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.

翻译：我们利用AOK Bayern公司的索赔数据,对Covid-19严重、关键或致命课程的先前风险因素进行分析。作为我们的主要方法贡献,我们避免事先对候选风险因素进行分组和预选。相反,我们采用了诊断、药品和程序医疗分类系统中的细细分类等级信息,导致33 000多个共变数。我们的方法比明确列出的发病群体更具有预测能力,但不需要事先了解问题。方法和估计系数提供给决策者,以便优先针对脆弱的亚群群体采取保护措施,并提供给那些在个别风险因素研究中愿意为大量混杂者进行调整的研究人员。