HIV-prevalence mapping using Small Area Estimation in Kenya, Tanzania, and Mozambique at the first sub-national level
Enrique M. Saldarriaga

TL;DR
This study employs Small Area Estimation with spatial modeling to produce detailed HIV prevalence maps at sub-national levels in Kenya, Tanzania, and Mozambique, aiding targeted health interventions.
Contribution
It introduces a spatial ICAR model combined with multistage sampling to improve local HIV prevalence estimates in these countries.
Findings
Identified high-prevalence regions such as Nyanza and Iringa.
Provided detailed HIV prevalence maps for targeted intervention.
Demonstrated the effectiveness of spatial models in epidemiological mapping.
Abstract
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…
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Taxonomy
TopicsHIV/AIDS Research and Interventions · Global Maternal and Child Health · Data-Driven Disease Surveillance
