The relative effects of non-pharmaceutical interventions on wave one Covid-19 mortality: natural experiment in 130 countries

  1. Jonathan Stokes
  2. Alex James Turner
  3. Laura Anselmi
  4. Marcello Morciano
  5. Thomas Hone

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Abstract

Background

Non-pharmaceutical interventions have been implemented around the world to control Covid-19 transmission. Their general effect on reducing virus transmission is proven, but they can also be negative to mental health and economies, and transmission behaviours can also change voluntarily, without mandated interventions. Their relative impact on Covid-19 attributed mortality, enabling policy selection for maximal benefit with minimal disruption, is not well established due to a lack of definitive methods.

Methods

We examined variations in timing and strictness of nine non-pharmaceutical interventions implemented in 130 countries and recorded by the Oxford COVID-19 Government Response Tracker (OxCGRT): 1) School closing; 2) Workplace closing; 3) Cancelled public events; 4) Restrictions on gatherings; 5) Closing public transport; 6) Stay at home requirements (‘Lockdown’); 7) Restrictions on internal movement; 8) International travel controls; 9) Public information campaigns. We used two time periods in the first wave of Covid-19, chosen to limit reverse causality, and fixed country policies to those implemented: i) prior to first Covid-19 death (when policymakers could not possibly be reacting to deaths in their own country); and, ii) 14-days-post first Covid-19 death (when deaths were still low, so reactive policymaking still likely to be minimal). We then examined associations with daily deaths per million in each subsequent 24-day period, which could only be affected by the intervention period, using linear and non-linear multivariable regression models. This method, therefore, exploited the known biological lag between virus transmission (which is what the policies can affect) and mortality for statistical inference.

Results

After adjusting, earlier and stricter school (− 1.23 daily deaths per million, 95% CI − 2.20 to − 0.27) and workplace closures (− 0.26, 95% CI − 0.46 to − 0.05) were associated with lower Covid-19 mortality rates. Other interventions were not significantly associated with differences in mortality rates across countries. Findings were robust across multiple statistical approaches.

Conclusions

Focusing on ‘compulsory’, particularly school closing, not ‘voluntary’ reduction of social interactions with mandated interventions appears to have been the most effective strategy to mitigate early, wave one, Covid-19 mortality. Within ‘compulsory’ settings, such as schools and workplaces, less damaging interventions than closing might also be considered in future waves/epidemics.

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  1. Version published to 10.1186/s12889-022-13546-6
  2. ScreenIT

    SciScore for 10.1101/2020.10.05.20206888: (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:
    There are key limitations of this analysis to consider when interpreting the findings. First, although we control for a range of potential confounders, there is a risk of unobserved time-varying confounding. However, other methods typically used to examine causal effects of interventions, such as difference-in-differences, are biased in settings with multiple policies implemented across time and geography, because pre-intervention trends for one intervention are impacted by any effects of pre-existing policies [28]. Instead, we use the known lag period between intervention effect and our outcome of interest to control for reverse causality as much as possible. Secondly, this study only examines the impact of nationally recorded policies, meaning subnational interventions were not captured. Furthermore, we were unable to measure compliance and regional variation in implementation, as well as voluntary changes in population behaviours. We were also unable to estimate longer-term effects due to limited statistical power and increased risk of bias due to reverse causality in later periods. A key limitation is the nature of these interventions. Whilst we examine them independently and look for separate effects, there are likely to be interaction effects. Additionally, we examine a narrow window of time in the initial stages of the outbreak. Alternative analysis methods will likely be necessary to reduce bias of reverse causality in these later periods, however. Thirdly, we are onl...

    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.

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  3. Version published to 10.1101/2020.10.05.20206888v1 on medRxiv