Abstract
BACKGROUND: Local estimates of HIV-prevalence provide information that can be used to target interventions and consequently increase the efficiency of resources. This enhanced allocation can lead to better health outcomes, including the control of the disease spread, and for more people. METHODS: In this study, we used the DHS data phase V to estimate HIV prevalence at the first-subnational level in Kenya, Tanzania, and Mozambique. We fitted the data to a spatial random effect intrinsic conditional autoregressive (ICAR) model to smooth the outcome. Further, we used a sampling specification from a multistage cluster design. RESULTS: We found that Nyanza (P(i) = 13.6%) and Nairobi (P(i) = 7.1%) in Kenya, Iringa (P(i) = 15.4%) and Mbeya (P(i) = 9.3%) in Tanzania, and Gaza (P(i) = 15.2%) and Maputo City (P(i) = 12.9%) in Mozambique are the regions with the highest prevalence of HIV, within country. Our results are based on publicly available data that through statistically rigorous methods, allowed us to obtain an accurate visual representation of the HIV prevalence at a regional level. CONCLUSIONS: These results can help in identification and targeting of high-prevalent regions to increase the supply of healthcare services to reduce the spread of the disease and increase the health quality of people living with HIV.