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Improving geographical accessibility modeling for operational use by local health actors

Authors
  • Ihantamalala, Felana Angella1, 2
  • Herbreteau, Vincent3
  • Révillion, Christophe4
  • Randriamihaja, Mauricianot1, 5
  • Commins, Jérémy3
  • Andréambeloson, Tanjona1, 5
  • Rafenoarimalala, Feno H.1
  • Randrianambinina, Andriamihaja6
  • Cordier, Laura F.1
  • Bonds, Matthew H.1, 2
  • Garchitorena, Andres1, 7
  • 1 NGO PIVOT, Ranomafana, Madagascar , Ranomafana (Madagascar)
  • 2 Harvard Medical School, Boston, USA , Boston (United States)
  • 3 UMR 228 Espace-Dev (IRD, UA, UG, UM, UR), Phnom Penh, Cambodia , Phnom Penh (Cambodia)
  • 4 Université de La Réunion, UMR 228 Espace-Dev (IRD, UA, UG, UM, UR), Saint-Pierre, La Réunion, France , Saint-Pierre (France)
  • 5 University of Fianarantsoa, Fianarantsoa, Madagascar , Fianarantsoa (Madagascar)
  • 6 Ministry of Health in Madagascar, Antananarivo, Madagascar , Antananarivo (Madagascar)
  • 7 MIVEGEC, Univ. Montpellier, CNRS, IRD, Montpellier, France , Montpellier (France)
Type
Published Article
Journal
International Journal of Health Geographics
Publisher
BioMed Central
Publication Date
Jul 06, 2020
Volume
19
Issue
1
Identifiers
DOI: 10.1186/s12942-020-00220-6
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundGeographical 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.MethodsWe used a participatory approach to map all the paths, residential areas, buildings and rice fields on OpenStreetMap (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.ResultsWe 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.ConclusionOur 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.

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