Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis
Open accessAbstract
BACKGROUND: An outbreak of coronavirus disease 2019 (COVID-19) occurred in Wuhan, China; the epidemic is more widespread than initially estimated, with cases now confirmed in multiple countries. AIMS: The aim of this meta-analysis was to assess the prevalence of comorbidities in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected patients and the risk of underlying diseases in severe patients compared to non-severe patients. METHODS: A literature search was conducted using the databases PubMed, EMBASE, and Web of Science through February 25, 2020. Odds ratios (ORs) and 95% confidence intervals (CIs) were pooled using random-effects models. RESULTS: Seven studies were included in the meta-analysis, including 1 576 infected patients. The results showed the most prevalent clinical symptom was fever (91.3%, 95% CI: 86-97%), followed by cough (67.7%, 95% CI: 59-76%), fatigue (51.0%, 95% CI: 34-68%) and dyspnea (30.4%, 95% CI: 21-40%). The most prevalent comorbidities were hypertension (21.1%, 95% CI: 13.0-27.2%) and diabetes (9.7%, 95% CI: 7.2-12.2%), followed by cardiovascular disease (8.4%, 95% CI: 3.8-13.8%) and respiratory system disease (1.5%, 95% CI: 0.9-2.1%). When compared between severe and non-severe patients, the pooled OR of hypertension, respiratory system disease, and cardiovascular disease were 2.36 (95% CI: 1.46-3.83), 2.46 (95% CI: 1.76-3.44) and 3.42 (95% CI: 1.88-6.22) respectively. CONCLUSION: We assessed the prevalence of comorbidities in the COVID-19 patients and found that underlying disease, including hypertension, respiratory system disease and cardiovascular disease, may be risk factors for severe patients compared with non-severe patients.
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
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.
Cites
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 · see citing works
Self-citationClinical 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 · see citing works
Self-citationWang, 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 · see citing works
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 · see citing works
Lu, R., Zhao, X., Li, J., Niu, P., Yang, B., Wu, H., Wang, W., Song, H., Huang, B., Zhu, N., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30251-8 · Cited by 11,907 · see citing works
Clinical characteristics of 140 patients infected with SARS‐CoV‐2 in Wuhan, China
Zhang, J., Dong, X., Cao, Y., Yuan, Y.-D., Yang, Y., Yan, Y., Akdiş, C. A., & Gao, Y. (2020) · Allergy · doi:10.1111/all.14238 · Cited by 4,004 · see citing works
Hui, D. S., I Azhar, E., Madani, T. A., Ntoumi, F., Kock, R., Dar, O., Ippolito, G., Mchugh, T. D., Memish, Z. A., Drosten, C., et al. (2020) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2020.01.009 · Cited by 3,809 · see citing works
CDC Weekly, C., & The Novel Coronavirus Pneumonia Emergency Response Epidemiology Team (2020) · China CDC Weekly · doi:10.46234/ccdcw2020.032 · Cited by 2,889 · see citing works
Liu, Y., Yang, Y., Zhang, C., Huang, F., Wang, F., Yuan, J., Wang, Z., Li, J., Li, J., Feng, C., et al. (2020) · Science China Life Sciences · doi:10.1007/s11427-020-1643-8 · Cited by 2,037 · see citing works
Clinical characteristics of novel coronavirus cases in tertiary hospitals in Hubei Province
Liu, K., Fang, Y.-Y., Deng, Y., Liu, W. Y., Wang, M., Ma, J.-P., Xiao, W. L., Wang, Y.-N., Zhong, M.-H., Li, C.-H., et al. (2020) · Chinese Medical Journal · doi:10.1097/cm9.0000000000000744 · Cited by 1,412 · see citing works
Self-citationThe Novel Coronavirus Pneumonia Emergency Response Epidemiology Team (2020) · PubMed · Cited by 1,277 · see citing works
Tuberculosis and diabetes mellitus: convergence of two epidemics
Dooley, K. E., & Chaisson, R. E. (2009) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(09)70282-8 · Cited by 934 · see citing works
Sex-Based Differences in Susceptibility to Severe Acute Respiratory Syndrome Coronavirus Infection
Channappanavar, R., Fett, C., Mack, M., Eyck, P. T., Meyerholz, D. K., & Perlman, S. M. (2017) · The Journal of Immunology · doi:10.4049/jimmunol.1601896 · Cited by 896 · see citing works
Badawi, A. F., & Ryoo, S. G. (2016) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2016.06.015 · Cited by 703 · see citing works
Sexual Dimorphism in Innate Immunity
Jaillon, S., Berthenet, K., & Garlanda, C. (2019) · Clinical reviews in allergy and immunology · doi:10.1007/s12016-017-8648-x · Cited by 659 · see citing works
Populations at risk for severe or complicated influenza illness: systematic review and meta-analysis
Mertz, D., Kim, T. H., Johnstone, J., Lam, P.-P., Science, M. E., Kuster, S. P., Fadel, S. A., Tran, D. T., Fernández, E., Bhatnagar, N., et al. (2013) · BMJ · doi:10.1136/bmj.f5061 · Cited by 632 · see citing works
Clinical Features Predicting Mortality Risk in Patients With Viral Pneumonia: The MuLBSTA Score
Guo, L., Wei, D., Zhang, X., Wu, Y., Li, Q. Y., Zhou, M., & Qu, J. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.02752 · Cited by 412 · see citing works
Alraddadi, B. M., Watson, J. T., Almarashi, A., Abedi, G. R., Turkistani, A., Sadran, M., Housa, A., AlMazroa, M. A., Alraihan, N., Banjar, A., et al. (2015) · Emerging infectious diseases · doi:10.3201/eid2201.151340 · Cited by 269 · see citing works
Patel, A. K., & Jernigan, D. B. (2020) · American Journal of Transplantation · doi:10.1111/ajt.15805 · Cited by 201 · see citing works
Summary of report on cardiovascular diseases in China, 2012.
