Evidence map›Paper›PMID 33781862›Full record

ArticleJournal of clinical epidemiology2021

Effect estimates of COVID-19 non-pharmaceutical interventions are non-robust and highly model-dependent.

Vincent Chin, John P A Ioannidis, Martin A Tanner, Sally Cripps

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of clinical epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023
    Pooled it
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  12. Flaws and uncertainties in pandemic global excess death calculations.European journal of clinical investigation · 2023
    Review
  13. COVID-19 and the ecological crisis: What do they have in common?Scandinavian journal of public health · 2023
    Article
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  17. What happened to the predicted COVID-19-induced suicide epidemic, and why?The Australian and New Zealand journal of psychiatry · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Vincent ChinAustralian Research Council Training Centre in Data Analytics for Resources and Environments, Sydney, New South Wales, Australia; School of Mathematics and Statistics, The University of Sydney, Sydney, New South Wales, Australia.
John P A IoannidisStanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, CA, USA; Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA; Department of Biomedical Data Sciences, Stanford University, Stanford, CA, USA; Department of Statistics, Stanford University, Stanford, CA, USA; Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA. Electronic address: jioannid@stanford.edu.
Martin A TannerDepartment of Statistics, Northwestern University, Evanston, IL, USA.
Sally CrippsAustralian Research Council Training Centre in Data Analytics for Resources and Environments, Sydney, New South Wales, Australia; School of Mathematics and Statistics, The University of Sydney, Sydney, New South Wales, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo compare the inference regarding the effectiveness of the various non-pharmaceutical interventions (NPIs) for COVID-19 obtained from different SIR models. STUDY DESIGN AND

settingWe explored two models developed by Imperial College that considered only NPIs without accounting for mobility (model 1) or only mobility (model 2), and a model accounting for the combination of mobility and NPIs (model 3). Imperial College applied models 1 and 2 to 11 European countries and to the USA, respectively. We applied these models to 14 European countries (original 11 plus another 3), over two different time horizons.

resultsWhile model 1 found that lockdown was the most effective measure in the original 11 countries, model 2 showed that lockdown had little or no benefit as it was typically introduced at a point when the time-varying reproduction number was already very low. Model 3 found that the simple banning of public events was beneficial, while lockdown had no consistent impact. Based on Bayesian metrics, model 2 was better supported by the data than either model 1 or model 3 for both time horizons.

conclusionInferences on effects of NPIs are non-robust and highly sensitive to model specification. In the SIR modeling framework, the impacts of lockdown are uncertain and highly model-dependent.

Indexed as

Models, StatisticalPhysical DistancingCommunicable Disease ControlCOVID-19EuropeHumansQuarantineSARS-CoV-2Bayesian statisticsCOVID-19Information criteriaModel comparisonNon-pharmaceutical interventionsSIR models

Identifiers

PMID33781862
PMCPMC7997643

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.