OBJECTIVE We examined trends in diabetic ketoacidosis (DKA) at diagnosis of type 1 diabetes at a large pediatric diabetes center between 2010 and 2017, overlapping with the Affordable Care Act’s overhaul of U.S. health care. RESEARCH DESIGN AND METHODS Colorado residents <18 years old who were diagnosed with type 1 diabetes from 2010 to 2017 and subsequently followed at the Barbara Davis Center for Diabetes were included. Logistic regression models were used to test associations among age, sex, race/ethnicity, insurance, language, year of diagnosis, and rural/nonrural residence and DKA at diagnosis. Linear regression models were used to test the association of each predictor with HbA_1c at diagnosis. RESULTS There were 2,429 subjects who met the inclusion criteria. From 2010 to 2017, the rate of DKA increased from 41 to 58%. It increased from 35.3 to 59.6% among patients with private insurance (odds ratio 1.10 [95% CI 1.05–1.15]; P < 0.0001) but remained unchanged (52.2–58.8%) among children with public insurance (1.03 [0.97–1.09]; P = 0.36). In the multivariable model, public insurance (1.33 [1.08–1.64]; P = 0.007), rural address (1.42 [1.08–1.86]; P = 0.013), and HbA_1c (1.32 [1.26–1.38]; P < 0.0001) were positively associated with DKA, whereas age, race/ethnicity, sex, and primary language were not. CONCLUSIONS The increase in the rate of DKA in patients with newly diagnosed type 1 diabetes was driven by patients with private insurance. This paradoxically occurred during a time of increasing health insurance coverage. More study is needed to understand the factors driving these changes.

Background: We describe the utilization of telemedicine visits (video or telephone) across the type 1 diabetes (T1D) Exchange Quality Improvement Collaborative (T1DX-QI) during the COVID-19 pandemic. Metrics, site-level survey results, and examples of interventions conducted to support telemedicine in T1D are shown. Materials and Methods: Thirteen clinics (11 pediatric, 2 adult) provided monthly telemedicine metrics between December 2019 and August 2020 and 21 clinics completed a survey about their telemedicine practices. Results: The proportion of telemedicine visits in T1DX-QI before the pandemic was <1%, rising to an average of 95.2% in April 2020 (range 52.3%–99.5%). Three sites initially used mostly telephone visits before converting to video visits. By August 2020, the proportion of telemedicine visits decreased to an average of 45% across T1DX-QI (range 10%–86.6%). The majority of clinics (62%) performed both video and telephone visits; Zoom was the most popular video platform used. Over 95% of clinics reported using CareLink™, Clarity ^® , Glooko™, and/or t:connect ^® to view device data, with only one center reporting automated data upload into the electronic medical record. The majority of centers had multidisciplinary teams participating in the video visits. All sites reported reimbursement for video visits, and 95% of sites reported coverage for telephone visits early on in the pandemic. Conclusions: There was rapid adoption of telemedicine in T1DX-QI during the COVID-19 pandemic. Future insurance reimbursement for telemedicine visits and the ideal ratio of telemedicine to in-person visits in T1D care remain to be determined.

The T1D Exchange established a learning platform by evaluating the current state of care and engaging 10 diabetes clinics in collaborative quality improvement (QI) activities. Participating clinics are sharing data and best practices to improve care delivery for people with type 1 diabetes. This article describes the design and initial implementation of this platform, known as the T1D Exchange Quality Improvement Collaborative. This effort has laid a foundation for learning from variation in type 1 diabetes care delivery via QI methodology and has demonstrated success in improving processes through iterative testing cycles and transparent sharing of data.

Aims: To investigate the short-term effects of the first wave of COVID-19 on clinical parameters in children with type 1 diabetes (T1D) from 82 worldwide centers participating in the Better Control in Pediatric and Adolescent Diabete S : W orking to Cr E ate C E n T ers of Reference (SWEET) registry. Materials and Methods: Aggregated data per person with T1D ≤21 years of age were compared between May/June 2020 (first wave), August/September 2020 (after wave), and the same periods in 2019. Hierarchic linear and logistic regression models were applied. Models were adjusted for gender, age-, and diabetes duration-groups. To distinguish the added burden of the COVID-19 pandemic, the centers were divided into quartiles of first wave COVID-19-associated mortality in their country. Results: In May/June 2019 and 2020, respectively, there were 16,735 versus 12,157 persons, 52% versus 52% male, median age 13.4 (Q1; Q3: 10.1; 16.2) versus13.5 (10.2; 16.2) years, T1D duration 4.5 (2.1; 7.8) versus 4.5 (2.0; 7.8) years, and hemoglobin A1c (HbA1c) 60.7 (53.0; 73.8) versus 59.6 (50.8; 70.5) mmol/mol [7.8 (7.0; 8.9) versus 7.6 (6.8; 8.6) %]. Across all country quartiles of COVID-19 mortality, HbA1c and rate of severe hypoglycemia remained comparable to the year before the first wave, while diabetic ketoacidosis rates increased significantly in the centers from countries with the highest mortality rate, but returned to baseline after the wave. Continuous glucose monitoring use decreased slightly during the first wave (53% vs. 51%) and increased significantly thereafter (55% vs. 63%, P < 0.001). Conclusions: Although glycemic control was maintained, a significant rise in DKA at follow-up was seen during first wave in the quartile of countries with the highest COVID mortality. Trial Registration: NCT04427189.

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.Results: Data from 266 patients with previously established type 1 diabetes aged <19 years with COVID‐19 were reported. Diabetic ketoacidosis (DKA) was the most common adverse outcome (n = 44, 72% of hospitalized patients). There were four hospitalizations for severe hypoglycemia, three hospitalizations requiring respiratory support (one of whom was intubated and mechanically ventilated), one case of multisystem inflammatory syndrome in children, and 10 patients who were hospitalized for reasons unrelated to COVID‐19 or diabetes. Hospitalized patients (n = 61) were more likely than nonhospitalized patients (n = 205) to have minority race/ethnicity (67% vs 39%, P < 0.001), public insurance (64% vs 41%, P < 0.001), higher A1c (11% [97 mmol/mol] vs 8.2% [66 mmol/mol], P < 0.001), and lower insulin pump and lower continuous glucose monitoring use (26% vs 54%, P < 0.001; 39% vs 75%, P < 0.001). Age and gender were not associated with risk of hospitalization. Higher A1c was significantly associated with hospitalization, with an odds ratio of 1.56 (1.34‐1.84) after adjusting for age, gender, insurance, and race/ethnicity.Conclusions: Higher A1c remained the only predictor for hospitalization with COVID‐19. Diabetic ketoacidosis is the primary concern among this group.