Detecting changes in population trends in infection surveillance using community SARS-CoV-2 prevalence as an exemplar.

Autor:	Pritchard E; Nuffield Department of Medicine, University of Oxford, Oxford, UK.; The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK., Vihta KD; Nuffield Department of Medicine, University of Oxford, Oxford, UK.; The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.; Department of Engineering, University of Oxford, Oxford, UK., Eyre DW; The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.; The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.; Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, UK., Hopkins S; The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.; Healthcare-Associated Infection and Antimicrobial Resistance Division, Public Health England, London, UK.; National Institute for Health Research, Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK., Peto TEA; Nuffield Department of Medicine, University of Oxford, Oxford, UK.; The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.; The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.; Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK., Matthews PC; Nuffield Department of Medicine, University of Oxford, Oxford, UK.; Francis Crick Institute, London, UK.; Division of Infection and Immunity, University College London, London UK.; Department of Infection, University College London Hospitals, London, UK., Stoesser N; Nuffield Department of Medicine, University of Oxford, Oxford, UK.; The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.; The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK.; Department of Infectious Diseases and Microbiology, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK., Studley R; Office for National Statistics, Newport, UK., Rourke E; Office for National Statistics, Newport, UK., Diamond I; Office for National Statistics, Newport, UK., Pouwels KB; The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.; Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK., Walker AS; Nuffield Department of Medicine, University of Oxford, Oxford, UK.; The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford, Oxford, UK.; The National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK., Infection Survey Team TC
Jazyk:	angličtina
Zdroj:	American journal of epidemiology [Am J Epidemiol] 2024 May 29. Date of Electronic Publication: 2024 May 29.
DOI:	10.1093/aje/kwae091
Abstrakt:	Detecting and quantifying changes in growth rates of infectious diseases is vital to informing public health strategy and can inform policymakers' rationale for implementing or continuing interventions aimed at reducing impact. Substantial changes in SARS-CoV-2 prevalence with emergence of variants provides opportunity to investigate different methods to do this. We included PCR results from all participants in the UK's COVID-19 Infection Survey between August 2020-June 2022. Change-points for growth rates were identified using iterative sequential regression (ISR) and second derivatives of generalised additive models (GAMs). Consistency between methods and timeliness of detection were compared. Of 8,799,079 visits, 147,278 (1.7%) were PCR-positive. Change-points associated with emergence of major variants were estimated to occur a median 4 days earlier (IQR 0-8) in GAMs versus ISR. When estimating recent change-points using successive data periods, four change-points (4/96) identified by GAMs were not found when adding later data or by ISR. Change-points were detected 3-5 weeks after they occurred in both methods but could be detected earlier within specific subgroups. Change-points in growth rates of SARS-CoV-2 can be detected in near real-time using ISR and second derivatives of GAMs. To increase certainty about changes in epidemic trajectories both methods could be run in parallel. (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)
Databáze:	MEDLINE
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