BACKGROUND: Workforce health is one of the primary and challenging issues, especially in industrialized countries. The purpose of the present study was to evaluate the ability to predict accident-proneness among Saveh Industry workers in Iran, based on an extended Health Belief Model, that included the construct of spiritual health.
METHODS: This descriptive-analytical study was conducted in 2022 on 384 workers in Saveh, Iran. The study aimed to explore relationships between accident proneness behavior, spiritual health, and health beliefs. The accident-proneness questionnaire consisted of two parts: the first part included demographic questions, and the second part comprised 9 sections covering personality traits, workplace harmful factors, miscellaneous factors, musculoskeletal disorders, safety culture, safety attitudes, job stress, organization interest, and degree of risk-taking. The Health Belief Model included 31 questions, while spiritual health was measured with the 20-question Paloutzian and Ellison questionnaire. The collected data were analyzed using SPSS version 26 software.
RESULTS: In terms of accident proneness, 229 (59.6%), exhibited high levels, 148 (38.5%) had medium levels, and 7 (1.8%) showed low levels of accident-proneness. Hierarchical multiple regression analysis showed that in the first model, variables of perceived self-efficacy, vulnerability, and severity independently predicted workers accident proneness, explaining a total of 43% of variance in accident proneness behavior. In the second step, perceived self-efficacy (β = 34%), perceived sensitivity (β = 27%), spiritual health (β = 16%), and perceived severity (β = 12%) were included, respectively, which explained a total of 46% of the variance of accident-prone behavior of workers.
CONCLUSIONS: Given the high rate of accident proneness observed in this study, there is a critical need for policymakers and health planners to design policies aimed at mitigating the risks associated with occupational accidents. Furthermore, the findings highlight the potential of integrating spiritual health into the Health Belief Model, as a conceptual framework for planning effective intervention programs to enhance workplace safety.

BACKGROUND: Workers\' compensation injuries entail burdensome financial and social costs. This study\'s objective was to describe cost and frequency of workplace injuries in aviation manufacturing in the state of Kansas using workers\' compensation data. Manufacturing incurs more workers\' compensation claims in Kansas than any other industry, and aviation contributes more of those claims than any other sub sector.
METHODS: Workers\' compensation insurance and reporting are required in the state of Kansas. Data were provided by the Kansas Department of Labor (KDOL) and included all closed workers\' compensation claims entailing indemnity and medical costs filed in the state from 2014 to 2022. Cost of claim data were normalized to 2022 U.S. dollars and data were analyzed as a function of percentage and claim cost by body part, type of injury, cause of injury, specific musculoskeletal disorder type, and as a function of age and gender injury rates.
RESULTS: Aviation claims entailed a median total cost of $26,941 and represented 8% of all closed claims filed in the state from 2014 to 2022. The grand total direct cost over the nine-year period was $75,404,147. Medical costs comprised 48.6% of all costs, indemnity 45.0%, and legal 6.4%. The most frequently injured body part was the hand/wrist (35.9%) followed by the shoulder (20.6%), and the most expensive body parts were related to the back. Overexertion (38.6%) was the most common cause followed by repetitive motion (22.8%). Work-related musculoskeletal disorders were the most common type accounting for 67.4% of all claims. Men and workers aged 55-64 incurred slightly higher claim rates than average. A sharp decrease in number of claims closed in 2021 coincided with production shutdowns the previous year related to the Covid-19 pandemic and design issues.
CONCLUSIONS: Aviation manufacturing is a key industry in Kansas and this study is the first known to describe costs and frequencies of workplace injuries in the sector using workers\' compensation data.
CONCLUSIONS: This guide to the most problematic and costly injuries in aviation manufacturing helps practitioners prioritize prevention strategies to most effectively reduce workplace injury and helps safety and health practitioners in prioritizing prevention efforts to reduce the most severe and costly aviation manufacturing injuries and illnesses. It also brings attention to some special considerations when working with safety data from 2020 to 2022 related to the Covid-19 pandemic.

