Collecting meaningful race and/or ethnicity data to improve public health outcomes is imperative and must be part of Australia’s national agenda.

Australia is an immigration nation and among the most successful multicultural countries globally.

Australia has settled 7.7 million people born overseas since the first federal immigration portfolio was created in 1945, and migrants represent 29.5% of the Australian population. Increased multiculturalism in Australia has brought with it complex dynamic and intersectional dimensions related to race, ethnicity, culture, and disparities in health and welfare.

Disparities in health outcomes manifest in various forms, with racism one consistent determinant. Lack of racial data is a barrier to overcoming structural racism. A high level of racism is associated with poor health care and outcome in a wide range of contexts, including participation in intervention innovations and primary health care, medical incidents, access to treatment and utilisation of health care, hospitalisation and rehabilitation, and maintenance. For example, racial minorities in Australia and other high income countries are less likely to receive clinically indicated, protocol-driven care than their white counterparts. Existing evidence suggests that the lack of adequate data on race and ethnicity obscures evidence of racism and holds back antiracism efforts in health policies, interventions and guidelines.

Is Australia falling behind in tracking racial disparities in health? - Featured Image
Racism is associated with poor health care and outcome in a wide range of contexts (Dragana Gordic / Shutterstock)

Although Australia does not collect data on race and ethnicity, data on cardiovascular health hospitalisation and outcome indicate that, compared with non-Aboriginal Australians, Aboriginal Australians have a coronary heart disease mortality that is two times higher and are 60% less likely to undergo angioplasty. The lower receipt rate of angioplasty is closely and significantly associated with symptoms deemed as non-cardiac (16%), prioritising non-invasive tests (8%), discharge against medical advice (11%), and unspecified/unclear reasons (36%). In countries where data on race exist, racial disparities have also been reported in all-cause mortality and recently in coronavirus disease 2019 (COVID-19) outcomes.

Racial and ethnic disparities in welfare have also been reported. For example, data from the United States suggest that migration is associated with significantly higher gains in economic outcomes among white men than men from ethnic minorities. For women, ethnic disparities in economic outcomes associated with migration are stable or narrow. Such gender differentials suggest that disparities in economic outcomes associated with migration widen among men but narrow or even become comparable among women. Data from Germany highlight the confounding and differential effect of educational attainment on racial disparities in welfare by migration status.

Reducing racial and ethnic disparities in Australia is a challenge due to the paucity of data on race and ethnicity. When compared to Organisation for Economic Co-operation and Development (OECD) countries, Australia is currently falling behind in tracking and addressing racial and ethnic inequities in health and social determinant factors. Several OECD countries such as the United Kingdom, the US, New Zealand and Canada not only have robust systems for capturing race and/or ethnicity data at various levels of service delivery in order to detect and address inequities in all their forms, they also have specific institutions or departments, strategic plans and associated budgets, and government acts or legislations that guide the collection and harmonisation of race and ethnicity data and drive transformative changes. For example, in these countries, government research funding institutions mandate the collection and reporting of individual-level data on race and/or ethnicity to monitor coverage, reach and inclusion in research funding. Health services collect data on race and/or ethnicity to depict racial and/or ethnic disparities and to design programs to bridge the disparity gap.

Apart from collecting data on Aboriginality, Australia predominantly relies on two key variables: ancestry and country of birth (with surrogate measures associated with language spoken at home and length of stay in Australia). The Australian Bureau of Statistics contends that the “ancestry” variable, when combined with the “country of birth” variable captures a person’s ethnic background. Such an approach can be misleading for many reasons. Cultural and social dimensions and life conditions that are associated with a person race and/or ethnicity are strong determinants of health than biological factors.

Data of the aggregate effect of social determinants of health on health outcomes suggest that health-related behaviours and socio-economic and physical environment factors account for ³ 80% of health outcomes in a population, and the remaining 10–20% are accounted for by other factors, including clinical care. Although biological dimensions are important at the individual level, they are not the main drivers of health at the population level. Race and ethnicity are social constructs, ancestry remains a biological parameter. Therefore, ancestry tells us nothing about a person’s cultural or social background, life conditions, or belief systems. These dimensions are better captured by social constructs.

