@article{fdi:010092743,
  title = {{C}ombining {O}pen{S}treet{M}ap mapping and route optimization algorithms to inform the delivery of community health interventions at the last mile},
  author = {{R}andriamihaja, {M}. and {I}hantamalala, {F}. {A}. and {R}afenoarimalala, {F}. {H}. and {F}innegan, {K}. {E}. and {R}akotonirina, {L}. and {R}azafinjato, {B}. and {B}onds, {M}. {H}. and {E}vans, {M}. {V}. and {G}architorena, {A}ndres},
  editor = {},
  language = {{ENG}},
  abstract = {{C}ommunity health programs are gaining relevance within national health systems and becoming inherently more complex. {T}o ensure that community health programs lead to equitable geographic access to care, the {WHO} recommends adapting the target population and workload of community health workers ({CHW}s) according to the local geographic context and population size of the communities they serve. {G}eographic optimization could be particularly beneficial for those activities that require {CHW}s to visit households door-to-door for last mile delivery of care. {T}he goal of this study was to demonstrate how geographic optimization can be applied to inform community health programs in rural areas of the developing world. {W}e developed a decision-making tool based on {O}pen{S}treet{M}ap mapping and route optimization algorithms in order to inform the micro-planning and implementation of two kinds of community health interventions requiring door-to-door delivery: mass distribution campaigns and proactive community case management (pro{CCM}) programs. {W}e applied the {V}ehicle {R}outing {P}roblem with {T}ime {W}indows ({VRPTW}) algorithm to optimize the on-foot routes that {CHW}s take to visit households in their catchment, using a geographic dataset obtained from mapping on {O}pen{S}treet{M}ap comprising over 100,000 buildings and 20,000 km of footpaths in the rural district of {I}fanadiana, {M}adagascar. {W}e found that personnel-day requirements ranged from less than 15 to over 60 per {CHW} catchment for mass distribution campaigns, and from less than 5 to over 20 for pro{CCM} programs, assuming 1 visit per month. {T}o illustrate how these {VRPTW} algorithms can be used by operational teams, we developed an "e-health" platform to visualize resource requirements, {CHW} optimal schedules and itineraries according to customizable intervention designs and hypotheses. {F}urther development and scale-up of these tools could help optimize community health programs and other last mile delivery activities, in line with {WHO} recommendations, linking a new era of big data analytics with the most basic forms of frontline care in resource poor areas.},
  keywords = {{MADAGASCAR}},
  booktitle = {},
  journal = {{PL}o{S} {D}igital {H}ealth},
  volume = {3},
  numero = {11},
  pages = {e0000621 [21 ]},
  year = {2024},
  DOI = {10.1371/journal.pdig.0000621},
  URL = {https://www.documentation.ird.fr/hor/fdi:010092743},
}