Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Meta-Analysis
. 2020 Oct;22(10):1915-1924.
doi: 10.1111/dom.14124. Epub 2020 Jul 16.

Prevalence of co-morbidities and their association with mortality in patients with COVID-19: A systematic review and meta-analysis

Awadhesh K Singh  1 , Clare L Gillies  2   3 , Ritu Singh  1 , Akriti Singh  4 , Yogini Chudasama  2   3 , Briana Coles  2 , Sam Seidu  2   3 , Francesco Zaccardi  2   3 , Melanie J Davies  3 , Kamlesh Khunti  2   3
Affiliations
Meta-Analysis

Prevalence of co-morbidities and their association with mortality in patients with COVID-19: A systematic review and meta-analysis

Awadhesh K Singh et al. Diabetes Obes Metab. 2020 Oct.

Abstract

Aim: To estimate the prevalence of both cardiometabolic and other co-morbidities in patients with COVID-19, and to estimate the increased risk of severity of disease and mortality in people with co-morbidities.

Materials and methods: Medline, Scopus and the World Health Organization website were searched for global research on COVID-19 conducted from January 2019 up to 23 April 2020. Study inclusion was restricted to English language publications, original articles that reported the prevalence of co-morbidities in individuals with COVID-19, and case series including more than 10 patients. Eighteen studies were selected for inclusion. Data were analysed using random effects meta-analysis models.

Results: Eighteen studies with a total of 14 558 individuals were identified. The pooled prevalence for co-morbidities in patients with COVID-19 disease was 22.9% (95% CI: 15.8 to 29.9) for hypertension, 11.5% (9.7 to 13.4) for diabetes, and 9.7% (6.8 to 12.6) for cardiovascular disease (CVD). For chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), cerebrovascular disease and cancer, the pooled prevalences were all less than 4%. With the exception of cerebrovascular disease, all the other co-morbidities presented a significantly increased risk for having severe COVID-19. In addition, the risk of mortality was significantly increased in individuals with CVD, COPD, CKD, cerebrovascular disease and cancer.

Conclusions: In individuals with COVID-19, the presence of co-morbidities (both cardiometabolic and other) is associated with a higher risk of severe COVID-19 and mortality. These findings have important implications for public health with regard to risk stratification and future planning.

Keywords: COVID-19; co-morbidities; coronavirus; meta-analysis; systematic review.

PubMed Disclaimer

Conflict of interest statement

All of the authors declare no competing interests or activities that could appear to have influenced the submitted work.

Figures

FIGURE 1
FIGURE 1
Flow diagram of literature search
FIGURE 2
FIGURE 2
Meta‐analyses of severe COVID‐19 by co‐morbidity
FIGURE 3
FIGURE 3
Meta‐analyses of mortality by co‐morbidity
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Wu F, Zhao S, Yu B, et al. A new coronavirus associated with human respiratory disease in China. Nature. 2020;579(7798):265‐269. - PMC - PubMed
    1. Worldometers . Coronavirus Update (Live). 2020. https://www.worldometers.info/coronavirus/. Accessed 4th May 2020.
    1. The National Health Commission of China. New coronavirus pneumonia prevention and control program, 6th ed. (in Chinese). 2020. http://en.nhc.gov.cn/2020-03/29/c_78468.htm. Accessed May 1, 2020.
    1. BBC News . ‘Window of Opportunity’ to Stop Coronavirus: @BBCNews. 2020. https://www.bbc.com/news/world-asia-china-51368873. Accessed 4th May 2020.
    1. Sun K, Chen J, Viboud C. Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population‐level observational study. Lancet. 2020;2(4):PE201‐PE208. - PMC - PubMed

Publication types

MeSH terms