Fine-scale malaria risk mapping from routine aggregated case data

Malaria Journal

Research

Nyasatu E Ntshalintshali¹  Arnaud Le Menach¹  Justin M Cohen¹  Petr Keil²  Andrew J Tatem³  Roland D Gosling⁴  Hugh JW Sturrock⁴  Michelle S Hsiang⁵ 

[1] Clinton Health Access Initiative, Boston, MA, USA;Department of Ecology and Evolutionary Biology, Yale University, New Haven, USA;Department of Geography and Environment, University of Southampton, Southampton, UK;Fogarty International Center, National Institutes of Health, Bethesda, USA;Flowminder Foundation, Stockholm, Sweden;Global Health Group, University of California, San Francisco, SF, USA;Global Health Group, University of California, San Francisco, SF, USA;Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA;

关键词: Malaria;    Health Facility;    Normalize Difference Vegetation Index;    Travel Time;    Land Surface Temperature;   

DOI  :  10.1186/1475-2875-13-421

 received in 2014-08-07, accepted in 2014-10-25,  发布年份 2014

来源: Springer

【 摘 要 】

BackgroundMapping malaria risk is an integral component of efficient resource allocation. Routine health facility data are convenient to collect, but without information on the locations at which transmission occurred, their utility for predicting variation in risk at a sub-catchment level is presently unclear.MethodsUsing routinely collected health facility level case data in Swaziland between 2011–2013, and fine scale environmental and ecological variables, this study explores the use of a hierarchical Bayesian modelling framework for downscaling risk maps from health facility catchment level to a fine scale (1 km x 1 km). Fine scale predictions were validated using known household locations of cases and a random sample of points to act as pseudo-controls.ResultsResults show that fine-scale predictions were able to discriminate between cases and pseudo-controls with an AUC value of 0.84. When scaled up to catchment level, predicted numbers of cases per health facility showed broad correspondence with observed numbers of cases with little bias, with 84 of the 101 health facilities with zero cases correctly predicted as having zero cases.ConclusionsThis method holds promise for helping countries in pre-elimination and elimination stages use health facility level data to produce accurate risk maps at finer scales. Further validation in other transmission settings and an evaluation of the operational value of the approach is necessary.

【 授权许可】

CC BY   
© Sturrock et al.; licensee BioMed Central Ltd. 2014