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