Spatial models for infants HIV/AIDS incidence using an integrated nested laplace approximation approach
Mutua, Susan Nzula
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Background: Kenya has made significant progress in the elimination of mother to child transmission of HIV through increasing access to HIV treatment and improving the health and wellbeing of women and children living with HIV. Despite this progress, broad geographical inequalities in infant HIV outcomes still exist. This study aimed at assessing the spatial distribution of HIV amongst infants, areas of abnormally high risk and associated risk factors for mother to child transmission of HIV. Methods Data were obtained from the Early infant diagnosis (EID) database that is routinely collected for infants under one year for the year 2017. We performed both areal and point-reference analysis. Bayesian hierarchical Poisson models with spatially structured random effects were fitted to the data to examine the effects of the covariates on infant HIV risk. Spatial random effects were modelled using Conditional autoregressive model (CAR) and stochastic partial differential equations (SPDEs). Inference was done using Integrated Nested Laplace Approximation. Posterior probabilities for exceedance were produced to assess areas where the risk exceeds 1. The Deviance Information Criteria (DIC) selection was used for model comparison and selection. Results: Among the models considered, CAR model (DIC = 306.36) performed better in terms of modelling and mapping HIV relative risk in Kenya. SPDE model outperformed the spatial GLM model based on the DIC statistic. The map of the spatial field revealed that the spatial random effects cause an increase or a decrease in the expected disease count in specific regions. Highly active antiretroviral therapy (HAART) and breastfeeding were found to be negatively and positively associated with infant HIV positivity respectively [-0.125, 95% Credible Interval = -0.348, -0.102], [0.178, 95% Credible Interval -0.051, 0.412]. Conclusion: The study provides relevant strategic information required to make investment decisions for targeted high impact interventions to reduce HIV infections among infants in Kenya.