Work serves as a critical means of obtaining resources, facilitating personal growth, realizing self-worth, and engaging in social interactions. However, work-related diseases pose significant threats to workers’ health and productivity, and impose considerable economic burdens. This article categorized work-related diseases into six major types, including musculoskeletal disorders, mental and behavioral disorders, cardiovascular and metabolic diseases, digestive system diseases, reproductive system diseases, and non-specific respiratory diseases, and summarized their risk factors, assessment methods, policy regulation, and prevention and control measures. Current research in this field predominantly relies on cross-sectional studies, which present limitations in causal inference and potential risks of bias. Future studies should expand sample sizes, optimize research designs, and establish multidimensional evaluation systems to comprehensively assess the health and economic impacts of work-related diseases. It is recommended to enhance the translation of research findings into practice, thereby providing a scientific basis for the occupational health protection system and promoting the well-being and sustainable development of the working population.

Objective:To evaluate the application effects of two different item response modes in the Worker-Occupation Fit Inventory (WOFI) .Methods:From June to October 2022, a stratified cluster sampling method was adopted to collect and evaluate on-site data on worker-occupation fit among medical workers in two tertiary grade A general hospitals in Sichuan Province and Henan Province (1162 participants). Ordinal categorical item response (hierarchical item response) and dichotomous item response are two commonly used item response modes in psychometric measurement. Item response theory was used to analyze and compare the evaluation effects of the two item response modes in the WOFI. The generalized partial credit model and the 2-parameter logistic model were used to compare the item difficulty, discrimination, and scale information volume under different response modes.Results:The discrimination values range of the items in the WOFI under the hierarchical item response and dichotomous item response modes was 0.98 to 2.06 and 1.24 to 2.72, respectively, and the item difficulty values range was -4.61 to 1.15 and -1.25 to 0.08, respectively. Under the hierarchical item response mode, the amount of information on the WOFI was concentrated in the range of θ (the real ability or trait level of the subject) of -4 to 2, while under the dichotomous item response mode, it was concentrated in the range of θ of-2 to 2. The scale information volume was insufficient when θ was greater than 2 for both response modes. Conclusion:The discrimination of the items of the WOFI is relatively close under the two item response modes, and the overall information volume meets the evaluation criteria. However, under the hierarchical item response mode, the item difficulty range and information volume distribution are wider. Therefore, the WOFI is more effective and reliable when using the hierarchical item response mode.

Objective:To investigate the relationship between work fit, work stress, and occupational fatigue among medical staff, and to analyze the independent and interactive effects of work fit and work stress on occupational fatigue.Methods:A stratified random sampling method was employed to select 1162 employees from two top-tier hospitals as the research subjects. A questionnaire survey was conducted to collect information on basic demographics, work stress, work fit, and occupational fatigue. Chi-square test, analysis of variance, and logistic regression equations were used to evaluate the interactive effects of work fit and work stress on occupational fatigue.Results:This study included 1162 medical Homo sapiens personnel, among whom 884 were job-adapted and 362 were in a stressful state. Univariate logistic regression analysis showed that the risk of occupational fatigue significantly increased in the 40-49 age group ( OR=1.89) and the bachelor's degree group ( OR=2.52, P<0.01). Smoking and alcohol consumption were also associated with a significantly higher risk of occupational fatigue ( P<0.01). In terms of occupational characteristics, compared to clinical doctors, nurses and pharmaceutical/technical Homo sapiens personnel had lower risks ( OR=0.57, 0.43, P<0.05). Compared to high-ranking Homo sapiens personnel, those with no title or junior titles exhibited lower risks ( OR=0.51, 0.43, P<0.05). Working hours exceeding 55 hours per week and night shifts 3-4 times per week significantly increased fatigue risk ( OR=1.94, 1.90, P<0.05). Occupational stress and job discomfort were identified as risk factors for occupational fatigue ( OR=11.94, 3.26, P<0.05). Job adaptation and occupational stress exhibited a multiplicative interaction on physical fatigue after adjusting for confounders[ OR (95% CI) =0.43 (0.22, 0.84) ]. Additionally, job adaptation and occupational stress demonstrated an additive interaction on mental fatigue, with RERI (95% CI) =2.64 (0.11, 5.59), API (95% CI) =0.38 (0.08, 0.67), and SI (95% CI) =1.78 (1.01, 3.14). Medical Homo sapiens personnel in a stressful state had a 7.25-fold higher risk of fatigue compared to those not in a stressful state, while those with job discomfort had a 2.01-fold higher risk compared to those in an adapted state. Conclusion:Occupational stress and job fit have interactive effects on physical fatigue and mental fatigue in medical Homo sapiens personnel. A stressful state combined with job discomfort increases the risk of fatigue occurrence, providing a basis for developing intervention strategies for occupational fatigue among medical Homo sapiens personnel.

