An Agenda for Advancing Research and Prevention at the Nexus of Work Organization, Occupational Stress, and Mental Health and Well-Being.

Work characteristics and worker well-being are inextricably connected. In particular, the characteristics of work organization shape and perpetuate occupational stress, which contributes to worker mental health and well-being outcomes. Consequently, the importance of understanding and addressing connections between work organization, occupational stress, and mental health and well-being-the focus of this Special Issue-increasingly demand attention from those affected by these issues. Thus, focusing on these issues in the long-haul truck driver (LHTD) sector as an illustrative example, the purpose of this commentary is as follows: (1) to outline current research approaches and the extant knowledge base regarding the connections between work organization, occupational stress, and mental health; (2) to provide an overview of current intervention strategies and public policy solutions associated with the current knowledge base to protect and promote worker mental health and well-being; and (3) to propose a two-pronged agenda for advancing research and prevention for workers during the 21st century. It is anticipated that this commentary, and this Special Issue more broadly, will both echo numerous other calls for building knowledge and engaging in this area and motivate further research within complementary current and novel research frameworks.

Complex systems and participatory approaches to address maternal health disparities: Findings from a system dynamics group model building project in North Texas.

Focusing on non-Hispanic Black women (NHBW) in North Texas, this study employed participatory system dynamics modeling to explore three hypotheses: (1) stakeholders will conceptualize structural racism is a pervasive macrostructural force that exerts downstream impacts to shape and perpetuate maternal health disparities among NHBW; (2) stakeholders will identify key causal forces and leverage points that exist across levels of influence; and (3) stakeholders will identify complex interactions, in the form of circular causality, that are present among the key causal forces and leverage points that shape NHBW maternal health disparities. Nine participants engaged in a virtual system dynamics group model-building session that focused on eliciting key variables, behavior-over-time graphs (BOTGs), causal loop diagram (CLD), and targets for action. Participants identified 83 key variables. BOTGs included an average of 6.56 notations and time horizons that, on average, started in 1956. The CLD featured 11 reinforcing and seven balancing feedback loops. Eleven targets for action were identified. Structural racism was revealed as a pervasive macrostructural force that shaped maternal health outcomes among NHBW. Key causal forces and leverage points were identified across levels of influence. Finally, feedback loops within the CLD exhibited circular causality.

Metabolic syndrome among commercial truck drivers: The relationship between condition prevalence and crashes.

BACKGROUND: Metabolic syndrome (MetS) is especially prevalent among US truck drivers. However, there has been limited research exploring associations between MetS conditions with roadway crashes among truck drivers. The objective of this paper is to assess relationships between specific combinations of individual MetS components and crashes and near-misses. METHODS: Survey, biometric, and anthropometric data were collected from 817 truck drivers across 6 diverse US states. Survey data focused on demographics and roadway safety outcomes, and anthropometric/biometric data corresponded to five MetS conditions (waist circumference blood pressure, hemoglobin A1c, triglycerides, and high-density lipoprotein [HDL] cholesterol). Logistic regression was used to calculate odds ratios of lifetime crashes and near-miss 1-month period prevalence associated with: 1) specific MetS conditions regardless of presence or absence of other MetS conditions, and 2) specific MetS conditions and counts of other accompanying MetS conditions. RESULTS: Hypertension was the MetS characteristic most strongly associated with lifetime crash and 1-month near-miss outcomes, while high triglycerides, low HDL cholesterol, and large waist circumference were most commonly present among groups of conditions associated with crashes and near-misses. Overall, an increasing number of specific co-occurring MetS conditions were associated with higher reporting of roadway crashes. CONCLUSIONS: Specific combinations and higher prevalence of MetS conditions were associated with increased frequency of reported crashes. Moreover, when the co-occurrence of MetS conditions is aggregated, a dose-response relationship with crashes appears. These results suggest that policy changes and interventions addressing MetS may increase driver health and reduce crash risk.

Exploring the complexity of firearm homicides in Harris County, Texas, from 2009 to 2021: Implications for theory and prevention.

