Evidence map›Paper›PMID 31691989›Full record

ArticleAmerican journal of industrial medicine2020

Musculoskeletal symptoms associated with workplace physical exposures estimated by a job exposure matrix and by self-report.

Marcus Yung, Ann Marie Dale, Skye Buckner-Petty, Yves Roquelaure, Alexis Descatha, Bradley A Evanoff

Open access · greenAbstract read
In one paragraph

Article in American journal of industrial medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 34% 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

3 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
  3. Self-Reported Musculoskeletal Disorders and Quality of Life in Supermarket Cashiers.International journal of environmental research and public health · 2020
    Article
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

6 authors at 3 institutions in 3 countries.

Marcus YungDivision of General Medical Sciences, Washington University School of Medicine, St Louis, Missouri.ORCID 0000-0003-3192-1470
Ann Marie DaleDivision of General Medical Sciences, Washington University School of Medicine, St Louis, Missouri.ORCID 0000-0002-5624-8967
Skye Buckner-PettyDivision of General Medical Sciences, Washington University School of Medicine, St Louis, Missouri.ORCID 0000-0003-1621-2334
Yves RoquelaureINSERM, U1085, IRSET (Institute de recherché en santé, environnement et travail), ESTER Team, University of Angers, Angers, France.ORCID 0000-0001-6918-0909
Alexis DescathaINSERM, U1085, IRSET (Institute de recherché en santé, environnement et travail), ESTER Team, University of Angers, Angers, France.ORCID 0000-0001-6028-3186
Bradley A EvanoffDivision of General Medical Sciences, Washington University School of Medicine, St Louis, Missouri.ORCID 0000-0003-0085-333X
Washington University in St. Louis · USInserm · FRConestoga College · CA

Funding

DEVELOPING A GENERAL POPULATION JOB EXPOSURE MATRIX FOR STUDIES OF WORK-RELATED MSDR01OH011076 · OH · WASHINGTON UNIVERSITY · PI EVANOFF, BRADLEY A · 2016 to 2018
$988k
ACL HHS R01OH011076Agence Nationale de la Recherche ANR-11-INBS-0002Caisse Nationale d'Assurance Maladie des Travailleurs Salariés (CNAMTS)Institut de Recherché en Santé Publique/Institut Thématique Santé PubliqueMinistère de la santé et des sports, Ministère délégué à la recherche, Institut national de la santé et de la recherche médicale, Institut national du cancer et Caisse nationale de solidarité pour l'autonomie, as well as Institute for research in public health (IReSP, CapaciT project)NIOSH CDC HHS R01 OH011076
6 · The paper itself

Abstract

backgroundA job-exposure matrix (JEM) is an efficient method to assign physical workplace exposures based on job titles. JEMs offer the possibility of linking work exposures to outcome data from national health registers that contain job titles. The French CONSTANCES JEM was constructed from self-reported physical work exposures of asymptomatic workers participating in a large general population study. We validated this general population JEM by testing its ability to demonstrate exposure-outcome associations for musculoskeletal disorders (MSD) symptoms.

methodsThe CONSTANCES JEM was evaluated by assigning exposure estimates to a validation sample of new participants in the CONSTANCES study (final n = 38 730). We used weighted Kappas to compare the level of agreement between JEM-assigned and self-reported exposures across job codes for each of the 27 physical exposure variables. We computed prevalence ratios and 95% confidence intervals using Poisson regression models adjusted for age and sex for pain at six body locations associated with work exposures estimated via individual self-report and by the JEM.

resultsAgreement between individual self-reported and JEM-assigned exposures ranged from κ = 0.16 to 0.71; generally, the level of agreement was fair to good. We observed consistent and significant associations between pain and both self-reported and JEM-assigned exposures at all body locations.

conclusionsThe CONSTANCES JEM replicated known associations between physical risk factors and prevalent MSD symptoms. Physical exposure JEMs can reduce some types of information bias, and open new avenues of research in the prevention of MSDs and other health conditions related to workplace physical activities.

Indexed as

Self ReportWorkplaceAdolescentAdultAgedFemaleFranceHumansMaleMiddle AgedMusculoskeletal DiseasesOccupational DiseasesOccupational ExposureRisk Factorsexposure assessmenthand painjob-exposure matrixmusculoskeletal disordersworkplace physical exposures

Identifiers

PMID31691989
PMCPMC9134319
OpenAlexW2983287797

What OpenQuestion holds

Textmetadata
LicenceTDM
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.