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Predictive symptoms for COVID-19 in the community: REACT-1 study of over 1 million people.
PLoS Med. 2021 09; 18(9):e1003777.PM

Abstract

BACKGROUND

Rapid detection, isolation, and contact tracing of community COVID-19 cases are essential measures to limit the community spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to identify a parsimonious set of symptoms that jointly predict COVID-19 and investigated whether predictive symptoms differ between the B.1.1.7 (Alpha) lineage (predominating as of April 2021 in the US, UK, and elsewhere) and wild type.

METHODS AND FINDINGS

We obtained throat and nose swabs with valid SARS-CoV-2 PCR test results from 1,147,370 volunteers aged 5 years and above (6,450 positive cases) in the REal-time Assessment of Community Transmission-1 (REACT-1) study. This study involved repeated community-based random surveys of prevalence in England (study rounds 2 to 8, June 2020 to January 2021, response rates 22%-27%). Participants were asked about symptoms occurring in the week prior to testing. Viral genome sequencing was carried out for PCR-positive samples with N-gene cycle threshold value < 34 (N = 1,079) in round 8 (January 2021). In univariate analysis, all 26 surveyed symptoms were associated with PCR positivity compared with non-symptomatic people. Stability selection (1,000 penalized logistic regression models with 50% subsampling) among people reporting at least 1 symptom identified 7 symptoms as jointly and positively predictive of PCR positivity in rounds 2-7 (June to December 2020): loss or change of sense of smell, loss or change of sense of taste, fever, new persistent cough, chills, appetite loss, and muscle aches. The resulting model (rounds 2-7) predicted PCR positivity in round 8 with area under the curve (AUC) of 0.77. The same 7 symptoms were selected as jointly predictive of B.1.1.7 infection in round 8, although when comparing B.1.1.7 with wild type, new persistent cough and sore throat were more predictive of B.1.1.7 infection while loss or change of sense of smell was more predictive of the wild type. The main limitations of our study are (i) potential participation bias despite random sampling of named individuals from the National Health Service register and weighting designed to achieve a representative sample of the population of England and (ii) the necessary reliance on self-reported symptoms, which may be prone to recall bias and may therefore lead to biased estimates of symptom prevalence in England.

CONCLUSIONS

Where testing capacity is limited, it is important to use tests in the most efficient way possible. We identified a set of 7 symptoms that, when considered together, maximize detection of COVID-19 in the community, including infection with the B.1.1.7 lineage.

Authors+Show Affiliations

Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom. MRC Centre for Environment and Health, Imperial College London, London, United Kingdom. Royal Surrey NHS Foundation Trust, Guildford, United Kingdom.Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom. MRC Centre for Environment and Health, Imperial College London, London, United Kingdom.Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom. MRC Centre for Environment and Health, Imperial College London, London, United Kingdom.MRC Centre for Global infectious Disease Analysis, Imperial College London, London, United Kingdom. Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom.MRC Centre for Global infectious Disease Analysis, Imperial College London, London, United Kingdom. Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom.Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom. Imperial College Healthcare NHS Trust, London, United Kingdom. National Institute for Health Research Imperial Biomedical Research Centre, London, United Kingdom.Imperial College Healthcare NHS Trust, London, United Kingdom. National Institute for Health Research Imperial Biomedical Research Centre, London, United Kingdom. Department of Infectious Disease, Imperial College London, London, United Kingdom.Imperial College Healthcare NHS Trust, London, United Kingdom. National Institute for Health Research Imperial Biomedical Research Centre, London, United Kingdom. Institute of Global Health Innovation, Imperial College London, London, United Kingdom.Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom. MRC Centre for Environment and Health, Imperial College London, London, United Kingdom. Health Data Research UK London, Imperial College London, London, United Kingdom.Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom. MRC Centre for Environment and Health, Imperial College London, London, United Kingdom. Imperial College Healthcare NHS Trust, London, United Kingdom. National Institute for Health Research Imperial Biomedical Research Centre, London, United Kingdom. Health Data Research UK London, Imperial College London, London, United Kingdom. UK Dementia Research Institute, Imperial College London, London, United Kingdom.

Pub Type(s)

Comparative Study
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, P.H.S.

