I’m writing a book on the politics of data for key populations, and it’s led me to think about “data deserts” – areas where no data is produced, so no programs or social services are provided.
It’s a particular problem for key populations (sex workers, men who have sex with men [MSM], people who inject drugs, trans* people and prisoners) in low-income countries. But surprisingly, it’s a problem for countries with higher income too.
Out of the 58 countries that the World Bank classifies as upper-middle income:
- 17 countries had NO official HIV prevalence data for ANY key population group
- 8 countries have HIV prevalence data for only ONE key population, but not the others
You can see these on the table at the end of this post.
What’s the reason for this? There are a range of reasons why countries may lack data on key populations. For small countries, such as the island countries of the Caribbean, getting the funding and technical expertise to study HIV prevalence can be a major challenge. Countries in or emerging from active armed conflict, such as Iraq, may have high income on paper, but also have weak health systems and many urgent competing priorities.
A number of the countries lacking data are plagued by government denialism about key populations. Criminalization of same-sex sexuality, drug use and/or sex work may make studying HIV among key populations so high-risk that health officials are reluctant to take on the task. Then there’s the case of Venezuela: in order to cling to power and the fiction that all is well with its crumbling health system, Venezuela’s leadership has actively suppressed health data, and fired a health ministerwho dared to publish it.
For some countries, too, the denialism combines with the growing dominance of a neoliberal logic straight from market-driven health economics: money is limited and resources must be prioritized. Thus small populations remain stuck at the bottom of a long list of priorities: studies and services for small groups are by definition not “cost-effective”.
That is especially the case for people who inject drugs and trans* people. When you look at the list below, all the countries are missing HIV prevalence data for people who inject drugs, and all but one are missing it for trans people, though HIV among both groups is extraordinarily high. Some countries may be reluctant to spend limited funds on research for those populations, arguing that injecting drug use is minimal and trans people are few (that argument is unlikely to fly in Equatorial Guinea, an emerging drug transit hub).
Other populations who are often vulnerable – persons with disabilities, indigenous people – also never get studied or counted, gaps that are not even visible because there is not even enough data on HIV among those groups to classify them as “key populations” in the first place.
Under the Global Fund’s new Eligibility Policy, a country with low general HIV prevalence needs data showing over 5% HIV prevalence among just one key population group in order to become eligible for funding, if other eligibility criteria are also met. It was hugely encouraging last month that the Fund approved a new Eligibility Policy including a provision that where countries lack official data on key populations, the Fund will work with UNAIDS to find other data sources to assess eligibility. Activists will now need to make sure that UNAIDS headquarters in Geneva get that data and can share it with the Fund.
Over the past two years, I’ve been able to observe the process of conducting key population size estimates by Caribbean Vulnerable Communitiesin the Eastern Caribbean, where officials have teamed up with community-based organizations to draw on their community networks to study HIV among key populations, and plan to use that data to step up prevention, testing and outreach programs. Those are the kinds of partnerships needed to help to cross the data desert and get water, and services, to those who direly need them.
You can explore further on the UNAIDS AIDSInfo website, which reports official data from national governments on HIV. UNAIDS also has a newer Key Populations Atlas site, with more data on key populations from NGOs, legal reviews and academic research (it’s not always better data, though – I’ve spotted a few issues in the legal data. Work on the site is still underway, so use with care and keep checking back in).
Upper-Middle-Income countries missing HIV prevalence data on three or more key populations groups
|Angola||4.7||No data||No data||No data|
|Bosnia & Herzegovina||No data||1.1||No data||No data|
|Costa Rica||No data||12.7||No data||No data|
|Croatia||No data||2.7||No data||No data|
|Dominica||No data||26.7||No data||No data|
|Equatorial Guinea||No data||No data||No data||No data|
|Gabon||No data||No data||No data||No data|
|Grenada||No data||No data||No data||No data|
|Iraq||No data||No data||No data||No data|
|Libya||No data||3.1||No data||No data|
|Macedonia||No data||No data||No data||No data|
|Maldives||No data||No data||No data||No data|
|Marshall Islands||No data||No data||No data||No data|
|Mongolia||No data||13.7||No data||No data|
|Namibia||No data||No data||No data||No data|
|Nauru||No data||No data||No data||No data|
|Palau||No data||No data||No data||No data|
|Russian Federation||No data||No data||No data||No data|
|Saint Lucia||No data||No data||No data||No data|
|Saint Vincent & the Grenadines||No data||No data||No data||No data|
|Suriname||5.8||No data||No data||No data|
|Tonga||No data||No data||No data||3.3|
|Turkey||No data||No data||No data||No data|
|Turkmenistan||No data||No data||No data||No data|
|Tuvalu||No data||No data||No data||No data|
|Venezuela||No data||No data||No data||No data|