A pilot study on psychosocial factors and perceptions of organizational health among a sample of U.S. waste workers.

Solid waste workers encounter a number of occupational hazards that are likely to induce stress. Thus, there are likely to be psychosocial factors that also contribute to their overall perceptions of organizational health. However, attitudes regarding the aforementioned among solid waste workers' have not been assessed. This descriptive, cross-sectional pilot study operationalized the INPUTS Survey to determine workers' perceptions of organizational health and other psychosocial factors of work. Percentage and mean responses to each INPUTS domain are presented in accordance with their survey manual. Pearson's chi-squared tests were run on count data; Fisher's exact tests were run for count data with fewer than five samples. ANOVAs were run on the continuous items. Due to a relatively low sample size (N = 68), two-sided p values < 0.1 were considered statistically significant. Most solid waste worker participants reported high decision authority, that they perceived their management to prioritize workplace health and safety, and had high job satisfaction. However, perceptions of support for health outside of the realm of occupational safety and health was lower. Addressing traditional occupational health hazards continues to take precedence in this industry, with less of a focus on how the social determinants of health may impact workplace health.

Risk perception or hazard perception? Examining misperceptions of miners' personal exposures to noise.

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.

Associations between Poorer Mental Health with Work-Related Effort, Reward, and Overcommitment among a Sample of Formal US Solid Waste Workers during the COVID-19 Pandemic.

Background: Effort-reward imbalance (ERI) and overcommitment at work have been associated poorer mental health. However, nonlinear and nonadditive effects have not been investigated previously. Methods: The association between effort, reward, and overcommitment with odds of poorer mental health was examined among a sample of 68 formal United States waste workers (87% male). Traditional, logistic regression and Bayesian Kernel machine regression (BKMR) modeling was conducted. Models controlled for age, education level, race, gender, union status, and physical health status. Results: The traditional, logistic regression found only overcommitment was significantly associated with poorer mental health (IQR increase: OR = 6.7; 95% CI: 1.7 to 25.5) when controlling for effort and reward (or ERI alone). Results from the BKMR showed that a simultaneous IQR increase in higher effort, lower reward, and higher overcommitment was associated with 6.6 (95% CI: 1.7 to 33.4) times significantly higher odds of poorer mental health. An IQR increase in overcommitment was associated with 5.6 (95% CI: 1.6 to 24.9) times significantly higher odds of poorer mental health when controlling for effort and reward. Higher effort and lower reward at work may not always be associated with poorer mental health but rather they may have an inverse, U-shaped relationship with mental health. No interaction between effort, reward, or overcommitment was observed. Conclusion: When taking into the consideration the relationship between effort, reward, and overcommitment, overcommitment may be most indicative of poorer mental health. Organizations should assess their workers' perceptions of overcommitment to target potential areas of improvement to enhance mental health outcomes.

Characterization of perceived biohazard exposures, personal protective equipment, and training resources among a sample of formal U.S. solid waste workers: A pilot study.

In the United States, the majority of waste workers work with solid waste. In solid waste operations, collection, sorting, and disposal can lead to elevated biohazard exposures (e.g., bioaerosols, bloodborne and other pathogens, human and animal excreta). This cross-sectional pilot study aimed to characterize solid waste worker perception of biohazard exposures, as well as worker preparedness and available resources (e.g., access to personal protective equipment, level of training) to address potential biohazard exposures. Three sites were surveyed: (1) a family-owned, small-scale waste disposal facility, (2) a county-level, recycling-only facility, and (3) an industrial-sized, large-scale facility that contains a hauling and landfill division. Survey items characterized occupational biohazards, resources to mitigate and manage those biohazards, and worker perceptions of biohazard exposures. Descriptive statistics were generated. The majority of workers did not report regularly coming into contact with blood, feces, and bodily fluids (79%). As such, less than one-fifth were extremely concerned about potential illness from biological exposures (19%). Yet, most workers surveyed (71%) reported an accidental laceration/cut that would potentially expose workers to biohazards. This study highlights the need for additional research on knowledge of exposure pathways and perceptions of the severity of exposure among this occupational group.

Linking environmental injustices in Detroit, MI to institutional racial segregation through historical federal redlining.