Firearm violence is a major health problem in the United States that clusters asymmetrically across geographic and demographic lines, and the persistence and unequal distribution of firearm violence suggests that novel causal explanations and theoretical frameworks may be warranted to guide preventive strategies. Thus, this study explores the following three hypotheses that are grounded in complex systems theory: 1) trends in firearm homicides risks have shifted heterogeneously in Harris County across endemic degree of risk; 2) firearm homicides clusters have remained resilient in Harris County across the study time period; and 3), the associations between known contextual correlates of firearm homicides and the distribution of firearm homicides risks in Harris County have manifested as nonlinear. Using a retrospective study design (n = 4,397) from January 1, 2009-June 31, 2021, medicolegal death investigation data from the Harris County Institute of Forensic Sciences and estimates of community characteristics from the American Community Survey were analyzed using Joinpoint trend analysis, kernel density geospatial analysis, and proportion tests. Trend analyses revealed that firearm homicides risks shifted heterogeneously across endemic degree of risk, with geographical areas with lower initial firearm homicides risks experiencing more profound upward shifts across the time period of the study. Geospatial analyses identified the resiliency of firearm homicides clusters across the study period, particularly in central, southern, and south-western districts of the city. Finally, the relationships between known contextual correlates and the distribution of firearm homicides risks in Harris County appeared to be nonlinear, particularly regarding ethnicity. This study provides data-driven results that suggest the plausibility of complex systems theory in advancing the understanding of causality in firearm homicides. Further, these findings support the urgent need for complex systems-informed preventive efforts that account for spatiotemporal heterogeneity, key interactions that generate nonlinearity, and latent feedback loops that underlie resiliency in firearm homicides.

Work, physical activity, and metabolic health: Understanding insulin sensitivity of long-haul truck drivers.

BACKGROUND: Long-haul truck drivers are disproportionately exposed to metabolic risk; however, little is known about their metabolic health and the role of physical activity and other risk factors in metabolic outcomes. OBJECTIVE: This study compares truck drivers' insulin sensitivity, and associations between metabolic risk factors and insulin sensitivity, with those of the general population. METHODS: Survey, anthropometric, and biometric data were collected from 115 long-haul truckers, which were then compared to the general population data using the National Health and Nutrition Examination Survey (NHANES) dataset. The quantitative insulin sensitivity check index (QUICKI) was used to estimate insulin sensitivity. RESULTS: Truck drivers had lower QUICKI scores than the general population cohort. Sagittal abdominal diameter and exercise were predictive for QUICKI among combined cohorts. Waist circumference and perceived health were more predictive for QUICKI among truck drivers, and sagittal abdominal diameter and income were more predictive for QUICKI among the general population. CONCLUSIONS: Long-haul truckers appear to represent a subset of the general population regarding the impact of physical activity and other metabolic risk factors on QUICKI. Accordingly, comprehensive efforts which target these factors are needed to improve truckers' physical activity levels and other metabolic risks.

Commercial truck drivers should be a priority population for COVID-19 vaccinations.

As COVID-19 vaccines become available, supply is expected to initially fall short of demand. In response, the Advisory Committee on Immunization Practices (ACIP) has issued guidance on which groups should be prioritized to receive vaccines. For the first phase of vaccine allocation, the ACIP recommended healthcare personnel and long-term care facility residents as recipients. This recommendation was based on risks endemic to these populations, as well as ethical principles related to benefits and harms, mitigating health inequalities, and promoting justice. Commercial truck drivers have played a vital and underappreciated role during the COVID-19 pandemic. Despite the indispensable role that commercial drivers play in distributing vaccines, they have not been recommended for vaccine allocation in the next phase (1b) by the ACIP. However, the rationale and ethical principles cited for the first vaccine phase suggest that these workers should be recommended for inclusion. By doing so, the acquisition and transmission of COVID-19 may be mitigated, which would benefit both these workers and the US public. Further, persistent vulnerabilities render commercial truck drivers susceptible to severe COVID-19 infection; therefore, vaccination during the next phase is imperative to curb the exacerbation of extant health inequities. Finally, because present-day COVID-19 vulnerabilities in these workers have been shaped by unjust policies over the past several decades, and because COVID-19 public health policies have excluded and potentially exacerbated the impacts of the pandemic for these workers, allocating vaccines to commercial truck drivers is a necessary step toward promoting justice.

