Evidence map›Paper›PMID 39838334›Full record

ArticleBMC public health2025

Documentation of lead exposure-associated comorbidity among 4538 workers in China.

Qian Hua, Pingyang Zhang, Yong Yang, Jingchao Ren, Shouming Cui, Yuxin Yang, Loulou Cai, Jia Ding, Shouyuan Cui, Mingyang Gao and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

16 authors.

Qian Hua *Shandong First Medical University & Shandong Academy of Medical Sciences, Shandong Institute of Occupational Health and Occupational Medicine, Jinan, 250062, China.
Pingyang Zhang *Department of Environmental Health, College of Preventive Medicine, Third Military Medical University, Chongqing, 400038, China.
Yong Yang *Shandong First Medical University & Shandong Academy of Medical Sciences, Shandong Institute of Occupational Health and Occupational Medicine, Jinan, 250062, China.
Jingchao RenSchool of Public Health, Chongqing Medical University, Chongqing, 400038, China.
Shouming CuiXinxiang Institute of Occupational Disease Prevention, Xinxiang, 453003, China.
Yuxin YangXinxiang Institute of Occupational Disease Prevention, Xinxiang, 453003, China.
Loulou CaiHenan International Collaborative Laboratory for Health Effects and Intervention of Air Pollution, School of Public Health, Xinxiang Medical University, Xinxiang, 453003, China.
Jia DingHenan International Collaborative Laboratory for Health Effects and Intervention of Air Pollution, School of Public Health, Xinxiang Medical University, Xinxiang, 453003, China.
Shouyuan CuiHenan International Collaborative Laboratory for Health Effects and Intervention of Air Pollution, School of Public Health, Xinxiang Medical University, Xinxiang, 453003, China.
Mingyang GaoHenan International Collaborative Laboratory for Health Effects and Intervention of Air Pollution, School of Public Health, Xinxiang Medical University, Xinxiang, 453003, China.
David C ChristianiEnvironmental Medicine and Epidemiology Program, Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA.
William AuPharmacy, Science and Technology, University of Medicine, Targu Mures, Romania.
Yanchun MaThe 902, Hospital Logistic Support Forces, Anhui, China.
Zhongjun DuShandong First Medical University & Shandong Academy of Medical Sciences, Shandong Institute of Occupational Health and Occupational Medicine, Jinan, 250062, China. duzj1981@163.com.
Guang-Hui ZhangDepartment of Environmental Health, College of Preventive Medicine, Third Military Medical University, Chongqing, 400038, China. zhgh221@tmmu.edu.cn.
Zhao-Lin XiaSchool of Public Health, Xinjiang Medical University, Urumqi, 830017, China. zlxia@shmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSystematic documentation of morbidity with exposure assessment in a large group of lead (Pb)-exposed workers in China.

methodsUsing the cluster sampling method, Pb-exposed workers were recruited from a factory in the Henan Province, China. The morbidity information and blood lead concentration (BLC) from each worker were collected. Relationships between the two sets of data were analyzed using the logistic regression and restricted cubic spline (RCS) models.

resultsAmong the 4538 recruited workers, there were 2780 males and 1758 females, with a median age of 34 years old, and BLC ranging from 7.70 to 722.70 μg/l. Among them, 1247 (27.4%) had multiple morbidities (comorbidity). Those with the highest BLC showed a 1.79-fold (significant) increase in comorbidities compared to those with the lowest BLC (95% confidence interval = 1.43, 2.25; p < 0.001). In addition, the comorbidities exhibited a BLC dose-response relationship (p < 0.001), after adjusting for potential confounding factors. The RCS curve analysis revealed a nonlinear relationship between BLC and risk of comorbidity (p-overall < 0.001, and p-nonlinear < 0.001). Sensitivity analysis further confirmed the dose-response relationship between BLC and the risk of developing comorbidities. Additionally, the data indicate that female workers were more susceptible to comorbidities.

conclusionOur study demonstrates that comorbidity was prevalent and was dose-dependent among lead exposed workers.

Indexed as

LeadLead PoisoningOccupational ExposureAdultChinaComorbidityFemaleHumansMaleMiddle AgedYoung AdultLeadComorbidityEnvironmental epidemiologyMetal toxicityMultiple morbiditiesOccupational exposure to lead

Identifiers

PMID39838334
PMCPMC11748346

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Registered trials

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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.