School of Natural Resources and Environment, University of Florida, Gainesville, USA.
School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, USA.
Malar J. 2021 Dec 3;20(1):455. doi: 10.1186/s12936-021-03991-w.
Access to healthcare is important in controlling malaria burden and, as a result, distance or travel time to health facilities is often a significant predictor in modelling malaria prevalence. Adding new health facilities may reduce overall travel time to health facilities and may decrease malaria transmission. To help guide local decision-makers as they scale up community-based accessibility, the influence of the spatial allocation of new health facilities on malaria prevalence is evaluated in Bunkpurugu-Yunyoo district in northern Ghana. A location-allocation analysis is performed to find optimal locations of new health facilities by separately minimizing three district-wide objectives: malaria prevalence, malaria incidence, and average travel time to health facilities.
Generalized additive models was used to estimate the relationship between malaria prevalence and travel time to the nearest health facility and other geospatial covariates. The model predictions are then used to calculate the optimisation criteria for the location-allocation analysis. This analysis was performed for two scenarios: adding new health facilities to the existing ones, and a hypothetical scenario in which the community-based healthcare facilities would be allocated anew. An interactive web application was created to facilitate efficient presentation of this analysis and allow users to experiment with their choice of health facility location and optimisation criteria.
Using malaria prevalence and travel time as optimisation criteria, two locations that would benefit from new health facilities were identified, regardless of scenarios. Due to the non-linear relationship between malaria incidence and prevalence, the optimal locations chosen based on the incidence criterion tended to be inequitable and was different from those based on the other optimisation criteria.
This study findings underscore the importance of using multiple optimisation criteria in the decision-making process. This analysis and the interactive application can be repurposed for other regions and criteria, bridging the gap between science, models and decisions.
获得医疗保健服务对于控制疟疾负担至关重要，因此，到医疗机构的距离或旅行时间通常是建模疟疾流行率的重要预测因素。新增医疗设施可能会减少整体到医疗机构的旅行时间，并可能降低疟疾传播。为了帮助指导地方决策者扩大基于社区的可达性，在加纳北部的邦普克鲁古-云尤区评估了新医疗设施的空间配置对疟疾流行率的影响。通过分别最小化三个全区目标：疟疾流行率、疟疾发病率和到医疗机构的平均旅行时间，进行了位置分配分析以找到新医疗设施的最佳位置。使用广义加性模型来估计疟疾流行率与到最近医疗机构的旅行时间以及其他地理空间协变量之间的关系。然后，使用模型预测来计算位置分配分析的优化标准。针对两种情况进行了此分析：向现有医疗设施添加新设施，以及假设社区医疗设施将重新分配的情况。创建了一个交互式网络应用程序，以方便有效地呈现此分析，并允许用户尝试其选择的医疗设施位置和优化标准。
无论采用哪种情况，使用疟疾流行率和旅行时间作为优化标准，都确定了两个需要新医疗设施的地点。由于疟疾发病率与流行率之间存在非线性关系，基于发病率标准选择的最佳位置往往不公平，与基于其他优化标准选择的最佳位置不同。
这项研究结果强调了在决策过程中使用多个优化标准的重要性。该分析和交互式应用程序可以针对其他地区和标准进行重新利用，从而弥合科学、模型和决策之间的差距。
