Can syndromic surveillance help forecast winter hospital bed pressures in England?

Roger Morbey, Andre Charlett, Iain Lake, James Mapstone, Richard Pebody, James Sedgwick, Gillian Smith, Alex Elliot

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Background Health care planners need to predict demand for hospital beds to avoid deterioration in health care. Seasonal demand can be affected by respiratory illnesses which in England are monitored using syndromic surveillance systems. Therefore, we investigated the relationship between syndromic data and daily emergency hospital admissions. Methods We compared the timing of peaks in syndromic respiratory indicators and emergency hospital admissions, between 2013 and 2018. Furthermore, we created forecasts for daily admissions and investigated their accuracy when real-time syndromic data were included. Results We found that syndromic indicators were sensitive to changes in the timing of peaks in seasonal disease, especially influenza. However, each year, peak demand for hospital beds occurred on either 29th or 30th December, irrespective of the timing of syndromic peaks. Most forecast models using syndromic indicators explained over 70% of the seasonal variation in admissions (adjusted R square value). Forecast errors were reduced when syndromic data were included. For example, peak admissions for December 2014 and 2017 were underestimated when syndromic data were not used in models. Conclusion Due to the lack of variability in the timing of the highest seasonal peak in hospital admissions, syndromic surveillance data do not provide additional early warning of timing. However, during atypical seasons syndromic data did improve the accuracy of forecast intensity.

Original languageEnglish
Article numbere0228804
JournalPLoS ONE
Volume15
Issue number2
DOIs
Publication statusPublished - 1 Feb 2020

Fingerprint

Dive into the research topics of 'Can syndromic surveillance help forecast winter hospital bed pressures in England?'. Together they form a unique fingerprint.

Cite this