Quantifying the impact of hospital catchment area definitions on hospital admissions forecasts: COVID-19 in England, September 2020 – April 2021

Publication date: Jul 12, 2023

Background. 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, and which are often not well-defined. Little work has been done to quantify the impact of hospital catchment area definitions on healthcare demand forecasting. Methods. We made forecasts of Trust-level hospital admissions using a scaled convolution of local cases (as defined by the hospital catchment area) and a 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 (WIS) 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, on the relative performance of each definition in retrospective vs. real-time settings, or at different spatial scales. Results. 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. Conclusions. Using catchment area definitions derived from context-specific data can improve local-level hospital admissions forecasts. Where context-specific data is not available, using catchment areas defined by carefully-chosen heuristics are 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.


Concepts Keywords
Geographies Admissions
Hospitalcatchment Area
London Areas
Tropical Catchment


Type Source Name
disease MESH COVID-19
disease IDO healthcare facility
disease MESH emergency
disease VO time
disease MESH Infectious Diseases
drug DRUGBANK Coenzyme M
disease VO population
disease VO vaccination coverage
disease IDO geographical region
disease MESH malaria
pathway KEGG Malaria
drug DRUGBANK Aspartame
disease MESH uncertainty
pathway REACTOME Reproduction
disease IDO pathogen
disease IDO country

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