Iran Occupational Health

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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.

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Background and aims: Work environments are constantly changing under the influence of various factors and newer risks are introduced. Rapid changes in science and technology, increasing the complexity of the industry, increased system integration and other factors have been shown to increase total risk in the past few decades. As well, risk management becomes increasingly critical in decreasing incidents, improving safety, and related outcomes. Risk identification is known as the heart of a risk assessment and management process. Risk assessment is a concept that outlines the way in which you Identify hazards and risk factors that are likely to cause damage (hazard identification),and evaluating any associated risks within a workplace (risk analysis, and risk evaluation). Risk assessment and management consists of an objective evaluation of risk in which assumptions and uncertainties are clearly considered and presented. This included identification of hazard (what can happen and why), the potential consequences, the likelihood of occurrence, the detectability and acceptability of the risk, and ways to decrease or reduce the probability and severity of the risk. Basically, it also involves documentation of the hazard identification, related risk assessment and its results, implementation of control methods, and review of the assessment, coupled with updates when necessary. 
With this view, the COVID 19 coronavirus and people infected with it, or suspected of being infected is considered a hazard. This is because of such a person, in addition to endangering their own health, is able to infect others, and threatening their health, especially their colleagues. Given the huge population of workers in the country, their daily commute and close relationship with family and friends, and coworkers it is essentially a need to present a suitable method for identification, evaluation, and management of risks associated with such coronavirus. In this regard, the present study was designed and implemented in order to design a rapid method for assessing and managing the risk of people suspected of being infected with the coronavirus in the workplace.
Methods: In this study, at first a framework for defining risk was introduced and appropriate criteria for hazard identification section were acquired using expert judgments. In the risk evaluation section, the number and nature of risk parameters, categorization, and descriptions of each of them with consideration to conventional risk evaluation and the opinions of experts were determined. At this stage, the opinions of experts on the importance of each criterion were collected. Then, using the single sample t-test in SPSS 21 software, important criteria were selected. Next, health risks assessment methods suitable for COVID 19 were collected and analyzed based on converging of selected criteria in hazard identification according to expert judgments and methods yielding the highest score was selected. To identify suitable accident analysis methods, related articles Searched in reputable databases such as Iran Medex, Science Database (SID)،Google scholar، Science Direct، PubMed، Scopus, and Web of Science. Keywords used included hazard identification, risk assessment and management, COVID 19, workplace, occupational, individual risk assessment, and health hazard analysis. In the following, by carefully examining the selected method and opinions extracted from experts, strengths and weaknesses of the selected methods were identified, and based on that, the proposed method "Rapid COVID Hazard Assessment", short for RCHA for workplaces application was developed. In the last step, the usefulness of the RCHA was examined by the successful application of it in six different workplaces.
Results: The RCHA method was introduced as the outcome of this study. In this method data sources for hazards, identification include engineering senses, knowledge management, patient history, personal interview, fever measurement, and examination of personal files. In terms of hazard identification, the introduced method is very similar to methods that benefit from a primary database such as preliminary hazard list and preliminary hazard analysis with the same pros and cons viz. low cost for employment, no quantitative data is needed, the possibility of use in the early stages of system life (early stages of disease formation), and rapid implementation. In this method, the risk obtained of multiplying three parameters included the severity of consequences, Probability of infection, and Individual health attitude level. The consequences severity parameter has four dimensions included personal life, the nature and type of workplace, Individual health status, and symptoms of Covid-19. In this parameter, each dimension has six classes, which are signed with symbols 1 to 6. Two other parameters that constitute risk has four classes with symbols of 1 to 4. The use of the three-dimensional method in risk assessment in this technique is similar to the approach used in several studies. The 3D risk matrix of this method is similar to the ones used in many well-known methods. Currently, literature reflects the fact that increasing the number of risk parameters can increase the accuracy of evaluation and provide more precise prioritization of identified risks. In the present study, due to the nature of the hazard, the targets are may be different and this issue in the analysis of risk is considered. The variety of factors take into account in estimating the severity of COVID 19 exposure is similar to the results of studies. According to research the degree of importance and therefore the weight of the risk parameters are not equal. The findings of this study also showed that the importance weight of the consequence of exposure is greater than the other two parameters. According to opinions of experts (risk assessment stage), the identified risks categorized at three levels including acceptable (X≤4), “as low as reasonably practicable- ALARP (440).In this study, 11 related methods were identified for the design of the RCHA technique. After the initial evaluations, the number of selected techniques reached 5 as follows: Health Hazard Analysis (HHA), Preliminary Hazard Analysis (PHA), Job Hazard Analysis (JHA), Healthcare Failure Modes and Effects Analysis (HFMEA) and Health Risk Assessments (HRAs). In the final step of the study, after performing the necessary training, the technique was tested separately and independently in two stages before and after in 6 units and organizations including the petrochemical industry, ceramic tile production, food production, steel, assembly of industrial parts and hospital.
Conclusion: The main purpose of the present study was to introduce a simple, rapid, low-cost, and precise method for screening infected or suspected people to COVID 19 in the workplace. So, after the identification of associated criteria and methods through a comprehensive survey, the principals of that were envisaged. Application of the RCHA in six types of industry in different provinces showed that HSE professionals are able to use it to identify sensitive and infected people in the shortest (acceptable) time.
Conflicts of interest: There is no conflict of interest.
Funding: This study is supported by Hamadan University of Medical Sciences, Iran (grant No. 9904031915).