A composite likelihood approach for estimating HIV prevalence in the presence of spatial variation.

Since 1990, the World Health Organization has recommended HIV surveillance among pregnant women as an essential surveillance activity for countries with generalized HIV epidemics. Despite the widespread availability and potential usefulness of antenatal HIV surveillance, analyses of such data present important challenges. Within an individual clinic, the HIV status of its attendees may be correlated because of similarities in HIV risk among women close in age. Between-clinic correlation may also arise as women often seek antenatal care at clinics located close to their home, and individuals living in nearby communities may share important characteristics or behaviours related to susceptibility. A general estimating equation-based approach for spatially-correlated, binary data such as that antenatal HIV surveillance based on a pairwise composite likelihood has been described. We present an extended version of this model that can accommodate penalized spline estimators and apply it to antenatal HIV surveillance data collected in 2011 in Botswana to estimate the effects of proximity to the 'hotspot' of the country's HIV epidemic and age on HIV prevalence. Finally, we compare the results with a logistic regression analysis, which ignores potential correlation of responses.