Cited by 62Diabetes Management and ResearchDiabetes Management and EducationDiabetes and associated disorders

Racial Disparities in Diabetes Technology Use and Outcomes in Type 1 Diabetes in a Safety-Net Hospital

Open access
Fantasia, Kathryn L, Wirunsawanya, Kamonkiat, Lee, Christopher, Rizo, Ivania·2021·Journal of Diabetes Science and Technology

Abstract

Background Limited data exist regarding diabetes technology use among adults with type 1 diabetes (T1D) in urban racially/ethnically diverse safety-net hospitals. We examined racial/ethnic differences in the use of continuous glucose monitor (CGM) and continuous subcutaneous insulin infusion (CSII) in this setting. Methods A retrospective review of 227 patients ≥ 18 years of age with T1D seen in an urban, safety-net endocrinology clinic during 2016-2017 was completed (mean age: 39; 80% English-speaking; 50% had public insurance). Diabetes technology use, defined as either CGM or CSII or both CGM and CSII, and clinical outcomes were examined by race/ethnicity. Results Overall, 30% used CGM and 26% used CSII. After adjusting for age, language, insurance, and annual income, diabetes technology use in non-White patients was significantly lower than in White patients, predominantly lower in Black (aOR 0.25 [95% CI 0.11-0.56]) and patients identified as other race/ethnicity (aOR 0.30 [95% CI 0.11-0.77]). At the highest household income level (≥$75,000/y), Black and Hispanic individuals were significantly less likely than White individuals to use diabetes technology ( P P Conclusions Racial/ethnic disparities in diabetes technology use and glycemic control were observed even after adjusting for sociodemographic factors. Further research should explore barriers to accessing diabetes technology in non-White populations.

Integrity signals

Registry

Crossref · 10.1177/1932296821995810

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

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

Claims the paper makes

We couldn’t retrieve this paper’s full text, 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?

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Fantasia, K. L., Wirunsawanya, K., Lee, C., & Rizo, I.
Affiliations
Boston University (US)
Corresponding author
  • Wirunsawanya, Kamonkiat
  • Rizo, Ivania
Venue
Journal of Diabetes Science and Technology
Published
2021-03-10
Volume
15
Issue
5
Pages
1010-1017
ISSN
1932-2968 (ISSN-L)
OpenAlex
W3134642622
Cited by
62
References
9
Citations by year
2026: 12 · 2025: 14 · 2024: 16 · 2023: 7 · 2022: 9 · 2021: 4
FWCI
6.72
Type
journal article
Language
en
Open access
Locations
  • Journal of Diabetes Science and Technology (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (UL1TR001430)
  • National Institute of Diabetes and Digestive and Kidney Diseases (T32DK007201)
  • National Center for Advancing Translational Sciences
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
Keywords
type 1 diabetes, racial disparities, diabetes technology use, continuous glucose monitoring, insulin pump therapy, glycemic control, HbA1c, safety-net hospitals, Black patients, Hispanic patients, diabetes technology access, household income
MeSH terms
Adult, Black or African American, Diabetes Mellitus, Type 1, Glycated Hemoglobin, Hispanic or Latino, Humans, Hypoglycemic Agents, Insulin, Insulin Infusion Systems, Retrospective Studies, Technology, White People, Healthcare Disparities, Safety-net Providers

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