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Shkembi A, Zelner J, Park SK, Neitzel R. Workplace Exposures Vary Across Neighborhoods in the US: Implications on Social Vulnerability and Racial/Ethnic Health Disparities. J Racial Ethn Health Disparities 2024:10.1007/s40615-024-02143-5. [PMID: 39212906 DOI: 10.1007/s40615-024-02143-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 08/15/2024] [Accepted: 08/17/2024] [Indexed: 09/04/2024]
Abstract
Ignoring workplace exposures that occur beyond the local residential context in place-based risk indices like the CDC's Social Vulnerability Index (SVI) likely misclassifies community exposure by under-counting risks and obscuring true drivers of racial/ethnic health disparities. To investigate this hypothesis, we developed several place-based indicators of occupational exposure and examined their relationships with race/ethnicity, SVI, and health inequities. We used publicly available job exposure matrices and employment estimates from the United States (US) Census to create and map six indicators of occupational hazards for every census tract in the US. We characterized census tracts with high workplace-low SVI scores. We used natural cubic splines to examine tract level associations between the percentage of racial/ethnic minorities (individuals who are not non-Hispanic White) and the occupational indicators. Lastly, we stratified each census tract into high/low occupational noise, chemical pollutant, and disease/infection exposure to examine racial/ethnic health disparities to diabetes, asthma, and high blood pressure, respectively, as a consequence of occupational exposure inequities. Our results show that racial/ethnic minority communities, particularly those that are also low-income, experience a disproportionate burden of workplace exposures that may be contributing to racial/ethnic health disparities. When composite risk measures, such as SVI, are calculated using only information from the local residential neighborhood, they may systematically under-count occupational risks experienced by the most vulnerable communities. There is a need to consider the role of occupational justice on nationwide, racial/ethnic health disparities.
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Affiliation(s)
- Abas Shkembi
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA.
| | - Jon Zelner
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Social Epidemiology and Population Health, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Sung Kyun Park
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Richard Neitzel
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI, 48109, USA
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Chen Z, Li W, Zhang H, Huang X, Tao Y, Lang K, Zhang M, Chen W, Wang D. Association of noise exposure, plasma microRNAs with metabolic syndrome and its components among Chinese adults. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 921:171123. [PMID: 38387587 DOI: 10.1016/j.scitotenv.2024.171123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/05/2024] [Accepted: 02/18/2024] [Indexed: 02/24/2024]
Abstract
AIMS We aimed to evaluate the association of occupational noise with metabolic syndrome (MetS) and its components, and to assess the potential role of miRNAs in occupational noise-associated MetS. METHODS A total of 854 participants were enrolled in our study. Cumulative noise exposure (CNE) was estimated in conjunction with workplace noise test records and research participants' employment histories. Logistic regression models adjusted for potential confounders were used to assess the association of CNE and miRNAs with MetS and its components. RESULTS We observed linear positive dose-response associations between occupational noise exposure and the prevalence of MetS (OR: 1.031; 95 % CI: 1.008, 1.055). And linear and nonlinear relationship were also found for the association of occupational noise exposure with high blood pressure (OR: 1.024; 95 % CI: 1.007, 1.041) and reduced high-density lipoprotein (OR: 1.051; 95 % CI: 1.031, 1.072), respectively. MiR-200a-3p, miR-92a-3p and miR-21-5p were inversely associated with CNE, or the prevalence of MetS and its components (all P < 0.05). However, we did not find any statistically significant mediation effect of miRNAs in the associations of CNE with MetS. Furthermore, the prevalence of bilateral hearing loss in high-frequency increased (OR: 1.036; 95 % CI: 1.008, 1.067) with CNE level rising, and participants with bilateral hearing loss in high-frequency had a significantly higher risk of MetS (OR: 1.727; 95 % CI: 1.048, 2.819). CONCLUSION Our study suggests that occupational noise exposure is associated with MetS and its components, and the role of miRNAs in noise-induced increasing MetS risk needs to be confirmed in future studies.
