Developing a Framework for Industrial Noise Risk Management Based on Noise Kurtosis and Its Adjustment.

OBJECTIVES: Noise risk control or management based on noise level has been documented, but noise risk management based on a combination of noise level and noise's temporal structure is rarely reported. This study aimed to develop a framework for industrial noise risk management based on noise kurtosis (reflecting noise's temporal structure) and its adjustment for the noise level. DESIGN: A total of 2805 Chinese manufacturing workers were investigated using a cross-sectional survey. The noise exposure data of each subject included LEX,8h, cumulative noise exposure (CNE), kurtosis, and kurtosis-adjusted LEX,8h (LEX,8h-K). Noise-induced permanent threshold shifts were estimated at 3, 4, and 6 kHz frequencies (NIPTS346) and 1, 2, 3, and 4 kHz frequencies (NIPTS1234). The prevalence of high-frequency noise-induced hearing loss prevalence (HFNIHL%) and noise-induced hearing impairment (NIHI%) were determined. Risk346 or Risk1234 was predicted using the ISO 1999 or NIOSH 1998 model. A noise risk management framework based on kurtosis and its adjustment was developed. RESULTS: Kurtosis could identify the noise type; Kurtosis combining noise levels could identify the homogeneous noise exposure group (HNEG) among workers. Noise kurtosis was a risk factor of HFNIHL or NIHI with an adjusted odds ratio of 1.57 or 1.52 (p < 0.01). At a similar CNE level, the NIPTS346, HFNIHL%, NIPTS1234, or NIHI% increased with increasing kurtosis. A nonlinear regression equation (expressed by logistic function) could rebuild a reliable dose-effect relationship between LEX,8h-K and NIPTS346 at the 70 to 95 dB(A) noise level range. After the kurtosis adjustment, the median LEX,8h was increased by 5.45 dB(A); the predicted Risk346 and Risk1234 were increased by 11.2 and 9.5%, respectively; NIPTS346-K of complex noise at exposure level <80, 80 to 85, and 85 to 90 dB(A), determined from the nonlinear regression equation, was almost the same as the Gaussian noise. Risk management measures could be recommended based on the exposure risk rating or the kurtosis-adjusted action levels (e.g., the lower and upper action levels were 80 and 85 dB(A), respectively). CONCLUSIONS: The kurtosis and its adjustment for noise levels can be used to develop an occupational health risk management framework for industrial noise. More human studies are needed to verify the risk management framework.

Effectiveness of Kurtosis-Adjusted Cumulative Noise Exposure in Assessing Occupational Hearing Loss Associated With Complex Noise.

OBJECTIVES: Occupational noise-induced hearing loss (NIHL) is one of the most prevalent occupational diseases worldwide. Few studies have been reported on applying kurtosis-adjusted noise energy (e.g., kurtosis-adjusted cumulative noise exposure, CNE-K) as a joint indicator for assessing NIHL. This study aimed to analyze the effectiveness of CNE-K in assessing occupational hearing loss associated with complex noise in typical manufacturing industries. DESIGN: A cross-sectional survey of 1404 Chinese manufacturing workers from typical manufacturing industries was conducted. General demographic characteristics, noise exposure data, and noise-induced permanent threshold shifts (NIPTS) at 3, 4, and 6 kHz (NIPTS 346 ) were collected and analyzed. The role of kurtosis in high-frequency noise-induced hearing loss (HFNIHL) was also analyzed. The degree of overlap of the two logistic curves (i.e., between complex noise CNE-K and HFNIHL%, and between Gaussian noise CNE and HFNIHL%) was used to evaluate the effectiveness of CNE-K, using a stratified analysis based on age, sex, industry, or job type. RESULTS: The binary logistic regression analysis showed that in addition to age, sex, exposure duration, and Eight-hour Continuous Equivalent A-weighted Sound Pressure Level (L Aeq,8h ), kurtosis was a key factor influencing HFNIHL% in workers (odds ratio = 1.18, p < 0.05), and its odds ratio increased with an increase in kurtosis value. Multiple linear regression analysis demonstrated that the contribution of kurtosis to NIPTS 346 was second to L Aeq,8h . Complex noise led to a higher risk of NIHL than Gaussian noise at frequencies of 3, 4, 6, and 8 kHz after adjusting for age, sex, and CNE ( p < 0.05). As kurtosis increased, the notch in the audiogram became deeper, and the frequency at which the notch began to deepen shifted from 3 to 1 kHz. The logistic curve between complex noise CNE-K and HFNIHL% nearly overlapped with that between Gaussian noise CNE and HFNIHL%, and the average difference in HFNIHL% between the two curves decreased from 8.1 to 0.4%. Moreover, the decrease of average difference in HFNIHL% between the two logistic curves was evident in several subgroups, such as male workers, aged <30 and 30 to 50 years, furniture and woodworking industries and gunning and nailing job types with relatively high kurtosis values. CONCLUSIONS: Kurtosis, as an indirect metric of noise temporal structure, was an important risk factor for occupational NIHL. Kurtosis-adjusted CNE metric could be more effective than CNE alone in assessing occupational hearing loss risk associated with complex noise.

