预测在中低收入国家引入紧急运输系统的影响:空间流行病学建模研究

Katie Scandrett, R. Lilford, Dmitri Nepogodiev, S. Katikireddi, Justine Davies, Stephen Tabiri, Samuel I Watson
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引用次数: 0

摘要

许多低收入和中等收入国家缺乏有组织的紧急运输系统,人们只能在发生医疗紧急情况时安排非正式的交通工具前往医院。对实施紧急交通系统的效果进行估算既不现实又昂贵,因此缺乏支持政策和投资决策的证据。我们开发了一个危及生命的紧急交通空间流行病学模型。该模型包含多个组成部分,既可预测不同情况下相关区域内的旅行时间,也可预测作为接受治疗时间函数的紧急状况下的存活率。我们回顾了不同模型参数的潜在相关数据源。我们将该模型应用于加纳北部产后出血急救运输的示例研究中。该模型预测,救护车服务的效果可能是短暂的,会根据当地情况(如人口密度和道路网络)而变化。在我们的应用实例中,救护车服务的引入可能会在一年内挽救 40 条生命(95% 可信区间为 5 至 111),也可能会在一年内使整个地区失去多达 107 条生命(95% 可信区间为-293 至-13),这取决于各种模型假设和参数规格。虽然我们的模型以及用于填充模型和通知参数选择的可用数据还有待改进,但我们相信这项工作为开创预测引入救护车系统影响的方法奠定了基础。我们的空间流行病学模型具有很大的灵活性,可根据需要进行更新,以最好地体现所选案例研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting the effects of introducing an emergency transport system in low-income and middle-income countries: a spatial-epidemiological modelling study
Many low-income and middle-income countries lack an organised emergency transportation system, leaving people to arrange informal transport to hospital in the case of a medical emergency. Estimating the effect of implementing an emergency transport system is impractical and expensive, so there is a lack of evidence to support policy and investment decisions. Alternative modelling strategies may be able to fill this gap.We have developed a spatial-epidemiological model of emergency transport for life-threatening conditions. The model incorporates components to both predict travel times across an area of interest under different scenarios and predict survival for emergency conditions as a function of time to receive care. We review potentially relevant data sources for different model parameters. We apply the model to the illustrative case study of providing emergency transport for postpartum haemorrhage in Northern Ghana.The model predicts that the effects of an ambulance service are likely to be ephemeral, varying according to local circumstances such as population density and road networks. In our applied example, the introduction of the ambulance service may save 40 lives (95% credible interval 5 to 111), or up to 107 lives (95% credible interval −293 to –13) may be lost across the region in a year, dependent on various model assumptions and parameter specifications. Maps showing the probability of reduced transfer time with the ambulance service may be particularly useful and allow for resource allocation planning.Although there is scope for improvement in our model and in the data available to populate the model and inform parameter choices, we believe this work provides a foundation for pioneering methodology to predict the effect of introducing an ambulance system. Our spatial-epidemiological model includes much oppurtunity for flexibility and can be updated as required to best represent a chosen case study.
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