Evidence map›Paper›PMID 42293317›Full record

ArticleFrontiers in epidemiology2026

Challenges and pitfalls in analyzing, reporting, and interpreting health effects related to occupational and environmental exposures.

Glinda S Cooper, Martha Powers, Krista Christensen, Suril S Mehta, Ruth M Lunn

Abstract read
In one paragraph

Article in Frontiers in epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Glinda S CooperRetired, National Center for Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC, United States.
Martha PowersRadiation Protection Division, Office of Air and Radiation, U.S. Environmental Protection Agency, Washington, DC, United States.
Krista ChristensenIndependent Researcher, Washington, DC, United States.
Suril S MehtaDivision of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, NC, United States.
Ruth M LunnDivision of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, NC, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Occupational and environmental epidemiology play a vital role in efforts to reduce preventable forms of disease and premature mortality worldwide by evaluating disease risk in relation to exposure sources, routes, and levels in a variety of industrial and community settings. There are considerable challenges in measuring these exposures due to wide variations in intensity, frequency, and duration. Because exposure levels and conditions often vary by site, comparing results across studies is also difficult. In this paper, we address these challenges and provide suggestions pertaining to the design, analysis, and reporting of individual studies as well as systemic reviews and meta-analyses. Key recommendations include reporting specific exposure levels within a study population and including quantitative bias assessment to address the impact of exposure measurement error on results within and across studies. These recommendations can improve the reporting quality and utility of occupational and environmental epidemiology, thereby strengthening its role in risk assessment.

Indexed as

biasenvironmental epidemiologymeasurement errormisclassificationoccupational epidemiologyrisk assessment

Identifiers

PMID42293317
PMCPMC13260551

What OpenQuestion holds

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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.