Associations of iron deficiency and depressive symptoms among young adult males and Females: NHANES 2017 to 2020.

Depression is one of the most prevalent mental health conditions throughout the lifespan. Notable differences in the prevalence of depression among females and males arise during adolescence and may peak during young adulthood. Since iron deficiency is a treatable condition that may contribute to depression, this topic among youth (18 to 25 years of age) needs to be further explored. Thus, our study examines the associations between three measures of iron (ferritin, serum iron, and transferrin saturation levels) with Patient Health Questionnaire-9 (PHQ-9) depressive symptoms and depression among young adult males and females using the National Health and Nutrition Examination Survey (NHANES) 2017-2020. Using multivariable Poisson and logistic regression models, adjusting for several demographic and clinical variables, we report 1) the prevalence of depression and 2) the associations between iron deficiency and depressive symptoms and depression among males and females. 917 participants were included in our study. More females (12.5 %) than males (6.8 %) had PHQ-9 depression. Males with ferritin (adjusted odds ratio [AOR] = 14.13, 95 % confidence interval [CI]: 1.51, 132.21), serum iron (AOR = 4.84, 95 % CI: 1.02, 22.92), and transferrin (AOR = 13.79, 95 % CI: 3.59, 53.06) deficiencies were at higher risk for depression, while females with ferritin deficiency (AOR = 0.34, 95 % CI: 0.11, 0.97) had a lower risk for depression. Our study highlights the need to focus on depression screening among young adults as well as risk factors for depression among this age group. Identifying risk factors and screening for iron deficiency, especially among females, should be considered as well.

Testing the reliability and validity of the modified Amsterdam Inventory for Auditory Disability and Handicap in career firefighters in the United States.

BACKGROUND: Firefighters are routinely exposed to loud noise that put them at risk for hearing loss. A reliable and valid measure to assess firefighters' hearing function is important. This study aims to test the reliability and validity of the modified Amsterdam Inventory for Auditory Disability and Handicap ((m)AIADH) in firefighters. METHOD: A cross-sectional study was conducted using a convenience sample of 239 career firefighters from six partnered fire departments in Central Texas and Northern California. The internal consistency, convergent and criterion validity, and the ability to discriminate groups by measured hearing, perceived hearing, and a combination of measured and perceived hearing, were examined using the total score and score for each of the five subscales of the (m)AIADH. RESULTS: The study participants were primarily men (93%). Satisfactory internal consistency was revealed for the (m)AIADH with Cronbach's alpha above 0.80 for all five subscales. Criterion analysis presented a moderate correlation between the (m)AIADH and the average of hearing threshold at high frequencies (4, 6, and 8 kHz). For convergent validity, the (m)AIADH was moderately to highly related with perceived hearing. There were statistically significant differences in the total (m)AIADH score and the five subscales for measured hearing thresholds except for "intelligibility in quiet." The (m)AIADH also showed a discriminative ability to distinguish between the group with good perceived hearing and the group with bad perceived hearing. CONCLUSION: The (m)AIADH is a reliable and valid measure to assess various dimensions of hearing function among firefighters.

Underreporting of workers' injuries or illnesses and contributing factors: a systematic review.

BACKGROUND: Accurate identification of work-related health problems is important to understand workplace safety issues and develop appropriate interventions. Although workers' reporting of work-related injuries or illnesses is the very first step of the reporting process, many workers may encounter challenges in reporting them to their management or workers' compensation (WC) programs. This systematic review aimed to identify the level of workers' underreporting of work-related injuries and illnesses and the contributing factors and reasons for underreporting among US workers. METHODS: This study searched PubMed (Medline), PsycINFO (ProQuest), CINAHL (EBSCOhost), EMBASE (Embase.com), and Social Science Citation Index (Web of Science) using search terms related to underreporting of work-related injury or illness. RESULTS: Twenty studies (17 quantitative and three mixed methods studies) were identified. The studies investigated reporting to management (n = 12), WC programs (n = 6), multiple organizations (n = 1), and not specified (n = 1). The timeframe used to measure reporting prevalence varied from three months to entire careers of workers, with the most common timeframe of 12 months. This review indicated that 20-91% of workers did not report their injuries or illnesses to management or WC programs. From quantitative studies, contributing factors for injury or illness underreporting were categorized as follows: injury type and severity, sociodemographic factors (e.g., age, gender, education, and race/ethnicity), general health and functioning, worker's knowledge on reporting, job and employment characteristics (e.g., work hour, job tenure, work shift, type of occupation, and physical demand), psychosocial work environment (e.g., supervisor support, coworker support, and safety climate), and health care provider factors. From the review of qualitative studies, the reasons for underreporting included the following: fear or concern, cumbersome time and effort in the reporting process, lack of knowledge regarding reporting, perceptions of injuries as not severe or part of the job, and distrust of reporting consequences. CONCLUSIONS: The review findings indicated that low wage earners, racial/ethnic minority workers, and workers who perceive a poor psychosocial work environment encounter more barriers to reporting a work-related injury or illness. This review also identified variations in the measurement of work-related injury reporting across studies and a lack of standardized measurement. TRIAL REGISTRATION: The review was registered in the PROSPERO, an international database of prospectively registered systematic reviews in health and social care (CRD42021284685).

