Utilization Patterns and Outcomes of People With Diabetes and COVID-19: Evidence From United States Medicare Beneficiaries in 2020.

Objective: Determine differences in utilization patterns, disease severity, and outcomes between patients with and without diabetes mellitus diagnosed with COVID-19 in 2020. Research Design and Methods: We used an observational cohort comprised of Medicare fee-for-service beneficiaries with a medical claim indicating a COVID-19 diagnosis. We performed inverse probability weighting between beneficiaries with and without diabetes to account for differences in socio-demographic characteristics and comorbidities. Results: In the unweighted comparison of beneficiaries, all characteristics were significantly different (P<0.001). Beneficiaries with diabetes were younger, more likely to be black, had more comorbidities, higher rates of Medicare-Medicaid dual-eligibility, and were less likely to be female. In the weighted sample, hospitalization rates for COVID-19 among beneficiaries with diabetes was higher (20.5% vs 17.1%; p < 0.001). Outcomes of hospitalizations were similarly worse among beneficiaries with diabetes: admissions to ICU during hospitalizations (7.78% vs. 6.11%; p < 0.001); in-hospital mortality (3.85% vs 2.93%; p < 0.001); and ICU mortality (2.41% vs 1.77%). Beneficiaries with diabetes had more ambulatory care visits (8.9 vs. 7.8, p < 0.001) and higher overall mortality (17.3% vs. 14.9%, p < 0.001) following COVID-19 diagnosis. Conclusion: Beneficiaries with diabetes and COVID-19 had higher rates of hospitalization, ICU use and overall mortality. While the mechanism of how diabetes impacts the severity of COVID-19 may not be fully understood, there are important clinical implications for persons with diabetes. A diagnosis of COVID-19 leads to greater financial and clinical burden than for their counterparts, persons without diabetes, including perhaps most significantly, higher death rates.

A proposed taxonomy for population-level prescription use patterns.

Recent and increasing discussion of prescription price transparency highlights the importance of defining, measuring and communicating prescription drug value. To help advance these goals, the authors propose a taxonomy of population-level prescription drug use patterns. The taxonomy assigns prescription use to one of five categories according to likely population-level health impact. The categories include effective, potentially discretionary, potentially harmful, wasteful, and lifestyle. The authors hope the proposed taxonomy will inform discussion of prescription drug value by providing estimates of population impact, especially the balance of anticipated benefit and harm.