@article{fdi:010079378,
  title = {{I}mproving geographical accessibility modeling for operational use by local health actors},
  author = {{I}hantamalala, {F}. {A}. and {H}erbreteau, {V}incent and {R}evillion, {C}. and {R}andriamihaja, {M}. and {C}ommins, {J}{\'e}r{\'e}my and {A}ndreambeloson, {T}. and {R}afenoarimalala, {F}. {H}. and {R}andrianambinina, {A}. and {C}ordier, {L}. {F}. and {B}onds, {M}. {H}. and {G}architorena, {A}ndres},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground {G}eographical accessibility to health facilities remains one of the main barriers to access care in rural areas of the developing world. {A}lthough 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. {T}he aim of this study was to develop very precise, context-specific estimates of geographic accessibility to care in a rural district of {M}adagascar to help with the design and implementation of interventions that improve access for remote populations. {M}ethods {W}e used a participatory approach to map all the paths, residential areas, buildings and rice fields on {O}pen{S}treet{M}ap ({OSM}). {W}e estimated shortest routes from every household in the {D}istrict to the nearest primary health care center ({PHC}) and community health site ({CHS}) with the {O}pen {S}ource {R}outing {M}achine ({OSMR}) tool. {T}hen, we used remote sensing methods to obtain a high resolution land cover map, a digital elevation model and rainfall data to model travel speed. {T}ravel speed models were calibrated with field data obtained by {GPS} tracking in a sample of 168 walking routes. {M}odel results were used to predict travel time to seek care at {PHC}s and {CHS}s for all the shortest routes estimated earlier. {F}inally, we integrated geographical accessibility results into an e-health platform developed with {R} {S}hiny. {R}esults {W}e mapped over 100,000 buildings, 23,000 km of footpaths, and 4925 residential areas throughout {I}fanadiana district; these data are freely available on {OSM}. {W}e 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}. {M}oreover, we identified areas in the {N}orth and {E}ast 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 {PHC}s and {CHS}s. {C}onclusion {O}ur study demonstrates how to improve geographical accessibility modeling so that results can be context-specific and operationally actionable by local health actors. {T}he importance of such approaches is paramount for achieving universal health coverage ({UHC}) in rural areas throughout the world.},
  keywords = {{M}adagascar ; {U}niversal health coverage ; {G}eographic barriers ; e-{H}ealth ; tools ; {MADAGASCAR} ; {IFANADIANA}},
  booktitle = {},
  journal = {{I}nternational {J}ournal of {H}ealth {G}eographics},
  volume = {19},
  numero = {1},
  pages = {27 [15 ]},
  ISSN = {1476-072{X}},
  year = {2020},
  DOI = {10.1186/s12942-020-00220-6},
  URL = {https://www.documentation.ird.fr/hor/fdi:010079378},
}