Iran Occupational Health

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  Background and aims: Power plants are the most important infrastructure for economic development with technological advances and sophisticated technology , are subject to risks and accidents. Despite of uncertainty and ambiguity , offer the Solution that can by combine information to identify hazards , assess and risk rank ing may effective to control and reduction of occupational accidents . This survey targeted to identify and assess hazards, determine and rank the effective safety risks in a combined cycle power plant.

  Methods: To identify hazard , was used documentation review , interviews with experts , brainstorming sessions , knowledge and experience group of experts in occupational health and safety engineering . For risk analysis , used fuzzy classification for severity and frequency by previous studies and expert opinion . Finally the risks were ranked based on using degree of Belief approach in fuzzy logic.

  Results: in the present study , among the 11 cases of identified hazards , explosion and fire gas turbines and bust steam pipes under pressure, with the degree of belief 0/56 , ranked in the first place and fell in tanks reservoirs and canals with degree of belief 0/21 had second place.

  Conclusion: So far , few risk assessment tools and techniques have been proposed in power plan t. In this study, despite of use subjective and qualitative variables , but for measuring these variables were used the absolute numbers and mathematical methods . Therefore this study is important and can be suitable and accurate approach to address the technical deficiencies in this section.

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Background and aims: taxi drivers are a large group of professional drivers, who spend so much time in driving. Considering traffic collisions as one of the Traffic Safety System in the country, modification of improper driving behaviors of this group can result in a significant decrease in accidents.

Methods: This analytical-descriptive study was conducted on 245 taxi drivers in 2013. A questionnaire aimed into two sections: demographic information and reasons for lack of night use of the automotive lighting system was designed. The content validity of the questionnaire was confirmed by the experts and professors’ comments and its reliability was confirmed by Cronbach's coefficient alpha (0.72).

Results: It was found that 26.9% of the drivers do not turn on their automotive lighting system over nigh. Most in main squares of the city (46.2%). Forgetfulness in turning car’s light on (14.28%) and depreciation on car lights (13.06%) are the major reasons. Moreover, a significant relationship was observed among variables like working in taxi driving as the main occupation (P-Value= 0.0001), lack of receiving warning from passengers (P-Value= 0.0001), the level of education (P-Value= 0.012) and lack of using automotive lighting system.

Conclusion: Considering that forgetfulness was the main reason for lack of using the lighting system and most of the drivers have not been received any warning or punishment from police because of lack of using lights overnight, it is essential to use appropriate training and exertion of regulations, as one of the ways to create correct culture of traffic and improve driver performance.

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Background and aims: Fatigue and sleepiness (decreased alertness), in addition to a negative impact on performance and quality of work, are considered as one of the leading causes of human error and accidents in work environments. There is much evidence indicating that physical factors in the workplace could affect fatigue, vitality, motivation, and productivity of individuals. A physical environment suitable for activity is formed by various factors, among which light is known as an essential element. Recent photobiological advances and recognition of intrinsically photosensitive retinal ganglion cells (ipRGCs) have shown that in addition to improving eyesight, light can affect the circadian and homeostatic regulations and melatonin suppression in human. Furthermore, light can have acute effects on the physiological, psychological, neurobehavioral, and neuroendocrine responses, such as improvement in the alertness and neurobehavioral performance, which are known as the non-visual or non-imaging forming effects (NIF) of light. However, the possible roles of these potential effects in the improvement of human safety and efficiency have not been thoroughly investigated.
 Some studies have shown that monochromatic blue light or blue-enriched white light (high correlated color temperature white light or high CCT) can enhance levels of alertness, and improve mood and cognitive function. Although recent evidence has indicated that monochromatic red light or red saturated white light (low correlated color temperature white light or low CCT) has also been able to induce such positive effects. There is still an open question left unanswered: “which of these lighting conditions (high CCT vs. low CCT) has a stronger effect on the alertness level and neurobehavioral function?” Therefore, the present study tested this hypothesis in a simulated office workplace environment with the recommended illumination level of 500 lx on the desk for daytime office work environments during the morning hours.
 
