Du Yuanzhen, Tao Xinrong, Chu Fengjen, Zou Yuanjie, Wang Jia, Ding Yu, Mu Min
School of Public Health, Anhui University of Science and Technology, Huainan, Anhui, People's Republic of China.
Noise Health. 2024;26(123):543-552. doi: 10.4103/nah.nah_29_24. Epub 2024 Dec 30.
This study aims to investigate the relationship between noise kurtosis and cardiovascular disease (CVD) risk while exploring the potential of kurtosis assessment in evaluating CVD risk associated with complex noise exposure in coal mines.
This cross-sectional study started in April 2021 and ended in November 2022. It involved 705 coal miners selected from 1045 participants. The participants underwent questionnaire surveys, physical examinations and assessment of individual noise exposure levels in the form of LAeq.8h. Individual CVD risk was evaluated by employing the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR). Logistic regression analyses were used to analyse the effect of noise kurtosis on the risk of developing CVD and adjust for confounders to determine independent effects. Stratified analyses were applied to examine the effect of different noise characteristics on risk.
In cardiovascular risk assessment using China-PAR, 637 and 68 individuals were classified as low and high risk, respectively. Notably, the group exposed to noise levels of 85-100 dB(A) exhibited a significantly higher risk than those exposed to noise levels of <85 dB(A). Regarding kurtosis (β), individuals with β < 3 had low cardiovascular risk, whereas those with β > 5 had high risk, with risk increasing alongside kurtosis. Correlation analysis indicated a strong association amongst noise kurtosis, cumulative noise exposure (CNE) and CVD risk after accounting for individual age and service duration. Logistic regression analysis identified kurtosis as the primary influencing factor for CVD risk (odds ratio = 3.46, 95% confidence interval: 1.68-7.13).
Given the pervasive presence of complex noise in the coal mining industry, kurtosis can serve as a valuable supplementary parameter for adjusting CNE, thus facilitating the assessment of CVD risk associated with complex noise exposure in coal mines.
本研究旨在探讨噪声峰度与心血管疾病（CVD）风险之间的关系，同时探索峰度评估在评估与煤矿复杂噪声暴露相关的CVD风险中的潜力。
这项横断面研究于2021年4月开始，2022年11月结束。研究对象为从1045名参与者中选取的705名煤矿工人。参与者接受了问卷调查、体格检查，并以等效连续A声级（LAeq.8h）的形式评估了个体噪声暴露水平。采用中国动脉粥样硬化性心血管疾病风险预测模型（China-PAR）评估个体CVD风险。采用逻辑回归分析来分析噪声峰度对发生CVD风险的影响，并对混杂因素进行校正以确定独立影响。应用分层分析来检验不同噪声特征对风险的影响。
在使用China-PAR进行的心血管风险评估中，分别有637人和68人被归类为低风险和高风险。值得注意的是，暴露于85-100 dB(A)噪声水平的组比暴露于<85 dB(A)噪声水平的组表现出显著更高的风险。关于峰度（β），β<3的个体心血管风险较低，而β>5的个体风险较高，风险随峰度增加。相关性分析表明，在考虑个体年龄和服务年限后，噪声峰度、累积噪声暴露（CNE）与CVD风险之间存在密切关联。逻辑回归分析确定峰度是CVD风险的主要影响因素（比值比=3.46，95%置信区间：1.68-7.13）。
鉴于煤矿行业中复杂噪声普遍存在，峰度可作为调整CNE的有价值的补充参数，从而有助于评估与煤矿复杂噪声暴露相关的CVD风险。
