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

• Published in: Malaria Journal
• Main Authors: Brandon D. Hollingsworth, Hilary Sandborn, Emmanuel Baguma, Emmanuel Ayebare, Moses Ntaro, Edgar M. Mulogo, Ross M. Boyce
• Format: Article in Journal/Newspaper
• Language: English
• Published: BMC 2023
• Subjects: Malaria; Uganda; Micro-epidemiology; Arctic medicine. Tropical medicine; RC955-962; Infectious and parasitic diseases; RC109-216; Arctic
• Online Access: https://doi.org/10.1186/s12936-023-04628-w; https://doaj.org/article/285f7ef8a7674d5489be519dbdfd2fec

Abstract 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 ...