Predicted geographic accessibility to healthcare in the Democratic Republic of the Congo, 2023

Analysis of routine surveillance data is an essential component of malaria programming, however the use of such data in quantifying malaria burden is challenging due to factors that may influence the reliability of data that is collected and reported through surveillance systems. Variations in health care facility use and access should be considered to ensure that malaria surveillance data is correctly interpreted and used effectively when informing decision making processes and malaria programming. Identification and quantification of underserved populations provides a more specific denominator for calculating malaria indicators and can provide additional context while interpreting routine surveillance data that is used for malaria planning and programming purposes. An approach for fitting health facility catchment models to service population statistics was therefore developed in collaboration with the Malaria Atlas Project (MAP) to predict the volume of spatial interaction between health zone (HZ) population totals and the time of cost of overcoming distance to access healthcare at a health facility or community care site in the DRC. Modeling outputs are available here as geospatial raster layers that quantify the mean number of accessible health facilities per pixel, as well as the proportion of the population per pixel that can feasibly access at least 2 health facilities, in the DRC.