CoChat
Sign Up
Cited by 216COVID-19 Clinical Research StudiesLong-Term Effects of COVID-19COVID-19 and healthcare impacts

The DOI matches its Crossref record; 123 of the 123 references it cites were found in a registry or an index.

Prevalence and predictors of death and severe disease in patients hospitalized due to COVID-19: A comprehensive systematic review and meta-analysis of 77 studies and 38,000 patients

Open access
Dorjee, Kunchok, Kim, Hyunju, Bonomo, Elizabeth, Dolma, Rinchen·2020·PLOS ONE
DOI 10.1371/journal.pone.0243191PMC PMC7721151

Published version of a preprint

Open this paper in CoChat — free account

Abstract

Introduction: Progression of COVID-19 to severe disease and death is insufficiently understood. Objective: Summarize the prevalence of risk factors and adverse outcomes and determine their associations in COVID-19 patients who were hospitalized. Methods: We searched Medline, Embase and Web of Science for case-series and observational studies of hospitalized COVID-19 patients through August 31, 2020. Data were analyzed by fixed-effects meta-analysis using Shore’s adjusted confidence intervals to address heterogeneity. Results: Seventy-seven studies comprising 38906 hospitalized patients met inclusion criteria; 21468 from the US-Europe and 9740 from China. Overall prevalence of death [% (95% CI)] from COVID-19 was 20% (18–23%); 23% (19–27%) in the US and Europe and 11% (7–16%) for China. Of those that died, 85% were aged≥60 years, 66% were males, and 66%, 44%, 39%, 37%, and 27% had hypertension, smoking history, diabetes, heart disease, and chronic kidney disease (CKD), respectively. The case fatality risk [%(95% CI)] were 52% (46–60) for heart disease, 51% (43–59) for COPD, 48% (37–63) for chronic kidney disease (CKD), 39% for chronic liver disease (CLD), 28% (23–36%) for hypertension, and 24% (17–33%) for diabetes. Summary relative risk (sRR) of death were higher for age≥60 years [sRR = 3.6; 95% CI: 3.0–4.4], males [1.3; 1.2–1.4], smoking history [1.3; 1.1–1.6], COPD [1.7; 1.4–2.0], hypertension [1.8; 1.6–2.0], diabetes [1.5; 1.4–1.7], heart disease [2.1; 1.8–2.4], CKD [2.5; 2.1–3.0]. The prevalence of hypertension (55%), diabetes (33%), smoking history (23%) and heart disease (17%) among the COVID-19 hospitalized patients in the US were substantially higher than that of the general US population, suggesting increased susceptibility to infection or disease progression for the individuals with comorbidities. Conclusions: Public health screening for COVID-19 can be prioritized based on risk-groups. Appropriately addressing the modifiable risk factors such as smoking, hypertension, and diabetes could reduce morbidity and mortality due to COVID-19; public messaging can be accordingly adapted.

Overview

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pone.0243191

Cites

123 of 123 references verified0 citation errors · 0 not checkable

Statistics

Verification

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.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Show all 123 references

Research works printed but not linked: 2

  1. A Qin, 2020, China Raises Coronavirus Death Toll by 50% in Wuhan, The New York Times, 2020
  2. United States Centers for Disease Control and Prevention, 2020, National Diabetes Statistics Report

Cited by

Sorted by citations

Items 201–216 of 216

Related

See all

Metadata

Authors
Dorjee, K., Kim, H., Bonomo, E., & Dolma, R.
Affiliations
  • Johns Hopkins University (US)
  • Brown University (US)
Corresponding author
Dorjee, Kunchok
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2020-12-07
Volume
15
Issue
12
Pages
e0243191
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0243191
PMC
PMC7721151
OpenAlex
W3111018862
Cited by
216
References
123
Citations by year
2026: 2 · 2025: 14 · 2024: 26 · 2023: 44 · 2022: 60 · 2021: 70
FWCI
2.06
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • medRxiv (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Institute of Allergy and Infectious Diseases (K01AI148583)
  • UNOPS STOP TB PARTNERSHIP TB REACH (STBP/TBREACH/GSA/W7-7692)
  • Johns Hopkins Alliance for a Healthier World (80045453)
Topics
  • Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
  • Health Sciences › Medicine › Clinical Neurology › Long-Term Effects of COVID-19
  • Health Sciences › Medicine › Oncology › COVID-19 and healthcare impacts
Keywords
COVID-19 pandemic, hospitalized patients, mortality, case fatality, risk factors, severe illness, chronic kidney disease, hypertension, diabetes mellitus, heart disease, chronic obstructive pulmonary disease, chronic liver disease, smoking, old age, sex differences, comorbidities, SARS-CoV-2, risk prediction model, prognostic factors, prognostic score, prognostic score 2
MeSH terms
COVID-19, SARS-CoV-2, Age Factors, China, Diabetes Mellitus, Europe, Female, Heart Diseases, Hospitalization, Humans, Hypertension, Male, Risk Factors, Smoking, United States, Comorbidity, Prevalence, Coronavirus Infections, Renal Insufficiency, Chronic, 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