Explaining health inequalities in Australia: the contribution of income, wealth and employment
Joanne Flavel![https://orcid.org/0000-0003-3228-7192](/media/client/orcid_16x16.png)
![https://orcid.org/0000-0002-2787-8580](/media/client/orcid_16x16.png)
![https://orcid.org/0000-0002-8573-4235](/media/client/orcid_16x16.png)
A Stretton Health Equity, Stretton Institute, University of Adelaide, Adelaide, SA 5005, Australia.
B College of Medicine and Public Health, Flinders University, Adelaide, SA 5042, Australia.
C Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK.
D Deakin University, Institute for Health Transformation, Melbourne, Vic. 3125, Australia.
Australian Journal of Primary Health 28(6) 474-481 https://doi.org/10.1071/PY21285
Submitted: 2 December 2021 Accepted: 13 June 2022 Published: 13 July 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of La Trobe University. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Background: Studies show widespread widening of socioeconomic and health inequalities. Comprehensive primary health care has a focus on equity and to enact this requires more data on drivers of the increase in inequities. Hence, we examined trends in the distribution of income, wealth, employment and health in Australia.
Methods: We analysed data from the Public Health Information Development Unit and Australian Bureau of Statistics. Inequalities were assessed using rate ratios and the slope index of inequality.
Results: We found that the social gradient in health, income, wealth and labour force participation has steepened in Australia, and inequalities widened between the quintile living in the most disadvantaged areas and the quintile living in the least disadvantaged areas.
Conclusion: Widening income, wealth and employment inequalities have been accompanied by increasing health inequalities, and have reinforced and amplified adverse health effects, leading to increased mortality inequality. Effective comprehensive primary health care needs to be informed by an understanding of structural factors driving economic and health inequities.
Keywords: Australia, economic inequality, health equity, health inequalities, social class, social determinants of health, social gradient, socioeconomic factors.
Introduction
Socioeconomic inequalities in health have widened in Australia (Adair and Lopez 2020). Brown et al. (2012) calculated the saving to the public purse if inequalities are reduced. The social determinants of health (SDH) are key drivers of health (CSDH 2008). This paper focuses on the role of employment, income and wealth, which influence people’s health profoundly and need to be recognised in terms of grounding primary health care in the realities of people’s lives (Baum et al. 2016).
There is a vast literature on the connection between employment status and health (Benach et al. 2007; Joyce et al. 2010; van der Noordt et al. 2014). For instance, people in lower socioeconomic groups are more exposed to adverse working conditions, including physically demanding work, shift work, and precarious and insecure employment (Benach et al. 2007). Likewise, inequalities in income and wealth have been associated with health inequities and gradients (Wilkinson and Pickett 2007).
The inequitable distribution of health is remediable with policies that address the SDH (CSDH 2008). Despite this, much research on improving population health has focused on the health system and/or targeted approaches that focus on individual behaviours rather than SDH (Balabanova et al. 2011; Katikireddi et al. 2013). This study contributes to a growing literature on health inequalities and SDH through examining measures of inequality that account for the social gradient by using all five quintiles and examining trends in SDH in addition to trends in three measures of mortality over three decades.
Methods
Study design
This study comprised descriptive quantitative analyses of Australian data from 1986 to 2017. This timeframe was chosen as availability of data was most comprehensive (some indicators were not available for the entire period). Data were collected that would enable descriptive analysis of the socioeconomic distribution of health, income, wealth and employment, and trends over time. This study goes beyond publicly available statistics reported by the Australian Bureau of Statistics (ABS), the Australian Institute of Health and Welfare and other published studies by examining all five quintiles in addition to differences between the most disadvantaged and least disadvantaged quintiles. Descriptive methods were chosen given that there are no individual data that include all the variables of interest (mortality and SDH), linked data for the variables and timeframe of interest were not available, limited comparability of many individual data sources over time, and the need for population data rather than cohort data to examine the full distribution of SDH.
