A predictive model for severe Covid-19 in the Medicare population: A tool for prioritizing primary and booster Covid-19 vaccination

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 multi-factor risk model and its population risk dashboard can be used to optimize Covid-19 vaccine allocation in the higher risk Medicare population.