Investigation of critical factors influencing the underestimation of hearing loss predicted by the ISO 1999 predicting model.

OBJECTIVE: To analyze factors influencing the underestimation of noise-induced permanent threshold shift (NIPTS) among manufacturing workers, providing baseline data for revising noise exposure standard. DESIGN: A cross-sectional study was designed with 2702 noise-exposed workers from 35 enterprises from 10 industries. Personal noise exposure level(LAeq,8h) and noise kurtosis level were determined by a noise dosimeter. Questionnaires and hearing loss tests were performed for each subject. The predicted NIPTS was calculated using the ISO 1999:2013 model for each participant, and the actual measured NIPTS was corrected for age and sex. The factors influencing the underestimation of NIPTS were investigated. RESULTS: The predicted NIPTS at each test frequency (0.5, 1, 2, 3, 4, or 6kHz) and mean NIPTS at 2, 3, 4, and 6kHz (NIPTS2346) using the ISO 1999:2013 model were significantly lower than their corresponding measured NIPTS, respectively (P < 0.001). The ISO model significantly underestimated the NIPTS2346 by 12.36 dB HL. The multiple linear regression analysis showed that noise exposure level, exposure duration, age, and kurtosis could affect the degree of underestimation of NIPTS2346. The generalized additive model (GAM) with (penalized) spline components showed nonlinear relationships between critical factors (age, exposure duration, noise level, and kurtosis) and the underestimated NIPTS2346.The underestimated NIPTS2346 decreased with an increase in exposure duration (especially over ten years). There was no apparent trend in the underestimated NIPTS2346 with age. The underestimated NIPTS2346 decreased with the increased noise level [especially > 90 dB(A)]. The underestimated NIPTS2346 increased with an increase in noise kurtosis after adjusting for the noise exposure level and exposure duration and ultimately exhibiting a linear regression relationship. CONCLUSIONS: The ISO 1999 predicting model significantly underestimated the noise-induced hearing loss among manufacturing workers. The degree of underestimation became more significant at the noise exposure condition of fewer than ten years, less than 90 dB(A), and higher kurtosis levels. It is necessary to apply kurtosis to adjust the underestimation of hearing loss and consider the applying condition of noise energy metrics when using the ISO predicting model.

Ethylbenzene induces hearing loss by triggering mitochondrial impairments and excess apoptosis in cochlear progenitor cells via suppressing the Wnt/ß-catenin signaling.

Ethylbenzene (EB) is widely distributed at low levels in the environment from vehicle emissions, industrial discharge, cigarette smoke, and in some food and consumer products. Evidence shows that EB exposure is associated with hearing loss, yet the mechanisms are unclear. This study aimed to explore the role of the Wnt/ß-catenin signaling pathway, which plays a key role during cochlear development, in EB-induced hearing loss. In vitro, we found that EB treatment decreased the viability of cochlear progenitor cells (CPCs), isolated from the cochleae of neonatal rats and crucial for cochlear hair cells generation and hearing construction, via inducing mitochondrial impairments and excessive apoptosis. These were accompanied by the inactivation of the Wnt/ß-catenin signaling cascade, as manifested by the decreased levels of related molecules ß-catenin, LEF-1 and Lgr5. These findings were further confirmed by knocking down ß-catenin and immunofluorescence analysis. Interestingly, adenovirus-mediated ß-catenin overexpression activated the Wnt/ß-catenin signaling network, alleviated mitochondrial impairments, reduced cell apoptosis, therefore promoting CPCs survival under EB treatment conditions. Finally, using adult Sprague-Dawley rats as an in vivo model with EB inhalation for 13 weeks, we found that exposure to EB decreased body weight gain, increased the hearing thresholds at different exposure stages, along with Wnt/ß-catenin signaling pathway suppression in cochlear tissue. More importantly, cochlear microinjection of recombinant lentivirus expressing ß-catenin significantly reversed EB-elicited these deleterious effects. Collectively, our results indicate that EB induces hearing loss by triggering mitochondrial impairments and excess apoptosis in CPCs via suppressing the Wnt/ß-catenin signaling, and provide clues for the possible therapy.

Estimation of Occupational Noise-Induced Hearing Loss Using Kurtosis-Adjusted Noise Exposure Levels.

