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.

Background: Diabetes is a risk factor for poor COVID-19 outcomes, but pediatric patients with type 1 diabetes are poorly represented in current studies. Methods: T1D Exchange coordinated a US type 1 diabetes COVID-19 registry.Forty-six diabetes centers submitted pediatric cases for patients with laboratory confirmed COVID-19. Associations between clinical factors and hospitalization were tested with Fisher's Exact Test. Logistic regression was used to calculate odds ratios for hospitalization.

Context COVID-19 morbidity and mortality are increased in type 1 diabetes (T1D), but few data focus on age-based outcomes. Objective To quantify the risk for COVID-19 related hospitalization and adverse outcomes by age in people with T1D. Design, Setting and Patients For this observational, multisite, cross-sectional study of patients with T1D and laboratory-confirmed COVID-19 from 56 clinical sites in the United States, data were collected from April 2020 to March 2021. The distribution of patient factors and outcomes across age groups (0-18, 19-40 and > 40 years) was examined. Descriptive statistics were used to describe the study population, and multivariate logistic regression models were used to analyze the relationship between age, adverse outcomes, and hospitalization. Main Outcome Measures Hospitalization for COVID-19. Results A total of 767 patients were analyzed. Fifty-four percent (n=415) were aged 0-18 years, thirty-two percent (n=247) were aged 19-40 years and fourteen percent (n=105) were aged >40 years. One-hundred and seventy patients were hospitalized, and 5 patients died. Compared to the 0-18 years age group, those >40 years of age had an adjusted odds ratio of 4.2 (95% confidence interval 2.28-7.83) for hospitalization after adjustment for gender, A1c, race, insurance type and comorbidities. Conclusions Age >40 years is a risk factor for patients with T1D and COVID-19, with children and younger adults experiencing milder disease and better prognosis. This indicates a need for age-tailored treatments, immunization, and clinical management of individuals affected by T1D.

Objective We examined United States (US) trends in diabetic ketoacidosis (DKA) among individuals with type 1 diabetes (T1D) during the COVID-19 pandemic at seven large US medical centers and factors associated with these trends. Methods We compared DKA events among children and adults with T1D during COVID-19 surge 1 (March-May 2020) and COVID-19 surge 2 (August-October 2020) to the same periods in 2019. Analysis was performed using descriptive statistics and Chi-square tests. Results We found no difference in the absolute number of T1D patients experiencing DKA in 2019 vs 2020. However, a higher proportion of non-Hispanic Blacks (NHB) experienced DKA in 2019 than non-Hispanic Whites (NHW) (44.6% vs 16.0%; p<0.001), and this disparity persisted during the COVID-19 pandemic (48.6% vs 18.6%; p<0.001). DKA was less common among patients on continuous glucose monitor (CGM) or insulin pump in 2020 compared to 2019 (CGM: 13.2% vs 15.0%, p<0.001; insulin pump: 8.0% vs 10.6%, p<0.001). In contrast to annual DKA totals, a higher proportion of patients had DKA during COVID-19 surges 1 and 2 compared to the same months in 2019 (surge 1: 7.1% vs 5.4%, p<0.001; surge 2: 6.6% vs 5.7%, p=0.001). Conclusions DKA frequency increased among T1D patients during COVID-19 surges with highest frequency among NHB. DKA was less common among patients using CGM or insulin pumps. These findings highlight the urgent need for improved strategies to prevent DKA among patients with T1D—not only under pandemic conditions, but under all conditions—especially among populations most affected by health inequities.

The optimal care of type 1 diabetes involves consistent glycemic management to avoid short-and long-term complications. However, despite advancements in diabetes technology and standards, achieving adequate glycemic levels in children and adolescents remains a challenge. This study aimed to identify factors associated with achieving the recommended A1C target of <7% from the United States–based multicenter T1D Exchange Quality Improvement Collaborative cohort, including 25,383 children and adolescents living with type 1 diabetes.