Association between safety leading indicators and safety climate levels.

OBJECTIVES: The aim of this study was to evaluate the association of leading indicators for occupational health and safety, particularly safety inspections and non-compliances, with safety climate levels. METHODS: Nordic Occupational Safety Climate Assessment Questionnaire was employed to evaluate safety climate in cross-sectional design. The geographically diverse population of the inspection body made it possible to conduct the survey across 10 provinces in Iran. 89 completed questionnaires were obtained with a response rate of 47%. Except for management safety justice, the internal consistency of other six dimensions was found to be acceptable (α≥0.7). RESULTS: Mean scores of dimensions ranged from 3.50 in trust in the efficacy of safety systems (SD=0.38) to 2.98 in workers' safety priority and risk non-acceptance (SD=0.47). Tukey HSD tests indicated a statistically significant difference of mean scores among groups undergoing different number of safety inspections and those receiving different number of non-compliances (p<0.05), with no significant differences based on safety training man-hours and sessions (p>0.05). Spearman's rank-order correlation showed no relationship between work experience and number of non-compliances (correlation coefficient=-0.04, p>0.05) and between safety training man-hours and number of non-compliances (correlation coefficient=-0.15, p>0.05). CONCLUSIONS: Our results indicate that safety climate levels are influenced by number of safety inspections and the resultant non-compliances. PRACTICAL APPLICATIONS: Findings suggest that safety non-compliances detected as a result of conducting safety inspections could be used to monitor the safety climate state. Establishing plans to conduct scheduled safety inspections and recording findings in the form of safety non-compliance and monitoring their trend could be used to monitor levels of safety climate.

Systemic Approach for Health Risk Assessment of Ambient Air Concentrations of Benzene in Petrochemical Environments: Integration of Fuzzy Logic, Artificial Neural Network, and IRIS Toxicity Method.

BACKGROUND: Reliable methods are crucial to cope with uncertainties in the risk analysis process. The aim of this study is to develop an integrated approach to assessing risks of benzene in the petrochemical plant that produces benzene. We offer an integrated system to contribute imprecise variables into the health risk calculation. METHODS: The project was conducted in Asaluyeh, southern Iran during the years from 2013 to 2014. Integrated method includes fuzzy logic and artificial neural networks. Each technique had specific computational properties. Fuzzy logic was used for estimation of absorption rate. Artificial neural networks can decrease the noise of the data so applied for prediction of benzene concentration. First, the actual exposure was calculated then it combined with Integrated Risk Information System (IRIS) toxicity factors to assess real health risks. RESULTS: High correlation between the measured and predicted benzene concentration was achieved (R2= 0.941). As for variable distribution, the best estimation of risk in a population implied 33% of workers exposed less than 1×10-5 and 67% inserted between 1.0×10-5 to 9.8×10-5 risk levels. The average estimated risk of exposure to benzene for entire work zones is equal to 2.4×10-5, ranging from 1.5×10-6 to 6.9×10-5. CONCLUSION: The integrated model is highly flexible as well as the rules possibly will be changed according to the necessities of the user in a different circumstance. The measured exposures can be duplicated well through proposed model and realistic risk assessment data will be produced.

Examining fatigue and insomnia symptoms among workers of a gas transmission industry in 2013.

BACKGROUND: Fatigue, which interferes with one's physical and mental operation, resulting in strength reduction and weakness, is considered one of the most important issues in the workplace. In addition, it can cause diseases, occupational accidents, and a reduction in an individual's efficiency. The aforementioned effects can be aggravated by fatigue in shift workers who experience sleep disturbance. The aim of this study was to investigate fatigue and the Insomnia Severity Index (ISI) among workers of a gas transmission industry in 2013. METHODS: This descriptive analytical study was conducted among 300 workers of the aforementioned industry and required data was collected via the face-to-face survey method and questionnaires. Data analysis was done with the following techniques: Mann-Whitney, variance analysis test, independent t-test, Kruskal-Wallis, Spearman's correlation test, and chi square test. RESULTS: The highest fatigue scores among fixed-dayshift and rotating-shift workers were 6 and 7, respectively, and the fatigue level for both groups was 4. The average of all symptoms associated with fatigue and the total score on the Insomnia Severity Index in rotating-shift workers were higher than for dayshift workers and there was a significant difference between them (P=0.001). Lack of concentration, exhaustion, and fatigue during work were the most common symptoms of fatigue among the aforementioned shift workers in this evaluation. CONCLUSION: The findings indicated that the level of fatigue and severity of insomnia among workers of this gas transmission industry is very high. Since this can lead to occupational accidents and efficiency reduction, it is necessary to provide workers with opportunities such as short breaks during working hours, rest and exercise during work, paying adequate attention to the workers' human needs, and improving work systems.