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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.)
View the document(introduction...)
View the document1. Introduction
Open this folder and view contents2. Why track behaviour?
View the document3. Linking behavioural data with HIV serosurveillance
Open this folder and view contents4. What is needed to understand and track behaviour?
View the document5. Do people tell the truth about their sexual and drug-taking behaviour?
Open this folder and view contents6. Recommended mix of data collection methods
View the document7. What next?
View the document8. Sustaining behavioural data collection over time
View the documentBibliography
View the documentAnnex

3. Linking behavioural data with HIV serosurveillance

Because the relationship between HIV incidence and prevalence grows increasingly complex as the epidemic matures, UNAIDS and its partners are promoting the strengthening and development of existing sentinel surveillance systems into "second generation" surveillance systems, in which behavioural data collection becomes an integral component. Second generation systems focus more closely on the segments of the general population where most new infections are concentrated, in particular young people. HIV prevalence among the young serves as a proxy for incidence because young people have only been sexually active for a short time.

This gives national programmes a rough indicator of ongoing incidence to complement currently collected prevalence data. The addition of behavioural data collection to the second generation surveillance system then allows serosurveillance and behavioural data to be used and compared concurrently, enabling national programmes to better understand and explain the currently observed trends in the national HIV epidemic.

In any system collecting sensitive data, such as information on serostatus or information on sexual or drug-related risk behaviours, steps must be taken to minimise biases. For this reason, antenatal clinic serosurveillance is usually conducted using unlinked anonymous blood samples that have been routinely taken for other purposes, such as syphilis testing. This approach reduces the bias introduced when people are asked to give a blood sample for HIV testing and refuse. Similarly, if one selects a sample of young women to answer questions on sexual and drug-using behaviours, some of them are likely to decline to be interviewed. Should one ask for both a blood sample and an interview on risk behaviour, especially from women late in their pregnancies, the combined refusal rate could prove quite high. Furthermore, ethical and practical operational difficulties aside, asking women in the later stages of pregnancy about their sexual behaviour and condom use will not generally yield results in any way typical of the female population at large. Similarly, sexual behavioural trends from male STD clients are difficult to interpret because such clients are, by definition, engaging in some type of high-risk behaviour.

Thus, in order to minimise biases, avoid jeopardising the validity of the serological data, and gather less biased behavioural data on the population as a whole, it is usually recommended that blood samples and risk-behaviour interviews be obtained from different individuals. But in order to establish a clear association between behaviour and HIV prevalence in the community, the data on HIV serostatus and behaviour have to be drawn from the same source population. These two needs are not incompatible. It is not necessary that blood and behavioural data be obtained from the same individuals, although this would have the strongest explanatory power, but only that the relationship between the population contributing the serological data and the population providing the behavioural data can be determined.

Determining these relationships requires carefully defining the population from which a key sentinel surveillance site (such as a large urban antenatal clinic) draws its attendees and collecting behavioural data from a random selection of households in the same catchment area. If this is done as part of national or regional behavioural surveys, it may mean deliberately oversampling in the catchment populations of key sentinel sites (that is, the population served by the particular site in question).

Because women attending antenatal clinics are not randomly chosen from the population, they may differ in significant ways from other members of the source population. For example, young women at antenatal clinics generally represent a portion of the total young female population that has become sexually active at an earlier age than average. In order to link the behavioural data with the HIV prevalence data, it is therefore recommended that a minimum set of sociodemographic questions be asked of all antenatal clinic attenders at sentinel sites. The questions should include age, parity, last birth interval, level of schooling, occupation, and length of time living in the area (as an indicator of migration). These indicators can then be compared with those collected from the population being asked about behaviour, allowing any systematic differences between the two groups to be identified and adjusted for in the analysis.

A more direct way of linking behaviour and serostatus is to undertake a population-based survey in which both HIV data and behavioural data are collected. However, success in this approach has been varied. Some countries successfully added testing for HIV serostatus to population-based behavioural surveys. Blood or saliva samples were taken and tested anonymously, after informed consent. (All participants were given the opportunity to be counseled and to choose to have their blood tested separately from the research project to learn their HIV status.) But in other countries, refusal rates for testing in such population-based surveys have been quite high, and the results are difficult to generalise. In addition, people not captured in a household survey (because they travel frequently, for example) may be disproportionately likely to be HIV infected.