Erhöhtes Risiko eines COVID-19-bedingten Krankenhausaufenthaltes für Arbeitslose: Eine Analyse von Krankenkassendaten von 1,28 Mio. Versicherten in Deutschland

  1. Morten Wahrendorf
  2. Christoph J. Rupprecht
  3. Olga Dortmann
  4. Maria Scheider
  5. Nico Dragano

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Abstract

Hintergrund und Ziel

Arbeitslosigkeit steht in Zusammenhang mit Armut und ist ein Risikofaktor für schlechte Gesundheit. Der vorliegende Beitrag untersucht, ob Arbeitslosigkeit das Risiko für einen COVID-19-bedingten Krankenhausaufenthalt für Männer und Frauen im erwerbsfähigen Alter in Deutschland erhöht.

Methoden

Die Auswertungen verwenden Krankenkassendaten der AOK Rheinland/Hamburg (vom 01.01.2020 bis zum 18.06.2020) mit Daten zu 1.288.745 Personen zwischen 18 und 65 Jahren. 4 Erwerbssituationen werden unterschieden: (1) reguläre Erwerbstätigkeit, (2) Niedriglohntätigkeit mit Sozialleistungen, (3) Arbeitslosigkeit mit Bezug von Arbeitslosengeld 1 (Alg I) und (4) Langzeitarbeitslosigkeit mit Bezug von Arbeitslosengeld 2 (Alg II). COVID-19-Krankenhausaufenthalte werden über Meldungen der Krankenhäuser anhand der ICD-Codes U07.1 und U07.2 bestimmt. Berechnet werden multiple logistische Regressionsmodelle (für Alter und Geschlecht adjustiert).

Ergebnisse

1521 Personen hatten im Beobachtungszeitraum einen Krankenhausaufenthalt mit COVID-19 als Haupt- oder als Nebendiagnose. Dies entspricht insgesamt einer Rate von 118 Fällen pro 100.000 Versicherten. Die Raten variieren je nach Erwerbssituation. Im Vergleich zu regulär Erwerbstätigen liegt das Odds Ratio im Falle von Langzeitarbeitslosigkeit (Alg II) bei 1,94 (KI 95 %: 1,74–2,15), für Empfänger von Alg I bei 1,29 (KI 95 %: 0,86–1,94) und für Niedriglohnverdiener bei 1,33 (KI 95 %: 0,98–1,82).

Schlussfolgerung

Die Ergebnisse stimmen mit früheren Studien aus den USA und Großbritannien zu sozioökonomischen Ungleichheiten bzgl. Risikos von COVID-19-Krankenhausaufenthalten überein. Dies liefert erste Hinweise dafür, dass sozioökonomische Unterschiede in Bezug auf schwere Verläufe von COVID-19 auch in Deutschland auftreten.

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  1. Version published to 10.1007/s00103-021-03280-6
  2. ScreenIT

    SciScore for 10.1101/2020.06.17.20133918: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variableFor this study, we rely on data of 2,799,119 persons who were insured at any time during the observation period from 01.01.2020 to 04.06.2020 and applied the following restrictions: First, we restricted the analyses to men and women between the ages of 18 and 65 at the beginning of the observation period (to avoid an age-bias).

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and Limitations: We were able to study the complete population of insured people of the insurance carrier who meet the inclusion criteria which guarantees a high internal validity of the sample. The large number of observations and the reporting of diagnosis on a daily basis must be considered as further a strength of the analyses. Our measure of unemployment is also based on official standardized records which meant that the reliability of the main exposure variable was high. Yet, the present study has important limitations and calls for future studies. First, we used data from only one statutory insurance carrier for people living in specific regions of Germany, while there are several other carriers in Germany. Our study population may therefore be selective. Specifically, it is well-known that the health insurance we have used in this study does historically over-represent persons with unemployment. This does surely prevent to draw conclusions about the composition of the labour force in Germany (e.g. percentage of unemployed). But it is unlikely that it affects the reported associations between unemployment and hospitalization. Second, our study relies on data and an observation period that covers an early period of the COVID-19 pandemic with rather low number of infections from January to March 2020, and we may question if findings will be even stronger once the Pandemic has progressed. Next, administrative data includes only limited information on socio-demog...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.06.17.20133918v1 on medRxiv