 Methods: In this study, 20 healthy paid volunteers (male; mean ± SD age, 27.6 ± 3.6 years) were selected and followed the experimental protocol. All participants were interviewed about the quality of sleep, lifestyle habits, and general health. The inclusion criteria were: not having any mental or physical health problem, having a good sleep quality according to the Persian version of Pittsburgh Sleep Quality Index (PSQI score<5 ), being neither extreme early nor extreme chronotype, having a regular sleep-wake state (bedtimes 22:00 to 24:00 p.m. and wake up between 07:00 and 08:00 a.m.), not smoking, not traveling to a different time zone or experiencing shift-work during three months prior the experiment, no history of eye diseases, and having a normal color blindness as evaluated by the Ishihara test. To control the effect of potential differences in the levels of alertness due to circadian variations and sleep pressure between the light conditions, the participants completed a sleep/wake log, starting one week prior to the beginning of the study. In addition, they were asked to keep a regular sleep/wake state during the study. The participants were also asked not to drink caffeine and/or alcohol about 12 h before the experiment. The aim of the study was described for all participants and they signed an informed consent before the commencement of the study. In addition, the protocol of the study was confirmed by the university ethics committee.
The present study had a repeated-measures design, and the participants were exposed to four light conditions for 140 minutes in a counterbalanced order with a one-week interval. The light conditions were dim light (DL, <5 lx, control), and a 500 lx light intensity on the desk level for high CCT white light (HWL, nominal CCT= 12000 K), low CCT white light (LWL, nominal CCT= 2700 K), and standard white light (SWL, nominal CCT= 4000 K).The study was performed in an air-conditioned room with an area of 19 m². The room’s windows were closed with light-blocking curtains to restrict the penetration of daylight into the experimental setting. Electroencephalogram (EEG) activity (5–7 Hz: theta, 5-9: alpha-theta, 8–12 Hz: alpha, and 13–30 Hz: beta), subjective sleepiness (Karolinska Sleepiness Scale, KSS), subjective mood (Visual Analogue Mood Scale, VAMS), cognitive performance tests (sustained attention, working memory, selective attention task, and inhibitory capacity) and subjective evaluation and beliefs of the participants about the light conditions were measured. The data were analyzed using the MATLAB software package (ver. R2012a, Math-Works, USA) and version 20.0 of the SPSS software (IBM, Armonk, NY, USA). Repeated-measures analysis of variance (ANOVA) was conducted, and where necessary, the Greenhouse–Geisser correction was applied. A 4 (light conditions) × 6 time intervals ANOVA was performed for the EEG activity measures (alpha, theta, beta, and alpha-theta) and CPT data. For the subjective sleepiness and mood, a 4 (light conditions) × 3 time intervals ANOVA was performed. Also, a 4 (light conditions) ANOVA was performed using each cognitive performance (GO/NO-GO, 2-Back, and divided attention) outcome measures. A 3 (light conditions) ANOVA was performed using each subjective evaluation and belief measures about the light conditions. The Bonferroni-adjusted post-hoc tests were applied to multiple comparisons (P<0.05).
 
Results: The means±standard error (SE) of the normalized alpha power was 1.083±0.018 for HWL, 1.147±0.021 for SWL, 1.069±0.021 for LWL, and 1.225±0.027 for DL condition. The Bonferroni-adjusted post-hoc tests indicated a significantly lower power under HWL (P=0.012) and LWL (P=0.006) compared to DL condition. The other comparisons revealed no significant differences. The means±SE of the normalized alpha-theta power was 1.009±0.012 for LWL, 1.053±0.014 for SWL, 1.024±0.016 for HWL, and 1.106±0.017 for DL condition. Post-hoc tests showed a significantly lower alpha-theta power under LWL (P<0.001) and HWL (P=0.033) compared to DL condition. The other comparisons indicated no significant differences. No significant main effect of the light conditions and interaction between them and the time intervals in the normalized beta and theta powers were observed.
 