Evaluation of an Ecological Model for Work-Related Musculoskeletal Disorders.

Work-related musculoskeletal disorder is a major occupational health problem worldwide. The ecological model by Sauter and Swanson proposes a way in which psychosocial factors can influence musculoskeletal disorders. Employing Chinn and Kramer's framework, we evaluated the ecological model and found it had a specific scope for the working populations and clear concepts. This model also demonstrated high clarity, consistency, generalizability, and accessibility aligning with a nursing metaparadigm. Empirical evidence supports the applicability of the ecological model in nursing research and practices, but more research is needed for nursing-specific application.

Filing a Workers' Compensation Claim for Low Back Pain and Associated Factors: Analysis of 2015 National Health Interview Survey.

OBJECTIVE: The aim of this study was to examine the degree of reporting work-related low back pain (LBP) by workers' compensation (WC) claim filing and associated factors among US workers. METHODS: We conducted multivariable logistic regression using the 2015 National Health Interview Survey data. RESULTS: Among 1203 US workers with self-reported work-related LBP diagnosis, only 19.6% filed for WC. Compared with non-Hispanic White workers, Black, Asian, and Hispanic workers were less likely to file for WC. Workers' compensation filing was less frequent among workers in independent work arrangement and who stopped working, changed jobs, or made a change in work and more frequent among workers 45 to 64 years of age and having high LBP frequency. CONCLUSIONS: Our study findings suggest underreporting of work-related LBP to the WC system is common. Barriers to WC filing should be addressed especially for racial/ethnic minorities and independent workers.

The short form effort-reward imbalance: Measure of occupational stress for firefighters.

PURPOSE: The original effort-reward imbalance (ERI) questionnaire has been widely used as a measure of occupational stress. The need for brief measurement of psychosocial stress at work has risen. This study examined the psychometric properties of a 16-item short form ERI (S-ERI) questionnaire. METHODS: A cross-sectional study was conducted with a convenience sample of 247 career firefighters in the United States. An internet-based survey contained the S-ERI questionnaire, and assessed perceived health, demographics, and work characteristics. Internal consistency was tested by item-total correlation and Cronbach's α coefficients. Factorial validity was assessed by confirmatory factor analysis (CFA), and criterion validity was examined by testing the S-ERI's association with perceived health, controlling for other factors. RESULTS: The study participants were primarily young and mid-aged White males (mean age = 42 years, 75% White non-Hispanic, 93% males). The S-ERI questionnaire showed satisfactory internal consistency, with Cronbach α coefficients above 0.80 for all three scales: effort 0.82, reward 0.83, and overcommitment 0.81. CFA showed all the S-ERI components had a good fit (goodness-of-fit index = 0.99, comparative fit index = 0.92, root mean square error of approximation = 0.06). In terms of criterion validity, significantly increased odds ratios (ORs) for perceived health (those indicating they were less healthy) were found in firefighters in the lowest tertile of reward (OR = 7.06, 95% confidence interval [CI]: 2.86-17.43), and highest tertile of overcommitment (OR = 2.29, 95% CI: 1.00-5.22), after controlling for other factors. CONCLUSION: The S-ERI questionnaire is a reliable and valid measure to assess occupational stress. We recommend the use of S-ERI as a measure of occupational stress when initiating efforts to improve firefighters' wellness.

Racial and Ethnic Differences in the Perceptions of Health, Work Environment and Experiences of Work-Related Symptoms Among Cleaning Workers.

This study explored racial and ethnic differences in perception of work environment, safe work practices, general health status, experience of work-related injury or illness and subsequent symptom reporting and health care seeking behaviors among cleaning workers. This study analyzed cross-sectional data obtained from 183 cleaning workers employed in a university hospital or a health sciences campus in Northern California. The sample included 120 Asians (65.6%), 37 Hispanics (20.2%), and 27 other ethnicities (14.2%); 85.7% were foreign-born. Asian workers perceived lower job control and supervisor support and higher job strain than other workers. The odds of perceiving general health as excellent or very good were lower among Asians compared to Hispanics and Others. Asians who experienced chemical-related symptoms were less likely than Hispanics and others to report the symptoms to their supervisor or seek healthcare. Our study findings indicated racial/ethnic differences in perceptions of work and general health, seeking healthcare, and reporting behaviors among cleaning workers. Asian workers, specifically, may need special attention to improve their experiences of work environments and health in the workplace.

Bayesian Approach to Multivariate Component-Based Logistic Regression: Analyzing Correlated Multivariate Ordinal Data.

