range was 30-120 days). The estimated median days

of use increased by more than a month in the 6 months after entry: for people with

dementia who were dispensed oral risperidone, the medicine was potentially used for

over 3 months (100 days), and half of these people may have used the medicine for

between 2 and 6 months (the interquartile range was 60-180 days).

199 Australia’s health 2020: data insights

Chapter

7

The AMH guidelines for prescribing risperidone for BPSD state that the maximum

period of use at a time should be 3 months (AMH 2019). However, where the facility

uses NRMC-compliant medicine charts, these charts are valid for up to 4 months at

a time and guidelines allow nurses to order medicines over the duration of the chart

(ACSQHC 2014), meaning that the volumes estimated here may be influenced by

medicine management practices within facilities.

New users and their use of selected medicines Looking at when people are first dispensed particular medicines provides additional

insight into how their patterns of use change around entry into residential aged

care. This section on ‘new users’ only includes people who were newly dispensed a

prescription for anti-dementia, antidepressant, antipsychotic, benzodiazepine or opioid

medicines, and again focuses on prescriptions dispensed in the 6 months before or

after a person’s entry into permanent residential aged care (Box 7.5).

Box 7.5: Who are ‘new users’ of the selected medicines?

‘New use’ was defined here in stages. First, broader prescribing patterns for the

selected medicines were examined to identify whether people had prescriptions

dispensed in the 12 months before and after their entry into permanent residential

aged care. This longer timeframe was used to capture a broader period of use

and to allow for seasonal patterns in dispensing (in particular, there is commonly

a peak towards the end of a calendar year as people have reached the PBS

safety net and stockpile medicines at lower cost for the coming calendar year). In

addition, to be counted as ‘new users’, people must not have had any prescriptions

dispensed for the selected medicine in the 12-24 months before entry.

The analysis then focused only on those people who had had no prescriptions of

interest dispensed in this longer retrospective period, but who went on to have

these prescriptions dispensed to them in the 6 months before or after their entry.

Medicine use considered to identify 'new use'

New use of interest

24 months before

12 months before

6 months before

6 months after

12 months after

Admission date

No use of selected medicines

200 Australia’s health 2020: data insights

Chapter

7

By this definition, the majority (38,400, or 86%) of the 45,000 people in the 3 ‘new entrant’

groups were new users of at least 1 of these medicines (anti-dementia, antidepressant,

antipsychotic, benzodiazepine or opioid medicines). Relatively few people were newly

prescribed anti-dementia medicines in the 6 months before or after their entry into

permanent residential aged care (1,500 people, or 3.3% of the 3 ‘new entrant’ groups).

On the other hand, 6,900 people (15%) were new users of antidepressants, and just

under 9,000 (20%) were new users of antipsychotic or benzodiazepine medicines.

Opioid medicines were newly prescribed to over 12,000 people (28%).

For most medicines, new use is more likely to be initiated after entry

For anti-dementia medicines only, new users were more likely to have the first

prescription dispensed in the 6 months before entry. For antidepressant, antipsychotic,

benzodiazepine and opioid medicines, new users were more likely to have their first

dispensing at or after entry (Figure 7.4).

Figure 7.4: Proportion of people in the 3 ‘new entrant’ groups who were new

users of selected medicine types, 2014 to 2016 (all years)(a)

(a) Proportion of people with at least 1 prescription dispensed in the 6 months before/after their admission into permanent residential aged care for whom that medicine type had not previously been dispensed.

Source: Linked aged care, MBS and PBS data.

0

2

4

6

8

10

12

14

16

Anti-dementia Antidepressant Antipsychotic Benzodiazepine Opioid

Per cent

Medicine type

Before After

201 Australia’s health 2020: data insights

Chapter

7

Identifying the halfway point in the distribution of days between the first prescription

being dispensed for a new user and their entry date into permanent residential aged

care showed a similar pattern. Anti-dementia medicines were commonly initiated

before entry, with the new users’ first prescription dispensed a median of 43 days

before the entry date. For the other medicine types, the timing of first dispensing

frequently coincided with entry into permanent residential aged care. For new users,

antidepressant medicines were dispensed a median of 3 days after the entry date,

while antipsychotic and benzodiazepine medicines were dispensed a median of

1 day after the entry date and opioid medicines a median of 0 days. In particular,

the short median time between the entry date and when a prescription for an

antipsychotic medicine was dispensed may indicate that there was little time to

trial a non-pharmacological approach.

Most new users have their first prescription written by a GP

A higher proportion of new prescriptions dispensed for all 5 medicines in the 6 months

after entry were written by a GP, compared with the 6 months before (Figure 7.5).

Figure 7.5: New users of selected medicines who had their first prescription

written by GP, by whether first prescription was dispensed before or after

admission, 2014 to 2016 (all years)(a)

(a) Proportion of people with at least 1 prescription dispensed in the 6 months before/after their admission into permanent residential aged care for whom that medicine type had not previously been dispensed.

Source: Linked aged care, MBS and PBS data.

Before After

0

20

40

60

80

100

Anti-dementia Antidepressant Antipsychotic Benzodiazepine Opioid

Per cent

Medicine type

202 Australia’s health 2020: data insights

Chapter

7

Multiple medicines may indicate potentially inappropriate use

Nine in 10 (90%) people who were newly dispensed antipsychotic medicines in the

6 months before or after entry also had at least 1 other prescription dispensed for

an antidepressant, benzodiazepine or opioid medicines in the same year. Combined

use of antidepressant, antipsychotic, benzodiazepine or opioid medicines could be

appropriate for some people, but can also indicate potentially inappropriate use of

medicines, as these medicines affect people in similar ways and they can further

compound existing health issues (AGS 2019; Box 7.3).

The data do not include exact timing of use or whether people adhered to directions in

using the medicines, but this group of people were potentially vulnerable to additional

harm from medicines. They were more likely to have dementia (69%) or to have been

rated ‘high’ across the 3 ACFI domains (27%) than people in the ‘new entrant’ groups

overall—and, combined, 1 in 5 (19%) had dementia and were rated ‘high’ on all 3 domains.

Conclusion Selected aspects of people’s health service and medicine use changed in the 6 months

after entry into permanent residential aged care from the 6 months that preceded

their entry. While people can experience specific acute events that trigger entry to

residential aged care, admission into permanent residential aged care can often

be accompanied by a long-term decline in people’s health and functional ability.

Sometimes this declining health can lead to admission into care, and sometimes

people’s overall care needs change incrementally. However, moving into permanent

residential aged care marks a change in people’s health service and medicine use that

may also be directly related to this change in their living conditions.

Almost everyone in the ‘new entrant’ groups had a GP attendance after entry to

permanent residential aged care, and the rate of GP use was considerably higher than

before entry. On the other hand, fewer people had a specialist attendance after entry;

the rates of specialist attendances declined; and the nature of specialist attendances

also changed, with fewer attendances by specialists in rehabilitation medicine after

entry than before. Dispensing patterns for selected medicines showed a similar

change, in that where people were dispensed these medicines in the 6 months after

entry into permanent residential aged care, a higher proportion of the prescriptions

were written by a GP than by a specialist, compared with the 6 months before entry.

In addition, the use of anti-dementia, antidepressant, antipsychotic, benzodiazepine

and opioid medicines changed between the 2 time periods, with a higher proportion

of people dispensed at least 1 prescription for most of these medicines after entering

care (with the exception of anti-dementia medicines, which remained relatively steady).

203 Australia’s health 2020: data insights

Chapter

7

The proportions varied depending on the medicine type and particular characteristics

of the person: for example, people with dementia were more likely to be dispensed

antipsychotic medicines (both before and after entry), and to be newly dispensed these

medicines, than were people without dementia.

The most common specific type of antipsychotic medicine (risperidone) is approved

for short-term management of BPSD, but the results here indicate that the volumes

dispensed can cover a longer period of risperidone use than recommended by

Australian prescribing guidelines. In addition, other antipsychotic medicines—as well as

antidepressant, benzodiazepine and opioid medicines—may all be used in residential

aged care to manage sleep disturbances, agitation and other behaviours of concern,

regardless of whether these constitute the most appropriate approach.

The primary evidence-based approaches for addressing BPSD are all psychosocial and

non-pharmacological, and often multidisciplinary, meaning that it can take time to

identify and implement the most appropriate care. Despite this, access to specialists

decreased following entry into permanent residential aged care, at the same time as

people were more likely to be prescribed medicines, including antipsychotic medicines.

The median time between admission and dispensing date would suggest that, for

some people, there was little time to trial a non-pharmacological approach before an

antipsychotic medicine was dispensed. As has been discussed, this does not mean the

medicine was used, but it may indicate that it is not always used as a last resort.

The analyses presented here did not consider changes in people’s use of health service

and medicine use beyond the 6 months before and after their entry into permanent

residential aged care. Furthermore, the analysis was somewhat limited by using

MBS-reimbursement data to describe GP and specialist use. It was also not possible

to consider the appropriateness of the use of these medicines, or the benefits and

harms that may be experienced. As people settle into their new living arrangements,

or their health and functional ability undergoes further changes, these patterns may

continue to evolve. Some people live in residential aged care facilities for years and, as

a vulnerable population, have been particularly impacted by COVID-19 (see Chapter 2:

‘Four months in: what we know about the new coronavirus disease in Australia’ for

more information).

The AIHW is undertaking ongoing work using linked data to examine different aspects

of the interfaces between aged care and health systems in Australia. (More information

is available through the AIHW website https://www.aihw.gov.au/reports/aged-care/

interfaces-between-the-aged-care-and-health-system/contents/summary).

While administrative data sources cannot fully account for the person’s experience,

this work shows the value of using existing data collections and these findings should

be considered in any redesign of the aged care system.

204 Australia’s health 2020: data insights

Chapter

7

References ACQSC (Aged Care Quality and Safety Commission) 2019. Regulation of physical and chemical restraint. Regulatory bulletin issue no. 2019-8.0. Sydney: ACQSC. Viewed 28 October 2019, https://www.agedcarequality.gov.au/sites/default/files/media/Regulatory_Bulletin_Issue_8.0_ v1.pdf.

ACSQHC (Australian Commission on Safety and Quality in Health Care) 2014. National Residential Medication Chart: user guide for nursing and care staff. Sydney: ACSQHC. Viewed 28 October 2019, https://www.safetyandquality.gov.au/sites/default/files/migrated/SAQ123_ NursesUserGuide_V6.pdf.

ACSQHC 2018. The third atlas of healthcare variation 2018. 5.5 Antipsychotic medicines dispensing, 65-years and over. Sydney: ACSQHC. Viewed 7 January 2020, https://www.

safetyandquality.gov.au/sites/default/files/migrated/5.5-Text-Antipsychotic-medicines-dispensing-65-years-and-over.pdf.

AGS (American Geriatrics Society) 2019. Updated AGS Beers Criteria for potentially inappropriate medication use in older adults. Journal of the American Geriatrics Society 67(4):674-94. https://doi.org/10.1111/jgs.15767.

AIHW (Australian Institute of Health and Welfare) 2018a. Older Australia at a glance: veterans. Cat. No. AGE 87. Canberra: AIHW. Viewed 14 January 2020, https://www.aihw.gov.au/reports/ older-people/older-australia-at-a-glance/contents/diversity/veterans.

AIHW 2018b. Survey of Health Care: selected findings for rural and remote Australians. Cat. no. PHE 220. Canberra: AIHW.

AIHW 2019. Interfaces between the aged care and health systems in Australia—first results. Cat. no. AGE 99. Canberra: AIHW.

AMH (Australian Medicines Handbook) 2019. Adelaide: AMH. Viewed 17 October 2019, https://amhonline.amh.net.au.

Arvanitakis Z, Shah RC & Bennet DA 2019. Diagnosis and management of dementia: review. JAMA 322(16):1589-99. https://doi.org/10.1001/jama.2019.4782.

Chokhavatia S, John ES, Bridgeman MB & Dixit D 2016. Constipation in elderly patients with noncancer pain: focus on opioid-induced constipation. Drugs & Aging 33(8):557-74. https://doi.org/10.1007/s40266-016-0381-2.

Cox CA, van Jaarsveld HJ, Houterman S, van der Stegen JCGH, Wasylewicz ATM, Grouls RJE et al. 2016. Psychotropic drug prescription and the risk of falls in nursing home residents. Journal of the American Medical Directors Association 17(12):1089-93.

Eagar K, Westera A, Snoek M, Kobel C, Loggie C & Gordon R 2019. How Australian residential aged care staffing levels compare with international and national benchmarks. Research paper 1. Wollongong: Centre for Health Service Development, Australian Health Services Research Institute, University of Wollongong.

Elliott R & Woodward M 2011. Medication-related problems in patients referred to aged care and memory clinics at a tertiary care hospital. Australasian Journal on Ageing 30(3):124-9.

Epstein N, Guo R, Farlow M, Singh J & Fisher M 2014. Medication for Alzheimer’s Disease and associated fall hazard: a retrospective cohort study from the Alzheimer’s Disease Neuroimaging Initiative. Drugs & Aging 31(2):125-9. http://doi.org/10.1007/s40266-013-0143-3.

205 Australia’s health 2020: data insights

Chapter

7

Fraser L-A, Liu K, Naylor KL, Hwang YJ, Dixon SN, Shariff SZ et al. 2015. Falls and fractures with atypical antipsychotic medication use: a population-based cohort study. JAMA Internal Medicine 175(3):450-2. http://dx.doi.org/10.1001/jamainternmed.2014.6930.

GAC (Guideline Adaptation Committee) 2016. Clinical practice guidelines and principles of care for people with dementia. Sydney: National Health and Medical Research Council. Viewed 7 January 2020, https://cdpc.sydney.edu.au/wp-content/uploads/2019/06/CDPC-Dementia-Guidelines_WEB.pdf.

Gadzhanova S & Reed R 2007. Medical services provided by general practitioners in residential aged-care facilities in Australia. Medical Journal of Australia 187(2):92-4. https://doi.org/10.5694/j.1326-5377.2007.tb01148.x.

GEN 2020a. People leaving aged care. Canberra: AIHW. Viewed 26 November 2019, https://www.gen-agedcaredata.gov.au/Topics/People-leaving-aged-care.

GEN 2020b. People using aged care. Canberra: AIHW. Viewed 26 November 2019, https://www.gen-agedcaredata.gov.au/Topics/People-using-aged-care.

GEN 2020c. People’s care needs in residential aged care. Canberra: AIHW. Viewed 26 November 2019, https://www.gen-agedcaredata.gov.au/Topics/Care-needs-in-aged-care.

Harrison SL, Cations M, Jessop T, Hilmer S, Sawan M & Brodaty H 2019. Approaches to deprescribing psychotropic medications for changed behaviours in long-term care residents living with dementia. Drugs & Aging 36(2):125-36. https://doi.org/10.1007/s40266-018-0623-6.

Harrison, SL, Sluggett JK, Lang C, Whitehead C, Crotty M, Corlis M et al. 2020a. Initiation of antipsychotics after moving to residential aged care facilities and mortality: a national cohort study. Aging Clinical and Experimental Research. https://doi.org/10.1007/s40520-020-01518-y.

Harrison S, Sluggett J, Lang C, Whitehead C, Crotty M, Corlis M et al. 2020b. The dispensing of psychotropic medicines to older people before and after they enter residential aged care. Medical Journal of Australia. https://doi.org/10.5694/mja2.50501.

Hearn L & Slack-Smith L 2014. Oral health care in residential aged care services: barriers to engaging health-care providers. Australian Journal of Primary Health 21(2):148-56. https://doi.org/10.1071/PY14029.

Hillen JB, Vitry A & Caughey GEW 2016. Trends in general practitioner services to residents in aged care. Australian Journal of Primary Health 22(6):517-22. https://doi.org/10.1071/PY15119.

Inacio MC, Harrison SL, Lang C, Sluggett JK & Wesselingh S 2019. Antipsychotic medicines dispensed before and after entering residential aged care: preliminary report and findings from the National Historical Cohort of the Registry of Older South Australians. Report prepared by The Registry of Older South Australians (ROSA) Research Team at the South Australian Health and Medical Research Institute (SAHMRI). Adelaide: SAHMRI. Viewed 28 October, https://agedcare.

royalcommission.gov.au/hearings/Documents/exhibits-2019/8-july/exhibit-6-1-darwin-general-tender-bundle/RCD.9999.0103.0001.pdf.

Lapane KL, Hume A, Ulbricth C & Gambassi G 2016. Safety of psychotropic drugs in the elderly. In: Spina E & Trifirò G (eds). Pharmacovigilance in psychiatry. Cham (Switzerland): Adis, 285-97.

Marx KA, Duffort N, Scerpella DL, Samus QM & Gitlin LN 2017. Evidence-based non-pharmacologic interventions for managing neuropsychiatric symptoms and mental health issues in residents in assisted living. Seniors Housing & Care Journal 25(1):71-83.

206 Australia’s health 2020: data insights

Chapter

7

Mavromaras K, Knight G, Isherwood L, Crettenden A, Flavel J, Karmel T et al. 2017. The aged care workforce, 2016. National Aged Care Workforce Census and Survey undertaken by National Institute of Labour Studies, Flinders University. Canberra: Department of Health.

McDerby N, Kosari S, Bail K, Shield A, Peterson G & Naunton M 2018. The effect of a residential care pharmacist on medication administration practices in aged care: a controlled trial. Journal of Clinical Pharmacy and Therapeutics 44(4):595-602. https://doi.org/10.1111/jcpt.12822.

Morin L, Laroche M-L, Texier G & Johnell K 2016. Prevalence of potentially inappropriate medication use in older adults living in nursing homes: a systematic review. Journal of the American Medical Directors Association 17(9):826.e-862.e9. https://doi.org/10.1016/j.

jamda.2016.06.011.

O’Mahony D, O’Sullivan D, Byrne S, O’Connor MN, Ryan C & Gallagher P 2015. STOPP/START criteria for potentially inappropriate prescribing in older people: version 2. Age and Ageing 44(2):213-8. https://doi.org/10.1093/ageing/afu145.

Pearson R, Mullan J, Ujvary E, Bonney A & Dijkmans-Hadley B 2018. Australian general practitioner attitudes to residential aged care facility visiting. Health & Social Care in the Community 26(4):e497-e504. https://doi.org/10.1111/hsc.12561.

Poudel A, Peel NM, Mitchell CA, Gray LC, Nissen LM & Hubbard RE 2015. Geriatrician interventions on medication prescribing for frail older people in residential aged care facilities. Clinical Interventions in Aging 10:1043-51.

Quality of Care Principles 2014. Section 96-1 of the Aged Care Act 1997. Canberra: Commonwealth of Australia. Viewed January 7 2020, https://www.legislation.gov.au/Details/ F2020C00096.

Quality of Care Amendment (Reviewing Restraints Principles) Principles 2019. Canberra: Commonwealth of Australia. Viewed January 7 2020, https://www.legislation.gov.au/Details/ F2019L01505.

Roughead EE, Gilbert AL & Woodward MC 2008. Medication use by Australian war veterans in residential aged-care facilities. Journal of Pharmacy Practice and Research 38(1):14-8.

RACGP (Royal Australian College of General Practitioners) 2020. RACGP aged care clinical guide (Silver Book, 5th edn). Melbourne: RACGP. Viewed April 23 2020, https://www.racgp.org.au/ clinical-resources/clinical-guidelines/key-racgp-guidelines/view-all-racgp-guidelines/silver-book.

RANZCP (Royal Australian & New Zealand College of Psychiatrists) 2016. Professional practice guideline 10: Antipsychotic medications as a treatment of behavioural and psychological symptoms of dementia. Viewed January 7 2020, https://www.ranzcp.org/files/resources/college_ statements/practice_guidelines/pg10-pdf.aspx.

RCACQS (Royal Commission into Aged Care Quality and Safety) 2019a. Interim report: neglect. Canberra: RCACQS.

RCACQS 2019b. Restrictive practices in residential aged care in Australia: background paper 4. Canberra RCACQS.

Shash D, Kurth T, Bertrand M, Dufouil C, Barberger-Gateau P, Berr C et al. 2016. Benzodiazepine, psychotropic medication, and dementia: a population-based cohort study. Alzheimer’s & Dementia 12(5):604-13. https://doi.org/10.1016/j.jalz.2015.10.006.

Somers M, Rose E, Simmonds D, Whitelaw C, Calver J & Beer C 2010. Quality use of medicines in residential aged care. Australian Family Physician 39(6):413-6.

207 Australia’s health 2020: data insights

Chapter

7

Stasinopoulos J, Bell JS, Ryan-Atwood TE, Tan EC, Ilomäki J, Cooper T et al. 2018. Frequency of and factors related to pro re nata (PRN) medication use in aged care services. Research in Social & Administrative Pharmacy 14(10):964-7. https://doi.org/10.1016/j.sapharm.2017.11.004.

Westaway K, Sluggett J, Alderman C, Moffat A, Procter N & Roughead E 2018. The extent of antipsychotic use in Australian residential aged care facilities and interventions shown to be effective in reducing antipsychotic use: a literature review. Dementia (London). https://doi.org/10.1177/1471301218795792.

