Higher COVID-19 mortality in low-income communities in the City of Cape Town – a descriptive ecological study

Introduction

South Africa is the most unequal country in the world, with a Gini Coefficient of 0.63^1,2, and within its cities the same inequities prevail. Post-apartheid Cape Town is a highly divided and unequal city, with socio-economic characteristics still differing along geographic lines³. In 2016, the city’s population was 4 232 276 and the Gini coefficient was 0.61⁴.

Since SARS-CoV-2 reached Cape Town in March 2020, the city has experienced two waves of COVID-19. The first wave peaked in June-July 2020, and the second more severe wave peaked in December 2020 – January 2021, and was driven largely by the new 501.YV2 (B1.351) variant^5,6.

Data from the United States (USA) has shown that low socio-economic status, and race as its proxy, were significantly associated with COVID-19 incidence and mortality⁷. Similar findings have been reported in the United Kingdom (UK)^8–10. Data from Brazil and Mexico further support the idea that the poorest population groups have lower survival from COVID-19^9,10.

Cape Town Metro has eight geographic sub-districts. Due to the city’s apartheid history, inequity tends to follow these geographic boundaries and the majority of the population resides in the historically Black African and Coloured (mixed ancestry) areas towards the southeast of the city (see Figure 1 map)¹¹. There is, however, still some heterogeneity within sub-districts. These low-income communities also tend to be overcrowded and denser, with less access to services like running water and electricity². The populations in these areas are also younger, with for instance 1.6% of the population in Khayelitsha being over 65 years of age, compared to 7.3% in Northern³.

Figure 1. Map of Cape Town Metro, with socio-economic index and population size taken from the 2011 Census³.

Graphic courtesy of the Western Cape Department of Health.

Since older age is the strongest risk factor for COVID-19 mortality¹², crude mortality rates may mask inequities in COVID-19 outcomes as poorer populations tend to be younger. We therefore aimed to compare the age- and sex-standardized mortality in different geographical areas of Cape Town known to have different income levels.

Methods

This descriptive ecological study was done using aggregate data routinely reported as part of the Western Cape Department of Health’s COVID-19 Surveillance Response, from 1 March 2020 to 28 February 2021. The main outcome of interest was the COVID-19 standardized death rate (SDR) and the main exposures were the economic indicators, for each sub-district. COVID-19 deaths include all deaths ascertained by the services, or through linkage to the national population register in patients with civil identifiers available, in individuals with laboratory confirmed COVID-19 (either PCR or antigen positive). This is limited to deaths within 28-days of a COVID-19 diagnosis or within 14-days of being discharged following admission due to COVID-19, and excludes deaths manually flagged as incidental by case managers or identified as non-natural deaths on the population register.

COVID-19 tests are performed in either the public National Health Laboratory Service or in various private laboratories. The Standardized Death Rate (SDR) is calculated by adjusting for the age and sex breakdown of each sub-district in the province, using the Western Cape Province as the standard population, using Microsoft Excel.

Data on economic indicators was taken from the latest Census in 2011, which is publicly available online³. Unemployment was defined as “persons who did not work, but who looked for work and were available to work in the reference period”, and the labor force was defined as persons aged 15–64 years³. The 2011 Census classified households with monthly incomes below ZAR3200, which when converted at the average 2011 exchange rate is around US$ 440¹³.

The co-morbidities amongst public sector patients are primarily reported from inferred health episodes at the Provincial Health Data Centre, where routine health data, such as laboratory tests and medication dispensed, is used to infer health conditions¹⁴. The co-morbidities of private patients are reliant on manual capture, and there is the risk of under-ascertainment in this latter group.

No ethics approval was sought for this study, as it relies upon aggregate data from routine reports.

Results

There were 7 643 total COVID-19 related deaths in the Cape Town Metro. The total crude death rate ranged from 1 217 in Northern, to 2 547 in Klipfontein. Adjusted for age, the total standardized death rate for COVID-19 was 1 640 per million overall but ranged from 920 in Northern to 2 686 in Khayelitsha sub-district (Table 1, Figure 2).

Figure 2. Total COVID-19 Standardized Death Rate (SDR) per million by sub-district.

Graphic courtesy of the Western Cape Department of Health.

Using the 2011 Census data, the eight sub-districts have differing levels of employment and monthly household income. Among the deceased COVID-19 cases, more private laboratory testing was done in the higher income sub-districts (Table 2). The scatter plots (Figure 3a and b) show a linear positive relationship between increasing COVID-19 SDR in a sub-district and (a) percentage of unemployment and (b) percentage of low-income households in a sub-district.

Figure 3a and 3b.

Total COVID-19 Standardized Death Rate (SDR) and percent of labor force unemployed (a) and household monthly income (b), taken from the 2011 Census³, and plotted for each Cape Town Metro sub-district.

Poorer sub-districts tended to be worse affected in the first wave with relative protection in the second wave. In contrast, wealthier sub-districts had a low SDR during the first wave but were worse affected in the second wave. A linear but inverse relationship was present between percentage unemployment rate in a sub-district and the ratio of Wave 2 to Wave 1 COVID-19 SDRs (Figure 4).

