Adjusting HIV prevalence for survey non-response

Adjusting HIV prevalence for survey non-response using mortality rates: an application of the method using surveillance data from Rural South Africa.

The main sources of HIV prevalence estimates are household and population-based surveys; however, high refusal rates may hinder the interpretation of such estimates. The study objective was to evaluate whether population HIV prevalence estimates can be adjusted for survey non-response using mortality rates. The data come from the longitudinal Africa Centre Demographic Information System (ACDIS), in rural South Africa. Mortality rates for persons tested and not tested in the 2005 HIV surveillance were available from routine household surveillance. Assuming HIV status among individuals contacted but who refused to test (non-response) is missing at random and mortality among non-testers can be related to mortality of those tested a mathematical model was developed. Non-parametric bootstrapping was used to estimate the 95% confidence intervals around the estimates. Mortality rates were higher among untested (16.9 per thousand person-years) than tested population (11.6 per thousand person-years), suggesting higher HIV prevalence in the former. Adjusted HIV prevalence for females (15-49 years) was 31.6% (95% CI 26.1-37.1) compared to observed 25.2% (95% CI 24.0-26.4). For males (15-49 years) adjusted HIV prevalence was 19.8% (95% CI 14.8-24.8), compared to observed 13.2% (95% CI 12.1-14.3). For both sexes (15-49 years) combined, adjusted prevalence was 27.5% (95% CI 23.6-31.3), and observed prevalence was 19.7% (95% CI 19.6-21.3). Overall, observed prevalence underestimates the adjusted prevalence by around 7 percentage points (37% relative difference). The authors developed a simple approach to adjust HIV prevalence estimates for survey non-response. The approach has three features that make it easy to implement and effective in adjusting for selection bias than other approaches. Further research is needed to assess this approach in populations with widely available HIV antiretroviral treatment.

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Editors’ note: Non-response in population-based HIV surveillance is a puzzler. Are people refusing to participate because they know they are HIV-positive or suspect that they may be? Or are they like those who accept to participate and do they therefore have HIV prevalence that is similar to participants? These researchers were keen to find a way to handle refusers in their analysis when 59% of residents in the 2005 surveillance round did not participate. Complex procedures (e.g. multiple imputations, regression equations) have been used in other settings where extensive information on individuals is available. Lacking such information, they used longitudinal demographic data from biannual household and annual individual surveillance to derive mortality rates for people who tested positive and negative as well as those who refused testing in the 2005 round. Mortality in the untested was significantly higher than in the tested population, even after adjusting for age and sex composition. Assuming that much of this difference could be accounted for by AIDS-related mortality, the prevalence estimates for this area of KwaZulu Natal rose 7 percentage points. This method cannot be used to adjust cross-sectional HIV prevalence data when prospective mortality data are not available. When you do have such mortality data, adjusting HIV prevalence estimates for non-participation using this method can improve tracking of the epidemic and the impact of the response