Exploring the Role of Social Network Structure in Disease Risk among U.S. Long-haul Truck Drivers in Urban Areas.

Objective: Using mixed methods, we explored properties of long-haul truckers' social networks potentially influencing STI/BBI acquisition and transmission. Methods: We recruited inner-city drug and sex network members (N = 88) for interviews. Blood and urine samples and vaginal swabs were collected to test for STIs/BBIs. Data were collected on participants' role in the network (trucker, sex worker, or intermediary), sexual and substance-use behaviors, and dyadic relationships with drug and/or sex contacts. We analyzed network data using UCINET. Results: Data revealed 2 major network clusters (58 male truckers, 6 male intermediaries, and 24 female sex workers; 27.3% STI/BBI positive). Overall, 18.8% of network members had more than one type of risky relationship with the same person (multiplexity), 11.4% of dyads were between 2 STI/ BBI positive people (assortative mixing), 36.4% were between one STI/BBI positive person and one negative person (disassortative mixing), 44.3% of people were connected to more than one person who was STI/BBI positive (concurrency), and 62.5% of nodes were just one path removed from an STI/BBI positive individual (bridging). Conclusion: Despite only 27.3% of the network being STI/BBI positive, our results revealed network characteristics (and potential intervention points) that amplify risk of disease spread within trucker-centered networks.

Work-Life Conflict among U.S. Long-Haul Truck Drivers: Influences of Work Organization, Perceived Job Stress, Sleep, and Organizational Support.

Work-life balance and job stress are critical to health and well-being. Long-haul truck driving (LHTD) is among the unhealthiest and most unsafe occupations in the U.S. Despite these disparities, there are no extant published studies examining the influence of work, stress and sleep outcomes on drivers' work-life balance. The current study investigated whether adverse work organization, stress, and poor sleep health among LHTDs are significantly associated with work-life conflict. Logistic regression was used to examine how work organization characteristics, job stress, and sleep influenced perceived stress and a composite measure of work-life conflict among a sample of 260 U.S. LHTDs. The pattern of regression results dictated subsequent analyses using structural equation modeling (SEM). Perceived job stress was the only statistically significant predictor for work-life balance. Fast pace of work, sleep duration and sleep quality were predictors of perceived job stress. SEM further elucidated that stress mediates the influences of fast work pace, supervisor/coworker support, and low sleep duration on each of the individual work-life balance indicators. There is an urgent need to address work conditions of LHTDs to better support their health, well-being, and work-life balance. Specifically, the findings from this study illustrate that scheduling practices and sleep outcomes could alleviate job stress and need to be addressed to more effectively support work-life balance. Future research and interventions should focus on policy and systems-level change.

The Impact of Work Organization, Job Stress, and Sleep on the Health Behaviors and Outcomes of U.S. Long-Haul Truck Drivers.

Background. Compared with other occupations, long-haul truck drivers (LHTD) engage in excessively unhealthy behaviors and experience disproportionately poor health outcomes. Health promotion efforts targeting LHTDs focus on improving individual-level behaviors; however, this occupation is replete with adverse work organization characteristics, high job stress, and compromised sleep health, which are hypothesized to cause poor health behaviors and outcomes among LHTDs. Therefore, the purpose of this study was to explore the connections between work characteristics, job stress, and sleep outcomes, and health behaviors and physical and mental health outcomes among LHTDs. Method. This was a cross-sectional study, using interviewer-administered surveys with LHTDs (n = 260). Bivariate correlation analysis was used to explore the associations among work organization, job stress, sleep health, and health behaviors and outcomes. Logistic regression analyses were used to determine whether these work organization, job stress, and sleep factors predicted health behaviors and outcomes. Results. Long work hours of more than 11 hours daily (odds ratio [OR] = 2.34) resulted in increased odds of high caffeine consumption. High job stress (OR = 0.48) and poor sleep quality (OR = 0.42) led to decreased odds for spending at least 1 hour daily for cooking/eating. Low sleep duration, less than 7 hours daily (OR = 2.55), led to increased odds of a physical health diagnosis. Both high job stress (OR = 3.58) and poor sleep quality (OR = 2.22) resulted in increased odds of a mental health diagnosis. Conclusion. Health promotion efforts targeting LHTDs need to be coupled with upstream policy, environmental, and systems-level change, especially at the governmental and trucking industry levels.

