Occupational Data Research Articles

Trends in Exposure to Respirable Dust and Respirable Crystalline Silica Among Lithium Mine Workers in Western Australia

BackgroundExposure to respirable dust (RES) and respirable crystalline silica (RCS) is common in mining operations and is associated with health effects such as pneumoconiosis, chronic obstructive pulmonary disease (COPD), interstitial pulmonary fibrosis, silicosis, lung cancer, and renal disease. MethodsThis study used industry occupational exposure data for respirable dust from two surface lithium mines in Western Australia for the period between 2017 and 2023. A total of 1122 samples were collected in workgroups across four departments - administration and support, mining, crushing and processing, and maintenance. ResultsThe study found that the overall RES concentration did not exceed the exposure standard. However, Crusher Dry/Wet Plant Personnel (0.558 mg/m3) and Workshop Boilermakers (0.842 mg/m3) recorded elevated exposure to RES. The highest mean exposures for RCS over the seven-year study period were measured for Management Administration & Technical (0.068 mg/m3), followed by Crusher Dry/Wet Plant Personnel (0.042 mg/m3), exceeding the ES. Maximum results for both RES (15.00 mg/m3) and RCS (2.50 mg/m3) indicated exceedances. ConclusionThe study demonstrated a decline in exposure to RES over the seven years of study from 0.472 mg/m3 to 0.151 mg/m3, with a slight increase in 2019 and 2022. A decline in the concentration of RCS was observed between 2019 -2021, followed by an increase after 2021. The mean concentration of RCS exceeded the exposure standard in 2023. Based on the study results and the established adverse health effects associated with exposure to silica, various control measuresto protect workers from RCS exposure should be considered.

Asthma control in severe asthma and occupational exposures to inhalable asthmagens

IntroductionWork-related asthma accounts for ≥25% of asthma in working-age populations, though the relationship between work exposures and symptoms is frequently missed, leading to poor health and employment outcomes. We hypothesised...

Pandemic of sensitivity to acrylate containing nail cosmetic among young Israeli women? Result of patch testing 2-hydroxyethyl methacrylate in the European baseline series.

2-Hydroxyethyl methacrylate (HEMA) was added into the European baseline series (EBS) in 2019. There is limited data regarding the frequency, relevance, and sources of exposure to HEMA. To investigate the frequency and clinical relevance of positive reactions to HEMA in the EBS in Israel, and explore sources of exposure. Retrospective cohort study that included all patients who underwent patch testing with the EBS in a tertiary center in Israel between 2020 and 2023. Positive reactions to HEMA were stratified by sex, six age groups, and year of study. Sources of exposure to HEMA as well as occupational data were recorded. A total of 1671 consecutive patients underwent HEMA patch testing, with 135 (8.1%) showing positive reactions to HEMA (130 females, 5 males). The prevalence in women (11.0%) was significantly higher compared to men (1.0%) (p < 0.001). Stratification by age and sex revealed the highest frequency of HEMA sensitivity of 16.7% among women younger than 30 years of age, with odds ratio of 2.3 (95%CI: 1.6-3.3, p < 0.001) compared to older women. There was an increase in frequency among women between the years 2022 and 2023 when compared to 2020-2021 (OR 1.7, 95%CI: 1.5-2.1, p < 0.01) attributable to COVID-19 pandemic and social restrictions. Among men the frequency fluctuations over the study period and age categories were nonsignificant. 111 (84%) were judged to be of clinical relevance and nail cosmetics was responsible for 95% of them. Of 111 patients with relevant reaction (110 females, 1 males), 20 (18%) had occupational contact dermatitis (18 nails stylists, 2 dentists). Other culprit products included sanitary pads (n = 4), medical adhesives (n = 3), and paints (n = 2). We report the highest frequency of HEMA sensitivity to date of 8.1%, that was most common among young women and in vast majority of cases was attributable to nail cosmetics. Our findings reflect the popularity of nail cosmetics in Israel as well as the global trend of increasing sensitivity to (meth)acrylates.

