Risk analysis of noise-induced hearing loss of workers in the automobile manufacturing industries based on back-propagation neural network model: a cross-sectional study in Han Chinese population.

OBJECTIVES: This study aims to predict the risk of noise-induced hearing loss (NIHL) through a back-propagation neural network (BPNN) model. It provides an early, simple and accurate prediction method for NIHL. DESIGN: Population based, a cross sectional study. SETTING: Han, China. PARTICIPANTS: This study selected 3266 Han male workers from three automobile manufacturing industries. PRIMARY OUTCOME MEASURES: Information including personal life habits, occupational health test information and occupational exposure history were collected and predictive factors of NIHL were screened from these workers. BPNN and logistic regression models were constructed using these predictors. RESULTS: The input variables of BPNN model were 20, 16 and 21 important factors screened by univariate, stepwise and lasso-logistic regression. When the BPNN model was applied to the test set, it was found to have a sensitivity (TPR) of 83.33%, a specificity (TNR) of 85.92%, an accuracy (ACC) of 85.51%, a positive predictive value (PPV) of 52.85%, a negative predictive value of 96.46% and area under the receiver operating curve (AUC) is: 0.926 (95% CI: 0.891 to 0.961), which demonstrated the better overall properties than univariate-logistic regression modelling (AUC: 0.715) (95% CI: 0.652 to 0.777). The BPNN model has better predictive performance against NIHL than the stepwise-logistic and lasso-logistic regression model in terms of TPR, TNR, ACC, PPV and NPV (p<0.05); the area under the receiver operating characteristics curve of NIHL is also higher than that of the stepwise and lasso-logistic regression model (p<0.05). It was a relatively important factor in NIHL to find cumulative noise exposure, auditory system symptoms, age, listening to music or watching video with headphones, exposure to high temperature and noise exposure time in the trained BPNN model. CONCLUSIONS: The BPNN model was a valuable tool in dealing with the occupational risk prediction problem of NIHL. It can be used to predict the risk of an individual NIHL.

Role of CASP7 polymorphisms in noise-induced hearing loss risk in Han Chinese population.

Genetic factors and gene-environment interaction may play an important role in the development of noise induced hearing loss (NIHL). 191 cases and 191 controls were selected by case-control study. Among them, case groups were screened from workers exposed to noise in binaural high-frequency hearing thresholds greater than 25 dB (A). Workers with hearing thresholds ≤ 25 dB (A) in any binaural frequency band were selected to the control group, based on matching factors such as age, exposure time to noise, and operating position. The blood samples from two groups of workers were subjected to DNA extraction and SNP sequencing of CASP3 and CASP7 genes using the polymerase chain reaction ligase detection reaction method. Conditional logistic regression correction was used to analyze the genetic variation associated with susceptibility to NIHL. There was an association between rs2227310 and rs4353229 of the CASP7 gene and the risk of NIHL. Compared with the GG genotype, the CC genotype of rs2227310 reduced the risk of NIHL. Compared with CC genotype, the TT genotype of rs4353229 reduced the risk of NIHL. Workers carrying the rs2227310GG and rs4353229CC genotype had an increased risk of NIHL compared to workers without any high-risk genotype. There were additive interaction and multiplication interaction between CASP7rs2227310 and CNE, and the same interaction between CASP7rs4353229 and CNE. The interaction between the CASP7 gene and CNE significantly increased the risk of NIHL. The genetic polymorphisms of CASP7rs2227310GG and CASP7rs4353229CC were associated with an increased risk of NIHL in Han Chinese population and have the potential to act as biomarkers for noise-exposed workers.

Relationship between occupational noise exposure and hypertension: A cross-sectional study in steel factories.

BACKGROUND: Hazardous exposure to occupational noise may be associated with an increased risk of cardiovascular disease and hypertension. This study was performed to assess the relationship between noise exposure and hypertension prevalence in steelworkers. METHODS: A cross-sectional survey using self-reported noise exposure and audiometrically measured hearing loss was performed. One thousand eight hundred and seventy-four workers were interviewed. Multiple logistic regression was used to calculate odds ratios for hypertension by noise exposure. Linear regression analysis was used to test associations between noise exposure and systolic blood pressure (SBP) and diastolic blood pressure (DBP). RESULTS: Occupational noise-exposed subjects had significantly higher blood pressure levels than nonexposed subjects (SBP: 123.18 ± (standard deviation) 12.44 vs 119.80 ± 12.50 mm Hg; DBP: 77.86 ± 9.34 vs 75.49 ± 8.73 mmHg). The prevalence of hypertension was approximately 5% in the control group without noise exposure or hearing impairment and increased from 6% to 21% across the range of increasing degree of hearing loss and, separately, of cumulative exposure time. Noise exposure (any) was associated with an increase in the prevalence of hypertension (odds ratio, 2.03, 95% confidence interval [CI]: 1.15-3.58). Noise-induced hearing loss and cumulative noise exposure time were positively correlated with BP (hearing loss: SBP: ß = .09, 95% CI: 0.04-0.15 mm Hg, DBP: ß = .11, 95% CI: 0.06-0.17 mm Hg; cumulative exposure time: SBP: ß = .10, 95% CI: 0.04-0.15 mm Hg, DBP: ß = .09, 95% CI: 0.04-0.15 mm Hg). CONCLUSIONS: Noise exposure measured in two different ways was strongly associated with the prevalence of hypertension in steelworkers. Reducing noise in the steel factory could be a way of decreasing the risk of hypertension in this population.

[A cross-sectional study on the industrial noise over-limit status in Guangzhou factories].

OBJECTIVE: To investigate the industrial noise over-limit status of the worksites in Guangzhou factories, so as to promote the prevention and control of occupational noise hazards. METHODS: 211 factories in Guangzhou were monitored and investigated. The analysis and assessment were developed for the properties and size of the factories, the districts of the factories being located, the industries of the factories being classified and the date of monitoring. RESULTS: In this understudied factories, most of them were national-owned and joint-ventures, medium size, located in urban, and mainly involved in the industries of manufacturing of motor vehicle, shipping, electron and electric equipment, and the industries of petroleum and chemicals. The prevalence of noise over-limit was higher in joint-ventures (36.0%) and private-run enterprises (31.2%). The over-limit status mainly presented in industries of textile, food and beverage processing, and leather producing, with getting prevalence of over-limit 46.7%, 43.1% and 41.3% respectively. Subsequence were industries of manufacturing of electron and electric equipment, motor vehicle and shipping, and industries of printing and goods producing for culture and sports, with the prevalence for all > 35%. Factories monitored during spring and summer also had higher prevalence of noise over-limit. The similar results were got after adjustment for each other using multivariable regression. The most common over-limit sites mainly focused on the operation of cutting and sawing, milling and planing, pressing, riveting, drilling, jointing, assembling and quality inspecting in industries of mechanism processing and manufacturing, on quality inspecting and packing in industries of pharmacy and food and beverage manufacture, on spinning and scutching in textile industry, and on cleaning and maintaining as assistant jobs, and patrolling and inspecting air-press machine, ventilation machine, dynamotor and pump. CONCLUSION: Noise in Guangzhou factories widely exists with different industries and districts. To strengthen noise occupational hazards prevention and control for the high risk districts, industries and worksites should be the key job in the future.