OBJECTIVES: Studies have shown that in addition to energy, kurtosis plays an important role in the assessment of hearing loss caused by complex noise. The objective of this study was to investigate how to use noise recordings and audiometry collected from workers in industrial environments to find an optimal kurtosis-adjusted algorithm to better evaluate hearing loss caused by both continuous noise and complex noise. DESIGN: In this study, the combined effects of energy and kurtosis on noise-induced hearing loss (NIHL) were investigated using data collected from 2601 Chinese workers exposed to various industrial noises. The cohort was divided into three subgroups based on three kurtosis (ß) levels (K 1 : 3 ≤ ß ≤ 10, K 2 : 10 <ß ≤ 50, and K 3 : ß > 50). Noise-induced permanent threshold shift at test frequencies 3, 4, and 6 kHz (NIPTS 346 ) was used as the indicator of NIHL. Predicted NIPTS 346 was calculated using the ISO 1999 model for each participant, and the actual NIPTS was obtained by correcting for age and sex using non-noise-exposed Chinese workers (n = 1297). A kurtosis-adjusted A-weighted sound pressure level normalized to a nominal 8-hour working day (L Aeq,8h ) was developed based on the kurtosis categorized group data sets using multiple linear regression. Using the NIPTS 346 and the L Aeq.8h metric, a dose-response relationship for three kurtosis groups was constructed, and the combined effect of noise level and kurtosis on NIHL was investigated. RESULTS: An optimal kurtosis-adjusted L Aeq,8h formula with a kurtosis adjustment coefficient of 6.5 was established by using the worker data. The kurtosis-adjusted L Aeq,8h better estimated hearing loss caused by various complex noises. The analysis of the dose-response relationships among the three kurtosis groups showed that the NIPTS of K 2 and K 3 groups was significantly higher than that of K 1 group in the range of 70 dBA ≤ L Aeq,8h < 85 dBA. For 85 dBA ≤ L Aeq,8h ≤ 95 dBA, the NIPTS 346 of the three groups showed an obvious K 3 > K 2 > K 1 . For L Aeq,8h >95 dBA, the NIPTS 346 of the K 2 group tended to be consistent with that of the K 1 group, while the NIPTS 346 of the K 3 group was significantly larger than that of the K 1 and K 2 groups. When L Aeq,8h is below 70 dBA, neither continuous noise nor complex noise produced significant NIPTS 346 . CONCLUSIONS: Because non-Gaussian complex noise is ubiquitous in many industries, the temporal characteristics of noise (i.e., kurtosis) must be taken into account in evaluating occupational NIHL. A kurtosis-adjusted L Aeq,8h with an adjustment coefficient of 6.5 allows a more accurate prediction of high-frequency NIHL. Relying on a single value (i.e., 85 dBA) as a recommended exposure limit does not appear to be sufficient to protect the hearing of workers exposed to complex noise.

Prevalence and risk factors analysis for low back pain among occupational groups in key industries of China.

BACKGROUND: With the acceleration of industrialization and population aging, low back pain (LBP) has become the leading cause of life loss years caused by disability. Thus, it places a huge economic burden on society and is a global public health problem that needs urgent solution. This study aimed to conduct an epidemiological investigation and research on a large sample of workers in key industries in different regions of China, determine the incidence and distribution characteristics of LBP, explore the epidemic law, and provide a reference basis for alleviating global public health problems caused by LBP. METHODS: We adopted a modified epidemiological cross-sectional survey method and a stratified cluster sampling method. All on-duty workers who fulfill the inclusion criteria are taken as the research participants from the representative enterprises in key industries across seven regions: north, east, central, south, southwest, northwest, and northeast China. The Chinese version of the musculoskeletal disease questionnaire, modified by a standardized Nordic questionnaire, was used to collect information, and 57,501 valid questionnaires were received. Descriptive statistics were used, and multivariate logistic regression analysis (p < 0.05) was performed to explore the association between musculoskeletal disorders and potential risk factors. RESULTS: LBP annual incidence among workers in China's key industries is 16.4%. There was a significant difference in LBP incidence among occupational groups across different industries (p < 0.05). The multivariate regression model showed the following as risk factors for LBP: frequent repetitive movements with the trunk, working in the same positions at a high pace, trunk position, frequently turning around with your trunk, often working overtime, lifting heavy loads (i.e., more than 20 kg), education level, staff shortage, working age (years), cigarette smoking, use of vibration tools at work, body mass index, lifting heavy loads (i.e., more than 5 kg), and age (years). Physical exercise, often standing at work, and absolute resting time were protective factors. CONCLUSION: LBP incidence among key industries and workers in China is high. Thus, it is urgent to take relevant measures according to the individual, occupational, and psychosocial factors of LBP to reduce the adverse impact of LBP on workers' health.

Occupational Hearing Loss Associated With Non-Gaussian Noise: A Systematic Review and Meta-analysis.

