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Objective: Continuous glucose monitor (CGM) use is associated with improved glucose control. We describe the effect of continued and interrupted CGM use on hemoglobin A1c (HbA1c) in youth with public insurance. Methods: We reviewed 956 visits from 264 youth with type 1 diabetes (T1D) and public insurance. Demographic data, HbA1c and two-week CGM data were collected. Youth were classified as never user, consistent user, insurance discontinuer, and selfdiscontinuer. Visits were categorized as never-user visit, visit before CGM start, visit after CGM start, visit with continued CGM use, visit with initial loss of CGM, visit with continued loss of CGM, and visit where CGM is regained after loss. Multivariate regression adjusting for age, sex, race, diabetes duration, initial HbA1c, and body mass index were used to calculate adjusted mean and delta HbA1c. Results: Adjusted mean HbA1c was lowest for the consistent user group (HbA1c 8.6%;[95%CI 7.9,9.3]). Delta HbA1c (calculated from visit before CGM start) was lower for visit after CGM start (−0.39%;[95%CI −0.78,−0.02]) and visit with continued CGM use (−0.29%;[95%CI −0.61,0.02]), whereas it was higher for visit with initial loss of CGM (0.40%;[95%CI −0.06,0.86]), visit with continued loss of CGM (0.46%;[95%CI 0.06,0.85]), and visit where CGM is regained after loss (0.57%;[95%CI 0.06,1.10]). Conclusions: Youth with public insurance using CGM have improved HbA1c, but only when CGM use is uninterrupted. Interruptions in use, primarily due to gaps in insurance coverage of CGM, were associated with increased HbA1c. These data support both initial and ongoing coverage of CGM for youth with T1D and public insurance. K E Y W O R D S diabetes technology, health policy, insurance, minority health, pediatric type 1 diabetes 1 | INTRODUCTION Optimal glucose control paired with improved quality of life is an important management goal for youth with type 1 diabetes (T1D) and providers who care for them. 1-3 Incorporation of continuous glucose This work was previously presented in a poster abstract form at the International Society for Pediatric and Adolescent Society's 45th Annual Conference.

COVID-19 threatens to overwhelm hospital facilities throughout the United States. We created an interactive, quantitative model that forecasts demand for COVID-19 related hospitalization based on county-level population characteristics, data from the literature on COVID-19, and data from online repositories. Using this information as well as user inputs, the model estimates a time series of demand for intensive care beds and acute care beds as well as the availability of those beds. The online model is designed to be intuitive and interactive so that local leaders with limited technical or epidemiological expertise may make decisions based on a variety of scenarios. This complements high-level models designed for public consumption and technically sophisticated models designed for use by epidemiologists. The model is actively being used by several academic medical centers and policy makers, and we believe that broader access will continue to aid community and hospital leaders in their response to COVID-19.