Westbury J, Gee P, Ling T, Brown D, Franks K, Bindoff I et al. 2018a. RedUSe: reducing antipsychotic and benzodiazepine prescribing in residential aged care facilities. Medical Journal of Australia 208(9):398-403. https://doi.org/10.5694/mja17.00857.

Westbury J, Gee P, Ling T, Kitsos A & Peterson G 2018b. More action needed: psychotropic prescribing in Australian residential aged care. Australian & New Zealand Journal of Psychiatry 53(2):136-47. https://doi.org/10.1177/0004867418758919.

WHO (World Health Organization) 2003. Introduction to drug utilization research. Geneva: WHO. Viewed 10 October 2019, http://www.whocc.no/filearchive/publications/drug_utilization_ research.pdf.

WHO 2019. WHO/DDD Index 2019. Oslo: WHO Collaborating Centre for Drug Statistics Methodology. Viewed 10 October 2019, https://www.whocc.no/atc_ddd_index.

209 Australia’s health 2020: data insights

Dementia data in Australia— understanding gaps and opportunities

8

210 Australia’s health 2020: data insights

Chapter

8

Dementia is one of Australia’s biggest health issues, causing substantial illness, high

levels of dependency and death. Dementia was the fourth leading cause of burden of

disease and injury in Australia in 2015 and the second leading cause in people aged

65 and over (AIHW 2019a). Furthermore, in 2018 it was the second leading cause of all

deaths in Australia and the leading cause of death for females (ABS 2019).

Box 8.1: What is dementia?

Dementia is a term used to describe a collection of symptoms that are progressive

in nature and caused by numerous conditions affecting brain function (WHO

2019). Dementia mainly occurs among people aged 65 and over but is not a

normal part of ageing. When it occurs in people under 65, it is known as

‘younger-onset dementia’ (Dementia Australia 2019). Dementia is commonly

associated with memory loss but can also affect speech, cognition, emotional

control, behaviour and mobility (WHO 2019).

There are many different types of dementia, with Alzheimer’s disease being

the most well-known. An increased risk of developing dementia is also linked

to the presence of other neurological conditions (such as Parkinson disease

and Huntington disease); prolonged alcohol abuse; HIV/AIDS; Down syndrome

and traumatic brain injury. It is possible to have multiple types of dementia at

once— known as ‘mixed dementia’—with the most common combination being

Alzheimer’s disease and vascular dementia.

The progression of dementia is complex and each person with dementia will

experience it differently. Disease progression varies but, on average, a person with

Alzheimer’s disease is expected to live 8-10 years following diagnosis (Musicco

et al. 2009). Factors impacting dementia progression include age of onset;

genetics; overall physical health; existing health conditions (such as diabetes and

cardiovascular disease); and type of dementia (Livingston et al. 2017).

While no cure for dementia exists, there are a number of management strategies

that can support a better quality of life as dementia progresses. Most people with

dementia live in the community and require considerable support from family

and friends, and through formal care arrangements such as community-based

aged-care services and respite facilities. People with advanced dementia

experience substantial cognitive and physical decline and require extensive

assistance with most or all activities of daily living. This care is typically provided in

permanent residential aged care, where it is estimated just over half of residents

have dementia (AIHW 2020c).

211 Australia’s health 2020: data insights

Chapter

8

It is estimated that between 400,000 and 459,000 Australians are living with dementia in 2020 (AIHW 2018; Dementia Australia 2020a). Dementia cost Australia $428 million in direct health expenditure in 2015-16 and, based on modelling undertaken by the National Centre for Social and Economic Modelling (NATSEM), an estimated $14.7 billion was spent on dementia-related health- and aged-care expenditure, productivity loss and other indirect costs in 2017 (AIHW 2019b; NATSEM 2017). Assuming no significant breakthrough in treatment, the number of people with dementia is projected to more than double between 2020 and 2050, placing an even greater demand on Australia’s health and aged-care systems (AIHW 2018; NATSEM 2017).

The Royal Commission into Aged Care Quality and Safety has exposed systemic issues in the current aged-care sector, and has called for fundamental reforms to an aged-care system that is failing to care appropriately for people who require care, including the growing number of people with dementia (RCACQS 2019b). With over half of people in residential aged-care facilities having dementia, and with a large proportion of people with dementia living at home, improving the quality of care and services available for older Australians, and of those with dementia, is essential.

Despite dementia being a major health challenge, there are significant gaps in robust Australian dementia data. For example, the exact number of Australians with dementia is not known, with current estimates based on small, outdated Australian studies and international data. Monitoring dementia—and its impact on individuals, their carers and Australia’s health and aged-care systems—is essential for the development of evidence-based health, aged care and social policy and associated service planning.

This article discusses:

• current issues and gaps in Australia’s dementia data and its impact on our knowledge of dementia in Australia

• recent investments made by the Australian Government to improve Australia’s dementia data

• other potential data development opportunities to ensure Australia has sufficient data to inform dementia policy and service planning.

In addition, it discusses novel findings from the 2020 AIHW report: Patterns of health service use by people with dementia in their last year of life.

See ‘Dementia’ https://www.aihw.gov.au/reports/australias-health/dementia for the latest available statistics on dementia in Australia.

See ‘International comparisons of health data’ https://www.aihw.gov.au/reports/ australias-health/international-comparisons-of-health-data for information on how the prevalence of dementia in Australia compares with other countries. Note: the Australian dementia prevalence rate shown in the international comparisons snapshot are produced by the Organisation for Economic Co-operation and Development (OECD).

212 Australia’s health 2020: data insights

Chapter

8

Due to methodological differences, these rates differ from the Australian dementia

prevalence estimates described in this article; the OECD dementia prevalence rates are

used for international comparisons only.

Dementia data gaps & implications Unlike other leading chronic conditions in Australia, there is no national approach

for monitoring and reporting dementia. Australia’s dementia statistics are derived

from a variety of data sources of varying quality, including administrative data from

government services (such as hospitals and aged-care services), survey data and

epidemiological studies (both Australian and international). Emerging evidence

suggests the incidence and prevalence rate of dementia is declining in several

high-income countries due to improvements in the prevention and management

of vascular risk factors for dementia (hypertension and cardiovascular disease)

(Roehr et al. 2018). However, it is not currently known whether rates in Australia

are also declining.

To understand why major dementia data gaps exist, it is important to understand

how dementia is diagnosed and managed; at which stages national data useful

for monitoring dementia are collected; and the limitations of the data. Figure 8.1

illustrates a potential care pathway for a person with dementia, along with the stages

at which national data are collected for use in monitoring dementia. While there are

opportunities to monitor dementia along most of the care pathway, each data source

has certain limitations, including capturing only a subset of people with dementia.

A summary of key data sources that can be used to monitor dementia, and their

benefits and limitations, is shown in Table 8.1.

213 Australia’s health 2020: data insights

Chapter

8

Figure 8.1: Dementia pathway and associated national data collections,

their coverage and limitations for reporting dementia

Dementia onset

Home-based care and support

Medical assessments & diagnosis

Dementia management

Residential aged-care End of life

GP and specialist care data: No national dementia-specific data

Prescription data:

PBS subsidy data available only for people diagnosed with Alzheimer’s disease and dispensed at least 1 of the 4 PBS-subsidised anti-dementia medications

Income support data: Recorded only for care recipients and carers receiving a Carer Payment and not for

other income support payments

Hospitals data:

Available, however dementia inconsistently coded in hospital data

Community aged-care data (including assessments): Currently only available in assessment data up to 2015 or if a person is receiving a dementia & cognition supplement

Residential aged-care data (including assessments): Available in current funding data, but assessment data currently

available only up to 2015

Deaths data: Available, however cause of death coding

standards change over time

National survey data:

Available, but questionable national representation; relies on self-reporting of dementia status or use of non-standardised diagnosis tools; and has limited information for population groups of interest

National data collections and dementia data limitations

PBS = Pharmaceutical Benefits Scheme

214 Australia’s health 2020: data insights

Chapter

8

Table 8.1: Summary of main national data sources for monitoring dementia, and their benefits and limitations for dementia monitoring (a)

Source Description Benefits Limitations

Pharmaceutical Benefits Scheme (PBS)

Information on PBS listed prescription medications, including those for people with Alzheimer’s disease who were prescribed dementia-specific medication.

- National coverage

- Routinely collected

- Not all people with dementia are prescribed dementia-specific medication

- The PBS currently subsidises dementia-specific medications only for people diagnosed with Alzheimer’s disease

Hospital admissions

Information about admitted patient activity in Australian hospitals and reason for admission.

- National coverage

- Routinely collected

- Inconsistent coding of dementia

- Under-diagnosis and under-disclosure of dementia

Emergency department presentations

Information about patient activity in Australian hospital emergency departments and their reason for admission.

- National coverage of public hospitals with emergency departments

- Routinely collected

- Missing data from private hospitals

- Inconsistent coding

- Under-diagnosis and under-disclosure of dementia

Aged-care assessments

Information on people assessed by Aged Care Assessment Teams (including some medical information) in order to receive a range of aged-care services.

- Detailed dementia diagnosis

- More likely to identify mild and moderate dementia

- Changes over time in how data are held and reported

- Data currently unavailable post 2015

- Includes only people who accessed formal aged-care services

Residential aged care

Information relating to the administration of residential aged-care subsidies, includes some medical information.

- National coverage of people in permanent residential aged care

- May under-estimate people with dementia

- Incomplete coverage in very remote areas

215 Australia’s health 2020: data insights

Chapter

8

Source Description Benefits Limitations

Income support and allowances Claims and payments data for recipients of

certain government income support and allowances with a medical diagnosis of Dementia (and their carers).

- National coverage

- Routinely collected

- Dementia may not be recorded if claim for payment is based on another medical condition

Deaths Information on deaths

in Australia and their underlying cause of death or associated cause of death.

- National coverage

- Routinely collected

- Dementia under-reported

- Unlikely that mild-to-moderate dementia will be recorded

Survey of Disability, Ageing and Carers

Large survey designed to measure the entire spectrum of disability, the underlying conditions and causes of disability, and disability-related need for assistance. It records dementia along with other health conditions.

- Nationally representative

- Comparable methods over time, allowing for time-series analysis

- Likely under-estimates number of people with dementia

- Unable to assess subgroups of interest (e.g. those with younger-onset dementia)

- No coverage in very remote areas

General practitioners and specialists(b)

No national dementia-specific data currently available. Dementia diagnoses captured in various practice management systems.

- Suitability of dementia data from practice management systems still being ascertained

(a) There are other datasets, not listed above, that can be used to monitor dementia when linked with the listed datasets. However, care must be taken as the limitations listed against each data source are likely to apply to the linked datasets as well.

(b) The Medicare Benefits Schedule (MBS), which captures information on general practitioners (GPs) and specialist services, does not capture dementia diagnosis information. The Bettering the Evaluation and Care of Health (BEACH) program, which captured information on conditions managed by GPs in Australia, ceased in 2016.

216 Australia’s health 2020: data insights

Chapter

8

Lack of national GP and specialist data collections

Dementia is a complex condition, with a diagnosis made after comprehensive cognitive

and medical evaluations. As there is no single conclusive diagnostic assessment

available, obtaining a diagnosis is often long and difficult. The pathway to a diagnosis

also varies from person to person and the stigma associated with dementia can

impede help-seeking and treatment, even when symptoms are present (Herrmann et

al. 2018). General practitioners (GPs) are often the first point of contact for a diagnosis,

with a referral made to other medical specialists or specialist memory services if

dementia is suspected.

GPs and other medical specialists, such as geriatricians, are essential in dementia

diagnosis and management. However, there are no national GP or specialist data

collections with dementia-specific diagnostic information. The Medicare Benefits

Schedule (MBS), which captures a wide range of medical services including

consultations, procedures and tests subsidised by the Australian Government, does

not contain specific items to identify dementia diagnosis. While various practice

management systems capture information on dementia diagnoses and can include a

large number of providers, the suitability of these datasets to monitor dementia is still

to be determined.

From 1998-2016, some information on conditions managed by GPs in Australia was

collected through the Bettering the Evaluation and Care of Health (BEACH) program.

This was a repeated cross-sectional study of GP clinical activity and comprised of

almost 1.7 million GP encounters from 10,300 individual GPs in 2015 (Britt et al. 2016).

Since the cessation of the BEACH program in 2016, there is no national data collection

with GP diagnostic data. There is also no national data collection with diagnostic

information from other specialists involved in diagnosing and managing dementia.

However, the AIHW is working to improve primary health care data through the

development of the National Primary Health Care Data Asset (AIHW 2020e),

which may lead to improvements for dementia reporting within the next decade.

Limitations in current administrative and survey data

Despite the lack of suitable GP and other specialist data, information on people with

dementia can be informed by other administrative data, including:

• death certificates

• admitted patient episodes of care

• emergency department presentations

• specialised mental health episodes of care

217 Australia’s health 2020: data insights

Chapter

8

• assessments for people seeking to access, or who are currently accessing,

aged-care services

• dispensing of government subsidised anti-dementia medications

• income support from a variety of Australian Government pensions and benefits for

people with dementia and/or their carers receiving financial assistance.

These data sets serve an important secondary purpose in monitoring dementia,

but they each have their limitations.

Dementia is known to be under-reported and/or inconsistently recorded in a number

of health administrative data collections (AIHW 2013, 2020a; Waller et al. 2017).

Reporting consistency has been affected by changes in clinical guidelines for recording

and managing dementia and increased awareness of dementia among health

professionals and the community. Dementia can be difficult to diagnose, and decisions

made by health professionals and clinical coders also impact the recording of dementia

in a single episode of care (Cummings et al. 2011). Further, changes in the International

Classification of Diseases (ICD-10) instructions for coding deaths data have resulted in

the assignment of some deaths to vascular dementia (F01) where previously they may

have been coded to cerebrovascular diseases (I60-I69) (ABS 2012).

The aged-care sector also provides information on people with dementia in Australia.

However, changes to government aged-care programs over time has resulted in

differences in the information captured on people with dementia accessing these

services. The Aged Care Funding Instrument (ACFI) captures information on the main

health conditions of people living in permanent residential aged care at the time

of appraisal. In contrast, information on the health conditions of people accessing

community-based aged-care services is inconsistently collected. For example, data are

not collected on the health conditions of people receiving Home Care Packages—a

program providing access to services to assist with daily living for people who want to

stay at home (AIHW 2020a). Data from the Aged Care Assessment Program provided

useful diagnosis information until June 2015, but data have been unavailable for

statistical purposes since that time.

In addition to administrative data, representative national surveys are pivotal for

dementia monitoring. They may capture people who do not access government

funded health, aged-care or income support services and can be used to validate

dementia measures based on administrative data. They can also provide information

that is usually missing from administrative data, such as personal experiences among

individuals with dementia and their carers. However, existing national surveys also

have limitations: a study by Anstey and others (2010) found prevalence estimates of

218 Australia’s health 2020: data insights

Chapter

8

probable dementia from national surveys differed from those of international

meta-analyses and pooled dementia studies with a focus on dementia and cognitive

decline. As a result, the authors concluded that existing national surveys were

unsuitable for reporting or estimating the prevalence of dementia or cognitive

impairment in Australia.

Limited data on groups of interest and broader initiatives

Australia is lacking comprehensive national data on dementia among population

groups of interest, including but not limited to Aboriginal and Torres Strait Islander

people; culturally and linguistically diverse (CALD) populations; veterans; people with

younger-onset dementia; and people with intellectual disabilities (Low et al. 2019).

Most studies of dementia in Indigenous Australians and CALD populations come from

site-specific epidemiological studies or national surveys (such as the Australian Bureau

of Statistics (ABS) Survey of Disability, Ageing and Carers (SDAC)). Small sample sizes in

national surveys limit analysis specifically for dementia in groups of interest (such as

people with younger-onset dementia). Additionally, site-specific epidemiological

studies are irregular, precluding recurrent analysis over time.

National information on unpaid carers—who provide essential care and support for

many people with dementia—and their health and wellbeing is available through the

SDAC, with most information limited to the ‘primary carer’. Information on all carers

of people with dementia is important considering the well-documented detrimental

health, emotional, social and financial outcomes often experienced by unpaid carers

of people with dementia (Brodaty & Donkin 2009; Connell et al. 2001). Income support

data are another source of information for informal carers. However, this information

is limited to only those eligible carers who applied for and received a government

carer payment or allowance. The AIHW has recently been tasked with constructing an

enduring longitudinal National Disability Data Asset to improve understanding of how

people with disability and their carers are supported through services, payments and

programs (PM&C 2019). The proposed dataset will link key administrative datasets,

making it a promising development for improving data on people with dementia and

their carers.

There is evidence that insufficient training among health and aged-care workers

contributes to the substandard care of people living with dementia (RCACQS 2019b;

SCRGSP 2020). Better data on dementia-relevant training among health- and aged-care

providers alongside currently available national data on the formal health workforce

(including their broad skills and qualifications) could be used to monitor care provision

and identify where further training is needed.

219 Australia’s health 2020: data insights

Chapter

8

Although there is no cure for dementia, there are a number of ways to maintain

quality of life for people with dementia that go beyond clinical and pharmacological

interventions. These include implementing person-centred care models; providing

cognitive training, rehabilitation and re-enablement; engaging in physical and social

activities tailored to people with dementia; and designing homes and communities

that support people with dementia. While many of these initiatives already exist across

Australia, there are no comprehensive data that can be used to monitor and report

on their availability, provision and efficacy (RCACQS 2019a). Capturing these data is

important for supporting and evaluating initiatives aimed at improving the quality of

life of people with dementia.

Implications of gaps in dementia data Issues with inconsistent administrative data and the lack of diagnostic data from GPs

and other specialists have contributed to uncertain Australian dementia estimates for

key population measures, including estimates of prevalence, incidence and burden of

disease. In 2020, the AIHW estimated there were 400,000 people living with dementia,

while NATSEM estimated 459,000 (AIHW 2018; Dementia Australia 2020a). Both studies

relied on modelling estimates from small-scale Australian and international studies

with known methodological limitations, but vary due to differences in data sources

and the methodologies employed to generate Australia-specific dementia rates. In fact,

of the 10 leading causes of disease burden in Australia, dementia has the lowest data

quality rating, due to the lack of up-to-date Australian-specific dementia prevalence

and severity data (AIHW 2019b).

The lack of national GP and other specialist data creates knowledge gaps with

respect to dementia diagnosis, including age of onset, existing health conditions

(comorbidities), risk factors, post-diagnosis support for people recently diagnosed, and

the prevalence of mild dementia or mild cognitive impairment. Improvements in GP

and other specialist diagnostic data would also improve understanding of how these

factors are changing over time, and help predict how they may change in the future.

People with dementia often have co-morbid conditions and complex care needs, and

need to transition between different care settings and health care providers (RCACQS,

2019a). However, existing datasets are currently unable to capture the complexity of

care and support often required by individuals with dementia. Furthermore, the risk

for developing dementia is linked to several modifiable risk factors, and the incidence

of key risk factors—such as cardiovascular disease, obesity and diabetes—is changing

quickly (Livingston et al. 2017). These data are essential to prepare for the emerging

challenges that dementia poses, including delivering high-quality services to the

220 Australia’s health 2020: data insights

Chapter

8

growing number of people with dementia and their carers; providing relevant training

for health and aged-care workers; and funding research into areas such as effective

prevention and treatment strategies.

Current dementia data gaps have a substantial impact on the development of

evidence-based dementia policy, service planning and provision; provision of support

and assistance to individuals with dementia and their carers; and evaluation of existing

guidelines, services and initiatives. This in turn limits the extent to which quality care is

provided and monitored, especially at local levels.

Improving Australia’s dementia data Recent international and national strategies to respond to the challenges dementia

poses, coupled with substantial Australian Government investments to improve

dementia research and data assets, are important advancements towards closing key

dementia data gaps.

The National Framework for Action on Dementia 2015-2019 (the Framework) was

developed under the Australian Health Ministers Advisory Council to guide

improvements in the quality of life for those living with dementia and their carers in

Australia. Priorities and actions identified in the Framework include:

• increasing dementia awareness

• reducing dementia risk

• reducing time to diagnosis

• ensuring access to care and ongoing support in all areas (particularly post-diagnosis

support, and support during and after hospital care, and palliative care support)

• promoting and supporting dementia research (Department of Health 2019).

The Framework also noted improving clinical coding of dementia in hospital data as an

action to provide better evidence for research. An evaluation of the effectiveness of the

Framework is currently under way and this will provide an opportunity to inform and

scope options for national strategies to address dementia in the future.

To coincide with outcomes of the Framework’s evaluation, a national dementia data

development plan (providing a comprehensive and co-ordinated approach to dementia

data improvements) would assist in improving Australia’s dementia data. Developed

in consultation with key national data stakeholders to ensure alignment with policy

and research priorities, the plan would outline responsibilities, steps, timeframes

and costs involved in improving dementia data. This would include steps to enhance

the quality of current data sources used for dementia monitoring; priorities for data

221 Australia’s health 2020: data insights

Chapter

8

integration and analysis; and suggestions for new data sources with the potential to

improve dementia information in remaining areas. Developing and implementing such

a plan would better enable policy makers and researchers to consistently examine key

dementia knowledge gaps, such as the impact of new policies and trends over time.