BACKGROUND: Tower cranes are commonly employed in construction projects, despite presenting significant hazards to the workforce involved.
METHODS: To address these safety concerns, a Knowledge-Based Decision-Support System for Safety Risk Assessment (KBDSS-SRA) has been developed. The system\'s capacity to thoroughly evaluate associated risks is illustrated through its utilization in various construction endeavors.
RESULTS: The system accomplishes the following goals: (1) compiles essential risk factors specific to tower crane operations, (2) identifies critical safety risks that jeopardize worker well-being, (3) examines and assesses the identified safety risks, and (4) automates the labor-intensive and error-prone processes of safety risk assessment. The KBDSS-SRA assists safety management personnel in formulating well-grounded decisions and implementing effective measures to enhance the safety of tower crane operations.
CONCLUSIONS: This is facilitated by an advanced computerized tool that underscores the paramount significance of safety risks and suggests strategies for their future mitigation.

BACKGROUND: Tractors and quad bikes pose a significant risk of fatal injuries among farmers, particularly affecting older farmers. This study aimed to explore the barriers and facilitators to the adoption of machine related safety behaviors among older farmers in Irish farm settings.
METHODS: Four focus groups were conducted via Zoom in February 2021. Nineteen Irish farmers from four farm types participated. The discussions were audio-recorded, transcribed verbatim, and analyzed using an inductive, reflexive thematic analysis approach. The themes identified were then mapped to the COM-B (Capability-Opportunity-Motivation) model, providing a systematic theoretical basis for designing a future intervention to reduce machine-related accidents.
RESULTS: The analysis identified five inductive themes that encompassed both barriers and facilitators in farm safety practices: (1) Capability to manage competing responsibilities; (2) Characteristics of the farm and its work environment; (3) Availability and affordability of resources; (4) Prevailing sociocultural opportunities; and (5) Perceived likelihood and cost-benefit analysis in safety decision-making. These themes captured the complex interplay of capability, opportunity, and motivation in farmers\' decision-making processes. The study also revealed limitations in existing interventions, such as voluntary guidelines and educational methods, in effectively addressing these barriers.
CONCLUSIONS: Farmers\' abilities (capability), prevailing sociocultural factors, resource availability (opportunity), and their perceived consequences and benefits (motivation) affect how safely they work with machines. The study emphasizes the need for comprehensive, theory-driven approaches that consider the interplay of capability, opportunity, and motivational factors that may support or impede machine safety. Understanding the challenges faced by Irish farmers highlights potential strategies for safety intervention, and these strategies should be co-designed with farmers and attentive to the local context.
CONCLUSIONS: The study provides a template for understanding farmers\' perspectives using the COM-B model. The findings can inform the development of theoretically informed intervention strategies based on the Behavior Change Wheel framework.

BACKGROUND: Most studies about accidents and about PTSD, respectively, have been conducted either on blue-collar workers, or on the entire working population. There are very few such studies on white-collar workers.
OBJECTIVE: To examine diagnosis-specific sickness absence (SA) and disability pension (DP) after a work accident or PTSD, respectively, among white-collar workers in the private retail and wholesale industry.
METHODS: A prospective population-based cohort study of all 192,077 such workers aged 18-67 (44% women) in Sweden in 2012, using linked microdata from nationwide registers. We identified individuals who had secondary healthcare due to work-related accidents (n = 1114; 31% women) or to PTSD (n = 216; 79% women) in 2012-2016. Their average number of net days of diagnosis-specific SA (in SA spells > 14 days) and DP were calculated for 365 days before and 365 days after the healthcare visit.
RESULTS: 35% of the women and 24% of the men had at least one new SA spell during the 365 days after healthcare due to work accidents. Among women, the average number of SA/DP days increased from 14 in the year before the visit to 31 days the year after; among men from 9 to 21 days. SA days due to fractures and other injuries increased most, while SA days due to mental diagnoses increased somewhat. 73% of women and 64% of men who had healthcare due to PTSD had at least one new SA spell in the next year. Women increased from 121 to 157 SA/DP days and men from 112 to 174. SA due to stress-related disorders and other mental diagnoses increased the most, while DP due to stress-related diagnoses and SA due to musculoskeletal diagnoses increased slightly.
CONCLUSIONS: About a quarter of those who had secondary healthcare due to work accidents, and the majority of those with such healthcare due PTSD, had new SA in the following year. SA due to injury and mental diagnoses, respectively, increased most, however, SA/DP due to other diagnoses also increased slightly. More knowledge is needed on factors associated with having or not having SA/DP in different diagnoses after work accidents and among people with PTSD.

