Exploring the Impact of Unsafe Behaviors on Building Construction Accidents Using a Bayesian Network

Gender differences in cardiovascular outcomes of kidney transplant recipients: A retrospective cohort study

The purpose of this study was to investigate gender differences in cardiovascular outcomes of kidney transplant recipients (KTRs). Here, a retrospective cohort study was conducted, and data from the National Health Insurance Research Database in Taiwan were used. In total, 2904 patients who had end-stage renal disease (ERSD) and received kidney transplantation (KT) were identified by propensity score matching (PSM) and were enrolled from 1997 to 2012, with follow-up ending in 2013. Besides, major adverse cardiovascular events (MACEs) were defined as a composite of all-cause mortality, nonfatal myocardial infarction, and nonfatal strokes. Apart from that, hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated by Cox regression, while the Bayesian network model was constructed to assess the importance of risk factors for MACEs. Furthermore, the original cohort was a sensitivity analysis. Women had a lower risk of MACEs compared with men (hazard ratio [HR]: 0.84; 95% CI: 0.72-0.98; P = .024). Beyond that, stratified analysis of age and waiting time for KT showed that the risk of MACEs was significantly lower in women than in men among KTRs aged > 50 years (HR: 0.79; 95% CI: 0.62-1.0; P = .05) or waiting time for KT ≤ 6 years (HR: 0.85; 95% CI: 0.72-0.99; P = .04). Bayesian network indicated that age is an important determinant of cardiovascular outcomes in KTRs, regardless of gender. In Taiwan, women had a lower risk of adverse cardiovascular outcomes than men in KTRs aged > 50 years or with a waiting time for KT ≤ 6 years. Furthermore, age is an important independent determinant for the prognosis of KTRs.

System dynamics simulation of occupational health and safety management causal model based on NetLogo

The occupational health and safety management factors of construction enterprises are critical influencing factors in their training management, and their causal principles are topics that warrant profound exploration. Drawing upon the conventional five factors, this study initially posited and authenticated a causal model among them, subsequently employing system dynamics on the NetLogo platform to dynamically simulate the model, and ultimately scrutinizing the interrelations and dynamic influence degree among the factors. The results show that the direct causes of management factors include human factors (weight coefficient of 0.583) and method factors (weight coefficient of 0.405), and environmental factors directly affect human factors (weight coefficient of 0.994), whereas material factors directly affect method factors (weight coefficient of 0.918). At the same time, it can be seen from the dynamic simulation results that the influence of human factors and method factors on management factors increases sharply in the nascent phase of the simulation cycle (the highest slope is .90), gradually decreases in the intermediary phase (the slope of the inflection point is .11), and is relatively stable in the final phase (the slope is less than 0.11). Three main conclusions have been drawn from this. Firstly, management factors are directly and positively affected by human factors and method factors respectively. Secondly, the interplay between diverse factors evinces a confluence of periodicity and exponential attributes. Thirdly, in each management cycle (set at 381 steps), the main focus is on controlling the causal factors in the early stages of management, with pivotal control points in steps 25 and 100, and the principal management factors comprising the management organization, operating procedures, and protective measures.

Data mining of the essential causes of different types of fatal construction accidents

Accident analysis is used to discover the causes of workplace injuries and devise methods for preventing them in the future. There has been little discussion in the previous studies of the specific elements contributing to deadly construction accidents. In contrast to previous studies, this study focuses on the causes of fatal construction accidents based on management factors, unsafe site conditions, and workers' unsafe actions. The association rule mining technique identifies the hidden patterns or knowledge between the root causes of fatal construction accidents, and one hundred meaningful association rules were extracted from the two hundred and fifty-three rules generated. It was discovered that many fatal construction accidents were caused by management factors, unsafe site circumstances, and risky worker behaviors. These analyses can be used to demonstrate plausible cause-and-effect correlations, assisting in building a safer working environment in the construction sector. The study findings can be used more efficiently to design effective inspection procedures and occupational safety initiatives. Finally, the proposed method should be tested in a broader range of construction situations and scenarios to ensure that it is as accurate as possible.