Data sources and indicators
Data were sourced from the Public Health Information Development Unit Social Health Atlas (SHA) on age-standardised premature mortality (deaths for people aged 0–74 years), avoidable mortality (deaths for people aged 0–74 years from avoidable causes) and infant mortality rates by Index of Relative Socio-economic Disadvantage (IRSD) quintile. Wealth data were obtained from the ABS covering Gini coefficients of household net worth and Gini coefficients of equivalised net worth. Income indicators drawn from the SHA covered key government income support indicators: receipt of unemployment benefits, low income and sole parent families. ABS data on equivalised household income inequality, income growth, occupation and industry were used. Data on unemployment and labour force participation were obtained from the SHA. Statistics on the top 1% income share were sourced from the world inequality database. We analysed data for the three time points that were available for each indicator from the SHA, as these provided the only data available by quintile of socioeconomic area disadvantage.
Social Health Atlas and Index of Relative Socio-economic Disadvantage
The SHA reports data by Population Health Area (PHA) and by socioeconomic disadvantage of area. PHAs have an average population of 22 000. The SHA ranks PHAs based on the IRSD score and divides the ranked PHAs into five quintiles (PHIDU 2022). Statistical Areas Level 2 (gazetted suburbs or rural localities) are grouped into quintiles by IRSD score for indicators derived from census data, and IRSD scores for each census were used to produce quintiles for that census year, and for the 2 years before and 2 years after that census year. The IRSD is one of the ABS Socio-Economic Indexes for Areas that can be mapped onto areas. The IRSD is a weighted sum of income, housing, education and employment variables that capture access to material and social resources, and factors enabling participation in society (ABS 2018). The IRSD is a commonly used indicator of socioeconomic status, as individual multidimensional measures of socioeconomic status are not available in routinely collected data in Australia.
Analyses
Data were extracted into Excel and Stata version 14. There are no linked data between health indicators and SDH, therefore, assessment of trends in inequality was undertaken by analysing rate ratios and the slope index of inequality (SII) for the SHA indicators. The SII is a regression-based indicator that quantifies the absolute linear association between socioeconomic rank and the indicator of interest (Moreno-Betancur et al. 2015). It is informed by all five quintiles and accounts for the social gradient. Rate ratios were calculated by dividing the rate for the quintile of the population living in the areas with the most disadvantaged IRSD scores by the rate for the quintile living in the least disadvantaged areas. Data used to produce the SII and rate ratios were population data (not a sample), therefore, uncertainty estimates were not needed. The SIIs and rate ratios were compared with those representing no inequality (1 for rate ratios, 0 for SIIs) to determine the extent of socioeconomic inequality for each indicator and changes in this over time. All assessments of inequality considered the underlying rates per quintile in determining the reliability of trends.
Ethical approval
This study involved secondary analysis of publicly available data sources, therefore, ethical approval was not required.
Results
Table 1 presents the rate ratios and the SIIs for health, income and employment for all indicators derived from the Social Health Atlas. Higher absolute values of SIIs represent higher inequality. Rate ratios represent the ratio of rates for the most disadvantaged quintile relative to the least disadvantaged quintile.
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Health
The SII showed an absolute rise for avoidable mortality from −17.15 in 1997–2000 to −20.40 in 2003–2007 to −21.36 in 2013–2017. The SII for premature mortality was lower in 2003–2007, but this was temporary and the SII in 2013–2017 was as high as that in 1987–1991, and it is notable that the SII for premature mortality was higher than that for avoidable mortality. The SII for infant mortality was unchanged over the three time points. Rate ratios steadily increased for premature mortality, avoidable mortality and infant deaths, and for each of these indicators, the rate for the quintile living in the most disadvantaged areas had risen to approximately double that of the least disadvantaged quintile in 2013–2017.
Fig. 1 illustrates the steepening of the social gradient in premature and avoidable mortality. This is most noticeable for avoidable mortality, where both the SII and rate ratio increased.
