Evidence map›Paper›PMID 35377319›Full record

ArticleJMIR public health and surveillance2022

Factors Associated With COVID-19 Death in the United States: Cohort Study.

Uan-I Chen, Hua Xu, Trudy Millard Krause, Raymond Greenberg, Xiao Dong, Xiaoqian Jiang

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Uan-I ChenSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States.ORCID 0000-0002-5938-9984
Hua XuSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States.ORCID 0000-0002-5274-4672
Trudy Millard KrauseDepartment of Management, Policy, and Community Heath, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States.ORCID 0000-0002-6740-4734
Raymond GreenbergDepartment of Population and Data Sciences, Peter O'Donnell School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, United States.ORCID 0000-0002-8858-8632
Xiao DongSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States.ORCID 0000-0001-7061-900X
Xiaoqian JiangSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States.ORCID 0000-0001-9933-2205

Funding

Open Health Natural Language Processing CollaboratoryU01TR002062 · NCATS · MAYO CLINIC ROCHESTER · PI JIANG, XIAOQIAN, LIU, HONGFANG · 2017 to 2021
$7.6M
Finding Combinatorial Drug Repositioning Therapy For Alzheimer'S Disease And Related DementiasR01AG066749 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI JIANG, XIAOQIAN, ZHENG, WENJIN JIM · 2020 to 2024
$4.4M
NCATS NIH HHS U01 TR002062NIA NIH HHS R01 AG066749
6 · The paper itself

Abstract

backgroundSince the initial COVID-19 cases were identified in the United States in February 2020, the United States has experienced a high incidence of the disease. Understanding the risk factors for severe outcomes identifies the most vulnerable populations and helps in decision-making.

objectiveThis study aims to assess the factors associated with COVID-19-related deaths from a large, national, individual-level data set.

methodsA cohort study was conducted using data from the Optum de-identified COVID-19 electronic health record (EHR) data set; 1,271,033 adult participants were observed from February 1, 2020, to August 31, 2020, until their deaths due to COVID-19, deaths due to other reasons, or the end of the study. Cox proportional hazards models were constructed to evaluate the risks for each patient characteristic.

resultsA total of 1,271,033 participants (age: mean 52.6, SD 17.9 years; male: 507,574/1,271,033, 39.93%) were included in the study, and 3315 (0.26%) deaths were attributed to COVID-19. Factors associated with COVID-19-related death included older age (80 vs 50-59 years old: hazard ratio [HR] 13.28, 95% CI 11.46-15.39), male sex (HR 1.68, 95% CI 1.57-1.80), obesity (BMI 40 vs <30 kg/m

conclusionsThis is one of the largest national cohort studies in the United States; we identified several patient characteristics associated with COVID-19-related deaths, and the results can serve as the basis for policy making. The study also offered directions for future studies, including the effect of other socioeconomic factors on the increased risk for minority groups.

Indexed as

COVID-19AdultBlack or African AmericanCohort StudiesHumansMaleMiddle AgedSARS-CoV-2United StatesWhite Peoplecohort studiesCOVID-19EHR datarisk factorssurvival analysis

Identifiers

PMID35377319
PMCPMC9132142

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.