OBJECTIVES: Epidemiological characteristics of occupational noise-induced hearing loss (NIHL) associated with non-Gaussian noise are still unclear and have been rarely reported in the literature. METHODS: The relationships between non-Gaussian noise exposure and occupational NIHL were analyzed based on the published papers. Systematic review and meta-analysis of epidemiological studies were performed. RESULTS: Of 78 epidemiological studies (47,814 workers) selected, there were seven cohort studies and 71 cross-sectional studies. The incidence of high-frequency NIHL (HFNIHL) and speech-frequency NIHL (SFNIHL) in the seven cohort studies was 10.9 and 2.9%, respectively. In 71 cross-sectional studies, the prevalence of HFNIHL and SFNIHL was 34.2 and 18.9%, respectively. The average hearing threshold level at the high frequencies was 42.1 ± 17.4 dB HL. Workers exposed to non-Gaussian noise had a higher risk of developing HFNIHL than those not exposed to noise (overall-weighted odds ratio [OR] = 4.46) or those exposed to Gaussian noise (overall-weighted OR = 2.20). The Chi-square trend test demonstrated that the prevalence of HFNIHL was positively correlated with age, cumulative noise exposure, and exposure duration (p < 0.001). CONCLUSIONS: Workers exposed to non-Gaussian noise suffered from greater NIHL than those exposed to Gaussian noise or not exposed to noise. Age, exposure duration, noise level, and noise temporal structure were the main risk factors for occupational NIHL. The A-weighted equivalent continuous sound pressure level (LAeq) is not a sufficient measurement metric for quantifying non-Gaussian noise exposure, and a combination of kurtosis and noise energy metrics (e.g., LAeq) should be used. It is necessary to reduce the exposure of non-Gaussian noise to protect the hearing health of workers.

Applying Kurtosis as an Indirect Metric of Noise Temporal Structure in the Assessment of Hearing Loss Associated With Occupational Complex Noise Exposure.

OBJECTIVE: The association of occupational noise-induced hearing loss (NIHL) with noise energy was well documented, but the relationship between occupational noise and noise temporal structure is rarely reported. The objective of this study was to investigate the principal characteristics of the relationship between occupational NIHL and the temporal structure of noise. METHODS: Audiometric and shift-long noise exposure data were collected from 3102 Chinese manufacturing workers from six typical industries through a cross-sectional survey. In data analysis, A-weighted 8-h equivalent SPL (LAeq.8h), peak SPL, and cumulative noise exposure (CNE) were used as noise energy indicators, while kurtosis (ß) was used as the indicator of noise temporal structure. Two NIHL were defined: (1) high-frequency noise-induced hearing loss (HFNIHL) and (2) noise-induced permanent threshold shift at test frequencies of 3, 4, and 6 kHz (noise-induced permanent threshold shift [NIPTS346]). The noise characteristics of different types of work and the relationship between these characteristics and the prevalence of NIHL were analyzed. RESULTS: The noise waveform shape, with a specific noise kurtosis, was unique to each type of work. Approximately 27.92% of manufacturing workers suffered from HFNIHL, with a mean NIPTS346 of 24.16 ± 14.13 dB HL. The Spearman correlation analysis showed that the kurtosis value was significantly correlated with the difference of peak SPL minus its LAeq.8h across different types of work (p < 0.01). For a kurtosis-adjusted CNE, the linear regression equation between HFNIHL% and CNE for complex noise almost overlapped with Gaussian noise. Binary logistic regression analysis showed that LAeq.8h, kurtosis, and exposure duration were the key factors influencing HFNIHL% (p < 0.01). The notching extent in NIPTS at 4 kHz became deeper with the increase in LAeq.8h and kurtosis. HFNIHL% increased most rapidly during the first 10 years of exposure. HFNIHL% with ß ≥ 10 was significantly higher than that with ß < 10 (p < 0.05), and it increased with increasing kurtosis across different CNE or LAeq.8h levels. When LAeq.8h was 80 to 85 dB(A), the HFNIHL% at ß ≥ 100 was significantly higher than that at 10 ≤ ß < 100 or ß < 10 (p < 0.05 and p < 0.01, respectively). CONCLUSIONS: In the evaluation of hearing loss caused by complex noise, not only noise energy but also the temporal structure of noise must be considered. Kurtosis of noise is an indirect metric that is sensitive to the presence of impulsive components in complex noise exposure, and thus, it could be useful for quantifying the risk for NIHL. It is necessary to re-evaluate the safety of permissible exposure limit of 85 dB(A) as noise with a high kurtosis value can aggravate or accelerate early NIHL.

New Metrics Needed in the Evaluation of Hearing Hazard Associated With Industrial Noise Exposure.

