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Accurate and representative data is vital for precisely reporting the impactof influenza in healthcare systems. Northern hemisphere winter 2022/23experienced the most substantial influenza wave since the COVID-19 pandemicbegan in 2020. Simultaneously, new data streams become available within healthservices because of the pandemic. Comparing these data, surveillance andadministrative, supports the accurate monitoring of population level diseasetrends. We analysed admissions rates per capita from four different collectionmechanisms covering National Health Service hospital Trusts in England over thewinter 2022/23 wave. We adjust for difference in reporting and extracted keyepidemic characteristics including the maximum admission rate, peak timing,cumulative season admissions and growth rates by fitting generalised additivemodels at national and regional levels. By modelling the admission rates percapita across surveillance and administrative data systems we show thatdifferent data measuring the epidemic produce different estimates of keyquantities. Nationally and in most regions the data correspond well for themaximum admission rate, date of peak and growth rate, however, in subnationalanalysis discrepancies in estimates arose, particularly for the cumulativeadmission rate. This research shows that the choice of data used to measureseasonal influenza epidemics can influence analysis substantially atsub-national levels. For the admission rate per capita there is comparabilityin the sentinel surveillance approach (which has other important functions),rapid situational reports, operational databases and time lagged administrativedata giving assurance in their combined value. Utilising multiple sources ofdata aids understanding of the impact of seasonal influenza epidemics in thepopulation.

Influenza Hospitalisations in England during the 2022/23 Season: do  different data sources drive divergence in modelled waves? A comparison of  surveillance and administrative data