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Affiliation(s)
- Zhaomin Chen
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education, Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Wenzhen Li
- Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong 999077, Hong Kong, China; Shenzhen Research Institute of the Chinese University of Hong Kong, Shenzhen 518000, China
| | - Haozhe Zhang
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education, Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Xuezan Huang
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education, Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yueqing Tao
- Department of Social Medicine and Health Management, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Kaiji Lang
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education, Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Meibian Zhang
- National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Weihong Chen
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education, Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Dongming Wang
- Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China; Key Laboratory of Environment and Health, Ministry of Education, Ministry of Environmental Protection, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
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Shkembi A, Smith LM, Neitzel RL. Risk perception or hazard perception? Examining misperceptions of miners' personal exposures to noise. Int J Hyg Environ Health 2023; 254:114263. [PMID: 37742520 DOI: 10.1016/j.ijheh.2023.114263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 09/26/2023]
Abstract
While perceptions of risk have been examined in the workplace to understand safety behavior, hazard perception has been overlooked, particularly for chemical, physical, and biological agents. This study sought to establish the prevalence of one type of mismatch in hazard perception, - noise misperception - among miners, to examine whether different types of noisy environments (e.g., continuous, highly variable, etc.) alter workers' misperception of their noise exposures, and to evaluate whether noise misperception is associated with hearing protection device (HPD) use behavior. In this cross-sectional study across 10 surface mines in the USA, 135 normal-hearing participants were surveyed on their perceptions of exposure to noise at work and were monitored for three shifts, each with personal noise dosimetry, to examine which workers had a mismatch in perceived versus true noise exposure by 8-hr, time-weighted average, NIOSH exposure limits (TWANIOSH). Mixed effects logistic regression and probit Bayesian Kernel Machine Regression (BKMR) models examining on the odds of noise misperception associated with four different noise metrics (kurtosis, crest factor, variability, and number of peaks >135 dB) were used to determine which types of noisy environments may influence noise misperception. The relationship between noise misperception and odds of not wearing HPDs during a work shift was further examined. Our findings showed that nearly 1 in 3 workers underestimated their exposure to noise when their true exposure was in fact hazardous (TWANIOSH≥85 dBA) for at least one shift, and 6% misperceived hazardous exposures for all shifts. Work shifts with highly kurtotic noise distributions (>3) had 3.1 (95% CI: 1.1 to 8.4) times significantly higher odds of resulting in misperceived noise; no other noise metric was significantly associated with noise misperception. BKMR modeling provided further evidence that kurtosis dominates this relationship, with an IQR increase in kurtosis significantly associated with 1.68 (95% CI: 1.13 to 2.50) higher odds of noise misperception. Although not statistically significant, misperception of hazardous noise exposure was associated with 3.2 (95% CI: 0.8 to 12.5) times higher odds of not using earplugs during a work shift. Misperception of noise occurs in the workplace, and likely occurs for other physical, chemical, and biological exposures. This hazard misperception may influence risk perceptions and worker behavior and reduce the effectiveness of behavior-related training. Elimination, substitution, or engineering controls of exposures is the best way to prevent hazard misperceptions and exposure-related diseases.
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Affiliation(s)
- Abas Shkembi
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA.
| | - Lauren M Smith
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Richard L Neitzel
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA
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Shkembi A, Smith LM, Bregg S, Neitzel RL. Evaluating Occupational Noise Exposure as a Contributor to Injury Risk among Miners. Ann Work Expo Health 2022; 66:1151-1161. [PMID: 36053031 DOI: 10.1093/annweh/wxac059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 07/21/2022] [Accepted: 08/03/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES This study: (i) assessed the relationship between noise exposure and injury risk, comprehensively adjusting for individual factors, psychosocial stressors, and organizational influences; (ii) determined the relative importance of noise on injuries; (iii) estimated the lowest observed adverse effect level (LOAEL) of noise on injury risk to determine the threshold of noise considered hazardous to injuries; and (iv) quantified the fraction of injuries that could be attributed to hazardous noise exposure. METHODS In this cross-sectional study at 10 US surface mine sites, traditional mixed effects, Poisson regression, and boosted regression tree (BRT) models were run on the number of reported work-related injuries in the last year. The LOAEL of noise on injuries was identified by estimating the percent increase in work-related injuries at different thresholds of noise exposure using a counterfactual estimator through the BRT model. A population attributable fraction (PAF) was quantified with this counterfactual estimator to predict reductions in injuries at the LOAEL. RESULTS Among 18 predictors of work-related injuries, mine site, perceived job safety, age, and sleepiness were the most important predictors. Occupational noise exposure was the seventh most important predictor. The LOAEL of noise for work-related injuries was a full-shift exposure of 88 dBA. Exposure ≥88 dBA was attributed to 20.3% (95% CI: 11.2%, 29.3%) of reported work-related injuries in the last year among the participants. CONCLUSIONS This study further supports hypotheses of a dose-response relationship between occupational noise exposure and work-related injuries, and suggests that exposures ≥88 dBA may increase injury risk in mining.
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Affiliation(s)
- Abas Shkembi
- Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA
| | - Lauren M Smith
- Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA
| | - Sandar Bregg
- Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA.,Michael & Associates, Inc., State College, PA, USA
| | - Richard L Neitzel
- Department of Environmental Health Sciences, University of Michigan, Ann Arbor, MI, USA
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