Evolution of Spatial Risk of Malaria Infection After a Pragmatic Chemoprevention Program in Response to Severe Flooding in Rural Western Uganda.

BACKGROUND: Malaria epidemics result from extreme precipitation and flooding, which are increasing with global climate change. Local adaptation and mitigation strategies will be essential to prevent excess morbidity and mortality. METHODS: We investigated the spatial risk of malaria infection at multiple timepoints after severe flooding in rural western Uganda employing longitudinal household surveys measuring parasite prevalence and leveraging remotely sensed information to inform spatial models of malaria risk in the 3 months after flooding. RESULTS: We identified clusters of malaria risk emerging in areas (1) that showed the greatest changes in Normalized Difference Vegetation Index from pre- to postflood and (2) where residents were displaced for longer periods of time and had lower access to long-lasting insecticidal nets, both of which were associated with a positive malaria rapid diagnostic test result. The disproportionate risk persisted despite a concurrent chemoprevention program that achieved high coverage. CONCLUSIONS: The findings enhance our understanding not only of the spatial evolution of malaria risk after flooding, but also in the context of an effective intervention. The results provide a "proof of concept" for programs aiming to prevent malaria outbreaks after flooding using a combination of interventions. Further study of mitigation strategies-and particularly studies of implementation-is urgently needed.

Comparing field-collected versus remotely-sensed variables to model malaria risk in the highlands of western Uganda.

BACKGROUND: Malaria risk is not uniform across relatively small geographic areas, such as within a village. This heterogeneity in risk is associated with factors including demographic characteristics, individual behaviours, home construction, and environmental conditions, the importance of which varies by setting, making prediction difficult. This study attempted to compare the ability of statistical models to predict malaria risk at the household level using either (i) free easily-obtained remotely-sensed data or (ii) results from a resource-intensive household survey. METHODS: The results of a household malaria survey conducted in 3 villages in western Uganda were combined with remotely-sensed environmental data to develop predictive models of two outcomes of interest (1) a positive ultrasensitive rapid diagnostic test (uRDT) and (2) inpatient admission for malaria within the last year. Generalized additive models were fit to each result using factors from the remotely-sensed data, the household survey, or a combination of both. Using a cross-validation approach, each model's ability to predict malaria risk for out-of-sample households (OOS) and villages (OOV) was evaluated. RESULTS: Models fit using only environmental variables provided a better fit and higher OOS predictive power for uRDT result (AIC = 362, AUC = 0.736) and inpatient admission (AIC = 623, AUC = 0.672) compared to models using household variables (uRDT AIC = 376, Admission AIC = 644, uRDT AUC = 0.667, Admission AUC = 0.653). Combining the datasets did not result in a better fit or higher OOS predictive power for uRDT results (AIC = 367, AUC = 0.671), but did for inpatient admission (AIC = 615, AUC = 0.683). Household factors performed best when predicting OOV uRDT results (AUC = 0.596) and inpatient admission (AUC = 0.553), but not much better than a random classifier. CONCLUSIONS: These results suggest that residual malaria risk is driven more by the external environment than home construction within the study area, possibly due to transmission regularly occurring outside of the home. Additionally, they suggest that when predicting malaria risk the benefit may not outweigh the high costs of attaining detailed information on household predictors. Instead, using remotely-sensed data provides an equally effective, cost-efficient alternative.