In 2015, the Australian Government committed an additional $200 million specifically

for dementia research in Australia over 5 years, with the National Health and Medical

Research Council National Institute for Dementia Research (NNIDR) established to

coordinate the strategic expansion of dementia research in Australia. The NNIDR

offered a series of Boosting Dementia Research Grants for dementia researchers,

with one round offering $3 million for projects aimed at strengthening Australia’s

national dementia data assets and capabilities. Two years of funding was awarded

in 2019 to 2 projects—one aiming to use national linked administrative data to

develop methods for improving Australia’s dementia statistics through a collaboration

between academics, the ABS and the AIHW, and the other aiming to link electronic

primary health care records to administrative data to develop methods for monitoring

dementia, risk factors and management (NHMRC 2019). The NNIDR was disestablished

on 30 June 2020.

As GP and other specialist diagnostic data are the biggest data gaps for estimating

dementia incidence and prevalence in Australia, better primary health care data have

the potential to greatly improve dementia monitoring. In 2018, the AIHW was funded to

develop a National Primary Health Care Data Asset. It is envisaged that the Data Asset will

contain reliable, detailed, high-quality data about primary health care, which could help

inform the diagnosis of dementia and its management in primary care (AIHW 2020e).

Maximising use of data linkage for dementia monitoring

Data linkage brings together data from multiple sources that relate to the same

individual or institution. Data linkage provides opportunities to substantially improve the

quality of dementia monitoring in Australia and has been used for dementia monitoring

internationally (Box 8.2). For example, an individual may not have a dementia diagnosis

recorded in hospitals data, but may be taking dementia-specific medications subsidised

by the PBS. In this case, by bringing together, or linking, administrative hospital and

medication data, dementia identification is improved. In addition, linked data are useful

to answer current dementia knowledge gaps, which include assessing:

• health outcomes and trajectories

• quality of care

• pathways through, and interfaces between, the health and aged-care systems

and interactions

222 Australia’s health 2020: data insights

Chapter

8

• service use and associated costs

• patterns of care and how variations in care impact health outcomes

• the experience and training of the formal health workforce

• informal carers of people with dementia

• population groups of interest

• social and economic outcomes for people with dementia and their carers

• the impact of policy changes on the delivery of health and aged-care services.

The dementia data improvement projects funded by the NNIDR Boosting Dementia

Research Grant are important initiatives for advancing dementia research at a

population level by leveraging data linkage.

Box 8.2: Examples of international dementia surveillance

In countries where national strategies and integrated systems for dementia

surveillance have been implemented, there have been substantial gains in

consistent monitoring. In the UK, data from different sources are brought together

to monitor quality outcomes for people with dementia and their carers, such as

the proportion of dementia patients whose dementia care plan has been reviewed

in the last 12 months, and the proportion of dementia carers (such as family and

friends) experiencing social isolation (PHE 2019).

In Sweden, national guidelines for quality dementia care and 7 clinical indicators,

are tracked in SveDem, the national dementia quality registry. SveDem provides

important population health indicators that encourage consistent approaches

to diagnosing dementia (such as conducting cognitive testing) and ensuring

high-quality care is provided to patients (such as limiting the use of antipsychotics)

(Religa et al. 2015).

The value of linking datasets is shown in a 2020 AIHW study, Patterns of health service

use by people with dementia in their last year of life (AIHW 2020d). Health-service usage

in the last year of life was examined for over 70,000 people who died in 2013 to assess

how dementia affects service use (GP and specialist services, admitted hospital care,

emergency department care and dispensing of prescriptions). The linked data set

223 Australia’s health 2020: data insights

Chapter

8

contained de-identified hospitals data from New South Wales and Victoria linked to the

MBS, PBS and deaths data. Aged-care service data was not included in the linked data

available for use in the study. The following results are for people who died aged 65

and over.

The study found that, with the exception of GP services, a smaller percentage of people

with dementia used each health service at least once in their last year of life, compared

with people without dementia (Figure 8.2). The greatest difference was seen in the use

of specialist services, followed by admitted patient care and emergency care.

Figure 8.2: Percentage of people who used a health service at least once in

their last year of life, by dementia status and type of health service, 2013

Notes

1. GP services excludes services provided to Department of Veteran Affairs (DVA) card holders where care is reimbursed through DVA, as well as services provided by salaried GPs in residential aged care or outpatients departments.

2. Analysis includes people who died in 2013 aged 65 or over and resided in New South Wales or Victoria.

Source: AIHW 2020d.

People with dementia People without dementia

90% 90%

86% 88%

68% 77%

66% 78%

33% 64%

Specialist service

Hospital admission

Emergency Department presentation

Prescription dispensed

GP service

224 Australia’s health 2020: data insights

Chapter

8

The frequency of health service use also varied based on if a person had dementia or

not. Compared with a person without dementia in their last year of life, a person with

dementia on average had:

• 3 more GP services

• 3 fewer specialist services

• 6 fewer prescriptions dispensed

• 2 fewer hospital admissions

• a similar number of emergency department presentations.

The type and usage of health services varied over the last 12 months of life, reflecting the

need for particular services at different end-of-life stages (Figure 8.3). For example, for

people with dementia, the percentage who used a GP service at least once increased in

the final month of life, while this pattern was not observed in people without dementia.

Figure 8.3: Health service use in the last 12 months of life, by dementia

status and month before death, 2013

Notes

1. The line for ‘Hospitalisations (dementia)’ is shown behind the ‘ED presentations (dementia)’ line, as the percentage of people with dementia who had a hospital admission was similar to the percentage who presented to the emergency department in the last 12 months of life.

2. ‘GP services’ excludes services provided to Department of Veteran Affairs (DVA) card holders where care is reimbursed through DVA, as well as services provided by salaried GPs in residential aged care or outpatients departments.

3. Analysis includes people who died in 2013 aged 65 or over and resided in New South Wales or Victoria.

Source: AIHW 2020d.

20

40

60

80

12 11 10 9 8 7 6 5 4 3 2 1

Month before death

Percentage of people using a health service at least once

Prescriptions dispensed Prescriptions dispensed

GP services GP services

Hospital admissions Hospital admissions

Emergency department presentations Emergency department presentations

Specialist services Specialist services

0

With dementia Without dementia

225 Australia’s health 2020: data insights

Chapter

8

Factors influencing health service use by people with dementia towards the end of life

were not able to be examined in this study. This is a recognised knowledge gap not

only in this study but also in international dementia research. It has been suggested

that service use at end of life by people with dementia may be influenced by place of

care; care needs and quality of care; advanced care planning; health care access; and

the number and type of comorbidities (Browne et al. 2016; Dyer et al. 2018; Forma

et al. 2011). Furthermore, the frequency of health service use by people with dementia

may not necessarily reflect the burden dementia places on the health and aged-care

systems. For example, the average cost of hospital care has been shown to be greater

for people with dementia than for those without dementia, and people with dementia

have higher ratings in 2 of the 3 domains of care assessed for people in aged-care

facilities than those without dementia (AIHW 2013, 2020b).

It was also not possible to identify people living in residential aged-care facilities in this

study. Different patterns of use among people in residential aged care could explain

differences in health care usage between people with and without dementia and is an

important area of future research. Another study also undertaken by the AIHW, using

linked data to explore the interface between the health and aged-care systems, found

that people aged 65 and over in residential aged care were less likely to see a specialist

than those receiving aged-care services in the community or those not receiving any

aged-care services. See Chapter 7: ‘Changes in people’s health service use around the

time of entering permanent residential aged care’ for more information.

Both the aforementioned AIHW studies used one-off linked datasets, which limits their

use for ongoing dementia monitoring. There are ongoing efforts to develop enduring

and regularly updated linked health data assets (referred to as Multi-source Enduring

Linked Data Assets (MELDAs)), which will provide new opportunities for dementia

monitoring and novel research, such as the inter-dependencies between health and

aged-care services, and how this changes by dementia progression, type and number

of comorbidities and care settings. See Chapter 1: ‘Health data in Australia’ for more

information on MELDAs and developments in person-centred data.

Leveraging electronic health records and developing a national dementia registry

The emerging availability of comprehensive electronic health records could help

provide better dementia data in the future—subject to the Australian community

supporting use of this data for research purposes and generating adequate uptake

by individuals and health care professionals. For dementia, this also relies on uptake

by GPs and other specialists involved in dementia diagnosis and management.

226 Australia’s health 2020: data insights

Chapter

8

Comprehensive clinical data from health care services held in electronic health records

have the potential to greatly improve dementia monitoring. For example, as there can

be shared care arrangements between GPs and other specialists when prescribing

dementia-specific medications, comprehensive health records would help better

understand patient journeys and health care use (Dementia Australia 2020b).

Statistical analysis of data stored in My Health Record—an electronic summary of

individuals’ clinical information—presents a potentially valuable future resource

to overcome the fragmented documentation of dementia across Australia’s health

and aged-care systems. It also presents a future opportunity to contribute essential

clinical data for a national dementia registry. Efforts to develop a clinical quality

registry that directly collects data generated by clinical processes for the diagnosis

and management of dementia, are already underway through the Australia Dementia

Network (ADNet) (NHMRC 2019).

By combining existing data collections with electronic clinical records and data from

clinical trials, a nationally-coordinated, clinically-based dementia registry is a future

possibility to assess not only incidence and prevalence but also dementia risk factors,

time of diagnosis, progression, comorbidities, treatment and management, quality of

care, service needs and health expenditure.

Conclusion Timely, comprehensive dementia data are needed to truly understand the existing

and emerging challenges dementia poses, as well as to develop and evaluate policies

and programs to most effectively combat these challenges. High-quality data are also

indispensable in supporting the Australian Government’s stated priority of monitoring

and improving the quality of care provided to older Australians—many of whom suffer

from dementia and are particularly vulnerable.

There are major gaps in the currently available dementia data, including a lack of

dementia diagnosis in GP and other specialist data; inconsistent reporting of dementia

diagnoses across different datasets and over time; irregular funding for studies

providing data on special groups of interest; and poor data integration across different

health care types. These gaps impede the development of robust estimates on key

population health indicators for dementia.

Nonetheless, there are also encouraging examples of innovative approaches to

overcome existing data gaps, with data linkage efforts already providing benefits,

including more accurately detecting dementia cases and tracking patterns of health care

use. Improved government funding has been essential in instigating these efforts,

227 Australia’s health 2020: data insights

Chapter

8

but continued investment will be needed to build on these initial achievements. Strategic

efforts through the development of a data improvement plan will help prioritise data

improvements and provide reliable monitoring and reporting of key dementia statistics.

References ABS (Australian Bureau of Statistics) 2012. Causes of death, Australia, 2012. ABS cat. no. 3303.0. Canberra: ABS. Viewed 21 October 2019, https://www.abs.gov.au/ausstats/abs@.nsf/ Lookup/3303.0main+features100012012.

ABS 2019. Causes of death, Australia 2018. ABS cat. no. 3303.0. Canberra: ABS. Viewed 3 October 2019, https://www.abs.gov.au/AUSSTATS/abs@.nsf/ookup/3303.0Main+Features12018?Open Document.

AIHW (Australian Institute of Health and Welfare) 2013. Dementia care in hospitals: costs and strategies. Cat.no. AGE 72. Canberra: AIHW.

AIHW 2018. Australia’s health 2018. Australia’s health series no. 16. AUS 221. Canberra: AIHW.

AIHW 2019a. Australian Burden of Disease Study: impact and causes of illness and death in Australia 2015. Australian Burden of Disease Study series no. 19. Cat. no. BOD 22. Canberra: AIHW.

AIHW 2019b. Disease expenditure in Australia. Cat. no. HWE 76. Canberra: AIHW. Viewed 28 August 2019, https://www.aihw.gov.au/reports/health-welfare-expenditure/disease-expenditure-australia/contents/australian-burden-of-disease-conditions.

AIHW forthcoming 2020a. Dementia data gaps and opportunities. Canberra: AIHW.

AIHW 2020b. GEN dashboard 2018-19: people using aged care 2018-19. Canberra: AIHW. Viewed 20 March 2020, https://www.gen-agedcaredata.gov.au/Resources/Dashboards/People-using-aged-care-2018%e2%80%9319

AIHW 2020c. GEN fact sheet 2018-19: people’s care needs in aged care. Canberra: AIHW. Viewed 20 March 2020, https://www.gen-agedcaredata.gov.au/Topics/Care-needs-in-aged-care

AIHW 2020d. Patterns of health service use by people with dementia in their last year of life: New South Wales and Victoria. Cat. no. AGE 102. Canberra: AIHW.

AIHW 2020e. Primary health care data development. Canberra: AIHW. Viewed 12 February 2020, https://www.aihw.gov.au/reports-data/health-welfare-services/primary-health-care/primary-health-care-data-development.

Anstey KJ, Burns RA, Birrell CL, Steel D, Kiely KM & Luszcz MA 2010. Estimates of probable dementia prevalence from population-based surveys compared with dementia prevalence estimates based on meta-analyses. BMC Neurology 10:62.

Britt H, Miller GC, Henderson J, Bayram C, Harrison C, Valenti L et al. General practice activity in Australia 2015-16. General practice series no. 40. Sydney: Family Medicine Research Centre, Sydney School of Public Health, University of Sydney.

Brodaty H & Donkin M 2009. Family caregivers of people with dementia. Dialogues in Clinical Neuroscience 11(2):218-28.

Browne J, Edwards DA & Rhodes KM 2017. Association of comorbidity and health service usage among patients with dementia in the UK: a population-based study. BMJ Open 7:3.

228 Australia’s health 2020: data insights

Chapter

8

Connell CM, Janevic MR & Gallant MP 2001. The costs of caring: impact of dementia on family caregivers. Journal of Geriatric Psychiatry and Neurology 14(4):179-87.

Cummings E, Maher R, Showell CM, Croft T, Tolman J, Vickers J, et al. 2011. Hospital coding of dementia: is it accurate? Health Information Management Journal 40(3):5-11.

Dementia Australia 2019. What is dementia? Canberra: Dementia Australia. Viewed 8 October 2019, https://www.dementia.org.au/about-dementia/what-is-dementia

Dementia Australia 2020a. Dementia statistics. Canberra: Dementia Australia. Viewed 6 February 2020, https://www.dementia.org.au/statistics.

Dementia Australia 2020b. Pharmacological treatment options. Canberra: Dementia Australia. Viewed 6 February 2020, https://www.dementia.org.au/information/for-health-professionals/ clinical-resources/pharmacological-treatment.

Department of Health 2019. National Framework for Action on Dementia 2015-2019. Canberra: Department of Health. Viewed 9 October 2019, https://agedcare.health.gov.au/ageing-and-aged-care-older-people-their-families-and-carers-dementia/national-framework-for-action-on-dementia-2015-2019.

Dyer SM, Enwu L, Gnanamanickam ES, Milte R, Easton T, Harrison SL et al. 2018. Clustered domestic residential aged care in Australia: fewer hospitalisations and better quality of life. Medical Journal of Australia 208 (10):433-8.

Forma L, Rissanen P, Aaltonen M, Raitanen J & Jylhä M 2011. Dementia as a determinant of social and health service use in the last two years of life 1996-2003. BMC Geriatrics 11(14).

Herrmann LK, Welter E, Leverenz J, Lerner AJ, Udelson N, Kanetsky C et al. 2018. A systematic review of dementia-related stigma research: can we move the stigma dial? The American Journal of Geriatric Psychiatry 26(3):316-31.

Livingston G, Sommerlad A, Orgeta V, Costafreda SG, Huntley J, Ames D et al. 2017. Dementia prevention, intervention, and care. The Lancet 390(10113):2673-4.

Low L-F, Barcenilla-Wong AL & Brijnath B 2019. Including ethnic and cultural diversity in dementia research. The Medical Journal of Australia 211(8):345-6.

Musicco M, Palmer K, Salamone G, Lupo F, Perri R, Mosti S et al. 2009. Predictors of progression of cognitive decline in Alzheimer’s disease: the role of vascular and sociodemographic factors. Journal of Neurology 256(8):1288-95.

NATSEM (National Centre for Social and Economic Modelling) 2017. Economic cost of dementia in Australia 2016-2056. Canberra: University of Canberra.

NHMRC (National Health and Medical Research Council) 2019. Boosting Dementia Research Grants. Canberra: NHMRC. Viewed 9 October 2019, https://www.nhmrc.gov.au/funding/find-funding/boosting-dementia-research-grants.

PHE (Public Health England) 2019. Dementia profile. London: PHE. Viewed 9 October 2019, https://fingertips.phe.org.uk/profile-group/mental-health/profile/dementia .

PM&C (Department of the Prime Minister and Cabinet) 2019. Australian Digital Council Communiqué, 5 April 2019. Canberra: PM&C. Viewed 18 February 2020, https://www.pmc.gov.

au/sites/default/files/publications/aust-digital-council-communique-050419.pdf.

RCACQS (Royal Commission into Aged Care Quality and Safety) 2019a. Dementia in Australia: nature, prevalence and care: background paper 3. Adelaide: RCACQS.

229 Australia’s health 2020: data insights

Chapter

8

RCACQS 2019b. Interim report: Neglect. Adelaide: RCACQS.

Religa D, Fereshtehnejad S-M, Cermakova P, Edlund A-K, Garcia-Ptacek S, Granqvist N et al. 2015. SveDem, the Swedish Dementia Registry—a tool for improving the quality of diagnostics, treatment and care of dementia patients in clinical practice. PLOS ONE 10(2):e0116538.

Roehr S, Pabst A, Luck T & Riedel-Heller SG 2018. Is dementia incidence declining in high-income countries? A systematic review and meta-analysis. Clinical Epidemiology 10:1233-47.

SCRGSP (Steering Committee for the Review of Government Service Provision) 2020. Report on Government Services 2020. Canberra: Productivity Commission.

Waller M, Mishra GD & Dobson AJ 2017. Estimating the prevalence of dementia using multiple linked administrative health records and capture-recapture methodology. Emerging Themes in Epidemiology 14(3):1—9.

WHO (World Health Organization) 2019. Dementia. Geneva: WHO. Viewed 16 July 2019, http://www.who.int/news-room/fact-sheets/detail/dementia.

231 Australia’s health 2020: data insights

Improving suicide and intentional self-harm monitoring in Australia

9

232 Australia’s health 2020: data insights

Chapter

9

Suicide and intentional self-harm are serious public health issues of concern to

governments and communities across Australia and around the world. In 2018,

3,046 deaths by suicide were registered in Australia (ABS 2019a). Each death by

suicide can have a lasting impact on families, friends and communities. The incidence

of intentional self-harm (which includes suicide attempts and non-suicidal self-injury)

is even greater, with the number of cases of hospitalised injury due to intentional

self-harm more than 10 times that of deaths by suicide—in 2016-17, there were

more than 33,000 cases of hospitalised injury due to intentional self-harm

(AIHW: Pointer 2019). The number of people who self-harmed but are not hospitalised

is largely unknown. Yet intentional self-harm and suicide may be prevented with timely,

evidence-based interventions. It is feared that suicide and intentional self-harm may

increase due to reductions in employment resulting from restrictions on business

activities designed to limit the transmission of COVID-19.

The prevalence, characteristics and methods of suicide and intentional self-harm vary

between different communities, demographic groups and over time. Collection of data

on suicide and intentional self-harm (including means and modifiable risk factors) is

an essential component of suicide prevention; it enables us to define the extent of the

problem, to identify trends and emerging areas of concern, and to highlight vulnerable

populations. Data underpins the appropriate targeting of prevention strategies and

research, and suicide and self-harm statistics are widely used as progress indicators

in Australia (AIHW 2009). For these reasons, it is important that monitoring of both

suicide and self-harm is as comprehensive and informative as possible.

This chapter provides an overview of the policy context for the monitoring of suicide

and intentional self-harm in Australia and examines the existing national sources of

data (administrative databases and surveys) currently used—including their strengths,

limitations and any data gaps. It also discusses potential new sources of data that

may enhance the evidence base, with particular reference to Aboriginal and Torres

Strait Islander people and to current serving, reserve and contemporary ex-serving

Australian Defence Force (ADF) personnel. The article does not discuss current

approaches to suicide prevention or emerging advice for service planning.

See ‘Suicide and intentional self-harm’, ‘Indigenous health and wellbeing’,

‘Indigenous life expectancy and deaths’ and ‘Health of veterans’ at

www.aihw.gov.au/australias-health/snapshots for more information.

233 Australia’s health 2020: data insights

Chapter

9

The AIHW recognises that each number reported here represents an individual

and wishes to acknowledge the devastating effects suicide and self-harm can have

on people, their families, friends and communities.

If this report raises any issues for you, these services can help:

• Lifeline 13 11 14

• Suicide Call Back Service 1300 659 467

• Kids Helpline 1800 55 1800

• MensLine Australia 1300 78 99 78

• Beyond Blue 1300 22 4636.

Crisis support services can be reached 24 hours a day.

Suicide prevention: a public health priority In 2013, the 66th World Health Assembly adopted the first Mental Health Action Plan

(2013-2020) of the World Health Organization (WHO) (WHO 2013). In its subsequent

report, Preventing suicide: a global imperative (WHO 2014), the WHO provided actionable

steps for the implementation of effective national suicide prevention strategies.

