In this short post we discuss further some of the challenges faced in planning HIV programmes for key populations. The post is based on discussions that were originally posted on our Facebook page.
So why are we in a situation where we have no data on some of the groups most affected by HIV? To characterize the widespread and generalized HIV epidemics found in mostly southern and eastern Africa, we have relied on surveillance systems that do not meaningfully assess the very populations known to be most affected by HIV in most parts of the world including sex workers, gay men and other men who have sex with men, people who use drugs, and trans populations. I understand the design and use of these systems–and there is no doubt that having a sampling frame made up of households is the most effective tool for population-based sampling to retrieve population-based estimates of HIV.
However, I also understand that for there to be household based transmission of HIV, there has to be sero-discordant relationship. And that tells me that there has to have been a primary infection event outside of the household. It is those risk factors for the extra-household primary infection events that we still seem to know very little about. And it seems to me that we may not have invested in this since maybe we don’t want to hear the answer.
Maybe it is because all around the world there are people that sell sex–as there always has been and always will be. All around the world, there are people who do not fit into heteronormative social expectations for sexuality and simple binary expressions of gender. And all around the world there are people who use drugs–some for fun and some because of untreated or undertreated mental health. And each of these people have specific needs for the prevention and treatment of the acquisition and transmission of HIV. But how can we fully understand these needs, if we don’t want to admit that populations exist everywhere. So we still, 30 some odd years later, find ourselves in a data paradox. A paradox where we know less about the needs of diverse populations in settings with the most stigma.
Does that mean we should not respond until we have “the data”? It took me a while to get how brilliant of a strategy this is–argue that the lack of data is a reason to not launch any programs or funding that may result in data. Ie, the lack of data feeds itself.
So the “data paradox” is this: decision-makers deny that most affected populations exist, or that they are relevant to the epidemic; so no research gets done on these populations; the lack of data feeds the denial; and so on.
But the other challenge we face is that decision-makers don’t always put resources towards programmes with key populations even when good data is available. Experience tells us that politicians, in particular, are very capable of ignoring data when it suits them to do so – and there are many countries where the quality of the data is reasonable enough but the investments in programming with the most affected groups are still all wrong. So while it is important to encourage better national level research and data that can eventually be plugged in to modelling and strategic planning exercises, we should also be aware that they won’t necessarily resolve everything.
Although there is no doubt that having the right laws and supportive leadership from the government or Ministry of Health makes a huge difference to how different problems get addressed, not all change comes from the top down. Similarly, the stigma and marginalisation that key populations face does not only come from the national level – it also has a lot to do with attitudes and behaviours of health care workers, law enforcement officers, and community members at the local level. These need to be addressed to. This discussion at the recent International AIDS Conference in Kuala Lumpur is an example of how research and programmes can be initiated at local level even without strong support from policy makers. I’ll also post something in a few days, and post a link here, about local, community-led research and how it can help groups get organised at local level.