Factors associated with repeated refusal to participate in longitudinal population-based HIV surveillance in rural South Africa: an observational study, regression analyses.

Background: For many estimation purposes, individuals who repeatedly refuse to participate in longitudinal HIV surveillance pose a bigger threat to valid inferences than individuals who participate at least occasionally. We investigate the determinants of repeated refusal to consent to HIV testing in a population-based longitudinal surveillance in rural South Africa.

Methods: We used data from two years (2005 & 2006) of the annual HIV surveillance conducted by the Africa Centre for Health and Population Studies, linking the HIV surveillance data to demographic and socioeconomic data. The outcome for the analysis was "repeated refusal". Demographic variables included sex, age, highest educational attainment, and place of residence. We also included a measure of wealth and the variable "ever had sex". To compare the association of each variable with the outcome, unadjusted odds ratios and standard errors were estimated. Multivariable logistic regression was used to estimate adjusted odds ratios and their standard errors. Data were analyzed using STATA 10.0.

Results: Of 15,557 eligible individuals, 46% refused to test for HIV in both rounds. Males were significantly more likely than females to repeatedly refuse testing. Holding all other variables constant, individuals in the middle age groups were more likely to repeatedly refuse testing compared with younger and older age groups. The odds of repeated refusal increased with increasing level of education and relative wealth. People living in urban areas were significantly more likely to repeatedly refuse an HIV test than people living in peri-urban or rural areas. Compared to those who had ever had sex, both males and females who had not yet had sex were significantly more likely to refuse to participate.

Conclusions: The likelihood of repeated refusal to test for HIV in this longitudinal surveillance increases with education, wealth, urbanization, and primary sexual abstinence. Since the factors determining repeated HIV testing refusal are likely associated with HIV status, it is critical that selection effects are controlled for in the analysis of HIV surveillance data. Interventions to increase consent to HIV testing should consider targeting the relatively well educated and wealthy, people in urban areas, and individuals who have not yet sexually debuted.