Vulnerability in the context of COVID-19 is not distributed evenly across all people and households. Globally, a range of factors – including race and social and economic status – have already been shown to shape vulnerability.
In the South African context, household composition and structure are already known to be linked to socio-economic vulnerability. They are likely to be relevant in the context of COVID-19 too. In particular, female-headed households and multigenerational households are likely to be at risk.
To explore these relationships, we created a series of interactive visualisations. They consider various household characteristics and exposure to factors which might increase the risk of contracting COVID-19, or of suffering economic and health impacts as a result of the pandemic.
Our analysis draws on Gauteng City Region Observatory’s Quality of Life V (2017/18) survey data. This defines the household as those people living in the same dwelling and eating together for four or more nights a week.
We focus on the following household characteristics: size; children; multiple generations; headship; and dwelling conditions. We also look at levels of vulnerability in households where respondents are over 60 years old, primary caregivers or migrants.
We have previously developed a set of indices to capture multiple forms of risk during the COVID-19 pandemic. Our current analysis shows how risks shift and overlap for different household characteristics.
We found that vulnerability in the context of COVID-19 is complex. Responses to the pandemic need to consider variations in forms and distribution of risk. For example, households in informal dwellings are more likely to have poor access to water and sanitation but they are less likely to have members with pre-existing health conditions. Female-headed households, larger households, and those where respondents are females with primary care-giving responsibilities have higher levels of risk for both transmission and the impacts of the lockdown. Women are more likely to live in larger households, so these patterns seem likely to place them at greater risk of transmission within the household.
Understanding how vulnerability shifts alongside household structure and attributes is crucial to delivering targeted support.
Our work follows the analysis established in the Gauteng City-Region Observatory’s March 2020 Map of the Month. This outlined two indices of factors that were expected to increase vulnerability related to the COVID-19 pandemic.
The first index considers factors which might increase the difficulty of preventing COVID-19 transmission. This includes, for example, maintaining high levels of personal hygiene and practising social distancing. These risk factors include living in a crowded dwelling, absence of piped water, shared or inadequate toilet facilities, dependence on public health care services, limited access to communication tools, and reliance on public transport.
The second index examines factors that are likely to increase health and socio-economic vulnerability in the context of a lockdown or widespread pandemic. These include existing health conditions and socio-economic conditions such as risk of hunger, ability to save money and access to medical aid.
Both indices are scored from 0-100, with 0 representing the lowest and 100 representing the highest level of risk.
Households living in informal dwellings are likely to experience the highest levels of vulnerability as measured by the first index. With a score of 50, these households are likely to face particular challenges in implementing prevention strategies. This is due to poor infrastructure and high levels of crowding within dwellings.
Despite these challenges, living in an informal dwelling is less predictive of risk in terms of the social and health issues. This is captured in the second index, when compared to the other household characteristics considered in this analysis.
Larger households, defined as those with five or more people, score higher in both indices, compared to smaller households. Similarly, households with five or more children also score higher. Scores on both indices also vary on the basis of household composition. For example, multigenerational households have the highest average score on the second index.
Household roles and responsibilities
Vulnerability also appears to vary according to roles and responsibilities within the household.
Female-headed households score higher on both indices compared to households headed by males or multiple adults together. Female respondents who - identified themselves as the primary carers of their children - also tend to live in households which score higher on both indices. This was in comparison to households in which respondents were male primary carers, joint carers, or did not have dependent children.
There is a mixed relationship between migrant status and scores on our vulnerability indices. Migrants from other provinces in South Africa live in households with higher scores on the first index than migrants from outside South Africa or those born in Gauteng province.
Internal migrants, along with respondents born in Gauteng, also score slightly higher on the second index than international migrants. These scores are explained by migrants reporting better overall health status, and living in households with fewer pre-existing health conditions and lower risk of hunger.
But these scores may understate the vulnerability of foreign migrants. These groups have been excluded from many governmental pandemic support initiatives, such as grant increases and food parcels.
Households living in informal dwellings are exposed to particular forms of risk. They are less likely to have piped water into their home or yard, and are more likely to have shared or inadequate sanitation. Residents in informal dwellings, whether South African or foreign, are also more likely to be migrants.
These same households are more likely to be single person households. This reduces household risk of hunger or pre-existing health conditions.
There is no particular household characteristic that is associated with high scores across all risk factors considered in our indices. As a result, supportive interventions and preventative measures need to be tailored to, and informed by, an understanding of household structure, as well as local conditions and challenges.