PURPOSE To examine whether trends in new-onset pediatric type 2 diabetes (T2D) -- inclusive of patients requiring hospitalization and patients managed outpatient -- were impacted during the COVID-19 pandemic, and to compare patient characteristics prior to and during COVID-19. METHODS A retrospective single-center medical-record review was conducted in a hospital which cares for 90% of Alabama’s pediatric T2D patients. Patients with new-onset T2D referred from March 2017-March 2021 were included. Counts of patients presenting per month (“monthly rates”) were computed. Linear regression models were estimated for the full sample and stratified by Medicaid and non-Medicaid insurance status. Patient characteristics prior to versus during COVID-19 were compared. RESULTS 642 patients presented with new-onset T2D over this period. Monthly rates were 11.1 ± 3.8 prior to COVID-19,19.3 ± 7.8 during COVID-19 (p=0.004). Monthly rates for Medicaid patients differed prior to and during COVID-19 (7.9 ± 3.4 vs 15.3 ± 6.6, p = 0.003) but not for non-Medicaid patients (3.3 ± 1.7 vs 4.0 ± 2.4, p=0.33). Regression results showed significant increases in monthly rates during COVID-19 for the full sample (β=, p<0.05) and for Medicaid enrollees (β=, p<0.05) Hospitalization-rate, severity of obesity, and hemoglobin A1c remained similar prior to and during COVID-19, though the proportion of male patients increased from 36.8% to 46.1% (p=0.021). CONCLUSIONS A rise in new-onset T2D was observed among Alabama’s youth during the COVID-19 pandemic, a burden that disproportionately affected Medicaid enrollees and males. Future research should explore the pathways through which the pandemic impacted pediatric T2D.

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.

Recent research confirms and expands on the previously known endocrinopathies in Down syndrome and provides more insight into potential underlying mechanisms.

Introduction Prevalence of diabetic ketoacidosis (DKA) complicated by severe hyperglycaemia and hyperosmolality and its outcomes in youth with type 2 diabetes (T2DM) are not well‐described. Our aim is to determine the frequency and clinical outcomes of isolated DKA, DKA with severe hyperglycaemia (DKA + SHG) and DKA with hyperglycaemia and hyperosmolality (DKA + HH) in youth with T2DM admitted for acute hyperglycaemic crisis. Methods Through retrospective medical record review, patients with T2DM were identified and categorized into isolated DKA, DKA + SHG (DKA + glucose ≥33.3 mmol/L) and DKA + HH (DKA + glucose ≥33.3 mmol/L + osmolality ≥320 mmol/kg). Results Forty‐eight admissions in 43 patients ages 9‐18 were included: 28 (58%) had isolated DKA, six (13%) had DKA + SHG and 14 (29%) had DKA + HH. Subgroups’ demographics and medical history were similar. Seventeen patients (35%) had acute kidney injury (AKI). Odds of AKI were higher in DKA + SHG and DKA + HH relative to isolated DKA ( P = .015 and .002 respectively). Frequency of altered mental status (AMS) was similar among groups. Three patients (6%) had concurrent soft‐tissue infections at presentation with no differences among subgroup. Three patients (6%) had other medical complications. These occurred only in patients with AKI and DKA + SHG or AKI and DKA + HH. Conclusions In youth with T2DM, severe hyperglycaemia ± hyperosmolality frequently complicates DKA. Youth with DKA and features of hyperglycaemic hyperosmolar syndrome, including isolated severe hyperglycaemia, have increased odds of AKI.

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