Evidence map›Paper›PMID 35352489›Full record

ArticleObesity (Silver Spring, Md.)2022

Associations between cardiometabolic disease severity, social determinants of health (SDoH), and poor COVID-19 outcomes.

Carrie R Howell, Li Zhang, Nengjun Yi, Tapan Mehta, Andrea L Cherrington, W Timothy Garvey

Abstract read
In one paragraph

Article in Obesity (Silver Spring, Md.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. 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.

Carrie R HowellDivision of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.ORCID 0000-0002-6554-6237
Li ZhangDepartment of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Nengjun YiDepartment of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Tapan MehtaDepartment of Health Services Administration, School of Health Professions, University of Alabama at Birmingham, Birmingham, Alabama, USA.
Andrea L CherringtonDivision of Preventive Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
W Timothy GarveyDepartment of Nutrition Sciences, School of Health Professions, University of Alabama at Birmingham, Birmingham, Alabama, USA.ORCID 0000-0003-0822-0860

Funding

University of Alabama at Birmingham's Diabetes Research CenterP30DK079626 · NIDDK · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Kirk Michael Habegger · 2013 to 2026
$19.5M
Molecular and Soc. Determinants of Obesity and Metabolic Disorders in YouthU54MD008176 · NIMHD · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI FOUAD, MONA N. · 2012 to 2016
$13.7M
Project 2: Leveraging Ongoing Home Visitation Programs to Address Obesity Disparities among Underserved, Low-Income Mothers and ChildrenU54MD000502 · NIMHD · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI FOUAD, MONA N., GARVEY, W TIMOTHY · 2017 to 2022
$11.8M
NIDDK NIH HHS P30 DK079626NIMHD NIH HHS U54 MD000502NIMHD NIH HHS U54 MD008176
6 · The paper itself

Abstract

objectiveThis study aimed to determine the ability of retrospective cardiometabolic disease staging (CMDS) and social determinants of health (SDoH) to predict COVID-19 outcomes.

methodsIndividual and neighborhood SDoH and CMDS clinical parameters (BMI, glucose, blood pressure, high-density lipoprotein, triglycerides), collected up to 3 years prior to a positive COVID-19 test, were extracted from the electronic medical record. Bayesian logistic regression was used to model CMDS and SDoH to predict subsequent hospitalization, intensive care unit (ICU) admission, and mortality, and whether adding SDoH to the CMDS model improved prediction was investigated. Models were cross validated, and areas under the curve (AUC) were compared.

resultsA total of 2,873 patients were identified (mean age: 58 years [SD 13.2], 59% were female, 45% were Black). CMDS, insurance status, male sex, and higher glucose values were associated with increased odds of all outcomes; area-level social vulnerability was associated with increased odds of hospitalization (odds ratio: 1.84, 95% CI: 1.38-2.45) and ICU admission (odds ratio 1.98, 95% CI: 1.45-2.85). The AUCs improved when SDoH were added to CMDS (p < 0.001): hospitalization (AUC 0.78 vs. 0.82), ICU admission (AUC 0.77 vs. 0.81), and mortality (AUC 0.77 vs. 0.83).

conclusionsRetrospective clinical markers of cardiometabolic disease and SDoH were independently predictive of COVID-19 outcomes in the population.

Indexed as

Cardiovascular DiseasesCOVID-19Bayes TheoremFemaleGlucoseHumansMaleMiddle AgedRetrospective StudiesSocial Determinants of HealthGlucose

Identifiers

PMID35352489
PMCPMC9088642

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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.