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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...

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Autores principales: Odugbesan, Ori, Addala, Ananta, Nelson, Grace, Hopkins, Rachel, Cossen, Kristina, Schmitt, Jessica, Indyk, Justin, Jones, Nana-Hawa Yayah, Agarwal, Shivani, Rompicherla, Saketh, Ebekozien, Osagie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Mary Ann Liebert, Inc., publishers 2022
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.
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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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