Key components were the strengthening of suicide surveillance by improving the

quality and timeliness of national data on deaths by suicide and suicide attempts,

and establishing an integrated data collection system to help identify specific groups,

vulnerable individuals and high-risk situations.

The policy context for suicide monitoring in Australia

Suicide has long been a significant health issue in Australia. More recently, there

has been an increased emphasis on suicide prevention by Australian governments.

In 2017, actions to address suicide as a priority area were included in the Fifth National

Mental Health and Suicide Prevention Plan (COAG Health Council 2017). This plan

committed all Australian governments to a collaborative national approach to mental

health planning and service delivery, including improving the quality and timeliness

of data collection on suicide; suicide attempts; and intentional self-harm in Australia.

These data would provide much needed information to those responsible for the

planning, funding, delivery and evaluation of suicide prevention strategies.

234 Australia’s health 2020: data insights

Chapter

9

Suicide prevention in Australia is a complex area of policy with interconnected

responsibilities. Governments, policy makers and service providers all have a role

in reducing deaths by suicide as well as cases of intentional self-harm. The reasons

for suicide are often complex and different for each individual. Research has shown

that a range of factors are commonly present in the histories of those who died by

suicide, including mental and behavioural disorders; physical illness; and psychosocial

factors (including alcohol and/or other drug problems; relationship or legal issues;

bereavement; impacts of chronic health conditions; disability; unemployment;

homelessness; and bullying) (ABS 2019b; Clapperton et al. 2019). Therefore, effective

suicide prevention requires action from a correspondingly broad range of government

agencies, including those responsible for health, education, employment, urban

planning, welfare and law enforcement agencies.

In acknowledgement of the devastating effects of suicide and the pivotal role

governments have to play in addressing it, the Australian Government has made

suicide prevention a national whole-of-government priority, and indicated a

commitment to the aspirational goal of working ‘Towards Zero’ deaths by suicide

(Department of Health 2019c). To this end, the first National Suicide Prevention Adviser

reporting directly to the Prime Minister has been appointed and a National Suicide

Prevention Taskforce has been established to coordinate collaboration between

government agencies and across different levels of government.

The AIHW is currently actively involved in data improvement activities to expand

the collection or availability of data on deaths by suicide and on the occurrence of

intentional self-harm in 2 specific populations: Indigenous Australians and current

serving, reserve and contemporary ex-serving ADF personnel. Rates of suicide

among Indigenous Australians are higher than those of the non-Indigenous

population. For the 5 years from 2014 to 2018, the age-standardised suicide rate

for Indigenous Australians was almost twice that of the non-Indigenous population

(23.7 vs 12.3 per 100,000 population) (ABS 2019a). In the 2019-20 Budget, the

Australian Government committed a further $15 million to suicide prevention for

Indigenous Australians (Department of Health 2019b). Also, on 5 February 2020, the

Australian Government announced the appointment of a National Commissioner for

Defence and Veteran Suicide Prevention. The AIHW and the Australian Commission

on Safety and Quality in Health Care, along with coronial and legal experts, will provide

technical expertise to support the Commissioner’s work.

235 Australia’s health 2020: data insights

Chapter

9

Improving the evidence base In Australia, the extent of intentional self-harm and suicidal behaviours in the broader community is largely unknown, as those presenting to emergency departments (EDs) or primary health care—or not seeking treatment—are not captured by clinical data. The ‘iceberg’ model has been used to represent the relative incidence of suicide and intentional self-harm and the difficulty of monitoring the incidence of suicide, intentional self-harm and suicidal behaviours (Arensman et al. 2017; Geulayov et al. 2018; McMahon et al. 2014; Pollock et al. 2018). In this model (Figure 9.1), the extent to which suicide, intentional self-harm and suicidal ideation are currently captured by administrative data sets can be represented as an ‘iceberg’ for which only the tip—representing suicide or intentional self-harm that results in hospital admission—is visible. Intentional self-harm that results in presentation to other health services (such as EDs, primary health care or ambulance services) or does not result in medical treatment are more common but largely hidden from view because they cannot be identified with clinical data—thus forming the ‘submerged’ part of the iceberg.

Figure 9.1: Iceberg model illustrating the extent to which suicide, intentional self-harm and suicidal behaviours are currently captured by clinical data in Australia

Adapted from: Pollock et al. 2018.

Deaths by suicide

Intentional self-harm resulting in hospital admission

Intentional self-harm

resulting in presentation to other health services (emergency department, ambulance or primary care services)

Visible (above surface)

Hidden from view (submerged)

Non-treated self-harm (not presenting to clinical services)

Suicidal ideation

Less common, more harm

More common, less harm

236 Australia’s health 2020: data insights

Chapter

9

Finally, suicidal behaviours, such as making a suicide plan or having suicidal thoughts, are

even more common; however, information about these behaviours may not be captured

by clinical data. Instead, an indication of the prevalence of these behaviours in the

community may be derived from surveys of representative samples of the population.

In recognition of the fact that data are critical to the development of effective suicide

and intentional self-harm prevention policies and services, the National Suicide and

Self-harm Monitoring System was announced as a component of the Australian

Government’s Prioritising Mental Health Package (Department of Health 2019a).

The aim of the monitoring system is to collate and coordinate data and information

on suicide, intentional self-harm and suicidal behaviours in Australia to improve their

coherence, accessibility, quality and timeliness. The AIHW has been funded $5 million

per year for 3 years (2019-20 to 2021-22) to deliver the monitoring system. Data

improvement activities to enhance the comprehensiveness of data, and the creation of

a monitoring system to support the accessibility and useability of data for stakeholders,

will make it a key resource to assist governments, services and communities to

improve suicide and intentional self-harm prevention strategies. The National Suicide

and Self-harm Monitoring System will draw on the expertise of the National Injury

Surveillance Unit (NISU) at Flinders University, a collaborating unit of the AIHW, and on

other subject matter experts as required.

Currently available national sources of data on suicide, intentional self-harm and

suicidal behaviours include mortality data; data on the provision of hospital services;

and population-based mental health surveys. These sources, and potential new

sources of data to fill gaps in our understanding of suicide, intentional self-harm and

suicidal behaviours in Australia, are summarised in Table 9.1 at the end of the chapter.

How the data are obtained, and the limitations of each data set, are also discussed

below. Examples of initiatives under the National Suicide and Self-harm Monitoring

system project are also described, including those that aim to make greater use of

existing data sources to identify populations at risk and to allow more timely,

localised responses.

237 Australia’s health 2020: data insights

Chapter

9

Deaths by suicide

The collation of national data on deaths by suicide in Australia requires the

collaboration of multiple state and national government bodies (Box 9.1). Despite the

fact that Australia has strong systems in place for the collection of death statistics,

accurate reporting of deaths by suicide is particularly challenging for a number of

reasons.

Generally, deaths due to external causes, including suspected suicides, are

referred to a coroner for investigation of the cause and, if applicable, the intention

of the deceased. Some deaths by suicide may be misclassified as ‘accidental’ or

‘undetermined’ due to the difficulty in determining the true intent of the deceased

and, as a result, may lead to under-reporting (Senate Community Affairs References

Committee 2010). Additionally, the international medical coding system used to

classify causes of death (the WHO International Statistical Classification of Diseases

and Related Health Problems, 10th revision: ICD-10), does not distinguish between

suicidal and non-suicidal intent (WHO 2016). While a decision has not yet been made

on whether or when ICD-11 will be adopted for coding deaths in Australia, the new

classification developed by the WHO has made provisions for a new dimension to

capture the ‘intended result’ (suicidal intent) of intentional self-harm (intent pending;

suicidal/non-suicidal intentional self-harm) (WHO 2019).

The quality of cause of death coding can be affected by the length of time required

for coronial processes to be finalised (ABS 2019a). To improve the quality of ICD

coding, all coroner-certified deaths are now subject to a revisions process (Box 9.1)

(ABS 2019a).

National data can mask regional variation due to significant demographic, economic or

cultural differences between different regions of Australia. Therefore, more granular,

regional-level mortality data can be useful in developing and monitoring local suicide

prevention strategies. However, a major challenge with suicide mortality data is

that suicide deaths are statistically rare events, meaning that it is difficult to achieve

the statistical power that is necessary to identify patterns and causation, or to draw

conclusions about reductions in the suicide rate (AHA 2014; AIHW 2011; Morris et al.

2018). These methodological challenges exist at the national level, where around

3,000 deaths per year are suicides, but for the reporting of suicide deaths at lower

regional levels (for example, state/territory, local government area) or by demographic

variables (for example, age and sex) these issues are compounded, as data are broken

down into even smaller population groups. To date, issues relating to data volatility and

robustness for measurements of rates and trends have had limited statistical analysis.

238 Australia’s health 2020: data insights

Chapter

9

Box 9.1: Suicide mortality data

The registration of deaths in Australia is required by law and is the responsibility

of state and territory Registries of Births, Deaths and Marriages. As part of the

registration process, information about the cause of death is supplied by the

medical practitioner certifying the death, or by a coroner. The information is

provided to the Australian Bureau of Statistics (ABS) for coding of causes of death

(according to the WHO International Statistical Classification of Diseases and

Related Health Problems, 10th revision; ICD-10) and compilation into aggregate

statistics on an annual basis (ABS 2019a). In addition, the ABS supplements these

data with information from the National Coronial Information System (NCIS), a

national database of coronial findings, post-mortem, toxicology and police reports

(ABS 2019a). These data are sent to the Australian Coordinating Registry (ACR)

who acts on behalf of all the Registries to coordinate release of the data. The

AIHW receives the data from the ACR and maintains these data in the National

Mortality Database (NMD), a historical (1964-2018), coherent and accurate

database for analysis, linkage and reporting purposes (AIHW 2019a).

Deaths that are referred to a coroner can take time to be finalised and the

coroner’s case closed. To account for this, the ABS undertakes a revisions process

for those deaths where coronial investigations remained open at the time

an initial cause of death was assigned. Usually, data are deemed preliminary

when first published, revised when published the following year and final when

published after a second year (ABS 2019a).

Problems with the reporting of small numbers in population groups—such as regional

areas (AIHW 2019d), certain demographic characteristics (AIHW 2019c), or in specific

populations including Indigenous Australians (AIHW: Kreisfeld & Harrison 2020) or current

serving, reserve and contemporary ex-serving ADF personnel (AIHW 2019f)—have been

avoided by aggregating multiple years of data to ensure confidentialisation and privacy.

State-based suicide registers

While the quality of Australian mortality data is high by world standards and historical

data are available, the annually available mortality data sets have limited usefulness

in informing time-sensitive responses. Delays between a death by suicide and its

reporting to policy makers and service providers can be an impediment to the early

detection of systematic trends and appropriate intervention responses aimed at

preventing further suicides.

239 Australia’s health 2020: data insights

Chapter

9

Coronial suicide registers have been established by some jurisdictions in Australia

(including Victoria, Queensland, Western Australia and Tasmania). Information from these

registers can be used to assist coroners formulate evidence-based recommendations

to prevent suicide and may also be shared with local governments and service providers

to better target and inform suicide prevention activities (Leske et al. 2019;

Sutherland et al. 2018; Tasmanian Department of Health and Human Services 2016).

These registers have the potential to provide timely data on deaths suspected to have

been by suicide and may be useful for identifying trends in locations or in the methods

used for suicide. For example, the Victorian suicide register enables basic information

on a suspected death by suicide (including cause of death, location of death, usual

place of residence, age, sex and occupational information) to be coded within

24-48 hours of the coroner being notified of a death suspected to be by suicide

(Sutherland et al. 2018). More comprehensive contextual information about

modifiable risk factors (for example, the deceased’s physical and mental health history,

interpersonal stressors, psychosocial factors, and other circumstances surrounding

their death) are also collected from a variety of sources, such as toxicology autopsy

and police reports; however, this information can take longer to be made available.

The establishment of such registers does not replace the collection of nationally

consistent deaths data but may enhance the development of timely interventions

and appropriate localised suicide prevention strategies.

Embedding psychosocial risk factors in national mortality data

Making greater use of existing data sets removes duplication of effort, reduces costs

and minimises reporting burden. In 2019, the ABS published results of a pilot study

to enhance the national Causes of Death data set, by coding psychosocial risk factors

for all coroner-referred deaths (including deaths by suicide) registered in 2017, via a

comprehensive manual review of reports included in the NCIS (ABS 2019b). Psychosocial

factors (for example, a past history of self-harm; relationship problems; legal issues;

bereavement; unemployment; homelessness; and disability) were identified in 63%

of all deaths by suicide. The findings of this pilot study were limited by the amount of

information that was voluntarily captured in post-mortem, toxicology or police reports

and coronial findings included in the NCIS. However, there is potential to enhance what

the NCIS captures by requiring the recording of risk-factor information in suspected

cases of suicide. The ABS is currently working to embed risk factors into the national

mortality data set to provide comprehensive information on the combination of factors

contributing to deaths by suicide. This data initiative is being funded as part of the

Suicide and Self-harm Monitoring System project to enable monitoring of emerging

trends and improve evaluation of the effectiveness of suicide prevention strategies.

240 Australia’s health 2020: data insights

Chapter

9

Data linkage with mortality data

Data integration can enrich the value and maximise the use, and re-use, of nationally

collected information, while preserving individual privacy and the security of sensitive

data. It also has the potential to expand the evidence base to better support research

and policy development. By combining information from existing surveys, administrative

data collections and censuses, a more complete picture of the circumstances of

individuals and households can emerge. Integrated data sets can also be combined with

additional point-in-time and/or longitudinal information to help assess the effectiveness

of policies and programs. A pilot project has shown that data integration has the

potential to provide valuable insights into contextual factors (for example, employment

and marital status) associated with deaths, including those by suicide (ABS 2016c).

This project combined national mortality data with data from the 2011 Census through

a process of probabilistic record linkage.

The integration of multiple, cross-agency data sets for use as a resource has evolved

more recently. The Multi-Agency Data Integration Project (MADIP) is a cross-agency

partnership between the Departments of Social Services, Health, Human Services,

Australian Taxation Office and the ABS. Its purpose is to create enduring, linked,

research data sets. Improvements in linkage and the ability to combine and repurpose

data will provide improved measurement of outcomes for population groups of interest

as well as richer statistics (ABS 2016b). As part of the Suicide and Self-harm Monitoring

System project interrogation of MADIP data will be used to better understand the social

determinants of death by suicide, such as educational attainment and housing tenure,

and to identify population groups at increased risk.

The AIHW has also been working with the Department of Health along with state

and territory health authorities to develop the National Integrated Health Services

Information Analysis Asset (NIHSI AA) which includes mortality data together with

hospital admissions, Medicare Benefits Schedule (MBS), Pharmaceutical Benefits

Scheme (PBS) and residential aged care data. This analysis asset will enable

examination of service-use patterns and the demographic profiles of those using

(and, by inference, those not using) services. The AIHW will analyse the NIHSI AA to

report on the service use of people in their last 12 months of life, including those who

died by suicide. The potential insights from this project and analysis of other integrated

data assets will greatly enhance our understanding of people-centred service use and

modifiable risk factors for suicide.

241 Australia’s health 2020: data insights

Chapter

9

Suicide attempts and intentional self-harm

The most common predictor of death by suicide or premature mortality of any

kind, including accidental drug overdose, is a personal history of a previous suicide

attempt or act of self-harm (ABS 2019b; Carr et al. 2017). As a consequence, monitoring

of the incidence, demographic patterns and methods used in suicide attempts or

instances of self-harm have the potential to improve the development of suicide

prevention strategies.

Hospitalisations for intentional self-harm

Currently in Australia, the national source of intentional self-harm data is the AIHW

National Hospital Morbidity Database (NHMD), a compilation of administrative data

supplied by state and territory health authorities for patients admitted to public and

private hospitals (AIHW 2019e). The NHMD includes demographic, length of stay

and diagnosis data including external causes of injury and poisoning, as well as the

procedures patients underwent in hospital, the place where the injury occurred and

the type of activity being undertaken by the person when injured (AIHW 2019b).

Diagnosis, intervention and external cause data are reported to the NHMD by all states

and territories using the International statistical classification of diseases and related

health problems, 10th revision, Australian modification (ICD-10-AM) and the Australian

Classification of Health Interventions (ACHI). Information from this database can be

useful to monitor trends in intentional self-harm over time and to provide, albeit

limited, insights into those at risk of further self-harm. Indigenous status is included in

the NHMD (AIHW 2019b) and the AIHW is investigating the feasibility of several options

on reporting intentional self-harm in ADF personnel.

The ICD-10 AM coding system does not distinguish between suicidal and non-suicidal

intentional self-harm (AIHW: Harrison & Henley 2014). However, suicidal ideation in

the absence of a mental health condition is captured and assigned using ICD-10-AM

(ACCD 2018). Although a decision has not yet been made on whether or when ICD-11

will be adopted for coding hospital admitted cases in Australia, the new classification

developed by the WHO has made provisions for a new dimension to capture the

suicidal/non-suicidal intention of intentional self-harm injuries (WHO 2019).

Hospitals data on intentional self-harm can only provide a partial picture of those

self-harming (Figure 9.1). Other sources of data on intentional self-harm and suicidal

behaviours, such as ED data, ambulance and police attendances, crisis line calls

and treatment provided by mental health or primary health care services, have the

potential to provide a more complete picture of these behaviours in Australia and

identify opportunities for improved intervention or postvention.

242 Australia’s health 2020: data insights

Chapter

9

Hospital emergency department data

The hospital ED is often the first point of contact with the health system for people

who have harmed themselves or who have suicidal thoughts (Perera et al. 2018).

State and territory health authorities provide ED data to the AIHW for collation

into the National Non-admitted Patient Emergency Department Care Database

(NNAPEDCD) (AIHW 2019g). The NNAPEDCD captures information on the patient’s

principal diagnosis (the diagnosis mainly responsible for the attendance) and

coexisting additional diagnoses. Unlike the NHMD, information on the external

cause of injury, the place of occurrence or the type of activity are not captured in the

NNAPEDCD (AIHW 2019g). This means presentations to the ED relating to suicide

attempts or intentional self-harm cannot be identified in the current national ED

data collection. Also, this data set may not capture the complexity of mental health

presentations to the ED (AIHW 2019g). Therefore, information on suicidal behaviours

is not coded or may only be captured in clinical notes. A technical report on the use

of ED data to improve the routine surveillance of all types of injuries concluded the

utility of the data source would be enhanced by including external cause data in the

NNAPEDCD (AIHW: Henley & Harrison 2018).

In recognition of the key need for better data around suicide attempts and

self-harm in ED data, the Australian Health Ministers’ Advisory Council has funded

the Mental Health Information Strategy Standing Committee to undertake 2 projects

in 2019-20 to support improvements in identification of suicide and self-harm-related

presentations to EDs. These include the development of a methodology to identify

presentations relating to suicide attempts within jurisdictional data and a scoping

paper outlining opportunities and barriers to developing a nationally consistent

data collection on suicide-related ED presentations, including recommendations

for data improvements. The AIHW has been funded to develop the scoping paper,

which will require engagement with a range of stakeholders, including the Mental

Health Information Strategy Standing Committee and Emergency Department data

custodians.

Police and ambulance attendance data

State- and territory-held police and ambulance attendance data may provide insights

into self-harming and suicidal behaviours in Australia at a stage when intervention to

prevent further harm or subsequent suicide may be possible. Currently, the clinical

coding of ambulance presentations data is limited, with data collected for some states

over a short period of time each year (Turning Point 2019) while police reports are not

standardised across Australia making national analysis difficult.

243 Australia’s health 2020: data insights

Chapter

9

A study by Turning Point and Monash University coded and analysed clinical patient

records from ambulance call-outs to men presenting with acute mental health issues,

intentional self-harm, suicidal behaviours, or alcohol or drug intoxication in the

Australian Capital Territory, the Northern Territory, Queensland, Tasmania, Victoria

and New South Wales between July 2015 and June 2016 (Turning Point 2019). The

study found that coded ambulance data are an important source of information to

establish the number and characteristics of presentations for mental illness, intentional

self-harm or suicidal behaviours—and may address evidence gaps by capturing

information that is not currently identified by other health morbidity data sets such as

ED or hospital admissions, including:

• details of the nature and background to the attendance, including information about

what was observed ‘on scene’ (such as bystander accounts, evidence of drug use and

suicide intent)

• types of people most at risk (for example, those who call an ambulance multiple

times or those with increasingly harmful behaviour)

• the location of the event (which may allow for geographic and temporal mapping)

• the clinical outcome.

Following this initial study, the National Suicide and Self-harm Monitoring System will

include the collection, improvement and dissemination of national coded ambulance

data for intentional self-harm and suicidal behaviours, as well as:

• alcohol and other drug-related ambulance attendances, including intentional alcohol

and other drug poisoning and type of drug involved

• mental health-related ambulance attendances, including the presence of symptoms,

history of mental illness and risk indicators both at the time of presentation and

through the life course.

This data set will address a significant gap in service-level data for populations at risk

of suicide or intentional self-harm. Such a data set may also provide opportunities for

data linkage to allow insights, for example, into service use patterns or cohort analysis.

