The DOI matches its Crossref record; 45 of the 46 references it cites were found in a registry or an index.
Social determinants of mortality from COVID-19: A simulation study using NHANES
Open accessAbstract
Background: The 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 findings: We 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. Conclusions: Substantial 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.
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.
Integrity signals
Registry
Cites
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Our ability to observe more specific geographic and demographic disparities is constrained by the limitations of available data, but we can infer from other analyses that caregiver loss is even more heavily concentrated in vulnerable populations than shown in our results.”
From Parental and Other Caregiver Loss Due to COVID-19 in the United States: Prevalence by Race, State, Relationship, and Child Age (2023) · cites this paper as [28]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Low-income individuals are at highest risk of death from COVID-19 and low-income counties faced disproportionately large shares of COVID-19 deaths, even after controlling for other indicators of wellbeing in two studies focused on individual and communitylevel associations with COVID-19 mortality rates of all persons [11, 28].”
From Parental and Other Caregiver Loss Due to COVID-19 in the United States: Prevalence by Race, State, Relationship, and Child Age (2023) · cites this paper as [28]
Experimental — accuracy not yet measured
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
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
Toprak, Ö., Kurt, H., Sarı, Y., Şarkış, C., Us, H., & Kırık, A. (2017) · Kidney & Blood Pressure Research · doi:10.1159/000468530 · Cited by 29,772
Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30566-3 · Cited by 27,263
A New Equation to Estimate Glomerular Filtration Rate
Levey, A. S., Stevens, L. A., Schmid, C. H., Zhang, Y. (., Castro, A. F., Feldman, H. I., Kusek, J. W., Eggers, P., Van Lente, F., Greene, T., et al. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-150-9-200905050-00006 · Cited by 25,603
An interactive web-based dashboard to track COVID-19 in real time
Dong, E., Du, H., & Gardner, L. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30120-1 · Cited by 10,048
Richardson, S., Hirsch, J. S., Narasimhan, M., Crawford, J. M., McGinn, T., Davidson, K. W., and the Northwell COVID-19 Research Consortium, Barnaby, D. P., Becker, L. B., Chelico, J. D., et al. (2020) · JAMA · doi:10.1001/jama.2020.6775 · Cited by 9,254
James, P. A., Oparil, S., Carter, B. L., Cushman, W. C., Dennison-Himmelfarb, C., Handler, J., Lackland, D. T., LeFevre, M. L., MacKenzie, T. D., Ogedegbe, O., et al. (2014) · JAMA · doi:10.1001/jama.2013.284427 · Cited by 7,950
Yang, J., Zheng, Y., Gou, X., Pu, K., Chen, Z., Guo, Q., Ji, R., Wang, H., Wang, Y., & Zhou, Y. (2020) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2020.03.017 · Cited by 4,206
Comorbidity and its impact on 1590 patients with covid-19 in China: a nationwide analysis
Guan, W.-J., Liang, W.-H., Zhao, Y., Liang, H.-R., Chen, Z.-S., Li, Y.-M., Liu, X.-Q., Chen, R.-C., Tang, C.-L., Wang, T., et al. (2020) · European Respiratory Journal · doi:10.1183/13993003.00547-2020 · Cited by 3,680
Flagged first, then by citations
1 of the 46 references couldn’t be matched to a known paper.
Research works printed but not linked: 13
- Centers for Disease Control and Prevention, 2020, CDC COVID data tracker
- JE Vespa, 2020, Those who served: America’s veterans from world war ii to the war on terror
- Centers for Disease Control and Prevention, 2020, Provisional death counts for coronavirus disease 2019 (COVID-19)
- Centers for Disease Control and Prevention, 2020, Provisional death counts for coronavirus disease 2019 (COVID-19)
- A Campbell, 2020, Deaths involving COVID-19, England and Wales: deaths occurring in May 2020
- Instituto de Salud Carlos III, 2020, Report no. 32. Situation of COVID-19 in Spain on 21 May 2020
- SARS-CoV-2 Surveillance Group, 2020, Characteristics of COVID-19 patients dying in Italy
- Santé Publique France, 2020, COVID-19: epidemiological update for 3 September 2020
- R Core Team, 2019, Version 4.0.2
- A Barbiero, 2015, GenOrd: simulation of discrete random variables with given correlation matrix and marginal distributions. Version 1.4.0, Comprehensive R Archive Network
- A Tomer, 2020, How to protect essential workers during COVID-19
- J Wu, 2020, 36,000 missing deaths: tracking the true toll of the coronavirus crisis, The New York Times
- E Brown, 2020, Which deaths count toward the covid-19 death toll? It depends on the state, The Washington Post
Cited by
Sorted by citations
Items 101–103 of 103
Chesnut, R. P., Aronson, K. R., & Perkins, D. F. (2025) · Depression and Anxiety · doi:10.1155/da/5572394
Engebretsen, S., Rø, G., & de Blasio, B. F. (2022) · Figshare · doi:10.6084/m9.figshare.19807912
Lorthe, E., Loizeau, A., Richard, V., Dumont, R., Zaballa, M.-E., Pennacchio, F., Lamour, J., L’Huillier, A. G., Baysson, H., Clares, N. F., et al. (2026) · BMJ Open · doi:10.1136/bmjopen-2026-116875
Metadata
- Authors
- Seligman, B., Ferranna, M., & Bloom, D. E.
- Affiliations
- Harvard University (US)
- Corresponding author
- Seligman, Benjamin
- Venue
- PLOS Medicine
- Publisher
- Public Library of Science (PLoS)
- Published
- 2021-01-11
- Volume
- 18
- Issue
- 1
- Pages
- e1003490
- ISSN
- 1549-1277 (ISSN-L)
- 1549-1676
- PMC
- PMC7799807
- OpenAlex
- W3119998633
- Cited by
- 103
- References
- 46
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 11 · 2023: 15 · 2022: 39 · 2021: 32
- FWCI
- 9.36
- Type
- journal article
- Language
- en
- Update notices
- correction (2021-12-29)
- Open access
- gold
- doi.org
- Licence
- public-domain
- creativecommons.org/publicdomain/zero/1.0/ (vor)
- Locations
- PLoS Medicine (publishedVersion, public-domain)
- Europe PMC (PubMed Central) (publishedVersion, public-domain)
- PubMed Central (publishedVersion, public-domain)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Veterans Affairs
- Bill and Melinda Gates Foundation (OPP1158136)
- Harvard T.H. Chan School of Public Health
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Social Sciences › Health › Health disparities and outcomes
- Keywords
- COVID-19 mortality, social determinants of health, racial and ethnic disparities, income inequality, educational attainment, veteran status, comorbidities, health disparities, NHANES, health care access, public health measures, age and sex differences in mortality, health equity policy, health workforce diversity, health workforce shortage
- MeSH terms
- COVID-19, SARS-CoV-2, Aged, Ethnicity, Female, Health Services Needs and Demand, Humans, Male, Mortality, Public Health, Socioeconomic Factors, United States, Comorbidity, Healthcare Disparities, Veterans Health, Quality Improvement, Social Determinants of Health
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