‘Not finding causal effect’ is not ‘finding no causal effect’ of school closure on COVID-19 [version 2; peer review: 1 approved, 2 approved with reservations]

Abstract

In a paper recently published in Nature Medicine, Fukumoto et al. tried to assess the government-led school closure policy during the early phase of the COVID-19 pandemic in Japan. They compared the reported incidence rates between municipalities that had and had not implemented school closure in selected periods from March–May 2020, where they matched for various potential confounders, and claimed that there was no causal effect on the incidence rates of COVID-19. However, the effective sample size (ESS) of their dataset had been substantially reduced in the process of matching due to imbalanced covariates between the treatment (i.e. with closure) and control (without closure) municipalities, which led to the wide uncertainty in the estimates. Despite the study title starting with “No causal effect of school closures”, their results are insufficient to exclude the possibility of a strong mitigating effect of school closure on incidence of COVID-19. In this replication/reanalysis study, we showed that the confidence intervals of the effect estimates from Fukumoto et al. included a 100% relative reduction in COVID-19 incidence. Simulations of a hypothetical 50% or 80% mitigating effect hardly yielded statistical significance with the same study design and sample size. We also showed that matching of variables that had large influence on propensity scores (e.g. prefecture dummy variables) may have been incomplete.

Keywords

• COVID-19
• school closure
• Japan
• causal inference
• reanalysis
• eng