Income and wealth
Our analysis of the distribution of disposable household income by income quintile found that the mean weekly equivalised disposable household income for the highest income quintile was 4.8 times higher than the lowest income quintile in 1994–1995, and increased to a peak of 5.6 times the lowest income quintile in 2007–2008. There was stagnation in disposable income for all income quintiles from 2013–2014 to 2017–2018. The income share of the top 1% has grown from 5.4% in 1983 to 12.4% in 2017, and grew even during the period of income stagnation (Fig. 2). Wealth inequality increased in Australia between 2003–2004 and 2017–2018 according to all measures. The Gini coefficient of household net worth rose from 0.573 to 0.621 between 2003–2004 and 2017–2018 (Fig. 3). The Gini coefficient of equivalised household net worth rose by a similar magnitude.
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The SII for low income, welfare-dependent families rose (in absolute terms) between 2006 and 2017, and the SII for female sole pensioners rose between 1996 and 2009, and had changed little in 2016 (Table 1). There was a higher SII for single parent families with children in 2016, despite the SII being lower in 2006 compared with 2001. Likewise, the SII for people receiving an unemployment benefit was higher in 2017 than in 1996, despite a lower SII in 2009. There were large increases in the rate ratios for all four income-related indicators
Employment
The SII for labour force participation rose from 1.96 in 1986 to 3.1 in 2009 and 3.56 in 2016, and rose for female labour force participation between 2006 and 2016. Rate ratios for overall labour force participation and female labour force participation worsened, with the percentage participating in the labour force for the quintile living in the most disadvantaged areas being 0.8 times that of the least disadvantage quintile in 2016 (and only 0.75 for female labour force participation). The absolute SII for unemployment was lower in 2009 and 2016 compared with 1986, and the rate ratio for unemployment was also lower in 2009 before increasing in 2016. The SII for jobless families with children was little changed in 2016 compared with 2001, but the rate for the quintile in the most disadvantaged quintile rose to five times that of the least disadvantaged. There were greater gains in labour force participation for the less disadvantaged quintiles, but less inequality in changes in unemployment by quintile (Fig. 4).
Low skilled jobs declined between 1996 and 2016 (Table 2). Health care and social assistance, construction, retail trade, accommodation and food services, professional, scientific and technical services, education and training, and public administration and safety saw increases in employment.
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Discussion
Our analysis shows that inequalities widened between the most and least disadvantaged quintiles of area disadvantage for health, income and employment in recent decades. The analysis using all five quintiles found that the social gradient in avoidable mortality, income, wealth and employment has steepened over this period. This steepening gradient has significance for comprehensive primary health care (CPHC), as the stresses caused an increase in related illnesses (Fisher and Baum 2010; Thoits 2010). Although we found overall improvements in health and employment, the gains were much greater for the 20% of the population living in the least disadvantaged areas.
The skill mix in the Australian labour market changed between 1996 and 2016, especially the decline of unskilled manufacturing jobs. Retail trade, accommodation and food services, health care, and social assistance showed an increase in employees, and these industries are more likely to offer casual and part-time employment, Although we could not examine associations between the SDHs and health directly, a large body of evidence points to how unequal distribution of income, wealth and employment are linked to increasing socioeconomic health inequalities (Marmot and Wilkinson 2005; Benach et al. 2007; Wilkinson and Pickett 2007; CSDH 2008; Barnay 2016). Our findings of increasing geographical inequality in labour force participation may be partially attributed to a mismatch between skills and available jobs in more disadvantaged areas due to the changed skill mix in the Australian labour market.
Increasing inequality in employment reinforces and exacerbates socioeconomic inequality in income and wealth. Those with higher incomes have higher levels of discretionary spending, whereas those on low incomes spend a much higher proportion of their income on essentials (Beech et al. 2014). Inequalities in standard of living lead to health inequalities by affecting the quality of housing, food and health care people can afford.
