ABSTRACT Occupational accidents (OA) are among the main causes of disabilities and death in developing and developed countries. The aims of this study were to identify the subgroups of OA

and assess the independent role of demographic characteristics on the membership of participants in each latent class. This cross-sectional study was performed on 290 workers between 2011

and 2017. Data gathering was done using the reports of accidents recorded in filed lawsuits. Descriptive statistical analysis was done using SPSS 16 and LCA was done using PROC LCA in

SAS9.2. For latent classes were identified; namely “critical due to distractions and lack of supervision” (40.1%), “critical due to lack of safety knowledge” (27.9%), “critical due to

this study showed that the majority of workers fell under the latent class of critical due to distractions and lack of supervision. In addition, it should be noted that although a relatively

small percentage of the workers are in the catastrophic class, the probability of occurring death is quite high in this class. Focusing on the education of workers and enhancing manager’s

supervision and employing educated workers could help in reducing severe and catastrophic OA. SIMILAR CONTENT BEING VIEWED BY OTHERS MAGNITUDE OF SELF-REPORTED NON-FATAL WORK-RELATED

INJURIES AND ASSOCIATED FACTORS AMONG CONSTRUCTION WORKERS IN ALETA WONDO, SIDAMA, ETHIOPIA Article Open access 05 February 2025 MIXED ADVERSE ERGONOMIC FACTORS EXPOSURE IN RELATION TO

WORK-RELATED MUSCULOSKELETAL DISORDERS: A MULTICENTER CROSS-SECTIONAL STUDY OF CHINESE MEDICAL PERSONNEL Article Open access 27 April 2025 WORK-RELATED INJURY CHARACTERIZATION AND FACTORS

AMONG FIRE BRIGADE WORKERS IN ETHIOPIA Article Open access 11 April 2025 INTRODUCTION Occupational accidents (OA) are a major issue in the workplace around the world, including in Iran.

Despite the fact that several definitions of OA have been developed, the exact meaning is represented in different sentences in the definitions. An OA is defined by the International Labor

Organization as "an unanticipated and unplanned event that results in specific damage or injury”1,2. Along with scientific advances and technological innovation, general welfare has

increased in human societies. Every year, millions of OA happen in the world, which causes injuries and economic losses. OA is among the main causes of disabilities and death in developing

or developed countries3,4. There have been several initiatives to decrease the prevalence of OA; however, it is still catastrophically high. According to the World Health Organization (WHO),

OA has still considered a health epidemic5,6. According to International Labor Organization (ILO), the developing countries are home to 60% of the world workforce, while only 5–15% of this

population has access to occupational health services7. In addition, according to international organizations’ reports, every year two million lethal accidents happen in the world and 268

million injury-causing accidents happen in work and industrial environments. These estimates indicate that the mere economic consequences of occupational accidents are about 4% of the gross

national product of developed countries8,9. Identifying the causes of and factors in accidents is an essential step to prevent such accidents. One of the key tools to preventing industrial

accidents is the descriptive-analytical examination of the accidents, which is performed to achieve a proper perception of these factors. Researchers from a variety of disciplines have tried

to elaborate on the types of accidents and the factors. Along with uncovering the causes of such accidents, such studies describe and analyze OA and lead to understanding and predicting the

accidents10. Considerable sums of money are spent in Iran every year to compensate for injuries and lost income of OA victims; this also affects the active workforce available in the

country3. Amiri et al. analyzed OA with high risk in construction works and showed that head, face, and neck injuries had the highest frequency compared to other accidents11,12. The European

Agency for Work Safety and Health estimated that 4.6 million OA happen in Europe every year, which means losing 146 million work hours. According to the agency, it is possible to motivate

managers and employers to prevent such accidents by highlighting the financial damages of such accidents13. In 2002, 15 Americans lost their lives at work every day on average and 20% of the

deaths were in the construction industry10. The descending trend of work accidents in the developed countries is undeniable, which indicates that it is possible to decrease the trend in

other countries using a set of efficient programming and preparation7. In order to obtain reliable models concerning a specific aspect of OA, an advanced data mining analysis should be

carried out on a set of detailed data, in which a data unit refers to a single object of observation. One of the methods that can be used for this purpose is latent class analysis (LCA). LCA

is an approach to identifying latent subgroups or classes among participants of a study14. This person-centered approach uses some indicator variables to identify latent classes. The

underlying basis of the information of these subgroups is the existing similarities regarding indicator variables. There is little information about OA in Iran. Although there are registered

data about these accidents in Iran, however, there is no information about subgroups of these accidents in Iran. Based on the above-mentioned background, the aims of this study were to

identify the subgroups of OA and assess the independent role of demographic characteristics on the membership of participants in each latent class after adjusting for other covariates.

