Objective: To explore the influencing factors for implementation of weight management services in primary medical and healthcare institutions, so as to provide references for implementing sustainable services of weight management. Methods: From May to June 2025, Pinghu City, Zhejiang Province was selected as the survey site. Personnel responsible for weight management in primary medical and healthcare institutions were selected as the survey subjects using a combined method of purposive sampling and snowball sampling. Based on the five core domains of the consolidated framework for implementation research (CFIR), a semi-structured interview outline for weight management services in primary medical and healthcare institutions was designed. Original data was collected through face-to-face semi-structured interviews. Interview data was organized and analyzed using framework analysis. Factors affecting weight management services were quantitatively analyzed by referencing CFIR's structural rating criteria. Results: A total of 21 participants completed interviews, covering positions in nutrition, endocrinology, traditional Chinese medicine, general practice, maternal health, and public health. There were 9 males and 12 females. Fifteen participants (71.43%) were aged 35 years and above, 18 (85.71%) held a bachelor's degree or higher, and 15 (71.43%) were frontline medical staff. Fifteen factors affecting weight management services were identified across five domains: innovation, outer setting, inner setting, individuals, and implementation process. Six barrier factors were identified: difficulties in policy implementation, time-consuming interventions, limited incentive measures, lack of professional skills, unclear weight-loss plans and goal setting, and imperfect follow-up and evaluation mechanisms. Three neutral factors were identified: the development and refinement of policies and regulations, the implementation of weight management training, and the optimization of the referral process within integrated healthcare systems (medical alliances / communities). Six facilitating factors were identified: the relatively significant advantages of lifestyle interventions, collaboration and coordination across multiple departments, cooperative communication among different units within the institution, the inherent convenience of primary care settings, a strong sense of professional responsibility, and the establishment of multidisciplinary teams. Conclusions The delivery of weight management services in primary medical and healthcare institutions is influenced by a wide array of factors across multiple domains. It requires policy support, multi-department coordination, a practice-oriented training system, optimized team resource allocation, incentives, and improved professional skills of medical staff to jointly promote long-term implementation.

Objective:To observe and analyze the levels of vitamin D（VD） and their influencing factors in children undergoing adenoidectomy and/or tonsillectomy. Methods:A total of 147 children who received adenoidectomy and/or tonsillectomy in our hospital from November 2018 to March 2019 were selected as the experimental groups, gender and age matched 147 healthy children of the same period were selected as the control group. The differences of VD levels between the two groups were compared, the factors affecting VD levels were investigated, and patients with VD deficiency/insufficiency in the experimental groups were followed up postoperatively. Results:The VD levels of the experimental groups were（19.6±6.6） ng/mL and those of the control groups were （22.5±6.5）ng/mL, which was significantly different （P<0.01）. The experimental groups were divided into inflammation groups and Sleeping disorder breathing（SDB）groups. The VD levels of the two groups were （19.1±6.7）ng/mL and （21.9±6.4）ng/mL, which was significantly different （P<0.05）. Regression analysis showed that VD levels were negatively correlated with age, body mass index （BMI）, adenoid hypertrophy, tonsil hypertrophy and Anti-streptolysin O（ASO）levels （P<0.05）. VD values were remeasured one year postoperatively in 23 of 72 children in the VD deficiency/deficiency groups, and there was a statistically significant difference between preoperative and postoperative VD values[（14.3±3.9）ng/mL and （17.1±5.5） ng/mL, respectively, P<0.05]. There was a significant difference in postoperative VD value between the inflammation groups and the SDB groups[ （15.6±5.9） ng/mL and （20.5±2.1） ng/mL, respectively, P<0.05]. Conclusion:Children who underwent adenoidectomy and/or tonsillectomy had lower VD levels than healthy children.VD levels decreased with increasing age,BMI and ASO values,and associated with the size of adenoid and tonsil. Preoperative VD levels were lower in the inflammation groups, adenoidectomy and/or tonsillectomy improved VD deficiency/insufficiency status, and postoperative elevation of VD levels was more pronounced in the SDB groups.
Humans ; Tonsillectomy ; Adenoidectomy ; Vitamin D/blood* ; Vitamin D Deficiency ; Male ; Female ; Postoperative Period ; Child ; Case-Control Studies ; Child, Preschool