Figure 4. Ratio of Wave 2 to Wave 1 COVID-19 SDRs and percent of labor forced unemployed, by sub-district.

For the deceased patients with COVID-19, different sub-districts also have differing age structures and co-morbidity burdens. Sub-districts with higher burdens of infectious diseases, particularly HIV and tuberculosis, tended to have younger COVID-19 deaths and an overall higher SDR (Figure 5 and Figure 6).

Figure 5. Age categories for the deceased COVID-19 patients, by sub-district.

Figure 6. Known co-morbidities in deceased COVID-19 patients, by sub-district (note that tuberculosis includes both previous and current disease).

Discussion

The study suggests that low-income sub-districts had higher COVID-19 SDRs. The SDR for Khayelitsha was almost three times higher than that of Northern, a more affluent sub-district. This finding of a strong socio-economic gradient in COVID-19 mortality is consistent with an expanding literature, and explanations for this association are multifactorial. Individuals in low-income communities with crowded housing, a reliance on public transport and higher numbers of essential workers are less able to implement and maintain social distancing and non-pharmaceutical interventions (NPIs)⁷. And they therefore are more likely to get infected with COVID-19, as demonstrated in several South African seroprevalence studies^15–17.

A SARS-CoV-2 seroprevalence study performed by the South African blood service in four of the nine provinces (Eastern Cape, Northern Cape, Free State and KwaZulu Natal) found substantial differences between different race groups, with Black African donors having seroprevalence rates above 60% and White donors having less than or around 20%¹⁵. While the authors note the limitations of using race as a proxy for socio-economic status, their study highlights the limitations of NPIs in communities with economic deprivation and high population density, or conversely how effective NPIs can be when they are feasible¹⁵. A study on shopping mall workers in Cape Town found a high seroprevalence was associated with informal housing, living in a sub-district with a low-income per household and having a low-earning occupation¹⁶.

In a study from the USA, amongst those diagnosed with COVID-19, poverty was also associated with a higher risk of hospitalization and intensive care unit admission¹⁸. Because of upstream social determinants of health, low-income communities have higher rates of chronic diseases that put them at risk of severe COVID-19 disease⁷. Younger age is usually a protective factor for severe COVID-19 disease. However, data from the USA and UK has shown that in communities of color, people of younger ages were still at risk of COVID-19 mortality, because high rates of co-morbidities, such as diabetes and chronic lung , are concentrated in these poor communities¹⁹. The diagnosis and management of these chronic conditions is also inferior, as these same communities often lack access to quality care²⁰.

Data from a population-based study in the Western Cape during the first wave of the pandemic showed that older age, male sex and diabetes were strongly associated with COVID-19 mortality¹². The study also found that HIV and current tuberculosis, both conditions strongly associated with poverty, were associated with COVID-19 mortality, with a 2.14 (95% confidence interval [CI] 1.70-2.70) and 2.70 (95% CI 1.81-4.04) adjusted hazard ratio, respectively¹². A population-based study from the UK also found a strong association between HIV and COVID-19 mortality²¹.

As data on co-morbidities is not collected in a standardized way across the public and private sectors for all cases diagnosed with COVID-19, this study was not able to formally assess the differing burdens of co-morbidities across the sub-districts.

If individuals in low-income communities require in-patient treatment, they are more likely to access the relatively under-resourced public health sector. An analysis of COVID-19 in-hospital mortality in South Africa, found that admission to a public sector compared to a private sector facility was associated with increased risk of mortality (adjusted odds ratio of 1.6; 95% CI 1.4-1.8)²². This may be due to later presentations to hospital, as well as differences in access to and unequal availability of critical care and other specialized resources or interventions in the public sector.

This study also showed that low-income sub-districts were worse affected in the first wave compared to the second wave, suggesting that infection in the first wave may have conferred some immunity. However, the science on re-infection is still very unclear, and particularly in the context of emerging variants, this should be interpreted cautiously²³.

Limitations of this study include its ecological nature, the lack of standardized co-morbidity data and the fact that the economic data is from 2011. However, while the percentages of individuals living in poverty in each sub-district might have changed with time, and even increased during the pandemic, a reduction in inequality between the sub-districts is unlikely to have occurred. The Western Cape Government reported increasing inequity in Cape Town from 2011 to 2018, with the Gini coefficient for the city increasing from 0.604 in 2011 to 0.617 in 2018¹¹. An analysis of data from the National Income Dynamics Study (NIDS) in 2017 and the first wave of the NIDS-Coronavirus Rapid Mobile Survey (NIDS-CRAM) suggested that income-related health inequality in the COVID-19 era increased six-fold compared with what was obtained in 2017²⁴.

The COVID-19 pandemic has exposed the longstanding structural drivers of health inequities globally²⁵. While all sectors of the population of Cape Town were affected by the pandemic, those living in low-income communities were at higher risk of SARS-CoV-2 infection and of COVID-19-related mortality. Health services need to be preferentially directed to those that need them the most, i.e. the poor. This is not only for COVID-19, but for any subsequent pandemic where low-income communities will remain vulnerable without improved access to care. It will however remain difficult to address the inequities of COVID-19 morbidity and mortality while poverty and income inequality persist.

Data availability

All data underlying the results are available as part of the article and no additional source data are required.