Australian burden of disease study: health equity through data disaggregation

ABSTRACT

This paper underscores the imperative for enhanced data disaggregation within the Australian Burden of Disease Studies (ABDS). While the ABDS offers a detailed assessment of disease burden, it falls short in providing systematic, disaggregated data – key to equitable healthcare and informed policymaking. Given Australia’s rich linguistic, ethnic, and cultural tapestry, aggregated health data often obscures the true extent of health disparities among diverse groups. The absence of detailed health data can precipitate unforeseen inequities with profound societal repercussions, including social unrest, diminished trust in healthcare and governance, economic downturns, educational gaps, mental health crises, political challenges, and stigmatisation. Such disparities undermine confidence in health systems and advisories, potentially fostering scepticism towards official health communications. Health disparities can lead to economic inadequacies, inflated healthcare expenditures, and stunted economic growth. Communities suffering from poor health are economically disadvantaged, and unexpected health inequities can derail educational achievements, particularly for students from underprivileged backgrounds who may face additional obstacles due to health-related absences or resource scarcity. The mental health burden from health disparities can escalate community-wide psychological distress, manifesting in heightened depression, anxiety, and trauma rates. The stigmatisation of certain groups exacerbates societal divisions and impedes unity. Politically, health inequities demand attention, with leaders facing scrutiny over their handling of these issues. Nevertheless, such challenges can catalyse community solidarity and advocacy for systemic change, fostering resilience. Addressing these disparities necessitates a comprehensive strategy encompassing disaggregated data access, equitable healthcare provision, policy reform, and community involvement. The ABDS 2023 report that Australians lost 5.6 million years of potential healthy life, with chronic disease imposing a more significant toll than premature mortality. The primary contributors to this burden were cancers, mental health conditions, and substance abuse disorders. However, this collective data fails to illuminate the disparate disease impacts on culturally, ethnically, and linguistically diverse populations. The ABD Study highlighted that in 2018, Indigenous Australians lost nearly 240,000 years of healthy life due to illness and early death, equating to 289 years per 1,000 individuals. Conditions like diabetes, cardiovascular diseases, renal disorders, strokes, and respiratory issues were more prevalent among individuals with limited English proficiency compared to fluent speakers. Gestational diabetes rates were notably higher in South Asian, Vietnamese, and African migrants compared to the native-born populace. The article emphasises the critical role of disaggregated health data in shaping effective health policies, optimising resource allocation, and promoting equitable health outcomes aligned with the Sustainable Development Goals. It advocates for integrating such data in public health practices to mitigate health disparities and enhance the welfare of all Australians. Ensuring the widespread availability of legally mandated disaggregated health data is vital for identifying, monitoring, and addressing health disparities within Australia’s multicultural society.

KEYWORDS:

• Health advocacy
• ethnicity
• health equity
• Burden of disease
• disaggregated data
• CALD population
• SDGs
• health policy

Introduction

The Australian Burden of Disease Study (Australian Bureau of Statistics, 2022a) was the first to project disease burden estimates and included estimates of disease burden due to COVID‑19. The ABDS is a pivotal research endeavour that has significantly enriched our comprehension of the diseases and injuries that bear Australia’s most substantial health burden. Its findings are paramount for our collective health (AIHW 2022). However, the lack of effort to gather systematic, disaggregated data remains a major drawback of such a significant study. There are other preventable Burden of Diseases studies conducted in Australia (Crosland et al., 2019; Institute for Health Metrics and Evaluation, 2024; Islam et al., 2023). However, those studies also lack disaggregated data.

The Australian Government collects and uses health data following the Australian Data Strategy and the Australian Data and Availability and Transparency Act 2022. The AIHW holds over 150 datasets covering various health and welfare topics, including disease, mental health, ageing, and more. These datasets are used by policymakers, researchers, and service providers. Health Data Australia (The Australian Research Data Commons partnership of the health research sector, ‘Australia’s leading research data infrastructure facility’) also provides a catalogue of clinical trial datasets collected nationwide by various institutions, which can be accessed for research and understanding health trends.