Community health care data

Community health care data on intentional self-harm and suicidal behaviours are

limited. Self-harming and suicidal behaviours occurring in the community may be

treated by general practitioners or community and residential mental health services;

however, data collections from these sources do not routinely capture this information.

244 Australia’s health 2020: data insights

Chapter

9

Also, although MBS, PBS or Repatriation Schedule of Pharmaceutical Benefits (RPBS)

data can provide information about medical services provided or prescriptions

processed, by themselves these sources cannot provide information about those at

risk of suicide, intentional self-harm or suicidal behaviours. Nevertheless, these sources

of data may be useful in terms of adding to data linkage projects.

Suicidal ideation

Measuring the incidence of suicidal ideation with routinely collected clinical data

is also limited because the majority of people with suicidal thoughts do not tend to

seek medical treatment (Geulayov et al. 2018). Self-reports of suicide attempts,

plans or thoughts in health surveys of representative samples of the population

provide information about suicidal behaviours in the community. On the basis of

survey data, suicidal behaviours are far more common than deaths by suicide or

intentional self-harm (Slade et al. 2009). People who experience suicidal ideation

and make suicide plans are at increased risk of suicide attempts, and people who

experience all forms of suicidal thoughts and behaviours are at greater risk of death

by suicide (Slade et al. 2009).

Survey data

The National Survey of Mental Health and Wellbeing (2007) indicated that, at some

point in their lives, 13% of Australians aged 16-85 years had had serious thoughts

about taking their own life (an estimated 2.1 million Australians), 4% (over 600,000)

made a suicide plan and 3% (over 500,000) had attempted suicide (Slade et al. 2009).

In 2007, the number of registered deaths by suicide in Australia was 2,229 (ABS 2016a).

The second Australian Child and Adolescent Survey of Mental Health and Wellbeing,

conducted between 2013 and 2014, captured information from Australian young

people aged 12-17 years about self-harming activity and suicidal behaviours

(Lawrence et al. 2015). Around 1 in 10 surveyed 12-17-year-olds (10.9%, equivalent

to an estimated 186,000 young people) reported having ever self-harmed and

about three quarters of these had harmed themselves in the previous 12 months

(8%, equivalent to an estimated 137,000 young people). Around 1 in 13 (7.5%, equivalent

to an estimated 128,000 young people) 12-17-year-olds had seriously considered

attempting suicide in the previous 12 months and of these, one third (or 2.4% of all

12-17-year-olds) reported having attempted suicide in the previous 12 months.

Both of these national surveys relied on self-reported responses, and therefore

should be interpreted with caution, as respondents may not feel comfortable

reporting on intentional self-harm or suicidal behaviours.

245 Australia’s health 2020: data insights

Chapter

9

In 2019, the Government announced funding for an Intergenerational Health and Mental Health Study (Hunt 2019). This study will include components on general and mental health, including lived experiences of suicide and related services, and will provide updated results to compare with the 1997 (ABS 1998) and 2007 National Survey of Mental Health and Wellbeing (Slade et al. 2009).

Crisis line calls and help-seeking websites

Several organisations in Australia provide tele-counselling for people in crisis, for example, Lifeline, MensLine Australia, Kids Helpline, Beyond Blue and the Suicide Call Back Service. Each helpline has its own data capture system; however, data are not standardised and governance agreements are not currently in place to allow de-identified data to be shared and analysed. Better use of these data may provide useful information on help-seeking behaviours and identify populations at risk of suicide, intentional self-harm and suicidal behaviours.

Suicide and intentional self-harm in specific populations Rates of suicide and intentional self-harm in Indigenous Australians and current serving, and contemporary ex-serving ADF personnel have been a cause of concern in Australia. However, there are significant challenges in monitoring suicide and intentional self-harm in these populations, which can make detecting changes in outcomes and assessing the impact of suicide prevention activities difficult. The AIHW is currently actively involved in data improvement activities to expand the collection and availability of data on deaths by suicide and the occurrence of intentional self-harm in these populations.

Aboriginal and Torres Strait Islander people

In 2018, 169 Indigenous Australians died by suicide, accounting for 5.3% of all Indigenous deaths (ABS 2019a). Age-standardised rates of Indigenous deaths by suicide have increased over time, from 20.2 per 100,000 persons in 2009-2013 to 23.7 per 100,000 persons in 2014-2018 (ABS 2019a).

Age-standardised suicide rates for Indigenous males have increased from 30.4 per 100,000 in 2009-2013 to 36.4 in 2014-18. The change in the rate for Indigenous females has been less marked (10.7 per 100,000 in 2009-2013 compared with 11.6 in 2014-2018) (ABS 2019a).

246 Australia’s health 2020: data insights

Chapter

9

Suicide is a pronounced issue for Indigenous youth—in the 5 years from 2014 to 2018,

suicide rates were highest for those aged 25-34 years (47.1 per 100,000) and 15-24

(40.5 per 100,000) but then declined with age to less than 10 per 100,000 for those

aged 65 and over (ABS 2019a).

Indigenous males are more likely than females to die by suicide—there were around

3 times as many deaths by suicide in Indigenous males (129) as females (40) in 2018

(ABS 2019a)—while Indigenous females were more likely than males to be hospitalised

for intentional self-harm (1,736 cases or 445 per 100,000 population, compared with

1,113 cases or 325 per 100,000 population) in 2016-17 (AIHW: Pointer 2019).

In the ABS Causes of Death data set, the Indigenous status of a deceased person is

captured through the death registration process; however, it is recognised that this does

not always occur, leading to under-identification (ABS 2019a). Due to these known data

quality issues, the ABS Causes of Death data set only reports rates of Indigenous deaths

(including those by suicide) in those states and territories that have official records

with reliable identification data for Indigenous people (New South Wales, Queensland,

Western Australia, South Australia and the Northern Territory) (ABS 2019a).

In order to improve suicide prevention activities targeted at Indigenous Australians,

and to accurately assess their progress, it will be critical to improve the evidence base

around this population group. Through the National Civil Registration and Statistics

Improvement Committee, the ABS is working closely with the state and territory

Registries of Births, Deaths and Marriages to progress towards improved identification

in a nationally consistent way.

There is a growing body of research literature around what works in the prevention

of Indigenous suicide, including a range of success factors that can be used to guide

interventions targeting at-risk groups and individuals (Dudgeon et al. 2016). The AIHW

will add to this evidence base by developing an online Indigenous mental health and

suicide-prevention clearinghouse. The clearinghouse will be an authoritative source on

the latest information and will include articles by subject matter experts; accessible data

and evidence on specific topic areas; and a register of relevant research and evaluations.

Current serving, reserve and contemporary ex-serving Australian Defence Force personnel

In 2016, in response to concerns within the ADF and the wider Australian community,

the Department of Veterans’ Affairs commissioned the AIHW to monitor the number

and rate of deaths by suicide in serving, reserve and ex-serving ADF personnel

(AIHW 2019g). To date, analysis includes current serving, reserve and contemporary

ex-serving ADF personnel who have at least 1 day of service from 2001.

247 Australia’s health 2020: data insights

Chapter

9

From 2001 to 2017 there were 419 certified deaths by suicide among men and women

with at least 1 day of ADF service since 1 January 2001 (AIHW 2019g). Of these, 229

(55%) occurred among contemporary ex-serving personnel. The crude rate of suicide

among contemporary ex-serving men between 2002 and 2017 was 27 per 100,000

population, which was 18% higher than in Australian men after adjusting for age.

The suicide rate in current serving and reserve men was 12 per 100,000 population

which was half the rate in Australian men after adjusting for age (AIHW 2019f).

Detailed analysis published in 2017 found that younger age, a short length of service

(less than 1 year), discharge with a rank other than a commissioned officer, or

involuntary discharge (particularly medical discharge) were risk factors for suicide

in ex-serving men (AIHW 2017). Between 2002 and 2017, the crude suicide rate in

contemporary ex-serving women was 15 per 100,000, which was higher than in

Australian women after adjusting for age; reporting of suicide rates for current

serving and reserve women is not possible at this time due to confidentiality

constraints and the small numbers in these cohorts.

Deaths by suicide in current serving and contemporary ex-serving ADF personnel

are identified using personnel management system data from the Department of

Defence, which includes those with service from 1 January 2001 (AIHW 2019f).

The AIHW is currently investigating the feasibility of using additional data sources

to extend the coverage of the available data. Further information on the use of

health services by ADF personnel following a suicide attempt or an incident of

intentional self-harm would also help to provide a more comprehensive

understanding of these behaviours in this population.

The important role of data Suicide and intentional self-harm are complex problems. Evidence-based interventions to help prevent suicide and self-harming behaviour require an understanding of the ‘who, when, where, and how’ of suicide and intentional self-harm in order to provide insights about ‘why’. While better data on its own cannot prevent suicide, bringing together multiple relevant data sources in a coherent manner is required to develop timely, targeted and effective prevention strategies.

The AIHW will work collaboratively and in consultation with all jurisdictions to design and implement the National Suicide and Self-harm Monitoring System so that it successfully brings together regional- and demographically-specific data on the incidence of suicide and intentional self-harm, and so better informs the planning and targeting of prevention and intervention strategies by governments, service providers and communities.

248 Australia’s health 2020: data insights

Chapter

9

Table 9.1: Overview of current and potential sources of data for suicide, intentional self-harm and suicidal behaviours in Australia

Measure Data source

Outcome as a number or %

(Most recent year available)

Data Source

Strengths Limitations Opportunities

Deaths by suicide

ABS Causes of Death data set/ AIHW NMD

3,046 (2018)

(a)

National coverage (epidemiological data set)

Mandatory collection

Includes all deaths

Method of suicide reported

High quality

Standardised, coded data set

Timely for an epidemiological data set

International comparison

Data are revised as more information becomes available

Not suitable for timely suicide surveillance because the data are updated annually

Issues with quality of geo-coded data for place of occurrence

Data quality issues with Indigenous status

Identification of psychosocial risk factors

Improved identification of Indigenous status

Improved geo-coding of incident and fatality location

Data linkage

NCIS n.a. National coverage

Standardised, coded data set

Demographic, contextual and circumstantial information on reportable deaths

Available by application, only to approved users Improved geo-coding of incident and fatality

location

Inclusion of risk factor information

249 Australia’s health 2020: data insights

Chapter

9

Measure Data source

Outcome as a number or %

(Most recent year available)

Data Source

Strengths Limitations Opportunities

Deaths by suicide, continued

Suicide registers

n.a. State-based

Timely

Can be used as a surveillance data set

Demographic, contextual and circumstantial information on all reportable deaths

No nationally consistent approach to data collection and reporting

Subject to change at time of use

Improved geo-coding of incident and fatality location

More timely data on suspected suicides

Hospitalisations for intentional self-harm

NHMD 33,131 cases

(2016-17) (b)

Administrative data from each state and territory

Standardised, coded data set

Indigenous status

Method of suicide reported

A record is included for each separation, not for each patient.

Improved identification of populations at risk

Data linkage

ED presentations for intentional self-harm

NNAPEDCD n.a. Administrative data from

each state and territory

Standardised, coded data set

Indigenous status

Does not capture the cause of the injury, intent and/or self-harm

Develop a methodology to identify intentional self-harm

Data linkage

250 Australia’s health 2020: data insights

Chapter

9

Measure Data source

Outcome as a number or %

(Most recent year available)

Data Source

Strengths Limitations Opportunities

Prevalence of suicide attempts in the community

National Survey of Mental Health and Wellbeing (2007)

3.3% lifetime prevalence

(c)

National sample of 8,841 Australian households, excluding very remote areas

Participants aged 16-85 years

Prevalence estimate

Self-reported survey data

Non-private dwellings excluded (e.g. hospitals, correctional facilities)

One-off, point-in-time data collection

Indigenous status

Update survey

Data linkage

Second Australian Child and Adolescent Survey of Mental Health and Wellbeing (2013-14)

2.4% of 12-17 year olds in the previous 12 months

National sample of 6,310 families with children aged 4-17 years (questions on self-harm and suicidal behaviours were only asked of those aged ≥12 years), excluding very remote areas

Prevalence estimate

Self-reported survey data

Non-private dwellings excluded (e.g. hospitals, correctional facilities)

Small sample size limiting modelling of risk factors

(d)

Data linkage

Ambulance call-outs n.a. State-based electronic

patient care records

Feasibility established for coding of ambulance clinical records for mental health, self-harm, alcohol or drugs attendances

(e)(f)

No system for the collation of nationally consistent paramedic data, including intentional self-harm and suicidal behaviours

Coding, collation and reporting of national ambulance presentations data

Data linkage

251 Australia’s health 2020: data insights

Chapter

9

Measure Data source

Outcome as a number or %

(Most recent year available)

Data Source

Strengths Limitations Opportunities

Prevalence of suicide attempts in the community, continued

Police data n.a. State-based police

incident forms

No system for the collation and reporting of nationally consistent police data

Reporting of national, standardised police data

Prevalence of suicidal ideation National Survey of

Mental Health and Wellbeing (2007)

13.3% lifetime prevalence

(c)

As above As above As above

Second Australian Child and Adolescent Survey of Mental Health and Wellbeing (2013-14)

7.5% of 12-17 year olds in the previous 12 months

As above As above As above

Crisis help line use n.a. Existing telephone and

web-based support services for suicide prevention (e.g. Lifeline, Kids Helpline, MensLine, Suicide Call Back Service, Headspace)

No national system for the collation and reporting of nationally consistent crisis support data

Coding, collation and reporting of national crisis support data.

252 Australia’s health 2020: data insights

Chapter

9

Measure Data source

Outcome as a number or %

(Most recent year available)

Data Source

Strengths Limitations Opportunities

Prevalence of suicidal ideation, continued

Primary health care (GP)

Existing clinical information systems Suicide and intentional self-harm data not

included

Coding, collation and reporting of national health care data

Treatment provided by mental health professionals

Existing community and residential mental health National Minimum Data Sets (NMDS)

Suicide and intentional self-harm data not included

Data linkage

(a) ABS 2019a. 2018 preliminary data; 2017 and 2016 revised data; 2015 and earlier finalised data

(b) AIHW: Pointer 2019

(c) at any point in the respondent’s lifetime

(d) Kyron et al. 2019

(e) Scott et al. 2018

(f) Turning Point 2019

Note: ABS = Australian Bureau of Statistics; ADF = Australian Defence Force; AIHW = Australian Institute of Health and Welfare; ED = emergency department; n.a. = Not available; NCIS = National Coronial Information System; NHMD = National Hospital Morbidity Database; NNAPEDCD = National Non-Admitted Patient Emergency Department Care Database; NMD = National Mortality Database.

253 Australia’s health 2020: data insights

Chapter

9

References ABS (Australian Bureau of Statistics) 1998. Mental health and wellbeing: profile of adults, Australia 1997. ABS cat. no. 4326.0. Canberra: ABS.

ABS 2016a. Causes of death, Australia, 2016. ABS cat. no. 3303.0. Canberra: ABS

ABS 2016b. Information paper: Transforming statistics for the future, Feb 2016. ABS cat. no. 1015.0. Canberra: ABS.

ABS 2016c. Research paper: Death registrations to Census linkage project—a linked dataset for analysis, Mar 2016. ABS cat. no. 1351.0.55.058. Canberra: ABS.

ABS 2019a. Causes of death, Australia, 2018. ABS cat. no. 3303.0. Canberra: ABS.

ABS 2019b. Research paper: Psychosocial risk factors as they relate to coroner-referred deaths in Australia, 2017. ABS cat. no. 1351.0.55.062. Canberra: ABS.

ACCD (Australian Consortium for Classification Development) 2018. The International Statistical Classification of Diseases and Related Health Problems, tenth revision, Australian Modification (ICD-10-AM), 11th edition. Adelaide: Independent Hospital Pricing Authority.

AHA (Australian Healthcare Associates) 2014. Evaluation of suicide prevention activities. Canberra: Department of Health. Viewed 2 January 2020, https://www1.health.gov.au/internet/ main/publishing.nsf/Content/mental-pubs-e-evalsuic.

AIHW (Australian Institute of Health and Welfare): Harrison JE, Pointer S & Elnour AA 2009. A review of suicide statistics in Australia. Injury research and statistics series no. 49. Cat. no. INJCAT 121. Adelaide: AIHW.

AIHW 2011. Principles on the use of direct age-standardisation in administrative data collections: for measuring the gap between Indigenous and non-Indigenous Australians. Cat. no. CSI 12. Canberra: AIHW.

AIHW: Harrison JE & Henley G 2014. Suicide and hospitalised self-harm in Australia: trends and analysis. Injury research and statistics series no. 93. Cat. no. INJCAT 169. Canberra: AIHW.

AIHW 2017. Incidence of suicide in serving and ex-serving Australian Defence Force personnel: detailed analysis 2001-2015. Cat. no. PHE 218. Canberra: AIHW. https://www.aihw.gov.au/ reports/veterans/incidence-of-suicide-in-adf-personnel-2001-2015/contents/table-of-contents.

AIHW: Henley G & Harrison JE 2018. Use of emergency department data to improve routine injury surveillance: technical report 2013-14. Cat. no. INJCAT 199. Canberra: AIHW. https://www.

aihw.gov.au/reports/injcat/199/emergency-department-data-routine-injury-2013-14/contents/ summary.

AIHW 2019a. About National Mortality Database. Canberra: AIHW. Viewed 8 November 2019, https://www.aihw.gov.au/about-our-data/our-data-collections/national-mortality-database/ about-nmd.

AIHW 2019b. Admitted patient care NMDS 2017-18. Canberra: AIHW. Viewed 8 November 2019, https://meteor.aihw.gov.au/content/index.phtml/itemId/641349.

AIHW 2019c. General Record of Incidence of Mortality (GRIM) data. Canberra: AIHW. Viewed 8 November 2019, https://www.aihw.gov.au/reports/life-expectancy-death/grim-books/contents/ general-record-of-incidence-of-mortality-grim-books.

254 Australia’s health 2020: data insights

Chapter

9

AIHW 2019d. Mortality Over Regions and Time (MORT) books. Canberra: AIHW. Viewed 8 November 2019, https://www.aihw.gov.au/reports/life-expectancy-death/mort-books/ contents/mort-books.

AIHW 2019e. National Hospitals Data Collection. Canberra: AIHW. Viewed 8 November 2019, https://www.aihw.gov.au/about-our-data/our-data-collections/national-hospitals-data-collection.

AIHW 2019f. National suicide monitoring of serving and ex-serving Australian Defence Force personnel: 2019 update. Cat. no. PHE 222. Canberra: AIHW. Viewed 3 January 2020, https://www.

aihw.gov.au/reports/veterans/national-veteran-suicide-monitoring/contents/summary.

AIHW 2019g. Non-admitted patient emergency department care NMDS 2017-18. Canberra: AIHW. Viewed 8 November 2019, https://meteor.aihw.gov.au/content/index.phtml/ itemId/651856.

AIHW: Pointer SC 2019. Trends in hospitalised injury, Australia 2007-08 to 2016-17. Injury research and statistics series no. 124. Cat. no. INJCAT 204. Canberra: AIHW.

AIHW: Kreisfeld R & Harrison JE 2020. Indigenous injury deaths: 2011-12 to 2015-16. Injury research and statistics series no. 130. Cat. no. INJCAT 210. Canberra: AIHW.

Arensman E, Corcoran P & McMahon E 2017. The iceberg model of self-harm: new evidence and insights. Lancet Psychiatry: 5(2):100-101.

Carr MJ, Ashcroft DM, Kontopantelis E, While D, Awenat Y, Cooper J et al. 2017. Premature death among primary care patients with a history of self-harm. Annals of Family Medicine 15(3):246-54.

Clapperton A, Newstead S, Bugeja L & Pirkis J 2019. Relative risk of suicide following exposure to recent stressors, Victoria, Australia. Australia and New Zealand Journal of Public Health 43(3):254-60.

COAG (Council of Australian Governments) Health Council 2017. The Fifth National Mental Health and Suicide Prevention Plan. Canberra: COAG Health Council.

Department of Health 2019a. Budget 2019-20: Prioritising Mental Health—National Suicide Information Initiative. Viewed 8 November 2019, https://www.health.gov.au/sites/default/files/ prioritising-mental-health-national-suicide-information-initiative_0.pdf.

Department of Health 2019b. Budget 2019-20: Prioritising Mental Health—Youth Mental Health and Suicide Prevention Plan. Viewed 16 October 2019, https://www.health.gov.au/sites/default/ files/prioritising-mental-health-youth-mental-health-and-suicide-prevention-plan_0.pdf.

Department of Health 2019c. The Prime Minister’s National Suicide Prevention Adviser. Viewed 8 November 2019, https://www1.health.gov.au/internet/main/publishing.nsf/Content/mental-national-suicide-prevention-adviser.

Dudgeon P, Milroy J, Calma T, Luxford Y, Ring I, Walker R et al. 2016. Solutions that work: what the evidence and our people tell us. Aboriginal and Torres Strait Islander Suicide Prevention Evaluation Project report. Crawley WA: University of Western Australia.

