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Recommendations for prioritizing COVID-19 vaccination have focused on the elderly at higher risk for severe disease. Existing models for identifying higher-risk individuals lack the needed integration of socio-demographic and clinical risk factors. Using multivariate logistic regression and random forest modeling, we developed a predictive model of severe COVID-19 using clinical data from Medicare claims for 16 million Medicare beneficiaries and socio-economic data from the CDC Social Vulnerability Index. Predicted individual probabilities of COVID-19 hospitalization were then calculated for population risk stratification and vaccine prioritization and mapping. The leading COVID-19 hospitalization risk factors were non-white ethnicity, end-stage renal disease, advanced age, prior hospitalization, leukemia, morbid obesity, chronic kidney disease, lung cancer, chronic liver disease, pulmonary fibrosis or pulmonary hypertension, and chemotherapy. However, previously reported risk factors such as chronic obstructive pulmonary disease and diabetes conferred modest hospitalization risk. Among all social vulnerability factors, residence in a low-income zip code was the only risk factor independently predicting hospitalization. This multifactor risk model and its population risk dashboard can be used to optimize COVID-19 vaccine allocation in the higher-risk Medicare population.

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Kaizen is a proven management technique that has a practical application for health care in the context of health care reform and the 2010 Institute of Medicine landmark report on the future of nursing. Compounded productivity is the unique benefit of kaizen, and its principles are change, efficiency, performance of key essential steps, and the elimination of waste through small and continuous process improvements. The kaizen model offers specific instruction for perioperative nurses to achieve process improvement in a five-step framework that includes teamwork, personal discipline, improved morale, quality circles, and suggestions for improvement.

Background: Public Health interventions to slow the spread of the Covid-19 pandemic focus on protecting individuals at risk for severe disease. Risk categorization is essential to effective pandemic response. However, existing risk models for severe Covid-19 lack needed integration of both socio-demographic and clinical risk factors, and geographic characteristics. Methods: We present an integrated multi-factor risk model for severe Covid-19 using de-identified Medicare claims from which we extracted demographic and clinical data for a cohort of 15 million Medicare beneficiaries with 770,000 Covid-19 cases, and socio-economic data at the county and zip code level from the CDC Social Vulnerability Index. The model and associated digital maps were developed as part of Project Salus of the Department of Defense Joint Artificial Intelligence Center, for use by the National Guard and other military personnel in their support mission to hospitals and local jurisdictions impacted by the pandemic. Results: The model affirms ethnicity (Black: OR 1.64; 95% CI 1.61-1.68, American Indian: OR 2.21; 95% CI 2.01-2.42), age over 85 (OR 1.75, 95% CI 1.69-1.81), the socio-economic factor of residing in a zip code in the lowest quartile of income (OR 1.23; 95% CI 1.21-1.26), ESRD (OR 2.35; 95% CI 2.25-2.45) and chronic lung disease (OR 1.95; 95% CI 1.90-2.00) as leading risk factors for Covid-19 hospitalizations, but reveals low risk for COPD (OR 1.15; 95% CI 1.13 -1.17) and minimal or no risk for diabetes (OR 1.03; CI 1.01-1.05), CHF (OR 1.10, 95% CI 1.08-1.12) or hypertension (OR 0.96; 95% CI 0.94-0.98), and demonstrates an association between prior herpes zoster immunization (OR 0.74; 95% CI 0.71-0.77), and to a lesser degree prior influenza and pneumococcal vaccines with less severe Covid-19. Conclusions: This multi-factor risk model and derived digital maps can be applied for use by national and local health authorities to augment existing tools for pandemic response, including monitoring of post Covid-19 sequelae, prioritization of Covid-19 vaccine, and vaccine monitoring for both safety and efficacy.