CRISP-DM-Based Data-Driven Approach for Building Energy Prediction Utilizing Indoor and Environmental Factors

The significant energy consumption associated with the built environment demands comprehensive energy prediction modelling. Leveraging their ability to capture intricate patterns without extensive domain knowledge, supervised data-driven approaches present a marked advantage in adaptability over traditional physical-based building energy models. This study employs various machine learning models to predict energy consumption for an office building in Berkeley, California. To enhance the accuracy of these predictions, different feature selection techniques, including principal component analysis (PCA), decision tree regression (DTR), and Pearson correlation analysis, were adopted to identify key attributes of energy consumption and address collinearity. The analyses yielded nine influential attributes: heating, ventilation, and air conditioning (HVAC) system operating parameters, indoor and outdoor environmental parameters, and occupancy. To overcome missing occupancy data in the datasets, we investigated the possibility of occupancy-based Wi-Fi prediction using different machine learning algorithms. The results of the occupancy prediction modelling indicate that Wi-Fi can be used with acceptable accuracy in predicting occupancy count, which can be leveraged to analyze occupant comfort and enhance the accuracy of building energy models. Six machine learning models were tested for energy prediction using two different datasets: one before and one after occupancy prediction. Using a 10-fold cross-validation with an 8:2 training-to-testing ratio, the Random Forest algorithm emerged superior, exhibiting the highest R2 value of 0.92 and the lowest RMSE of 3.78 when occupancy data were included. Additionally, an error propagation analysis was conducted to assess the impact of the occupancy-based Wi-Fi prediction model’s error on the energy prediction model. The results indicated that Wi-Fi-based occupancy prediction can improve the data inputs for building energy models, leading to more accurate energy consumption predictions. The findings underscore the potential of integrating the developed energy prediction models with fault detection systems, model predictive controllers, and energy load shape analysis, ultimately enhancing energy management practices.

Surnames in Local Newspapers and Social Mobility

This study aims to investigate social mobility by introducing an innovative data source: surnames in newspapers. The core hypothesis posits that frequent mentions in newspapers are indicative of greater social relevance. Through the collection of local newspaper data in the municipality of Modena, Italy, from 1921 to 2011, we examine variations in the relative representation of surnames in newspapers with respect to their presence in registry data. The results suggest that surnames in newspapers are not a random sample of the population, supporting the assumption that they reflect social significance. Surnames belonging to privileged groups exhibit a higher representation compared to other social groups. This higher relative representation seems to be transmitted to future generations and converges over time toward the mean, with variations depending on the considered high-status group. This kind of analysis could contribute to identifying different mobility patterns at the local level and represents a useful alternative when established data sources, such as income, education, and occupational data, are not available.

Examining the Completeness of Industry and Occupation Data Among Patients Newly Diagnosed with Cancer in North Carolina

Background Occupational exposure to toxic substances has been associated with cancer. Including industry and occupation (I/O) in cancer surveillance can help identify potential links between occupational exposures and cancers using data already collected to monitor statewide cancer trends. I/O reporting is required in cancer registries and recently in death records. In this study, we examined the completeness of I/O information in North Carolina cancer and death certificate data. Methods The North Carolina Central Cancer Registry (NC CCR) is the sole repository of North Carolina cancer incidence data. NC CCR (2020–2021) and death certificate data (2020–2022) were analyzed for completeness of I/O. NC CCR incidence records for 1995–2021 diagnoses were linked to North Carolina death files (2020–2022). Results NC CCR data had I/O in 17% to 51% of the cases, depending on cancer type. I/O was included in 41% of 2020 death certificates and increased to a 99.9% inclusion in 2021 and 2022 death certificates. In 2020, 20,018 (18%) of 109,677 total deaths matched with a cancer record during NC CCR data linkage. In 2021, 20,225 (16%) of 119,237 total deaths matched with a cancer record, and in 2022, 20,367 (17%) of 116,406 total deaths matched to a cancer record. Limitations Data presented here only examine the completeness of 10 cancer types that the NC CCR regularly analyzes using the most recent data available (2021). Conclusion Health care providers should routinely record I/O using the standards explained in the training for providers on I/O data collection, provided by the North Carolina Department of Health and Human Services, and should increase their frequency of reporting I/O to NC CCR. Death certificate data may provide I/O data for individuals whose information is missing from cancer registry data.

The Role of Paternal Occupational Data in Pregnancy Cohort Study in Puerto Rico (P.R.).

The Role of Paternal Occupational Data in Pregnancy Cohort Study in Puerto Rico (P.R.).

Occupational inequalities in mortality from cardiovascular disease, 2020-2021.