MATERIALS AND METHODS STUDY POPULATION AND SAMPLING FRAMEWORK This cross-sectional study was carried out on OA recorded between 2011 and 2017. Information gathering was done using accident

reports in filed lawsuits. Information was collected from studying the completed incident report form. These forms are the report form of accidents caused by the work of the Iranian Labor

Office. The inclusion criteria were at least one year of work record, no physical impairment, and no chronic disease. Accidents cases with incomplete information were excluded. The variables

under study were age, marital status, education, shift work (morning, evening, night); individual causes of the accident (lack of skill and experience, fatigue and excessive sleepiness,

distraction, and lack of safety knowledge); managerial causes of the accident (lack of adequate and accurate supervision, wrong order, lack of occupational safety and health training);

severity of accident (death, temporary debilitation, and permanent debilitation); and injured member (the eyes, head, face, neck, waist, arm, forearm, wrist, hand fingers, feet, knees, and

toes). STATISTICAL ANALYSIS Descriptive statistics were used to investigate the characteristics of workers and OA type, reason, severity, and distribution. Then, LCA was performed six times,

using one to six classes to identify the best model that can fit the data. To find the best model, each candidate model was fitted 20 times with different starting values. To choose the

final model, a few indices were calculated and compared across six models. These indices were likelihood-ratio statistics G2, Akaike information criteria (AIC), Bayesian information criteria

(BIC), entropy, and log-likelihood value. In addition to these indices, interpretability, and parsimony of a model could help in the selection of the final model15,16. Four indicator

variables were used for the subgrouping workers. These variables were personal causes of the accident (four categories), managerial causes of the accident (three categories), the severity of

accident (three categories), and injured limb (four categories). After identifying the optimal model (four-class model), an LCA was performed with covariates to detect the effect of

predictors of latent class membership16. To this end, four variables were included in the analysis including age, marital status, education, and shift work. It should be noted that the

“critical due to distractions and lack of supervision” class was considered as the reference class when investigating predictors of class membership. Descriptive statistical analyses were

performed using SPSS 16. The LCA was performed using PROC LCA in SAS 9.2 (_P_-value < 0.05). ETHICAL APPROVAL The study was approved by the Ethical Committee of Ardabil University of

Medical Sciences, Iran (Code of ethics: IR. ARUMS. REC.1398.073). CONSENT TO PARTICIPATE The informed consent was waived by the Institutional Review Board of Ardabil University of Medical

Sciences (Ethical ID: IR. ARUMS. REC.1398.073). The authors confirm that all methods were carried out in accordance with relevant guidelines and regulations. CONSENT TO PUBLISH All the

authors agreed to publish the data in this journal. RESULT AND DISCUSSION The demographic characteristics of the study participants are given in Table 1. According to Table 1, the most

common causes of OA were distractions and lack of adequate and accurate supervision. Also, OA has mainly caused disability in workers, and the eyes, head, face, and neck have suffered the

most damage. Table 2 lists different measures of model selection for classes one to six. The number of parameters was relatively high in this study, which could be related to the large

categories of indicator variables. When there are numerous parameters/categories involved, the distribution of the G2 reference statistics is unknown. Therefore, no P-value is reported for

testing the efficiency of the models. Under these circumstances, AIC and BIC have a more highlighted role in the selection of the best model. According to Table 2, the lowest BIC value was

found for the two-class model and the lowest value of AIC value was found for the four-class model. Considering these criteria and the interpretability of the results, the four-class model

was chosen for the subgrouping of the workers. Table 3 represents the four-class latent model. Industry workers in our study were grouped into “critical due to distractions and lack of

supervision” class (40.1%), “critical due to lack of safety knowledge” class (27.9%), “critical due to fatigue and lack of supervision” class (13.1%), and “catastrophic” class (18.8%).