The ABDS 2023 study for 220 diseases and injuries in Australia by the Australian Institute of Health and Welfare (AIHW, 2023) analysis reveals that in 2023, 5.6 million years of healthy life were lost. Living with disease was a more significant burden for Australians (54%) than dying young (46%). The category of diseases with the highest burden in 2023 was malignancies (17%), followed by mental health and drug use disorders (15%). Furthermore, AIHW reported that coronary heart diseases, anxiety disorders, COPD, dementia, and back pain were the top 5 burden diseases in Australia (AIHW, 2023).

The National Health Survey (NHS) 2022 is the most recent in Australia-wide health surveys. It is designed to collect a wide range of information about the health of Australians, including the prevalence of health conditions and the prevalence of health risk factors (such as smoking and vaping, alcohol consumption, and physical activity) relevant to demographic and socioeconomic characteristics. Eight in ten (81.4%) people had at least one long-term health condition, and one in two (49.9%) had at least one chronic condition. Mental and behavioural conditions (26.1%), back problems (15.7%), and arthritis (14.5%) were the most common chronic conditions.

Comparisons of Australia’s disease burden over time show that variations in the burden of a given disease or injury over time may result from population ageing, changes in population size and composition caused by migration, variations in disease prevalence (including epidemics), or adjustments to the way causes are reported or coded in health data.

According to the Australian Institute of Health and Welfare (AIHW), individuals who arrived in Australia more than 10 years before the 2021 Census were considered early arrivals, and those with low English proficiency were more likely to have one or more long-term health conditions compared to those with high proficiency (23%). Individual ailments that are highly prevalent among early immigrants with poor English proficiency include dementia (1.3% versus 0.5%) and mental health disorders (6.8% versus 3.9%). Australian-born individuals followed a similar pattern to that seen in early arrivals. Additionally, the prevalence of diabetes, heart disease, kidney disease, stroke, and lung problems was higher in those with low English proficiency than in those with high fluency (AIHW, 2023). Likewise, gestational diabetes was found to be higher among South Asian, Vietnamese and African migrants than the Australian-born population (Nisar et al., 2021).

In a limited way, AIHW (2023) explored the prevalence of chronic health conditions reported by people from culturally and linguistically diverse (CALD) backgrounds in Australia using data on long-term health conditions and 4 CALD indicators collected through the Australian Bureau of Statistics (ABS) 2021 Census of Population and Housing.

There are other critical studies, such as Australian Suicide and self-harm monitoring data (Australian Institute of Health and Welfare, 2024), that also ignored the need for disaggregated data to monitor critical public health incidents and subsequent health inequity.

Nevertheless, their analysis aggregated broadly as Culturally and Linguistically Diverse (CALD) population groups, thereby obscuring disparities in specific subgroups. This further underscores the importance of gathering disaggregated data that is more significant to health equity. It is not just a goal but a crucial step towards a healthier and more equitable society. Although the Australian Bureau of Statistics (2022a) surveyed cultural diversity in Australia, such data is not linked to public health and disease burden.

During the pandemic, the Australian Immunisation Register (AIR) was linked with the Multi-Agency Data Integration Project (MADIP) data asset to enable detailed analysis of vaccination coverage rates among priority groups, including people from culturally and linguistically diverse (CALD) backgrounds; however, such effort was not applied widely to other health conditions.

Why is data disaggregation important?

Public Health policymakers, practitioners, clinicians, researchers, advocates and civil society can use disaggregated health data to identify health disparities in subgroups that might not be obvious otherwise and more carefully track health outcomes in at-risk and vulnerable populations. Breaking down data into smaller units like age, sex, geographic area, and socioeconomic status helps to capture health inequalities within a population. This is essential for designing targeted health initiatives and policies, ensuring no one is left behind, especially in alignment with the SDGs. It supports operational and clinical decision-making by identifying factors that make some populations more vulnerable.

During events like pandemics, disaggregated data is crucial in allocating limited health resources efficiently and planning policies effectively. Disaggregated data helps discover confounders in clinical trials, which is vital for developing interventions like vaccines. Data disaggregation involves dividing data into smaller groups based on demographic factors, such as nationality, immigration status, race, ethnicity, gender expression, or socioeconomic status (Rubin et al., 2018). The 2030 Agenda for Sustainable Development Goals (SDGs) target 17.18 calls for countries to increase data availability disaggregated by income, gender, age, race, ethnicity, migratory status, disability status, geographical location, and other characteristics relevant in national contexts (UN Department of Economic and Social Affairs Goal 17, 2024; UN General Assembly, 2015).

