The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Comparison of seroprevalence of SARS-CoV-2 infections with cumulative and imputed COVID-19 cases: Systematic review
Open accessAbstract
Background: Accurate seroprevalence estimates of SARS-CoV-2 in different populations could clarify the extent to which current testing strategies are identifying all active infection, and hence the true magnitude and spread of the infection. Our primary objective was to identify valid seroprevalence studies of SARS-CoV-2 infection and compare their estimates with the reported, and imputed, COVID-19 case rates within the same population at the same time point. Methods: We searched PubMed, Embase, the Cochrane COVID-19 trials, and Europe-PMC for published studies and pre-prints that reported anti-SARS-CoV-2 IgG, IgM and/or IgA antibodies for serosurveys of the general community from 1 Jan to 12 Aug 2020. Results: Of the 2199 studies identified, 170 were assessed for full text and 17 studies representing 15 regions and 118,297 subjects were includable. The seroprevalence proportions in 8 studies ranged between 1%-10%, with 5 studies under 1%, and 4 over 10%—from the notably hard-hit regions of Gangelt, Germany; Northwest Iran; Buenos Aires, Argentina; and Stockholm, Sweden. For seropositive cases who were not previously identified as COVID-19 cases, the majority had prior COVID-like symptoms. The estimated seroprevalences ranged from 0.56–717 times greater than the number of reported cumulative cases–half of the studies reported greater than 10 times more SARS-CoV-2 infections than the cumulative number of cases. Conclusions: The findings show SARS-CoV-2 seroprevalence is well below “herd immunity” in all countries studied. The estimated number of infections, however, were much greater than the number of reported cases and deaths in almost all locations. The majority of seropositive people reported prior COVID-like symptoms, suggesting that undertesting of symptomatic people may be causing a substantial under-ascertainment of SARS-CoV-2 infections.
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QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies
Whiting, P. F., Rutjes, A. W. S., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., Leeflang, M. M. G., Sterne, J. A. C., Bossuyt, P. M. M., & the QUADAS-2 Group* (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-8-201110180-00009 · Cited by 14,199
Antibody responses to SARS-CoV-2 in patients with COVID-19
Long, Q.-X., Liu, B.-Z., Deng, H.-J., Wu, G.-C., Deng, K., Chen, Y.-K., Liao, P., Qiu, J.-F., Lin, Y., Cai, X.-F., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0897-1 · Cited by 2,876
Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections
Long, Q.-X., Tang, X.-J., Shi, Q.-L., Li, Q., Deng, H.-J., Yuan, J., Hu, J.-L., Xu, W., Zhang, Y., Lv, F.-J., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0965-6 · Cited by 2,686
Pollán, M., Pérez-Gómez, B., Pastor-Barriuso, R., Oteo, J., Hernán, M. A., Pérez-Olmeda, M., Sanmartín, J. L., Fernández-García, A., Cruz, I., Fernández de Larrea, N., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)31483-5 · Cited by 1,639
High Contagiousness and Rapid Spread of Severe Acute Respiratory Syndrome Coronavirus 2
Sanche, S., Lin, Y. T., Xu, C., Romero-Severson, E., Hengartner, N., & Ke, R. (2020) · Emerging Infectious Diseases · doi:10.3201/eid2607.200282 · Cited by 1,619
Humoral Immune Response to SARS-CoV-2 in Iceland
Gudbjartsson, D. F., Norddahl, G. L., Melsted, P., Gunnarsdottir, K., Holm, H., Eythorsson, E., Arnthorsson, A. O., Helgason, D., Bjarnadottir, K., Ingvarsson, R. F., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2026116 · Cited by 903
Levin, A. T., Hanage, W. P., Owusu-Boaitey, N., Cochran, K. B., Walsh, S. P., & Meyerowitz-Katz, G. (2020) · European Journal of Epidemiology · doi:10.1007/s10654-020-00698-1 · Cited by 844
A full systematic review was completed in 2 weeks using automation tools: a case study
Clark, J., Glasziou, P., Del Mar, C., Bannach-Brown, A., Stehlik, P., & Scott, A. M. (2020) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2020.01.008 · Cited by 687
Self-citationSeroprevalence of antibodies to SARS-CoV-2 in 10 sites in the United States, March 23–May 12, 2020
Havers, F. P., Reed, C., Lim, T., Montgomery, J. M., Klena, J. D., Hall, A. J., Fry, A. M., Cannon, D. L., Chiang, C.-F., Gibbons, A., et al. (2020) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2020.4130 · Cited by 647
What reinfections mean for COVID-19
Iwasaki, A. (2021) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30783-0 · Cited by 230
Cited by
Levin, A. T., Hanage, W. P., Owusu-Boaitey, N., Cochran, K. B., Walsh, S. P., & Meyerowitz-Katz, G. (2020) · European Journal of Epidemiology · doi:10.1007/s10654-020-00698-1 · Cited by 844
(2020) · doi:10.3138/jammi-2020-0030 · Cited by 664
COVID-19 herd immunity: where are we?
Fontanet, A., & Cauchemez, S. (2020) · Nature Reviews Immunology · doi:10.1038/s41577-020-00451-5 · Cited by 537
Global seroprevalence of SARS-CoV-2 antibodies: A systematic review and meta-analysis
Bobrovitz, N., Arora, R. K., Cao, C., Boucher, E., Liu, M., Donnici, C., Yanes-Lane, M., Whelan, M., Perlman-Arrow, S., Chen, J., et al. (2021) · PLOS ONE · doi:10.1371/journal.pone.0252617 · Cited by 246
Levin, A. T., Owusu-Boaitey, N., Pugh, S., Fosdick, B. K., Zwi, A. B., Malani, A., Soman, S., Besançon, L., Kashnitsky, I., Ganesh, S., et al. (2022) · BMJ Global Health · doi:10.1136/bmjgh-2022-008477 · Cited by 208
Metadata
- Authors
- Byambasuren, O., Dobler, C. C., Bell, K., Rojas, D. P., Clark, J., McLaws, M.-L., & Glasziou, P.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2021-04-02
- Volume
- 16
- Issue
- 4
- Pages
- e0248946
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W3139820633
- Cited by
- 250
- References
- 11
- FWCI
- 5.50
- Type
- journal article
- Language
- en
- Open access
- gold
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- Byambasuren, O., Dobler, C. C., Bell, K., Rojas, D. P., Clark, J., McLaws, M.-L., & Glasziou, P. (2021). Comparison of seroprevalence of SARS-CoV-2 infections with cumulative and imputed COVID-19 cases: Systematic review. PLOS ONE. https://doi.org/10.1371/journal.pone.0248946
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