关键词:
Combined noise and dust
Dust
ECG
Electrocardiogram
Hypertension
Noise
摘要:
Objective To explore the impact of noise and dust exposure in the mechanical manufacturing industry on the risk of hypertension and abnormal electrocardiogram in workers, and their combined effects, in order to provide support for the prevention and treatment of occupational related diseases among workers. Methods In January 2024, A convenience sampling method was used to study 2802 on-the-job workers who underwent occupational health check-ups from January 2023 to December 2023 at a machinery manufacturing enterprise in Baotou, Inner Mongolia. Blood pressure and electrocardiogram results were analyzed in the noise group, dust group, dust noise group and control group according to the exposure factors. For count data, the chi-square test was employed to analyze differences among groups. Additionally, a binary logistic regression model was utilized to assess the impact of dust and noise exposure on the prevalence of hypertension and electrocardiogram abnormalities. Results The stratified analysis results showed that the differences in hypertension prevalence among the four groups were statistically significant (P<0.001) in males, age groups >30-40 years, >40-50 years, >50 years, different exposure durations, and different enterprise sizes. For ECG abnormalities, significant differences were observed among the four groups (P<0.001) in males, the age group >30-40 years, different enterprise sizes, and those with exposure durations ≤15 years. The trend test for hypertension prevalence across different age groups revealed that as age increased, the prevalence of hypertension showed an upward trend in the noise-exposed group, dust-exposed group, and combined dust-noise-exposed group (χ2=10.76, 4.25, 6.60, P<0.001, 0.039, 0.010) . Binary regression model analyses revealed that the the risk of hypertension in the noise group, dust group and dust noise group was 2.63 times (OR=2.63, 95%CI: 1.89~3.67, P<0.001) , 2.36 times (OR=2.36, 95%CI: 1.76~3.16, P<0.001) and 2