Wang Yixiao, Mei Peng, Zhao Yunfei, Lu Jie, Zhang Hongbing, Zhang Zhi, Zhao Yuan, Zhu Baoli, Wang Boshen
School of Public Health, Nanjing Medical University, Nanjing 211166, China.
Jiangsu Provincial Center for Disease Prevention and Control (Jiangsu Provincial Academy of Preventive Medicine), Nanjing 210009, China.
Audiol Res. 2025 Jul 23;15(4):91. doi: 10.3390/audiolres15040091.
Hearing loss is increasingly prevalent and poses a significant public health concern. While both aging and occupational noise exposure are recognized contributors, their interactive effects and gender-specific patterns remain underexplored. This cross-sectional study analyzed data from 135,251 employees in Jiangsu Province, China. Demographic information, noise exposure metrics, and hearing thresholds were obtained through field measurements, questionnaires, and audiometric testing. Multivariate logistic regression, restricted cubic spline modeling, and interaction analyses were conducted. Machine learning models were employed to assess feature importance. A nonlinear relationship between age and high-frequency hearing loss (HFHL) was identified, with a critical inflection point at 37.8 years. Noise exposure significantly amplified HFHL risk, particularly in older adults (OR = 2.564; 95% CI: 2.456-2.677, < 0.001), with consistent findings across genders. Men exhibited greater susceptibility at high frequencies, even after adjusting for age and co-exposures. Aging and noise exposure have a joint association with hearing loss (OR = 2.564; 95% CI: 2.456-2.677, < 0.001) and an interactive association (additive interaction: RERI = 2.075, AP = 0.502, SI = 2.967; multiplicative interaction: OR = 1.265; 95% CI: 1.176-1.36, < 0.001). And machine learning also confirmed age, gender, and noise exposure as key predictors. Aging and occupational noise exert synergistic effects on auditory decline, with distinct gender disparities. These findings highlight the need for integrated, demographically tailored occupational health strategies. Machine learning approaches further validate key risk factors and support targeted screening for hearing loss prevention.
听力损失日益普遍,是一个重大的公共卫生问题。虽然衰老和职业性噪声暴露都是公认的影响因素,但其交互作用和性别特异性模式仍未得到充分研究。这项横断面研究分析了来自中国江苏省135251名员工的数据。通过现场测量、问卷调查和听力测试获取了人口统计学信息、噪声暴露指标和听力阈值。进行了多变量逻辑回归、受限立方样条建模和交互分析。采用机器学习模型评估特征重要性。研究发现年龄与高频听力损失(HFHL)之间存在非线性关系,临界拐点为37.8岁。噪声暴露显著增加了高频听力损失风险,尤其是在老年人中(OR = 2.564;95% CI:2.456 - 2.677,P < 0.001),不同性别结果一致。即使在调整年龄和共同暴露因素后,男性在高频段仍表现出更高的易感性。衰老和噪声暴露与听力损失存在联合关联(OR = 2.564;95% CI:2.456 - 2.677,P < 0.001)和交互关联(相加交互作用:RERI = 2.075,AP = 0.502,SI = 2.967;相乘交互作用:OR = 1.265;95% CI:1.176 - 1.36,P < 0.001)。机器学习也证实年龄、性别和噪声暴露是关键预测因素。衰老和职业性噪声对听力下降具有协同作用,且存在明显的性别差异。这些发现凸显了制定综合的、针对不同人群的职业健康策略的必要性。机器学习方法进一步验证了关键风险因素,并支持针对听力损失预防的靶向筛查。