OBJECTIVES: To evaluate (1) the accuracy of the International Organization for Standardization (ISO) standard ISO 1999 [(2013), International Organization for Standardization, Geneva, Switzerland] predictions of noise-induced permanent threshold shift (NIPTS) in workers exposed to various types of high-intensity noise levels, and (2) the role of the kurtosis metric in assessing noise-induced hearing loss (NIHL). DESIGN: Audiometric and shift-long noise exposure data were acquired from a population (N = 2,333) of screened workers from 34 industries in China. The entire cohort was exclusively divided into subgroups based on four noise exposure levels (85 ≤ LAeq.8h < 88, 88 ≤ LAeq.8h < 91, 91 ≤ LAeq.8h < 94, and 94 ≤ LAeq.8h ≤ 100 dBA), two exposure durations (D ≤ 10 years and D > 10 years), and four kurtosis categories (Gaussian, low-, medium-, and high-kurtosis). Predicted NIPTS was calculated using the ISO 1999 model for each participant and the actual measured NIPTS was corrected for age and sex also using ISO 1999. The prediction accuracy of the ISO 1999 model was evaluated by comparing the NIPTS predicted by ISO 1999 with the actual NIPTS. The relation between kurtosis and NIPTS was also investigated. RESULTS: Overall, using the average NIPTS value across the four audiometric test frequencies (2, 3, 4, and 6 kHz), the ISO 1999 predictions significantly (p < 0.001) underestimated the NIPTS by 7.5 dB on average in participants exposed to Gaussian noise and by 13.6 dB on average in participants exposed to non-Gaussian noise with high kurtosis. The extent of the underestimation of NIPTS by ISO 1999 increased with an increase in noise kurtosis value. For a fixed range of noise exposure level and duration, the actual measured NIPTS increased as the kurtosis of the noise increased. The noise with kurtosis greater than 75 produced the highest NIPTS. CONCLUSIONS: The applicability of the ISO 1999 prediction model to different types of noise exposures needs to be carefully reexamined. A better understanding of the role of the kurtosis metric in NIHL may lead to its incorporation into a new and more accurate model of hearing loss due to noise exposure.

Analysis of correlation between window duration for kurtosis computation and accuracy of noise-induced hearing loss prediction.

Kurtosis is considered an important metric for evaluating noise-induced hearing loss (NIHL). However, how to select window duration to calculate kurtosis remains unsolved. In this study, two algorithms were designed to investigate the correlation between window duration for kurtosis computation and the accuracy of NIHL prediction using a Chinese industrial database. Pure-tone hearing threshold levels (HTLs) and full-shift noise were recorded from each subject. In the statistical comparison, subjects were divided into high- and low-kurtosis groups based on kurtosis values computed over different window durations. Mann-Whitney U test was used to compare the difference in group HTLs to find the optimal window duration to best distinguish these two groups. In the support vector machine NIHL prediction model, kurtosis obtained from different window durations was used as a feature of the model for NIHL evaluation. The area under the curve was used to evaluate the performances of models. Fourteen window durations were tested for each algorithm. Results showed that 60 s was an optimal window duration that allows for both efficient computation and high accuracy for NIHL evaluation at test frequencies of 3, 4 and 6 kHz, and the geometric mean of kurtosis sequence was the best metric in NIHL evaluation.

Associations of noise kurtosis, genetic variations in NOX3 and lifestyle factors with noise-induced hearing loss.

BACKGROUND: Noise-induced hearing loss (NIHL) is a complex disease caused by environmental and genetic risk factors. This study was to explore the association of noise kurtosis, triphosphopyridine nucleotide oxidase 3 (NOX3) and lifestyles with NIHL. METHODS: This case-control study included 307 patients with NIHL and 307 matched control individuals from Zhejiang province of China. General characteristics, noise exposure data, the exfoliated cells of the oral mucosa, and lifestyle details of individuals were collected. The kompetitive allele specific polymerase chain reaction (KASP) method was used to analyze the genotypes of three single nucleotide polymorphisms (SNPs) of NOX3. RESULTS: People who exposed to complex noise had a higher risk of NIHL than those exposed to steady noise (adjusted: OR = 1.806, P = 0.002). The GT genotype of additive model and TT + GT genotype of dominant model in NOX3 rs12195525 decreased the risk of NIHL (adjusted: OR = 0.618, P = 0.043; OR = 0.622, P = 0.036). Smoking and exposure to high video volume increased the risk of NIHL (adjusted: OR = 1.486, P = 0.038; OR = 1.611, P = 0.014). Oppositely, regular physical exercise decreased the risk of NIHL (adjusted: OR = 0.598, P = 0.004). A positive interaction was found between complex noise and lifestyles including high video volume exposure and no physical exercise in the additive models (RERI = 1.088, P < 0.001; RERI = 1.054, P = 0.024). A positive interaction was also found between NOX3 rs12195525 GG genotype and lifestyles including smoking and high video volume exposure in the additive models (RERI = 1.042, P = 0.005; RERI = 0.774, P = 0.044). CONCLUSIONS: Noise temporal structure, NOX3 rs12195525 polymorphism, and the three lifestyles of smoking, video volume, and physical exercise were related to the NIHL. There were the interactions between noise temporal structure and the lifestyle of video volume or physical exercise, as well as between NOX3 and the lifestyle of smoking or video volume. These results provide a theoretical basis for the prevention and genetic testing of NIHL.

Characteristics of iron status, oxidation response, and DNA methylation profile in response to occupational iron oxide nanoparticles exposure.

