MSc.SS Theses and Dissertations (2019)

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    Spatial models for infants HIV/AIDS incidence using an integrated nested laplace approximation approach
    (Strathmore University, 2019) Mutua, Susan Nzula
    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.
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    Distributions of zero-inflated models with application to HIV exposed infants
    (Strathmore University, 2019) Nekesa, Faith Victory
    The instances of data with excess zeros are commonly found in many disciplines, including the public health. Several models have been proposed when analyzing this kind of data. The World Health Organization (WHO) indicates that majority of the 1.8 million children who are at the present with HIV in sub-Saharan Africa got the HIV virus from their mothers probably during delivery, pregnancy or through breastfeeding, but the study shows there is a drop in the rate of infections due to interventions that have been put in place. Here we attempt to fit zero-inflated models to data in this setting. The objective is to systematically compare distributions of the various zero-inflated models with an application to HIV Exposed Infants (HEI). We revisit zero-inflated models, conducted the simulations and applied the models to HEI data. The models performance were evaluated by Akaike Information Criteria(AIC).The simulation results indicated ZAP had the lowest AIC value of 467.95 at 80% of zeros. The real data showed ZAP as the best fit for the simulation data since it had the lowest AIC value. From the simulations results of the AIC value and the real data results, it is clear that ZAP is the best fitting model.
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    A Bayesian approach to Geo spatial analysis of HIV viral load data
    (Strathmore University, 2019) Kareko, Joy Hilda Mukami
    HIV is currently ranked among the leading causes of death in Kenya and in the world, with an estimated 1.5 Million Kenyans living with HIV and 28,000 deaths recorded annually as a result of AIDS related illnesses. In 2014, UNAIDS launched a 90-90-90 strategy the aim was to diagnose 90 per cent of all HIV- positive persons, provide antiretroviral therapy (ART) for 90 percent of those diagnosed, and achieve viral suppression for 90 per cent of those treated by 2020. This study is motivated by the need to assess the 3rd 90; viral suppression for 90 per cent of those ART treated and seeks to analyze one statistical paradigm (Bayesian) that have conventionally been used for geospatial trends. Use of Bayesian approach has been used previously to assess the prevalence and incidence of diseases however, this dissertation seeks to evaluate Bayesian Approach to spatial trends of HIV Viral Load Suppression in Kenya. We revisit the theoretical framework of the Bayesian Approach and apply real data from the Kenyan setting spanning from 2012 to 2017. Results show Bayesian Approach to be robust, in depth and entails more information when modelling spatio-trends of Viral Load suppression. Further, First Line ART regimen, HIV-TB co-infection and retention rates are significant predictors of Viral Load suppression spread.