Language

eng

PubMed ID

34582457

Citation

Elliott, Joshua, et al. "Predictive Symptoms for COVID-19 in the Community: REACT-1 Study of Over 1 Million People." PLoS Medicine, vol. 18, no. 9, 2021, pp. e1003777.
Elliott J, Whitaker M, Bodinier B, et al. Predictive symptoms for COVID-19 in the community: REACT-1 study of over 1 million people. PLoS Med. 2021;18(9):e1003777.
Elliott, J., Whitaker, M., Bodinier, B., Eales, O., Riley, S., Ward, H., Cooke, G., Darzi, A., Chadeau-Hyam, M., & Elliott, P. (2021). Predictive symptoms for COVID-19 in the community: REACT-1 study of over 1 million people. PLoS Medicine, 18(9), e1003777. https://doi.org/10.1371/journal.pmed.1003777
Elliott J, et al. Predictive Symptoms for COVID-19 in the Community: REACT-1 Study of Over 1 Million People. PLoS Med. 2021;18(9):e1003777. PubMed PMID: 34582457.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Predictive symptoms for COVID-19 in the community: REACT-1 study of over 1 million people. AU - Elliott,Joshua, AU - Whitaker,Matthew, AU - Bodinier,Barbara, AU - Eales,Oliver, AU - Riley,Steven, AU - Ward,Helen, AU - Cooke,Graham, AU - Darzi,Ara, AU - Chadeau-Hyam,Marc, AU - Elliott,Paul, Y1 - 2021/09/28/ PY - 2021/05/10/received PY - 2021/08/20/accepted PY - 2021/9/28/entrez PY - 2021/9/29/pubmed PY - 2021/10/9/medline SP - e1003777 EP - e1003777 JF - PLoS medicine JO - PLoS Med VL - 18 IS - 9 N2 - BACKGROUND: Rapid detection, isolation, and contact tracing of community COVID-19 cases are essential measures to limit the community spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to identify a parsimonious set of symptoms that jointly predict COVID-19 and investigated whether predictive symptoms differ between the B.1.1.7 (Alpha) lineage (predominating as of April 2021 in the US, UK, and elsewhere) and wild type. METHODS AND FINDINGS: We obtained throat and nose swabs with valid SARS-CoV-2 PCR test results from 1,147,370 volunteers aged 5 years and above (6,450 positive cases) in the REal-time Assessment of Community Transmission-1 (REACT-1) study. This study involved repeated community-based random surveys of prevalence in England (study rounds 2 to 8, June 2020 to January 2021, response rates 22%-27%). Participants were asked about symptoms occurring in the week prior to testing. Viral genome sequencing was carried out for PCR-positive samples with N-gene cycle threshold value < 34 (N = 1,079) in round 8 (January 2021). In univariate analysis, all 26 surveyed symptoms were associated with PCR positivity compared with non-symptomatic people. Stability selection (1,000 penalized logistic regression models with 50% subsampling) among people reporting at least 1 symptom identified 7 symptoms as jointly and positively predictive of PCR positivity in rounds 2-7 (June to December 2020): loss or change of sense of smell, loss or change of sense of taste, fever, new persistent cough, chills, appetite loss, and muscle aches. The resulting model (rounds 2-7) predicted PCR positivity in round 8 with area under the curve (AUC) of 0.77. The same 7 symptoms were selected as jointly predictive of B.1.1.7 infection in round 8, although when comparing B.1.1.7 with wild type, new persistent cough and sore throat were more predictive of B.1.1.7 infection while loss or change of sense of smell was more predictive of the wild type. The main limitations of our study are (i) potential participation bias despite random sampling of named individuals from the National Health Service register and weighting designed to achieve a representative sample of the population of England and (ii) the necessary reliance on self-reported symptoms, which may be prone to recall bias and may therefore lead to biased estimates of symptom prevalence in England. CONCLUSIONS: Where testing capacity is limited, it is important to use tests in the most efficient way possible. We identified a set of 7 symptoms that, when considered together, maximize detection of COVID-19 in the community, including infection with the B.1.1.7 lineage. SN - 1549-1676 UR - https://www.unboundmedicine.com/medline/citation/34582457/Predictive_symptoms_for_COVID_19_in_the_community:_REACT_1_study_of_over_1_million_people_ L2 - https://dx.plos.org/10.1371/journal.pmed.1003777 DB - PRIME DP - Unbound Medicine ER -