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

Abstract

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, 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 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.

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 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.

Keywords: COVID-19; Forecasting; Healthcare demand; Hospital catchment area; Infectious disease; Mathematical modelling.

Fig. 1

Comparison of six LTLA-level hospital catchment area definitions for University Hospitals Bristol And Weston NHS Foundation Trust. The hospital catchment areas are defined as follows: by the marginal distribution of hospital admissions to Trusts June 2020–May 2021 (“marginal”); the nearest Trust for each LTLA (“nearest”); any Trust within a 40-km radius (shown by the red dashed circle) of the LTLA (“nearby”); by the distribution of emergency, or elective, hospital admissions in 2019 (“emergency” and “elective”, respectively); and by the distribution of COVID-19 hospital admissions June 2020–May 2021 (“covid”). In each panel, the colour denotes the proportion of all patients from that LTLA that are admitted to University Hospitals Bristol And Weston: darker colours indicate a higher proportion, and white indicates zero admissions. The Trust’s main site is marked by a red cross

Fig. 2

Example of retrospective forecasts made 13 December 2020 for Mid And South Essex NHS Foundation Trust. These forecasts are made based on UTLA-level catchment area definitions and using future observed cases. Shown are median forecasts (line) and 50% and 90% quantile forecasts (dark and light ribbon, respectively). The black solid line shows admissions observed up to the forecast date (13 December, marked by a vertical dotted line), while the black dashed line and points show realised admissions, for reference

Fig. 3

Forecasting performance under different hospital catchment area definitions (by UTLA) using future observed cases. A Median interval score (taken over all forecast dates and locations) for each forecast horizon, with the values highlighted for a 7-day forecast horizon in the grey-shaded region. B Median interval score for each forecast date; 7-day forecast horizon. C Median interval score for the 40 acute NHS Trusts with the most total COVID admissions (descending top to bottom); 7-day forecast horizon. Trusts are defined by their three-letter organisational code; see [15] for a full list of Trust codes and names

Fig. 4

Forecasting performance under different hospital catchment area definitions using future forecast cases. A Median interval score (taken over all forecast dates and locations) for each forecast horizon, with the values highlighted for a 14-day forecast horizon in the grey-shaded region. B Median interval score for each forecast date; 14-day forecast horizon. C Median interval score for the top 40 acute NHS Trusts (by total COVID-19 admissions); 14-day forecast horizon