Defining health facility catchment areas for malaria microstratification using routine data: A demonstration of concept using Malawi as a case study

Background Many countries in Sub-Saharan Africa are advancing toward malaria elimination following years of sustained reductions in prevalence and incidence. However, transmission remains localized in specific areas with suitable conditions, forming malaria hotspots. Identifying these hotspots and disrupting transmission chains through targeted interventions is crucial for elimination. A key step in this process is sub-national malaria risk stratification, which informs malaria control programs on high-burden areas requiring prioritization. Methods For Malawi, we identified health facility catchment areas as the appropriate stratification level for assessing community-level transmission. To delineate these catchments, we integrated road network, land cover, and elevation data, with a walking speeds scenario to model travel time using a least-cost path algorithm. Population estimates for each catchment area were derived from gridded population data. Results We generated contiguous catchment areas and associated population estimates for the entire country. To demonstrate microstratification, we selected Balaka district as a case study to stratify malaria burden based on incidence and prevalence. In 2021, incidence and prevalence patterns were similar across catchments. By 2023, high-burden catchments based on incidence fell from eight to three, while average prevalence rose from 0.12–0.16%, with one facility reaching the highest burden category. Conclusion Our findings contribute to a more granular understanding of malaria burden at the facility level. Identifying high-burden facilities within districts provides a practical, operationally relevant approach for malaria control programs to implement targeted interventions effectively.