By Laura Dixie
Manager
22 July 2020


Contributors

Hugh Miller

The socioeconomic gradient of health and COVID-19


By Laura Dixie | 22 July 2020 | Contributors Hugh Miller



By Laura Dixie | 22 July 2020


Contributors Hugh Miller


COVID-19 is more likely to present severely for older people and people with pre-existing medical conditions1. Pre-existing conditions, along with other health outcomes, are not equally distributed along socioeconomic lines. In this article we explore this relationship and what it means for Australia's experience of COVID-19.

People in lower socioeconomic areas tend to have poorer health. The NY Times recently explored this ‘socioeconomic gradient of health’ in its article Who Is Most Likely to Die From the Coronavirus? 2. The article referenced a University of California, Los Angeles study Trends in Health Equity in the United States by Race/Ethnicity, Sex, and Income, 1993-2017 whose authors concluded “what we now know about population health is that it is determined largely by social and economic policy factors”.

We decided it would be interesting to see how these results translate to an Australian context. While we share many commonalities with the United States, there are some important differences. We have a very different healthcare system, lower income inequality and a different ethnic makeup. However, we know Australia certainly has its own ‘socioeconomic gradient of health’. The 2018 edition of the Australian Institute of Health and Welfare’s report Australia’s health found people with their socioeconomic status in the lowest fifth of the population were 1.5 times more likely to die from all causes than the fifth of the population with highest socioeconomic status3.

Pulling apart the relationship between socioeconomic status and health is complex. A full analysis would correct for effects such as age, gender and ethnic background, all of which strongly contribute to the picture of Australia’s health. However, a focus on income is helpful as it brings the topic of inequality into sharp relief.

Are people with lower incomes at higher risk of severe COVID-19?

Using Australian Bureau of Statistics (ABS) data4,5 we looked at how the prevalence of four important risk factors for severe COVID-19 vary by household income. The four risk-factors are:

  • Asthma
  • Diabetes mellitus (‘diabetes’)
  • Heart, stroke and vascular disease (‘cardiovascular disease’)
  • Having three or more chronic conditions – i.e. at least three of: arthritis; asthma; back problems; cancer; chronic obstructive pulmonary disease; diabetes mellitus; heart, stroke and vascular disease; kidney disease; mental and behavioural conditions; and osteoporosis.

The charts in Figure 1 compare the prevalence of these conditions to the median household income in a geographical area. Each point on the chart is one ABS ‘Statistical Area 2’ region, or SA2 region. An SA2 region is typically about the size of a postcode, or approximately 10,000 residents. The median prevalence is shown as a black horizontal line.

Figure 1 – Rates of disease by median household income

Areas with lower household incomes have much higher rates of disease and chronic conditions. About 32% of SA2 regions have median household incomes below $50,000. Visually, most SA2s in this category have higher than median rates of the risk factors. For these regions:

  • 95% have above-median rates of ≥3 chronic conditions
  • 86% have above-median rates of asthma
  • 84% have above-median rates of diabetes
  • 92% have above-median rates of cardiovascular disease.

These rates are incredibly high when compared regions with higher household incomes. For example, the proportion of regions with above median rates of cardiovascular disease is three times higher where the median income is less than $50,000, compared to regions with median incomes of $50,000 or more.

How does the socioeconomic gradient of health vary across different states and territories?

Figure 2 takes the SA2 regions within each state and territory and:

  • Orders the SA2 regions by median household income and groups them into deciles (where decile 1 is the group of SA2 regions with the lowest median household incomes, and decile 10 is the group of SA2 regions with the highest median household incomes)
  • Graphs how the incidence of the four risk factors (the coloured lines) varies by household income.

To make the different states and territories more comparable, each set of lines is centred around the median for that state or territory.

Figure 2 – Difference compared to the median by household income decile for all States and Territories

In New South Wales:

  • The rate of cardiovascular disease and the rate of diabetes are both 50% above the median for the lowest household income decile.
  • The rate of cardiovascular disease and the rate of diabetes are 20% lower and 30% than the median among the highest household income decile.
  • The combined effect can be seen in the rate of having three or more chronic conditions. This is 80% above the median for the lowest household income decile. It is 40% lower than the median among the highest household income decile.

New South Wales has the steepest health gradient for multiple chronic diseases out of all states and territories. The gradients are similar to those seen in the United States.