Objective To investigate the relationship between biological aging and the rate of cognitive decline in middle-aged and elderly populations.Methods Longitudinal tracking data of cognitive function were obtained from the China Health and Retirement Longitudinal Study(CHARLS).We employed the Klemera and Doubal method(KDM)to estimate biological age(BA),and calculate the biological aging index(BAI)and biological aging type(BAT).A multivariate linear regression model was employed to analyze the relationships between baseline BAI,BAT,and cognitive function scores.Based on the baseline analysis,a mixed-effects model was used to examine the longitudinal associations between baseline BAI,BAT,and cognitive function during follow-up.Results A total of 5 897 participants were included in the study.BAI was found to be negatively associated with baseline cognitive function scores,with the partial regression coefficient(β)(95％CI)being-0.185(—0.231,—0.139)(P＜0.001).Compared with the lagged aging group,the premature aging group had lower cognitive function scores(β[95％CI]:—0.741[—0.966,—0.516]).For age and sex,for each additional year of baseline BAI,cognitive function scores declined by an average of 0.012(95％CI:—0.019,—0.005)points per year after adjusting for age and sex,and declined by 0.011(95％CI:—0.018,—0.004)points per year after adjusting for other covariates.Compared with participants with lagged aging,those with premature aging experienced,on average,an additional decline of 0.042(95％CI:—0.075,0.009)points per year in cognitive function scores after adjusting for age and sex alone,and by 0.039(95％CI:—0.072,—0.007)points per year after adjusting for other covariates.Conclusion Biological aging affects the rate of cognitive decline in middle-aged and elderly populations.A higher BAI is associated with a faster decline in cognitive function.Compared with those with lagged aging,individuals with premature aging exhibit a more rapid rate of cognitive decline.

Objective:To identify and evaluate the important risk factor set of anxiety and depression in occupational population, establish a risk prediction model, and provide scientific basis for making targeted mental health protection plan and promoting the mental health of workers.Methods:In August 2016, a cluster random sampling method was used to investigate 807 employees who underwent physical examination in a hospital as research objects. The simplified Chinese version of the core job content questionnaire, Athens Insomnia Scale, AIS-5 and Symptom Check List-90 (SCL-90) were used for the Occupational stress, insomnai and negative emotional symptom investigation. Chi-square and Fisher exact probability method were used for data analysis, and Bayesian network was used for model construcion and analysis.Results:The score of occupational stress was 0.88±0.15, and the incidence of occupational stress was 18.09% (146/807). AIS-5 scores were (3.03±2.82), and the incidence of insomnia was 15.99% (129/807). Depression (16.89±5.73) scores, anxiety (12.36±4.11) scores. Depression (16.89±5.73) score, anxiety (12.36±4.11) score, the detection rate was 8.55% (69/755), 7.31% (59/762). Gender, illness, education, insomnia and occupational stress were correlated with depression ( P<0.01), while education, illness, insomnia and anxiety were correlated ( P<0.05). When both occupational stress and insomnia existed, the detection rate of depression was the highest (0.4006) . Conclusion:Insomnia was a valid predictor of anxiety and depression, suggesting that occupational groups should pay attention to sleep quality and managers should rationalize work tasks in order to reduce the risk of anxiety and depression.

Objective:To evaluate the application effects of two different item response modes in the Worker-Occupation Fit Inventory (WOFI) .Methods:From June to October 2022, a stratified cluster sampling method was adopted to collect and evaluate on-site data on worker-occupation fit among medical workers in two tertiary grade A general hospitals in Sichuan Province and Henan Province (1162 participants). Ordinal categorical item response (hierarchical item response) and dichotomous item response are two commonly used item response modes in psychometric measurement. Item response theory was used to analyze and compare the evaluation effects of the two item response modes in the WOFI. The generalized partial credit model and the 2-parameter logistic model were used to compare the item difficulty, discrimination, and scale information volume under different response modes.Results:The discrimination values range of the items in the WOFI under the hierarchical item response and dichotomous item response modes was 0.98 to 2.06 and 1.24 to 2.72, respectively, and the item difficulty values range was -4.61 to 1.15 and -1.25 to 0.08, respectively. Under the hierarchical item response mode, the amount of information on the WOFI was concentrated in the range of θ (the real ability or trait level of the subject) of -4 to 2, while under the dichotomous item response mode, it was concentrated in the range of θ of-2 to 2. The scale information volume was insufficient when θ was greater than 2 for both response modes. Conclusion:The discrimination of the items of the WOFI is relatively close under the two item response modes, and the overall information volume meets the evaluation criteria. However, under the hierarchical item response mode, the item difficulty range and information volume distribution are wider. Therefore, the WOFI is more effective and reliable when using the hierarchical item response mode.