Wang, W., Hu, S. S., Kong, L., et al. (2014) · PubMed · doi:10.3967/bes2014.085 · Cited by 185 · see citing works
Connecting Type 1 and Type 2 Diabetes through Innate ImmunityPreprint
Odegaard, J. I., & Chawla, A. (2012) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a007724 · Cited by 185 · see citing works
Liu, M., Liu, S.-W., Wang, L.-J., Bai, Y.-M., Zeng, X., Guo, H., Liu, Y., Jiang, Y., Dong, W., He, G. X., et al. (2018) · Diabetes & Metabolism · doi:10.1016/j.diabet.2018.08.008 · Cited by 155 · see citing works
Zhang, M. Q., Wang, X. H., Chen, Y. L., Zhao, K., Cai, Y. Q., An, C. L., Lin, M., & Mu, X. D. (2020) · PubMed · doi:10.3760/cma.j.issn.1001-0939.2020.0013 · Cited by 114 · see citing works
Lau, D., Eurich, D. T., Majumdar, S. R., Katz, A., & Johnson, J. A. (2013) · Thorax · doi:10.1136/thoraxjnl-2012-203109 · Cited by 63 · see citing works
Hong, K., Cheong, H. J., Choi, W. S., Lee, J., Wie, S., Baek, J. H., Kim, H. Y., Jeong, H. W., & Kim, W. J. (2013) · Journal of Infection and Chemotherapy · doi:10.1016/j.jiac.2013.07.001 · Cited by 38 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Renin-angiotensin-aldosterone system inhibitors in patients with Covid-19
Vaduganathan, M., Vardeny, O., Michel, T., McMurray, J. J. V., Pfeffer, M. A., & Solomon, S. D. (2020) · New England Journal of Medicine · doi:10.1056/nejmsr2005760 · Cited by 2,101 · see citing works
Covid-19: risk factors for severe disease and death
Jordan, R. E., Adab, P., & Cheng, K. K. (2020) · BMJ · doi:10.1136/bmj.m1198 · Cited by 1,653 · see citing works
The pivotal link between ACE2 deficiency and SARS-CoV-2 infection
Verdecchia, P., Cavallini, C., Spanevello, A., & Angeli, F. (2020) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2020.04.037 · Cited by 1,237 · see citing works
“[56] Again, hypertension and history of cardiovascular disease were significantly more prevalent among the more severe patients.”
