The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study
Open accessAbstract
Background Data for front-line health-care workers and risk of COVID-19 are limited. We sought to assess risk of COVID-19 among front-line health-care workers compared with the general community and the effect of personal protective equipment (PPE) on risk. Methods We did a prospective, observational cohort study in the UK and the USA of the general community, including front-line health-care workers, using self-reported data from the COVID Symptom Study smartphone application (app) from March 24 (UK) and March 29 (USA) to April 23, 2020. Participants were voluntary users of the app and at first use provided information on demographic factors (including age, sex, race or ethnic background, height and weight, and occupation) and medical history, and subsequently reported any COVID-19 symptoms. We used Cox proportional hazards modelling to estimate multivariate-adjusted hazard ratios (HRs) of our primary outcome, which was a positive COVID-19 test. The COVID Symptom Study app is registered with ClinicalTrials.gov, NCT04331509. Findings Among 2 035 395 community individuals and 99 795 front-line health-care workers, we recorded 5545 incident reports of a positive COVID-19 test over 34 435 272 person-days. Compared with the general community, front-line health-care workers were at increased risk for reporting a positive COVID-19 test (adjusted HR 11·61, 95% CI 10·93–12·33). To account for differences in testing frequency between front-line health-care workers and the general community and possible selection bias, an inverse probability-weighted model was used to adjust for the likelihood of receiving a COVID-19 test (adjusted HR 3·40, 95% CI 3·37–3·43). Secondary and post-hoc analyses suggested adequacy of PPE, clinical setting, and ethnic background were also important factors. Interpretation In the UK and the USA, risk of reporting a positive test for COVID-19 was increased among front-line health-care workers. Health-care systems should ensure adequate availability of PPE and develop additional strategies to protect health-care workers from COVID-19, particularly those from Black, Asian, and minority ethnic backgrounds. Additional follow-up of these observational findings is needed. Funding Zoe Global, Wellcome Trust, Engineering and Physical Sciences Research Council, National Institutes of Health Research, UK Research and Innovation, Alzheimer's Society, National Institutes of Health, National Institute for Occupational Safety and Health, and Massachusetts Consortium on Pathogen Readiness.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Clinical characteristics of coronavirus disease 2019 in China
Guan, W.-J., Ni, Z.-Y., Hu, Y., Liang, W.-H., Ou, C.-Q., He, J.-X., Liu, L., Shan, H., Lei, C.-L., Hui, D. S. C., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2002032 · Cited by 30,246
Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30566-3 · Cited by 27,263
Garg, S., Kim, L., Whitaker, M., O’Halloran, A., Cummings, C., Holstein, R., Prill, M., Chai, S. J., Kirley, P. D., Alden, N. B., et al. (2020) · MMWR. Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm6915e3 · Cited by 2,335
Real-time tracking of self-reported symptoms to predict potential COVID-19
Menni, C., Valdes, A. M., Freidin, M. B., Sudre, C. H., Nguyen, L. H., Drew, D. A., Ganesh, S., Varsavsky, T., Cardoso, M. J., El-Sayed Moustafa, J. S., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0916-2 · Cited by 1,517
Self-citationDigital disease detection - harnessing the Web for public health surveillance
Brownstein, J. S., Freifeld, C. C., & Madoff, L. C. (2009) · New England Journal of Medicine · doi:10.1056/nejmp0900702 · Cited by 819
Sourcing Personal Protective Equipment During the COVID-19 Pandemic