Objective:To investigate the relationship between work fit, work stress, and occupational fatigue among medical staff, and to analyze the independent and interactive effects of work fit and work stress on occupational fatigue.Methods:A stratified random sampling method was employed to select 1162 employees from two top-tier hospitals as the research subjects. A questionnaire survey was conducted to collect information on basic demographics, work stress, work fit, and occupational fatigue. Chi-square test, analysis of variance, and logistic regression equations were used to evaluate the interactive effects of work fit and work stress on occupational fatigue.Results:This study included 1162 medical Homo sapiens personnel, among whom 884 were job-adapted and 362 were in a stressful state. Univariate logistic regression analysis showed that the risk of occupational fatigue significantly increased in the 40-49 age group ( OR=1.89) and the bachelor's degree group ( OR=2.52, P<0.01). Smoking and alcohol consumption were also associated with a significantly higher risk of occupational fatigue ( P<0.01). In terms of occupational characteristics, compared to clinical doctors, nurses and pharmaceutical/technical Homo sapiens personnel had lower risks ( OR=0.57, 0.43, P<0.05). Compared to high-ranking Homo sapiens personnel, those with no title or junior titles exhibited lower risks ( OR=0.51, 0.43, P<0.05). Working hours exceeding 55 hours per week and night shifts 3-4 times per week significantly increased fatigue risk ( OR=1.94, 1.90, P<0.05). Occupational stress and job discomfort were identified as risk factors for occupational fatigue ( OR=11.94, 3.26, P<0.05). Job adaptation and occupational stress exhibited a multiplicative interaction on physical fatigue after adjusting for confounders[ OR (95% CI) =0.43 (0.22, 0.84) ]. Additionally, job adaptation and occupational stress demonstrated an additive interaction on mental fatigue, with RERI (95% CI) =2.64 (0.11, 5.59), API (95% CI) =0.38 (0.08, 0.67), and SI (95% CI) =1.78 (1.01, 3.14). Medical Homo sapiens personnel in a stressful state had a 7.25-fold higher risk of fatigue compared to those not in a stressful state, while those with job discomfort had a 2.01-fold higher risk compared to those in an adapted state. Conclusion:Occupational stress and job fit have interactive effects on physical fatigue and mental fatigue in medical Homo sapiens personnel. A stressful state combined with job discomfort increases the risk of fatigue occurrence, providing a basis for developing intervention strategies for occupational fatigue among medical Homo sapiens personnel.

Objective:To identify and evaluate the important risk factor set of anxiety and depression in occupational population, establish a risk prediction model, and provide scientific basis for making targeted mental health protection plan and promoting the mental health of workers.Methods:In August 2016, a cluster random sampling method was used to investigate 807 employees who underwent physical examination in a hospital as research objects. The simplified Chinese version of the core job content questionnaire, Athens Insomnia Scale, AIS-5 and Symptom Check List-90 (SCL-90) were used for the Occupational stress, insomnai and negative emotional symptom investigation. Chi-square and Fisher exact probability method were used for data analysis, and Bayesian network was used for model construcion and analysis.Results:The score of occupational stress was 0.88±0.15, and the incidence of occupational stress was 18.09% (146/807). AIS-5 scores were (3.03±2.82), and the incidence of insomnia was 15.99% (129/807). Depression (16.89±5.73) scores, anxiety (12.36±4.11) scores. Depression (16.89±5.73) score, anxiety (12.36±4.11) score, the detection rate was 8.55% (69/755), 7.31% (59/762). Gender, illness, education, insomnia and occupational stress were correlated with depression ( P<0.01), while education, illness, insomnia and anxiety were correlated ( P<0.05). When both occupational stress and insomnia existed, the detection rate of depression was the highest (0.4006) . Conclusion:Insomnia was a valid predictor of anxiety and depression, suggesting that occupational groups should pay attention to sleep quality and managers should rationalize work tasks in order to reduce the risk of anxiety and depression.