Although the growing development and application of iron oxide nanoparticles (IONPs) may pose exposure risk and adverse health outcomes, biological changes due to occupational exposure remain unexplored. This cross-sectional study recruited 23 workers at a plant that manufactures IONPs and 23 age- and sex-matched controls without metal-rich occupational hazards exposure. Exposure metrics at worksites were monitored, and iron status, oxidation markers, and methylation profiles of genomic DNA in peripheral blood were measured using corresponding enzyme-linked immunosorbent assays and methylation-specific polymerase chain reaction (PCR), respectively. The mass concentration, number counting, and surface area concentration of airborne particles at the worksite significantly increased during the work process of manufacturing/handling IONPs. Overall, compared to controls, workers exhibited increased 5-hydroxymethylcytosine (5hmC) levels without changes in 5-methylcytosine (5mC), hepcidin methylation, iron, soluble transferrin receptor (sTfR), ferritin, hepcidin, 8-hydroxydeoxyguanosine, and glutathione. A positive correlation was found between 5hmC and IONP exposure year with adjustment for age, sex, and cotinine using partial correlation analyses (r = 0.521, p < 0.001). After stratification of INOPs exposure and 5hmC levels, the univariate general linear model with adjustment for age, sex, and cotinine found that the estimated mean levels of 5mC and sTfR in subjects with low and high 5hmC levels among controls were 11% and 14.4% (p ≤ 0.01) and 80.9 nM and 70.3 nM (p < 0.05), respectively. The estimated mean levels of sTfR in workers and controls with low 5hmC levels were 88.3 nM and 68.7 nM (p ≤ 0.01). Multivariate linear regression analyses suggested an association between sTfR and 5hmC (standardized ß = -0.420, p = 0.014) and female sex (standardized ß = 0.672, p < 0.001) for subjects with low 5hmC levels. These findings suggest that increased 5hmC could be differentially employed to monitor an epigenetic signature with steady iron homeostasis for occupational IONP-exposed individuals who are likely to experience early but specific decreased sTfR, especially for females concurrent with the onset of increment in 5hmC at low level.

Machine Learning Models for the Hearing Impairment Prediction in Workers Exposed to Complex Industrial Noise: A Pilot Study.

OBJECTIVES: To demonstrate the feasibility of developing machine learning models for the prediction of hearing impairment in humans exposed to complex non-Gaussian industrial noise. DESIGN: Audiometric and noise exposure data were collected on a population of screened workers (N = 1,113) from 17 factories located in Zhejiang province, China. All the subjects were exposed to complex noise. Each subject was given an otologic examination to determine their pure-tone hearing threshold levels and had their personal full-shift noise recorded. For each subject, the hearing loss was evaluated according to the hearing impairment definition of the National Institute for Occupational Safety and Health. Age, exposure duration, equivalent A-weighted SPL (LAeq), and median kurtosis were used as the input for four machine learning algorithms, that is, support vector machine, neural network multilayer perceptron, random forest, and adaptive boosting. Both classification and regression models were developed to predict noise-induced hearing loss applying these four machine learning algorithms. Two indexes, area under the curve and prediction accuracy, were used to assess the performances of the classification models for predicting hearing impairment of workers. Root mean square error was used to quantify the prediction performance of the regression models. RESULTS: A prediction accuracy between 78.6 and 80.1% indicated that the four classification models could be useful tools to assess noise-induced hearing impairment of workers exposed to various complex occupational noises. A comprehensive evaluation using both the area under the curve and prediction accuracy showed that the support vector machine model achieved the best score and thus should be selected as the tool with the highest potential for predicting hearing impairment from the occupational noise exposures in this study. The root mean square error performance indicated that the four regression models could be used to predict noise-induced hearing loss quantitatively and the multilayer perceptron regression model had the best performance. CONCLUSIONS: This pilot study demonstrated that machine learning algorithms are potential tools for the evaluation and prediction of noise-induced hearing impairment in workers exposed to diverse complex industrial noises.

Screening of noise-induced hearing loss (NIHL)-associated SNPs and the assessment of its genetic susceptibility.

BACKGROUND: The aim of this study was to screen for noise-induced hearing loss (NIHL)-associated single nucleotide polymorphisms (SNPs) and to construct genetic risk prediction models for NIHL in a Chinese population. METHODS: Four hundred seventy-six subjects with NIHL and 476 matched controls were recruited from a cross-sectional survey on NIHL in China. A total of 83 candidate SNPs were genotyped using nanofluidic dynamic arrays on a Fluidigm platform. NIHL-associated SNPs were screened with a multiple logistic model, and a genetic risk model was constructed based on the genetic risk score (GRS). The results were validated using a prospective cohort population. RESULTS: Seven SNPs in the CDH23, PCDH15, EYA4, MYO1A, KCNMA1, and OTOG genes were significantly (P < 0.05) associated with the risk of NIHL, whereas seven other SNPs were marginally (P > 0.05 and P < 0.1) associated with the risk of NIHL. A positive correlation was observed between GRS values and odds ratio (OR) for NIHL. Two SNPs, namely, rs212769 and rs7910544, were validated in the cohort study. Subjects with higher GRS (≧9) showed a higher risk of NIHL incidence with an OR of 2.00 (95% CI = 1.04, 3.86). CONCLUSIONS: Genetic susceptibility plays an important role in the incidence of NIHL. GRS values, which are based on NIHL-associated SNPs. GRS may be utilized in the evaluation of genetic risk for NIHL and in the determination of NIHL susceptibility.

