Estimating the cost-of-illness associated with the COVID-19 outbreak in China from January to March 2020

  1. Huajie Jin
  2. Haiyin Wang
  3. Xiao Li
  4. Weiwei Zheng
  5. Shanke Ye
  6. Sheng Zhang
  7. Jiahui Zhou
  8. Mark Pennington

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Abstract

Background COVID-19, an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), swept through China in 2019-2020, with over 80,000 confirmed cases reported by end of March 2020. This study estimates the economic burden of COVID-19 in 31 provincial-level administrative regions in China between January and March 2020. Methods The healthcare and societal cost of COVID-19 was estimated using bottom-up approach. The main cost components included identification, diagnosis and treatment of COVID-19, compulsory quarantine and productivity losses for all affected residents in China during the study period. Input data were obtained from government reports, clinical guidelines, and other published literature. The primary outcomes were total health and societal costs. Costs were reported in both RMB and USD (2019 value). Outcomes The total estimated healthcare and societal cost associated with the outbreak is 4.26 billion RMB (0.62 billion USD) and 2,647 billion RMB (383 billion USD), respectively. The main components of routine healthcare costs are inpatient care (41.0%) and medicines (30.9%). The main component of societal costs is productivity losses (99.8%). Hubei province incurred the highest healthcare cost (83.2%) whilst Guangdong province incurred the highest societal cost (14.6%). Interpretation This review highlights a large economic burden of the recent COVID-19 outbreak in China. These findings will aid policy makers in making informed decisions about prevention and control measures for COVID-19. Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.15.20102863: (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:
    Strengths and limitations:

    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.

  2. Version published to 10.1101/2020.05.15.20102863v1 on medRxiv