Workers in the “critical due to distractions and lack of supervision” class had a high probability of being distracted and having an accident. In this class, among managerial causes of the

accident, lack of adequate and accurate supervision had the highest probability. Workers in the “critical due to lack safety knowledge” class had a high probability of lacking safety

knowledge and becoming disabled. Workers in the “critical due to fatigue and lack of supervision” class had a high probability of being fatigued, having excessive sleepiness, and becoming

disabled. In this class, among managerial causes of the accident, lack of adequate and accurate supervision had a higher probability. Finally, workers in the “catastrophic” class had a high

probability of death. In this class, the probability of eyes, head, face, and neck injuries was high. Only one significant predictor of latent class membership was found (Table 4), implying

different distribution of latent class membership across this factor. Being illiterate significantly increased the odds of membership in “critical due to fatigue and lack of supervision” (OR

 = 4.05) and “catastrophic” (OR = 18.99) classes compared to “Critical due to distractions and lack of supervision” classes. Therefore, the level of education is effective in the occurrence

of OA. In this study, we evaluated the pattern of OA among industry workers with the LCA approach. We were able to identify four distinct classes of OA named as critical to distractions and

lack of supervision, critical due to lack of safety knowledge, critical due to fatigue and lack of supervision, and catastrophic that represented 40.1%, 27.9%, 13.1%, and 18.8% of the

workers in our study sample, respectively. To the best of our knowledge, there are only a few studies that have employed the LCA approach to detect the latent classes of OA. Moreover,

researchers have used various variables to subgroup workers. Some of these studies are discussed below: Nowakowska and Pajęcki applied LCA to identify OA patterns. They found three severe

accident patterns and two light accident patterns17. Farnia et al. used LCA to find causation in occupational fatalities in Italy. The authors selected the eight-class model. In their study,

most of the factors fell in the class “fall from height or vehicle rollover due to incorrect practice18. Although the number of classes was different in the mentioned studies in comparison

to our findings, however, inconsistent with other studies our study demonstrated that some workers fell under catastrophic class with a high probability of death. OA usually causes severe or

even lethal damage to individuals. They also create large losses to society and the economy. Detecting the mechanisms that frequently cause such accidents can help us develop efficient

tools to improve work safety17,19. The present study represented that among the individual factors of accidents, lack of safety knowledge was the main cause. This personal factor had a high

probability in latent class 2 (Critical due to lack of safety knowledge). Also, fatigue and excessive sleepiness, and distractions had a high probability of occurring in latent classes 3 and

1 respectively. However, among individual factors, lack of skills and experience had no important role in the clustering of workers. Therefore, in order to prevent OA, it is necessary to

pay more attention to fatigue and excessive sleepiness, distractions, and lack of safety knowledge. Zhang et al. showed in their study on the main causes of accidents that the key factors in

lack of safety culture were negligence of safety codes and regulations, negligence in paying attention to safety priorities, limited participation in operational parts, and not paying

attention to safety education. Therefore emphasized the role of departments, safety communications, safety participation, and supervision of safety culture improvement measures to decrease

OA20. Kelly et al. analyzed human factors in accidents and showed that workers experienced distraction and fatigue at work. Therefore, human factors are the main elements of accidents. The

common factors in decision making and skill-based errors and communicational errors were coordinating and programming. Increasing awareness of activities and specific occupational situation,

educations focused on better decision-making and revising basic skills were essential for preventing accidents21. Baby et al. examined the role of individual factors and safety atmosphere

in OA and showed that these factors along with workers’ health conditions were significantly related to OA. Individual factors including age, type of job, education, and experience had a

notable effect on the safety behavior of workers. All the personal factors had a notable effect on the safety atmosphere. The study highlighted the need for safety participation, safety

knowledge, safety education, and intervention to decrease personal problems at work22. Our findings indicated that most of the workers suffer from an OA disability (Table 1). The most

commonly injured limbs in the present study were the eyes, head, face, and neck. Izadi et al.23 found that less than 1% of OA resulted in death in Iran from 2007 to 2016 and the highest

