Assessing the spatial-temporal spread of drug-resistant tuberculosis in Kenya.
Abdullahi, Suleiman H.
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Background: Tuberculosis (TB) remains one of the world biggest public health threat. Over the last three decades there has been a remarkable decline in the TB incidence and mortality globally. However, the emergence and increasing spread of a drug resistant strains of Mycobacterium tuberculosis is threatening to derail the effort to eradicate tuberculosis. Kenya is among the 27 high MDR-TB burden countries that account for more than 85% of estimated MDR-TB cases in the World. Also ranked 13th among the 22 high-burden nations, that collectively account for up to 80% of the global TB Cases. Drug-resistant TB is not evenly distributed across Kenya, therefore identifying the Spatio-temporal pattern and areas of high risks of DR-TB will help government prioritise resources and allow for efficient deployment of interventions that are often in limited supply to the areas where they are most urgently needed. Objectives: The purpose of this study was to utilized spatial methods to assess and predict the spatial risk distribution of drug resistant TB in Kenya. Study design and sites: A retrospective cohort study using longitudinal data of notified cases of drug-resistant TB from the Kenyan national DR-TB surveillance database. The study covered all the 47 counties of Kenya, using county as spatial unit of analysis. Material and methods: Exploratory spatial data analysis (ESDA), and Bayesian spatial model were employed to estimate the spatial risk pattern of drug resistance TB using county level data obtained from the national DR-TB surveillance database for the period of five years (January, 2012 to December, 2016). Results: Between 2012 and 2016, there has been a remarkable change in the distribution of the empirical Bayes Smoothed notification rate and excess risk of DR-TB. The EBS maps revealed a significant temporal pattern in the distribution of DR-TB cases over the five years period (2012-2016). The local Moran test for the year 2016 has identified a significant clustering of counties with high risk of DR-TB.
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