It goes without saying that measuring race will always generate controversy, especially when constructed within social realities linked to biological connotations (eg, racial inferiority v racial superiority), with potential political manipulations. However, when grounded in socio-cultural contexts linked social goods, it becomes a meaningful indicator with practical extensions in policy formulation, health service delivery, and socio-political outcomes, such as civic engagement.

The use of country of birth is also problematic and tend to generate data packaged under the collective “culturally and linguistically diverse” terminology. This collective terminology does not necessarily measure race or ethnicity and leads to non-specific ethnic groups such as Australian-born versus overseas-born or English-speaking versus non-English speaking. The use of non-committal collective terminologies has serious negative policy consequences:

  • It makes visible minorities invisible. For example, the Australian Bureau of Statistics reported lower age-standardised COVID-19 death rates among sub-Saharan Africa than the Australian-born population. However, these findings do not account for the white privilege, as 50.84% of the 372 151 enumerated sub-Saharan African migrants are predominantly white South Africans (Afrikaner and British descent) and white Zimbabweans (English and Scottish), who are socio-demographically, economically and ethnically different from their black African Australian counterparts.
  • It leads to distorted allocation of funds. Reliance on aggregated cultural diversity data rather than explicit data on race and/or ethnicity can lead to false assumptions, with associated distorted allocation of funds. When reporting on the health of migrants in Australia, the healthy migrant effect is often evoked. Although often generalised to apply to all migrants, the healthy migrant effect fails to consider the health of forced migrants such as refugees and humanitarian entrants, who remain invisible minorities in significant numbers. When estimating the healthy migrant effect, researchers tend to use the wrong comparator. For example, when domestic migrants (host nation-born interstate movers) are included in the models and used as a comparator, the nativity health gap narrows significantly. In addition, there is the issue of “the salmon bias hypothesis”, where critically ill migrants return to their countries of origin either prior to death to convalesce and/or possibly die and be buried in their ancestral lands or to escape integration challenges.
  • It legitimises and institutionalises racism. The racial data dashboard by the COVID Tracking Project and other scholars found that, when data on race and ethnicity are consistently and systematically omitted in health policies and interventions or are incomplete, findings can “misrepresent the racial and ethnic identities of people who face the greatest structural health risks”. These studies emphasise that health disparities exist across and within racial categories, hence, any misrepresentation of findings based on incomplete or inadequate racial data can polarise and victimise certain migrant groups. For example, in the absence of racial data in Australia, despite no evidence of actual gang activity, it is not uncommon for crimes committed by few African migrant youth to be generalised to all African communities under the umbrella of “African gangs”.

Although criticism of assessing race and/or ethnicity exists, collecting meaningful race and/or ethnicity data to achieve public goods and support public interests is imperative and must be part of Australia’s national agenda. Bridging the racial and ethnic disparities will require concerted whole-of-government efforts to standardise indicators to collect consistent and reliable data within and across government jurisdictions. The recent Australian Government’s announcement that it plans to include ethnicity data in the next census in 2026 is a step in the right direction.

Distinguished Professor Andre M N Renzaho is a Professor within the Translational Health Research Institute, School of Medicine at Western Sydney University.

The statements or opinions expressed in this article reflect the views of the authors and do not necessarily represent the official policy of the AMA, the MJA or InSight+ unless so stated. 

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2 thoughts on “Is Australia falling behind in tracking racial disparities in health?

  1. Andre Renzaho says:

    a great point, Brian

  2. Brian Cooper says:

    There is a similar issue with disability data, which lacks demographic insight, epidemographic insight or intersectional insight. There is an urgent need to ensure that the lifecycle stage, early or late-late onset conditions, are recognised in the data collection so appropriate policies can be developed.

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