During the career life cycle, workers may face various health problems such as occupational injuries, occupational diseases, and work-related diseases. How to comprehensively protect the health of workers is a crucial scientific issue that needs to be solved urgently. Workers show the characteristics of co-exposure to multiple occupational risks or co-existence of multiple health conditions in their occupational activities. Occupational injuries are closely related to occupational diseases and work-related diseases. To carry out prevention and control of occupational injuries in the context of "big health", we should further strengthen the systematic approach and highlight the concept of "overall process" and "all-round". That is to establish an occupational injury surveillance system covering the whole process of surveillance-assessment-intervention and the evaluation of intervention effects, and to set up the joint prevention and control strategy of occupational injuries, occupational diseases, and work-related diseases. This will promote the implementation of efficient and intensive health management at government, society, employers, workers and other levels to achieve all-round protection of workers' health. When exploring the possible effects of job burnout, occupational stress, comorbidity, and other factors on occupational injuries, the introduction of machine learning methods provides a new approach to identifying and analyzing the influencing factors of occupational injuries and to exploring potential underlying mechanisms.

Background As a pillar industry in China, the manufacturing sector has a high incidence of non-fatal occupational injuries. The factors influencing non-fatal occupational injuries in this industry are closely related at various levels, including individual, equipment, environment, and management, making the analysis of these influencing factors complex. Objective To identify influencing factors of non-fatal occupational injuries among manufacturing workers, providing a basis for targeted interventions and surveillance. Methods A total of 2243 frontline workers from cable and shipbuilding enterprises were selected as study subjects to investigate the incidence of non-fatal occupational injuries and collect information at four levels: individual, equipment, management, and environment in past 12 months. Data balancing was performed using resampling, and LASSO regression was used to select factors of non-fatal occupational injuries. The influence degree and type of variables were judged based on the magnitude of the estimated coefficients of each variable, where variables with estimated coefficients > 0 are risk factors, and those <0 are protective factors. The area under the receiver operating characteristic (ROC)curve (AUC) was used to test the performance of the model, with an AUC value > 0.7 indicating good model performance. Results Among the 2243 frontline workers, males accounted for 77.7% (1742 out of 2243), with the main age range being 40-49 years old, representing 29.5% (661 out of 2243), 82.7% of the workers (1854 out of 2243) were married, and 55.6% (1248 out of 2243) had a junior middle school education level. The average monthly income for 51.0% (1144 out of 2243) of the workers was between 5000 and 6999 Chinese Yuan. The incidence of non-fatal occupational injuries among the manufacturing workers was 8.4% (189/2243) in the past 12 months. Among the 22 factors associated with the occurrence of non-fatal occupational injuries (P<0.05), 10 were individual-level factors, including gender, smoking, alcohol consumption, colleague relationships, average exercise duration, job burnout, work fatigue, musculoskeletal disorders, cardiovascular diseases, and neurological and sensory organ diseases; 3 were equipment-level factors, including equipment operability, hazardous workpieces, and safety hazards; 5 were environmental-level factors, including low temperatures, special operations, noise, workspace size, and dirty and disorderly environment; and 4 were management-level factors, including daily working hours, weekly working days, overtime, and pre-job technical training. The AUC value of the LASSO regression model was 0.704 and the final model retained a total of 10 variables. Among them, there were 7 risk factors for non-fatal occupational injuries (coefficient > 0), including safety hazards, musculoskeletal disorders, dangerous workpieces, job burnout, dirty and disorderly environment, smoking, and male gender; and 3 protective factors (coefficient < 0), including pre-job technical training, good colleague relationship, and long working days per week. Conclusion Manufacturing enterprises need to focus on the incidence of non-fatal occupational injuries and conduct targeted interventions for non-fatal occupational injuries by controlling potential safety hazards, providing pre-job technical training, reducing dangerous workpieces, rectifying working environment, and reasonably arranging working hours.