Occupational health disparities among U.S. long-haul truck drivers: the influence of work organization and sleep on cardiovascular and metabolic disease risk.

OBJECTIVE: The organization of work has undergone vast transformations over the past four decades in the United States and has had profound impacts on worker health and wellbeing. The profession of commercial truck driving is one of the best examples. Particularly for long-haul truck drivers, changes in work organization have led to disproportionately poor physiological, psychological, and sleep health outcomes. METHODS: The present study examined disparities in cardiometabolic disease risk among long-haul truck drivers and the general population, and the influence of work organization and sleep in generating these outcomes. Researchers collected survey data from 260 drivers, and blood assay samples from 115 of those drivers, at a large highway truck stop in North Carolina. Comparisons were made for cardiovascular and metabolic risk against the 2011-2012 National Health and Nutrition Examination Survey (NHANES). In addition, logistic regression was used to explore predictive relationships between work organization and sleep and risk for cardiovascular and metabolic disease. RESULTS: There were statistically significant mean differences between the long-haul truck driver sample and the NHANES sample for both cardiovascular (3.71 vs. 3.10; p <0.001) and metabolic (4.31 vs. 3.09; p <0.001) disease risk. The truck driver sample was less physically active and had lower HDL cholesterol along with greater levels of smoking, BMI, and metabolic syndrome diagnosis. More years of driving experience and poor sleep quality were statistically significant predictors for both cardiovascular and metabolic disease risk. CONCLUSIONS: Study findings implicate elements of the occupational milieu experienced by long-haul truck drivers that induce disproportionate cardiometabolic disease risk. Sleep quality, largely compromised by poor work conditions and workplace environments, plays a significant role in increased risks for cardiometabolic disease. There is an urgent need for longitudinal studies of this critical occupational sector as well as intervention research centered on policy and systems level change.

Can subjective sleep problems detect latent sleep disorders among commercial drivers?

INTRODUCTION: Long-haul truck drivers experience poor sleep health and heightened accident rates, and undiagnosed sleep disorders contribute to these negative outcomes. Subjective sleep disorder screening tools may aid in detecting drivers' sleep disorders. This study sought to evaluate the value of subjective screening methods for detecting latent sleep disorders and identifying truck drivers at-risk for poor sleep health and safety-relevant performance. MATERIALS AND METHODS: Using cross-sectional data from 260 long-haul truck drivers, we: 1) used factor analysis to identify possible latent sleep disorders; 2) explored the construct validity of extracted sleep disorder factors by determining their associations with established sleep disorder risk factors and symptoms; and 3) explored the predictive validity of resulting sleep disorder factors by determining their associations with sleep health and safety-relevant performance. RESULTS: Five latent sleep disorder factors were extracted: 1) circadian rhythm sleep disorders; 2) sleep-related breathing disorders; 3) parasomnias; 4) insomnias; 5) and sleep-related movement disorders. Patterns of associations between these factors generally corresponded with known risk factors and symptoms. One or more of the extracted latent sleep disorder factors were significantly associated with all the sleep health and safety outcomes. DISCUSSION: Using subjective sleep problems to detect latent sleep disorders among long-haul truck drivers may be a timely and effective way to screen this highly mobile occupational segment. This approach should constitute one component of comprehensive efforts to diagnose and treat sleep disorders among commercial transport operators.

Moving alcohol prevention research forward-Part II: new directions grounded in community-based system dynamics modeling.

