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结合开放街道地图映射和路线优化算法,为最后一英里的社区卫生干预措施的提供提供信息。

Combining OpenStreetMap mapping and route optimization algorithms to inform the delivery of community health interventions at the last mile.

作者信息

Randriamihaja Mauricianot, Ihantamalala Felana Angella, H Rafenoarimalala Feno, Finnegan Karen E, Rakotonirina Luc, Razafinjato Benedicte, H Bonds Matthew, V Evans Michelle, Garchitorena Andres

机构信息

NGO Pivot, Ranomafana, Ifanadiana, Madagascar.

ED 168 CBS2, University of Montpellier, Montpellier, France.

出版信息

PLOS Digit Health. 2024 Nov 7;3(11):e0000621. doi: 10.1371/journal.pdig.0000621. eCollection 2024 Nov.

Abstract

Community health programs are gaining relevance within national health systems and becoming inherently more complex. To 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 (CHWs) according to the local geographic context and population size of the communities they serve. Geographic optimization could be particularly beneficial for those activities that require CHWs to visit households door-to-door for last mile delivery of care. The 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. We developed a decision-making tool based on OpenStreetMap 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 (proCCM) programs. We applied the Vehicle Routing Problem with Time Windows (VRPTW) algorithm to optimize the on-foot routes that CHWs take to visit households in their catchment, using a geographic dataset obtained from mapping on OpenStreetMap comprising over 100,000 buildings and 20,000 km of footpaths in the rural district of Ifanadiana, Madagascar. We 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 proCCM programs, assuming 1 visit per month. To 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. Further 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.

摘要

社区卫生项目在国家卫生系统中的重要性日益凸显,且本身也变得愈发复杂。为确保社区卫生项目能实现医疗服务的公平地理可及性,世界卫生组织建议根据社区的当地地理环境和人口规模,调整社区卫生工作者(CHW)的目标人群和工作量。地理优化对于那些要求社区卫生工作者逐户上门提供最后一公里医疗服务的活动可能特别有益。本研究的目的是展示如何应用地理优化来为发展中世界农村地区的社区卫生项目提供信息。我们基于开放街道地图(OpenStreetMap)制图和路线优化算法开发了一个决策工具,以便为两种需要逐户上门服务的社区卫生干预措施的微观规划和实施提供信息:大规模分发活动和主动社区病例管理(proCCM)项目。我们应用带时间窗的车辆路径问题(VRPTW)算法,利用从开放街道地图上获取的地理数据集优化社区卫生工作者在其服务区域内逐户家访的步行路线,该数据集涵盖了马达加斯加伊法纳迪亚纳农村地区的10多万栋建筑和2万公里的人行道。我们发现,假设每月进行1次家访,大规模分发活动中每个社区卫生工作者服务区域的人员日需求量从不到15人天到超过60人天不等,而proCCM项目则从不到5人天到超过20人天不等。为说明业务团队如何使用这些VRPTW算法,我们开发了一个“电子健康”平台,可根据可定制的干预设计和假设来可视化资源需求、社区卫生工作者的最佳时间表和行程。按照世界卫生组织的建议,进一步开发和扩大这些工具有助于优化社区卫生项目及其他最后一公里交付活动,将大数据分析的新时代与资源匮乏地区最基本的一线医疗服务形式联系起来。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d237/11542841/8b35b300aefe/pdig.0000621.g001.jpg

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