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Spatiotemporal analysis of the first wave of COVID-19 hospitalisations in Birmingham, UK.
Watson, Samuel I; Diggle, Peter J; Chipeta, Michael G; Lilford, Richard J.
  • Watson SI; Institute of Applied Health Research, University of Birmingham, Birmingham, UK s.i.watson@bham.ac.uk.
  • Diggle PJ; Faculty of Health and Medicine, Lancaster University, Lancaster, UK.
  • Chipeta MG; Epidemiology and Population Health, University of Liverpool, Liverpool, UK.
  • Lilford RJ; Big Data Institute, University of Oxford, Oxford, UK.
BMJ Open ; 11(10): e050574, 2021 Oct 04.
Artigo em Inglês | MEDLINE | ID: covidwho-1450605
ABSTRACT

OBJECTIVES:

To evaluate the spatiotemporal distribution of the incidence of COVID-19 hospitalisations in Birmingham, UK during the first wave of the pandemic to support the design of public health disease control policies.

DESIGN:

A geospatial statistical model was estimated as part of a real-time disease surveillance system to predict local daily incidence of COVID-19.

PARTICIPANTS:

All hospitalisations for COVID-19 to University Hospitals Birmingham NHS Foundation Trust between 1 February 2020 and 30 September 2020. OUTCOME

MEASURES:

Predictions of the incidence and cumulative incidence of COVID-19 hospitalisations in local areas, its weekly change and identification of predictive covariates.

RESULTS:

Peak hospitalisations occurred in the first and second weeks of April 2020 with significant variation in incidence and incidence rate ratios across the city. Population age, ethnicity and socioeconomic deprivation were strong predictors of local incidence. Hospitalisations demonstrated strong day of the week effects with fewer hospitalisations (10%-20% less) at the weekend. There was low temporal correlation in unexplained variance. By day 50 at the end of the first lockdown period, the top 2.5% of small areas had experienced five times as many cases per 10 000 population as the bottom 2.5%.

CONCLUSIONS:

Local demographic factors were strong predictors of relative levels of incidence and can be used to target local areas for disease control measures. The real-time disease surveillance system provides a useful complement to other surveillance approaches by producing real-time, quantitative and probabilistic summaries of key outcomes at fine spatial resolution to inform disease control programmes.
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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Estudo experimental / Estudo observacional / Estudo prognóstico Limite: Humanos País/Região como assunto: Europa Idioma: Inglês Revista: BMJ Open Ano de publicação: 2021 Tipo de documento: Artigo País de afiliação: Bmjopen-2021-050574

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Texto completo: Disponível Coleções: Bases de dados internacionais Base de dados: MEDLINE Assunto principal: COVID-19 Tipo de estudo: Estudo experimental / Estudo observacional / Estudo prognóstico Limite: Humanos País/Região como assunto: Europa Idioma: Inglês Revista: BMJ Open Ano de publicação: 2021 Tipo de documento: Artigo País de afiliação: Bmjopen-2021-050574