Applications of GIS and Spatial Statistics for Malaria Research in Rural Zambia: Evaluation of Risk Factors and Risk Mapping in Nchelenge District and Elimination Strategies in Macha

Abstract

Objective: The goals of this dissertation project were to understand malaria transmission dynamics in two different settings in Zambia. The specific aims in Nchelenge District, an uncontrolled transmission setting, were to describe the individual-, household- and environmental-level risk factors for malaria (Paper 1); and to generate and validate seasonal malaria risk maps (Paper 2). The specific aims in Macha District, a low transmission setting, were to describe factors associated with sustained bednet use (Paper 3), and determine the efficiency of reactive case detection and focal drug administration in treating infections missed by passive surveillance (Paper 4). Methods: Both sites are part of the International Center for Excellence in Malaria Research (ICEMR) for southern Africa. Satellite images are used to generate sampling frames, and households randomly selected for enrollment. Questionnaires, blood samples, mosquitoes and GPS coordinates are collected. Multilevel models with random effects were built for the odds of RDT positivity in Nchelenge District (Paper 1). Logistic regression and prediction models were used to create seasonal malaria risk maps and validated using RMSE in Nchelenge District (Paper 2). A multi-level longitudinal model with random intercepts was generated to determine factors associated with bednet use in Macha District (Paper 3). A simulation model was constructed to predict the distribution of RDT and PCR cases of malaria, to determine the efficiency of reactive case detection and focal drug administration in Macha District (Paper 4). Results: Age, report of symptoms, and proximity to certain ecological features increased risk of malaria infection, and varied by season (Paper 1). Risk maps accurately predicted household malaria risk; prediction was best in the rainy season and for smaller households (<4 members) (Paper 2). Several factors including presence of nuisance mosquitoes and distance to healthcare facilities affected reported bednet use (Paper 3). Reactive case detection identified and treated RDT positive cases that cluster around index households; focal drug administration would treat PCR positive RDT negative cases missed otherwise (Paper 4). Conclusions: In high transmission settings, spatial targeting of high-risk areas and populations is necessary to reduce malaria transmission; risk maps and school-based interventions may be suggested. In a low transmission setting, sustained use of personal protective measures and implementation of active case detection strategies to treat every remaining case is necessary for elimination