|Meeting the Behavioural Data Collection Needs of National HIV/AIDS and STD Programmes (Implementing AIDS Prevention and Care Project - Joint United Nations Programme on HIV/AIDS - United States Agency for International Development, 1998, 41 p.)|
|2. Why track behaviour?|
For the first decade or so of the HIV epidemic, many countries concentrated resources on tracking the spread of the virus itself. Industrialised countries focused on AIDS case reporting, while many developing nations, particularly those of sub-Saharan Africa, set up sentinel surveillance systems to detect the spread of HIV. After stripping personal identifiers from blood samples taken for other purposes - most commonly antenatal syphilis testing of pregnant women - sentinel surveillance systems test blood for HIV. This data is thought to give some indication of the levels of HIV infection in the general population.
However, because a person may be infected with HIV for a decade or more without showing any symptoms, HIV prevalence rates can reflect a combination of recent infections and infections that are many years old. Consequently, the prevalence rate is very slow to reflect changes in new infections. Prevalence that is stable or falling may mean that people are behaving more safely and fewer are becoming infected than in previous years. It may, however, simply reflect the fact that HIV-infected people are dropping out of the tested population because they have died, moved away, or are too sick to go to the health facility where they might be tested. It may mean that nearly everyone with risk behaviour is already infected, or that the group of people tested has changed over time. Indeed, the relationship between HIV incidence and prevalence is so complex that in some cases falling prevalence may mask a still rising incidence of HIV infections, especially among young people.
Clearly, then, HIV prevalence rates do not serve as a good indicator of changes in new infections or as a measure of the success of programmes designed to reduce new infections. What are the alternatives? HIV incidence is costly and problematic to measure, since it involves testing the same group of individuals repeatedly over time or using costly testing methods on large numbers of people to detect a small number of new infections. Other physical markers that track sexual risk behaviour more closely than HIV are curable sexually transmitted diseases (STDs). Bacterial STD prevalence rates more closely reflect incidence rates because they are usually treated with antibiotics upon detection. However, surveillance of STDs in most countries is of lower quality than HIV surveillance. It is also extremely incomplete in the many countries where most surveillance data are collected in the public sector, while most treatment occurs in the private sector.
Although measuring changes in new HIV and STD infections is difficult, it is possible to track changes in the behaviours that lead to those infections. There are several reasons to do this, and they vary in importance according to how widespread HIV is in a country and which communities are affected.
Behavioural risk is not distributed uniformly throughout a population. On average, some subpopulations or communities may have higher levels of risk behaviour than others. Which subpopulations or communities are particularly vulnerable can vary greatly from country to country and may need to be defined locally in terms of occupation, migration status, sexual orientation, geographic location, income level, or any number of other factors. Behavioural data can help identify those subpopulations or communities at risk locally and can suggest the pathways the virus might follow if nothing is done to brake its spread. It can also indicate the levels of risk behaviour in the general population and the behavioural "bridges" between the general population and more vulnerable subpopulations. If these "bridges" are strong, arresting HIV transmission in vulnerable subpopulations or communities early becomes an urgent and critical component of slowing the spread in the population as a whole.
This kind of behavioural information can act as a call to arms for many people - politicians, religious and community leaders, and people who may themselves be at risk - signalling that the threat of HIV is very real even in areas where it is not yet visible. Such data are a powerful tool in pressing for action.
A country monitoring the HIV epidemic is doing so because it wants to slow the spread of the virus through effective prevention programmes. Effective prevention is prevention that enables people to adopt safer behaviours and protect themselves from the risk behaviour of their partners. But effective prevention requires more than just knowing who is at risk. It also requires understanding why they engage in risk behaviour, motivating them to reduce their risk, developing their prevention knowledge and skills, improving their access to the means of prevention in ways that are appropriate and accessible to them, and providing a supportive social and policy environment for behaviour change. These requirements create a strong need for qualitative data to illuminate and clarify the determinants of risk in specific subpopulations and situations. Unless the context and forms of risk behaviour are well understood in each specific vulnerable subpopulation or risk situation, it is not possible to provide and effectively support relevant safe alternative behaviours. Thus, behavioural research data can help communities and programme planners design initiatives carefully focused on breaking the links in the chain of transmission in a particular country, region, or group.
In addition, behavioural research data can quantitatively indicate who is most at risk of contracting or passing on HIV infection, and why. Such data can document levels of risk in specific communities that may be particularly vulnerable to rapid HIV spread or identify characteristics of individuals who may have higher risk, allowing prevention efforts to be prioritised and directed so as to have the greatest impact.
A good behavioural data collection system can give a picture of changes in sexual and drug-taking behaviour over time, both in the general population and in vulnerable subpopulations. The system will record a reduction in risky sex just as it will record persistent risk behaviour or shifts in the pattern of risk. These changes can provide an indication of the success of the overall package of activities aimed at promoting safe behaviour and reducing the spread of HIV, both in the general population and in specific vulnerable subpopulations.
Showing that behaviour can and does change following national efforts to reduce risky sex and drug taking is essential to building support for ongoing prevention activities. However, while behavioural data can help evaluators document these changes, it is important to realise that it can not show a direct causal link between an intervention and a particular level of behaviour change. Most people are exposed to many sources of information and make decisions based on many - and complex - criteria. Information or activities provided as part of a prevention programme will contribute to what people decide and how they behave, but there may be many other factors in the equation. Reported behavioural data alone rarely make it possible to isolate and attribute change to a single component of a programme.
As discussed earlier, changing behaviour and a consequent reduction in new HIV infections is just one possible reason for changes in HIV prevalence data. It is, of course, the most encouraging explanation to those trying to reduce the spread of the virus. But without collecting data that show trends in behaviour over time, program evaluators will not be able to ascertain whether behaviour change contributes to changes in HIV prevalence.
Focusing entirely on HIV prevalence without complementary behavioural data can also be misleading. When HIV prevalence stabilises - and even when it stabilises at very high levels - there is often a tendency to become complacent: the problem has peaked, it won't get any worse. This can be a dangerous fallacy. For example, prevalence among injecting drug users in Bangkok has been stable for almost a decade, but careful studies of cohorts of drug users have shown that they continue to become infected at a rate between 5 and 10 percent per year. Stable prevalence results because the number of newly infected drug users roughly equals the number dropping out due to death and to ceasing injection.
Behavioural data showing no change in high levels of risk activities, or continued risk in certain age groups or sections of the population, should ring alarm bells even when HIV prevalence seems stable. Several factors unrelated to intervention effects can contribute to observed stabilisation or decreases in HIV prevalence in a given setting. These include mortality (especially in mature epidemics), saturation effects in subpopulations at higher risk, differential migration patterns related to the epidemic, sampling bias, and errors in data collection and analysis.
Although comparisons across regions, cultures, and countries must be made with extreme caution, behavioural data can also help explain differences in levels of infection between one region and another. This is particularly true when indicators of risk behaviour are standardised across all studies and surveys, with the same wording and reference periods.