Evidence map›Paper›PMID 41954439›Full record

ArticleScience progress

Association between occupational hazard exposure and elevated total cholesterol among automobile manufacturing plant workers: A retrospective cross-sectional study based on latent class analysis.

Huixia Ji, Dandan Liu, Ye Chen

Abstract read
In one paragraph

Article in Science progress. 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
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0citing papers in PubMed
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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

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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

3 authors.

Huixia JiDepartment of Occupational Disease, Nanjing Prevention and Treatment Center for Occupational Diseases, Nanjing, Jiangsu, China.ORCID 0000-0003-2103-2637
Dandan LiuDepartment of Occupational Disease, Nanjing Prevention and Treatment Center for Occupational Diseases, Nanjing, Jiangsu, China.
Ye ChenDepartment of Occupational Disease, Nanjing Prevention and Treatment Center for Occupational Diseases, Nanjing, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

ObjectiveTo investigate the prevalence of elevated total cholesterol (TC) among workers in an automobile manufacturing plant, as well as to examine the impact of occupational exposure to hazardous factors on their TC levels.MethodsThis was a retrospective cross-sectional study. A latent class analysis was performed using occupational health examination data collected to classify the nine major occupational hazards into five distinct exposure profiles. Multivariate logistic regression models were employed to identify risk factors associated with elevated TC.ResultsA total of 191 workers (27.0%) were diagnosed with elevated TC in the study. Based on latent class analysis, occupational hazards were classified into five distinct groups: Low Exposure Group, Noise Exposure Group, Dust Exposure Group, Organic toxins Exposure Group, and Combined Noise and Harmful Gases Exposure Group. Multivariate logistic regression revealed that exposure to organic toxins and combined noise and harmful gases group was significantly associated with increased odds of elevated TC, with adjusted ORs (odds ratio) of 2.76 (95% confidence interval [CI]: 1.47-5.30, p=0.002) and 1.95 (95% CI: 1.05-3.70, p=0.038), respectively. Subgroup analyses indicated non-smokers and non-drinkers showed consistently elevated risk.ConclusionOccupational exposure to organic toxins, noise, and harmful gases may contribute to elevated TC levels in automobile manufacturing workers, highlighting the need for prevention strategies in occupational health settings.

Indexed as

AutomobilesCholesterolManufacturing IndustryOccupational ExposureAdultCross-Sectional StudiesFemaleHumansLatent Class AnalysisMaleMiddle AgedRetrospective StudiesRisk FactorsWorking ConditionsCholesterolautomobile manufacturing workerscross-sectional studylatent class analysisoccupational hazard factorstotal cholesterol

Identifiers

PMID41954439
PMCPMC13070155

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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.