Development of an automatic classifier for the prediction of hearing impairment from industrial noise exposure.

The ISO-1999 [(2013). International Organization for Standardization, Geneva, Switzerland] standard is the most commonly used approach for estimating noise-induced hearing trauma. However, its insensitivity to noise characteristics limits its practical application. In this study, an automatic classification method using the support vector machine (SVM) was developed to predict hearing impairment in workers exposed to both Gaussian (G) and non-Gaussian (non-G) industrial noises. A recently collected human database (N = 2,110) from industrial workers in China was used in the present study. A statistical metric, kurtosis, was used to characterize the industrial noise. In addition to using all the data as one group, the data were also broken down into the following four subgroups based on the level of kurtosis: G/quasi-G, low-kurtosis, middle-kurtosis, and high-kurtosis groups. The performance of the ISO-1999 and the SVM models was compared over these five groups. The results showed that: (1) The performance of the SVM model significantly outperformed the ISO-1999 model in all five groups. (2) The ISO-1999 model could not properly predict hearing impairment for the high-kurtosis group. Moreover, the ISO-1999 model is likely to underestimate hearing impairment caused by both G and non-G noise exposures. (3) The SVM model is a potential tool to predict hearing impairment caused by diverse noise exposures.

Use of the kurtosis statistic in an evaluation of the effects of noise and solvent exposures on the hearing thresholds of workers: An exploratory study.

The aim of this exploratory study was to examine whether the kurtosis metric can contribute to investigations of the effects of combined exposure to noise and solvents on human hearing thresholds. Twenty factory workers exposed to noise and solvents along with 20 workers of similar age exposed only to noise in eastern China were investigated using pure-tone audiometry (1000-8000 Hz). Exposure histories and shift-long noise recording files were obtained for each participant. The data were used in the calculation of the cumulative noise exposure (CNE) and CNE adjusted by the kurtosis metric for each participant. Passive samplers were used to measure solvent concentrations for each worker exposed to solvents over the full work shift. Results showed an interaction between noise exposure and solvents for the hearing threshold at 6000 Hz. This effect was observed only when the CNE level was adjusted by the kurtosis metric.

Childhood pesticide poisoning in Zhejiang, China: a retrospective analysis from 2006 to 2015.

BACKGROUND: Pesticide poisoning in children has been a serious public health issue around the world, especially in the developing countries where agriculture is still one of the largest economic sectors. The purpose of this study was to analyze epidemiological characteristics of acute pesticide poisoning in children from Zhejiang province, China. METHODS: The pesticide poisoning cases for children were retrieved from Occupational Disease Surveillance and Reporting System, Zhejiang Provincial Center for Disease Control and Prevention, China. The incident cases, deaths, and fatality rate of child pesticide poisoning from 2006 through 2015 were calculated. RESULTS: During the study period, totally 2952 children were poisoned by pesticides, with 66 deaths, resulting in a fatality rate of 2.24%. Among them, there were 1607 male cases with 28 deaths, and 1345 female cases with 38 deaths. Most of the cases occurred in preschool children (1349) and adolescent age group (1269). Organophosphate and carbamate insecticides were the cause of most poisonings (1130), leading to 34 deaths. The highest fatality rate (3.13%) was due to poisoning by herbicides and fungicides, causing 14 deaths out of 448 cases. Poisoning occurred mostly in rural areas (78%). And most pesticide poisoning occurred in the summer (896) and fall (811), while fewest poisoning cases in the winter (483) but with the highest fatality rate (3.52%). CONCLUSIONS: This study shows that pesticide poisoning of children is a major health problem in Zhejiang, suggesting preventive strategies should be conducted to control childhood pesticide poisoning.

The Use of the Kurtosis-Adjusted Cumulative Noise Exposure Metric in Evaluating the Hearing Loss Risk for Complex Noise.