OBJECTIVES: To identify the most pervasive environmental exposures driving environmental disparities today associated with historical redlining in Detroit. METHODS: We overlaid Detroit's 1939 Home Owners' Loan Corporation (HOLC) shapefile from the Mapping Inequality project onto the EPA EJScreen and the DOT National Transportation Noise maps to analyze differences in current demographic and environmental indicators between historically redlined (D-grade) and non-redlined neighborhoods using simple linear regression and a boosted classification tree algorithm. RESULTS: Historically redlined neighborhoods in Detroit experienced significantly higher environmental hazards than non-redlined neighborhoods in the form of 12.1% (95% CI: 7.2-17.1%) higher levels of diesel particulate matter (PM), 32.2% (95% CI: 3.3-69.3%) larger traffic volumes, and 65.7% (95% CI: 8.6-152.8%) higher exposure to hazardous road noise (LEQ(24h) >70 dBA). Historically redlined neighborhoods were situated near 1.7-times (95% CI: 1.4-2.1) more hazardous waste sites and twice as many (95% CI: 1.5-2.7) risk management plan (RMP) sites than non-redlined neighborhoods. The lifetime cancer risk from inhalation of air toxics was 4.4% (95% CI: 2.9-6.6%) higher in historically redlined communities, and the risk of adverse respiratory health outcomes from air toxics was 3.9% (95% CI: 2.1-5.6%) higher. All factors considered together, among the environmental hazards considered, the most pervasive hazards in historically redlined communities are proximity to RMP sites, hazardous road noise, diesel PM, and cancer risk from air pollution. CONCLUSIONS: Historically redlined neighborhoods may have a disproportionately higher risk of developing cancer and adverse respiratory health outcomes from air toxics. Policies targeting air and noise pollution from transportation sources, particularly from sources of diesel exhaust, in historically redlined neighborhoods may ameliorate some of the impacts of structural environmental racism from historical redlining in Detroit.

Evaluating Occupational Noise Exposure as a Contributor to Injury Risk among Miners.

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.

Effort-Reward Imbalance among a Sample of Formal US Solid Waste Workers.

Background: Solid waste workers are exposed to a plethora of occupational hazards and may also experience work-related stress. Our study had three specific hypotheses: (1) waste workers experience effort−reward imbalance (ERI) with high self-reported effort but low reward, (2) unionized workers experience greater ERI, and (3) workers with higher income have lower ERI. Methods: Waste workers from three solid waste sites in Michigan participated in this cross-sectional study. We characterized perceived work stress using the short-version ERI questionnaire. Descriptive statistics and linear tests for trend were assessed for each scale. Linear regression models were constructed to examine the relationship between structural factors of work stress and ERI. Gradient-boosted regression trees evaluated which factors of effort or reward best characterize workers' stress. Results: Among 68 participants, 37% of workers reported high effort and low reward from work (ERI > 1). Constant pressure due to heavy workload was most indicative of ERI among the solid waste workers. Union workers experienced 79% times higher ERI than non-unionized workers, while no significant differences were observed by income, after adjusting for confounders. Conclusions: Organizational-level interventions, such as changes related to workload, consideration of fair compensation, and increased support from supervisors, can decrease work stress.

Beware the Grizzlyman: A comparison of job- and industry-based noise exposure estimates using manual coding and the NIOSH NIOCCS machine learning algorithm.

Recently, the National Institute for Occupational Safety and Health (NIOSH) released an updated version of the NIOSH Industry and Occupation Computerized Coding System (NIOCCS), which uses supervised machine learning to assign industry and occupational codes based on provided free-text information. However, no efforts have been made to externally verify the quality of assigned industry and job titles when the algorithm is provided with inputs of varying quality. This study sought to evaluate whether the NIOCCS algorithm was sufficiently robust with low-quality inputs and how variable quality could impact subsequent job estimated exposures in a large job-exposure matrix for noise (NoiseJEM). Using free-text industry and job descriptions from >700,000 noise measurements in the NoiseJEM, three files were created and input into NIOCCS: (1) N1, "raw" industries and job titles; (2) N2, "refined" industries and "raw" job titles; and (3) N3, "refined" industries and job titles. Standardized industry and occupation codes were output by NIOCCS. Descriptive statistics of performance metrics (e.g., misclassification/discordance of occupation codes) were evaluated for each input relative to the original NoiseJEM dataset (N0). Across major Standardized Occupational Classifications (SOC), total discordance rates for N1, N2, and N3 compared to N0 were 53.6%, 42.3%, and 5.0%, respectively. The impact of discordance on the major SOC group varied and included both over- and under-estimates of average noise exposure compared to N0. N2 had the most accurate noise exposure estimates (i.e., smallest bias) across major SOC groups compared to N1 and N3. Further refinement of job titles in N3 showed little improvement. Some variation in classification efficacy was seen over time, particularly prior to 1985. Machine learning algorithms can systematically and consistently classify data but are highly dependent on the quality and amount of input data. The greatest benefit for an end-user may come from cleaning industry information before applying this method for job classification. Our results highlight the need for standardized classification methods that remain constant over time.

Noise exposure and mental workload: Evaluating the role of multiple noise exposure metrics among surface miners in the US Midwest.

