Unmasking Fever: A Rapid Diagnostic Approach to Fever Surveillance in Madagascar

Abstract

Non-malarial fever (NMF) characterizes a large, but understudied disease burden globally. Use of new rapid diagnostic tests (RDTs) to diagnose NMFs offers a promising opportunity to better understand the distribution of infectious diseases. However, cost effective deployment of such tests would be strengthened by characterization of the spatial distributions of NMF, and associated landscape (human population density, temperature) and individual (e.g., use of bednets etc) features. Using Demographic and Health Survey (DHS) data from Madagascar (2011–2021), this study examines the prevalence, predictors, and spatial distribution of NMF among children under five. Data from 29,371 individuals were analyzed using generalized additive models (GAMs) incorporating environmental and demographic predictors. Fever prevalence ranged from 12.4 - 16%, with NMF accounting for 88.4% of the cases tested across all survey years. Population density was the most consistent predictor of NMF, showing a non-linear (broadly increasing and saturating) relationship with fever risk. Predicted probability of NMF ranged from 2.9 to 19.4% across the island. Our results indicate that most fevers in children in Madagascar across this timespan are not attributable to malaria and are spatially heterogeneous. We conclude by discussing how increased diagnostic capacity and geographic targeting in disease surveillance programs could uncover the ecology of other important disease agents in Madagascar