Background Workers in the cable manufacturing industry are exposed to high-speed machinery and equipment for a long time, coupled with heavy workload, which poses significant risks to their physical health. However, the issue of occupational injuries in this industry has not received enough attention yet. Objective To understand the incidence of occupational injury of workers in cable manufacturing industry and to analyze the influencing factors. Method A basic information questionnaire and an occupational injury questionnaire were developed to investigate the occupational injuries of 1 343 workers in a cable manufacturing enterprise in the past year, and a total of 1 225 valid questionnaires were recovered, with an effective rate of 91.2%. Descriptive statistics were used to characterize the causes, injury locations, injury types, and other characteristics of employees’ occupational injuries. Chi-square test was used to analyze the occupational injury status of groups with different demographic characteristics, occupational characteristics, lifestyles, and interpersonal relationships. Logistic regression was used to analyze the influencing factors of occupational injuries. Result The incidence of occupational injuries among workers in a cable manufacturing enterprise in the past year was 8.6%, which mainly happened in male workers (80.0%) and occurred from May to July in summer (45.7%). The main causes were mechanical injuries (32.4%) and object blows (27.6%). The main sources of damage were machinery and equipment (36.2%) as well as raw materials and products (15.2%). The main injuries were located in upper limbs (53.3%) and lower limbs (22.9%). The main types of injuries were fractures (33.3%) and abrasions/contusions/puncture wounds (19.0%). The results of univariate analysis showed that there were statistically significant variations in the incidence of occupational injuries by gender, overtime, pre-job training, years of service in current position, alcohol consumption, physical exercise per week, and co-worker relationship (P<0.05). The logistic regression model showed that workers who exercised less than twice a week, did not participate in pre-job training, worked overtime, and had fair/poor/very poor colleague relationship had a higher risk of occupational injury, while women had a lower risk of occupational injury. Conclusion The distribution of occupational injury population is mainly male, and the time distribution is mainly from May to July. Gender, physical exercise, pre-job training, overtime, and colleague relationship are the influencing factors of occupational injuries. We should strengthen pre-job training, arrange work hours reasonably, and create a good working atmosphere to reduce the occurrence of occupational injuries.

Background Social psychological factors have emerged as a key area of research in occupational injury prevention. Occupational stress, a significant component of social psychology, has garnered widespread attention due to its potential impact on occupational injury. Objective To analyze the factors influencing occupational stress among cable manufacturing workers and explore the relationship between occupational stress and occupational injury, and to provide scientific evidence for reducing occupational stress and injury. Methods A questionnaire on basic demographics, occupational injury, and occupational stress (Effort-Reward Imbalance, ERI) was used to investigate 1343 workers in a cable enterprise. Chi-square test was employed to analyze the distribution of occupational stress across different worker characteristics. Logistic regression was used to identify factors influencing occupational stress, and a decision tree model was applied to assess the impact of occupational stress on injury risk. Results A total of 1225 valid questionnaires were collected, yielding a response rate of 91.2%. The positive rate of occupational stress was 28.7%, while the incidence of occupational injury in the past year was 8.6%. The results of univariate analysis revealed that workers who had experienced occupational injuries had a higher positive rate of occupational stress compared to those without injuries (P<0.05), and there were significant differences in the distribution of occupational stress by genders, overtime work, shift work, daily working hours, fatigue levels, sleep duration, family relationships, and colleague relationships (P<0.05). The logistic regression results showed that individuals who worked shifts, had fair/poor/very colleague relationships, and frequently worked overtime had a higher risk of occupational stress (P<0.05). The final decision tree model confirmed that occupational stress was a risk factor for occupational injury. Additional factors influencing occupational injury included physical exercise, colleague relationships, pre-job training participation, and gender. Workers who exercised less than twice per week and experienced occupational stress had a significantly higher risk of injury (23.6%) compared to those without occupational stress (10.9%). Conclusion Shift work, colleague relationships, and overtime work are correlated with the positive rate of occupational stress among cable manufacturing workers. Implementing a reasonable work schedule and fostering a positive work environment are recommended. Reducing occupational stress may help lower the risk of occupational injuries in this industry.

