I spent part of January working on my book manuscript, The Uncounted: Politics of Data in Global Health. When I began writing this in 2017, I was just interested in the data paradox: in which criminalized, stigmatized key populations, who lack data to prove they exist, get no funding for programs that save their lives, reinforcing the lack of data. But as I get deeper into the work, I’m noticing the growing dominance of cost-effectiveness language and tools, and how economic values are shaping how we think about priorities in global health finance.
Cost-effectiveness tools and language increasingly shape how health resources are divvied up: both at the national level, where health officials decide how much money (if any) to invest in HIV programs for criminalized, stigmatized key populations; and at the global level, where aid agencies decide which countries to prioritize for funding.
I’m even starting to hear community activists adopt health economics terms like “allocative efficiency” when they advocate for key populations services. This is what Wendy Brown brilliantly calls “the model of the market” — neoliberalism reshapes everything in the form of economics, “converting every human need into a profitable enterprise,” displacing other values –ultimately, she argues, undermining democracy.
Cost-effectiveness can be a great way to push for efficiency and evidence-based priorities. But when there is no data to demonstrate, quantitatively, that a given population needs services, or when that population is very small, it can be tough to make a case that programs to save their lives are cost-effective. Thanks to those data gaps, cost-effectiveness arguments in HIV priority-setting routinely disfavor some key populations– transgender women, male sex workers – in ways that reinforce societal discrimination.
Some populations who may face special vulnerability to HIV for social and political reasons are even more invisible – trans men, indigenous peoples, persons with disabilities, and others. They’re not officially “key populations”, according to WHO, so no data may exist at all to make the case to fund HIV services. For survivors of sexual violence, the most hidden of all, the problems are even more complex. Because only a fraction of survivors ever report the crimes, their urgent medical needs are routinely underestimated and under-resourced. Some well-intentioned efforts to gather data about the extent of sexual violence risk exposing survivors to retaliation and more violence — the data paradox, again.
The research for this is interdisciplinary. The book draws on ethnographic field work and conversations with activists in the Caribbean, where aid agencies are withdrawing support and governments have to decide where to allocate what little funding they have; and in Sub-Saharan Africa, where donors are investing big funds and are under pressure to show results. In Africa, we’re starting to see increasingly aggressive forms of data-gathering – biometrics, DNA mapping, and more. These local views have been useful in the deconstruction of the assumptions and data that underly mathematical models, cost effectiveness analysis software, and global aid policies. Excitingly, in both the Caribbean and in Africa, communities are increasingly pushing for more ownership of the data and control over how it is collected – what Sukti Dhital neatly calls “democratizing data”.
There’s no grant money for this. I got to spend January in Grenada working on this beast because I spent the rest of the year consulting for UN agencies, AIDS NGOs, and civil society delegations on the Board of the Global Fund. The experience of observing and thinking from within institutions is valuable during the writing process. It’s always inspiring to watch how hard civil society activists work at the business of global health governance, where there is so much politics and technical lingo to master; and how they endlessly push for transparency, accountability, and democratic governance. Hopefully, the finished book will be a useful tool for them, too…updates to follow.