Research Paradigm of Network Approaches in Construction Safety and Occupational Health

Construction safety accidents seriously threaten the lives and health of employees; however, the complexity of construction safety problems continues to increase. Network approaches have been widely applied to address accident mechanics. This study aims to review related studies on construction safety and occupational health (CSOH) and summarize the research paradigm of recent decades. We solicited 119 peer-reviewed journal articles and performed a bibliometric analysis as the foundation of the future directions, application bottlenecks, and research paradigm. (1) Based on the keyword cluster, future directions are divided into four layers: key directions, core themes, key problems, and important methods. (2) The network approaches are not independently applied in the CSOH research. It needs to rely on different theories or be combined with other methods and models. However, in terms of approach applications, there are still some common limitations that restrict its application and development. (3) The research paradigm of network analysis process can be divided into four stages: description, explanation, prediction, and control. When the same network method encounters different research objects, it focuses on different analysis processes and plays different roles.

Comparison of the predictive value of four insulin resistance surrogates for the prevalence of hypertension: a population-based study

BACKGROUND

Several studies have investigated the association of insulin resistance (IR) surrogates and the risk of hypertension. However, it is unclear whether there exist differences between different IR surrogates and hypertension risk. Therefore, this study aimed to explore the association of four IR surrogates (triglyceride-glucose index (TyG index), triglyceride-glucose index with body mass index (TyG-BMI), triglycerides/high-density lipoprotein cholesterol ratio (TG/HDL-c), and metabolic score for IR (METS-IR)) with the prevalence of hypertension.

METHODS

This is a cross-sectional study with a total of 117,056 participants. Data were extracted from a computerized database established by Rich Healthcare Group in China, which included all medical records of participants who received a health check-up from 2010 to 2016. IR surrogates were grouped into quartiles as continuous variables, and multivariate logistic regression was performed to estimate the association between different IR surrogate levels and the prevalence of hypertension. Results were expressed as odds ratios (ORs) and 95% confidence intervals (CIs). Missing data were accounted by multiple imputation. These analyses were considered as the sensitivity analysis. Meanwhile, the Bayesian network (BN) model was constructed to further evaluate the relationship between baseline characteristics and the four IR surrogates and the prevalence of hypertension, as well as the importance of every single variable for the prevalence of hypertension.

RESULTS

Multivariate logistic regression analysis revealed that TyG-BMI and METS-IR were independent risk factors for the prevalence of hypertension that increased significantly with increasing TyG-BMI and METS-IR (p for trend < 0.001). The area under the TyG-BMI curve (AUC) was 0.681 [95% CI: 0.677-0.685], and the cut-off value was 199.5, with a sensitivity and specificity of 65.57% and 61.18%, respectively. While the area under the METS-IR curve (AUC) was 0.679 [95% CI: 0.674-0.683], and the cut-off value was 33.61, with a sensitivity and specificity of 69.67% and 56.67%, respectively. The BN model presented that among these four IR surrogates and related variables, TyG-BMI was the most important predictor of hypertension prevalence, with a significance of 34%. The results before and after multiple imputation were similar.

CONCLUSION

TyG-BMI and METS-IR were independent risk factors for the prevalence of hypertension. TyG-BMI and METS-IR had good predictive value for the prevalence of hypertension, and TyG-BMI was superior to METS-IR.

Relationship Between Lipid Profiles and Hypertension: A Cross-Sectional Study of 62,957 Chinese Adult Males

Background

Patterns of dyslipidemia and incidence of hypertension have been rarely reported in Asian populations with inconsistent findings. To accumulate further evidence in Asian populations, the study aimed to investigate the relationship between lipid profiles and hypertension in Chinese adult males.

