Effect of underlying comorbidities on the infection and severity of COVID-19 in South Korea

  1. Wonjun Ji
  2. Kyungmin Huh
  3. Minsun Kang
  4. Jinwook Hong
  5. Gi Hwan Bae
  6. Rugyeom Lee
  7. Yewon Na
  8. Hyoseon Choi
  9. Seon Yeong Gong
  10. Yoon-Hyeong Choi
  11. Kwang-Pil Ko
  12. Jeong-Soo Im
  13. Jaehun Jung

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Abstract

Background

The coronavirus disease (COVID-19) pandemic is an emerging threat worldwide. It is still unclear how comorbidities affect the risk of infection and severity of COVID-19.

Methods

A nationwide retrospective case-control study of 65,149 individuals, aged 18 years or older, whose medical cost for COVID-19 testing were claimed until April 8, 2020. The diagnosis of COVID-19 and severity of COVID-19 infection were identified from the reimbursement data using diagnosis codes and based on whether respiratory support was used, respectively. Odds ratios were estimated using multiple logistic regression, after adjusting for age, sex, region, healthcare utilization, and insurance status.

Results

The COVID-19 group (5,172 of 65,149) was younger and showed higher proportion of females. 5.6% (293 of 5,172) of COVID-19 cases were severe. The severe COVID-19 group had older patients and a higher male ratio than the non-severe group. Cushing syndrome (Odds ratio range (ORR) 2.059-2.358), chronic renal disease (ORR 1.292-1.604), anemia (OR 1.132), bone marrow dysfunction (ORR 1.471-1.645), and schizophrenia (ORR 1.287-1.556) showed significant association with infection of COVID-19. In terms of severity, diabetes (OR 1.417, 95% CI 1.047-1.917), hypertension (OR 1.378, 95% CI 1.008-1.883), heart failure (ORR 1.562-1.730), chronic lower respiratory disease (ORR 1.361-1.413), non-infectious lower digestive system disease (ORR 1.361-1.418), rheumatoid arthritis (ORR 1.865-1.908), substance use (ORR 2.790-2.848), and schizophrenia (ORR 3.434-3.833) were related with severe COVID-19.

Conclusions

We identified several comorbidities associated with COVID-19. Health care workers should be more careful when diagnosing and treating COVID-19 when the patient has the above-mentioned comorbidities.

Take Home message

Comorbidities that might be associated with COVID-19 infection and severe clinical course were identified, which could assist in formulating public health measures to mitigate the risk in groups with increased risk.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.08.20095174: (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 variablenot detected.

    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:
    This study had a few limitations. Firstly, this study was limited to data from the nationwide claim database of subjects who received laboratory test of COVID-19. Thus, the database including those who were tested via “Drive Through” or local health centers and treated in non-medical facility was not included in this study. Therefore, the number of officially announced population was different from that of the analyzed population, which was used in this study. Despite this limitation, this study was conducted only for those who performed the laboratory test for COVID-19 on insurance claims database. Therefore, the negative control group had a confirmed a negative result of SARS-CoV-2 infection. Thus, the comparison between this negative control group and case group helped in proper evaluation of the risk factors for COVID 19 occurrence. Another limitation was the inability of the data source to provide the information on the severity of the comorbidities. Finally, we could not evaluate the detailed mechanism of the relationship between comorbidities and the diagnosis or severity of COVID-19. However, most comorbidities identified in each individual’s health insurance claim data could be used for this study. It could be possible to discover previously unknown or unexpected risk factors based on a data driven approach.

    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

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  2. Version published to 10.1101/2020.05.08.20095174 on medRxiv
  3. Version published to 10.3346/jkms.2020.35.e237