BackgroundInfluentialEstimating the burden of SARS-CoV-2 in France
Salje, H., Tran Kiem, C., Lefrancq, N., Courtejoie, N., Bosetti, P., Paireau, J., Andronico, A., Hozé, N., Richet, J., Dubost, C.-L., et al. (2020) · Science · doi:10.1126/science.abc3517 · Cited by 1,038 · see citing works
Immune response, inflammation, and the clinical spectrum of COVID-19
García, L. F. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01441 · Cited by 739 · see citing works
Sallam, M., Dababseh, D., Eid, H., Al-Mahzoum, K., Al-Haidar, A., Taim, D., Yaseen, A., Ababneh, N. A., Bakri, F. G., & Mahafzah, A. (2021) · Vaccines · doi:10.3390/vaccines9010042 · Cited by 675 · see citing works
An epidermal patch for the simultaneous monitoring of haemodynamic and metabolic biomarkers
Sempionatto, J. R., Lin, M., Yin, L., De la paz, E., Pei, K., Sonsa-ard, T., de Loyola Silva, A. N., Khorshed, A. A., Zhang, F., Tostado, N., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00685-1 · Cited by 658 · see citing works
Association of Social and Demographic Factors with COVID-19 Incidence and Death Rates in the US
Karmakar, M., Lantz, P. M., & Tipirneni, R. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.36462 · Cited by 580 · see citing works
Banerjee, A., Pasea, L., Harris, S., Gonzalez-Izquierdo, A., Torralbo, A., Shallcross, L., Noursadeghi, M., Pillay, D., Sebire, N., Holmes, C., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30854-0 · Cited by 518 · see citing works
Care of patients with liver disease during the COVID-19 pandemic: EASL-ESCMID position paper
Boettler, T., Newsome, P. N., Mondelli, M. U., Maticic, M., Cordero, E., Cornberg, M., & Berg, T. (2020) · JHEP Reports · doi:10.1016/j.jhepr.2020.100113 · Cited by 477 · see citing works
de Almeida-Pititto, B., Dualib, P. M., Zajdenverg, L., Dantas, J. R., de Souza, F. D., Rodacki, M., Bertoluci, M. C., & Brazilian Diabetes Society Study Group (SBD) (2020) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-020-00586-4 · Cited by 391 · see citing works
Prevalence of comorbidities among individuals with COVID-19: A rapid review of current literature
Bajgain, K. T., Badal, S., Bajgain, B. B., & Santana, M. J. (2021) · American Journal of Infection Control · doi:10.1016/j.ajic.2020.06.213 · Cited by 328 · see citing works
Dorjee, K., Kim, H., Bonomo, E., & Dolma, R. (2020) · PLOS ONE · doi:10.1371/journal.pone.0243191 · Cited by 216 · see citing works
Heer, C. D., Sanderson, D. J., Voth, L. S., Alhammad, Y. M. O., Schmidt, M. S., Trammell, S. A. J., Perlman, S., Cohen, M. S., Fehr, A. R., & Brenner, C. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.ra120.015138 · Cited by 192 · see citing works
Lancet COVID-19 Commission Statement on the occasion of the 75th session of the UN General Assembly
Sachs, J. D., Abdool Karim, S., Aknin, L., Allen, J., Brosbøl, K., Cuevas Barron, G., Daszak, P., Espinosa, M. F., Gaspar, V., Gaviria, A., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)31927-9 · Cited by 171 · see citing works
SARS-CoV-2 infection, COVID-19 pathogenesis, and exposure to air pollution: What is the connection?
Woodby, B., Arnold, M. M., & Valacchi, G. (2021) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14512 · Cited by 160 · see citing works
Esposito, A. J., Menon, A. A., Ghosh, A. J., Putman, R. K., Fredenburgh, L. E., El-Chemaly, S. Y., Goldberg, H. J., Baron, R. M., Hunninghake, G. M., & Doyle, T. J. (2020) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202006-2441le · Cited by 142 · see citing works
Flaherty, G. T., Hession, P., Liew, C. H., Lim, B. C. W., Leong, T. K., Lim, V., & Sulaiman, L. H. (2020) · Tropical Diseases, Travel Medicine and Vaccines · doi:10.1186/s40794-020-00118-y · Cited by 127 · see citing works
Juanjuan, L., Santa-Maria, C. A., Hongfang, F., Lingcheng, W., Pengcheng, Z., Yuanbing, X., Yuyan, T., Zhongchun, L., Bo, D., Meng, L., et al. (2020) · Clinical Breast Cancer · doi:10.1016/j.clbc.2020.06.003 · Cited by 124 · see citing works
COVID-19 and ethnicity: who will research results apply to?
