The burden of illness and death due to coronavirus (COVID-19) is not being shouldered equally. Across the world, men, older people, and people with long-term health conditions are at higher risk of serious illness and death from the virus. But inequalities in outcomes go beyond these factors alone. We have seen higher numbers of deaths from COVID-19, than in the general population, among: black and minority ethnic communities, people living in socioeconomically deprived areas, those working in professions like social care, and those who require care in their own homes or a care home.

The reasons for higher COVID-19 death rates among these groups need to be understood and tackled. But, when we know that women are more likely to work in social care than men, or that black and minority ethnic people may be more likely to be exposed to COVID-19 in their living circumstances or professions, it should shape our analysis and public health response.

In this blog we examine the data on socioeconomic deprivation, gender and COVID-19 mortality, to show how acting on only one aspect of possible inequality can prevent us observing and tackling the intersection with others. Doing so will help national and local government, public health teams, the health service, and the scientific community, develop strategies and act on some of the steepest inequalities we see, as part of our public health response.

Gender, socioeconomic deprivation and the direct effects of COVID-19

COVID-19 studies around the world point to the virus taking a greater toll on men. At all ages, COVID-19 related hospitalisations in England and across Britain, intensive care admissions and deaths in and outside of hospitals are higher among men.

As for many other diseases, evidence of a striking socioeconomic gradient is also emerging for COVID-19. 25% of critical care patients with COVID-19 are from the most socioeconomically deprived fifth of areas and 15% are from the least deprived. The age-adjusted death rate in the most deprived tenth of areas is double that of the least deprived areas.

But the effects of socioeconomic deprivation may be exacerbated by other social factors. Intersectionality theory is a framework to consider whether disadvantage on multiple social dimensions combines to affect outcomes. It was first laid out as a way of understanding how racism and sexism combine to affect the experiences of black women but has been extended to consider discrimination and disadvantage where other identities intersect. One recently highlighted intersection is that of gender and socioeconomic deprivation. Despite overall increases in life expectancy, socioeconomic inequalities in life expectancy have increased for men and women since 2010. And for women in the most deprived tenth of areas, according to The Marmot review 10 years on, life expectancy actually declined between 2013–2015 and 2016–2018.

Additional analysis of ONS figures released on 1 May 2020 suggests the socioeconomic gradient of the COVID-19 death rate could be steeper for women than men. For men, the age-standardised rate in the least deprived decile was 35.9 deaths per 100,000 population and in the most deprived decile the rate was 114% higher at 76.7 per 100,000. For women, the age-adjusted rates were lower at 17.0 deaths per 100,000 population in the least deprived decile and 39.6 per 100,000 in the most deprived decile, but the percentage difference (133%) was larger. Whatever factors are contributing to the greater risk of dying with COVID-19 in more deprived areas could be operating more strongly for women. All-cause mortality for the same period did not show a steeper socioeconomic gradient among women. We do not know why this might be, and further analysis using data over a longer period is needed to look at the all-cause and COVID-19 death rates among men and women.

We should be cautious when interpreting this finding. Using the data available, it is not possible to quantify the uncertainty or variability around the estimates or use a robust statistical test on the interaction between gender and deprivation. Over the time period for which data was available, deaths in London contributed a considerable proportion and the final impact may look different when later deaths across the UK are included.

Why might women in deprived areas be more at risk?

Identifying the underlying drivers is key to understanding and acting to reduce this inequality. From our analysis1 (shown in Figure 2), we know that the number of long-term conditions increases by deprivation. The inequality between those living in the least deprived compared to the most deprived areas is greater among women than men, and this is statistically robust. Given the known link between existing conditions and COVID-19 deaths, a gender difference in the socioeconomic gradient of COVID-19 deaths is plausible.

In addition to being more likely to have underlying conditions that may increase their risk of serious infection, women living in deprived areas might also be more exposed to the virus. Occupations like social care, teaching or many roles in health care may face an increased risk of infection, as they often require close contact with others. We have seen this play out in the data. When adjusted for age and gender, social care workers have twice the rate of death due to COVID-19 compared to those of working age. In the UK, 75% of people in such roles are women and many of these roles are not well paid. Indeed, ONS analysis shows that most women younger than 65 who died with COVID-19 were care workers or home care workers.

Importantly, unpaid roles were not included in the ONS analysis of deaths by occupation. The majority of women with no occupation recorded on their death certificate were full-time carers or working in other voluntary roles. Socioeconomically disadvantaged women are also over-represented among unpaid carers and may additionally face greater exposure to COVID-19 in this role.

Taking an intersectional approach to our COVID-19 response

There is already a huge amount of effort and research attempting to understand the reasons for the higher age-adjusted COVID-19 death rates in men, people living in deprived communities, and black and minority ethnic communities, and this should continue.

But the findings we present here should also motivate further research to understand how multiple characteristics and identities overlap to deepen inequalities. What are the everyday circumstances and challenges that contribute to increased deaths due to COVID-19? Here, we have focused on women and deprivation, but we also need to understand the compounding influence of ethnicity too.

As a first step, further analysis using updated, individual level data on deaths involving COVID-19 with ethnicity, gender, socioeconomic deprivation, and occupation will be needed to probe these associations. In addition, data on infection risk and other factors, such as providing unpaid care outside the household and marital or cohabitation status, could also shed light on how gender, deprivation and ethnicity, interact to affect COVID-19 risk. Those using the data, and interpreting the results, must also be alert to bias in data collection that often fails to recognise the work of women. And that the recording of clinical characteristics in electronic health records can too be influenced by structural inequalities.

This analysis illustrates one combination of social factors that may be linked to increased risk of COVID-19. An intersectional approach to inequalities encourages us to think about other combinations that could accentuate risk and to question whether a universal public health approach alone will ensure those high-risk groups are protected from the impact of the virus. As planning starts for changes to the lockdown, it is crucial that these risk factors are understood so that government can tailor the most relevant public health support. This support will need to provide for different social groups based on gender, socioeconomic deprivation and other intersecting attributes.

Mai Stafford (@stafford_xm) is Principal Data Analyst at the Health Foundation.

Sarah Deeny (@SarahDeeny) is Assistant Director of Data Analytics at the Health Foundation.

1. Analysis based on a random sample of 300,000 individuals in the Clinical Research Practice Datalink active on 1 April 2014. 37 long-term conditions that predict all-cause hospitalisation and mortality were counted. Some of these conditions overlap with the shielded patient list. Approval to use the data was obtained from an Independent Scientific Advisory Committee protocol 17_150Rmn2

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