Background: Mapping health facility catchment areas is important for estimating the population that uses the health facility, as a denominator for capturing spatial patterns of disease burden across space. Mapping activities to generate catchment areas are expensive exercises and are often not repeated on a regular basis.
Methods: In this work, we demonstrated the generation of facility catchment areas in Blantyre, Malawi using crowdsourced road data and open-source mapping tools. We also observed travel speeds associated with different means of transportation were made in five randomly selected residential communities within Blantyre city. AccessMod version 5.8 was used to process the generated data to quantify travel time and catchment areas of health facilities in Blantyre city.
Results: When these catchments are compared with georeferenced patients originating communities (based on malaria records), an average of 90.3 percent of the patients come from communities within the generated catchments.
Conclusions: The study suggests that crowdsourced data resources can be used for the delineation of catchment areas and this information can confidently be used in efforts to stratify the burden of diseases such as malaria.
Plain language summary: Knowing which areas health facilities serve is important for estimating the number of people they cover, tracking disease cases, and planning targeted health programmes. However, facility service areas change over time, especially as new facilities open, old ones close, communities grow, and transport networks improve. In Malawi, the last update of facility catchment areas was in the early 2000s. Since then, new health facilities have been built, populations have shifted, and roads have changed. This study mapped the areas that health facilities in Blantyre city serve and checked their accuracy using patient visit records. The results showed that the mapped areas match where patients travel from. These updated service areas, when combined with routine health data, could help improve understanding of disease patterns in the future.
