HIERARCHICAL BAYESIAN LOGISTIC MODELLING OF THE PREVALENCE AND THE DETERMINANTS OF TUNGA PENETRANS INFESTATION IN CENTRAL, KENYA

Tungiasis is an important parasitic disease of global public health concern. However, there is limited data on its epidemiology in many poor-resource areas which prevent the implementation of targeted control measures. A cross-sectional study was implemented to determine the prevalence of Tunga spp. (Tunga penetrans) and associated determinants of infestation among humans in Central Kenya. We used a questionnaire to collate the information needed to determine the Multidimensional Poverty Index of the visited homesteads. Soil samples were taken randomly from each homestead to establish the type and percentage of each of the primary soil nutrients. From images taken of either the feet, hands, toes or fingers of each person in the visited homestead, categorization on infestation with Tunga penetrans, was done. For data analysis, we used WinBUGS, a Markov chain Monte Carlo method in complex Bayesian models. Reduced hierarchical Bayesian logistic (RHBL) model for the prevalence of jigger infestation was developed through the Bayesian approach. For a unit decrease in the poverty index at level 1 there was an increase in the prevalence of jigger infestation by 80.17%. A unit increase in clay percentage in the soil contributed to a 35.91 times increase in jigger infestation prevalence. Incorporating structured and unstructured random effects led to the spatial hierarchical Bayesian logistic (SHBL) model showing Murang'a county to have a higher prevalence (2.575 times) of jigger infestation compared to Nyeri county, while Kiambu county had a lower prevalence. From the models, a unit change in the poverty level and clay soil percentage increased the prevalence of jigger infestation substantially. It was used to identify the geographical areas where broad intervention strategies would be needed in Central, Kenya.