欧丝嘉, 陈兆乾, 俞家恒, 黄海娟, 荣幸, 阮燕梅, 张玉侠, 曾文锋, 王致. 基于倾向性评分的汽车制造业工人职业性噪声暴露与血脂异常的关联研究J. 职业卫生与应急救援, 2026, 44(2): 147-152. DOI: 10.16369/j.oher.issn.1007-1326.2026.250639
引用本文: 欧丝嘉, 陈兆乾, 俞家恒, 黄海娟, 荣幸, 阮燕梅, 张玉侠, 曾文锋, 王致. 基于倾向性评分的汽车制造业工人职业性噪声暴露与血脂异常的关联研究J. 职业卫生与应急救援, 2026, 44(2): 147-152. DOI: 10.16369/j.oher.issn.1007-1326.2026.250639
OU Sijia, CHEN Zhaoqian, YU Jiaheng, HUANG Haijuan, RONG Xing, RUAN Yanmei, ZHANG Yuxia, ZENG Wenfeng, WANG Zhi. Association between occupational noise exposure and dyslipidemia among automobile manufacturing workers using propensity score matchingJ. Occupational Health and Emergency Rescue, 2026, 44(2): 147-152. DOI: 10.16369/j.oher.issn.1007-1326.2026.250639
Citation: OU Sijia, CHEN Zhaoqian, YU Jiaheng, HUANG Haijuan, RONG Xing, RUAN Yanmei, ZHANG Yuxia, ZENG Wenfeng, WANG Zhi. Association between occupational noise exposure and dyslipidemia among automobile manufacturing workers using propensity score matchingJ. Occupational Health and Emergency Rescue, 2026, 44(2): 147-152. DOI: 10.16369/j.oher.issn.1007-1326.2026.250639

基于倾向性评分的汽车制造业工人职业性噪声暴露与血脂异常的关联研究

Association between occupational noise exposure and dyslipidemia among automobile manufacturing workers using propensity score matching

  • 摘要:
    目的 了解汽车制造业工人血脂异常的流行现状,探讨职业性噪声暴露与血脂异常之间的关联,为高风险工人血脂异常的防控提供科学依据。
    方法 2024年选取广州市某汽车制造企业工人作为研究对象,通过问卷调查与临床检查收集工人的一般人口学信息及血脂水平。采用累积噪声暴露量(CNE)评估个体噪声暴露水平,在运用倾向评分匹配(PSM)法平衡年龄、月收入、轮班情况、夜班情况和佩戴听力保护用品情况等组间混杂因素后,采用logistic回归模型分析职业噪声暴露与血脂异常的关联,并进一步通过交互作用检验探索潜在的效应修饰因素。
    结果 PSM匹配前,高噪声暴露组(CNE ≥ 86.6 dB·年)血脂异常检出率为38.3%(369/963),高于低暴露组(26.1%,251/963)(P < 0.001)。经PSM匹配后,共纳入605对研究对象,对其的logistic回归分析显示:相对于低噪声暴露组,高噪声暴露组发生血脂异常的风险升高至1.295倍(95%CI:1.013 ~ 1.656,P = 0.039)。交互作用分析结果显示,年龄、BMI、教育程度、收入、吸烟、饮酒、体育锻炼、轮班、夜班、听力保护装置佩戴及肉类、蔬菜摄入量等潜在效应修饰因子对职业噪声暴露与血脂异常之间关联的修饰效应均无统计学意义(交互项均P > 0.05)。
    结论 职业性噪声暴露与血脂异常之间存在关联。建议工作场所积极推行噪声防控措施,包括引进低噪声生产设备、实施工种交叉轮岗制度等,以有效降低职业性噪声暴露所带来的健康风险。

     

    Abstract:
    Objective To investigate the prevalence of dyslipidemia among workers and its association with occupational noise exposure in the automobile manufacturing industry, providing a scientific basis for the prevention and control of dyslipidemia in high-risk workers.
    Methods In 2024, workers from an automobile manufacturing enterprise in Guangzhou were studied, and their general demographic information and clinical examination data of blood lipid levels were collected. Cumulative noise exposure (CNE) was used to assess individual noise exposure levels. After balancing confounding factors, including age, monthly income, shift work, night shifts, and use of hearing protection devices, between high noise-exposed and low noise-exposed groups using propensity score matching (PSM), a logistic regression model was employed to analyze the association between occupational noise exposure and dyslipidemia. Interaction analyses were further conducted to explore potential effect modifiers.
    Results Before PSM, the detection rate of dyslipidemia in the high noise-exposed group (CNE ≥ 86.6 dB·year) was 38.3% (369/963), which was significantly higher than that in the low noise-exposed group (26.1%, 251/963, P < 0.001). Following PSM matching for the aforementioned confounding factors, a total of 605 pairs of study subjects were included in the analysis. Logistic regression analysis revealed that, compared to the low noise-exposed group, the risk of dyslipidemia was 1.295 times higher in the high noise-exposed group (95%CI: 1.013 to 1.656, P = 0.039). Results from the interaction analysis indicated that potential effect modifiers, including age, BMI, educational level, income, smoking status, alcohol consumption, physical activity, shift work, night shifts, use of hearing protection devices, and intake of meat and vegetables, had no statistically significant modifying effect on the association between occupational noise exposure and dyslipidemia (all interaction terms: P > 0.05).
    Conclusions Occupational noise exposure was associated with dyslipidemia. It is recommended that workplaces actively implement noise control measures, including the introduction of low-noise production equipment and the adoption of job rotation systems, to effectively reduce the health risks associated with occupational noise exposure.

     

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