Our findings provide detail on the increasing inequality in SDH, and the corresponding increase in health inequality, by examining the gap between the most and least disadvantaged. The growing gap results from lower socioeconomic groups living and working in difficult circumstances. These conditions mean they are more susceptible to poorer health outcomes through poor employment conditions, and lack of resources for social and economic participation (CSDH 2008).
CPHC professionals need to be aware of the impact of the structural inequalities on their patients and local communities, and provide comprehensive responses that take into account people’s living circumstances. Primary health networks can use data on inequalities for intersectoral collaboration, and advocacy for policies likely to reduce economic inequalities.
Socioeconomic factors may be invisible to CPHC practitioners unless they are open to examining the changing socioeconomic dynamics in their patients’ lives. Barriers to quality care can be reduced through policies, such as bulk billing patients, and practitioners asking people about their life circumstances (Browne-Yung et al. 2019). In addition, CPHC practitioners are powerful advocates for policy action on the SDH, especially when they understand the changing dynamics.
A government committed to reducing inequalities could reduce income inequality by increasing government income support to above the poverty line. Wealth inequality could be reduced by implementing more progressive taxation. Increasing the stock and quality of public housing would reduce rental stress and food insecurity. Fairer employment contracts and better working conditions would contribute towards reducing employment inequality. Finally, monitoring of health inequalities needs to be improved. In particular, small area data would be useful to CPHC services.
This study’s strength is the use of multiple data sources, which draw on population data, and enable more detailed estimates of inequality in health and SDH over three decades, allowing examination of the changes in the social gradient for multiple indicators. IRSD rankings are used in Australia as a proxy for socioeconomic status, and here provide estimates of inequality for indicators where individual data are not available and over a longer time period than for most individual data (Mather et al. 2014). However, area-based data have been found to underestimate the extent of health inequality between individuals (Mather et al. 2014), and our estimates of health inequality should therefore be treated as conservative. Inferences also cannot be made about individuals based on aggregate, area-based population data (Mather et al. 2014), and reciprocal relationships cannot be examined.
Conclusions
This study provides new detail on population trends in inequality in health and SDH in Australia. Australia has become a less equal society in the past three decades, not just due to differences between the most disadvantaged and least disadvantaged, but due to a steepening of the social gradient that leaves all but the least disadvantaged worse off. CPHC can play a vital role in advocating for policies that reduce inequalities in income, wealth and employment, and being aware of the constraints people face, especially during health crises, such as the current COVID-19 pandemic.
Data availability
Data used in this manuscript are publicly available, and can be found on the Public Health Information Development Unit website (https://phidu.torrens.edu.au/social-health-atlases/data) and the ABS website (https://www.abs.gov.au/statistics).
Conflicts of interest
The authors declare no conflicts of interest.
Declaration of funding
This work was supported by a Flinders Foundation Health Seed Grant. The funder had no involvement in preparation of data or the manuscript or the decision to submit for publication.
Acknowledgements
We thank John Glover for advice related to the use of the Social Health Atlas.
References
ABS (2018) Technical Paper: Socio-Economic Indexes for Areas 2016. ABS.Adair T, Lopez A (2020) Widening inequalities in premature mortality in Australia, 2006-16. Australian Population Studies 4, 37–56.
| Widening inequalities in premature mortality in Australia, 2006-16.Crossref | GoogleScholarGoogle Scholar |
Balabanova D, McKee M, Mills A (Eds) (2011) ‘Good health at low cost’ 25 years on: what makes a successful health system?.’ (School of Hygeine and Tropical Medicine: London, UK)
Barnay T (2016) Health, work and working conditions: a review of the European economic literature. The European Journal of Health Economics 17, 693–709.
| Health, work and working conditions: a review of the European economic literature.Crossref | GoogleScholarGoogle Scholar | 26280132PubMed |
Baum F, Freeman T, Sanders D, Labonté R, Lawless A, Javanparast S (2016) Comprehensive primary health care under neo-liberalism in Australia. Social Science & Medicine 168, 43–52.
| Comprehensive primary health care under neo-liberalism in Australia.Crossref | GoogleScholarGoogle Scholar |
Beech A, Dollman R, Findlay R, Cava GL (2014) ‘The distribution of household spending in Australia.’ RBA Bulletin. (Reserve Bank of Australia: Sydney)
Benach J, Muntaner C, Santana V (2007) Employment conditions and health inequalities. Final report to the WHO Commission on Social Determinants of Health.
