Our focus in this article is to discuss the importance of sex-disaggregated data in enabling our understanding of the real impact of the COVID-19 pandemic; therefore, providing an understanding of what is understood by sex-disaggregated data and gender is a precursory task.
What is sex-disaggregated data?
Any data on individuals collected and analysed separately based on the biological categorisation of that person as male, female or intersex.
What is gender?
Refers to the sociocultural meanings we place on the above categories of sex. It is how women are treated because they are perceived as female. While gender is constructed, both have real consequences for women and men.
The COVID-19 pandemic has directly affected women's and men's social, economic and biological health. However, the overall effect of the pandemic is much greater than these direct impacts, and available evidence suggests that the indirect effects are shaped by marginalisation and disadvantage. As gender plays a significant role in increasing marginalisation and disadvantage, gender shapes the adverse effects experienced by females and males.
To prevent the effects of the COVID-19 pandemic from further exacerbating existing gender gaps on the African continent, we need to collect data on the differential impacts experienced by females, males and intersex.
To help address a problem, the data available must be sufficiently descriptive of the problem - for policy and other interventions to be designed, the unknowns need to become known.
What do we know about COVID-19 and Sex and Gender Disaggregated data on the African continent?
Overall, publicly available sex-disaggregated data are still incomplete for multiple health and well-being aspects across the continent. Our recent search for publicly available subnational level COVID-19 data in Africa found that these countries provided sex-disaggregated COVID-19 data.
Figures 1, 2 and 3 are data visualisations of sex-disaggregated data created by three different African countries. They highlight some of the differential impacts of the pandemic that can be understood when case, death or vaccination data is categorised via sex.
Figure 1 is a Population Pyramid (a pair of back-to-back histograms for each sex). It shows us the distribution of a population in all age groups (Y-axis) and both sexes (colours). In the above Population Pyramids, we can see that for the Nigerian population, more males have gotten and died from COVID-19 (larger green and red bars).
Figure 2 is a Doughnut chart (Pie Chart with the centre cut out). It helps show proportions and percentages between categories by dividing a circle into proportional segments. In the above doughnut chart, we can see that the arc length for the male category (blue) is longer than for the female category (pink); therefore, visually, we can see that a higher percentage of males are vaccinated against COVID-19. Because we know more males than females are being vaccinated, we could investigate what may be the cause and create an enabling environment for females to get vaccinated.
Figure 3 is a bar chart showing discrete, numerical comparisons across the female, male and unknown categories of sex. From this bar chart, we can see that the majority of clinical statuses (31 700 cases) were recorded without being assigned, female or male.
Additionally, we can see that for eSwantini, there were more COVID-19 cases for females (9050) in comparison to males (8502).
Data availability was greatest for indicators related to the direct impacts of the COVID-19 pandemic, such as cases, deaths or vaccinations. Table 1, shows that all sex-disaggregated data published related to these direct indicators. However, no data on the portals related to sexual and reproductive health care, disruptions in schooling, safety, and/or gender-based violence.
Data availability is still - broadly - a significant challenge in the region, and what this kind of case study starts to reveal is how this gap becomes even more pronounced when seeking to answer more complex social, political and economic questions (the types of questions social change work necessarily needs to try to answer).
What do we know about the direct impacts of COVID-19 and Gender?
A substantial amount of research shows that the direct health effects of the COVID-19 pandemic have affected men more than women: COVID-19 incidence, hospitalisation, and death rates are higher among men than women across locations. Conversely, existing evidence indicates that the indirect effects of COVID-19 have affected women disproportionately. The economic impacts of the COVID-19 pandemic have affected women more than men in some countries because they tend to be employed disproportionately in sectors that are harder hit by COVID-19, such as the hospitality industry or the informal sector (e.g., domestic workers). - Flor, Luisa S et al. (2022)
A general lack of sex-disaggregated data on COVID means that answering many of the potential gender-related questions about the disease remains a niche topic, dealt with by academics and largely concerning communities outside the Global South. This kind of invisibility for African women adds a datafied layer of discrimination, preventing us from designing well-targeted interventions in response to the disease. As we move toward engaging on the longer-term socio and economic impacts of COVID, as opposed to the immediate health impacts, the importance of such data will only grow.
Society is at a pivotal moment where investment in the empowerment of women and girls is critically needed to ensure that progress towards gender equality does not get stalled or reversed because of the COVID-19 pandemic. We cannot let the social and economic fallouts from the pandemic continue into the post-COVID era. Action must be taken now to not only reverse the current disparities but to further close the gaps present before the pandemic began. - Flor, Luisa S et al. (2022)
What to learn more about this topic?
The source for impacts of COVID-19 on Gender can be found here.
Notes on the publicly available sex-disaggregated data from the African countries above:
- eSwatini (Provides Cumulative cases and Deaths disaggregated by Sex, however, the data was last updated in December 2021);
- Guinea (Provides Cumulative cases and Number Vaccinated disaggregated by Sex);
- Mozambique (Provides Cumulative cases disaggregated by Sex only);
- Nigeria (Provides Cumulative cases and Deaths disaggregated by Sex);
- Rwanda (Provides Confirmed cases and Deaths disaggregated by Sex);
- Saint Helena ( Provides Cases over the 14 days disaggregated by Sex);
- South Africa (Provides Cumulative Cases disaggregated by Sex at the Provincial and Municipal levels); and
- Togo (Provides Confirmed cases and Deaths disaggregated by Sex).