Background Light gradient boosting machine (LightGBM) has become a popular choice in prediction models due to its high efficiency and speed. However, the "black box" issues in machine learning models lead to poor model interpretability. At present, few studies have evaluated the severity of occupational injuries from the perspective of LightGBM model and model interpretability. Objective To evaluate the application value of LightGBM models and model interpretability methods in occupational injury prediction. Methods The Mine Safety and Health Administration (MSHA) occupational injury data set of mining industry workers from 1983 to 2022 was used. Injury severity (death/fatal occupational injury and permanent/partial disability) was used as the outcome variable, and the predictor variables included the month of occurrence, age, sex, time of accident, time since beginning of shift, accident time interval from shift start, total experience, total mining experience, experience at this mine, cause of injury, accident type, activity of injury, source of injury, body part of injury, work environment type, product category, and nature of injury. Feature sets were screened using least absolute shrinkage and selection operator (Lasso) regression. A LightGBM model was then employed to predict occupational injury, with area under curve (AUC) of the model serving as the primary evaluation metric; an AUC closer to 1 indicates better predictive performance of the model. The interpretability of the model was evaluated using Shapley additive explanations (SHAP). Results Through Lasso regression, 7 key influencing factors were identified, including accident time interval from shift start, experience at this mine, cause of injury, accident type, body part of injury, nature of injury, and work environment type. A LightGBM model, constructed based on feature selection via Lasso regression, demonstrated good predictive performance with an AUC value of 0.9941 (95%CI: 0.9917, 0.9966), accuracy of 0.9743, specificity of 0.9781, and sensitivity of 0.9640. The predicted probability of fatal occupational injuries showed high consistency with the actual probability of fatal occupational injuries. In the occupational injury prediction model, the importance of each indicator was analyzed through its SHAP value, and it was found that the body part of injury and the nature of injury were the two main features that affected the prediction results of the model, and the impacts of other features were relatively small. The distribution of SHAP values across body part of injury was broad, with significant impacts on the model's prediction of fatal risk, particularly for injuries to the head and neck, as well as multi-part injuries. The nature of injury also exerted influences on the model in different directions, with suffocation/drowning, crushing, and multi-part injuries having a greater impact on the risk of fatal occupational injuries. Conclusion LightGBM model is capable of efficiently processing large-scale data and providing high-precision prediction results. Research on model interpretability aids in more accurately exploring and analyzing various key risk factors for fatal occupational injuries among mining workers, and further reveals the complex interactions among these factors. This, in turn, enables better preventive intervention and protection measures, as well as optimal resource allocation for labor workers.

Background Diseases severely affect the efficiency of workers. Comorbidity refers to the coexistence of two or more chronic diseases or health problems in the same individual. Previous studies have primarily focused on occupational injuries caused by environmental exposures, while the analysis of the epidemiological characteristics of self-reported diseases and occupational injuries among manufacturing workers has been insufficient. Objective To analyze the distribution of self-reported diseases and occupational injuries among manufacturing workers, the strength of correlation between different diseases, and common disease combinations, and to preliminarily explore the relationship between self-reported diseases and occupational injuries. Methods A cross-sectional survey was conducted to investigate the occupational injuries of 2382 workers in 2 manufacturing enterprises over the past year, and 2355 questionnaires were valid after cleaning. The questionnaire included three parts: basic information, occupational injury occurrence, and disease prevalence. The self-reported diseases diagnosed by physicians included musculoskeletal diseases, cardiovascular diseases, respiratory diseases, mental health problems, neurological and sensory organ diseases, digestive organ diseases, reproductive and urinary organ diseases, skin diseases, tumors, endocrine and metabolic system diseases, blood diseases and birth defects, a total of 11 types. Log-binomial model was used to calculate the prevalence ratio (PR) value of the association between different diseases, and UpSet diagram was used to present the disease combination pattern. Results In total, 40.2% (946/2355) of the study participants reported at least one disease, while 8.6% (203/2355), 5.2% (122/2355), and 5.9% (140/2355) reported having two, three, and four or more diseases, respectively. A higher prevalence was observed for musculoskeletal diseases, neurological and sensory organ diseases, digestive organ diseases, and skin diseases, which accounted for 18.0% (423/2355), 13.8% (324/2355), 9.3% (218/2355), and 9.1% (214/2355), respectively. Workers with musculoskeletal disorders, cardiovascular diseases, or neurological and sensory organ diseases reported significantly higher incidence of occupational injuries compared with workers without the corresponding diseases (P<0.001, P=0.041, and P=0.006, respectively). Musculoskeletal diseases were strongly associated with comorbidities of neurological and sensory organ diseases (PR values were 1.86 and 1.98, respectively), and the other patters were musculoskeletal diseases and digestive organ diseases (PR and OR values were 1.57 and 2.35, respectively), and musculoskeletal diseases and cardiovascular diseases (PR and OR values were 1.46 and 2.22, respectively). The largest binomial comorbidity group involved musculoskeletal diseases and neurological and sensory organ diseases, accounting for 5.4% (25/465) of the comorbid workers. Conclusion Among the self-reported diseases of manufacturing workers, musculoskeletal diseases, neurological and sensory organ diseases, digestive organ diseases, and skin diseases are more common. Musculoskeletal diseases and neurological and sensory organ diseases are the most common in all comorbidities. Musculoskeletal diseases, cardiovascular diseases, and neurological and sensory organ diseases are associated with occupational injuries. In the prevention and control of occupational injuries, joint prevention strategies targeting multiple diseases should be strengthened.