Background Studies on the relationships of environmental noise exposure with negative emotions and sleep quality have long been reported. Self-perceived noise intensity is not only related to environmental noise exposure, but also reflects an individual's susceptibility to noise; however, few studies on self-perceived noise intensity, negative emotions, and sleep quality have been reported, and it is not clear whether negative emotions play a mediating role in the relationship between self-perceived noise intensity and sleep quality. Objective To analyze the mediating role of negative emotions (anxiety, depression, and stress) in the relationship between self-perceived noise intensity and sleep quality in noise-exposed workers, and to provide a scientific basis for addressing psychological problems and sleep quality induced by noise exposure. Methods Stratified cluster sampling was used to select noise-exposed workers from a large equipment manufacturing plant in Chengdu from May to June 2023, and demographic characteristics were investigated using a self-designed general information questionnaire; self-perceived noise intensity was reported by the study subjects as the noise intensity of their workplaces; sleep quality was synthesized from three indicators: night sleep duration, self-perceived sleep quality, and sleep-related symptoms; the Anxiety-Depression-Stress Scale (DASS-21 scale) was used to evaluate anxiety, depression, and stress of a worker. Structural equation modeling was used to analyze the pathways of negative emotions (anxiety, depression, and stress) between self-perceived noise intensity and sleep quality. Results The positive rates of negative emotions were 33.1% (anxiety symptoms, 215/649), 26.0% (depression symptoms, 169/649), and 14.0% (stress symptoms, 91/649), respectively in a total of 649 noise-exposed workers. Self-perceived noise intensity was positively correlated with sleep quality score (r=0.218, P<0.001) and negative emotions (anxiety, depression, and stress) (r=0.167, 0.145, 0.167, P<0.001); sleep quality score was positively correlated with negative emotions (anxiety, depression, and stress) (r=0.512, 0.447, 0.513, P<0.001). The results of path analysis showed that the negative emotions (anxiety, depression, and stress) partially mediated between self-perceived noise intensity and sleep quality (β=0.123，P<0.001). Self-perceived noise intensity was positively correlated with negative emotions and sleep quality, and there was a path of self-perceived noise intensity → negative emotion → sleep quality (P<0.001), and the mediating effect accounted for 42.71%. Conclusion The positive rates of anxiety, depression, and stress are high among the noise-receiving workers in this manufacturing industry, and negative emotions characterized by anxiety, depression, and stress partially mediate the relationship between self-perceived noise intensity and sleep quality.

Objective To explore the risk factors for developing chronic pulmonary heart disease in patients with pneumoconiosis.Methods The medical records of pneumoconiosis patients admitted to an occupational disease hospital in Sichuan Province between January 2012 and November 2021 were collected.Kaplan-Meier(K-M)method,or product-limit method,was used to plot the incidence curves of pulmonary heart disease in the pneumoconiosis patients.Cox proportional hazard regression model was used to analyze the influencing factors associated with chronic pulmonary heart disease in patients with pneumoconiosis.Results A total of 885 pneumoconiosis patients were included in this study.The follow-up time was 12 to 115 months and the median follow-up time was 43 months.A total of 138 patients developed chronic pulmonary heart disease and the incidence density of pulmonary heart disease was 38.50/1000 person-years.Multivariate Cox proportional hazard regression analysis showed that the influencing factors of pneumoconiosis inpatients developing chronic pulmonary heart disease included the following,being 50 and older(hazard ratio[HR]=1.85,95%confidence interval[CI]:1.25-2.74),stage Ⅲ pneumoconiosis(HR=2.43,95%CI:1.48-4.01),resting heart rate≥100 beats/min(HR=2.62,95%CI:1.63-4.21),the complication of chronic obstructive pulmonary disease(COPD)(HR=4.52,95%CI:2.12-9.63),underweight(HR=2.40,95%CI:1.48-3.87),overweight and obesity(HR=0.54,95%CI:0.34-0.86),and triacylglycerol(TG)(HR=0.69,95%CI:0.49-0.99).Conclusion Old age,stage Ⅲ pneumoconiosis,high resting heart rate,low BMI,and the complication of COPD are risk factors for chronic pulmonary heart disease in pneumoconiosis patients,while overweight and obesity and TG are protective factors.Early identification of the risk factors and the adoption of the corresponding prevention measures are the key to preventing chronic pulmonary heart disease in patients with pneumoconiosis.