Geulayov G, Casey D, McDonald KC, Foster P, Pritchard K, Wells C et al. 2018. Incidence of suicide, hospital-presenting non-fatal self-harm, and community-occurring non-fatal self-harm in adolescents in England (the iceberg model of self-harm): a retrospective study. Lancet Psychiatry 5(2):167-74.

Hunt, the Hon. G 2019. Building a mentally and physically healthy Australia. Media release by the Minister for Health. 14 August. Canberra.

255 Australia’s health 2020: data insights

Chapter

9

Kyron M, Carrington-Jones P, Page A, Bartlett J & Lawrence D 2019. Factors differentiating adolescents who consider suicide and those who attempt: results from a National Survey of Australian Adolescents. Australian Journal of Psychology 1-11. https://doi.org/10.1111/ ajpy.12267.

Lawrence D, Johnson S, Hafekost J, Boterhoven de Haan K, Sawyer M, Ainley J et al. 2015. The mental health of children and adolescents: report on the second Australian Child and Adolescent Survey of Mental Health and Wellbeing. Canberra: Department of Health.

Leske S, Crompton D & Kõlves K 2019. Suicide in Queensland: annual report 2019. Mt Gravatt Qld: Australian Institute for Suicide Research and Prevention, Griffith University.

McMahon EM, Keeley H, Cannon M, Arensman E, Perry IJ, Clarke M et al. 2014. The iceberg of suicide and self-harm in Irish adolescents: a population-based study. Social Psychiatry and Psychiatric Epidemiology 49(12):1929-35.

Morris JK, Tan J, Fryers P & Bestwick J 2018. Evaluation of stability of directly standardized rates for sparse data using simulation methods. Population Health Metrics 16(19).

Perera J, Wand T, Bein KJ, Chalkley D, Ivers R, Steinbeck KS et al. 2018. Presentations to NSW emergency departments with self-harm, suicidal ideation, or intentional poisoning, 2010-2014. Medical Journal of Australia 208(8):348-53.

Pollock NJ, Healey GK, Jong M, Valcour JE, Mulay S 2018. Tracking progress in suicide prevention in Indigenous communities: a challenge for public health surveillance in Canada. BMC Public Health 18(1320).

Scott D, Crossin R, Ogeil R, Smith K, Lubman DL 2018. Exploring harms experienced by children aged 7 to 11 using ambulance attendance data: a 6-year comparison with adolescents aged 12-17. International Journal of Environmental Research in Public Health 15(7):1385-1398.

Senate Community Affairs References Committee 2010. The hidden toll: suicide in Australia. Canberra: Senate Community Affairs Committee Secretariat.

Slade T, Johnston A, Teesson M, Whiteford H, Burgess P, Pirkis J et al. 2009. The mental health of Australians 2: report on the 2007 National Survey of Mental Health and Wellbeing. Canberra: Department of Health and Ageing.

Sutherland G, Milner A, Dwyer J, Bugeja L, Woodward A, Robinson J et al. 2018. Implementation and evaluation of the Victorian Suicide Register. Australian and New Zealand Journal of Public Health. 42(3):296-302.

Tasmanian Department of Health and Human Services 2016. Tasmanian Suicide Prevention Strategy (2016-2020): working together to prevent suicide. Hobart: Tasmanian Department of Health and Human Services. Viewed 2 December 2019, https://www.dhhs.tas.gov.au/__data/ assets/pdf_file/0014/214412/151152_DHHS_Suicide_Prevention_Strategy_Final_WCAG.pdf.

Turning Point 2019. Beyond the emergency: a national study of ambulance responses to men’s mental health. Richmond Vic: Turning Point.

WHO (World Health Organization) 2013. Mental health action plan 2013-2020. Geneva: WHO.

WHO 2014. Preventing suicide: a global imperative. Geneva: WHO.

WHO 2016. International Statistical Classification of Diseases and Related Health Problems, tenth revision. Geneva: WHO.

WHO 2019. ICD-11: International Classification of Diseases for Mortality and Morbidity Statistics, eleventh revision: reference guide. Geneva: WHO.

257 Australia’s health 2020: data insights

Longer lives, healthier lives?

10

258 Australia’s health 2020: data insights

Chapter

10

Over many decades, life expectancy in Australia has increased substantially. People

born in the early 1900s were expected to live, on average, to around age 55, compared

with people born after 2010 who are expected to live, on average, to age 80 or more.

But are longer lives also healthier lives? It is important to differentiate years lived in

full health from years lived in ill health: are people who live longer also staying sick

for longer—and thus increasing the amount of ill health in the country? If more of the

years gained are expected to be affected by disease and injury, this has an impact on

quality of life of individuals. It will also have implications for health planning and future

health system costs and demand for aged-care and community services, particularly

for older Australians.

Older Australians experience a significant proportion of the burden of ill health. In 2015,

Australians aged 65 and over represented 15% of the population but experienced one-third

(33%) of the burden of ill health. Chronic conditions—musculoskeletal disorders, neurological

conditions, cardiovascular diseases and respiratory diseases—accounted for 60% of this

burden (AIHW 2019a). See ‘Burden of disease’ https://www.aihw.gov.au/reports/

australias-health/burden-of-disease for more information.

Whether or not the amount of ill health experienced by older Australians has increased

has been the subject of ongoing debate. There are 3 main theories of healthy ageing

that offer a useful framework for assessing improvements in health and increases in

life expectancy. These theories are referred to as:

• expansion of morbidity—where increasing life expectancy is accompanied by more

illness and injury before death. As chronically ill people survive for longer, we can

expect an increase in the proportion of their lives spent with illness (Greunberg 1975)

• compression of morbidity—where increasing life expectancy is accompanied by

better health. As the population ages, there is a delay in the age of onset of disease,

and we can expect a reduction in the proportion of life spent in ill health (Fries 1980)

• dynamic equilibrium—where the proportion of the lifetime spent living with illness

remains relatively constant over time. As life expectancy increases, so does the onset

and progression of disease—but as diseases grow more prevalent they may also be

less severe (Howse 2006).

This chapter provides some unique insights to help us assess whether there has been

an expansion or compression of morbidity—or an equilibrium between morbidity and

mortality—as older Australians are living longer. Using burden of disease analysis—

specifically health-adjusted life expectancy (HALE) (Box 10.1), which combines

health-related quality of life (years lived with disability (YLD)) and life expectancy into

a single measure—can help determine which of these theories best describes the

picture of health in Australia.

259 Australia’s health 2020: data insights

Chapter

10

Box 10.1: Terms used in this chapter

Health-adjusted life expectancy (HALE)

HALE extends the concept of life expectancy by considering the time spent

living with the health consequences of disease and injury. It provides a more

comprehensive picture of health than other summary measures (for example,

life expectancy, infant mortality and disease prevalence). The measure reflects the

average number of years of life expected in full health. Over a period of 1 year, a

person at any age can potentially live a year in full health or spend some of the

year living with illness. Illnesses vary by duration and severity, so the amount of

time lost to ill health is measured by combining the duration and severity of the

illness; the remaining time in that 1 year is considered as time in full health.

HALE uses YLD rates and life expectancy estimates in its calculation.

Years lived with disability (YLD)

YLD quantifies the average experience of health loss, based on the prevalence of

all health conditions adjusted for the severity and comorbidity of diseases. YLD

rates expressed per person can be interpreted as the proportion of the year that

each person, on average, lost due to ill health, thereby providing a measure of

average ill health in the population during that year.

This chapter focusses on HALE at age 65—a measure that represents the number of

years of life expectancy at this age that could be expected to be lived in full health.

Focussing on this age group highlights trends in the health of Australia’s ageing

population and helps to describe whether or not the years of life (expectancy) gained

are healthy years. As statistics at the national level can mask disparities between

different population groups, this chapter also explores differences in HALE for

Australians from different socioeconomic areas.

At a national level, what is evident from these analyses is that, for people aged 65,

with continuing increases in life expectancy, the proportion of their lifetime spent in

ill health has remained constant (that is, supporting the ‘dynamic equilibrium’ theory).

However, this picture is not the same for all population groups. Available data suggest

there has been an expansion of morbidity for people living in the lowest socioeconomic

areas, and a compression of morbidity in the highest socioeconomic areas.

These findings, and the analyses underpinning them, are discussed in more detail below.

260 Australia’s health 2020: data insights

Chapter

10

Living longer Advances in disease prevention and treatment over the 20th and 21st centuries have resulted in large reductions in mortality rates in Australia. While reductions were more dramatic in the first half of last century, age-standardised mortality rates have still declined by 59% since 1967, from around 1,300 deaths per 100,000 persons to 552 in 2015 (Figure 10.1). Also, in the first half of last century, deaths due to chronic disease (such as cancer and cardiovascular diseases) were on the rise, while deaths due to infectious diseases were declining. Chronic diseases are now more prevalent and are responsible for the majority of deaths: in 2015, 58% of deaths were due to cancer and cardiovascular diseases compared with 23% in 1915 (AIHW 2019b).

For the latest mortality and life expectancy statistics please refer to ‘Causes of Death’ https://www.aihw.gov.au/reports/australias-health/causes-of-death and ‘How healthy are Australians’ https://www.aihw.gov.au/reports/australias-health/how-healthy-are-australians.

Increasing longevity is occurring at all ages. For example, if you were aged 65 in 1905, you would have been expected to live on average for 11.3 more years, compared with an average of 19.6 more years if you were aged 65 in 2015. Similarly, if you were aged 85 in 1905, you would have been expected to live on average for 3.7 more years, compared with an average of 6.2 more years if you were aged 85 in 2015.

Deaths per 100,000 population

All causes

All other causes

Cancers and cardiovascular diseases

Infectious diseases (incl influenza and pneumonia)

0

500

1,000

1,500

2,000

2,500

1915 1925 1935 1945 1955 1965 1975 1985 1995 2005 2015

External causes

Figure 10.1: Age-standardised mortality rates, by causes of death, 1915-2015

Source: AIHW 2019b.

261 Australia’s health 2020: data insights

Chapter

10

During the 40 years to 2015, the average male life expectancy—at birth and at ages

65 and 85—increased by 16%, 50% and 40% respectively. There were similar but less

pronounced increases among females (increasing by 10%, 30% and 33% respectively).

That is, life expectancy has been increasing for those of older ages as well as for

newborns (Table 10.1). For Australians aged 65, life expectancy increased by 6.5 years

over this period for males (from 13.1 years in 1976 to 19.6 in 2015) and by 5.2 years for

females (from 17.1 to 22.3 years) (ABS 2019).

Table 10.1: Life expectancy at selected ages and time periods, by sex, 1901-2016

Age (years) 1901-1910 1932-1934 1953-1954 1975-1977 1994-1996 2014-2016

Males

0 55.2 63.5 67.1 69.6 75.2 80.5

25 40.6 44.4 45.5 46.9 51.5 56.2

45 24.8 26.9 27.2 28.3 32.8 37.1

65 11.3 12.4 12.3 13.1 15.8 19.6

85 3.7 3.9 4.0 4.5 5.2 6.2

Females

0 58.8 67.1 72.8 76.6 81.1 84.6

25 43.4 47.2 50.2 53.1 56.9 60.1

45 27.6 29.7 31.4 34.0 37.5 40.6

65 12.9 14.2 15.0 17.1 19.6 22.3

85 4.2 4.3 4.5 5.5 6.4 7.3

Note: Life expectancy is based on limited historical data. Reference years were selected to approximate 20-year intervals.

Source: ABS 2019.

Measuring whether longer lives are healthier lives To understand whether the amount of ill health experienced in the population

is increasing or decreasing requires a comprehensive measure of the health

of a population that is comparable over time and between population groups.

A comprehensive measure needs to combine the prevalence of all diseases and the

degree of impact of these diseases on health.

Burden of disease analysis provides such a metric: it measures the impact of diseases

on health by quantifying the health loss due to all health conditions. YLD rates provide

a measure of the average number of years spent living with disease or injury per

person in the population.

262 Australia’s health 2020: data insights

Chapter

10

Figure 10.2 shows YLD rates by age for people aged 65 and over. In both 2003 and 2015, YLD rates increased progressively with age. For example, the YLD rate for those aged 65-69 in 2015 was 167 YLD per 1,000 persons, compared with 318 for those aged 85-89. That is, on average, as people age they lose more healthy years due to ill health. This figure also suggests there has been a decline in YLD rates in the older age groups between 2003 and 2015.

Figure 10.2: Years lived with disability (YLD) rates among people aged 65

and over, by age group, 2003 and 2015

Source: AIHW 2019a.

HALE uses YLD rates in its calculation and reflects the average number of years an individual can expect to live in full health, taking into account mortality and disease/injury. HALE data presented below use life expectancy estimates based on Australian mortality rates and YLD rates from the Australian Burden of Disease Study (ABDS) 2015 (AIHW 2019a).

Compression, expansion or equilibrium? Assessment of how the relationship between life expectancy and HALE has changed over time (by analysing the ratio and difference between the 2 measures) provides an opportunity to examine which of the scenarios of healthy ageing—compression or expansion of morbidity, or equilibrium—provides the best insight into whether longer lives are healthier lives.

2003 2015

0

100

200

300

400

500

600

65 -69 70 -74 75 -79 80 -84 85 -89 90 -94 95 -99 100+

YLD rates (years lived with disability per 1,000 population)

Age

263 Australia’s health 2020: data insights

Chapter

10

The national picture—equilibrium

Figure 10.3 presents the remaining years of life at each age for males and females

(life expectancy) apportioned into the time spent in 2 health states: full health and

ill health. Life expectancy at birth (age 0) shows that boys born in 2015 would be

expected to live to 80.4 years with 71.5 of these years in full health and 8.9 in ill health,

while girls would be expected to live 84.6 years with 74.4 of these in full health and

10.2 years in ill health.

Figure 10.3: Life expectancy, by years spent in full health and ill health,

by sex and age, 2015

Source: AIHW 2019a.

Males aged 65 years in 2015 have a life expectancy of 19.6 years, during which a

total amount of 15.0 years would be expected in full health (indicated by the dotted

line in Figure 10.3) with 4.6 healthy years lost due to ill health. For females, of their

remaining 22.3 years, 16.8 years would be expected to be in full health (Figure 10.3)

and 5.5 healthy years lost due to ill health.

Changes over time

Extending the analysis to compare changes over time in life expectancy, HALE and

years lost due to ill health can aid understanding of the extent to which gains in life

expectancy are accompanied by a decrease or increase in living with ill health: that is,

the compression or expansion of morbidity.

0

10

20

30

40

50

60

70

80

90

0

Remaining years of life

Males aged 65

0

10

20

30

40

50

60

70

80

90

0

Age

Life expectancy

Full health

Ill health

Females aged 65

20 40 60 80 100

Males

20 40 60 80 100

Females

264 Australia’s health 2020: data insights

Chapter

10

Over the period 2003 to 2015, males gained 1.8 years of life expectancy—with 1.5 of

these years in full health and 0.3 years in ill health. Females gained 1.2 years of life

expectancy—with 0.8 of these years in full health and 0.4 years in ill health. So, for both

men and women, the number of years expected in full health and in ill health increased

over the period 2003 to 2015 (Table 10.2).

While life expectancy and HALE increased over this period, the proportion of time

spent in full health at age 65 was similar at each time point (around 75% and 76% for

both males and females).

This analysis indicates that, while increasing life expectancy is associated with some

extra time in ill health, the proportion of people’s lives spent in ill health remains about

the same. At the national level, for people aged 65, changes in morbidity are in keeping

with changes in mortality: that is, there is no indication that morbidity is compressing

or expanding among this age group. Rather, the picture reflects a dynamic equilibrium

between morbidity and mortality.

Table 10.2: Life expectancy at age 65 by years in full health and ill health, by sex, 2003 and 2015

  Number of years  

Proportion of life expectancy

  2003 2015   2003 2015

Men

Expected years of life at age 65

In full health (HALE) 13.5 15.0 75.8 76.5

In ill health 4.3 4.6 24.2 23.5

Total (life expectancy) 17.8 19.6 100.0 100.0

Women

Expected years of life at age 65

In full health (HALE) 16.0 16.8 75.8 75.3

In ill health 5.1 5.5 24.2 24.7

Total (life expectancy) 21.1 22.3   100.0 100.0

Sources: ABS 2019; AIHW 2019a.

265 Australia’s health 2020: data insights

Chapter

10

Expansion in the lowest socioeconomic areas and compression in the highest socioeconomic areas Like many aspects of population health, the national picture for HALE (of equilibrium

in morbidity and mortality as we age) is not shared by all Australians. There is a

clear trend of increased life expectancy and years lived in full health in higher

(more advantaged) socioeconomic areas (Figure 10.4).

Men and women aged 65-69 living in the lowest (least advantaged) socioeconomic

areas had shorter life expectancy and a smaller percentage of life in full health,

compared with those living in the highest socioeconomic areas:

• In 2015, men aged 65-69 in the lowest socioeconomic areas had a life expectancy

of 18.1 years compared with 21.7 years in the highest socioeconomic areas.

For women, these figures were 21.2 and 23.6 years respectively.

• Similar differentials are apparent for years in full health: men and women in the

lowest socioeconomic areas experienced 3.6 and 2.7 fewer years, respectively in full

health, than those in the highest socioeconomic areas.

• The proportion of life expectancy spent in full health in the lowest socioeconomic

areas was lower than in the highest areas. For men it was 74.6% in the lowest

socioeconomic areas compared with 78.8% in the highest areas and for women,

these figures were 74.1% and 78.0%, respectively.

266 Australia’s health 2020: data insights

Chapter

10

Figure 10.4: Life expectancy at age 65-69, by years in full health and ill

health, by sex and socioeconomic area, 2015

Note: Socioeconomic areas are based on the socioeconomic characteristics of the population and are presented as quintiles (fifths). Quintile 1 (Q1) represents the 20% of the population with the lowest socioeconomic characteristics. The level of socioeconomic position increases through to the 20% of the population with the highest socioeconomic characteristics (Q5).

Source: AIHW 2019a.

Changes over time

The ABDS 2015 is the first study to provide consistent estimates of non-fatal burden

for socioeconomic areas for 2 points in time, 2011 and 2015. While the ABDS 2015

also produced burden of disease estimates for the reference year 2003, these are not

available by subnational populations (including by socioeconomic area).

Available data over this 4-year period suggest there are some differences over time in

HALE and life expectancy by socioeconomic area. For people aged 65-69 living in the

lowest (least advantaged) socioeconomic areas:

• life expectancy increased over time for men and stayed the same for women. For

men, it rose from 17.7 years in 2011 to 18.1 years in 2015. For women,

life expectancy was 21.2 years at both time points

• HALE decreased over time for women and stayed the same for men. Men expected

13.5 years in full health in 2015 (and 13.6 years in 2011). Women expected 15.7 years

in full health in 2015 down from 16.2 years in 2011

Full health Ill health

13.5 14.2

15.0

15.9

17.1

15.7 16.2

17.0 17.7

18.4 4.5 4.5 4.5

4.9

4.6 5.6 5.4 5.0

5.3

5.2

0

10

20

30

40

50

60

70

80

90

100

0

5

10

15

20

25

Q1

(lowest)

Q2 Q3 Q4 Q5

(highest)

Q1

(lowest) Q2 Q3 Q4 Q5

(highest)

Per cent remaining years in full health

Remaining years

Socioeconomic area

Per cent remaining life in full health

Men Women

267 Australia’s health 2020: data insights

Chapter

10

• years in ill health increased over time, from 4.1 to 4.5 years for men and from 4.9

to 5.6 years for women (Figure 10.5)

• proportion of life expectancy in full health decreased over time, from 77.1% to 74.8%

for men and from 76.8% to 73.8% for women.

In contrast, for people aged 65-69 living in the highest (most advantaged)

socioeconomic areas:

• life expectancy increased over time—rising from 20.9 years in 2011 to 21.7 years in

2015 for men and for women, rising from 22.9 to 23.6 years

• HALE increased over time. Men could expect to live 17.1 years in full health in 2015,

up from 16.2 years in 2011. Similarly, women could be expected to live 18.4 years in

full health in 2015, up from 17.5 years in 2011

• years in ill health decreased over time—from 4.7 to 4.6 years for males and from

5.5 to 5.2 years for females (Figure 10.5)

• proportion of life expectancy in full health increased over time—from 77.6% to 78.7%

for males and from 76.1% to 78.0% for females.

Figure 10.5: Life expectancy and health-adjusted life expectancy (HALE),

at ages 65-69, by sex and socioeconomic area, 2011-2015

Notes

1. Lowest SE group refers to the approximate 20% of the population living in areas with the lowest socioeconomic characteristics.

2. Highest SE group refers to the approximate 20% of the population living in areas with the highest socioeconomic characteristics.

Source: AIHW 2019a.

0

5

10

15

20

25

2011 2012 2013 2014 2015

Remaining years of life

Year

Men

0

5

1 0

1 5

2 0

2 5

2011 2012 2013 2014 2015

Women

Highest SE group 5: Life expectancy

Lowest SE group 1: Life expectancy

Highest SE group 5: HALE

Lowest SE group 1: HALE

268 Australia’s health 2020: data insights

Chapter

10

In summary, for men and women aged 65-69 living in the lowest (least advantaged)

socioeconomic areas, the number and proportion of expected healthy years declined

over a relatively short period of time (2011 to 2015). In contrast, it increased over time

for men and women living in the highest (most advantaged) socioeconomic areas.

