Reproducibility of health claims in meta-analysis studies of COVID quarantine (stay-at-home) orders

TL;DR

This study assesses the reproducibility of claims in meta-analyses on COVID quarantine effects, revealing concerns about research transparency and reliability in key health outcomes.

Contribution

It introduces a p-value plotting method to independently evaluate the reproducibility of meta-analyses in COVID research.

Findings

Three meta-analyses raise questions about quarantine benefits/risks

One meta-analysis on suicidal ideation is unreliable

Highlights issues of research transparency and reproducibility

Abstract

The coronavirus pandemic (COVID) has been an extraordinary test of modern government scientific procedures that inform and shape policy. Many governments implemented COVID quarantine (stay-at-home) orders on the notion that this nonpharmaceutical intervention would delay and flatten the epidemic peak and largely benefit public health outcomes. The overall research capacity response to COVID since late 2019 has been massive. Given lack of research transparency, only a small fraction of published research has been judged by others to be reproducible before COVID. Independent evaluation of published meta-analysis on a common research question can be used to assess the reproducibility of a claim coming from that field of research. We used a p-value plotting statistical method to independently evaluate reproducibility of specific research claims made in four meta-analysis studies related to benefits/risks of COVID quarantine orders. Outcomes we investigated included: mortality, mental health symptoms, incidence of domestic violence, and suicidal ideation (thoughts of killing yourself). Three of the four meta-analyses that we evaluated (mortality, mental health symptoms, incidence of domestic violence) raise further questions about benefits/risks of this form of intervention. The fourth meta-analysis study (suicidal ideation) is unreliable. Given lack of research transparency and irreproducibility of published research, independent evaluation of meta-analysis studies using p-value plotting is offered as a way to strengthen or refute (falsify) claims made in COVID research.