Socioeconomic inequalities in COVID-19 outcomes in south Africa

Tshipetane, Anza

Socioeconomic inequalities in COVID-19 outcomes in south Africa

Thesis / Dissertation

2025

Publisher

University of Cape Town

Department

Department of Public Health and Family Medicine

Faculty

Faculty of Health Sciences

Abstract

Background: This research addressed the crucial issue of socioeconomic inequalities associated with COVID-19 outcomes at the district level in South Africa. While previous literature has frequently relied on income as a measure of socioeconomic status, it's essential to recognize that socioeconomic standing is multifaceted and dynamic. In this study, we delved into these multidimensional factors shaping a population's SES, employing an intersectional approach that considers demographic, economic, and structural aspects of socioeconomic status. Our analysis aimed to understand how each of these dimensions, as well as their collective influence, impacts COVID-19 cases, recoveries, and deaths. The insights underline the need for targeted policy interventions that bolster support for vulnerable groups, enhance economic resilience through job creation and social grants, and improve public infrastructure to reduce overcrowding and ensure equitable access to basic utilities. Methods: The study utilised data amalgamated by the National Policy Data Observatory (NPDO), which synthesizes information from the National COVID-19 outcomes surveillance spanning from November 2019 to July 2022 and socioeconomic indicator data from the 2021 General Household Survey (GHS) . The study used 12 socioeconomic indicators across 53 districts to create four analytically backed domains of socioeconomic standing namely: sociodemographic index (average number of persons per household, proportion of female- headed households, and age dependency ratio and proportion of individuals over the age of 60), economic index (income, subsidised households, poverty and adult unemployment), infrastructural index (water, electricity, toilets and proportion of informal dwellings) and overall district socioeconomic index using the Principal Component Analysis technique. The relationship between these indices with COVID-19 cases, recoveries, and fatalities were further investigated using the Wagstaff concentration index of inequality and concentration curves. Results: Using factor loadings attained from PCA, we grouped our variables into the 3 domains of socioeconomic standing, validating the intersectional framework conceptualised in the study. The analysis revealed that when districts were ranked by the sociodemographic index, COVID-19 outcomes showed a pro-poor distribution across cases, recoveries, and fatalities, with concentration indices of -0.27 (p=0.01), -0.27 (p=0.01), and -0.16 (p=0.08) respectively. Groups experiencing greater sociodemographic deprivation, including those in complex households such as households with the elderly and female-headed households, suffered severe outcomes. The results showed a similar pattern with the economic index, with concentration indices of -0.22 (p=0.04), -0.21 (p=0.04), and -0.16 (p=0.07) for cases, recoveries, and mortalities respectively. This aligns with empirical literature where unemployment and lack of income exacerbated poor outcomes. The infrastructural index showed a pro-rich distribution with the concentration curve below the line of equality and concentration indices of +0.34 (p=0.0005), +0.34 (p=0.0006), and +0.30 (p=0.005) for cases, recoveries, and mortalities respectively. This along with evidence of dense population in metropolitans such as city of Johannesburg, eThekwini and City of Cape Town suggests that urbanization and subsequent overcrowding in well-developed areas increased the risk of spreading infection and worsening COVID-19 outcomes, supporting the narrative of calling COVID-19 the urban disease. Finally, the multidimensional SES index produced an overall pro-poor distribution with the concentration curve lying above the line of equality and concentration indices of -0.28 (p=0.01), -0.27 (p=0.01), and -0.20 (p=0.02) for cases, recoveries, and mortalities respectively. Using the sociodemographic, economic, and infrastructural indices, the analysis provided a statistically significant description of inequality in cases and recoveries but not in mortalities. Conclusion: The research provides compelling evidence of a 'social gradient' in health at the district level in South Africa. The results clearly demonstrate that poor socioeconomic status, as indicated by our broad multidimensional SES index, exacerbates negative health outcomes, with the main drivers being sociodemographic characteristics and economic deprivation. This indicates potential pressure points that policymakers can focus on.

Keywords

Covid-19

South Africa

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