The lack of disaggregated health data in the public domain has policy and program implications for monitoring health inequity, resource allocation, and developing prevention interventions. Gigli (2021) discussed the importance of data disaggregation as a research tool to examine the heterogeneity of the paediatric population. It emphasises how this approach can reveal systemic health disparities among children and their families that might be concealed when children are treated as a homogeneous group. Zhao et al. (2022) reported the value of a national burden-of-disease study by comparing the estimates between the Australian Burden of Disease Study 2015 and the Global Burden of Disease Study (Zhao et al., 2022).

Lassi et al. (2024) called for investing in understanding the health and well-being of South Asian migrants in Australia. Considering the differential disease burden, understanding the epidemiology of disease and health conditions in South Asian migrants is crucial for tailoring preventive health services and cohort studies on migrants (Lassi et al., 2024).

The Australian Burden of Disease Study on the impact and causes of illness and death in Aboriginal and Torres Strait Islander people 2018 found that, in 2018, Indigenous Australians lost almost 240,000 years of healthy life due to ill-health and premature death – equivalent to 289 years for every 1,000 people (Australian Government, 2022).

On an aggregate level, the health disparity between Australia’s First Nation people and non-Indigenous Australians is a significant issue. There are vast and substantial variations in life expectancy, leading causes of death, the burden of disease, and social determinants of health. Indigenous males born in 2015–2017 could expect to live 71.6 years, and Indigenous females 75.6 years. This is lower compared to non-Indigenous Australians, particularly in remote areas.

For Indigenous Australians, the leading causes of death include coronary heart disease, diabetes, chronic lower respiratory diseases, lung cancers, and intentional self-harm. About 34% of the health gap is attributed to social determinants like employment, education, housing, and income, while 19% is due to health risk factors such as smoking and obesity. However, there are ongoing efforts to address these disparities through the ‘Closing the Gap’ targets, which aim to improve the health and well-being of Indigenous Australians.

However, this aggregated data did not indicate the health equity implications of the multilingual population’s diversity. Australia’s First Nation people are multilingual, with over 250 Indigenous languages, including 800 dialects. Each language is specific to a particular place and people. In some areas, like Arnhem Land in the Northern Territory, many different languages are spoken over a small area. In other areas, like the immense Western Desert, dialects of one language are spoken.

The data from the 2021 Australian Census shows that 29.1% of all Australians were born overseas (Lassi et al., 2024), with more than half (51.5%) of the entire population having a parent born overseas. The top countries of birth for people born outside of Australia are England (927,490), India (673,352), China (549,618), New Zealand (530,492) and the Philippines (293,892). The countries of birth with the most significant percentage growth since the last census in 2016 include Nepal (124%), India (48%), Pakistan (45%), Iraq (38%) and the Philippines (26%) (Australian Bureau of Statistics, 2022d).

Several key Australian Government health strategies prioritise people from CALD backgrounds. However, reporting on the health needs of CALD communities in Australia is complex and arduous. CALD encompasses a broad spectrum of factors, including nationality, ancestry, place of birth of parents, languages spoken, and religious affiliation. Even within these groupings, there can be significant variations. For instance, individuals born in the same nation might not identify with the same culture or speak the same language. Many data collections only focus on one aspect of CALD or may not collect any information. This limited approach is inadequate to fully grasp the health needs of individuals from CALD backgrounds, as a comprehensive range of data is often necessary.

Though limited, the available disaggregated data reveal important health inequality patterns in Australia. A significant public health concern is the burden of Non-Communicable Diseases (NCDs) among CALD populations. Interacting with social and health conditions, poor health literacy and multimorbidity can hinder the management of NCDs. Structural disadvantages and vulnerabilities, along with inadequate health systems, can impede the ability of CALD populations to access necessary health services (Khatri & Assefa, 2022). These core distinctions underpin important yet modifiable chronic non-communicable diseases that might be overlooked in aggregated data.

The COVID-19 epidemic brought attention to the difficulties in obtaining health care information and services, as well as the disproportionate effects this had on populations of migrants and refugees. According to the COVID-19 mortality data in Australia, those who were born outside of Australia saw a higher death rate during the pandemic. The research also noted that more than 70% of COVID-19 fatalities during the Delta wave were foreign-born individuals (Australian Bureau of Statistics, 2022d).

