Evidence map›Paper›PMID 36589933›Full record

ArticleFrontiers in public health2022

The prediction of occupational health risks of benzene in the printing industry through multiple occupational health risk assessment models.

Bin Shi, Shibiao Su, Cuiju Wen, Tianjian Wang, Haijuan Xu, Ming Liu

Abstract read
In one paragraph

Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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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.

Bin ShiGuangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, China.
Shibiao SuGuangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, China.
Cuiju WenGuangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, China.
Tianjian WangGuangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, China.
Haijuan XuGuangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, China.
Ming LiuGuangdong Province Hospital for Occupational Disease Prevention and Treatment, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Benzene poisoning is a common occupational poisoning event in the printing industries. Up to now there is still a lack of research data on risk assessment of benzene operations in enclosed workshops. It is crucial to assess the risk level of these positions and put forward effective measures and suggestions. Methods: The information of selected companies and air samples were collected through on-site investigation, data collation and sample testing were carried out according to the requirements of Chinese standards. The Control of Substances Hazardous to Health (COSHH) Essential, the EPA non-carcinogenic risk assessment model, the Singapore exposure index method and the Chinese semi-quantitative risk assessment models were used to assess the risks of benzene. Results: The exposed groups all worked more than 8 h per day, and the cleaning, pasting, and packaging groups used general ventilation rather than local ventilation. 28.6% of the printing group and 16.7% of the pasting group had benzene concentrations that exceeded the permissible concentration-time weighted average (PC-TWA) in China. Over 60.0% of the work groups were evaluated at high risk and over 20% of the work groups were evaluated at high cancer risk by the risk assessment models. Conclusion: The Chinese exposure index method and the synthesis index method may have a stronger practicability. The printing and pasting groups may have a higher risk for benzene exposure. It is necessary to increase protective measures and strengthen occupational hygiene management to reduce risks.

Indexed as

Occupational ExposureOccupational HealthBenzeneIndustryRisk AssessmentBenzenebenzeneoccupational healthoccupational hygiene managementprinting industryrisk assessment

Identifiers

PMID36589933
PMCPMC9797023

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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.