Like any health issue, the impacts of COVID-19 are strongly gendered.
Men are believed to be more susceptible to the virus, while women are more exposed in other ways: to an increased care burden, to heightened incidences of gender-based violence, to limited work opportunities, and to job losses.
Understanding these gendered experiences and responding accordingly requires data that is disaggregated. It means going beyond the totals of people accessing facilities or losing their jobs, but disaggregating figures on the balance of men and women facing these challenges, including inequalities and discriminatory effects. For instance, in Bangladesh and Pakistan, women are less likely to receive information about the virus and to be covered by health insurance than men.
For many countries in the world, disaggregated data is incredibly hard to come by; the surveys required are too costly, samples taken aren’t large enough, or the surveys were only done at the broad household-level with no insights gleaned on the differences between people within those households. This presents a major analytical and humanitarian challenge always, but especially during a global pandemic. From our experiences in OECD countries battling COVID-19, we know we need gender-specific response strategies. But without the data, how can we craft those strategies?
One useful emerging tool available to countries is gridded population data. Despite being around for the past two decades (the Center for International Earth Science Information Network launched its first Gridded Population of World dataset in 1995), the recent boom in available satellite imagery and mapping tools has made various datasets more prevalent.
As explained in a new report from SDSN TReNDS and the POPGRID Data Collaborative, Leaving No One Off The Map: A Guide For Gridded Population Data For Sustainable Development, gridded (or raster) population maps represent the distribution of population in rows and columns of grid cells, typically defined by their latitude-longitude coordinates. An increasing number of data providers are also combining information from censuses with satellite-derived geospatial features to redistribute population data and produce gridded population datasets.
In short, redistributing population data across grid cells while combing satellite imagery and other sources can provide more accurate, timely population figures — which can also be overlaid with other datasets in a systematic way to derive new insights, especially on population demographics.
A good example is data on access to health facilities. Current estimates across Africa are often derived from outdated census information mapped against surveys of health facilities. With gridded population data from groups like WorldPop, overlaid with new infrastructure surveys, we can have much more timely, accurate maps of people’s access to health facilities, as well as gender-disaggregated datasets. These datasets provide critical information — including women’s access to facilities — and help us to better identify vulnerable populations and ensure that they have the support they need.
Gridded population data and a whole host of other data innovations will be essential for countries to develop tailored pandemic responses that are inclusive of everyone. Fortunately, two gridded population datasets, Gridded Population of the World and WorldPop are already providing disaggregated datasets — based on age and sex — that countries can use for their COVID-19 response efforts. And groups like SDSN TReNDS and Data2X are continuing to encourage governments, academia, civil society organizations, and the private sector to lend their support by providing training, access to resources, and much more.
Without this kind of technical support, the gendered effects of this crisis will go unseen in much of the developing world, with catastrophic consequences for the lives affected.