Methods

We conducted a cross-sectional study based on the data from the DATADRYAD database. The overall population was divided into hypertensive and non-hypertensive groups based on baseline blood pressure levels. For continuous variables, Mann-Whitney test was performed between two groups, while Kruskal-Wallis and Dunn tests were used among multiple groups. The chi-square test was carried out for dichotomous variables. Spearman's correlation coefficient was employed to assess the association between systolic blood pressure (SBP), diastolic blood pressure (DBP) and lipid profiles, whereas the relationship between lipid profiles and the incidence of hypertension was evaluated using multivariate logistic regression. The Bayesian network (BN) model was adopted to investigate the relationship between clinical characteristics and hypertension, and the importance of related predictor to the incidence of hypertension was obtained to make conditional probability analysis.

Results

Finally, totally 62,957 participants were included in this study. In the lipid profiles, total cholesterol (TC), low-density cholesterol (LDL-c), and non- high-density lipoprotein cholesterol (non-HDL-c) were higher in the hypertensive population (p <0.001). In the fully multivariate model, for every 1 mg/dl increase in TC, LDL-c and non-HDL, the risk of hypertension increased by 0.2% [1.002 (1.001–1.003)], 0.1% [1.001 (1.000–1.002)], and 0.1% [1.001 (1.000–1.002)]. Meanwhile, HDL-c became positively associated with the incidence of hypertension (p for trend < 0.001) after adjusting for the body mass index (BMI), and 1 mg/dl increment in HDL-c increased the risk of hypertension by 0.2% [1.002 (1.000–1.002)] after fully adjusting for multiple variables. Furthermore, the BN showed that the importance of age, BMI, fasting plasma glucose (FPG), and TC to the effect of hypertension is 43.3, 27.2, 11.8, and 5.1%, respectively.

Conclusion

Elevated TC, LDL-c, and non-HDL-c were related to incidence of hypertension in Chinese adult males, whereas triglycerides (TG) was not significantly associated. The relationship between HDL-c and hypertension incidence shifted from no association to a positive correlation after adjusting for the BMI. Moreover, the BN model displayed that age, the BMI, FPG, and TC were strongly associated with hypertension incidence.

Differences in Lipid Profiles and Atherogenic Indices Between Hypertensive and Normotensive Populations: A Cross-Sectional Study of 11 Chinese Cities

Background

Several previous studies have reported that dyslipidemia is associated with the risk of hypertension, but these studies are mainly conducted in European and US populations, with a very few studies in the Asian population. Moreover, the effects of atherosclerotic indices, including atherogenic coefficient (AC) and atherogenic risk of plasma (AIP), on hypertension in Asians have not been well described so far.

Methods

From 2010 to 2016, altogether 211,833 Chinese adults were ultimately recruited at the health centers in 11 Chinese cities (including Shanghai, Beijing, Nanjing, Suzhou, Shenzhen, Changzhou, Chengdu, Guangzhou, Hefei, Wuhan, and Nantong). Differences in continuous variables between the two groups were analyzed by the Mann-Whitney test, while those in categorical variables were examined by the Chi-squared test. Logistic regression was applied to evaluate the association between lipid profiles and the risk of hypertension. The predictive values of AC and AIP for the incidence of hypertension were analyzed using the area under the receiver operating characteristic (ROC) curve. Meanwhile, Bayesian network (BN) models were performed to further analyze the associations between the different covariates and the incidence of hypertension.

Results

A total of 117,056 participants were included in the final analysis. There were significant differences in baseline characteristics between normotension and hypertension groups (p < 0.001). In multivariate logistic regression, the risk of hypertension increased by 0.2% (1.002 [1.001-1.003]), 0.2% (1.002 [1.001-1.003]), and 0.2% (1.002 [1.001-1.003]) per 1 mg/dl increase in total cholesterol (TC), low-density lipoprotein (LDL), and non-high-density lipoprotein cholesterol (non-HDL-c), respectively. However, after adjusting for body mass index (BMI), an increase in HDL level was associated with a higher risk of hypertension (p for a trend < 0.001), and the risk of hypertension increased by 0.6% per 1 mg/dl increase in HDL-c (1.006 [1.003-1.008]). In women, AC had the highest predictive value for the incidence of hypertension with an area under the curve (AUC) of 0.667 [95% confidence interval (CI): 0.659-0.674]. BN models suggested that TC and LDL were more closely related to the incidence of hypertension.