This indicates an expansion of morbidity in the lowest socioeconomic areas, and a

compression of morbidity in the highest socioeconomic areas.

See ‘Health across socioeconomic groups’ https://www.aihw.gov.au/reports/

australias-health/health-across-socioeconomic-groups for more information on

socioeconomic disparities.

Future work While this work focusses on HALE at a specific age (age 65), the same analysis can be

undertaken for different age groups (for example at ages 45 or 85) to assess whether

the patterns and conclusions drawn here in regards to the 3 theories of healthy ageing

(compression or expansion of morbidity, or equilibrium) differ by age.

A more detailed report on this topic is planned to be published by the AIHW in late

2020 which will include analysis for different age groups.

It is important to note that the analyses shown here are based on burden of disease

data currently available. For the national analysis, a longer period of data was

available, enabling a comparison over time between 2003 and 2015. For assessment

by socioeconomic area, there were only 2 time points (2011 and 2015) available at

the time of this analysis. More time points are needed for continued monitoring of

this important measure of the health of Australians. In addition, this analysis uses

socioeconomic areas which have some limitations compared with individual-based

measures. It reflects the overall or average socioeconomic characteristics of the

population of an area; it does not show how individuals living in the same area might

differ from each other (AIHW 2016), or how the characteristics of people who live in an

area may change over time.

The AIHW is currently undertaking work to update Australia’s burden of disease

estimates to the 2018 reference year, which will include estimates of HALE. This will

extend the time series to examine whether the patterns of healthy ageing presented

here are changing over time.

269 Australia’s health 2020: data insights

Chapter

10

Further reading The following reports provide further information on HALE and theories of

healthy ageing:

• AIHW 2012. Changes in life expectancy and disability in Australia 1998 to 2009.

Bulletin no. 111. Cat. no. AUS 166. Canberra: AIHW.

• AIHW 2017. Health-adjusted life expectancy in Australia: expected years lived in

full health 2011. Australian Burden of Disease Study series no.16. Cat. no. BOD 17.

Canberra: AIHW.

• AIHW 2019. Australian Burden of Disease Study: impact and causes of illness and

death in Australia 2015. Australian Burden of Disease series no. 19. Cat. no. BOD 22.

Canberra: AIHW.

References ABS (Australian Bureau of Statistics) 2019. Australian historical population statistics, 2016. ABS cat. no. 3105.0.65.001. Canberra: ABS. Viewed 24 April 2020, https://www.abs.gov.au/ AUSSTATS/abs@.nsf/Lookup/3105.0.65.001Main+Features12016?OpenDocument.

AIHW (Australian Institute of Health and Welfare) 2016. Australia’s health 2016. Australia’s health series no. 15. Cat.no. AUS 199. Canberra: AIHW.

AIHW 2019a. Australian Burden of Disease Study: impact and causes of illness and death in Australia 2015. Australian Burden of Disease series no. 19. Cat. no. BOD 22. Canberra: AIHW.

AIHW 2019b. General Record of Incidence of Mortality (GRIM) data. Canberra: AIHW. Viewed 11 September 2019, https://www.aihw.gov.au/reports/life-expectancy-death/grim-books/ contents/grim-books.

Fries JF 1980. Aging, natural death, and the compression of morbidity. The New England Journal of Medicine 303(3):130-5.

Greunberg EM 1975. The failures of success. The Milbank Quarterly 83(4):779-800.

Howse K 2006. Increasing life expectancy and the compression of morbidity: a critical review of the debate. Working paper 206. Oxford: Oxford Institute of Population Ageing.

270 Australia’s health 2020: data insights

Acknowledgments

In addition to the individual acknowledgments below, many other staff from the AIHW contributed their time and expertise. We gratefully acknowledge the work of the publishing, design, website, media, communications, executive and parliamentary teams, as well as the support and advice from the author liaison group for Australia’s health snapshots, data custodians, subject matter experts, data visualisation specialists and the statistical advisor.

Steering committee Fadwa Al-Yaman, Michael Frost, Matthew James, Richard Juckes, Andrew Kettle, Gabrielle Phillips, Barry Sandison, Adrian Webster, Louise York

Project management team Sara Bignell, Simone Brown, Pooja Chowdhary, Sayan De, Elise Guy, Dinesh Indraharan, Sarah Kamppi

Authors

The AIHW gratefully acknowledges the co-authorship of Associate Professor Sanjaya Senanayake of the Australian National University for Chapter 2 ‘Four months in: what we know about the new coronavirus disease in Australia’.

Valuable contributions in support of authors were made by Fleur de Crespigny, Louise Gates, Tim Howle, Chris Killick-Moran, Melinda Leake, Miriam Lum On, Anna O’Mahony, Claire Sparke, Louise Tierney, Jason Thomson.

Lilia Arcos-Holzinger

Michelle Barnett

Sara Bignell

Karen Bishop

Simone Brown

Therese Chapman

Pooja Chowdhary

Tracy Dixon

Vergil Dolar

Melanie Dunford

Mardi Ellis

Ingrid Evans

Patrick Gorman

Michelle Gourley

Elise Guy

Jenna Haddin

Imogen Halstead

Dinesh Indraharan

Matthew James

Jenni Joenpera

Richard Juckes

Bokyung Kim

Claire Lee-Koo

Lynelle Moon

Kien Nguyen

Tuan Phan

Anna Reynolds

Adrian Webster

Imaina Widagdo

Bronwyn Wyatt

271 Australia’s health 2020: data insights

External reviewers Thanks to the following experts for reviewing Australia’s health 2020: data insights articles:

Justine Boland—Australian Bureau of Statistics

Professor Henry Brodaty AO—University of New South Wales

Mark Cooper-Stanbury

Dr Stephen Duckett—Grattan Institute

Dr Michael Falster—University of New South Wales Centre for Big Data Research in Health

Dr Matt Fisher—Flinders University

John Goss—University of Canberra Health Research Institute

Professor James Harrison—Flinders University

Professor Michael Kidd AM—World Health Organization Collaborating Centre on Family Medicine and Primary Care

Professor Martyn Kirk—Australian National University

Associate Professor Rosemary Korda—Australian National University

Professor Louise Maple-Brown—Menzies School of Health Research; Royal Darwin Hospital

Dr Lynelle Moon

Associate Professor Michael Murray AM—Austin Health

Marissa Otuszewski—Australian Government Department of Health

Deb Reid—D A Reid and Company

Professor Libby Roughead—University of South Australia

Dr Janet Sluggett—Centre for Medicine Use and Safety, Monash University

Professor Renuka Visvanathan—University of Adelaide National Health and Medical Research Council Centre of Research Excellence in Frailty and Health Ageing

Dr Gavin Wheaton—Women’s and Children’s Hospital (Adelaide)

Shannon White—National Health Funding Body

Associate Professor Jongsay Yong—University of Melbourne

Thanks also to the following organisations and Australian Government departments and agencies:

Aged Care Quality and Safety Commission

Australian Bureau of Statistics

Department of Health

Department of Veterans’ Affairs

Everymind

National Indigenous Australians Agency

Royal Commission into Aged Care Quality and Safety

Services Australia

272 Australia’s health 2020: data insights

Abbreviations

ABDS Australian Burden of Disease Study

ABF Activity-Based Funding

ABS Australian Bureau of Statistics

ACFI Aged Care Funding Instrument

ACHI Australian Classification of Health Interventions

ACR Australian Coordinating Registry

ACSQHC Australian Commission on Safety and Quality in Health Care

ADF Australian Defence Force

ADNet Australia Dementia Network

AHPF Australian Health Performance Framework

AIHW Australian Institute of Health and Welfare

AMH Australian Medicines Handbook

ANZDATA Australian and New Zealand Dialysis and Transplant Registry

ARF acute rheumatic fever

ATC Anatomical Therapeutic Chemical (Classification System)

BEACH Bettering the Evaluation and Care of Health

BPSD behavioural and psychological symptoms of dementia

CKD chronic kidney disease

CNOS Canadian National Occupancy Standard

CDNA Communicable Diseases Network Australia

COAG Council of Australian Governments

COPD Chronic obstructive pulmonary disease

DALY Disability-adjusted life year

DATA Data Availability and Transparency Act

DDD defined daily dose

DVA Department of Veterans’ Affairs

ED Emergency Department

EIU Economist Intelligence Unit

ESKD end-stage kidney disease

GAS group A streptococcus

GDP Gross Domestic Product

GP General Practitioner

HALE Health-adjusted life expectancy

HPV human papillomavirus

273 Australia’s health 2020: data insights

ICD International Classification of Disease

ICD-10 AM International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Australian Modification

ICD-10 International Classification of Disease, 10 th edition

ICD-11 International Classification of Disease, 11 th edition

IHPA Independent Hospital Pricing Authority

IQWiG Institute for Quality and Efficiency in Health Care (Germany)

IRSD Index of Relative Socio-economic Disadvantage

MADIP Multi-Agency Data Integration Project

MBS Medicare Benefits Schedule

MELDA Multi-source Enduring Linked Data Asset

METeOR Metadata Online Registry

NATSEM National Centre for Social and Economic Modelling

NATSIHS National Aboriginal and Torres Strait Islander Health Survey

NCIS National Coronial Information System

NDLDP National Data Linkage Demonstration Project

NHFB National Health Funding Body

NHMD National Hospital Morbidity Database

NHPA National Health Performance Authority

NHRA National Health Reform Agreement

NIHSI AA National Integrated Health Services Information Analysis Asset

NISU National Injury Surveillance Unit

NMD National Mortality Database

NMDS National Minimum Data Set

NNAPEDCD National Non-Admitted Patient Emergency Department Care Database

NNDSS National Notifiable Diseases Surveillance System

NNIDR National Health and Medical Research Council National Institute for Dementia Research

NRMC National Residential Medication Chart

NZ MoH New Zealand Government Ministry of Health

OECD Organisation for Economic Co-operation and Development

OM otitis media

ONDC Office of the National Data Commissioner

PBS Pharmaceutical Benefits Scheme

PPH Potentially preventable hospitalisation

PRN pro re nata (as needed)

PSGN post-streptococcal glomerulonephritis

274 Australia’s health 2020: data insights

RACGP Royal Australian College of General Practitioners

RCACQS Royal Commission into Aged Care Quality and Safety

RHD rheumatic heart disease

RPBS Repatriation Pharmaceutical Benefits Scheme

SDAC Survey of Disability, Ageing and Carers

SE socioeconomic

SHA System of Health Accounts

SHI Statutory Health Insurance

WHO World Health Organization

YLD Years lived with disability

Symbols

% per cent

$ Australian dollars, unless otherwise specified

< less than

> more than

≤ less than or equal to

≥ more than or equal to

.. no data/insufficient data

‘000 thousands

mg/mmol microgram per millimole

mL millilitre

m2 met

n.a. not available

275 Australia’s health 2020: data insights

Glossary

Aboriginal or Torres Strait Islander: A person of Aboriginal and/or Torres Strait Islander descent who identifies as an Aboriginal and/or Torres Strait Islander. See also Indigenous.

acute care: Care provided to patients admitted to hospital that is intended to cure illness, alleviate symptoms of illness or manage childbirth.

acute rheumatic fever: An autoimmune response to infection of the throat (and possibly of the skin) by group A streptococcus bacteria. See also rheumatic heart disease.

additional diagnosis: The diagnosis of a condition or recording of a complaint—either coexisting with the principal diagnosis or arising during the episode of admitted patient care (hospitalisation), episode of residential care or attendance at a health care establishment—that requires the provision of care. Multiple diagnoses may be recorded.

ADF personnel: Serving, reserve and ex-serving members of the Australian Defence Force, civilian personnel employed by the Department of Defence are excluded.

administrative data: This refers to information that is collected, processed, and stored in automated information systems. Administrative data include enrolment or eligibility information, claims information, and managed care encounters.

administrative data collection: Data set that results from the information collected for the purposes of delivering a service or paying the provider of the service. This type of collection is usually complete (that is, all in-scope events are collected), but it may not be fully suitable for population-level analysis because the data are collected primarily for an administrative purpose.

admission: An admission to hospital. The term hospitalisation is used to describe an episode of hospital care that starts with the formal admission process and ends with the formal separation process. The number of separations has been taken as the number of admissions; hence, ‘admission rate’ is the same as ‘separation rate’.

admitted patient: A patient who undergoes a hospital’s formal admission process to receive treatment and/or care and ends with a formal separation process.

aged-care services: Daily living and nursing-care services provided through residential, home or flexible care arrangements run by governments, not-for-profit organisations or private businesses.

age-standardisation: A method of removing the influence of age when comparing populations with different age structures. This is usually necessary because the rates of many diseases vary strongly (usually increasing) with age. The age structures of the different populations are converted to the same ‘standard’ structure, and then the disease rates that would have occurred with that structure are calculated and compared.

Alzheimer’s disease: A degenerative brain disease caused by nerve cell death resulting in shrinkage of the brain. A common form of dementia.

antibodies: Blood proteins produced in response to and counteracting a specific antigen. Antibodies combine chemically with substances which the body recognizes as alien, such as bacteria, viruses, and foreign substances in the blood.

anti-dementia medicines: A group of medicines that can be used to manage the symptoms of dementia in people with mild-to-moderate Alzheimer’s disease.

276 Australia’s health 2020: data insights

antidepressant medicines: A group of medicines that can be used to manage the symptoms of certain mental health conditions, particularly depression and anxiety.

antigen: A toxin or other foreign substance which induces an immune response in the body, especially the production of antibodies.

antipsychotic medicines: A group of medicines that can be used to manage the symptoms of certain mental health conditions, particularly schizophrenia.

antiviral: A drug or treatment effective against viruses.

anxiety disorders: A group of mental disorders marked by excessive feelings of apprehension, worry, nervousness and stress. Includes generalised anxiety disorder, obsessive-compulsive disorder, panic disorder, post-traumatic stress disorder and various phobias.

associated cause(s) of death: Any condition(s), diseases and injuries—other than the underlying cause of death—considered to contribute to a death. See also cause of death.

asymptomatic transmission: Refers to transmission of an infectious agent from a person who does not develop symptoms.

average length of stay (ALOS): The average of the length of stay for admitted patient episodes (hospitalisations). ALOS is calculated by dividing total patients days in a given period by the total number of hospital separations in that period.

back pain and problems: A range of conditions related to the bones, joints, connective tissue, muscles and nerves of the back. Back problems are a substantial cause of disability and lost productivity.

Basic Reproduction Number (R0): The reproduction number when there is no immunity from past exposures or vaccination, nor any deliberate intervention in disease transmission.

behavioural and psychological symptoms of dementia (BPSD): Behaviours and feelings that are commonly experienced by people with dementia, including agitation, wandering, delusions and anxiety. These symptoms may relate to the dementia itself or indicate another underlying cause, such as illness, pain or fear.

benzodiazepine medicines: A group of medicines that can be used to manage the symptoms of certain mental health conditions, particularly anxiety disorders.

built environment: The built environment refers to the human-made surroundings where people live, work and recreate. It includes buildings and parks as well as supporting infrastructure such as transport, water and energy networks (Coleman 2017).

burden of disease and injury: The quantified impact of a disease or injury on an individual or population, using the disability-adjusted life year (DALY) measure.

cancer (malignant neoplasm): A large range of diseases where some of the body’s cells become defective, begin to multiply out of control, invade and damage the area around them, and can then spread to other parts of the body to cause further damage.

carer: A person who cares for another person (often a relative or friend) and has the responsibility for making decisions about that person’s daily care. In the Australian Bureau of Statistics Survey of Disability, Ageing and Carers, a carer is defined as a person who provides any informal assistance (help or supervision) to people with disability or older people, with assistance being ongoing, or likely to be ongoing, for at least 6 months.

277 Australia’s health 2020: data insights

cataract: A mostly degenerative condition in which the lens of the eye clouds over, obstructing the passage of light to the retina and causing vision impairment and, potentially, blindness.

cause of death: All diseases, morbid conditions or injuries that either resulted in or contributed to death—and the circumstances of the accident or violence that produced any such injuries— that are entered on the Medical Certificate of Cause of Death.

child: A person aged 0-14 unless otherwise stated.

chronic: Persistent and long lasting.

chronic kidney disease (CKD): All conditions of the kidney, lasting at least 3 months, where a person has had evidence of kidney damage and/or reduced kidney function, regardless of the specific cause.

chronic obstructive pulmonary disease (COPD): Serious, progressive and disabling long-term lung disease where damage to the lungs (usually because of both emphysema and chronic bronchitis) obstructs oxygen intake and causes increasing shortness of breath. By far the greatest cause of COPD is cigarette smoking.

clinical guidelines: Systematically developed statements to inform practitioner and patient decisions on appropriate health care for specific clinical circumstances.

cohort: A group of people who share a similar characteristic (for example, age).

comorbidity: A situation where a person has 2 or more health problems at the same time.

complication: A secondary problem that arises from a disease, injury or treatment (such as surgery) that makes the patient’s condition worse and treatment more complicated.

Compression of Morbidity: A theory of healthy ageing suggesting that the lifetime burden of illness could be reduced if the onset of chronic illness could be postponed.

condition (health condition): A broad term that can be applied to any health problem, including symptoms, diseases and certain risk factors, such as high blood cholesterol and obesity. Often used synonymously with ‘disorder’ or ‘problem’.

confidence interval: A range determined by variability in data, within which there is a specified (usually 95%) chance that the true value of a calculated parameter lies.

constant prices: Account for inflation by removing the effect of changes in prices over time. This allows for comparisons of spending over different time periods to be made. Constant price estimates indicate what expenditure would have been had the same prices applied across all years. See also real expenditure.

contemporary ex-serving (Australian Defence Force): Australian Defence Force members who have had at least 1 day of full-time or reserve service on or after 1 January 2001 and who have since been discharged from the Australian Defence Force.

coronary heart disease: A disease due to blockages in the heart’s own (coronary) arteries, expressed as angina or a heart attack. Also known as ischaemic heart disease.

current prices: ‘Expenditure at current prices’ refers to expenditure that is not adjusted for movements in price (inflation) from one year to another and therefore represents the dollar amount spent in that year.

current serving (Australian Defence Force): Australian Defence Force members who have had at least 1 day of full-time service on or after 1 January 2001 and are still serving in the Australian Defence Force.

278 Australia’s health 2020: data insights

DALY: See disability-adjusted life year.

data linkage: Bringing together (linking) of information from 2 or more different data sources that are believed to relate to the same entity (for example, to the same individual or the same institution). This linkage can yield more information about the entity and, in certain cases, provide a time sequence—the term is used synonymously with ‘record linkage’ and ‘data integration’.

deidentified: A process which involves the removal or alteration of personal identifiers, followed by the application of additional techniques or controls to remove, obscure, aggregate, alter and/or protect data so that it is no longer about an identifiable (or reasonably identifiable) individual.

dementia: A group of conditions that affect the brain: dementia is generally progressive and characterised by symptoms such as impaired thinking, behaviour and ability to perform the activities of daily living. Common types of dementia are Alzheimer’s disease, vascular dementia and mixed types of dementia.

demographics: Statistical data relating to population groups, such as age, sex, economic status, education level and employment status, among others.

depression: A mood disorder with prolonged feelings of being sad, hopeless, low and inadequate, with a loss of interest or pleasure in activities and often with suicidal thoughts or self-blame.

depressive disorders: A group of mood disorders with prolonged feelings of being sad, hopeless, low and inadequate, with a loss of interest or pleasure in activities and often with suicidal thoughts or self-blame.

determinant: Any factor that can increase the chances of ill health (risk factors) or good health (protective factors) in a population or individual.

diabetes (diabetes mellitus): A chronic condition where the body cannot effectively use its main energy source, the sugar glucose. This is due to a relative or absolute deficiency in insulin, a hormone produced by the pancreas that helps glucose to enter the body’s cells from the bloodstream and to be processed by them. Diabetes is marked by an abnormal build-up of glucose in the blood and it can have serious short- and long-term effects. The 3 main types of diabetes are type 1 diabetes, type 2 diabetes and gestational diabetes.

diabetic nephropathy: Damage to the blood-filtering capillaries in the kidneys, caused by high blood sugar levels.

diabetic retinopathy: A complication of diabetes. Refers to damage to the blood vessels in the retina which can result in blindness.

dialysis: An artificial method of treating kidney failure by removing waste substances from the blood and regulating levels of circulating chemicals—functions usually performed by the kidneys.

digital health: The electronic management of health information. This includes using technology to collect and share a person’s health information. It can be as simple as a person wearing a device to record how much exercise they do each day, to health care providers sharing clinical notes about an individual.

disability-adjusted life year (DALY): A year of healthy life lost, either through premature death or equivalently through living with ill health due to illness or injury. It is the basic unit used in burden of disease and injury estimates.

279 Australia’s health 2020: data insights

disease: A physical or mental disturbance involving symptoms (such as pain or feeling unwell), dysfunction or tissue damage, especially if these symptoms and signs form a recognisable clinical pattern.

disorder (health disorder): A term used synonymously with condition.

