Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, Keppel St, London, WC1E 7HT, UK.
BMC Med. 2024 Apr 17;22(1):163. doi: 10.1186/s12916-024-03369-0.
Defining healthcare facility catchment areas is a key step in predicting future healthcare demand in epidemic settings. Forecasts of hospitalisations can be informed by leading indicators measured at the community level. However, this relies on the definition of so-called catchment areas or the geographies whose populations make up the patients admitted to a given hospital, which are often not well-defined. Little work has been done to quantify the impact of hospital catchment area definitions on healthcare demand forecasting.
We made forecasts of local-level hospital admissions using a scaled convolution of local cases (as defined by the hospital catchment area) and delay distribution. Hospital catchment area definitions were derived from either simple heuristics (in which people are admitted to their nearest hospital or any nearby hospital) or historical admissions data (all emergency or elective admissions in 2019, or COVID-19 admissions), plus a marginal baseline definition based on the distribution of all hospital admissions. We evaluated predictive performance using each hospital catchment area definition using the weighted interval score and considered how this changed by the length of the predictive horizon, the date on which the forecast was made, and by location. We also considered the change, if any, in the relative performance of each definition in retrospective vs. real-time settings, or at different spatial scales.
The choice of hospital catchment area definition affected the accuracy of hospital admission forecasts. The definition based on COVID-19 admissions data resulted in the most accurate forecasts at both a 7- and 14-day horizon and was one of the top two best-performing definitions across forecast dates and locations. The "nearby" heuristic also performed well, but less consistently than the COVID-19 data definition. The marginal distribution baseline, which did not include any spatial information, was the lowest-ranked definition. The relative performance of the definitions was larger when using case forecasts compared to future observed cases. All results were consistent across spatial scales of the catchment area definitions.
Using catchment area definitions derived from context-specific data can improve local-level hospital admission forecasts. Where context-specific data is not available, using catchment areas defined by carefully chosen heuristics is a sufficiently good substitute. There is clear value in understanding what drives local admissions patterns, and further research is needed to understand the impact of different catchment area definitions on forecast performance where case trends are more heterogeneous.
在疫情环境下，定义医疗保健设施的服务区是预测未来医疗需求的关键步骤。通过在社区层面测量的领先指标，可以为住院预测提供信息。然而，这依赖于所谓的服务区或构成给定医院收治患者的人口的地理区域的定义，而这些定义往往不明确。在量化医院服务区定义对医疗需求预测的影响方面，很少有工作。
我们使用当地病例的缩放卷积（根据医院服务区定义）和延迟分布来预测当地医院的住院人数。医院服务区的定义来自简单的启发式方法（人们被收治到最近的医院或任何附近的医院）或历史住院数据（2019 年所有急诊或择期住院或 COVID-19 住院），再加上基于所有住院分布的边缘基线定义。我们使用每个医院服务区定义的加权区间得分来评估预测性能，并考虑预测期的长短、预测日期以及位置如何改变这一性能。我们还考虑了在回顾性与实时环境或不同空间尺度下，每个定义的相对性能是否发生了变化。
医院服务区定义的选择影响住院人数预测的准确性。基于 COVID-19 住院数据的定义在 7 天和 14 天的预测期内产生了最准确的预测结果，并且是所有预测日期和地点中表现最好的两个定义之一。“附近”启发式方法的表现也很好，但不如 COVID-19 数据定义稳定。不包括任何空间信息的边缘分布基线是排名最低的定义。与未来观察到的病例相比，使用病例预测时，定义的相对性能更大。所有结果在服务区定义的不同空间尺度上都是一致的。
使用从特定于上下文的数据中派生的服务区定义可以提高当地医院住院预测的准确性。在没有特定于上下文的数据的情况下，使用经过精心选择的启发式方法定义的服务区是一个足够好的替代方案。了解是什么驱动了当地住院模式具有明显的价值，并且需要进一步研究在病例趋势更加异质的情况下，不同服务区定义对预测性能的影响。
