Spatial Temporal Modelling of Tuberculosis in Kenya Using Small Area Estimation

Abstract

Tuberculosis (TB) is a disease which continues to be of public health importance, and is characterized with varying distribution across regions depending on socio-economic status, HIV burden and efficiency of the health system. Kenya is one of the 22 high burden countries that contribute 80% of global TB burden. This study aims to assess spatial and temporal distribution of TB using small area estimation methodology. The spatial reference regions considered were the 47 Kenyan counties. The small area estimates were mapped to produce smear positive TB and favorable treatment outcomes maps. The covariates considered were gender, HIV positive proportion, directly observed Treatment (DOTs), average weight, average Body Mass Index (BMI) and average age. The significant covariates were used in the model to generate the relative risks, posterior probability means and the associated standard deviations which were then used to generate the spatial temporal maps. The spatial temporal maps generated showed distribution clustering of TB cases in a number of counties over the years (2012-2014). From the results of all notified TB cases, only average BMI was excluded from the spatial temporal model since it was not statistically significant (P-value > 0.05). The variables gender, HIV+, DOTs by, Weight, BMI and age were included in the spatial temporal model. The estimated risk of case notification rates per 100,000 found to be highest in the following counties Marsabit, Isiolo, Nairobi, Lamu, Mombasa, Machakos, Kajiado, Makueni, Kisumu, Siaya and Homabay The risk maps from the small area estimates can be used by policy makers to target and develop interventions which address the real challenges which occur in the public health arena.