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FN1Authors' addresses: Gordon C. McCord, School of Global Policy and Strategy, University of California, San Diego, CA, E-mail: email@example.com. Jesse K. Anttila-Hughes, Department of Economics, University of San Francisco, San Francisco, CA, E-mail: firstname.lastname@example.org.
- The American Society of Tropical Medicine and Hygiene,
- Source: The American Journal of Tropical Medicine and Hygiene, Volume 96, Issue 3, Mar 2017, p. 616 - 623
A Malaria Ecology Index Predicted Spatial and Temporal Variation of Malaria Burden and Efficacy of Antimalarial Interventions Based on African Serological Data
Reducing the global health burden of malaria is complicated by weak reporting systems for infectious diseases and a paucity of vital statistics registration. This limits our ability to predict changes in malaria health burden intensity, target antimalarial resources where needed, and identify malaria impacts in retrospective data. We refined and deployed a temporally and spatially varying Malaria Ecology Index (MEI) incorporating climatological and ecological data to estimate malaria transmission strength and validate it against cross-sectional serology data from 39,875 children from seven sub-Saharan African countries. The MEI is strongly associated with malaria burden; a 1 standard deviation higher MEI is associated with a 50–117% increase in malaria risk and a 3–5 g/dL lower level of Hg. Results show that the relationship between malaria ecology and disease burden is attenuated with sufficient coverage of insecticide treated nets (ITNs) or indoor residual spraying (IRS). Having both ITNs and IRS reduce the added risk from adverse malaria ecology conditions by half. Readily available climate and ecology data can be used to estimate the spatial and temporal variation in malaria disease burden, providing a feasible alternative to direct surveillance. This will help target resources for malaria programs in the absence of national coverage of active case detection systems, and facilitate malaria research using retrospective health data.