OBJECTIVE: To test a kurtosis-adjusted cumulative noise exposure (CNE) metric for use in evaluating the risk of hearing loss among workers exposed to industrial noises. Specifically, to evaluate whether the kurtosis-adjusted CNE (1) provides a better association with observed industrial noise-induced hearing loss, and (2) provides a single metric applicable to both complex (non-Gaussian [non-G]) and continuous or steady state (Gaussian [G]) noise exposures for predicting noise-induced hearing loss (dose-response curves). DESIGN: Audiometric and noise exposure data were acquired on a population of screened workers (N = 341) from two steel manufacturing plants located in Zhejiang province and a textile manufacturing plant located in Henan province, China. All the subjects from the two steel manufacturing plants (N = 178) were exposed to complex noise, whereas the subjects from textile manufacturing plant (N = 163) were exposed to a G continuous noise. Each subject was given an otologic examination to determine their pure-tone HTL and had their personal 8-hr equivalent A-weighted noise exposure (LAeq) and full-shift noise kurtosis statistic (which is sensitive to the peaks and temporal characteristics of noise exposures) measured. For each subject, an unadjusted and kurtosis-adjusted CNE index for the years worked was created. Multiple linear regression analysis controlling for age was used to determine the relationship between CNE (unadjusted and kurtosis adjusted) and the mean HTL at 3, 4, and 6 kHz (HTL346) among the complex noise-exposed group. In addition, each subject's HTLs from 0.5 to 8.0 kHz were age and sex adjusted using Annex A (ISO-1999) to determine whether they had adjusted high-frequency noise-induced hearing loss (AHFNIHL), defined as an adjusted HTL shift of 30 dB or greater at 3.0, 4.0, or 6.0 kHz in either ear. Dose-response curves for AHFNIHL were developed separately for workers exposed to G and non-G noise using both unadjusted and adjusted CNE as the exposure matric. RESULTS: Multiple linear regression analysis among complex exposed workers demonstrated that the correlation between HTL3,4,6 and CNE controlling for age was improved when using the kurtosis-adjusted CNE compared with the unadjusted CNE (R = 0.386 versus 0.350) and that noise accounted for a greater proportion of hearing loss. In addition, although dose-response curves for AHFNIHL were distinctly different when using unadjusted CNE, they overlapped when using the kurtosis-adjusted CNE. CONCLUSIONS: For the same exposure level, the prevalence of NIHL is greater in workers exposed to complex noise environments than in workers exposed to a continuous noise. Kurtosis adjustment of CNE improved the correlation with NIHL and provided a single metric for dose-response effects across different types of noise. The kurtosis-adjusted CNE may be a reasonable candidate for use in NIHL risk assessment across a wide variety of noise environments.

Exposure characteristics of ferric oxide nanoparticles released during activities for manufacturing ferric oxide nanomaterials.

The exposure characteristics of Fe2O3 nanoparticles (NPs) released in a factory were investigated, as exposure data on this type of NP is absent. The nature of the particles was identified in terms of their concentrations [i.e. number concentration (NC(20-1000 nm)), mass concentration (MC(100-1000 nm)), surface area concentration (SAC(10-1000 nm))], size distribution, morphology and elemental composition. The relationships between different exposure metrics were determined through analyses of exposure ranking (ER), concentration ratios (CR), correlation coefficients and shapes of the particle concentration curves. Work activities such as powder screening, material feeding and packaging generated higher levels of NPs as compared to those of background particles (p < 0.01). The airborne Fe2O3 NPs exhibited a unimodal size distribution and a spindle-like morphology and consisted predominantly of the elements O and Fe. Periodic and activity-related characteristics were noticed in the temporal variations in NC(20-1000 nm) and SAC(10-1000 nm). The modal size of the Fe2O3 NPs remained relatively constant (ranging from 10 to 15 nm) during the working periods. The ER, CR values and the shapes of NC(20-1000 nm) and SAC(10-1000 nm) curves were similar; however, these were significantly different from those for MC(100-1000 nm). There was a high correlation between NC(20-1000 nm) and SAC(10-1000 nm), and relatively lower correlations between the two and MC(100-1000 nm). These findings suggest that the work activities during the manufacturing processes generated high levels of primary Fe2O3 NPs. The particle concentrations exhibited periodicity and were activity dependent. The number and SACs were found to be much more relevant metrics for characterizing NPs than was the mass concentration.

[An analysis of characteristics of exposure to nanoparticles in a workplace manufacturing iron oxide nanoparticles].

OBJECTIVE: To investigate the characteristics of exposure to iron oxide nanoparticles in workplace. METHODS: The real-time particle number (NC), surface area (SAC), and mass (MC) concentrations of nanoparticles were measured in various locations of a selected workplace manufacturing iron oxide nanoparticles. The collected particles were analyzed for morphology and elemental composition. RESULTS: The average NCs and SACs in milling site (16,566 pt/cm3, 106.082 µm2/cm3), packaging site (12,386 pt/cm3, 89.861 µm2/cm3), shipping site (13,808 pt/cm3, 102.071 µm2/cm3), and product storage room (17,192 pt/cm, 115.044 µm2/cm3) of the yellow powder (α-Fe2O3 . nH2O) were all significantly higher than the workplace background concentrations (11,420 pt/cm3, 85.026 µm2/cm3) (all P<0.05). The NC was highly correlated with the SAC (r= 0.784), while both NC and SAC were loosely correlated with the MC (r1=0.323, r2=0.331). Scanning electron microscopy revealed a spindle-like shape of the iron oxide nanoparticle; the chemical composition of the collected particles contained 19.33 weight percent iron (Fe). CONCLUSION: The milling site and product storage room of the yellow powder are exposed to a higher concentration of nanoparticles, which are mainly composed of iron oxide nanoparticles. The NC is highly correlated with the SAC.