Evidence map›Paper›PMID 35338091›Full record

ArticleBMJ evidence-based medicine2022

Factors influencing estimated effectiveness of COVID-19 vaccines in non-randomised studies.

John P A Ioannidis

Abstract read
In one paragraph

Article in BMJ evidence-based medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
–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

28 citing papers in PubMed.

  1. Article
  2. Interim 2025/26 influenza vaccine effectiveness estimates with immuno-epidemiological considerations for A(H3N2) subclade K protection, Canada, January 2026.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2026
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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

1 author.

John P A IoannidisStanford Prevention Research Center, Department of Medicine and Department of Epidemiology and Population Health, and Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA jioannid@stanford.edu.ORCID 0000-0003-3118-6859

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-randomised studies assessing COVID-19 vaccine effectiveness need to consider multiple factors that may generate spurious estimates due to bias or genuinely modify effectiveness. These include pre-existing immunity, vaccination misclassification, exposure differences, testing, disease risk factor confounding, hospital admission decision, treatment use differences, and death attribution. It is useful to separate whether the impact of each factor admission decision, treatment use differences, and death attribution. Steps and measures to consider for improving vaccine effectiveness estimation include registration of studies and of analysis plans; sharing of raw data and code; background collection of reliable information; blinded assessment of outcomes, e.g. death causes; using maximal/best information in properly-matched studies, multivariable analyses, propensity analyses, and other models; performing randomised trials, whenever possible, for suitable questions, e.g. booster doses or comparative effectiveness of different vaccination strategies; living meta-analyses of vaccine effectiveness; better communication with both relative and absolute metrics of risk reduction and presentation of uncertainty; and avoidance of exaggeration in communicating results to the general public.

Indexed as

COVID-19COVID-19 VaccinesBiasHospitalizationHumansUncertaintyCOVID-19 VaccinesCOVID-19vaccination

Identifiers

PMID35338091
PMCPMC9691814

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.