Dataset Information

Comparative assessment of methods for short-term forecasts of COVID-19 hospital admissions in England at the local level.

ABSTRACT:

Background

Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources.

Methods

We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all, and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the Weighted Interval Score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known.

Results

All models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons.

Conclusions

Assuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings.

SUBMITTER: Meakin S

PROVIDER: S-EPMC8547529 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundForecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources.MethodsWe made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the weighted interval score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known.ResultsAll models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons.ConclusionsAssuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings.

Project description:Background and aimsExcessive alcohol consumption has a substantial impact on public health services. A key element determining alcohol availability is alcohol outlet density. This study investigated the relationship between on-trade and off-trade outlets and hospital admission rates in local neighbourhoods.DesignNational small-area level ecological study.Setting and participantsAll 32 482 lower layer super output census areas (LSOAs) in England (42 227 108 million people aged 15+ years). Densities for six outlet categories (outlets within a 1-km radius of residential postcode centroids, averaged for all postcodes within each LSOA) were calculated.MeasurementsMain outcome measures were admissions due to acute or chronic conditions wholly or partially attributable to alcohol consumption from 2002/03 to 2013/14.FindingsThere were 1 007 137 admissions wholly, and 2 153 874 admissions partially, attributable to alcohol over 12 years. After adjustment for confounding, higher densities of on-trade outlets (pubs, bars and nightclubs; restaurants licensed to sell alcohol; other on-trade outlets) and convenience stores were associated with higher admission rate ratios for acute and chronic wholly attributable conditions. For acute wholly attributable conditions, admission rate ratios were 13% (95% confidence interval = 11-15%), 9% (7-10%), 12% (10-14%) and 10% (9-12%) higher, respectively, in the highest relative to the lowest density categories by quartile. For chronic wholly attributable conditions, rate ratios were 22% (21-24%), 9% (7-11%), 19% (17-21%) and 7% (6-9%) higher, respectively. Supermarket density was associated with modestly higher acute and chronic admissions but other off-trade outlet density was associated only with higher admissions for chronic wholly attributable conditions. For partially attributable conditions, there were no strong patterns of association with outlet densities.ConclusionsIn England, higher densities of several categories of alcohol outlets appear to be associated with higher hospital admission rates for conditions wholly attributable to alcohol consumption.