Sigmoid sinus thrombophlebitis(SST) as a severe complication of otogenic infections in children, its early diagnosis and treatment are crucial for improving prognosis. This study reports three cases (aged 2 years, 9 months to 4 years) of otogenic SST in children diagnosed by imaging, all secondary to acute otitis media. The clinical features mainly included recurrent high fever, ear pain, and postauricular swelling, with one case complicated by abducens nerve palsy and otorrhea. Imaging characteristics revealed: HRCT of the temporal bone showed destruction of the anterior wall of the sigmoid sinus; characteristic MRI findings of the ear included high T2WI signal in the sigmoid sinus area, ring enhancement post-contrast, and restricted diffusion on DWI; MRV of the ear clearly displayed the extent of venous sinus thrombosis. Treatment involved a comprehensive approach of surgical drainage combined with sensitive antibiotics and anticoagulant therapy, and all children achieved clinical cure. Through literature review, it was found that SST in children has an insidious onset, and high vigilance is required when otogenic infection patients present with the "otitis media triad" (fever, ear pain, headache) accompanied by neurological symptoms. Imaging examination is crucial for early diagnosis, and standardized treatment (clearance of infection foci and adequate course of anti-infection and anticoagulation) can significantly improve prognosis, providing a reference for the clinical management of SST in children.

To examine the evolution of surgical techniques and trends in overall inpatient burden for pediatric adenoidectomy in Beijing tertiary hospitals from 2013 to 2022. A retrospective observational study was conducted using the regional health information platform of Beijing. Data from children aged ≤14 years who underwent adenoidectomy between 2013 and 2022 were extracted, including total hospitalization cost, length of stay(LOS), surgical material cost, surgical fee, operative technique, perioperative antimicrobial drugs cost, coagulation factor cost, and blood transfusion cost. The Mann-Kendall trend test was used to assess temporal changes in total hospitalization expenses and the structure of cost components. The results showed that over the 10-year period from 2013 to 2022, a total of 25 989 children underwent adenoidectomy in tertiary hospitals. The proportion of children aged ≤6 years increased from 59.83% to 76.11%, showing a significant upward trend ( Z=2.15, P=0.032). Only one case required surgical hemostasis due to postoperative bleeding. During the ten-year period, the median hospitalization cost for adenoidectomy in tertiary hospitals was ￥12 425.82 (￥11 307.43, ￥14 955.42).Overall hospitalization cost demonstrated a fluctuating downward pattern, decreasing from ￥15 229.73 in 2013 to ￥13 927.52 in 2022, this declining trend was not statistically significant( Z=-0.54, P=0.592). In contrast, the surgical costs showed an upward trend over the decade increasing from ￥1 856.22 in 2013 to ￥3 726.45 in 2022, which was statistically significant ( Z=3.22, P=0.001), while the cost of surgical materials showed no significant increase ( Z=1.79, P=0.074).Concurrently, the average LOS decreased remarkably from 10.56 days in 2013 to 3.26 days in 2022 ( Z=-3.94, P<0.001). The cost of perioperative antimicrobial drugs decreased ( Z=-3.94, P<0.001), while the cost of coagulation factors and blood transfusion remained unchanged ( Z=0.54, P=0.592; Z=0.56, P=0.578). Comparison between 2013-2017 and 2018-2022 showed a significant increase in the use of coblation from 28.9% to 42.5% ( χ2=638.7, P<0.001).Furthermore, in the coblation group, total hospitalization cost decreased by 27.73%, surgical cost increased by 94.98%, surgical material cost decreased by 10.33%, LOS shortened by 56.24%, and antimicrobial drug cost increased by 43.03%. In contrast, the non-coblation group showed a 23.94% increase in total hospitalization cost, a 57.08% increase in surgical procedure cost, a 33.88% increase in material cost, and a 30.14% reduction in LOS and a 26.0% decrease in antimicrobial drugs cost. In conclusion,from 2013 to 2022, total hospitalization cost for pediatric adenoidectomy in Beijing tertiary hospitals remained stable. Compared to non-coblation techniques, coblation was associated with a shorter LOS, lower total costs, a higher proportion of surgical fees, and a decreased proportion of material costs, without a significant increase in overall healthcare costs.