BACKGROUND AND AIMS: Given the complexity of factors contributing to alcohol misuse, appropriate epistemologies and methodologies are needed to understand and intervene meaningfully. We aimed to (1) provide an overview of computational modeling methodologies, with an emphasis on system dynamics modeling; (2) explain how community-based system dynamics modeling can forge new directions in alcohol prevention research; and (3) present a primer on how to build alcohol misuse simulation models using system dynamics modeling, with an emphasis on stakeholder involvement, data sources and model validation. Throughout, we use alcohol misuse among college students in the United States as a heuristic example for demonstrating these methodologies. METHODS: System dynamics modeling employs a top-down aggregate approach to understanding dynamically complex problems. Its three foundational properties-stocks, flows and feedbacks-capture non-linearity, time-delayed effects and other system characteristics. As a methodological choice, system dynamics modeling is amenable to participatory approaches; in particular, community-based system dynamics modeling has been used to build impactful models for addressing dynamically complex problems. RESULTS: The process of community-based system dynamics modeling consists of numerous stages: (1) creating model boundary charts, behavior-over-time-graphs and preliminary system dynamics models using group model-building techniques; (2) model formulation; (3) model calibration; (4) model testing and validation; and (5) model simulation using learning-laboratory techniques. CONCLUSIONS: Community-based system dynamics modeling can provide powerful tools for policy and intervention decisions that can result ultimately in sustainable changes in research and action in alcohol misuse prevention.

Moving alcohol prevention research forward-Part I: introducing a complex systems paradigm.

BACKGROUND AND AIMS: The drinking environment is a complex system consisting of a number of heterogeneous, evolving and interacting components, which exhibit circular causality and emergent properties. These characteristics reduce the efficacy of commonly used research approaches, which typically do not account for the underlying dynamic complexity of alcohol consumption and the interdependent nature of diverse factors influencing misuse over time. We use alcohol misuse among college students in the United States as an example for framing our argument for a complex systems paradigm. METHODS: A complex systems paradigm, grounded in socio-ecological and complex systems theories and computational modeling and simulation, is introduced. Theoretical, conceptual, methodological and analytical underpinnings of this paradigm are described in the context of college drinking prevention research. RESULTS: The proposed complex systems paradigm can transcend limitations of traditional approaches, thereby fostering new directions in alcohol prevention research. By conceptualizing student alcohol misuse as a complex adaptive system, computational modeling and simulation methodologies and analytical techniques can be used. Moreover, use of participatory model-building approaches to generate simulation models can further increase stakeholder buy-in, understanding and policymaking. CONCLUSIONS: A complex systems paradigm for research into alcohol misuse can provide a holistic understanding of the underlying drinking environment and its long-term trajectory, which can elucidate high-leverage preventive interventions.

Work, sleep, and cholesterol levels of U.S. long-haul truck drivers.

Long-haul truck drivers in the United States experience elevated cardiovascular health risks, possibly due to hypercholesterolemia. The current study has two objectives: 1) to generate a cholesterol profile for U.S. long-haul truck drivers; and 2) to determine the influence of work organization characteristics and sleep quality and duration on cholesterol levels of long-haul truck drivers. Survey and biometric data were collected from 262 long-haul truck drivers. Descriptive analyses were performed for demographic, work organization, sleep, and cholesterol measures. Linear regression and ordinal logistic regression analyses were conducted to examine for possible predictive relationships between demographic, work organization, and sleep variables, and cholesterol outcomes. The majority (66.4%) of drivers had a low HDL (<40 mg/dL), and nearly 42% of drivers had a high-risk total cholesterol to HDL cholesterol ratio. Sleep quality was associated with HDL, LDL, and total cholesterol, and daily work hours were associated with LDL cholesterol. Workday sleep duration was associated with non-HDL cholesterol, and driving experience and sleep quality were associated with cholesterol ratio. Long-haul truck drivers have a high risk cholesterol profile, and sleep quality and work organization factors may induce these cholesterol outcomes. Targeted worksite health promotion programs are needed to curb these atherosclerotic risks.