Livingston, E., Desai, A., & Berkwits, M. (2020) · JAMA · doi:10.1001/jama.2020.5317 · Cited by 637
Rapid implementation of mobile technology for real-time epidemiology of COVID-19
Drew, D. A., Nguyen, L. H., Steves, C. J., Menni, C., Freydin, M., Varsavsky, T., Sudre, C. H., Cardoso, M. J., Ourselin, S., Wolf, J., et al. (2020) · Science · doi:10.1126/science.abc0473 · Cited by 389
Self-citationEffectiveness of N95 Respirator Decontamination and Reuse against SARS-CoV-2 Virus
Fischer, R. J., Morris, D. H., van Doremalen, N., Sarchette, S., Matson, M. J., Bushmaker, T., Yinda, C. K., Seifert, S. N., Gamble, A., Williamson, B. N., et al. (2020) · Emerging Infectious Diseases · doi:10.3201/eid2609.201524 · Cited by 310
Cited by
COVID-19 Vaccine Hesitancy Worldwide: A Concise Systematic Review of Vaccine Acceptance Rates
Sallam, M. (2021) · Vaccines · doi:10.3390/vaccines9020160 · Cited by 2,128
De Kock, J. H., Latham, H. A., Leslie, S. J., Grindle, M., Munoz, S.-A., Ellis, L., Polson, R., & O’Malley, C. M. (2021) · BMC Public Health · doi:10.1186/s12889-020-10070-3 · Cited by 940
Antonelli, M., Penfold, R. S., Merino, J., Sudre, C. H., Molteni, E., Berry, S., Canas, L. S., Graham, M. S., Klaser, K., Modat, M., et al. (2022) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(21)00460-6 · Cited by 843
Self-citationPrasad, K., McLoughlin, C., Stillman, M., Poplau, S., Goelz, E., Taylor, S., Nankivil, N., Brown, R., Linzer, M., Cappelucci, K., et al. (2021) · EClinicalMedicine · doi:10.1016/j.eclinm.2021.100879 · Cited by 541
Li, Y., Scherer, N., Felix, L., & Kuper, H. (2021) · PLOS ONE · doi:10.1371/journal.pone.0246454 · Cited by 536
Metadata
- Authors
- Nguyen, L. H., Drew, D. A., Graham, M. S., Joshi, A. D., Guo, C.-G., Ma, W., Mehta, R. S., Warner, E. T., Sikavi, D. R., Lo, C.-H., Kwon, S., Song, M., Mucci, L. A., Stampfer, M. J., Willett, W. C., Eliassen, A. H., Hart, J. E., Chavarro, J. E., Rich-Edwards, J. W., Davies, R., Capdevila, J., Lee, K. A., Lochlainn, M. N., Varsavsky, T., Sudre, C. H., Cardoso, M. J., Wolf, J., Spector, T. D., Ourselin, S., Steves, C. J., Chan, A. T., Albert, C. M., Andreotti, G., Bala, B., Balasubramanian, B. A., Beane-Freeman, L. E., Brownstein, J. S., Bruinsma, F. J., Coresh, J., Costa, R., Cowan, A. N., Deka, A., Deming-Halverson, S. L., Elena Martinez, M., Ernst, M. E., Figueiredo, J. C., Fortuna, P., Franks, P. W., Freeman, L. B., Gardner, C. D., Ghobrial, I. M., Haiman, C. A., Hall, J. E., Kang, J. H., Kirpach, B., Koenen, K. C., Kubzansky, L. D., Lacey, Jr., Le Marchand, L., Lin, X., Lutsey, P., Marinac, C. R., Martinez, M. E., Milne, R. L., Murray, A. M., Nash, D., Palmer, J. R., Patel, A. V., Pierce, E., Robertson, M. M., Rosenberg, L., Sandler, D. P., Schurman, S. H., Sewalk, K., Sharma, S. V., Sidey-Gibbons, C. J., Slevin, L., Smoller, J. W., Steves, C. J., Tiirikainen, M. I., Weiss, S. T., Wilkens, L. R., & Zhang, F.
- Venue
- The Lancet Public Health
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 5
- Issue
- 9
- Pages
- e475-e483
- ISSN
- 2468-2667 (ISSN-L)
- OpenAlex
- W3046400528
- Cited by
- 2,395
- References
- 8
- FWCI
- 79.10
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study. https://app.cochat.ai/papers/10.1016/s2468-2667(20)30164-x
- The paper
- Nguyen, L. H., Drew, D. A., Graham, M. S., Joshi, A. D., Guo, C.-G., Ma, W., Mehta, R. S., Warner, E. T., Sikavi, D. R., Lo, C.-H., Kwon, S., Song, M., Mucci, L. A., Stampfer, M. J., Willett, W. C., Eliassen, A. H., Hart, J. E., Chavarro, J. E., Rich-Edwards, J. W., . . . Zhang, F. (2020). Risk of COVID-19 among front-line health-care workers and the general community: a prospective cohort study. The Lancet Public Health. https://doi.org/10.1016/s2468-2667(20)30164-x
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.