Improving geographical accessibility modeling for operational use by local health actors

Abstract

Geographical accessibility to health facilities remains one of the main barriers to access care in rural areas of the developing world. Although methods and tools exist to model geographic accessibility, the lack of basic geographic information prevents their widespread use at the local level for targeted program implementation. The aim of this study was to develop very precise, context-specific estimates of geographic accessibility to care in a rural district of Madagascar to help with the design and implementation of interventions that improve access for remote populations. We used a participatory approach to map all the paths, residential areas, buildings and rice fields on OpenStreetMap (OSM). We estimated shortest route from every household in the District to the nearest primary health care center (PHC) and community health site (CHS) with the Open Source Routing Machine (OSMR) tool. Then, we used remote sensing methods to obtain a high resolution land cover map, a digital elevation model and rainfall data to model travel speed. Travel speed models were calibrated with field data obtained by GPS tracking in a sample of 168 walking routes. Model results were used to predict travel time to seek care at PHCs and CHSs for all the shortest route estimated earlier. Finally, we integrated geographical accessibility results into an e-health platform developed with R Shiny. We mapped over 100,000 buildings, 23,000 km of footpaths, and 4,925 residential areas throughout Ifanadiana district; this data is freely available on OSM. We found that over three quarters of the population lived more than one hour away from a PHC, and 10-15% lived more than one hour away from a CHS. Moreover, we identified areas in the North and East of the district where the nearest PHC was further than 5 hours away, and vulnerable populations across the district with poor geographical access (>1 hour) to both PHCs and CHSs. Our study demonstrates how to improve geographical accessibility modeling so that results can be context-specific and operationally actionable by local health actors. The importance of such approaches is paramount for achieving universal health coverage in rural areas throughout world.

Estimating the local spatio-temporal distribution of disease from routine health information systems: the case of malaria in rural Madagascar

“…In Ifanadiana, a mountainous landscape, poor road infrastructure and a sparsely distributed population make it difficult for patients to access health centers. More than 95% of paths are not accessible by vehicle, and three fourths of the population live more than an hour’s walk of a public health center 36,37 , a commonly accepted threshold of low geographic access 53-56 . All these factors could have led to significant underreporting of malaria, at levels compatible with estimates presented here.…”

“…Low access to health care was strongly associated with substantial financial and geographic barriers 35 . For instance, only one fourth of the population lives within an hour’s travel of a health center 36,37 .…”

“…Finally, we used a geographic information system containing data on locations of all health centers, more than 20,000 km of footpaths, over 100,000 buildings, and nearly 5,000 residential areas in the district. This was obtained following a participatory complete mapping of Ifanadiana in 2018-2019, from very high resolution satellite images available through OpenStreetMap 37 . This data was queried on QGIS via the QuickOSM plugin and was used to estimate shortest path distances between health centers and each Fokontany.…”

Geographic Barriers to Achieving Universal Health Coverage in a rural district of Madagascar

“…A detailed description of the methods used to estimate distance and travel time to PHC is available in Ihantamalala et al . (2020) [57].…”

Networks of Care in Rural Madagascar for Achieving Universal Health Coverage in Ifanadiana District

“…Long distances and travel time to a health facility greatly affect people's ability to access care; in Ifanadiana District, 70% of the population lives at least 5 km from a health facility and 49% of the population lives more than 10 km from a health facility. 4 To reduce geographic barriers to care, Ifanadiana has one village-based community health site for each of its 196 fokontany (the smallest administrative unit in Madagascar that is composed of one or several villages). Per national policy, each community health site has two community health workers (CHWs), who provide basic health services and ensure a communication link between communities and the health system.…”