Conclusions

Overall, lipid profiles were significantly abnormal in the hypertensive population than in the normotensive population. TC and LDL were strongly associated with the incidence of hypertension. TC, LDL, and non-HDL-c levels show a positive association, HDL-c shows a negative association, while TG is not significantly associated with the risk of hypertension. After adjusting for BMI, HDL-c turns out to be positively associated with the risk of hypertension. In addition, AC has a good predictive value for the incidence of hypertension in women.

A Mathematical Modeling of Evaluating China’s Construction Safety for Occupational Accident Analysis

China has seen a rapid increase in its construction industry in recent years; however, safety conditions of their workers have not improved owing to low education levels and increasing age trend of construction personnel. This study analyzed construction occupations in China from 2010 to 2018 using descriptive analysis, ANOVA and factor analysis. The results showed May, July and August as the deadliest months during the peak of construction activities in the year. No particular day was established as having a higher risk than other days in the week. The most vulnerable times of the day are from 9 AM to 10 AM and 2 PM to 4 PM. A mathematic modeling based on factor analysis, which is the construction safety evaluation score equation, was developed to illustrate regional distribution, and Qinghai Province ranked the worst in construction safety in China. Problems such as poor labor and environment safety management procedures and false reporting or concealed reporting of construction accidents were revealed. Suggestions for improving China’s construction safety were also generated. This study enriched statistical analysis results of construction accidents in China and evaluation modeling with an abundant database will serve as a reference for stakeholders and researchers to improve the construction safety situation in China.

Exploring Empirical Rules for Construction Accident Prevention Based on Unsafe Behaviors

This paper is aimed at exploring rules for construction accident prevention based on unsafe behaviors. The literature review demonstrates a clear connection between construction accident prevention and unsafe behaviors, followed by a 2-year field investigation resulting in 2207 observations based on convenient sampling with 95% confidence and 5% limit of errors in the 50–50 category. There are 80.43% unsafe behaviors categorized into “Regulations for the Occupational Safety and Health Equipment and Measures”, where there are 66.37% of regulations and law VII violations, linking fall prevention with the most cases (94.48%) of Fall Protection and Structure Strengthening. The Apriori yields 13 association rules, where the top 3 rules show that 44.11% of the Passage and lighting category is linked to construction equipment inspections; 29.41% of the high-pressure gas category is linked to construction equipment inspections; 100% of the fire prevention category is linked to fire protection unsafe behavior. The findings clarify the association rules that can prevent workers from accidents in construction sites.

Exploring the Formation Mechanism of Unsafe Construction Behavior and Testing Efficient Occupational Health and Safety (OHS) Programs

Safety education and training for employees is important to ensure the safety of construction and improve the safety awareness of employees. It is difficult to meet the needs of the new situation of safety production with traditional safety education and training consequences of construction enterprises. To address this deficiency, this research analyzed the formation mechanism of safety behavior of personnel at different levels in construction enterprises from the perspective of behaviorist psychology and studied their different needs for safety training, designing training programs for simple safety behavior and complex safety behavior according to the degree of difficulty of the training content. This research also developed and tested models of training frequency and training times and carried out corresponding experimental research. Through the experiments of “safety behavior by wearing a helmet” and “fire control consciousness training”, it was found that the training of consciousness of relatively complex safety behavior should follow the principles that each training should last longer and the training interval should not be too short, so as to minimize the cost of safety education and training while ensuring the quality of safety education and training.

Association of Lipid Levels With the Prevalence of Hypertension in Chinese Women: A Cross-Sectional Study Based on 32 Health Check Centers

BACKGROUND

Dyslipidemia is strongly associated with the development of hypertension. In our previous study, it was shown that elevated TC, LDL-c, and non-HDL-c were associated with the prevalence of hypertension in Chinese men, whereas the relationship between HDL-c and hypertension shifted from no association to a positive association after adjusting for the BMI. To further accumulate epidemiological evidence in Asian women, this study aimed to investigate the relationship between lipid profile and prevalence of hypertension in Chinese adult women.

