You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
Improving geographical accessibility modeling for operational use by local health actors
Improving geographical accessibility modeling for operational use by local health actors
AbstractBackgroundGeographical 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 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.ResultsWe 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.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 in rural areas throughout world.
- INSTITUT DE RECHERCHE AGRICOLE POUR LE DEVELOPPEMENT Cameroon
- Institut de Recherche Pour Le developpement
- Institut de Recherche pour le Développement France
- Institut de Recherche pour le Développement (IRD), Institut Méditerranéen docéanologie
- Institut de Recherche Pour le Développement Burkina Faso
Microsoft Academic Graph classification: education.field_of_study business.industry Population Psychological intervention Developing country Sample (statistics) Land cover Geography Community health Global Positioning System Rural area education business Environmental planning
Microsoft Academic Graph classification: education.field_of_study business.industry Population Psychological intervention Developing country Sample (statistics) Land cover Geography Community health Global Positioning System Rural area education business Environmental planning
1212
1. Tracking Universal Health Coverage : 2017 Global Monitoring Report. 2017.
2. Dror DM, Shahed Hossain SA, Majumdar A, Koehlmoos TLP, John D, Panda PK. What 8. Stenberg K, Hanssen O, Edejer TT, Bertram M, Brindley C, Meshreky A, et al. Articles 11. Ebener S, Guerra-Arias M, Campbell J, Tatem AJ, Moran AC, Amoako Johnson F, et al.
14. Hanson C, Gabrysch S, Mbaruku G, Cox J, Mkumbo E, Manzi F, et al. Access to 18. Ruktanonchai CW, Ruktanonchai NW, Nove A, Lopes S, Pezzulo C, Bosco C, et al.
19. Kenny A, Basu G, Ballard M, Griffiths T, Kentoffio K, Niyonzima JB, et al. Remoteness 20. Kuupiel D, Adu KM, Apiribu F, Bawontuo V, Adogboba DA, Ali KT, et al. Geographic 21. Munyaneza F, Ntaganira J, Nyirazinyoye L, Birru E, Nisingizwe MP, Gupta N, et al.
28. Nguyen HT, Zombré D, Ridde V, De Allegri M. The impact of reducing and eliminating 29. Langlois É V., Karp I, De Dieu Serme J, Bicaba A. Effect of a policy to reduce user fees 30. De Allegri M, Tiendrebéogo J, Müller O, Yé M, Jahn A, Ridde V. Understanding home 31. De Allegri M, Ridde V, Louis VR, Sarker M, Tiendrebéogo J, Yé M, et al. Determinants of 38. Noor AM, Amin AA, Gething PW, Atkinson PM, Hay SI, Snow RW. Modelling distances 39. Bailey PE, Keyes EB, Parker C, Abdullah M, Kebede H, Freedman L. Using a GIS to 46. World Bank. World Bank Open Data. 2019.
47. World Health Organization. Global Health Observatory Data Repository. 2019.
53. Miller AC, Ramananjato RH, Garchitorena A, Rabeza VR, Gikic D, Cripps A, et al.
54. Fondation OpenStreetMap (OSMF). OpenStreetMap. 2004.
55. HOT. Humanitarian OpenStreetMap Team. Mapping our world together. 2013.
57. Shcherb V, Pelykh A, Mueller H. Offline mobile maps & navigation. 2010.
- 1
- 2
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).8 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10% citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).8 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!
- INSTITUT DE RECHERCHE AGRICOLE POUR LE DEVELOPPEMENT Cameroon
- Institut de Recherche Pour Le developpement
- Institut de Recherche pour le Développement France
- Institut de Recherche pour le Développement (IRD), Institut Méditerranéen docéanologie
- Institut de Recherche Pour le Développement Burkina Faso
- University of La Réunion Réunion
- Harvard University United States
- Université de La Réunion
- Institut de recherche pour le développement
- INSTITUT DE RECHERCHE EN ELEVAGE POUR LE DEVELOPPEMENT Chad
- INSTITUT de RECHERCHE pour le DEVELOPPEMENT - MEDITERRANEAN INSTITUTE of OCEANOGRAHPY
- Institut de Recherche pour le Developpement (IRD), France France
- INSTITUT DE RECHERCHE AGRICOLE POUR LE DEVELOPPEMENT Cameroon
- Institut de Recherche pour le Développement Congo
- Institut de Recherche pour le Développement New Caledonia
- Harvard Medical School United States
- Institut de Recherche pour le Développement Bolivia
- Harvard University United States
- Institut de Recherche pour le Développement IRD - (UMR 208 Paloc)
- UNIVERSITE DE LA REUNION France
- Institut de Recherche Agricole pour le Développement Cameroon
- Institut de Recherche pour le Développement Cote d'Ivoire
- INSTITUT DE RECHERCHE POUR LE DEVELOPPEMENT - IRD
- Institut de Recherche pour le Développement Benin
- Institut de Recherche pour le Développement Tunisia
- French National Centre for Scientific Research France
- Insitut de recherche et Developpement
- Institut de Recherche pour le Développement Senegal
- Université de Fianarantsoa Madagascar
- University of Montpellier France
AbstractBackgroundGeographical 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 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.ResultsWe 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.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 in rural areas throughout world.