To address these issues, health systems and services must focus on culturally appropriate health interventions and disaggregated data and ensure multilingual health resources. Moreover, holistic policy interventions are required to improve the social determinants of health for CALD populations, such as living and working conditions and socioeconomic disparities (Queensland Health, 2023).

The Australian Multicultural Bill (2018) enshrines multiculturalism and diversity principles in law. This is consistent with state legislation and ensures multilateral and ongoing support for multiculturalism. The Bill also outlines annual reporting requirements for Commonwealth entities. These reporting requirements include collecting statistical data during the period to enable the development of policies, programs, and practices sensitive and responsive to Australia’s multicultural character.

The ABS (2022c) recommend the ‘Standards for Statistics on Cultural and Language Diversity’ (the Standards) to standardise collecting and reporting information on CALD. The Standards include a Minimum Core set of indicators, including the country of birth of a person, primary language other than English spoken at home, proficiency in spoken English and Indigenous status. The Standards also recommend a set of non-core indicators, which includes the year of arrival in Australia (Australian Bureau of Statistics, 2022b).

Conclusion

The lack of attention to the need for disaggregated data to monitor health inequity is not a drawback of ABDS alone. The Global Burden of Disease Study (GBD2021) is one of the largest and most comprehensive efforts to quantify health loss across places and over time. It draws on the work of nearly 12,000 collaborators across more than 160 countries and territories. GBD 2021 includes more than 607 billion estimates of 371 diseases and injuries and 88 risk factors in 204 countries and regions. Attention to the need for disaggregated data would have made the study more meaningful for monitoring the global health inequity of sub-population groups.

We propose concerted efforts to gather disaggregated data by specific CALD populations to improve data equity and health equity. This disaggregation can be achieved partially by implementing established guidelines such as those outlined by The National Health Data System, which comprises several linkage systems such as the ABS’ Person Level Integrated Data Asset (PLIDA), the National Disability Data Asset (NDDA), Population Health Research Network (PHRN) linkage nodes, state and territory linkage nodes and other tailored linkages and assets.

The ABS is the critical agency entrusted with data collection in Australia. The ABS Act 1975 and the Census and Statistics Act 1905 are the principal legislation determining the functions and responsibilities of the ABS. As part of the Government’s ongoing commitment to giving Australians greater access to relevant government information quickly and easily, the Senate Order was amended in 1998. It now requires departments to list these files on their Internet websites. Further, Legal and policy initiates are essential for collecting disaggregated and standardising racial or ethnic categories across databases to promote comparability. Developing creative research strategies and enhanced sample techniques to better represent racial or ethnic groups that are regionally and remotely distributed or numerically small to ensure health equity.

Australia needs to improve its statistical capacity to gather disaggregated data through a multifaceted approach that includes reforming legal frameworks, enhancing health data infrastructure, greater institutional coordination, ensuring adequate financial resources to support desegregated data collection, utilising advancements in technology to enhance data collection and Improving data use and services effectively to inform policy and that services are provided to meet the needs of equity in health.

In conclusion, there is legal and policy impetus to collect and disseminate CALD-specified disaggregated data, but such data is not widely available. The ABDS should take note of this data gap. Disaggregated data is essential for evidence-informed policymaking, tracking health inequity, ‘leaving no one behind’ and achieving progress towards health-related Sustainable Development Goals.

Authors’ contributions

JT Conceptualised the manuscript, conducted the literature survey, developed the study, performed the analysis, wrote the manuscript, and handled the submission. BG and FH conducted a literature survey, wrote the manuscript together, and contributed to the final revision. All the authors read and approved the final manuscript.

Ethical statement

We certify that all the data and materials in this manuscript are databases and that the literature is in the public domain.

Contributions to the submission have been received explicitly from all co-authors. Authors whose names appear on the submission have contributed sufficiently to the scientific work and share collective responsibility and accountability for the comments and observation.

The original manuscript has not been published and will not be submitted elsewhere for publication while being considered by Critical Public Health. No data have been fabricated or manipulated (including images) to support our conclusions. No data, text, or theories by others are presented as if they were our own.

Acknowledgements

The Corresponding author is the editor-in-chief of Critical Public Health

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

We do not analyse or generate datasets because our work proceeds with a theoretical approach. All references are cited.

Additional information

Funding

No external funding was received to prepare this paper.