Evidence map›Paper›PMID 32171256›Full record

SynthesisBMC medical research methodology2020

Evaluation of confounding in epidemiologic studies assessing alcohol consumption on the risk of ischemic heart disease.

Joshua D Wallach, Stylianos Serghiou, Lingzhi Chu, Alexander C Egilman, Vasilis Vasiliou, Joseph S Ross, John P A Ioannidis

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in BMC medical research methodology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 12 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 12 pooled it
4.6field-weighted citation impact, top 4% of its field
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, 12 syntheses or guidelines pooled it, 54 citations in OpenAlex.

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  16. COPD Management: Bridging the Gap Between Living Well and Living Longer.International journal of chronic obstructive pulmonary disease · 2025
    Article
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  18. Global rural health disparities in Alzheimer's disease and related dementias: State of the science.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023
    Review
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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

7 authors at 3 institutions in 1 country.

Joshua D WallachDepartment of Environmental Health Sciences, Yale School of Public Health, 60 College Street, 4th Floor, Room 411, New Haven, CT, 06510, USA. joshua.wallach@yale.edu.ORCID 0000-0002-2816-6905
Stylianos SerghiouMeta-Research Innovation Center at Stanford (METRICS), Stanford School of Medicine, 1265 Welch Rd, MSOB X306, Stanford, CA, 94305, USA.
Lingzhi ChuDepartment of Environmental Health Sciences, Yale School of Public Health, 60 College Street, 4th Floor, Room 411, New Haven, CT, 06510, USA.
Alexander C EgilmanCollaboration for Research Integrity and Transparency (CRIT), Yale School of Medicine, New Haven, CT, 06510, USA.
Vasilis VasiliouDepartment of Environmental Health Sciences, Yale School of Public Health, 60 College Street, 4th Floor, Room 411, New Haven, CT, 06510, USA.
Joseph S RossCenter for Outcomes Research and Evaluation, Yale-New Haven Health System, New Haven, CT, 06510, USA.
John P A IoannidisMeta-Research Innovation Center at Stanford (METRICS), Stanford School of Medicine, 1265 Welch Rd, MSOB X306, Stanford, CA, 94305, USA.
Yale University · USStanford Health Care · USYale New Haven Health System · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Yale-Mayo FDA Center for Excellence in Regulatory Science and InnovationU01FD005938 · FDA · YALE UNIVERSITY · PI Joseph Solomon Ross · 2016 to 2026
$25.4M
Understanding hospital value: provider, hospital and community effectsR01HS022882 · AHRQ · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI HORWITZ, LEORA · 2014 to 2023
$2.9M
Optimizing Medical Device Post-Market Surveillance for Public ValueU01FD004585 · FDA · YALE UNIVERSITY · PI KRUMHOLZ, HARLAN MARC · 2012 to 2016
$592k
AHRQ HHS R01 HS022882FDA HHS U01 FD004585FDA HHS U01 FD005938NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

backgroundAmong different investigators studying the same exposures and outcomes, there may be a lack of consensus about potential confounders that should be considered as matching, adjustment, or stratification variables in observational studies. Concerns have been raised that confounding factors may affect the results obtained for the alcohol-ischemic heart disease relationship, as well as their consistency and reproducibility across different studies. Therefore, we assessed how confounders are defined, operationalized, and discussed across individual studies evaluating the impact of alcohol on ischemic heart disease risk.

methodsFor observational studies included in a recent alcohol-ischemic heart disease meta-analysis, we identified all variables adjusted, matched, or stratified for in the largest reported multivariate model (i.e. potential confounders). We recorded how the variables were measured and grouped them into higher-level confounder domains. Abstracts and Discussion sections were then assessed to determine whether authors considered confounding when interpreting their study findings.

results85 of 87 (97.7%) studies reported multivariate analyses for an alcohol-ischemic heart disease relationship. The most common higher-level confounder domains included were smoking (79, 92.9%), age (74, 87.1%), and BMI, height, and/or weight (57, 67.1%). However, no two models adjusted, matched, or stratified for the same higher-level confounder domains. Most (74/87, 85.1%) articles mentioned or alluded to "confounding" in their Abstract or Discussion sections, but only one stated that their main findings were likely to be affected by residual confounding. There were five (5/87, 5.7%) authors that explicitly asked for caution when interpreting results.

conclusionThere is large variation in the confounders considered across observational studies evaluating the impact of alcohol on ischemic heart disease risk and almost all studies spuriously ignore or eventually dismiss confounding in their conclusions. Given that study results and interpretations may be affected by the mix of potential confounders included within multivariate models, efforts are necessary to standardize approaches for selecting and accounting for confounders in observational studies.

Indexed as

Alcohol DrinkingMyocardial IschemiaEpidemiologic StudiesHumansReproducibility of ResultsAdjustmentAlcohol exposureBiasConfoundingIschemic heart diseaseObservational studies

Identifiers

PMID32171256
PMCPMC7071725
OpenAlexW3010879785

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.