{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T17:55:00Z","timestamp":1792000500779,"version":"4.4.0"},"reference-count":47,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Technology &amp; Therapeutics"],"published-print":{"date-parts":[[2022,9]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method:<\/jats:title>\n                    <jats:p>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\u2013Ethnic identity. Bias was identified if providers: (1) recommended more technology for patients with an English name (Racial\u2013Ethnic bias) or private insurance (insurance bias), or (2) Race\/Ethnicity or insurance was ranked high (Racial\u2013Ethnic and insurance bias, respectively) in recommending diabetes technology. Provider characteristics were analyzed using descriptive statistics and multivariate logistic regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Result:<\/jats:title>\n                    <jats:p>\n                      Insurance-mediated implicit bias was common in our cohort (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      \u2009=\u200966, 61%). Providers who were identified to have insurance-mediated bias had greater years in practice (5.3\u2009\u00b1\u20095.3 years vs. 9.3\u2009\u00b1\u20099 years,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      \u2009=\u20090.006). Racial\u2013Ethnic-mediated implicit bias was also observed in our study (\n                      <jats:italic toggle=\"yes\">n<\/jats:italic>\n                      \u2009=\u200937, 34%). Compared with those without Racial\u2013Ethnic bias, providers with Racial\u2013Ethnic bias were more likely to state that they could recognize their own implicit bias (89% vs. 61%,\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      \u2009=\u20090.001).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1089\/dia.2022.0042","type":"journal-article","created":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T12:19:19Z","timestamp":1653308359000},"page":"619-627","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":108,"title":["Implicit Racial\u2013Ethnic and Insurance-Mediated Bias to Recommending Diabetes Technology: Insights from T1D Exchange Multicenter Pediatric and Adult Diabetes Provider Cohort"],"prefix":"10.1177","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4538-0628","authenticated-orcid":false,"given":"Ori","family":"Odugbesan","sequence":"first","affiliation":[{"name":"T1D Exchange, QI &amp; Population Health Department, Boston, Massachusetts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ananta","family":"Addala","sequence":"additional","affiliation":[{"name":"Stanford University, Division of Pediatric Endocrinology &amp; Diabetes, Lucile Packard Children's Hospital, Stanford, California, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Grace","family":"Nelson","sequence":"additional","affiliation":[{"name":"Le Bonheur Children's Hospital, Pediatric Endocrinology, Memphis, Tennessee, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachel","family":"Hopkins","sequence":"additional","affiliation":[{"name":"SUNY Upstate Medical Center, Division of Endocrinology and Metabolism, Syracuse, New York, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristina","family":"Cossen","sequence":"additional","affiliation":[{"name":"Children's Healthcare of Atlanta, Division of Pediatric Endocrinology, Atlanta, Georgia, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0733-2000","authenticated-orcid":false,"given":"Jessica","family":"Schmitt","sequence":"additional","affiliation":[{"name":"The University of Alabama Pediatric Endocrinology and Diabetes at Birmingham Hospital, Birmingham, Alabama, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Indyk","sequence":"additional","affiliation":[{"name":"Nationwide Children Hospital, Division of Endocrinology, Columbus, Ohio, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nana-Hawa Yayah","family":"Jones","sequence":"additional","affiliation":[{"name":"Cincinnati Children's Hospital, Division of Endocrinology, Cincinnati, Ohio, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4506-3702","authenticated-orcid":false,"given":"Shivani","family":"Agarwal","sequence":"additional","affiliation":[{"name":"Yeshiva University Albert Einstein College of Medicine, Division of Endocrinology, Bronx, New York, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saketh","family":"Rompicherla","sequence":"additional","affiliation":[{"name":"T1D Exchange, QI &amp; Population Health Department, Boston, Massachusetts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8473-249X","authenticated-orcid":false,"given":"Osagie","family":"Ebekozien","sequence":"additional","affiliation":[{"name":"T1D Exchange, QI &amp; Population Health Department, Boston, Massachusetts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2022,8,24]]},"reference":[{"key":"e_1_3_6_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecl.2019.10.006"},{"key":"e_1_3_6_3_2","doi-asserted-by":"publisher","DOI":"10.2337\/cd21-0027"},{"key":"e_1_3_6_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecl.2019.10.009"},{"key":"e_1_3_6_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(19)31142-0"},{"key":"e_1_3_6_6_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc20-0257"},{"key":"e_1_3_6_7_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2021.6530"},{"key":"e_1_3_6_8_2","first-page":"e160","article-title":"Diabetes technology use for management of type 1 diabetes is associated with fewer adverse COVID-19 outcomes: findings from the T1D Exchange COVID-19 Surveillance registry","author":"Noor N","unstructured":"Noor N, , Ebekozien O, , Levin L, et al.: Diabetes technology use for management of type 1 diabetes is associated with fewer adverse COVID-19 outcomes: findings from the T1D Exchange COVID-19 Surveillance registry. 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