incidence of these OA was seen in the industrial sectors during all years. There are some theories for the occurrence of severe OA. The causes of this OA are different and depend on many

factors such as demographic and occupational characteristics, organizational culture, psychosocial and economic factors, etc. from an economic view, in addition to disability, many workdays

are lost in OA. But many countries especially developing ones often encounter with lack of resources to maintain an effective safety management system. As the results showed, lack of

adequate and accurate supervision was the main factor among managerial factors of OA. Therefore, periodic monitoring and improving the quality of monitoring can be helpful in preventing

accidents. Study Tau et al. found that the human factor had a role in 71% of accidents. Among the human factors, the main factor was “lack of supervision”, which had a direct and indirect

effect on the recorded accidents. Implemented procedures for safety management were mostly inefficient because of poor supervision. Supervision can affect safety culture as a supervisor has

a notable role in the improvement of work methods and processes and the removal of system weak spots. Inefficient supervision is an indicative of inefficient safety culture, which can

increase the rate of accidents24. Also, Soltanzadeh et al. showed in their study on the causal factors in the severity of OA that personal and organizational factors, HSE educational

factors, and risk management systems had a significant relationship with the severity of the accidents25. CONCLUSION Workers in the “critical due to distractions and lack of supervision”

class had a high probability of being distracted and having an accident. And in the “critical due to fatigue and lack of supervision” the class had a high probability of being fatigued,

having excessive sleepiness, and becoming disabled. In these two classes, among managerial causes of the OA, lack of adequate and accurate supervision had a higher probability. Workers in

the “critical due to lack safety knowledge” class had a high probability of lacking safety knowledge and becoming disabled. And in the “catastrophic” class had a high probability of death.

In this class, the probability of eyes, head, face, and neck injuries was high. Among the individual factors of OA, lack of safety knowledge was the main cause. Also, fatigue and excessive

sleepiness, and distractions had a high probability of occurring. In order to prevent OA, it is necessary to pay more attention to fatigue and excessive sleepiness, distractions, and lack of

safety knowledge. Lack of adequate and accurate supervision was the main factor among managerial factors of OA. Therefore, periodic monitoring and improving the quality of monitoring can be

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Download references ACKNOWLEDGEMENTS We hereby express our sincere gratitude to the Vice President for Research at Ardabil University of Medical Sciences who approved and financially

supported this research. AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of Occupational Health Engineering, School of Health, Ardabil University of Medical Sciences, Ardabil, Iran

Behzad Saranjam & Amin Babaei-Pouya * Health Department, Ardabil University of Medical Sciences, Ardabil, Iran Islam Shirinzadeh * Students Research Committee, School of Health, Ardabil

University of Medical Sciences, Ardabil, Iran Kobra Davoudi & Zahra Moammeri * Department of Public Health, School of Health, Ardabil University of Medical Sciences, Ardabil, Iran Abbas

Abbasi-Ghahramanloo Authors * Behzad Saranjam View author publications You can also search for this author inPubMed Google Scholar * Islam Shirinzadeh View author publications You can also

search for this author inPubMed Google Scholar * Kobra Davoudi View author publications You can also search for this author inPubMed Google Scholar * Zahra Moammeri View author publications

You can also search for this author inPubMed Google Scholar * Amin Babaei-Pouya View author publications You can also search for this author inPubMed Google Scholar * Abbas

Abbasi-Ghahramanloo View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS We confirm that this manuscript has not been published elsewhere and is

not under consideration by another journal. All authors have approved the manuscript and agree with submission to Scientific Reports. The authors have no conflicts of interest to declare.

All authors reviewed the manuscript. CORRESPONDING AUTHORS Correspondence to Amin Babaei-Pouya or Abbas Abbasi-Ghahramanloo. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no

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ARTICLE Saranjam, B., Shirinzadeh, I., Davoudi, K. _et al._ Latent class analysis of occupational accidents patterns among Iranian industry workers. _Sci Rep_ 12, 7512 (2022).

https://doi.org/10.1038/s41598-022-11498-w Download citation * Received: 27 December 2021 * Accepted: 25 April 2022 * Published: 07 May 2022 * DOI: https://doi.org/10.1038/s41598-022-11498-w

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