Most other states show significant gradients also. For example, Queensland has a particularly steep curve for diabetes. Tasmania and the ACT have the shallowest socioeconomic health gradients. ACT is perhaps understandable since it has relatively lower income inequality and is highly urbanised. The flatter curve for Tasmania reflects slightly different dynamics. Tasmania has the least income inequality of all the states/territories, combined with a higher overall rate for each of the risk factors (in part due to an older age profile).

Older people are more likely to have chronic illnesses. This means differences in the age profile will contribute to the slopes. We note, firstly that previous research found the socioeconomic gradient still exists within age bands6. Secondly that when we used the rates of disease among those aged 60 and over (rather than in the SA2), the gradient was still present albeit shallower.

The socioeconomic gradient of health exists in all states and territories. It is steepest in New South Wales, Queensland and Victoria, and shallower in the ACT and Tasmania.

What does this add up to?

The socioeconomic gradient of health is not new, and it exists for a range of reasons. For instance, those with higher incomes tend to have better housing and working conditions and are less exposed to dangerous environments, among other things7. But the heightened risk of poor health outcomes during COVID-19 draws attention to its implications.

As at early July, Diabetes Australia reports people with diabetes represent 7% of diagnosed cases of COVID, 17% of hospitalised cases, 24% of ICU stays, and 25% of deaths in Australia8. A meta-analysis on COVID-19 and diabetes found COVID-19 patients with diabetes have more than two times the risk of developing severe COVID-19 and nearly two times the risk of mortality9. Another meta-analysis explored various comorbidities for COVID-19 patients and found hypertension, respiratory system disease, and cardiovascular disease were 1.4-2.4 times more likely among severe-COVID-19 cases than non-severe10.

It is still early days in our understanding of the COVID-19 virus. It is likely some time away before we know the exact elevation in risk of severe COVID-19 associated with having an existing chronic illness. These early numbers indicate those with a chronic illness may be at two-to-four times the risk of severe COVID-19.

What does this mean? In NSW, for example, areas with the lowest decile of household incomes have rates of chronic diseases 1.6-2.1 times higher than among areas with the highest decile of household incomes. Applying the Diabetes Australia ratios directly, this implies if there was an outbreak in these regions, they could see 3-8 times the rate of severe-COVID-19 cases.

What should we take from this? As the threat of a second wave of cases looms in Australia, it is important to remember the risk of developing severe complications from COVID-19 is not distributed equally along socioeconomic lines. In the short term, this has implications for how governments think about staffing, testing, and hospital capacity. In the longer term, it has the opportunity to spark a continued focus on ‘health justice’, or as the authors of the UCLA study comment “when our politics starts to work better for those left behind, then their health will improve”.


References
1World Health Organisation, COVID-19: vulnerable and high-risk groups. Available from: https://www.who.int/westernpacific/emergencies/covid-19/information/high-risk-groups
2NY Times, Who Is Most Likely to Die From the Coronavirus? Yaryna Serkez June 4, 2020. Available from: https://www.nytimes.com/interactive/2020/06/04/opinion/coronavirus-health-race-inequality.html
3Australia’s health 2018, The Australian Institute of Health and Welfare. Available from: https://www.aihw.gov.au/reports/australias-health/australias-health-2018/contents/table-of-contents
4Australian Bureau Statistics. National Health Survey: Small Area Estimates, 2017–18 — Australia. Available from: https://absstats.maps.arcgis.com/apps/MapSeries/index.html?appid=bacd58f73b554c329f431ceb02ef9ab8
5Australian Bureau Statistics. Census 2016 – total household income. Available from: http://stat.data.abs.gov.au/Index.aspx?DataSetCode=ABS_C16_T21_SA#
6Mather et al. Variation in health inequalities according to measures of socioeconomic status and age. Australian and New Zealand Journal of Public Health (2014). Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/1753-6405.12239
7World Health Organisation. Social determinants of health. Available from: https://www.who.int/social_determinants/thecommission/finalreport/key_concepts/en/
8Diabetes Australia, https://www.diabetesaustralia.com.au/covid19 accessed 9 July 2020
9Kumar et al. Is diabetes mellitus associated with mortality and severity of COVID-19? A meta-analysis, Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2020. Available from: https://www.sciencedirect.com/science/article/pii/S1871402120301090
10Jang et al. Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis, International Journal of Infectious Diseases 2020. Available from: https://www.ijidonline.com/article/S1201-9712(20)30136-3/abstract



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