This study examined associations between metrics of noise exposure and mental workload. In this cross-sectional study, five occupational noise metrics computed from full-shift dosimetry were evaluated among surface mine workers in the US Midwest. Mental workload was evaluated using a modified, raw NASA-TLX and clustered with a k-means clustering algorithm. Mixed effects logistic regression and Bayesian Kernel Machine Regression (BKMR) was utilized for analysis. Average noise exposure, the difference between peak and mean noise exposure, and the number of peaks >135 dB were each strongly associated with mental workload, while the kurtosis and standard deviation of noise throughout a shift were not. An exposure-response relationship between average noise exposure and mental workload may exist, with elevated risk of high mental workload beginning at 80 dBA. These results suggest that high noise exposure may be an independent risk factor of high mental workload, and impulse events and the difference between the peak and mean noise exposure may have interactive effects with average noise exposure.

Fraction of acute work-related injuries attributable to hazardous occupational noise across the USA in 2019.

INTRODUCTION: The contribution of hazardous noise-a ubiquitous exposure in workplaces-to occupational injury risk is often overlooked. In this ecological study, the fraction of US workplace acute injuries resulting in days away from work in 2019 attributable to hazardous occupational noise exposure was estimated. METHODS: Using the NoiseJEM, a job exposure matrix of occupational noise, and 2019 Occupational Employment and Wage Statistics data, the proportion of workers experiencing hazardous occupational noise (≥85 dBA) was estimated for every major US Standard Occupational Classification (SOC) group. Population attributable fractions (PAFs) were calculated for each major SOC group using the relative risk (RR) taken from a published 2017 meta-analysis on this relationship. RESULTS: About 20.3 million workers (13.8%) are exposed to hazardous levels of occupational noise. Nearly 3.4% of acute injuries resulting in days away from work in 2019 (95% CI 2.4% to 4.4%) were attributable to hazardous occupational noise, accounting for roughly 14 794 injuries (95% CI 10 367 to 18 994). The occupations with the highest and the lowest PAFs were production (11.9%) and office and administrative support (0.0%), respectively. DISCUSSION: Hazardous noise exposure at work is an important and modifiable factor associated with a substantial acute occupational injury burden.

Retrospective assessment of the association between noise exposure and nonfatal and fatal injury rates among miners in the United States from 1983 to 2014.

BACKGROUND: Mining is a significant economic force in the United States but has historically had among the highest nonfatal injury rates across all industries. Several factors, including workplace hazards and psychosocial stressors, may increase injury and fatality risk. Mining is one of the noisiest industries; however, the association between injury risk and noise exposure has not been evaluated in this industry. In this ecological study, we assessed the association between noise exposure and nonfatal and fatal occupational injury rates among miners. METHODS: Federal US mining accident, injury, and illness data sets from 1983 to 2014 were combined with federal quarterly mining employment and production reports to quantify annual industry rates of nonfatal injuries and fatalities. An existing job-exposure matrix for occupational noise was used to estimate annual industry time-weighted average (TWA, dBA) exposures. Negative binomial models were used to assess relationships between noise, hearing conservation program (HCP) regulation changes in 2000, year, and mine type with incidence rates of injuries and fatalities. RESULTS: Noise, HCP regulation changes, and mine type were each independently associated with nonfatal injuries and fatalities. In multivariate analysis, each doubling (5 dB increase) of TWA was associated with 1.08 (95% confidence interval: 1.05, 1.11) and 1.48 (1.23, 1.78) times higher rate of nonfatal injuries and fatalities, respectively. HCP regulation changes were associated with 0.61 (0.54, 0.70) and 0.49 (0.34, 0.71) times lower nonfatal injury and fatality rates, respectively. CONCLUSION: Noise may be a significant independent risk factor for injuries and fatalities in mining.

Work Task Association with Lead Urine and Blood Concentrations in Informal Electronic Waste Recyclers in Thailand and Chile.

The informal recycling of electronic waste ("e-waste") is a lucrative business for workers in low- and middle-income countries across the globe. Workers dismantle e-waste to recover valuable materials that can be sold for income. However, workers expose themselves and the surrounding environment to hazardous agents during the process, including toxic metals like lead (Pb). To assess which tools, tasks, and job characteristics result in higher concentrations of urine and blood lead levels among workers, ten random samples of 2 min video clips were analyzed per participant from video recordings of workers at e-waste recycling sites in Thailand and Chile to enumerate potential predictors of lead burden. Blood and urine samples were collected from participants to measure lead concentration. Boosted regression trees (BRTs) were run to determine the relative importance of video-derived work variables and demographics, and their relationship with the urine and blood concentrations. Of 45 variables considered, five job characteristics consisting of close-toed shoes (relative importance of 43.9%), the use of blunt striking instruments (14%), bending the back (5.7%), dismantling random parts (4.4%), and bending the neck (3.5%) were observed to be the most important predictors of urinary Pb levels. A further five job characteristics, including lifting objects <20 lbs. (6.2%), the use of screwdrivers (4.2%), the use of pliers/scissors (4.2%), repetitive arm motion (3.3%), and lifting objects >20 pounds (3.2%) were observed to be among the most important factors of blood Pb levels. Overall, our findings indicate ten job characteristics that may strongly influence Pb levels in e-waste recycling workers' urine and blood.