The DOI matches its Crossref record; 61 of the 64 references it cites were found in a registry or an index.
Cancer associates with risk and severe events of COVID ‐19: A systematic review and meta‐analysis
Open accessAbstract
Evidence is mounting to indicate that cancer patients may have more likelihood of having coronavirus disease 2019 (COVID-19) but lack consistency. A robust estimate is urgently needed to convey appropriate information to the society and the public, in the time of ongoing COVID-19 pandemic. We performed a systematic review and meta-analysis through a comprehensive literature search in major databases in English and Chinese, and two investigators conducted publication selection and data extraction independently. A meta-analysis was used to obtain estimates of pooled prevalence of cancer in patients with COVID-19 and determine the association of cancer with severe events, after assessment of potential heterogeneity, publication bias, and correction for the estimates when necessary. Total 38 studies comprising 7094 patients with COVID-9 were included; the pooled prevalence of cancer was estimated at 2.3% (95% confidence limit [CL] [0.018, 0.029]; P < .001) overall and 3.2% (95% CL [0.023, 0.041]; P < .001) in Hubei province; the corresponding estimates were 1.4% and 1.9% after correction for publication bias; cancer was significantly associated with the events of severe cases (odds ratio [OR] = 2.20, 95% CL [1.53, 3.17]; P < .001) and death (OR = 2.97, 95% CL [1.48, 5.96]; P = .002) in patients with COVID-19, there was no significant heterogeneity and a minimal publication bias. We conclude that cancer comorbidity is associated with the risk and severe events of COVID-19; special measures should be taken for individuals with cancer.
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“This is particularly true of older adults with cancer, who may be among the most vulnerable to the effects of COVID-19 due to their immunocompromised status and increased presence of comorbidities [1,2,3,4,5].”
From The Psychosocial Impact of COVID-19 on Older Adults with Cancer: A Rapid Review (2022) · cites this paper as [4]
In the citing paper
“Older adults diagnosed with both cancer and COVID-19 face increased mortality, hospitalization, and ICU admission [3,4,5].”
From The Psychosocial Impact of COVID-19 on Older Adults with Cancer: A Rapid Review (2022) · cites this paper as [4]
Citations
Cites
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group (2009) · BMJ · doi:10.1136/bmj.b2535 · Cited by 83,823
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Self-citationPreferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.
(2009) · PubMed Central · Cited by 44,859
Quantifying heterogeneity in a meta‐analysis
Higgins, J. P. T., & Thompson, S. G. (2002) · Statistics in Medicine · doi:10.1002/sim.1186 · Cited by 36,763
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
Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30211-7 · Cited by 21,415
Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., et al. (2020) · JAMA · doi:10.1001/jama.2020.1585 · Cited by 20,148
La certeza de lo impredecible: Cultura Educación y Sociedad en tiempos de COVID19
Martínez González, M. (2020) · DSPACE System - Metalibrary (University of the Coast) · Cited by 18,463
Wu, Z., & McGoogan, J. M. (2020) · JAMA · doi:10.1001/jama.2020.2648 · Cited by 17,016
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3 of the 64 references couldn’t be matched to a known paper.
Research works printed but not linked: 6
- Fang XW, 2020, Clinical characteristics and treatment analysis of 79 patients infected with SARS‐CoV‐2, Chin Pharmacol Bull, 36, 453
- Li D, 2020, Clinical characteristics of 80 patients with COVID‐19 in Zhozhou city, Chin J Infect Control, 19, 227
- Li YL, 2020, Comparative study for clinical features between COVID‐19 patients with conventional type and heavy/critical type, Pract J Cardiac Cereb Pneumal Vasc Dis, 28, 14
- Sun DF, 2020, Clinical analysis of 30 patients infected with SARS‐CoV‐2, Zhejiang Clin Med, 22, 354
- Wang F, 2020, Clinical characteristics of 29 patients with COVID‐19 in Ningxia city, Ningxia Med J, 42, 263
- Tuo JY, 2016, Cancer incidence and mortality in cancer registries in Hubei Province, 2012, Cancer Res Prev Treat, 43, 974
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Mesas, A. E., Cavero‐Redondo, I., Álvarez‐Bueno, C., Cabrera, M. A. S., de Andrade, S. M., Sequí‐Domínguez, I., & Martínez‐Vizcaíno, V. (2020) · PLoS ONE · doi:10.1371/journal.pone.0241742 · Cited by 245
Treskova, M., Haas, L., Reda, S. A., Pilic, A., Borodova, A., Karimi, K., Koch, J., Nygren, T. M., Scholz, S. M., Schönfeld, V., et al. (2021) · BMC Medicine · doi:10.1186/s12916-021-02058-6 · Cited by 219
Galmiche, S., Nguyen, L. B. L., Tartour, É., de Lamballerie, X. N., Wittkop, L., Loubet, P., & Launay, O. (2021) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2021.09.036 · Cited by 207
Digital Health Applications in Oncology: An Opportunity to Seize
Parikh, R. B., Basen-Enquist, K. M., Bradley, C. J., Estrin, D. L., Levy, M. A., Lichtenfeld, J. L., Malin, B., McGraw, D., Meropol, N. J., Oyer, R. A., et al. (2022) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djac108 · Cited by 81
Li, H., Zhang, Y., Shu, H., Lv, W., Su, C., & Nie, F. (2021) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2021.121412 · Cited by 68
Metadata
- Authors
- Tian, Y., Qiu, X., Wang, C., Zhao, J., Jiang, X., Niu, W., Huang, J., & Zhang, F.
- Affiliations
- Beijing University of Chinese Medicine (CN)
- China-Japan Friendship Hospital (CN)
- Peking University (CN)
- Corresponding author
- Niu, Wenquan
- Huang, Jinchang
- Zhang, Fengyu
- Venue
- International Journal of Cancer
- Publisher
- Wiley
- Published
- 2020-08-15
- Volume
- 148
- Issue
- 2
- Pages
- 363-374
- ISSN
- 0020-7136 (ISSN-L)
- 1097-0215
- PMC
- PMC7404763
- OpenAlex
- W3042427819
- Cited by
- 129
- References
- 64
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 6 · 2023: 22 · 2022: 42 · 2021: 41 · 2020: 7
- FWCI
- 3.01
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Cancer (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (81573736)
- Topics
- Health Sciences › Medicine › Oncology › COVID-19 and healthcare impacts
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Keywords
- COVID-19 pandemic, cancer comorbidity, disease severity, mortality, risk factors, Hubei Province, publication bias
- MeSH terms
- COVID-19, SARS-CoV-2, Humans, Neoplasms, Risk Factors, Severity of Illness Index, Comorbidity, Prevalence, Risk Assessment, Pandemics
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