In recent years previous declines in cardiovascular disease (CVD) have stalled. There are occupational risk factors for CVD mortality. This study seeks to examine inequalities in CVD mortality for working-age adults in the United States by occupation. Death certificate data for CVD deaths were obtained from the National Center for Health Statistics. Occupation data from these death certificates were coded to major occupation groups. Using information about the number of workers employed in these occupations obtained from the American Community Survey, we calculated mortality rates and rate ratios (RRs), adjusted for covariates associated with CVD mortality. After adjusting for age, sex, race/ethnicity, and educational attainment, workers in 11 occupations had significantly elevated RRs: food preparation and serving; construction and extraction; arts, design, entertainment, sports, and media; life, physical, and social science; farming, fishing, and forestry; legal; protective services; building and grounds cleaning and maintenance; healthcare practitioners and technical; personal care and service; and community and social services. Occupation appears to be a significant predictor of CVD mortality. Further research is needed to assess how occupational risk factors contribute to changing trends for CVD mortality. Interventions are needed to address workplace risk factors for CVD.

Scaling traffic variables from sensors sample to the entire city at high spatiotemporal resolution with machine learning: applications to the Paris megacity

Abstract Road transportation accounts for up to 35% of carbon dioxide and 49% of nitrogen oxides emissions in the Paris region. However, estimates of city traffic patterns are often incomplete and of coarse spatio-temporal resolution, even where extensive networks of sensors exist. This study uses a machine learning approach to analyze data from 2086 magnetic road sensors across Paris, generating a detailed dataset of hourly traffic flow and road occupancy covering 6846 road segments from 2018 to 2022. Our model captures flow and occupancy with a Symmetric Mean Absolute Percentage Error of 37% and 54% respectively, providing high-resolution insights into traffic patterns. These insights allow for the creation of a comprehensive map of hourly transportation patterns in Paris, offering a robust framework for assessing traffic variables for each significant road link in the city. The model's ability to incorporate an emission factor based on the mean speed of the vehicle fleet, derived from flow and occupancy data, holds promise for developing a detailed CO2 and pollutant inventory. This methodology is not limited to Paris; it can be applied to other urban centers with similar data availability, highlighting its potential as a versatile tool for sustainable urban monitoring.

Burden and social distribution of occupational psychosocial exposures in the United States workforce, 2022.

To characterize the burden and social distribution of occupational psychosocial exposures in the United States (US). We merged 2022 US employment and demographic data from the Current Population Survey (CPS) with occupational characteristic data from the Occupational Information Network (O*NET), wage data from the Occupational Employment and Wage Statistics Survey, and hours worked from the CPS, to estimate the number and proportion of US workers at risk of exposure to 19 psychosocial hazards. We additionally estimated the number and proportion of US workers over- or underrepresented in exposure burden. Of the exposures examined, US workers were most commonly employed in occupations with high time pressure (67.5 million US workers exposed; 43.2% US workers exposed), high emotional labor (57.1 million; 36.6%), and low wages (47.8 million; 30.6%). The burden of exposures was uneven across sociodemographic strata, attributable to occupational segregation. The full data set is available online at https://deohs.washington.edu/us-exposure-burden. Work-related psychosocial exposures are ubiquitous and should be considered in occupational and public health research, policy, and interventions to reduce the burden of disease and health inequities in the United States.

Chromatin and gene regulation in archaea.

The chromatinisation of DNA by nucleoid-associated proteins (NAPs) in archaea 'formats' the genome structure in profound ways, revealing both striking differences and analogies to eukaryotic chromatin. However, the extent to which archaeal NAPs actively regulate gene expression remains poorly understood. The dawn of quantitative chromatin mapping techniques and first NAP-specific occupancy profiles in different archaea promise a more accurate view. A picture emerges where in diverse archaea with very different NAP repertoires chromatin maintains access to regulatory motifs including the gene promoter independently of transcription activity. Our re-analysis of genome-wide occupancy data of the crenarchaeal NAP Cren7 shows that these chromatin-free regions are flanked by increased Cren7 binding across the transcription start site. While bacterial NAPs often form heterochromatin-like regions across islands with xenogeneic genes that are transcriptionally silenced, there is little evidence for similar structures in archaea and data from Haloferax show that the promoters of xenogeneic genes remain accessible. Local changes in chromatinisation causing wide-ranging effects on transcription restricted to one chromosomal interaction domain (CID) in Saccharolobus islandicus hint at a higher-order level of organisation between chromatin and transcription. The emerging challenge is to integrate results obtained at microscale and macroscale, reconciling molecular structure and function with dynamic genome-wide chromatin landscapes.

