The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
A systematic review and meta-analysis of geographic differences in comorbidities and associated severity and mortality among individuals with COVID-19
Open accessAbstract
Several comorbidities have been shown to be associated with coronavirus disease 2019 (COVID-19) related severity and mortality. However, considerable variation in the prevalence estimates of comorbidities and their effects on COVID-19 morbidity and mortality have been observed in prior studies. This systematic review and meta-analysis aimed to determine geographical, age, and gender related differences in the prevalence of comorbidities and associated severity and mortality rates among COVID-19 patients. We conducted a search using PubMed, Scopus, and EMBASE to include all COVID-19 studies published between January 1st, 2020 to July 24th, 2020 reporting comorbidities with severity or mortality. We included studies reporting the confirmed diagnosis of COVID-19 on human patients that also provided information on comorbidities or disease outcomes. We used DerSimonian and Laird random effects method for calculating estimates. Of 120 studies with 125,446 patients, the most prevalent comorbidity was hypertension (32%), obesity (25%), diabetes (18%), and cardiovascular disease (16%) while chronic kidney or other renal diseases (51%, 44%), cerebrovascular accident (43%, 44%), and cardiovascular disease (44%, 40%) patients had more COVID-19 severity and mortality respectively. Considerable variation in the prevalence of comorbidities and associated disease severity and mortality in different geographic regions was observed. The highest mortality was observed in studies with Latin American and European patients with any medical condition, mostly older adults (≥ 65 years), and predominantly male patients. Although the US studies observed the highest prevalence of comorbidities in COVID-19 patients, the severity of COVID-19 among each comorbid condition was highest in Asian studies whereas the mortality was highest in the European and Latin American countries. Risk stratification and effective control strategies for the COVID-19 should be done according to comorbidities, age, and gender differences specific to geographical location.
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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
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000097 · Cited by 64,042
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA Statement.
(2009) · PubMed Central · Cited by 44,859
Meta-analysis in clinical trials
DerSimonian, R., & Laird, N. (1986) · Controlled Clinical Trials · doi:10.1016/0197-2456(86)90046-2 · Cited by 39,552
Wang, G., Ma, R., Qiao, G., Wada, K., Aizawa, Y., & Satoh, T. (2015) · Integrative Medicine International · doi:10.1159/000368408 · Cited by 18,762
Yang, X., Yu, Y., Xu, J., Shu, H., Xia, J., Liu, H., Wu, Y., Zhang, L., Yu, Z., Fang, M., et al. (2020) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(20)30079-5 · Cited by 10,066
Wu, C., Chen, X., Cai, Y., Xia, J., Zhou, X., Xu, S., Huang, H., Zhang, L., Zhou, X., Du, C., et al. (2020) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2020.0994 · Cited by 8,249
Slim, K., Nini, E., Forestier, D., Kwiatkowski, F., Panis, Y., & Chipponi, J. (2003) · ANZ Journal of Surgery · doi:10.1046/j.1445-2197.2003.02748.x · Cited by 7,603
Sex differences in immune responses
Klein, S. L., & Flanagan, K. L. (2016) · Nature Reviews Immunology · doi:10.1038/nri.2016.90 · Cited by 6,370
Ruan, Q., Yang, K., Wang, W., Jiang, L., & Song, J. (2020) · Intensive Care Medicine · doi:10.1007/s00134-020-05991-x · Cited by 4,898
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Metadata
- Authors
- Thakur, B., Dubey, P., Benitez, J., Torres, J. P., Reddy, S., Shokar, N., Aung, K., Mukherjee, D., & Dwivedi, A. K.
- Affiliations
- Texas Tech University Health Sciences Center (US)
- Texas Tech University (US)
- Corresponding author
- Dwivedi, Alok Kumar
- ORCID
- Venue
- Scientific Reports
- Published
- 2021-04-20
- Volume
- 11
- Issue
- 1
- Pages
- 8562-8562
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC8058064
- OpenAlex
- W3152572687
- Cited by
- 284
- References
- 59
- Citations by year
- 2026: 11 · 2025: 17 · 2024: 53 · 2023: 66 · 2022: 95 · 2021: 41
- FWCI
- 7.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Clinical Neurology › Long-Term Effects of COVID-19
- Keywords
- COVID-19 pandemic, comorbidities, disease severity, mortality, regional variation, hypertension, obesity, diabetes mellitus, cardiovascular disease, chronic kidney disease, cerebrovascular disease, age differences, sex differences, risk stratification
- MeSH terms
- COVID-19, Age Factors, Aged, Aged, 80 and over, Asia, Europe, Female, Humans, Latin America, Male, Severity of Illness Index, Sex Factors, Comorbidity, Prevalence
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