Scandrett Katie, Lilford Richard, Nepogodiev Dmitri, Katikireddi Srinivasa Vittal, Davies Justine, Tabiri Stephen, Watson Samuel I
Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
NIHR Birmingham Biomedical Research Centre, Birmingham, UK.
BMJ Public Health. 2024 Feb 20;2(1):e000321. doi: 10.1136/bmjph-2023-000321. eCollection 2024 Jun.
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.
许多低收入和中等收入国家缺乏有组织的紧急运输系统，这使得人们在遇到医疗紧急情况时只能自行安排非正规的交通工具前往医院。评估实施紧急运输系统的效果既不切实际又成本高昂，因此缺乏支持政策和投资决策的证据。替代建模策略或许能够填补这一空白。
我们开发了一种针对危及生命状况的紧急运输空间流行病学模型。该模型包含多个组成部分，既能预测不同情景下感兴趣区域内的出行时间，又能根据获得救治的时间预测紧急情况下的生存率。我们审查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紧急运输的案例研究。
该模型预测，救护车服务的效果可能是短暂的，会因人口密度和道路网络等当地情况而有所不同。在我们的应用示例中，引入救护车服务可能挽救40条生命（95%可信区间为5至111），或者根据各种模型假设和参数设定，该地区一年内可能会多失去多达107条生命（95%可信区间为-293至-13）。显示救护车服务减少转运时间概率的地图可能特别有用，有助于进行资源分配规划。
尽管我们的模型以及用于填充模型和为参数选择提供信息的数据仍有改进空间，但我们认为这项工作为预测引入救护车系统效果的开创性方法奠定了基础。我们的空间流行病学模型具有很大的灵活性，可以根据需要进行更新，以最好地呈现所选案例研究。
