On gender, the case data and why an anthropologist cares

Last night, I was talking to a reporter with the Washington Post about gender and Ebola. She contacted me because she saw a tweet I wrote asking about sex disaggregated data for the outbreak. None of my ‘Ebola tweeps’ — some of them data wonks — knew of any good sources. I looked at the ministry of health updates and WHO data, but found nothing about sex or gender. It seems that much of what we hear, and much of what we know, is based on conjecture and speculation. Liberian President Ellen Johnson-Sirleaf apparently reported that 75% of the cases were women (I haven’t found the link but it’s one of the things that prompted the reporter to request an interview). It is not clear where these figures come from, but I suspect it is an educated guess related to who takes care of sick people at home and within health facilities, who deals with bodies in funerary rites, and other factors involving the differences between women’s and men’s daily practices. It might even be a hunch that frontline workers have communicated. I can’t be sure. But this article suggests that Ebola is affecting ‘breadwinners’; who are these people?

After my conversation with the reporter, I decided to look more carefully for data that might provide some clue about transmission and infection patterns as they relate to gender. I worry that because we have so little numerical data beyond these cases and their location (or maybe it’s just not available for public consumption?), however, the numerous consultants deploying to the region run the risk of basing many of their decisions about containing the epidemic on speculation and conjecture. Apparently, MSF has some anthropologists on staff now; I am hoping that the anthropologist has the willingness and foresight to not only provide fine-grained analysis of social and cultural practices that place people at risk (our work tends to be valued for being arcane, esoteric), but also to mine the numerical stuff for other clues. Perhaps s/he can suggest better ways to slice and dice the numbers.

But back to the original question: Why does gender matter? It might not at all. But my sense is that we might not know whether it does or not if we don’t have sex disaggregated data to look at how the epidemic has developed over time. And we don’t just need gender data for the cases, but for the health workforce, hospital cleaning staff and the like. This is how messages get targeted. This is how people follow contacts, trace movements, contain patients. Knowing how people do the work that they do, where they do it, and under what context they may be at risk for infection, is paramount as the new infections and cases show no sign of slowing.