ABSTRACT
AIMS: We examined diabetes status (no diabetes; type 1 diabetes [T1D]; type 2 diabetes [T2D]) and other demographic and clinical factors as correlates of coronavirus disease 2019 (COVID-19)-related hospitalization. Further, we evaluated predictors of COVID-19-related hospitalization in T1D and T2D. METHODS: We analyzed electronic health record data from the de-identified COVID-19 database (December 2019 through mid-September 2020; 87 US health systems). Logistic mixed models were used to examine predictors of hospitalization at index encounters associated with confirmed SARS-CoV-2 infection. RESULTS: In 116,370 adults (>=18 years old) with COVID-19 (93,098 no diabetes; 802 T1D; 22,470 T2D), factors that independently increased risk for hospitalization included diabetes, male sex, public health insurance, decreased body mass index (BMI; <25.0-29.9 kg/m2), increased BMI (>25.0-29.9 kg/m2), vitamin D deficiency/insufficiency, and Elixhauser comorbidity score. After further adjustment for concurrent hyperglycemia and acidosis in those with diabetes, hospitalization risk was substantially higher in T1D than T2D and in those with low vitamin D and elevated hemoglobin A1c (HbA1c). CONCLUSIONS: The higher hospitalization risk in T1D versus T2D warrants further investigation. Modifiable risk factors such as vitamin D deficiency/insufficiency, BMI, and elevated HbA1c may serve as prognostic indicators for COVID-19-related hospitalization in adults with diabetes.
ABSTRACT
Background: The T1D Exchange Quality Improvement Collaborative (T1DX-QI) is a national quality improvement network focused on improving health outcomes for patients with diabetes by sharing and using data to refine best practices among 41 participating clinics. Due to Covid-restrictions, engagement among learning health networks has shifted to remote platforms, and may limit engagement among members. Clinics participate in regular collaborative-wide calls and one-on-one QI check-ins with a QI coach. Clinics can also participate in Committees and have access to an EMR-based data benchmarking platform. Methods: T1DX-QI tracked network engagement quarterly, using the measures of: 1) Collaborative Calls;2) Coaching Calls;3) Committee participation;and 4) Data Benchmarking Platform Use. A radar chart (Figure 1) displays the percentage of clinics meeting the four engagement measures by quarter. Results: Findings show an increase in data benchmarking platform use among clinics from 75% in Q1 to 86.7% in Q4. The remaining measures sustain an 85% engagement or higher from Q1-Q4, with increases in subsequent quarters. Concurrently, the number of clinics participating increased from 30 in Q1 to 41 at Q4. Conclusions: Over the past year, T1DX-QI growth increased and engagement among clinics increased and sustained across various measured components. Future tracking will include data sharing via other platforms.