Premenstrual syndrome among medical versus non-medical workers and its association with work-related quality of life

BackgroundPremenstrual syndrome (PMS) is a commonly underestimated disorder that negatively impacts a woman’s life. Medical workers, who live a more stressful life, may report an increased rate of PMS. Studies on the relationship between PMS and work-related quality of life for medical professionals are scarce, particularly in the Arab world. This study aimed to compare the frequency of PMS among medical versus non-medical workers at Zagazig University and to assess the association between PMS and their work-related quality of life.MethodsA comparative cross-sectional study was conducted. The sample population consisted of 48 medical and 48 non-medical female workers aged 18–45 years from Zagazig University. The two groups filled out a questionnaire with 3 parts: sociodemographic and occupational data, the Premenstrual Symptoms Screening Tool (PSST), and the Work-Related Quality of Life Scale (WRQL).ResultsSevere PMS was reported in 45.8% of medical workers versus 20.8% of non-medical workers with a statistically significant difference between both groups (p = 0.009). Binary logistic regression showed that being a medical worker, clinical specialty, ≥ 8 years of work, ≥ 24 working hours per week, and having a non-set hourly schedule were predictors for severe PMS. PMS was found to be a statistically significant predictor of poor WRQL (p < 0.001). There was a highly significant negative correlation between the PMS score and the WRQL score (r = − 0.302, p < 0.001).ConclusionAmong medical workers, PMS is more common and more severe, and WRQL is worse and negatively correlated with PMS. We suggest further studies with larger samples to prove this association and planning for public health programs to screen for and manage PMS among medical workers in our community.

Caitlin Clark's surprising economic impact

Increases in attendance for University of Iowa's women's basketball games caused by star player Caitlin Clark have led to claims of large economic benefits for Iowa City. This paper uses daily hotel occupancy data to analyze the visitor inflow arising from Clark's stardom. The findings show a surprising pattern: hotel occupancy did increase substantially during Clark's junior season but showed a much smaller increase during her senior season, even with considerably higher game attendance. Local fans substituting from men's games to women's games, thereby leaving fewer women's basketball seats available for out-of-town visitors, appears to explain at least some of the smaller hotel occupancy effect in Clark's senior season.

Downscaling occupational employment data from the state to the Census tract level

The lack of detailed occupational employment data at more granular geographic levels presents significant challenges in forecasting and analyzing local and regional employment changes in the era of the new technological revolution. This study aims to develop detailed occupational employment data by downscaling state-level employment information to the Census tract level. We introduce two downscaling algorithms that leverage employment, population, and sociodemographic composition data sourced from the American Community Survey, the Current Population Survey, and the Occupational Employment and Wage Statistics. This approach allows us to create a tract-level employment dataset covering 808 occupations. Such data are crucial for examining the effects of expected technological and demographic shifts on employment at this scale, which is critical for understanding tax base implications and job mobility opportunities. We demonstrate the value of these datasets by examining employment projections for two occupations anticipated to decline due to technological advancements in the near future.

Internet of things and deep learning-enhanced monitoring for energy efficiency in older buildings

Retrofitting older buildings for energy efficiency is paramount in today's sustainability and environmental awareness era. Older buildings contribute greatly to energy waste since they typically lack new energy-efficient technology. Reducing carbon emissions, lowering energy bills, and extending the life of these historic landmarks all depend on fixing the energy inefficiency that plagues these buildings. Despite advanced technologies' remarkable progress, the potential of the Internet of Things and deep learning in older buildings has not been unexplored. Major obstacles include expensive and out-of-date infrastructure and the difficulty of incorporating new technology into historically significant structures. Existing research has mostly ignored older infrastructures' unique requirements and limitations in favour of current or newly built services. In addition, comprehensive research on integrating deep learning with the Internet of Things in this specific environment has been lacking. Smart building energy management is made possible by the Internet of Things (IoT) and deep learning. Architectural limitations, outmoded infrastructure, and the necessity for non-invasive retrofitting solutions all contribute to the difficulty of energy monitoring and improvement in older buildings. This research proposes combining the IoT with Deep Learning-enhanced Predictive Energy Modeling (DL-PEM) to make an energy management system that can change and adapt to the needs of older buildings. Data from IoT sensors is collected on occupancy, temperature, lighting, and equipment usage and then analyzed using Deep Learning models to determine the most efficient energy consumption patterns. Beyond its energy-saving potential, this method has many potential uses. Spotting and fixing structural problems before they become major can improve occupant comfort, reduce maintenance costs, and pave the way for predictive maintenance. Integration with the Smart grid and demand response programs can be facilitated, too, improving the reliability of the power system as a whole. Our IoT and Deep Learning-based energy management solution optimizes energy usage, reduces expenses, and mitigates environmental impact in older buildings, as shown by extensive simulation studies. The system's performance is compared to more conventional methods, and its flexibility is evaluated in various building and usage contexts. The experimental outcomes show the suggested DL-PEM model increases the energy demand forecasting analysis, energy consumption analysis, thermal comfort optimization analysis, seasonal variation analysis, and occupancy data analysis.