Treweek, S., Forouhi, N. G., Narayan, K. M. V., & Khunti, K. (2020) · The Lancet · doi:10.1016/s0140-6736(20)31380-5 · Cited by 119 · see citing works
Social determinants of mortality from COVID-19: A simulation study using NHANES
Seligman, B., Ferranna, M., & Bloom, D. E. (2021) · PLOS Medicine · doi:10.1371/journal.pmed.1003490 · Cited by 103 · see citing works
Type 2 diabetes mellitus and COVID-19: a narrative review
Corrao, S., Pinelli, K., Vacca, M., Raspanti, M., & Argano, C. (2021) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.609470 · Cited by 97 · see citing works
COVID-19-Related Fear and Health-Related Safety Behavior in Oncological Patients
Musche, V., Bäuerle, A., Steinbach, J., Schweda, A., Hetkamp, M., Weismüller, B., Kohler, H., Beckmann, M., Herrmann, K., Tewes, M., et al. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01984 · Cited by 99 · see citing works
National French Survey of Coronavirus Disease (COVID-19) Symptoms in People Aged 70 and Over
Annweiler, C., Sacco, G., Salles, N., Aquino, J.-P., Gautier, J., Berrut, G., Guérin, O., & Gavazzi, G. (2021) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa792 · Cited by 95 · see citing works
Epilepsy in time of COVID-19: A survey-based study
Fonseca, E., Quintana, M., Lallana, S., Luis Restrepo, J., Abraira, L., Santamarina, E., Seijo‐Raposo, I., & Toledo, M. (2020) · Acta Neurologica Scandinavica · doi:10.1111/ane.13335 · Cited by 94 · see citing works
Cardiovascular disease and <scp>COVID</scp> ‐19: Australian and New Zealand consensus statement
Zaman, S., MacIsaac, A. I., Jennings, G. L., Schlaich, M. P., Inglis, S. C., Arnold, R., Kumar, S., Thomas, L., Wahi, S., Lo, S., et al. (2020) · Medical Journal of Australia · doi:10.5694/mja2.50714 · Cited by 73 · see citing works
Does having a high-risk pregnancy influence anxiety level during the COVID-19 pandemic?
Sinaci, S., Ozden Tokalioglu, E., Ocal, D., Atalay, A., Yilmaz, G., Keskin, H. L., Erdinc, S. O., Sahin, D., & Moraloglu Tekin, O. (2020) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2020.10.055 · Cited by 68 · see citing works
Abdominal imaging findings in patients with SARS-CoV-2 infection: a scoping review
Lui, K., Wilson, M. P., & Low, G. (2021) · Abdominal Radiology · doi:10.1007/s00261-020-02739-5 · Cited by 56 · see citing works
Ahmed-Hassan, H., Sisson, B., Shukla, R. K., Wijewantha, Y., Funderburg, N. T., Li, Z., Hayes, D., Demberg, T., & Liyanage, N. P. M. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01979 · Cited by 47 · see citing works
Anti-inflammatory effects of GLP-1 in patients with COVID-19
Sazgarnejad, S., Yazdanpanah, N., & Rezaei, N. (2022) · Expert Review of Anti-infective Therapy · doi:10.1080/14787210.2021.1964955 · Cited by 21 · see citing works
Kim, D. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1117280 · Cited by 21 · see citing works
(2024) · Journal of Community Medicine & Public Health · doi:10.29011/2577-2228.100460 · Cited by 19 · see citing works
Detecting racial/ethnic health disparities using deep learning from frontal chest radiography
Pyrros, A., Rodríguez-Fernández, J. M., Borstelmann, S. M., Gichoya, J. W., Horowitz, J. M., Fornelli, B., Siddiqui, N., Velichko, Y., Koyejo, O., & Galanter, W. (2022) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2021.09.010 · Cited by 15 · see citing works
“Racial/ethnic minority populations tend to have a greater degree of underlying comorbidities, which can increase the risk for mortality from COVID-19 [2-4].”
Background
- Authors
- Yang, J., Zheng, Y., Gou, X., Pu, K., Chen, Z., Guo, Q., Ji, R., Wang, H., Wang, Y., & Zhou, Y.
- Affiliations
- First Hospital of Lanzhou University (CN)
- Lanzhou University (CN)
- Corresponding author
- Wang, Yuping
- Zhou, Yongning
- Venue
- International Journal of Infectious Diseases
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 94
- Pages
- 91-95
- ISSN
- 1201-9712 (ISSN-L)
- PMC
- PMC7194638
- OpenAlex
- W3023787531
- Cited by
- 4,206
- References
- 25
- Citations by year
- 2026: 38 · 2025: 102 · 2024: 250 · 2023: 449 · 2022: 749 · 2021: 1350 · 2020: 1263 · 2019: 2
- FWCI
- 45.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- International Journal of Infectious Diseases (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Lanzhou University Institutional Repository (submittedVersion, cc-by-nc-sa)
- Lanzhou University Institutional Repository (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Neurology › Long-Term Effects of COVID-19
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Keywords
- COVID-19 pandemic, SARS-CoV-2, comorbidities, underlying disease, disease severity, hypertension, diabetes mellitus, cardiovascular disease, respiratory diseases, clinical symptoms, risk factors
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Cough, Fever, Humans, Pneumonia, Viral, Risk Factors, Comorbidity, Prevalence, Coronavirus Infections, Pandemics
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
