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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 fatigue and lack

of supervision” (13.1%), and “catastrophic” (18.8%). After adjusting for other studied covariates, 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” class. Results of 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.

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.

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).

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