Applications of component-based models have gained much attention as a means of accompanying dimension reduction in the regression setting and have been successfully implemented to model a univariate outcome in the behavioral and social sciences. Despite the prevalence of correlated ordinal outcome data in the fields, however, most of the extant component-based models have been extended to address the multivariate ordinal issue with a simplified but unrealistic assumption of independence, which may lead to biased statistical inferences. Thus, we propose a Bayesian methodology for a component-based model that accounts for unstructured residual covariances, while regressing multivariate ordinal outcomes on pre-defined sets of predictors. The proposed Bayesian multivariate ordinal logistic model re-expresses ordinal outcomes of interest with a set of latent continuous variables based on an approximate multivariate t-distribution. This contributes not only to developing an efficient Gibbs sampler, a Markov Chain Monte Carlo algorithm, but also to facilitating the interpretation of regression coefficients as log-transformed odds ratio. The empirical utility of the proposed method is demonstrated through analyzing a subset of data, extracted from the 2009 to 2010 Health Behavior in School-Aged Children study that investigates risk factors of four different forms of bullying perpetration and victimization: physical, social, racial, and cyber.

Bayesian Mixture Model of Extended Redundancy Analysis.

Extended redundancy analysis (ERA), a generalized version of redundancy analysis (RA), has been proposed as a useful method for examining interrelationships among multiple sets of variables in multivariate linear regression models. As a limitation of the extant RA or ERA analyses, however, parameters are estimated by aggregating data across all observations even in a case where the study population could consist of several heterogeneous subpopulations. In this paper, we propose a Bayesian mixture extension of ERA to obtain both probabilistic classification of observations into a number of subpopulations and estimation of ERA models within each subpopulation. It specifically estimates the posterior probabilities of observations belonging to different subpopulations, subpopulation-specific residual covariance structures, component weights and regression coefficients in a unified manner. We conduct a simulation study to demonstrate the performance of the proposed method in terms of recovering parameters correctly. We also apply the approach to real data to demonstrate its empirical usefulness.

Gender differences in experience and reporting of acute symptoms among cleaning staff.

BACKGROUND: Cleaning tasks pose risks of hazardous chemical exposure and adverse health effects for cleaning workers. We examined gender differences among cleaning staff in the experience of chemical-related symptoms and in reporting to supervisors. METHODS: We analyzed cross-sectional reports from 171 university hospital or campus cleaning staff on chemical exposures to cleaning products, experience of acute symptoms, reporting of symptoms to supervisors, as well as demographic and psychosocial factors (risk perception, job demand/control, supervisor/co-worker support, and safety climate). Results were analized using multivariable logistic regression, adjusting for demographic, job, and psychosocial factors. Interactions of gender and psychosocial variables were also examined. RESULTS: Men and women reported different frequencies for exposure-related tasks. Acute symptoms of chemical exposure were more prevalent in women compared with men (46.0% vs. 25.4%; adjusted odds ratio [OR] = 2.63; 95% confidence interval [CI] 1.27-5.46). Women were more concerned about exposure to cleaning chemicals (p = 0.029) but reported symptoms to their supervisor less often than men (18.5% vs. 40.6%, adjusted OR = 0.28; 95% CI 0.09-0.93). More supervisor support was significantly associated with less frequent symptom experience among women (OR = 0.83; 95% CI 0.70-0.99). Asian workers and less educated workers were less likely than others to report symptoms to supervisors. Gender differences in symptom reporting to supervisors were not explained by psychosocial factors. CONCLUSIONS: Women may have increased susceptibility or perception of symptoms from cleaning compared to men, but this may be mitigated by supervisor support. Female Asian workers with lower education may perceive more significant barriers in reporting work-related symptoms to supervisors. Further research is needed to explore factors related to underreporting.

Bayesian Extended Redundancy Analysis: A Bayesian Approach to Component-based Regression with Dimension Reduction.

Extended redundancy analysis (ERA) combines linear regression with dimension reduction to explore the directional relationships between multiple sets of predictors and outcome variables in a parsimonious manner. It aims to extract a component from each set of predictors in such a way that it accounts for the maximum variance of outcome variables. In this article, we extend ERA into the Bayesian framework, called Bayesian ERA (BERA). The advantages of BERA are threefold. First, BERA enables to make statistical inferences based on samples drawn from the joint posterior distribution of parameters obtained from a Markov chain Monte Carlo algorithm. As such, it does not necessitate any resampling method, which is on the other hand required for (frequentist's) ordinary ERA to test the statistical significance of parameter estimates. Second, it formally incorporates relevant information obtained from previous research into analyses by specifying informative power prior distributions. Third, BERA handles missing data by implementing multiple imputation using a Markov Chain Monte Carlo algorithm, avoiding the potential bias of parameter estimates due to missing data. We assess the performance of BERA through simulation studies and apply BERA to real data regarding academic achievement.