PLoS ONE (Jan 2021)

Scenario modelling as planning evidence to improve access to emergency obstetric care in eastern Indonesia.

  • Frederika Rambu Ngana,
  • A A I N Eka Karyawati

DOI
https://doi.org/10.1371/journal.pone.0251869
Journal volume & issue
Vol. 16, no. 6
p. e0251869

Abstract

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The rate of maternal deaths in remote areas in eastern Indonesia-where geographic conditions are difficult and the standard of infrastructure is poor-is high. Long travel times needed to reach emergency obstetric care (EMOC) is one cause of maternal death. District governments in eastern Indonesia need effective planning to improve access to EMOC. The aim of this study was to develop a scenario modelling tool to be used in planning to improve access to EMOC in eastern Indonesia. The scenario model was developed using the geographic information system tool in NetLogo. This model has two inputs: the location of the EMOC facility (PONED) and the travel cost of moving across geographical features in the rainy and dry seasons. We added a cost-benefit analysis to the model: cost is the budget for building the infrastructure; benefit is the number of people who can travel to the EMOC in less than 1 hour if the planned infrastructure is built. We introduced the tool to representative midwives from all districts of Nusa Tenggara Timur province and to staff of Kupang district planning agency. We found that the tool can model accessibility to EMOC based on weather conditions; compare alternative infrastructure planning scenarios based on cost-benefit analysis; enable users to identify and mark poor infrastructure; and model travel across the ocean. Lay people can easily use the tool through interactive scenario modelling: midwives can use it for evidence to support planning proposals to improve access to EMOC in their district; district planning agencies can use it to choose the best plan to improve access to EMOC. Scenario modelling has potential for use in evidence-based planning to improve access to EMOC in low-income and lower-middle-income countries with poor infrastructure, difficult geography conditions, limited budgets and lack of trained personnel.