Characterization and assessment of exposure risks of healthcare personnel during the COVID-19 pandemic.

Healthcare personnel have the right to decent, healthy, and safe working conditions during COVID-19. Despite our best efforts to safeguard them against SARS-CoV-2 infection, a substantially large number of healthcare personnel fell ill and succumbed to COVID-19. This study aimed to characterize the SARS-CoV-2 exposures among healthcare personnel and determine the risk level of those events, thereby identifying and focusing on the key areas that influence workplace safety. A pre-tested questionnaire was used to collect data from healthcare personnel on the type of exposure, place, and use of preventive measures, along with demographic and occupational data. Exposure events were categorized into low, moderate, and high-risk by using a risk categorization protocol. Doctors were primarily found to be involved in the aerosol-generating procedure, which had 94 times higher odds (95% CI: 43.9926-201.17) for high-risk exposures than other activities. In contrast, nurses were more likely to experience close-contact exposures (OR: 2.77, 95% CI: 1.44-5.33). Both critical care units and operation theaters were identified to have higher odds (OR: 2.34, 95% CI: 1.33-4.23 and OR: 2.31; 95% CI: 0.99-5.42, respectively) than the wards for high-risk exposures. Use of personal protective equipment was poor, with breaches reported in 10.2% of all exposure events. The lacunae in practice were addressed by repeated training and counseling and by ensuring the adequacy of resources in required areas. The assessment was beneficial for safeguarding healthcare personnel and assuring a safer workplace during the early phase of the COVID-19 pandemic.

[Translated article] Temporary work disability following trapezial resection and suspension arthroplasty for thumb carpometacarpal osteoarthritis

PurposeThe aim of this study is to assess the time to return to work (TRW) in patients undergoing trapezial resection using the Mini TightRope® system at our centre and to investigate factors that might delay return to work following this surgery. Material and methodsA retrospective study was conducted on patients who underwent Trapezial resection and suspensionplasty using the Mini TightRope® system between 2015 and 2016, with a minimum one-year follow-up. Telephone interviews were conducted along with a review of medical records and radiology reports, as well as temporary work disability documents, collecting epidemiological and occupational data. Groups were compared based on age, gender, dominant hand, biomechanical occupational requirements of the patients, and whether they had experienced prior temporary work disability. ResultsA total of 36 patients (29 women and 7 men) with an average age of 55.7 years were included. The median time to return to work was 126 days. Self-employed workers re-entered the workforce 72 days earlier on average; workers who had experienced prior temporary work disability had a greater total temporary work disability duration and took 91 days longer to return to work compared to those who had not. ConclusionsPatients employed by others and those who had experienced prior temporary work disability before the surgery had longer temporary work disability periods. In our study, no differences were observed based on gender, dominant hand, or biomechanical work demands of the intervened patients.

Spatiotemporal information enhanced multi-feature short-term traffic flow prediction.

Accurately predicting traffic flow is crucial for optimizing traffic conditions, reducing congestion, and improving travel efficiency. To explore spatiotemporal characteristics of traffic flow in depth, this study proposes the MFSTBiSGAT model. The MFSTBiSGAT model leverages graph attention networks to extract dynamic spatial features from complex road networks, and utilizes bidirectional long short-term memory networks to capture temporal correlations from both past and future time perspectives. Additionally, spatial and temporal information enhancement layers are employed to comprehensively capture traffic flow patterns. The model aims to directly extract original temporal features from traffic flow data, and utilizes the Spearman function to extract hidden spatial matrices of road networks for deeper insights into spatiotemporal characteristics. Historical traffic speed and lane occupancy data are integrated into the prediction model to reduce forecasting errors and enhance robustness. Experimental results on two real-world traffic datasets demonstrate that MFSTBiSGAT successfully extracts and captures spatiotemporal correlations in traffic networks, significantly improving prediction accuracy.