Slip, trip, and fall (STF) accidents cause high rates of absence from work in many companies. During the 2022 reporting period, the German Social Accident Insurance recorded 165,420 STF accidents, of which 12 were fatal and 2485 led to disability pensions. Particularly in the traffic, transport and logistics sector, STF accidents are the most frequently reported occupational accidents. Therefore, an accurate detection of near-falls is critical to improve worker safety. Efficient detection algorithms are essential for this, but their performance heavily depends on large, well-curated datasets. However, there are drawbacks to current datasets, including small sample sizes, an emphasis on older demographics, and a reliance on simulated rather than real data. In this paper we report the collection of a standardised kinematic STF dataset from real-world STF events affecting parcel delivery workers and steelworkers. We further discuss the use of the data to evaluate dynamic stability control during locomotion for machine learning and build a standardised database. We present the data collection, discuss the classification of the data, present the totality of the data statistically, and compare it with existing databases. A significant research gap is the limited number of participants and focus on older populations in previous studies, as well as the reliance on simulated rather than real-world data. Our study addresses these gaps by providing a larger dataset of real-world STF events from a working population with physically demanding jobs. The population studied included 110 participants, consisting of 55 parcel delivery drivers and 55 steelworkers, both male and female, aged between 19 and 63 years. This diverse participant base allows for a more comprehensive understanding of STF incidents in different working environments.

OBJECTIVE: To analyze the contribution of alcohol and drug intoxication to fatal occupational injuries and sudden death at the workplace in Moscow.
METHODS: A number of death cases of various organizations\' employees equal 357 in Moscow in 2023 were investigated. The mean age of the deceased was 48.29±13.9 years, 92.4% of them were men.
RESULTS: Ethanol in blood has been determined in 15% of the deceased. Narcotic drugs and psychotropic medications have been found in 6.7% of cases. Signs of chronic intoxication have been established in 16.5% of the deceased. Chronic intoxication accompanied or aggravated the course of 70% of cardiomyopathies. The proportion of deceased in an accident at an industry or construction site equal 23.9%, as well as 1/2 of the deceased in an accident on the street and in a residential building were impaired by alcohol.
CONCLUSIONS: The study of the contribution of alcohol and drug consumption to occupational mortality will allow to plan measures for reducing the mortality of working-age population.
UNASSIGNED: Анализ вклада алкогольной и наркотической интоксикации в смертельный производственный травматизм и внезапную смерть на рабочем месте в Москве.
UNASSIGNED: Исследованы 357 случаев смерти работников различных организаций на территории Москвы в 2023 г. Средний возраст погибших составил 48,29±13,9 года, 92,4% погибших — мужчины.
UNASSIGNED: У 15% погибших в крови обнаружен этанол. В 6,7% случаев были выявлены наркотические вещества и психотропные лекарственные препараты. У 16,5% погибших установлены признаки хронической интоксикации. Хроническая интоксикация сопутствовала или отягощала течение 70% кардиомиопатий. В состоянии опьянения находились 23,9% погибших в результате происшествия на производстве и стройке, 1/2 погибших в результате происшествия на улице и в жилом здании.
UNASSIGNED: Изучение вклада употребления алкоголя и наркотиков в смертность на производстве позволит планировать меры по снижению смертности трудоспособного населения.

