Improving geographical accessibility modeling for operational use by local health actors

Abstract

Background: 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. Methods: We used a participatory approach to map all the paths, residential areas, buildings and rice fields on Open-StreetMap (OSM). We estimated shortest routes 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 routes estimated earlier. Finally, we integrated geographical accessibility results into an e-health platform developed with R Shiny. Results: We mapped over 100,000 buildings, 23,000 km of footpaths, and 4925 residential areas throughout Ifanadiana district; these data are 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 1 h away from a CHS. Moreover, we identified areas in the North and East of the district where the nearest PHC was further than 5 h away, and vulnerable populations across the district with poor geographical access (> 1 h) to both PHCs and CHSs. Conclusion: 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 (UHC) in rural areas throughout the world.

Estimating the local spatio‐temporal distribution of malaria from routine health information systems in areas of low health care access and reporting

“…The population of children under five years old was estimated 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 [35]. This data was queried on QGIS via the QuickOSM plugin and was used to estimate shortest path distances between health centers and each Fokontany.…”

District-level health system strengthening for universal health coverage: evidence from a longitudinal cohort study in rural Madagascar, 2014-2018

“…Rates increased to 60% to 85% across the idHSS catchment in 2016 and 2018, except for a remote area in one of the intervention communes (centre-right of the map), where populations have to travel over 4 hours by foot each way to reach the nearest health facility. 46 In the majority of the control catchment area, healthcare seeking BMJ Global Health behaviours for childhood illnesses remained consistently below 50% during the study period (figure 2). The idHSS intervention implemented by the MoPH-PIVOT partnership was associated with reductions in economic and geographical inequalities for most indicators assessed (figure 3, online supplemental tables S5 and S6).…”

“…40 To estimate geographical inequalities, the Open Source Routing Machine (OSRM) engine was used to accurately estimate the shortest path distance between the villages in each cluster and the nearest health centre. For this, we had previously mapped the entire district of Ifanadiana on OpenStreetMap, resulting in over 23 000 km of footpaths and 5000 residential areas mapped (see 46 for details). For each indicator, we estimated wealth-specific and geographic-specific averages (bottom two quantiles vs top three quantiles) as well as composite indicators of inequality, such as relative concentration index and slope index of inequality.…”

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

“…Although PIVOT initially focused its efforts on supporting the MOPH's facility-based care (via the CHRD, CSBs, and the referral network), evaluations of health-care access in Ifanadiana showed that while coverage of most essential health services rapidly improved over time, major geographic barriers to care left remote communities with persistent unmet primary care needs. 12,18 It was clear that PIVOT's financial and technical support needed to shift to include community-based care delivery via community health sites. Yet, the geography in Ifanadiana is such that many communities have poor geographic access to community health sites.…”

“…Yet, the geography in Ifanadiana is such that many communities have poor geographic access to community health sites. 18 To respond to this persistent challenge, and at the request of the MoPH, an innovative pilot to more actively meet the primary care needs of clients in their homes and communities was designed. The commune of Ranomafana has piloted a two-pronged approach to service delivery: one CHW is available at the community health site, while other CHWs travel a circuit of households throughout the fokontany to proactively identify sick children and provide basic care following a roster.…”