The means±SE of the normalized subjective sleepiness were 1.129± 0.045 for LWL, 1.25±0.057 for SWL, 1.127±0.052 for HWL, and 1.499±0.074 for DL condition. Post-hoc tests showed that HWL (P=0.002) and LWL (P=0.001) conditions significantly decreased the sleepiness compared to the DL condition. The other comparisons revealed no significant differences. Furthermore, the present study results indicated that the means±SE of the normalized mood scores were 1.048±0.013 for SWL, 1.066±0.011 for HWL, 0.942±0.014 for DL, and 1.158±0.02 for LWL condition. Post-hoc tests indicated that the participants had significantly better mood under the LWL (P<0.001), SWL (P=0.001), and HWL (P<0.001) conditions compared to the DL condition. Furthermore, the LWL enhanced the participants' mood state as compared to the SWL (P<0.001) and HWL (P=0.009) conditions.
 The means±SE of the normalized mean reaction time for continuous performance test (CPT) were 0.998±0.011 for SWL, 0.977±0.011 for HWL, 1.066±0.017 for DL, and 0.985±0.011 for LWL condition. The post‐hoc with Bonferroni‐adjusted pairwise comparison revealed a significantly lower mean reaction time under HWL (P<0.001), LWL (P=0.009), and SWL (P=0.026) conditions compared to the DL condition. Furthermore, the means±SE of the normalized mean reaction time for GO/NO-GO task were 305.13±13.959 ms for HWL, 308.91±13.78 ms for SWL, 304.64±14.11 ms for LWL, and 327.49±17.7 ms for DL condition. Post-hoc tests indicated a significantly lower mean reaction time under LWL (P=0.033) and HWL (P=0.034) conditions compared to DL condition. The means±SE of the normalized mean reaction time for 2–Back task were 394.24±32.22 ms for SWL, 387.92±31.64 ms for HWL, 398.62±31.81 ms for DL, and 384.16±30.66 ms for LWL condition. Post-hoc tests revealed a significantly lower reaction time under LWL (P=0.001) and HWL (P=0.027) conditions compared to DL condition. The means±SE of the normalized mean reaction time for selective attention task were 4.16.25±10.262 ms for SWL, 415.95±10.292 ms for HWL, 408.05±10.75 ms for LWL, and 437.75±9.618 ms for DL condition. Post-hoc tests showed a significantly lower reaction time under LWL (P=0.001), HWL (P=0.027), and SWL (P=0.02) conditions compared to DL condition. The Bonferroni-adjusted post-hoc tests did not show any significant differences between HWL, LWL, and SWL light conditions in the CPT, GO/NO-GO, selective attention, and 2–Back tasks.
The participants believed that there was no significant difference between the light conditions (SWL, LWL, and HWL) according to the subjective appraisals of light, including brightness, distribution, activating, adequacy amount, and color. Also, the participants reported that the light conditions did not significantly improve their performance. In contrast, the volunteers stated that the LWL (P=0.006) condition was more effective in improving their mood status compared to the SWL condition. Also, about the pleasantness of light, the participants preferred the LWL (P=0.037) over the HWL condition.
 
Conclusion: Under natural conditions (healthy participants and with regular sleep-wake cycle), both the low and high CCT lights (500 lx at the desk) improved alertness and performance compared to the DL condition during the morning hours. In contrast, compared to the SWL, no significant improvement in alertness and cognitive performance during inhibitory capacity, working memory, selective attention, and sustained attention tasks was observed. Briefly, it can be concluded that in addition to the relative preferences of the LWL (2700 K) light condition by the participants, it has had a significant impact on improving the mood of the participants. Hence, the designing and application of lighting interventions by using low correlated color temperature lighting sources can be beneficial for reducing fatigue and sleepiness and improving performance and mood during the morning hours although more studies are required to determine the optimal parameters for lighting interventions.
 
 