Application of Bayesian Decision Analysis to Estimate Occupational Noise Levels from Limited Exposure Data

In recent years, Bayesian statistical modeling approaches have emerged as valuable tools in Industrial Hygiene (IH), particularly in situations with limited exposure data. This paper delves into the application of Bayesian Statistical Models, specifically Bayesian Decision Analysis (BDA), to analyze occupational noise exposure data within two Similar Exposure Groups (SEGs) of groundskeeping workers, addressing the pervasive issue of high noise levels exceeding 85 dBA. The primary aim is to estimate exposure levels based on the location of the exposure profiles for the SEGs within the AIHA® Exposure Control Categories (ECCs) 0-4, later modified and expanded to AIHA-ECC 0-5. This study employs BDA to estimate noise exposures among groundskeepers, emphasizing the scarcity of literature on BDA in noise exposure rating. Groundskeepers, facing potential risk of hearing impairment due to prolonged exposure, constitute a crucial and critical demographic, with nearly one million individuals in the United States alone. Noise data from the two SEGs was analyzed by using the Exposure Assessment Strategies Committee—EASC- AIHA® IHData Analyst for BDA modeling, complemented with the AIHA IHSTAT™ for certainty rating. The resulting posterior graphs were utilized to categorize exposure profiles into respective AIHA ECCs relative to noise OEL. These findings provide valuable insights, indicating a posterior probability of approximately 43.5% and 56.5% with 95% confidence that noise exposure levels fall within AIHA ECC 3 and AIHA ECC 4, respectively. However, while the 95% percentile decision statistics suggest exposure values lower than the OEL, the Upper Tolerance Limit (UTL) significantly surpass the OEL indicating low certainty regarding these exposure values for employees. Notably, the UTLs suggest that the SEGs are experiencing noise levels above the legally required 90 dBA for more than 5% of the time, which is an unacceptable scenario. Recommendations include implementing protective strategies such as regular training, surveillance, and monitoring, alongside engineering control strategies and mandatory use of personal protective equipment. These results underscore the critical need for refining judgments about noise exposures based on variability within and between employees, suggesting the use of additional statistical tools such as ANOVA (single-multivariate), MATLAB, and Python.

Occupational Characteristics and Working Hours of Stroke and Myocardial Infarction Patients at a Hospital in Bangkok: A Descriptive Study

OBJECTIVE The aim of this study was to investigate occupational risk factors, particularly focusing on working hours, in the patients with cerebrovascular diseases and cardiovascular diseases to help reduce the incidence of those diseases in the future. METHODS This descriptive study was conducted retrospectively. Patients age 18 to 65 with a first diagnosis of acute stroke and acute myocardial infarction in the years 2019 to 2022 were included. Demographic data were obtained from in-patient records. Occupational data, including working hours and other missing data, were obtained through telephone interviews with the patients. RESULTS After excluding individuals who were unemployed at the time of the disease incident, deceased, unwilling or unreachable to participate, a total of 146 patients were included in this study. Of those, 115 participants (78.8%) were first diagnosed with acute stroke and 31 participants (21.2%) were first diagnosed with acute myocardial infarction during the study period. The median and interquartile range of weekly working hours of all participants, participants with acute stroke, and participants with acute myocardial infarction were 56 (25), 56 (35), and 59 (27) hours per week, respectively. The range of working hours per week was from 4 to 112 hours. The majority of participants reported not engaging in dangerous work as defined by Thailand’s Ministry of Labour, reported no significant occupational hazards, stated they had minimal stress and that they had control over their own job. CONCLUSIONS The results indicate that the majority of stroke and myocardial infarction patients from this center worked ≥ 55 hours per week. Although the majority reported having no significant occupational factors, recognizing and incorporating a history of occupational factors could be beneficial in disease diagnosis, disease prevention, prevention of recurrence, and aiding the return-to-work process of the patients. KEYWORDS overwork, cerebrovascular disease, cardiovascular disease, myocardial infarction, stroke, occupational disease, long working hours