METHODS

This is a cross-sectional study including 54,099 Chinese women aged>20 years at 32 health screening centers in 11 cities from 2010-2016. The original data were obtained from DATADRYAD database (www.datadryad.org). Besides, the overall women were classified into non-hypertensive and hypertensive groups based on baseline blood pressure levels. Differences between the two groups were examined by Man-Whitney test or Chi-square test. Spearman's correlation coefficient was employed to evaluate the correlation between systolic blood pressure (SBP), diastolic blood pressure (DBP) and lipid profiles. Multivariate logistic regression was performed to estimate the relationship between different lipid levels and the prevalence of hypertension. Odds ratios (ORs) and 95% confidence intervals (CIs) indicated the risk of lipid and hypertension. Bayesian model (BN) model was constructed to further assess the relationship between baseline characteristics and the prevalence of hypertension, as well as the importance of each variable for the prevalence of hypertension.

RESULTS

Compared to the non-hypertensive population, the hypertensive population was older, and had the higher body mass index (BMI), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-c), serum creatinine (Scr), fasting blood glucose (FPG), blood urea nitrogen (BUN), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and non-high-density lipoprotein cholesterol (non-HDL-c), but HDL-c and the presence concerning the family history of diabetes were lower. Multivariate logistic regression analysis revealed that TC, LDL-c, and non-HDL-c showed a positive trend with hypertension risk (p for trend < 0.05) whereas TC and HDL-c were not significantly associated with hypertension prevalence. Moreover, each 1 mg/dl increase in TC, LDL, and non-HDL hypertension prevalence increased by 0.2% [1.002 (1.000-1.003)], 0.2% [1.002 (1.000-1.004)], and 0.2% [1.002(1.001-1.004)], respectively. BN suggested that the importance of age, BMI, FPG, non-HDL-c on the prevalence of hypertension was 52.73%, 24.98%, 11.22%, and 2.34%, respectively.

CONCLUSION

Overall, in Chinese adult women, TC, LDL-c and non-HDL-c levels were higher and HDL-c level was lower in the hypertensive population, whereas TG did not differ significantly from the non-hypertensive population. Meanwhile, TC, LDL-c, and non-HDL-c were positively associated with prevalence of hypertension, and HDL-c was negatively associated with prevalence of hypertension but became nonsignificant after full adjustment for variables. Moreover, BN model suggested that age, BMI, FPG, and non-HDL-c had a greater effect on the development of hypertension.

Semantic Network Analysis Using Construction Accident Cases to Understand Workers' Unsafe Acts

Unsafe acts by workers are a direct cause of accidents in the labor-intensive construction industry. Previous studies have reviewed past accidents and analyzed their causes to understand the nature of the human error involved. However, these studies focused their investigations on only a small number of construction accidents, even though a large number of them have been collected from various countries. Consequently, this study developed a semantic network analysis (SNA) model that uses approximately 60,000 construction accident cases to understand the nature of the human error that affects safety in the construction industry. A modified human factor analysis and classification system (HFACS) framework was used to classify major human error factors-that is, the causes of the accidents in each of the accident summaries in the accident case data-and an SNA analysis was conducted on all of the classified data to analyze correlations between the major factors that lead to unsafe acts. The results show that an overwhelming number of accidents occurred due to unintended acts such as perceptual errors (PERs) and skill-based errors (SBEs). Moreover, this study visualized the relationships between factors that affected unsafe acts based on actual construction accident case data, allowing for an intuitive understanding of the major keywords for each of the factors that lead to accidents.

