Do Lockdowns Bring about Additional Mortality Benefits or Costs? Evidence based on Death Records from 300 Million Chinese People

  1. Jinlei Qi
  2. Dandan Zhang
  3. Xiang Zhang
  4. Peng Yin
  5. Jiangmei Liu
  6. Yuhang Pan
  7. Tanakao Takana
  8. Peiyu Xie
  9. Zhaoguang Wang
  10. Shuocen Liu
  11. George F. Gao
  12. Guojun He
  13. Maigeng Zhou

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

After the COVID-19 outbreak, China immediately adopted stringent lockdown policies to contain the virus. Using comprehensive death records covering around 300 million Chinese people, we estimate the impacts of city and community lockdowns on non-COVID-19 mortality outside of Wuhan. Employing a difference-in-differences method, we find that lockdowns reduced the number of non-COVID-19 deaths by 4.9% (cardiovascular deaths by 6.2%, injuries by 9.2%, and non-COVID-19 pneumonia deaths by 14.3%). The health benefits are likely driven by significant reductions in air pollution, traffic, and human interactions. A back-of-the-envelope calculation shows that more than 32,000 lives could have been saved from non-COVID-19 diseases/causes during the 40 days of the lockdown on which we focus. The results suggest that the rapid and strict virus countermeasures not only effectively controlled the spread of COVID-19 but also brought about massive unintended public health benefits. These findings can help better inform policymakers around the world about the benefits and costs of city and community lockdowns policies in dealing with the COVID-19 pandemic.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.08.28.20183699: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    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:
    Strengthen and limitations of study: This study has several major strengths. First, we use the largest mortality database in China, covering a quarter of the Chinese population. The data are nationally representative, and our findings are unlikely to be affected by potential data misreporting in a specific city (i.e., Wuhan). Second, this study examines the effects of lockdowns on a variety of causes of death in China, which provides insights on the overall health implications of lockdown policies. Third, we explore the mechanism of lockdowns’ impacts on cause-specific deaths and show that improvement in air quality, reduction in accidents, and less human interactions can help explain the impacts. However, a few limitations should be noted here. First, due to the data unavailability in personal daily activities and family care at the household level, we are unable to study the effect of lockdowns on morbidity, especially for those who were concerned and did not go to hospitals when getting sick. Second, our research only focuses on the short-term effect of the lockdown policies, so our findings cannot be applied to the long-term case. In the long run, there could be a harvesting effect (normally negative) on mortality rates, especially for mortality due to chronic diseases, which might be caused by the delay of medication and physical examinations during lockdowns. Also, if lockdowns were sustained for an extended period of time, low-income households would suffer a lot, and ...

    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.
    • Thank you for including a protocol registration statement.

    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 10.1101/2020.08.28.20183699v3 on medRxiv
  3. Version published to 10.1101/2020.08.28.20183699v2 on medRxiv
  4. Version published to 10.1101/2020.08.28.20183699v1 on medRxiv