A Predictive Model for Severe Covid-19 in the Medicare Population: A Tool for Prioritizing Scarce Vaccine Supply

Abstract

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.