|dc.description.abstract||Background: Malaria screen-and-treat (called Step-D in Zambia) is a reactive case detection strategy in which passively detected malaria cases trigger community health workers (CHWs) to screen for secondary cases within a 140-meters of the initial case’s household using rapid diagnostic tests (RDTs). Few studies have evaluated whether an evidence-based strategy using environmental variables that characterize the immediate surroundings of a household can improve the efficiency of secondary case identification. This study extended the screening radius to 250-meters and assessed whether local environmental factors can guide CHWs to identify secondary cases more efficiently.
Methods: Demographic information, malaria diagnosis, and household characteristics were obtained from household visit survey tools. Households were stratified into malaria positive or negative secondary households using RDT and qPCR results (excluding index cases). ArcGIS was used to generate environmental variables such as number of animal pens within 100-meters, distance to nearest animal pen, nearest stream category, and main road, as well as, distance and elevation difference between households. Generalized estimating equations (GEE) were used to estimate the average cross-sectional effect for the difference in odds of a malaria positive vs. negative secondary household for each environmental variable. For the secondary analysis, GEE was used to estimate the cross-sectional difference in odds of a positive vs. negative household for each environmental predictor.
Results: 4,202 individuals in 692 households were enrolled in the Enhanced Step D program between January 12, 2015 and July 26, 2017. 165 participants tested positive for malaria, 66% of whom resided in index households. The overall parasite prevalence in index households was 8.6% for the study period (2015-2017), while that in secondary households was 1.9%. Excluding index cases, stratification resulted in 488 negative secondary and 45 positive secondary households. Results from the primary regression revealed that if the nearest stream was category 3, 4, or 5, there was significantly higher odds of being a positive secondary household (OR stream category 3: 1.5; p=0.03, OR stream category 4: 1.9; p<0.01, OR stream category 5: 1.5; p<0.01). Similar trends were observed in the secondary analysis for stream categories, in addition, to a significant OR for the presence of animal pens (OR: 5.5; p<0.001).
Conclusion: Screening for secondary households within a low-transmission setting in southern Zambia could be optimized by using both local-scale indicators such as the presence of animal pens and large-scale indicators such as streams as environmental guidance tools.||