Safety climate and risk perception of forestry workers: a case study of motor-manual tree felling in Indonesia

Timber harvesting processes, especially motor-manual felling, are hazardous to forestry workers' health and safety. The purpose of this study is to examine forestry workers' mental safety models (at the supervisor and operator levels) using the Nordic safety climate questionnaire. This study also examines how operators and their families perceive workplace risks (dread and unknown risk factors). The safety climate analysis revealed that supervisors misunderstand management safety priority, competence, empowerment and justice. Additionally, this study found that operators do not yet prioritize safety. There was a lack of safety communication and operators' skepticism about the current safety system. These findings highlight the critical importance of implementing safety measures into operators' work environments. The risk perception analysis revealed that family members had a greater risk aversion to dread risk factors than operators. As a result, we see a possibility for family members to act as safety-net figures, bolstering the operators' safety values.

Effect of Occupational Health and Safety Training for Chinese Construction Workers Based on the CHAID Decision Tree

Background: Occupational health and safety (OHS) training is an important way to prevent construction safety risks. However, the effectiveness of OHS training in China is questionable. In this study, the CHAID (chi-squared automatic interaction detection) decision tree, chi-square analysis, and correlation analysis were used to explore the main, secondary, weak, unrelated, and expectation factors affecting the effectiveness of training. It is the first to put forward the "five-factor method" of training effectiveness. It is found that training effectiveness is positively correlated with job responsibilities, OHS training, and job satisfaction. It is also significantly related to job certificate, training time, training method, and working time. However, the effectiveness of training has nothing to do with personal age, marital status, educational level, job type, and whether or not they have experienced industrial accidents. And the workers on site expect the enterprise to provide security and opportunities such as physical safety, training and learning, and future career development. The results show that OHS system training should be strengthened in the construction industry, and classified training should be carried out according to post responsibility, training methods, job satisfaction, and working hours.

A study of the shift in fatal construction work-related accidents during 2012-2019 in Turkey

Objectives. The construction sector is one of the sectors with the highest number of occupational accidents and diseases in the world in terms of working conditions. According to the 'Communiqué on Occupational Hazard Classes on Occupational Health and Safety' related to Occupational Health and Safety Law No. 6331, the construction sector is considered a 'very dangerous works' class. Methods. Occupational accidents that occurred between 2012 and 2019 are examined according to occupational groups, working environments, etc. Feature importance and Kendall, Pearson and Spearman correlations were used for analysis. Results. From the studies, it is determined that fatal accidents in the construction sector in Turkey are caused by falling from height with a high rate. When the correlation values were examined, it was determined that the column 'accident type' had a negative relationship with the 'injured part of the body' and a positive relationship with the 'accident environment' column. Conclusion. A total 51% of 3517 fatal accidents examined occurred in the construction of buildings. Most deaths in the construction sector in Turkey are caused by falling from height, like many countries (41.6%). Statistics shows that despite the relevant regulations, the construction sector in Turkey is seen as a weak safety culture.

Analysis of the Characteristics of Fatal Accidents in the Construction Industry in China Based on Statistical Data

Background: Construction activities not only provide the necessary conditions for citizens to live, but also cause fatal accidents. Methods: This study aimed to reveal the characteristics of fatal accidents in the construction industry in China based on statistical data. From 2010 to 2019, there were 6005 fatal accidents in China’s construction industry causing 7275 deaths. The important features of these fatal accidents, such as the type, time of occurrence, site location, severity, and geographical region of the accident, were carefully analyzed. Results: There were 258 major and severe construction accidents causing 1037 deaths, accounting for 4.3% and 14.25% of the total number of construction accidents and deaths in this period, respectively. As an important finding, more deaths occurred in August and on Mondays. The greatest number of construction accidents took place along openings and edges, accounting for 22.9% of all fatal accidents. Taking into account their economic development level and number of employees, Qinghai and Hainan experienced a higher mortality rate than Jiangsu. Falls from a high place were the dominant type of construction accident, accounting for 51.66% of all accidents. However, collapses were the primary type of major and severe construction accident, accounting for 60.09% of such accidents. The predicted number of construction deaths in 2020 is 887 according to the GM(1,1) model. Corresponding safety measures should be adopted to improve the working environment of the construction industry. Implications: The implications of these results with respect to the characteristics of construction accidents can be regarded as the foundation for accident prevention in practice.