Disease mapping of Leishmaniasis outbreak in Afghanistan: spatial hierarchical Bayesian analysis

Abstract

Abstract ObjectiveTo analyze the spatial pattern of Leishmaniasis disease in Afghanistan, using provincial level geo-referenced data. The disease is contracted through bites from sand flies and is the third most common vector-borne disease. Leishmaniasis is a serious health concern in Afghanistan with about 250 000 estimated new cases of cutaneous infection nationwide and 67,000 cases in Kabul. This makes Kabul the city with the largest incidence of the disease worldwide. MethodsWe use a Bayesian hierarchical Poisson model to estimate the influence of hypothesized risk factors on the relative risk of the disease. We use random components to take into account the lack of independence of the risk between adjacent areas. ResultsStatistical inference is carried out using Markov Chain Monte Carlo simulation. The final model specification includes altitude, two random components (intercept and slope) and utilizes a conditional autoregressive prior with a deviance information criterion of 247.761. Spatial scan statistics confirm disease clusters in the North-Eastern and South-Eastern regions of Afghanistan with a p-value of less than 0.0001. ConclusionsThe study confirms disease clusters in the North-Eastern and South-Eastern regions of Afghanistan. Our findings are robust with respect to the specification of the prior distribution and give important insights into the spatial dynamics of Leishmaniasis in Afghanistan.