Mapping the Risk of Snakebite in Sri Lanka - A National Survey with Geospatial Analysis. - Dileepa Senajith Ediriweera; Anuradhani Kasturiratne; Arunasalam Pathmeswaran; Nipul Kithsiri Gunawardena; Buddhika Asiri Wijayawickrama; Shaluka Francis Jayamanne; Geoffrey Kennedy Isbister; Andrew Dawson; Emanuele Giorgi; Peter John Diggle; David Griffith Lalloo; Hithanadura Janaka de Silva (PLoS Neglected Tropical Diseases)
Background There is a paucity of robust epidemiological data on snakebite, and data available from hospitals and localized or time-limited surveys have major limitations. No study has investigated the incidence of snakebite across a whole country. We undertook a community-based national survey and model based geostatistics to determine incidence, envenoming, mortality and geographical pattern of snakebite in Sri Lanka. Methodology/principal findings The survey was designed to sample a population distributed equally among the nine provinces of the country. The number of data collection clusters was divided among districts in proportion to their population. Within districts clusters were randomly selected. Population based incidence of snakebite and significant envenoming were estimated. Model-based geostatistics was used to develop snakebite risk maps for Sri Lanka. 1118 of the total of 14022 GN divisions with a population of 165665 (0.8%of the country's population) were surveyed. The crude overall community incidence of snakebite, envenoming and mortality were 398 (95% CI: 356-441), 151 (130-173) and 2.3 (0.2-4.4) per 100000 population, respectively. Risk maps showed wide variation in incidence within the country, and snakebite hotspots and cold spots were determined by considering the probability of exceeding the national incidence. Conclusions/significance This study provides community based incidence rates of snakebite and envenoming for Sri Lanka. The within-country spatial variation of bites can inform healthcare decision making and highlights the limitations associated with estimates of incidence from hospital data or localized surveys. Our methods are replicable, and these models can be adapted to other geographic regions after re-estimating spatial covariance parameters for the particular region.