We analyse prior risk factors for severe, critical or fatal courses of Covid-19 based on a retrospective cohort using claims data of the AOK Bayern. As our main methodological contribution, we avoid prior grouping and pre-selection of candidate risk factors. Instead, fine-grained hierarchical information from medical classification systems for diagnoses, pharmaceuticals and procedures are used, resulting in more than 33,000 covariates. Our approach has better predictive ability than well-specified morbidity groups but does not need prior subject-matter knowledge. The methodology and estimated coefficients are made available to decision makers to prioritize protective measures towards vulnerable subpopulations and to researchers who like to adjust for a large set of confounders in studies of individual risk factors.
@article{arxiv.2102.02697,
title = {Covid-19 risk factors: Statistical learning from German healthcare claims data},
author = {Roland Jucknewitz and Oliver Weidinger and Anja Schramm},
journal= {arXiv preprint arXiv:2102.02697},
year = {2021}
}