Socioeconomic-Related Inequalities in COVID-19 Vulnerability in South Africa.

Individuals' vulnerability to the risk of COVID-19 infection varies due to their health, socioeconomic, and living circumstances, which also affect the effectiveness of implementing non-pharmacological interventions (NPIs). In this study, we analysed socioeconomic-related inequalities in COVID-19 vulnerability using data from the nationally representative South African General Household Survey 2019. We developed a COVID-19 vulnerability index, which includes health and social risk factors for COVID-19 exposure and susceptibility. The concentration curve and concentration index were used to measure socioeconomic-related inequalities in COVID-19 vulnerability. Recentred influence function regression was then utilised to decompose factors that explain the socioeconomic-related inequalities in COVID-19 vulnerability. The concentration index estimates were all negative and highly significant (p < 0.01), indicating that vulnerability to COVID-19 was more concentrated among the poor. According to the decomposition analysis, higher income and education significantly (p < 0.01) positively impacted lowering socioeconomic-related COVID-19 vulnerability. Living in an urban region, being Black, and old all had significant (p < 0.01) positive impacts on increasing socioeconomic-related COVID-19 vulnerability. Our findings contribute to a better understanding of socially defined COVID-19-vulnerable populations in South Africa and the implications for future pandemic preparedness plans.

Spatial inequality through the prism of a pandemic: Covid-19 in South Africa.

In this paper, we examine whether pre-existing socio-economic inequalities relate to inequalities in vulnerability to COVID-19 infection in the context of South Africa using the 2016 Community Survey, which allows for a detailed geographical disaggregation of the analysis. For this purpose, we use a set of indicators of a household's vulnerability to COVID-19 infection and then aggregate these into an index of COVID-19 vulnerability. We use these indicators and their aggregate indices to profile COVID-19 vulnerability at the national, provincial and municipal levels as well providing an urban/rural breakdown. We find that pre-existing socio-economic inequalities are related to inequalities in vulnerability to COVID-19 infection. Poorer households are more vulnerable to infection due to their living conditions. While close to 30% of the population in the poorest two wealth quintiles may be considered vulnerable to the virus due to multiple vulnerability factors, the corresponding figure for the richest two wealth quintiles is less than 2%. There are also stark spatial inequalities in COVID-19 vulnerability. This has implications for budget allocations in response to the COVID-19 pandemic, especially as some of the government relief funding has been and will be apportioned according to municipal need.