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Implicit Racial–Ethnic and Insurance-Mediated Bias to Recommending Diabetes Technology: Insights from T1D Exchange Multicenter Pediatric and Adult Diabetes Provider Cohort
BACKGROUND: Despite documented benefits of diabetes technology in managing type 1 diabetes, inequities persist in the use of these devices. Provider bias may be a driver of inequities, but the evidence is limited. Therefore, we aimed to examine the role of race/ethnicity and insurance-mediated provi...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Mary Ann Liebert, Inc., publishers
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9422789/ https://www.ncbi.nlm.nih.gov/pubmed/35604789 http://dx.doi.org/10.1089/dia.2022.0042 |
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author | Odugbesan, Ori Addala, Ananta Nelson, Grace Hopkins, Rachel Cossen, Kristina Schmitt, Jessica Indyk, Justin Jones, Nana-Hawa Yayah Agarwal, Shivani Rompicherla, Saketh Ebekozien, Osagie |
author_facet | Odugbesan, Ori Addala, Ananta Nelson, Grace Hopkins, Rachel Cossen, Kristina Schmitt, Jessica Indyk, Justin Jones, Nana-Hawa Yayah Agarwal, Shivani Rompicherla, Saketh Ebekozien, Osagie |
author_sort | Odugbesan, Ori |
collection | PubMed |
description | BACKGROUND: Despite documented benefits of diabetes technology in managing type 1 diabetes, inequities persist in the use of these devices. Provider bias may be a driver of inequities, but the evidence is limited. Therefore, we aimed to examine the role of race/ethnicity and insurance-mediated provider implicit bias in recommending diabetes technology. METHOD: We recruited 109 adult and pediatric diabetes providers across 7 U.S. endocrinology centers to complete an implicit bias assessment composed of a clinical vignette and ranking exercise. Providers were randomized to receive clinical vignettes with differing insurance and patient names as proxy for Racial–Ethnic identity. Bias was identified if providers: (1) recommended more technology for patients with an English name (Racial–Ethnic bias) or private insurance (insurance bias), or (2) Race/Ethnicity or insurance was ranked high (Racial–Ethnic and insurance bias, respectively) in recommending diabetes technology. Provider characteristics were analyzed using descriptive statistics and multivariate logistic regression. RESULT: Insurance-mediated implicit bias was common in our cohort (n = 66, 61%). Providers who were identified to have insurance-mediated bias had greater years in practice (5.3 ± 5.3 years vs. 9.3 ± 9 years, P = 0.006). Racial–Ethnic-mediated implicit bias was also observed in our study (n = 37, 34%). Compared with those without Racial–Ethnic bias, providers with Racial–Ethnic bias were more likely to state that they could recognize their own implicit bias (89% vs. 61%, P = 0.001). CONCLUSION: Provider implicit bias to recommend diabetes technology was observed based on insurance and Race/Ethnicity in our pediatric and adult diabetes provider cohort. These data raise the need to address provider implicit bias in diabetes care. |
format | Online Article Text |
id | pubmed-9422789 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Mary Ann Liebert, Inc., publishers |
record_format | MEDLINE/PubMed |
spelling | pubmed-94227892022-08-30 Implicit Racial–Ethnic and Insurance-Mediated Bias to Recommending Diabetes Technology: Insights from T1D Exchange Multicenter Pediatric and Adult Diabetes Provider Cohort Odugbesan, Ori Addala, Ananta Nelson, Grace Hopkins, Rachel Cossen, Kristina Schmitt, Jessica Indyk, Justin Jones, Nana-Hawa Yayah Agarwal, Shivani Rompicherla, Saketh Ebekozien, Osagie Diabetes Technol Ther Original Articles BACKGROUND: Despite documented benefits of diabetes technology in managing type 1 diabetes, inequities persist in the use of these devices. Provider bias may be a driver of inequities, but the evidence is limited. Therefore, we aimed to examine the role of race/ethnicity and insurance-mediated provider implicit bias in recommending diabetes technology. METHOD: We recruited 109 adult and pediatric diabetes providers across 7 U.S. endocrinology centers to complete an implicit bias assessment composed of a clinical vignette and ranking exercise. Providers were randomized to receive clinical vignettes with differing insurance and patient names as proxy for Racial–Ethnic identity. Bias was identified if providers: (1) recommended more technology for patients with an English name (Racial–Ethnic bias) or private insurance (insurance bias), or (2) Race/Ethnicity or insurance was ranked high (Racial–Ethnic and insurance bias, respectively) in recommending diabetes technology. Provider characteristics were analyzed using descriptive statistics and multivariate logistic regression. RESULT: Insurance-mediated implicit bias was common in our cohort (n = 66, 61%). Providers who were identified to have insurance-mediated bias had greater years in practice (5.3 ± 5.3 years vs. 9.3 ± 9 years, P = 0.006). Racial–Ethnic-mediated implicit bias was also observed in our study (n = 37, 34%). Compared with those without Racial–Ethnic bias, providers with Racial–Ethnic bias were more likely to state that they could recognize their own implicit bias (89% vs. 61%, P = 0.001). CONCLUSION: Provider implicit bias to recommend diabetes technology was observed based on insurance and Race/Ethnicity in our pediatric and adult diabetes provider cohort. These data raise the need to address provider implicit bias in diabetes care. Mary Ann Liebert, Inc., publishers 2022-09-01 2022-08-24 /pmc/articles/PMC9422789/ /pubmed/35604789 http://dx.doi.org/10.1089/dia.2022.0042 Text en © Ori Odugbesan, et al., 2022; Published by Mary Ann Liebert, Inc. https://creativecommons.org/licenses/by-nc/4.0/This Open Access article is distributed under the terms of the Creative Commons Attribution Noncommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited. |
spellingShingle | Original Articles Odugbesan, Ori Addala, Ananta Nelson, Grace Hopkins, Rachel Cossen, Kristina Schmitt, Jessica Indyk, Justin Jones, Nana-Hawa Yayah Agarwal, Shivani Rompicherla, Saketh Ebekozien, Osagie Implicit Racial–Ethnic and Insurance-Mediated Bias to Recommending Diabetes Technology: Insights from T1D Exchange Multicenter Pediatric and Adult Diabetes Provider Cohort |
title | Implicit Racial–Ethnic and Insurance-Mediated Bias to Recommending Diabetes Technology: Insights from T1D Exchange Multicenter Pediatric and Adult Diabetes Provider Cohort |
title_full | Implicit Racial–Ethnic and Insurance-Mediated Bias to Recommending Diabetes Technology: Insights from T1D Exchange Multicenter Pediatric and Adult Diabetes Provider Cohort |
title_fullStr | Implicit Racial–Ethnic and Insurance-Mediated Bias to Recommending Diabetes Technology: Insights from T1D Exchange Multicenter Pediatric and Adult Diabetes Provider Cohort |
title_full_unstemmed | Implicit Racial–Ethnic and Insurance-Mediated Bias to Recommending Diabetes Technology: Insights from T1D Exchange Multicenter Pediatric and Adult Diabetes Provider Cohort |
title_short | Implicit Racial–Ethnic and Insurance-Mediated Bias to Recommending Diabetes Technology: Insights from T1D Exchange Multicenter Pediatric and Adult Diabetes Provider Cohort |
title_sort | implicit racial–ethnic and insurance-mediated bias to recommending diabetes technology: insights from t1d exchange multicenter pediatric and adult diabetes provider cohort |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9422789/ https://www.ncbi.nlm.nih.gov/pubmed/35604789 http://dx.doi.org/10.1089/dia.2022.0042 |
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