TY - JOUR
T1 - Mapping and quantifying travel time to define health facility catchment areas in Blantyre city in Malawi
AU - Kalonde, Patrick Ken
AU - Tsoka, Owen
AU - Chiepa, Blessings
AU - Baluwa, Chifuniro
AU - Nkolokosa, Clinton
AU - Mategula, Donnie
AU - Muthukrishnan, Suresh
AU - Feasey, Nicholas
AU - Henrion, Marc Y.R.
AU - Stanton, Michelle C.
AU - Ray, Nicolas
AU - Terlouw, Dianne Jannette
AU - Longbottom, Joshua
AU - Chirombo, James
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025/6/11
Y1 - 2025/6/11
N2 - 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.
AB - 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.
U2 - 10.1038/s43856-025-00845-3
DO - 10.1038/s43856-025-00845-3
M3 - Article
AN - SCOPUS:105007898219
SN - 2730-664X
VL - 5
JO - Communications Medicine
JF - Communications Medicine
IS - 1
M1 - 227
ER -