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.

International, national and local trends in the spread of COVID-19: a geographic view of COVID-19 spread and the role to be played by coproduction

Background COVID-19, a respiratory disease caused by the SARS-CoV-2 virus, emerged in 2019 and led to a worldwide pandemic in 2020. The COVID-19 pandemic has been a massive natural experiment in the formation of mitigation strategies to prevent cases and to provide effective healthcare for those afflicted. Regional differences in the impact of the pandemic on morbidity and mortality have been driven by political and regional differences in the coproduction of public health and social policy. We explored the United States (US) experience of COVID-19 for trends and correlations with other nations and also at the national, regional, state and local levels. Objective To identify geographic and temporal trends in the spread of COVID-19 in the United States. Methods Population data on COVID-19 cases and mortality were acquired on a daily basis from multiple publicly available databases, including the New York Times and Johns Hopkins University. At each geographic level (national, state and county), geographic entities’ reported cases were evaluated for correlations using linear least-squares methods to identify patterns of correlation in the cases independent of scale. We evaluated for two specific characteristics: (i) the nature of the curvature of the line linking across percentile scores, ranging from concave to convex and (ii) the area under this curve, indicating how effectively a selected region (nation, state and county) is linked to its entire containing unit (world, country and state). We used this approach to identify three distinct COVID behavior phenotypes, each of which consisted of a number of states in the USA. Results We found that COVID activity in the USA follows a unique trend compared to other countries and that within the USA during the first year of the pandemic, three initial COVID phenotypes emerged: (i) the metropolitan outbreak (early outbreak phenotype);(ii) the regional outbreak (summer peak phenotype) and (iii) trans-regional outbreak (fall/winter peak phenotype), which, taken in sum, represent the overall USA national trend. Each phenotype has specific behavioral characteristics and is composed of a cluster of different states experiencing different conditions. Conclusion Our findings suggest a new opportunity for public health strategy in the pandemic, namely to apply targeted public health approaches to address the specific needs of each phenotype. In the future, we should create databases that capture key health and hardship data elements at the smallest geographic level possible and use these to track trends, predict the future and apply targeted coproduction approaches to more effectively and efficiently safeguard population health, economic vitality and social well-being.

Impact of the COVID-19 pandemic: How our response is shaping the future of cystic fibrosis care.

The findings of this body of work are presented in the eight articles included in this supplement. The impact and perspectives of adult and pediatric care teams and patient/families are covered with special attention to mental health care, the financial and personnel impacts within care programs, the experiences of vulnerable and underrepresented patient populations, and implementation of remoting monitoring. Commentaries from colleagues provide a broader perspective, offering reflections on the findings and their implications regarding the future CF care model.

A 'COVID Compass' for navigating the pandemic.

INTRODUCTION: The greatest challenge confronting political, public health, business, education and social welfare leaders in the COVID pandemic era is to restore the economy, businesses and schools without further risking public health. The 'COVID Compass' project aims to provide helpful information to guide local decisions by tracking state and local policies over time and their impact on a balanced set of outcomes-health metrics, economic trends and social hardship indicators. METHODS: We selected a parsimonious set of 'local level' health, economic and hardship outcomes and linked them to 'local level' actions aimed to decrease COVID-19 health effects and to mitigate hardship for people, businesses and the economy. Data trends will be released frequently (e.g. weekly and monthly) to show changes in health economic and social hardship 'outcomes' (based on quantitative data), alongside policy, health care, public health and individual/social 'actions' (based on both qualitative and quantitative data). RESULTS: Work on initial analytic and visualization prototypes of the COVID Compass is currently in progress at national, state and local levels. CONCLUSION: Building a national, regional and local integrated database platform that captures upstream policies, actions and behaviors and links them to downstream health, economic and social hardship outcomes will offer a more comprehensive view of the data necessary for decision-makers and citizens to more effectively and intelligently monitor and mitigate harms caused by the pandemic.