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Background and aims: Human errors are major causes of the accident that occurring in the industries. However, attributing incidents to human error, regardless of the nature of human error, cannot be useful in preventing accidents. Identifying organizational and supervisory factors that affecting human errors, as well as determining the interactions between these factors, can be used in the management of appropriate control strategies to reduce the accidents. The Human Factors Analysis and Classification System framework (HFACS) is one of the most important and comprehensive qualitative tools to identify human and organizational contributing factors involved in an accident. Until now, several studies have tried to integrate the HFACS with a quantitative analysis tool in order to determine the interactions between human and organizational factors to reduce accidents. There are many types of quantitative tools that researchers usually used for this purpose. Fuzzy analytical hierarchy process, analytical network process, and artificial neural network are the most used analytical quantitative tools in this regard. Powerful graphical probability-based modeling approaches have been less well considered for quantitative analysis of the interaction and relationship between different variables. Bayesian network (BN) is one of the most important quantitative tools in this regard. BN is a probabilistic graphical model that uses for various types of inference such as diagnostic and predictive. Belief updating or sensitivity analysis is one of the exclusive feature of BN that researchers using this feature can examine the sensitivity of one “target variable” to changes in other variables. In the modeling, sensitivity analysis is used to rank the influence of input variables on the predicting of output variables. This study aimed to integrate the HFACS framework and BN to identify different factors that influence unsafe acts and determine the relationships and interactions among identified those factors to provide appropriate intervention strategies for preventing accidents in the future.
Methods: In this study, the accidents occurred in one of the largest mines in Iran that occurred during a period of 5 years (2011-2015) were collected, and then accidents with serious consequences such as fatalities, disabling injuries, or considerable property damage were screened. In the next step, all contributing factors in each accident were identified using an accident analysis team by root cause analysis (RCA) approach. RCA is a problem-solving approach that is applied to identify the root causes of problems. A total of 250 accidents analysis results were collected and classified in one of the 13 groups of the HFACS framework, and a database was created. According to the structure of the HFACS framework, the BN model was developed. HFACS is a 4 levels hierarchy of human and organizational errors, in which higher levels can influence directly lower levels and this pattern can help to the develop a BN graphical model. Causal factors at the 4 levels of the HFACS consist the nodes of the BN model. In the next step, for each node, states were defined that show different values of the variable. In this study, except for unsafe acts node that had three states (skill based, decision, and perceptual), other nodes had two states; yes (node involved in an accident) and no (node not involved in an accident). The main hypothesis of the HFACS framework is that deficiency at the higher level casual factors can lead to deficiency at the lower level casual factors. Hence, in the present study, all causal factors (parents nodes) at the higher level were connected to the lower level causal factors (child nodes) edge with arcs. For instance, causal factors of unsafe supervision (level 3) that include inadequate supervision, planned inappropriate operations, failure to correct a known problem, and supervisory violations are parents of environmental factors, personnel factors, and condition of operator nods which belong to preconditions for unsafe acts (level 2). After the graphical structure of the BN model was developed, using database that obtained in the previous section and the expectation–maximization (EM) algorithm model was trained. In a BN the conditional probability tables (CPTs) are used to determine quantitative relationships among a set of variables. The EM algorithm is one of the common methods to calculate. There are several approaches for conducting a sensitivity analysis but the mutual information (MI) approach is most common. In order to determine the factors with greatest impact on unsafe acts, the MI approach was used and the sensitivity analysis was performed. In probability theory, the MI of two random variables is a measure of the mutual dependence between the two variables. In the current study, Netica version 5.24 was used to perform calculations and analyses.
Results: The results of this study showed that at the level of unsafe acts, skill-based errors (%67.3) had the highest prior probability technique errors were the most skill based errors that were detected. Also at the level of unsafe conditions, environmental factors (%74.8) had the highest prior probability. Inadequate installation and improper housekeeping were the most frequently identified environmental factors that led to accidents. At the levels of unsafe supervision and organizational influences, inappropriate planned operation (%60.6) and organizational processes (%35.3) had the highest prior probability, respectively. Inadequate task/safety plan from unsafe supervision level and lack of standard operation procedures from organizational influences level were the most frequently identified deficiency in the selected accidents. The results of the sensitivity analysis demonstrated that the environmental factors from level 2, inappropriate planned operation from level 3, and organizational processes from level 4 had the greatest impact on unsafe acts. Based on the analysis results, several strategies were made to reduce the unsafe acts of employees.
Conclusion: In the current study, by integrating the HFACS framework as a qualitative tool and BN as a powerful quantitative tool, a human factors analysis model was developed. The results of this study indicated that the environmental factors and inappropriate planned operation had the most effect on the unsafe acts. Although organizational influences play a role as indirect factors on the unsafe acts, paying attention to eliminating defects at this level can be useful in reducing accidents. Different forms of unsafe acts require various interventions, therefore, the use of BN model can be helpful in determining strategies tailored to the specificities of the unsafe acts.