Work and Chronic Disease: Comparison of Cardiometabolic Risk Markers Between Truck Drivers and the General US Population.

OBJECTIVE: US long-haul truck drivers experience a wide array of excess cardiometabolic disease (CMD) risks unique to their occupation. How these risks translate to, and potentially induce, elevations in the clinical CMD risk profile of this population is unknown. METHODS: A non-experimental, descriptive, cross-sectional design was employed to collect anthropometric and biometric data from 115 long-haul truckers to generate for the first time a comprehensive CMD risk marker profile, which was then compared with the general US population. The relationships between CMD risk markers and CMD outcomes were examined for both populations. RESULTS: The long-haul trucker sample presented elevated CMD risk markers, generally scoring significantly worse than the general population. Associations between CMD risk markers and disease states varied between both populations. CONCLUSIONS: US long-haul truck drivers' distinctive CMD risk profile indicates occupationally-linked CMD pathogenesis.

Understanding the role of sleep quality and sleep duration in commercial driving safety.

INTRODUCTION: Long-haul truck drivers in the United States suffer disproportionately high injury rates. Sleep is a critical factor in these outcomes, contributing to fatigue and degrading multiple aspects of safety-relevant performance. Both sleep duration and sleep quality are often compromised among truck drivers; however, much of the efforts to combat fatigue focus on sleep duration rather than sleep quality. Thus, the current study has two objectives: (1) to determine the degree to which sleep impacts safety-relevant performance among long-haul truck drivers; and (2) to evaluate workday and non-workday sleep quality and duration as predictors of drivers' safety-relevant performance. MATERIALS AND METHODS: A non-experimental, descriptive, cross-sectional design was employed to collect survey and biometric data from 260 long-haul truck drivers. The Trucker Sleep Disorders Survey was developed to assess sleep duration and quality, the impact of sleep on job performance and accident risk, and other relevant work organization characteristics. Descriptive statistics assessed work organization variables, sleep duration and quality, and frequency of engaging in safety-relevant performance while sleepy. Linear regression analyses were conducted to evaluate relationships between sleep duration, sleep quality, and work organization variables with safety composite variables. RESULTS: Drivers reported long work hours, with over 70% of drivers working more than 11h daily. Drivers also reported a large number of miles driven per week, with an average of 2,812.61 miles per week, and frequent violations of hours-of-service rules, with 43.8% of drivers "sometimes to always" violating the "14-h rule." Sleep duration was longer, and sleep quality was better, on non-workdays compared on workdays. Drivers frequently operated motor vehicles while sleepy, and sleepiness impacted several aspects of safety-relevant performance. Sleep quality was better associated with driving while sleepy and with job performance and concentration than sleep duration. Sleep duration was better associated with accidents and accident risk than sleep quality. DISCUSSION: Sleep quality appears to be better associated with safety-relevant performance among long-haul truck drivers than sleep duration. Comprehensive and multilevel efforts are needed to meaningfully address sleep quality among drivers.

Overcoming Barriers in Unhealthy Settings: A Phenomenological Study of Healthy Truck Drivers.

We investigated the phenomenon of sustained health-supportive behaviors among long-haul commercial truck drivers, who belong to an occupational segment with extreme health disparities. With a focus on setting-level factors, this study sought to discover ways in which individuals exhibit resiliency while immersed in endemically obesogenic environments, as well as understand setting-level barriers to engaging in health-supportive behaviors. Using a transcendental phenomenological research design, 12 long-haul truck drivers who met screening criteria were selected using purposeful maximum sampling. Seven broad themes were identified: access to health resources, barriers to health behaviors, recommended alternative settings, constituents of health behavior, motivation for health behaviors, attitude toward health behaviors, and trucking culture. We suggest applying ecological theories of health behavior and settings approaches to improve driver health. We also propose the Integrative and Dynamic Healthy Commercial Driving (IDHCD) paradigm, grounded in complexity science, as a new theoretical framework for improving driver health outcomes.