  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%) and Nairobi (P=7.8%)
in Kenya, Iringa (P=16.2%) and Dar es Salaam (P=10.1%) in Tanzania, and Gaza
(P=13.7%) and Maputo City (P=12.7%) in Mozambique are the regions with the
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.