Dynamic Equilibrium: A theory of healthy ageing which suggests that the proportion of the lifetime spent living with illness remains relatively constant over time, because there is a trade-off between increasing prevalence and decreasing severity of diseases.

Effective Reproduction Number (Re): The reproduction number when there is some immunity or some intervention measures in place.

end-stage kidney disease (ESKD): The most severe form of chronic kidney disease (CKD), also known as Stage 5 CKD or kidney failure. It occurs when kidney function has deteriorated so much that it is no longer sufficient to sustain life, and kidney replacement therapy (KRT) in the form of dialysis or kidney transplantation is required for the patient to survive.

epidemic: Widespread occurrence of an infectious disease in a community at a particular time.

Expansion of Morbidity: A theory of healthy ageing which suggests that increasing life expectancy will be accompanied by higher prevalence of disease, resulting in more disability from illness and injury before death.

exponential growth: Growth that increases at a consistent rate. While it starts slowly, it can rapidly result in enormous quantities.

fomites: Objects or materials, such as clothes, utensils, and furniture, which are likely to carry infectious agents.

general practitioner (GP): A medical practitioner who provides primary, comprehensive and continuing care to patients and their families in the community.

genomics: The study of genes and their functions, and related techniques. Genomics addresses all genes and their interrelationships to identify their combined influence on the growth and development of the organism.

gestational diabetes: A form of diabetes that is first diagnosed during pregnancy (gestation). It may disappear after pregnancy but signals a high risk of diabetes occurring later on. See also diabetes (diabetes mellitus), type 1 diabetes and type 2 diabetes.

gross domestic product (GDP): A statistic commonly used to indicate national wealth. It is the total market value of goods and services produced within a given period after deducting the cost of goods and services used up in the process of production but before deducting allowances for the consumption of fixed capital.

group A streptococcus infection (GAS): Is caused by bacteria known as Group A (beta-haemolytic) Streptococcus, GAS is a common infection that can cause sore throats (pharyngitis), scarlet fever or impetigo (skin sores).

health: The World Health Organization (WHO) defines health as a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.

health and aged-care system: The interaction of funding, institutions, workforce and resources that support the delivery of health and aged-care services.

280 Australia’s health 2020: data insights

health hardware: The physical equipment needed to support good health in a domestic setting, including safe electrical systems, access to water, facilities for washing people and clothing, facilities for storing and preparing food, and waste removal systems.

health literacy: The ability of people to access, understand and apply information about health and the health care system so as to make decisions that relate to their health.

health outcome: A change in the health of an individual or population due wholly or partly to a preventive or clinical intervention.

health promotion: A broad term to describe activities that help communities and individuals increase control over their health behaviours. Health promotion focuses on addressing and preventing the root causes of ill health, rather than on treatment and cure.

health status: The overall level of health of an individual or population, taking into account aspects such as life expectancy, level of disability, levels of disease risk factors and so on.

health-adjusted life expectancy (HALE): The average number of years that a person at a specific age can expect to live in full health, taking into account years lived in less than full health due to the health consequences of disease and/or injury.

hearing loss: Any hearing threshold response in either ear, to any sound stimuli, that is outside the normal range, measured using audiometry (the testing of a person’s ability to hear various sound frequencies). Hearing loss in a population describes the number of people who have abnormal hearing. Hearing loss may affect one ear (unilateral) or both ears (bilateral).

hospital services: Services provided to a patient who is receiving admitted patient services or non-admitted patient services in a hospital.

hospitalisation: An episode of hospital care that starts with the formal admission process and ends with the formal separation process (synonymous with admission and separation). An episode of care can be completed by the patient’s being discharged, being transferred to another hospital or care facility, or dying, or by a portion of a hospital stay starting or ending in a change of type of care (for example, from acute to rehabilitation).

household: A group of two or more related or unrelated people who usually live in the same dwelling, and who make common provision for food or other essentials for living; or a single person living in a dwelling who makes provision for his or her own food and other essentials for living, without combining with any other person.

housing adequacy: A measure to assess whether a dwelling is overcrowded. The number of bedrooms a dwelling should have in order to avoid crowding, as determined by the Canadian National Occupancy Standard. This standard assesses bedroom requirements based on the following criteria:

• there should be no more than 2 people per bedroom

• children aged under 5 of different sexes may reasonably share a bedroom

• children aged 5 and over of opposite sexes should have separate bedrooms

• children aged under 18 and of the same sex may reasonably share a bedroom

• single household members aged 18 and over should have a separate bedroom, as should parents or couples.

281 Australia’s health 2020: data insights

illness: A state of feeling unwell, although the term is also often used synonymously with disease.

immunisation: The process of both receiving a vaccine and becoming immune to the disease as a result.

immunity: The ability of an organism to resist a particular infection or toxin by the action of specific antibodies or sensitized white blood cells.

incidence: The number of new cases (of an illness, injury or event, and so on) occurring during a given period. Compare with prevalence.

incubation period: The time from the moment of exposure to an infectious agent until signs and symptoms of the disease appear.

indicator: A key statistical measure selected to help describe (indicate) a situation concisely so as to track change, progress and performance; and to act as a guide for decision making.

Indigenous status: Whether or not a person identifies as being of Aboriginal and/or Torres Strait Islander origin.

Indigenous: A person of Aboriginal and/or Torres Strait Islander descent who identifies as an Aboriginal and/or Torres Strait Islander. See also Aboriginal or Torres Strait Islander.

infectious disease: Disease or illness caused by infectious organisms or their toxic products. The disease may be passed directly or indirectly to humans through contact with other humans, animals or environments where the organism is found. Also referred to as a communicable disease.

interoperability: The ability of different information systems, devices and applications (‘systems’) to access, exchange, integrate and cooperatively use data in a coordinated manner.

intentional self-harm: Attempted suicide, as well as cases where people have intentionally hurt themselves, but not necessarily with the intention of suicide (for example, acts of self-mutilation).

International Statistical Classification of Diseases and Related Health Problems (ICD): The World Health Organization’s internationally accepted classification of death and disease. The Tenth Revision (ICD-10) is currently in use. The ICD-10-AM is the Australian Modification of the ICD-10; it is used for diagnoses and procedures recorded for patients admitted to hospitals.

life course: A series of life stages that people are normally expected to pass through as they progress from birth to death. For example, stages often included are: birth and infancy, childhood, youth, working age, and older age.

life expectancy: An indication of how long a person can expect to live, depending on the age they have already reached. Technically, it is the number of years of life left to a person at a particular age if death rates do not change. The most commonly used measure is life expectancy at birth.

long-term condition: A term used to describe a health condition that has lasted, or is expected to last, at least 6 months. See also chronic diseases.

macular degeneration: A progressive deterioration of the macula of the retina (the central inner-lining of the eye). It is often positively related to old age (usually referred to as ‘age-related macular degeneration’), and results in a loss of central vision.

282 Australia’s health 2020: data insights

Medicare: A national, government-funded scheme that subsidises the cost of personal medical services for all Australians and aims to help them afford medical care. The Medicare Benefits Schedule (MBS) is the listing of Medicare services subsidised by the Australian Government. The schedule is part of the wider Medicare Benefits Scheme (Medicare).

medicines that act on the central nervous system: A group of medicines that have an effect on the central nervous system (brain and spinal cord). These are used for many different conditions.

mental illness (or mental disorders): Disturbances of mood or thought that can affect behaviour and distress the person or those around them, so that the person has trouble functioning normally. They include anxiety disorders, depression and schizophrenia.

mesothelioma: An aggressive form of cancer occurring in the mesothelium—the protective lining of the body cavities and internal organs, such as the lungs, heart and bowel.

modifiable risk factor: A risk factor where the level of associated risk can be increased or decreased through changes in behaviours or exposures.

monitoring (of health): A process of keeping a regular and close watch over important aspects of public health and health services, using various measurements, and then regularly reporting on the situation, enabling health systems and society more generally to plan and respond accordingly. The term is often used interchangeably with surveillance, although surveillance may imply more urgent watching and reporting—such as the surveillance of infectious diseases and epidemics. Monitoring can also be applied to individuals, such as hospital care where a person’s condition must be closely assessed over time.

morbidity: The ill health of an individual and levels of ill health in a population or group.

mortality: Number or rate of deaths in a population during a given time period.

My Health Record: An online platform for storing the health information of individuals, including their Medicare claims history, hospital discharge information, diagnostic imaging reports and details of allergies and medications.

non-admitted patient: A patient who receives care from a recognised non-admitted patient service/clinic of a hospital, including emergency departments and outpatient clinics.

non-fatal burden: The quantified impact on a population of ill health due to disease or injury, measured as years lived with disability (YLD).

non-Indigenous: People who have declared that they are not of Aboriginal or Torres Strait Islander descent. Compare with Other Australians.

obesity: Marked degree of overweight, defined for population studies as a body mass index of 30 or over. See also overweight.

occupational exposures and hazards: Chemical, biological, psychosocial, physical and other factors in the workplace that can potentially cause harm.

283 Australia’s health 2020: data insights

odds ratio: A measure of the association between an exposure and an outcome. The odds ratio represents the odds that an outcome will occur, given a particular exposure, compared with the odds of the outcome’s occurring in the absence of that exposure. The value of the odds ratio is interpreted as:

• An odds ratio close or equal to 1 means that the exposure has little or no effect on the odds of the outcome’s occurring

• An odds ratio greater than 1 means that the exposure increases the odds of the outcome’s occurring

• An odds ratio less than 1 means that the exposure decreases the odds of the outcome’s occurring.

opioid medicines: A group of medicines that can be used to relieve pain and relax muscles, some of which may be used in palliative care.

Other Australians: People who have declared that they are not of Aboriginal or Torres Strait Islander descent, and people whose Indigenous status is unknown. Compare with non-Indigenous.

otitis media: All forms of inflammation and infection of the middle ear. Active inflammation or infection is nearly always associated with a middle ear effusion (fluid in the middle ear space).

outcome (health outcome): A health-related change due to a preventive or clinical intervention or service. (The intervention may be single or multiple, and the outcome may relate to a person, group or population, or be partly or wholly due to the intervention.)

out-of-pocket costs: The total costs incurred by individuals for health care services, over and above any refunds from the MBS, the PBS or private health insurance funds.

overcrowding: Situation in a dwelling where one or more additional bedrooms would be required to adequately house its inhabitants, according to the Canadian National Occupancy Standard. See also housing adequacy.

overweight: Defined for the purpose of population studies as a body mass index of 25 or over. See also obesity.

pandemic: A new infectious disease that is rapidly spreading across a large region, or worldwide, and affecting large numbers of people.

permanent residential aged care: Care for older people provided in residential aged-care facilities (also often called ‘nursing homes’). People live in the facility, either in private or shared rooms, and commonly receive assistance with activities of daily living (such as eating and personal care), as well as nursing care. Many facilities also provide respite residential aged care for short-term stays.

person-centred data: An approach to analysis which focusses on the experiences and outcomes of individuals, rather than organising information by specific topics, services or systems.

personalised medicine: a type of medical care in which treatment is customized for an individual patient.

Pharmaceutical Benefits Scheme (PBS): A national, government-funded scheme that subsidises the cost of a wide variety of pharmaceutical drugs, covering all Australians, to help them afford standard medications. The PBS lists all the medicinal products available under the PBS and explains the uses for which subsidies can apply (see Repatriation Pharmaceutical Benefits Scheme ).

284 Australia’s health 2020: data insights

population health: Typically, the organised response by society to protect and promote health and to prevent illness, injury and disability. Population health activities generally focus on:

• prevention, promotion and protection rather than on treatment

• populations rather than individuals

• the factors and behaviours that cause illness.

It can also refer to the health of particular subpopulations, and comparisons of the health of different populations.

post-streptococcal glomerulonephritis: Inflammation of the kidneys by certain strains of streptococcus bacteria, associated with a previous infection of the skin or throat.

post-traumatic stress disorder (PTSD): PTSD is a form of anxiety disorder in which a person has a delayed and prolonged reaction after being in an extremely threatening or catastrophic situation such as a war, natural disaster, terrorist attack, serious accident or witnessing violent deaths.

potentially preventable hospitalisation (PPH): Hospital separations for specified conditions which could potentially have been prevented through the provision of appropriate health interventions and early disease management for individuals. The proposed preventive measures could usually have been delivered in primary care and community-based care settings (including by general practitioners, medical specialists, dentists, nurses and allied health professionals). PPH conditions are classified as vaccine-preventable, chronic and acute. Descriptions of each PPH condition can be found at ‘Disparities in potentially preventable hospitalisations across Australia: Exploring the data’.

premature deaths (or premature mortality): Deaths that occur at a younger age than a selected cut-off. The age below which deaths are considered premature can vary depending on the purpose of the analysis and the population under investigation. In this report, deaths among people aged under 75 are considered premature.

presenteeism: The practice of going to work despite being sick or unwell.

pesymptomatic transmission: Transmission of an infectious disease before the infected person displays symptoms.

prevalence: The number or proportion (of cases, instances, and so forth) in a population at a given time. Compare with incidence.

primary health care: Services delivered in general practices, community health centres, Aboriginal health services and allied health practices (for example, physiotherapy, dietetic and chiropractic practices) and which come under numerous funding arrangements.

principal diagnosis: The diagnosis established, after study, to be chiefly responsible for an episode of patient care (hospitalisation), residential care or attendance at a health care establishment. Diagnoses are recorded using the relevant edition of the International statistical classification of diseases and related health problems, 10th revision, Australian modification (ICD-10-AM).

private patient: A person admitted to a private hospital, or person admitted to a public hospital who decides to choose the doctor(s) who will treat them or to have private ward accommodation. This means they will be charged for medical services, food and accommodation.

285 Australia’s health 2020: data insights

pro re nate (PRN) medicines: Medicines prescribed to be taken as required (as opposed to medicines that are prescribed to be taken regularly, for example 3 times a day).

psychological distress: Unpleasant feelings or emotions that affect a person’s level of functioning and interfere with the activities of daily living. This distress can result in having negative views of the environment, others and oneself, and manifest as symptoms of mental illness, including anxiety and depression.

psychosocial: Involving both psychological and social factors

public health: Activities aimed at benefiting a population, with an emphasis on prevention, protection and health promotion (as distinct from treatment tailored to individuals with symptoms). Examples include provision of a clean water supply and good sewerage, conduct of anti-smoking education campaigns, and screening for diseases such as cancer of the breast and cervix.

public patient: A person admitted to hospital who has agreed to be treated by doctors of the hospital’s choice and to accept shared ward accommodation. Such patients are admitted and treated at no charge and are mostly funded through public sector health or hospital service budgets.

rate: One number (the numerator) divided by another number (the denominator). The numerator is commonly the number of events in a specified time. The denominator is usually the population ‘at risk’ of the event. Rates—crude, age-specific and age-standardised—are generally multiplied by a number such as 100,000 to create whole numbers.

real expenditure: Expenditure that has been adjusted to remove the effects of inflation (for example, expenditure for all years compiled using 2017-18 prices). Removing the effects of inflation allows comparisons to be made between expenditures in different years on an equal dollar-for-dollar basis. Changes in real expenditure measure the change in the volume of goods and services produced (see constant prices).

remoteness classification: Each state and territory is divided into several regions based on road distance that must be travelled to access goods and services (such as general practitioners, hospitals and specialist care). These regions are categorised using the Accessibility/Remoteness Index of Australia and (from 2011 onwards) defined as Remoteness Areas by the Australian Statistical Geographical Standard in each Census year. The 5 Remoteness Areas are Major cities, Inner regional, Outer regional, Remote and Very remote.

Repatriation Pharmaceutical Benefits Scheme (RPBS): An Australian Government scheme that provides a range of pharmaceuticals and wound dressings at a concessional rate for the treatment of eligible veterans, war widows/widowers and their dependants.

reserve (Australian Defence Force): Australian Defence Force members who have had at least 1 day of reserve service on or after 1 January 2001.

respiratory condition: A condition affecting the airways and characterised by symptoms such as wheezing, shortness of breath, chest tightness and cough. Conditions include asthma and chronic obstructive pulmonary disease (COPD)—which includes emphysema and chronic bronchitis.

rheumatic heart disease: Damage to the heart valves as a result of one or more episodes of acute rheumatic fever.

286 Australia’s health 2020: data insights

risk: The probability of an event occurring during a specific period of time.

risk factor: A factor that represents a greater risk of a health disorder or other unwanted condition or event. Some risk factors are regarded as causes of disease; others are not necessarily so. Along with their opposites, protective factors, risk factors are known as determinants.

secondary use of data: any application of data beyond the reason for which they were first collected (known as the primary use or purpose).

separation: The formal process where a hospital records the completion of an episode of treatment and/or care for an admitted patient.

sexual violence: The occurrence, attempt or threat of sexual assault experienced by a person since the age of 15. Sexual violence can be perpetrated by partners in a domestic relationship, former partners, other people known to the victims, or strangers.

sexually transmissible infection: An infectious disease that can be passed from one person to another by sexual contact. Examples include chlamydia and gonorrhoea infections.

smoker: Someone who reports smoking daily, weekly or less than weekly.

social determinants of health: The circumstances in which people are born, grow up, live, work and age, and the systems put in place to deal with illness. These circumstances are in turn shaped by a wider set of forces including economics, social policies and politics.

social exclusion: A situation where people do not have the resources, opportunities and capabilities they need to learn, work, engage with or have a voice in their communities. Composite measures of social exclusion weight indicators such as income level, access to education, unemployment, poor English, health services and transport, and non-material aspects such as stigma and denial of rights. These measures are typically divided into three levels: marginal exclusion, deep exclusion and very deep exclusion.

Socio-Economic Indexes for Areas (SEIFA): A set of indexes, created from Census data, that aim to represent the socioeconomic position of Australian communities and identify areas of advantage and disadvantage. The index value reflects the overall or average level of disadvantage of the population of an area; it does not show how individuals living in the same area differ from each other in their socioeconomic group. This report uses the Index of Relative Socio-Economic Disadvantage.

socioeconomic position: An indication of how ‘well off’ a person or group is. In this report, socioeconomic areas are mostly reported using the Socio-Economic Indexes for Areas, typically for five groups ( quintiles)—from the most disadvantaged (worst off or lowest socioeconomic area) to the least disadvantaged (best off or highest socioeconomic area).

specialist services: Services that support people with specific or complex health conditions and issues, who are generally referred by primary health care providers. These services are often described as ‘secondary’ health care services. In many cases, a formal referral is required for an individual to be able to access the recommended specialist service

substance use disorder: A disorder of harmful use and/or dependence on illicit or licit drugs, including alcohol, tobacco and prescription drugs

suicidal behaviours: The collective term for suicidal ideation, suicide plans and suicide attempts.

suicidal ideation: Serious thoughts about ending one’s own life.

287 Australia’s health 2020: data insights

suicide: An action to deliberately end one’s own life.

surveillance: Systematic ongoing collection, collation, and analysis of data and the timely dissemination of information to those who need to know so that action can be taken.

telemedicine: The remote delivery of health care services, such as health assessments or consultations, over the telecommunications infrastructure

trachoma: An eye disease caused by infection with Chlamydia trachomatis bacteria.

transmission: The act of transferring something, such as an infectious disease, from one person to another.

type 1 diabetes: A form of diabetes mostly arising among children or younger adults and marked by a complete lack of insulin. Insulin replacement is needed for survival.

type 2 diabetes: The most common form of diabetes, occurring mostly in people aged 40 and over, and marked by reduced (or less effective) insulin.

underlying cause of death: The primary or main cause of death: the condition, disease or injury that initiated the sequence of events leading directly to death, or the circumstances of the accident or violence that produced the fatal injury. See also cause of death and associated cause(s) of death.

vaccination: Treatment with a vaccine to produce immunity against a disease.

vaccine: A substance used to stimulate the production of antibodies and provide immunity against one or several diseases, prepared from the causative agent of a disease, its products, or a synthetic substitute, treated to act as an antigen without inducing the disease.

virus: An infective agent that typically consists of a nucleic acid molecule in a protein coat, is too small to be seen by light microscopy, and is able to multiply only within the living cells of a host

wellbeing: A state of health, happiness and contentment. It can also be described as judging life positively and feeling good. For public health purposes, physical wellbeing (for example, feeling very healthy and full of energy) is also viewed as critical to overall wellbeing. Because wellbeing is subjective, it is typically measured with self-reports, but objective indicators (such as household income, unemployment levels and neighbourhood crime) can also be used.

workforce: People who are employed or unemployed (not employed but actively looking for work). Also known as the labour force.

years lived with disability (YLD): A measure calculated as the prevalence of a condition, multiplied by a severity weight for that condition (that is, its disabling effect). YLD represent the non-fatal burden of disease or disability.

Australia’s health 2020: data insights

Australian Institute of Health and Welfare

Australia’s health 2020: data insights presents an overview of health data in Australia and explores selected health topics in 10 original articles.

Australia’s health 2020 is the 17th biennial health report of the Australian Institute of Health and Welfare. This edition has a new format and expanded product suite:

Australia’s health

data insights 2020

• Australia’s health 2020: data insights

• Australia’s health snapshots

• Australia’s health 2020: in brief.