Journal article
Studying the Long-term Impact of COVID-19 in Kids (SLICK). Healthcare use and costs in children and young people following community-acquired SARS-CoV-2 infection: protocol for an observational study using linked primary and secondary routinely collected healthcare data from England, Scotland and Wales
- Abstract:
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Introduction: SARS-CoV-2 infection rarely causes hospitalisation in children and young people (CYP), but mild or asymptomatic infections are common. Persistent symptoms following infection have been reported in CYP but subsequent healthcare use is unclear. We aim to describe healthcare use in CYP following community-acquired SARS-CoV-2 infection and identify those at risk of ongoing healthcare needs.
Methods and analysis: We will use anonymised individual-level, population-scale national data linking demographics, comorbidities, primary and secondary care use and mortality between 1 January 2019 and 1 May 2022. SARS-CoV-2 test data will be linked from 1 January 2020 to 1 May 2022. Analyses will use Trusted Research Environments: OpenSAFELY in England, Secure Anonymised Information Linkage (SAIL) Databank in Wales and Early Pandemic Evaluation and Enhanced Surveillance of COVID-19 in Scotland (EAVE-II). CYP aged ≥4 and <18 years who underwent SARS-CoV-2 reverse transcription PCR (RT-PCR) testing between 1 January 2020 and 1 May 2021 and those untested CYP will be examined.
The primary outcome measure is cumulative healthcare cost over 12 months following SARS-CoV-2 testing, stratified into primary or secondary care, and physical or mental healthcare. We will estimate the burden of healthcare use attributable to SARS-CoV-2 infections in the 12 months after testing using a matched cohort study of RT-PCR positive, negative or untested CYP matched on testing date, with adjustment for confounders. We will identify factors associated with higher healthcare needs in the 12 months following SARS-CoV-2 infection using an unmatched cohort of RT-PCR positive CYP. Multivariable logistic regression and machine learning approaches will identify risk factors for high healthcare use and characterise patterns of healthcare use post infection.
Ethics and dissemination: This study was approved by the South-Central Oxford C Health Research Authority Ethics Committee (13/SC/0149). Findings will be preprinted and published in peer-reviewed journals. Analysis code and code lists will be available through public GitHub repositories and OpenCodelists with meta-data via HDR-UK Innovation Gateway.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 966.2KB, Terms of use)
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- Publisher copy:
- 10.1136/bmjopen-2022-063271
Authors
- Funder identifier:
- https://ror.org/029chgv08
- Grant:
- 215091/Z/18/Z
- Funder identifier:
- https://ror.org/03x94j517
- Grant:
- MC_PC_19004
- Funder identifier:
- https://ror.org/001aqnf71
- Grant:
- MR/V015737/1
- MC_PC_20059
- Publisher:
- BMJ Publishing Group
- Journal:
- BMJ Open More from this journal
- Volume:
- 12
- Issue:
- 11
- Article number:
- e063271
- Place of publication:
- England
- Publication date:
- 2022-11-10
- Acceptance date:
- 2022-10-20
- DOI:
- EISSN:
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2044-6055
- Pmid:
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36356998
- Language:
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English
- Keywords:
- Pubs id:
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1305003
- Local pid:
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pubs:1305003
- Deposit date:
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2024-09-27
Terms of use
- Copyright holder:
- Swann et al
- Copyright date:
- 2022
- Rights statement:
- © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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