Brown L, Thurecht L, Nepal B (2012) ‘The cost of inaction on the social determinants of health.’ (NATSEM, University of Canberra: Canberra)
Browne-Yung K, Freeman T, Battersby M, McEvoy DR, Baum F (2019) Developing a screening tool to recognise social determinants of health in Australian clinical settings. Public Health Research & Practice 29, e28341813
| Developing a screening tool to recognise social determinants of health in Australian clinical settings.Crossref | GoogleScholarGoogle Scholar |
CSDH (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. World Health Organization, Geneva. Available at https://www.who.int/publications/i/item/WHO-IER-CSDH-08.1 [Accessed March 2022]
Fisher M, Baum F (2010) The social determinants of mental health: implications for research and health promotion. Australian & New Zealand Journal of Psychiatry 44, 1057–1063.
| The social determinants of mental health: implications for research and health promotion.Crossref | GoogleScholarGoogle Scholar |
Joyce K, Pabayo R, Critchley JA, Bambra C (2010) Flexible working conditions and their effects on employee health and wellbeing. Cochrane Database of Systematic Reviews 2010, CD008009
| Flexible working conditions and their effects on employee health and wellbeing.Crossref | GoogleScholarGoogle Scholar |
Katikireddi SV, Higgins M, Smith KE, Williams G (2013) Health inequalities: the need to move beyond bad behaviours. Journal of Epidemiology and Community Health 67, 715–716.
| Health inequalities: the need to move beyond bad behaviours.Crossref | GoogleScholarGoogle Scholar | 23486925PubMed |
Marmot M, Wilkinson R (2005) ‘Social determinants of health.’ (OUP Oxford: Oxford)
Mather T, Banks E, Joshy G, Bauman A, Phongsavan P, Korda RJ (2014) Variation in health inequalities according to measures of socioeconomic status and age. Australian and New Zealand Journal of Public Health 38, 436–440.
| Variation in health inequalities according to measures of socioeconomic status and age.Crossref | GoogleScholarGoogle Scholar | 24962890PubMed |
Moreno-Betancur M, Latouche A, Menvielle G, Kunst AE, Rey G (2015) Relative index of inequality and slope index of inequality: a structured regression framework for estimation. Epidemiology 26, 518–527.
| Relative index of inequality and slope index of inequality: a structured regression framework for estimation.Crossref | GoogleScholarGoogle Scholar | 26000548PubMed |
PHIDU (2022) Social Health Atlas of Australia. Available at https://phidu.torrens.edu.au/social-health-atlases/indicators-and-notes-on-the-data/social-health-atlases-of-australia-contents [Accessed 26 April 2022]
Thoits PA (2010) Stress and health: major findings and policy implications. Journal of Health and Social Behavior 51, S41–S53.
| Stress and health: major findings and policy implications.Crossref | GoogleScholarGoogle Scholar | 20943582PubMed |
van der Noordt M, Ijzelenberg H, Droomers M, Proper KI (2014) Health effects of employment: a systematic review of prospective studies. Occupational and Environmental Medicine 71, 730–736.
| Health effects of employment: a systematic review of prospective studies.Crossref | GoogleScholarGoogle Scholar | 24556535PubMed |
Wilkinson RG, Pickett KE (2007) The problems of relative deprivation: why some societies do better than others. Social Science & Medicine 65, 1965–1978.
| The problems of relative deprivation: why some societies do better than others.Crossref | GoogleScholarGoogle Scholar |