BACKGROUND: Work-related fatalities represent an important global public health threat, accounting for over 300,000 deaths annually. Despite preventive strategies, fatalities persist, necessitating comprehensive investigations. Autoptic investigations, when ordered, offer comprehensive data on fatal accidents, in particular with detailed information about the type of accident, lesions, and type of work, enabling a thorough analysis of various factors contributing to workplace deaths. This study investigates work-related fatalities in Milan and Monza Brianza, Lombardy, analyzing patterns, industries, and factors leading to death among workers.
METHODS: This retrospective study analyzed all fatal work accident data from the Autopsy Database of the Legal Medicine Institute of Milan from January 2000 to December 2022. We also estimated the risk of fatal accidents per 1.00.000 workers using ISTAT data for the different work sectors.
RESULTS: Of the 17,841 violent deaths in Milan and Monza Brianza, 308 resulted from work-related accidents. The majority were male (95.1%). The secondary sector showed the higher estimated risk (27 /106 vs. 19/106 in primary and 2.3/106 in tertiary sector) accounted for 78.5% of fatalities, with specialized workers (35.1%) and construction workers (25%) being the most affected. Falls from above were the leading cause of death (36.7%). A decreasing trend in fatal accidents over the study period was observed, with July having the greatest frequency. The most common causes of death were polytrauma (36.4%) and head injuries (19.2%). Non-Italian workers constitute a noteworthy percentage of fatal cases (24%).
CONCLUSIONS: The secondary sector has the most fatal accidents at work despite a decrease in accidents over the observed time period. There is a need for rigorous analysis and interventions, using forensic autopsy case data to help understand causes, and collaboration between institutions is encouraged to develop effective preventive policies.

UNASSIGNED: Construction worker safety remains a major concern even as task automation increases. Although safety incentives have been introduced to encourage safety compliance, it is still difficult to accurately measure the effectiveness of these measures. A simple count of accident rates and lower numbers do not necessarily mean that workers are properly complying with safety regulations. To address this problem, this study proposes an image-based approach to monitor moment-by-moment worker safety behavior and evaluate the effects of different safety incentive scenarios.
UNASSIGNED: By capturing workers\' safety behaviors using a model integrated with OpenPose and spatiotemporal graph convolutional network, this study evaluated the effects of safety-incentive scenarios on workers\' compliance with rules while on the job. The safety incentive scenarios in this study were designed as 1) varying the type (i.e., providing rewards and penalties) of incentives and 2) varying the frequency of feedback about ones\' own compliance status during tasks. The effects of the scenarios were compared to the average compliance rates of three safety regulations (i.e., personal protective equipment self-monitoring hazard avoidance, and arranging the safety hook) for each scenario.
UNASSIGNED: The results show that 1) rewarding a good-compliance is more effective when there is no feedback on compliance status, and 2) penalizing non-compliance is more effective when there are three feedbacks during the tasks.
UNASSIGNED: This study provides a more accurate assessment of safety incentives and their effectiveness by focusing on safe behaviors to promote safety compliance among construction workers.

In order to study the relationship between China\'s safety production indicators and economic and social indicators, the development trend of indicator data in the past 20 years was statistically analyzed, and qualitative and quantitative research was conducted using grey relational analysis and multiple linear regression analysis methods. In the past two decades, there has been a significant improvement in the number of deaths, work-related injuries, and occupational patients in China\'s safety production, and the country\'s three categories of 14 economic and social indicators have achieved rapid development. Using the grey relation analysis method, the grey correlation degree between the number of deaths, work-related injuries, and occupational patients in China over the past twenty years and 14 economic and social indicators was obtained. The ranking of economic and social indicators that affect the number of deaths, work-related injuries, and occupational patients varies greatly. A multiple linear regression model was established for the number of deaths, work-related injuries, occupational diseases, and 14 economic and social indicators. The rationality of the model was verified from four aspects: R2, F-value, P-value, and deviation between actual and fitted values. Provide guidance for the development of safety production indicators and economic and social indicators in China through research.