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close this bookMeeting 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.)
close this folder2. Why track behaviour?
View the document(introduction...)
View the document2.1 Behavioural data serves as an early warning system for HIV and STDs
View the document2.2 Behavioural data informs effective programme design and direction
View the document2.3 Tracking behaviour improves programme evaluation
View the document2.4 Changes in behaviour help explain changes in HIV prevalence
View the document2.5 Behavioural data can help explain variations in prevalence

2.4 Changes in behaviour help explain changes in HIV prevalence

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.