张晋蔚, 丘丛玺, 阮燕梅, 荣幸, 麦诗琪, 唐侍豪, 苏艺伟, 叶翠萍, 王致. 噪声作业工人高血压影响因素分析及风险预测[J]. 职业卫生与应急救援, 2019, 37(4): 316-322. DOI: 10.16369/j.oher.issn.1007-1326.2019.04.005
引用本文: 张晋蔚, 丘丛玺, 阮燕梅, 荣幸, 麦诗琪, 唐侍豪, 苏艺伟, 叶翠萍, 王致. 噪声作业工人高血压影响因素分析及风险预测[J]. 职业卫生与应急救援, 2019, 37(4): 316-322. DOI: 10.16369/j.oher.issn.1007-1326.2019.04.005
ZHANG Jinwei, QIU Congxi, RUAN Yanmei, RONG Xing, MAI Shiqi, TANG Shihao, SU Yiwei, YE Cuiping, WANG Zhi. Study on risk factors of hypertension among noise-exposed workers and prediction model[J]. Occupational Health and Emergency Rescue, 2019, 37(4): 316-322. DOI: 10.16369/j.oher.issn.1007-1326.2019.04.005
Citation: ZHANG Jinwei, QIU Congxi, RUAN Yanmei, RONG Xing, MAI Shiqi, TANG Shihao, SU Yiwei, YE Cuiping, WANG Zhi. Study on risk factors of hypertension among noise-exposed workers and prediction model[J]. Occupational Health and Emergency Rescue, 2019, 37(4): 316-322. DOI: 10.16369/j.oher.issn.1007-1326.2019.04.005

噪声作业工人高血压影响因素分析及风险预测

Study on risk factors of hypertension among noise-exposed workers and prediction model

  • 摘要:
    目的 探讨噪声作业工人患高血压的影响因素,建立高血压风险模型及个体预测工具。
    方法 选取2017年2家汽车制造企业的4 951名男性噪声作业工人为研究对象,以职业健康检查及现场调查资料为基础,分析高血压患病情况。采用lasso-logistic回归分析法筛选作业工人患高血压的影响因素,建立列线图预测模型,并使用ROC曲线和决策曲线分析评价模型的实用性,应用自抽样法进行模型内部验证。
    结果 4 951名研究对象平均年龄(29.9±4.5)岁,高血压检出率为6.65%。lasso-logistic回归分析得出:累积噪声暴露量90.1~95.0 dB(A)·年组、95.1~100.0 dB(A)·年组及> 100.0 dB(A)·年组发生高血压风险分别是≤90.0 dB(A)·年组的4.666倍、11.810倍、9.785倍(P均 < 0.01);听阈提高组发生高血压的风险是正常组的1.348倍(P < 0.05);年龄31~35岁组、≥36岁组发生高血压的风险分别是≤25岁组的3.669倍、7.353倍(P均 < 0.01);高血红蛋白组发生高血压的风险是正常组的1.498倍(P < 0.01);对噪声作业工人高血压有影响的其他危险因素还包括超重或肥胖、脂肪肝、空腹高血糖(OR值为1.448~5.839,P < 0.05)。高血压风险预测列线图模型的ROC曲线下面积为0.705,经内部验证后,C指数为0.696。决策曲线分析表明,列线图模型在阈值概率超过0.03的情况下进行高血压的预防干预具有意义。
    结论 噪声作业工人有更高的患高血压的风险,年龄、BMI和其他个体因素对血压也有影响。基于lassologistic回归制作的高血压风险列线图预测模型具有一定的准确性和可操作性。

     

    Abstract:
    Objective To explore the influencing factors of hypertension among occupationally noise-exposed workers, and to establish a risk model of predicting hypertension for individual in this specific population.
    Methods A total of 4 951 male occupationally noise-exposed workers in two automobile manufacturing enterprises were studied in 2017. The prevalence of hypertension was analyzed based on occupational health examination and survey data. The influencing factors of hypertension were analyzed by lasso-logistic regression, and the predictive nomogram model was evaluated and validated by ROC curve, decision curve and bootstrap internal verification.
    Results The prevalence of hypertension among 4 951 subjects averagely aged(29.9 ±4.5) years old was 6.65%. The cumulative noise exposure(CNE) level played an important role and lasso-logistic regression analysis showed that the risk of hypertension among workers with higher CNE of 90.1-95.0 dB(A)·a, 95.1-100.0 dB (A)·a and more than 100.0 dB(A)·a was 4.666, 11.810, 9.785(P < 0.01), compared with CNE of lower than 90.1 dB(A)·a, respectively. The workers with elevated hearing threshold had higher risk (1.348) compared with workers with normal hearing threshold(P < 0.01). The risk of hypertension among workers with age of 31-35 years and more than 35 years was 3.669, 7.353 (P < 0.01) compared with age of lower than 26 years, respectively (P < 0.01). The risk of hypertension among workers with hyperhemoglobin was 1.498 times higher compared with the normal group (P < 0.01).The analysis showed that other risks of hypertension of these noise-exposed workers were overweight or obesity, fatty liver, and hyperglycemia and the risks varied from 1.448 to 5.839(P < 0.05). The AUC of ROC curve of the hypertensive risk predictive nomogram model was 0.705 and bootstrap internal verification showed a C index of 0.696. Decision curve analysis showed that the intervention could be meaningful and useful while hypertension risk threshold probability was higher than 0.03 in the predictive nomogram.
    Conclusion The occupationally noise-exposed workers had a higher risk of hypertension and their age, BMI and other individual also affected their blood pressure. The hypertensive risk predictive nomogram model based on lasso-logistic regression was operable.

     

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