CoChat
Sign Up
Cited by 16Health disparities and outcomesDiabetes, Cardiovascular Risks, and LipoproteinsHealthcare Systems and Public Health

Diabetes mortality across the 30 biggest U.S. cities: Assessing overall trends and racial inequities

Buscemi, Joanna, Saiyed, Nazia, Silva, Abigail, Ghahramani, Fereshteh, Benjamins, Maureen R.·2021·Diabetes Research and Clinical Practice
DOI 10.1016/j.diabres.2021.108652
Open this paper in CoChat — free account

Abstract

Aims National data suggest that diabetes mortality disproportionately affects Blacks compared to whites. We aimed to (1) calculate diabetes mortality rates (where diabetes was an underlying cause of death) among the general population of the U.S. and the largest 30 cities; (2) calculate Black/white mortality rate ratios and rate differences; and (3) compare changes in mortality rates and inequities across two 5-year periods (2008-2012 (T1) and 2013-2017 (T2)).Methods We used vital statistics mortality data and American Community Survey population estimates.Results The U.S. diabetes mortality rate at T1 was 20.91 per 100,000, and significantly increased to 21.05 at T2. El Paso had the highest diabetes mortality rate at both time points (T1 = 33.06; T2 = 35.98), while San Francisco had the lowest rate (T1 = 11.41; T2 = 13.18). The U.S. Black mortality rate was 2.21 times higher than the white rate at T2 (95%CI [2.19-2.23]). Eleven cities had significantly higher rate ratios than the U.S. at T2. The Black:white rate ratio in Washington, D.C. was approximately three times higher than the national rate ratio.Conclusions This city-level data is important to inform more targeted local policy interventions and programming to promote health equity, particularly within cities with the greatest inequities.

Integrity signals

Registry

Crossref · 10.1016/j.diabres.2021.108652

Cites

29 of 29 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

  • Newer therapeutic agents including GLP-1 receptor agonists and GIP plus GLP dual agonists may decrease cardiovascular disease and diabetic kidney disease.

    “In addition, these newer therapeutic agents may decrease cardiovascular disease (CVD) 20 and diabetic kidney disease (DKD), 21 the major life-shortening complications of diabetes.[22][23][24]”

    From Effectiveness of Semaglutide and Tirzepatide in Overweight and Obese Adults with Type 1 Diabetes (2025) · cites this paper as [23]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Buscemi, J., Saiyed, N., Silva, A., Ghahramani, F., & Benjamins, M. R.
Affiliations
  • DePaul University (US)
  • Sinai Urban Health Institute (US)
  • Loyola University Chicago (US)
Corresponding author
Buscemi, Joanna
ORCID
Venue
Diabetes Research and Clinical Practice
Publisher
Elsevier BV
Published
2021-03
Volume
173
Pages
108652
ISSN
0168-8227 (ISSN-L)
DOI
10.1016/j.diabres.2021.108652
OpenAlex
W3119367983
Cited by
16
References
29
Citations by year
2025: 4 · 2024: 2 · 2023: 2 · 2022: 4 · 2021: 4
FWCI
1.66
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Diabetes Research and Clinical Practice (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Health › Health disparities and outcomes
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Health Sciences › Medicine › Epidemiology › Healthcare Systems and Public Health
Keywords
diabetes mortality, racial disparities, health disparities, health equity, urban health, mortality trends, Washington, D.C., El Paso
MeSH terms
Cities, Diabetes Mellitus, Female, Humans, Male, Mortality, United States, Racial Groups

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