The first few posts on Where There Is No Data have set the scene by discussing the challenges faced by marginalised populations in HIV programmes. We’ve also talked about the fact that having good data isn’t always enough – policy decisions tend to be influenced as much by politics and prejudices as they are by evidence. Nonetheless, while data isn’t everything, getting better data is important. It can begin to shine a light on problems that have been ignored; and when there is political commitment to tackling these problems, it can help make sure that health programmes are properly designed.
Policy makers, funding agencies and NGOs often talk about different types of data and research, but not everyone is familiar with these. When we talk about data for health programmes what do we mean?
- Data that describes the burden of health problems including the prevalence and incidence of these conditions, and how they are distributed among different sections of the population. In most countries, data on HIV prevalence (i.e., the percentage of the population that is infected with HIV) is derived from a range of sources: surveys of a representative sample of households during which respondents are asked to participate in anonymous HIV testing; surveys of specific population groups such as pregnant women, men who have sex with men, sex workers (these are often called “sentinel surveillance” surveys); and routine data – for instance, the proportion of people volunteering for HIV testing who are found to be HIV positive. None of these methods provides a complete picture of HIV prevalence but, if all of them are used fairly regularly, they can help give a good overview of what is happening. In some countries, special studies are carried out to measure HIV incidence (i.e. the rate of growth of the epidemic in a given period of time such as a year), but this type of study is rarely carried out at national level.
- Prevalence and incidence studies can also help to identify how a health problem is distributed in the population – whether it affects men or women more, whether some age groups are disproportionately affected, and which behaviours or sub-populations are most affected. A lot of this information can be gained from household surveys and routine data. However, because in many societies people are reluctant to talk openly about sex and sexuality, and in particular because of the stigma against behaviours such as sex work, they are often under-represented. Moreover, knowing how a health problem is distributed in the population is not just a matter of knowing the prevalence of the problem in each sub-population group. It also means knowing the size of each these sub-population groups is. This is useful for planning programmes and ensuring that resources are allocated in the right places. Once again though there are particular challenges in estimating numbers of marginalised populations. Sex between men, sex work, and drug use are behaviours not identities, and people with these behaviours often have good reasons to avoid being counted or included in surveys. In many locations there has been little or no research on these groups and informal, community level research is needed before any formal surveys can be carried out.
- Information about what makes people vulnerable or at risk. The same surveys described above can help provide an indication of why some people are more vulnerable or at risk than others. In the context of HIV, researchers often ask respondents about their sexual behaviour, condom use and so on – although once again, peoples’ responses to these questions are not always reliable. Moreover it is not enough to know whether people have good enough knowledge about HIV or whether they use condoms or whether they have access to health care. It is also important to know why these things happen. Is it because programmes are not reaching them? What role does stigma and discrimination play? Getting the answers to these questions often requires a different approach: one that engages much more with the people concerned, and ideally one that is led by them. These factors also often vary from place to place and can change over time, so it is important to have mechanisms that enable communities to collect, understand and act on local data in a regular way.
- Data on what types of programme or “intervention” are effective. Many policy makers rely on experimental research methods to provide evidence on the effectiveness of programmes or interventions for preventing or treating health problems. Although they are expensive to conduct these studies help provide an estimate of how effective different approaches are. There is considerable debate surrounding the reliability of this sort of study for evaluating social change programmes, although their use in this area is growing. In any case, because they are experiments, generally conducted in controlled conditions, they will not necessarily be as effective or work in the same way when implemented at scale. For this reason programmes have to find ways of continually monitoring their impact and of identifying any unintended consequences. Once again a combination of large scale survey and routine data and more qualitative, community based approaches is needed.
- There is increasing interest in good quality programme related data – for instance, data on the coverage of programmes (how many people they reach and who these people are); data on how programmes are funded (where does the money come from? Communities? The government? Donors?); and data on what it costs to implement different types of programme. Good data on costs, combined with good data on effectiveness can be used to make sure the most cost-effective programmes get funded – in other words that available resources have the biggest possible impact.
Readers working on HIV and AIDS may be familiar with concepts such as “know your epidemic”, “know your response” and “strategic investment”. The description of the different types of data described above is somewhat simplistic; however they are the basic building blocks behind these concepts. It is particularly useful for community actors, key or marginalised populations to know about these types of data and concepts so that they can clearly articulate the gaps when speaking to policy makers.
We will explain all of these types of data in more detail on this site – including how reliable are, what sorts of skills are required to collect them, and the methods and the costs of collecting them. There are a number of free online courses and resources where you can learn more about different research techniques and we will post links to these too. Finally, we will talk about the importance of data generated by communities and the challenge of trying to get this sort of data taken seriously.