HIV-prevalence mapping using Small Area Estimation in Kenya, Tanzania, and Mozambique at the first sub-national level
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Local estimates of HIV-prevalence provide information that can be used to target interventions and consequently increase the efficiency of the resources. This closer-to-optimal allocation can lead to better health outcomes, including the control of the disease spread, and for more people. Producing reliable estimates at smaller geographical levels can be challenging and careful consideration of the nature of the data and the epidemiologic rational is needed. In this paper, we use the DHS data phase V to estimate HIV prevalence at the first-subnational level in Kenya, Tanzania, and Mozambique. We fit the data to a spatial random effect intrinsic conditional autoregressive (ICAR) model to smooth the outcome. We also use a sampling specification from a multistage cluster design. We found that Nyanza (P=14.2 in Kenya, Iringa (P=16.2 (P=13.7 highest prevalence of HIV, within country. Our results are based on statistically rigorous methods that allowed us to obtain an accurate visual representation of the HIV prevalence in the subset of African countries we chose. 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.
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