# COVID-19 in the years 2020 to 2022 in Germany: effects of comorbidities and co-medications based on a large-scale database analysis

## TL;DR

This study analyzed German health data to identify how pre-existing conditions and medications affected the severity of COVID-19 from 2020 to 2022.

## Contribution

The study demonstrates the use of claims data to identify risk factors for severe COVID-19 and highlights the role of comorbidities and medications.

## Key findings

- Immunodeficiencies and immunosuppressives were the strongest risk factors for hospitalization due to COVID-19.
- Diabetes, hypertension, and depression were also associated with increased hospitalization risk.
- Claims data proved effective for identifying risk factors and could support future pandemic surveillance.

## Abstract

The SARS-CoV-2 pandemic was a challenge for health care systems worldwide. People with pre-existing chronic diseases have been identified as vulnerable patient groups. Furthermore, some of the drugs used for these chronic diseases such as antihypertensive drugs have been discussed as possible influencing factors on the progression of COVID-19. This study examines the effect of medication- and morbidity-associated risk factors suspected to moderate the disease course and progression of COVID-19.

The study is based on claims data of the Techniker Krankenkasse, Germany’s largest statutory health insurance. The data cover the years 2020 to 2022 and include insured persons with COVID-19 diagnosis from both the outpatient and inpatient sectors and a control of insured persons without COVID-19 diagnosis. We conducted a matched case-control study and matched each patient with an inpatient diagnosis of COVID-19 to (a) 10 control patients and (b) one patient with an outpatient diagnosis of COVID-19 to form two study cohorts. We performed a descriptive analysis to describe the proportion of patients in the two cohorts who were diagnosed with comorbidities or medication use known to influence the risk of COVID-19 progression. Multiple logistic regression models were used to identify risk factors for disease progression.

In the first study period the first study cohort comprised a total of 150,018 patients (13,638 cases hospitalised with COVID-19 and 136,380 control patients without a COVID-19 infection). Study cohort 2 included 27,238 patients (13,619 patients hospitalised with COVID-19 and 13,619 control patients with an outpatient COVID-19 diagnosis). Immunodeficiencies and use of immunosuppressives were strongest risk modifying factors for hospitalization in both study populations. Other comorbidities associated with hospitalization were diabetes, hypertension, and depression.

We have shown that hospitalisation with COVID-19 is associated with past medical history and medication use. Furthermore, we have demonstrated the ability of claims data as a timely available data source to identify risk factors for COVID-19 severity based on large numbers of patients. Given our results, claims data have the potential to be useful as part of a surveillance protocol allowing early-stage access to epidemiological data in future pandemics.

The online version contains supplementary material available at 10.1186/s12889-024-21110-7.