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BMJ Publishing Group, Thorax, 3(75), p. 262-268, 2020

DOI: 10.1136/thoraxjnl-2019-213764

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Geospatial and seasonal variation of bronchiolitis in England: a cohort study using hospital episode statistics

Journal article published in 2020 by Kate Marie Lewis ORCID, Bianca De Stavola ORCID, Pia Hardelid ORCID
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

BackgroundRates of hospital admissions for bronchiolitis vary seasonally and geographically across England; however, seasonal differences by area remain unexplored. We sought to describe spatial variation in the seasonality of hospital admissions for bronchiolitis and its association with local demographic characteristics.MethodsSingleton children born in English National Health Service hospitals between 2011 and 2016 (n=3 727 013) were followed up for 1 year. Poisson regression models with harmonic functions to model seasonal variations were used to calculate weekly incidence rates and peak timing of bronchiolitis admissions across English regions and clinical commissioning groups (CCGs). Linear regression was used to estimate the joint association of population density and deprivation with incidence and peak timing of bronchiolitis admissions at the CCG level.ResultsBronchiolitis admission rates ranged from 30.9 per 1000 infant-years (95% CI 30.4 to 31.3) in London to 68.7 per 1000 (95% CI 67.9 to 69.5) in the North West. Across CCGs, there was a 5.3-fold variation in incidence rates and the epidemic peak ranged from week 49.3 to 52.2. Admission rates were positively associated with area-level deprivation. CCGs with earlier peak epidemics had higher population densities, and both high and low levels of deprivation were associated with earlier peak timing.ConclusionsApproximately one quarter of the variation in admission rates and two-fifths of the variation in peak timing of hospital admissions for bronchiolitis were explained by local demographic characteristics. Implementation of an early warning system could help to prepare hospitals for peak activity and to time public health messages.