Evidence map›Paper›PMID 33428624›Full record

ArticlePLoS medicine2021

Social determinants of mortality from COVID-19: A simulation study using NHANES.

Benjamin Seligman, Maddalena Ferranna, David E Bloom

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in PLoS medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 66 papers.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed
12.4field-weighted citation impact, top 1% of its field
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

66 citing papers in PubMed, 109 citations in OpenAlex.

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  6. The impact of Covid-19 in Chhattisgarh, India: A descriptive study.Journal of family medicine and primary care · 2024
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  20. Statistical model for factors correlating with COVID-19 deaths.International journal of disaster risk reduction : IJDRR · 2022
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6 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Benjamin SeligmanNew England Geriatric Research, Education, and Clinical Center, VA Boston Healthcare System, Boston, Massachusetts, United States of America.ORCID 0000-0002-8223-5924
Maddalena FerrannaDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
David E BloomDepartment of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.ORCID 0000-0003-3731-3345
Harvard Global Health Institute · USHarvard University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 epidemic in the United States is widespread, with more than 200,000 deaths reported as of September 23, 2020. While ecological studies show higher burdens of COVID-19 mortality in areas with higher rates of poverty, little is known about social determinants of COVID-19 mortality at the individual level. METHODS AND

findingsWe estimated the proportions of COVID-19 deaths by age, sex, race/ethnicity, and comorbid conditions using their reported univariate proportions among COVID-19 deaths and correlations among these variables in the general population from the 2017-2018 National Health and Nutrition Examination Survey (NHANES). We used these proportions to randomly sample individuals from NHANES. We analyzed the distributions of COVID-19 deaths by race/ethnicity, income, education level, and veteran status. We analyzed the association of these characteristics with mortality by logistic regression. Summary demographics of deaths include mean age 71.6 years, 45.9% female, and 45.1% non-Hispanic white. We found that disproportionate deaths occurred among individuals with nonwhite race/ethnicity (54.8% of deaths, 95% CI 49.0%-59.6%, p < 0.001), individuals with income below the median (67.5%, 95% CI 63.4%-71.5%, p < 0.001), individuals with less than a high school level of education (25.6%, 95% CI 23.4% -27.9%, p < 0.001), and veterans (19.5%, 95% CI 15.8%-23.4%, p < 0.001). Except for veteran status, these characteristics are significantly associated with COVID-19 mortality in multiple logistic regression. Limitations include the lack of institutionalized people in the sample (e.g., nursing home residents and incarcerated persons), the need to use comorbidity data collected from outside the US, and the assumption of the same correlations among variables for the noninstitutionalized population and COVID-19 decedents.

conclusionsSubstantial inequalities in COVID-19 mortality are likely, with disproportionate burdens falling on those who are of racial/ethnic minorities, are poor, have less education, and are veterans. Healthcare systems must ensure adequate access to these groups. Public health measures should specifically reach these groups, and data on social determinants should be systematically collected from people with COVID-19.

Indexed as

Public HealthSocioeconomic FactorsAgedComorbidityCOVID-19EthnicityFemaleHealthcare DisparitiesHealth Services Needs and DemandHumansMaleMortalityQuality ImprovementSARS-CoV-2Social Determinants of HealthUnited States

Identifiers

PMID33428624
PMCPMC7799807
OpenAlexW3119998633

What OpenQuestion holds

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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.