Occupant Injury Severity Research Articles

Epidemiology of injury patterns for 4- to 10-year-olds in side and oblique impacts

Objectives: With regard to the pediatric population involved in vehicle side impact collisions, epidemiologic data can be used to identify specific injury-producing conditions and offer possible safety technology effectiveness through population-based estimates. The objective of the current study was to perform a field data analysis to investigate injury patterns and sources of injury to 4- to 10-year-olds in side and oblique impacts to determine the potential effect of updated side impact regulations and airbag safety countermeasures.Methods: The NASS-CDS, years 1991 to 2014, was analyzed in the current study. The Abbreviated Injury Scale (AIS) 2005–Update 2008 was used to determine specific injuries and injury severities. Injury distributions were examined by body region as specified in the AIS dictionary and the Maximum AIS (MAIS). Children ages 4 to 10 were examined in this study. All occupant seating locations were investigated. Seating positions were designated by row and as either near side, middle, or far side. Side impacts with a principal direction of force (PDOF) between 2:00 and 4:00 as well as between 8:00 and 10:00 were included. Restraint use was documented only as restrained or unrestrained and not whether the restraint was being used properly. Injury distribution by MAIS, body region, and source of injury were documented. Analysis regarding occupant injury severity, body region injured, and injury source was performed by vehicle model year to determine the effect of updated side impact testing regulation and safety countermeasures. Because the aim of the study was to identify the most common injury patterns and sources, only unweighted data were analyzed.Results: Main results obtained from the current study with respect to 4- to 10-year-old child occupants in side impact were that a decrease was observed in frequency of MAIS 1–3 injuries; injuries to the head, face, and extremities; as well as injuries caused by child occupant interaction with the vehicle interior and seatback support structures in 1998 model year passenger cars and newer.Conclusions: Results from this study could be useful in design advances of pediatric anthropomorphic test devices, child restraints, as well as vehicles and their safety countermeasure systems.

Associations between upper extremity injury patterns in side impact motor vehicle collisions with occupant and crash characteristics

IntroductionSide impact motor vehicle collisions (MVC) represent a significant burden of mortality and morbidity caused by automotive injury within the United States. The objective of this study was to evaluate the relationship between upper extremity (UE) injury patterns and contact sources in side impact MVC with occupant and crash variables. MethodsCrash Injury Research and Engineering Network data obtained from 1998 to 2012 were used to evaluate UE injuries in side impact crashes. First row drivers and passengers that were at least 16 years old with complete crash information were included. Side impact crashes were defined to have an area of deformation to the side of the vehicle and a principal direction of force between 60° and 120° or 240° and 300°. Injuries were stratified by type, anatomic location, and Abbreviated Injury Scale (AIS) severity. Occupant variables included age, sex, height, weight, body mass index, and Injury Severity Score. Vehicle and crash variables included in the analysis were change in vehicle velocity at the time of impact, maximum door intrusion, maximum B-pillar intrusion, seat track position, belt use, vehicle type, impact type, and injury source. Statistical analysis of the UE injury data included descriptive statistics, linear regression analyses with occupant variables, and logistic regression analyses with vehicle and crash variables. ResultsThere were 903 UE injuries among 408 case occupants. The most common injury type was soft tissue injury (72.5%). The majority of fractures were proximal to and including the humerus (70.3%) with the clavicle being the most common fracture location (N = 89). AIS 2+ UE injuries were associated with a significantly higher mean occupant Injury Severity Score than AIS 1 UE injuries (p = 0.01). Contact with the door was the leading cause of UE injury (34.2%). The odds (OR [95% confidence interval], p-value) of an AIS 2+ UE injury due to contact with the B-pillar (5.3 [3.1, 9.1], <0.0001), door (1.9 [1.3, 2.7], 0.0006), and steering wheel/assembly (2.7 [1.1, 6.3], 0.03) were significantly higher than all other injury sources combined. Scapula fractures were significantly associated with rearward seat track positions (1.46 [1.04, 2.05], 0.03). ConclusionsThis study provides insight into UE injury patterns in side impact MVC. The clavicle was the most common UE fracture location. Contact with the door resulted in the highest number of UE injuries and the B-pillar resulted in the most severe injuries. Additionally, exposure to greater B-pillar intrusion was associated with increased odds of scapula and clavicle fractures in side impacts.

Examining Injury Severity of Moped and Motorcycle Occupants with Real-Time Traffic and Weather Data

AbstractCurrently studies exploring the effect of real-time traffic and weather characteristics on crash severity are relatively limited, and studies focusing on powered two-wheelers (PTWs) are eve...

Able or unable to work? Life trajectory after severe occupational injury

Purpose: To study the probabilities and permanence of return to work, inability to work and rehabilitation, and to explore the connection between these life situations and later working after a severe occupational injury.Materials and methods: A historical cohort of Finnish workers with a severe occupational injury during 2008 (N = 11,585) were followed up annually on the outcomes of return to work over a 5-year observation period. We examined transition probabilities from one life situation to another with Markov chain analysis, and applied logistic regression with generalized estimating equations to assess the effect of register-based determinants on return to work.Results: Within the five anniversaries, 85% of the injured were working, 9% were unable to work (fully or partly) and 2% received rehabilitation. Age, gross annual income, type of work, injured body part, injury type and the injured’s annual condition subsequent to the work injury were significant determinants of return to work.Conclusions: The probability of return to work decreased with time, but, on average, one-fifth of the injured workers succeeded in return to work after being unable to work on the previous anniversary, which indicates that it is worthwhile to conduct efforts for this target group in order to promote return to work.Implications for RehabilitationThe current life situation of the injured should be taken into account when promoting return to work, as it is a strong predictor of later working after a serious occupational injury.Rehabilitation and return to work programs should start in time due to declining return to work rates as the disability continues.Return to work on a part-time basis could be a good option during the early phases of recovery, since a notable proportion of those partly unable to work on the first anniversary returned later to full-time workers.The probability of recovery is relatively high even for those with long-term disabilities, so the promotion of return to work is highly recommended also for this target group.

Quality of life at 6years after occupational injury.

Occupational injuries have considerable impact on workers' lives. However, data regarding workers' health-related quality of life (HRQOL) at several years after the injury are lacking. This study assessed workers' HRQOL at 6years after occupational injury and determined related factors in each HRQOL domain. Workers who sustained an occupational injury in 2009 and who responded to a previous survey at 3 or 12months after their injury were followed up in 2015. A total of 1715 participants were candidates for this study. The Taiwanese version of the World Health Organization Quality of Life scale-abbreviated version (WHOQOL-BREF) was used to assess their HRQOL. Multiple linear regression analysis identified predictive factors for HRQOL at 6years after occupational injury. A total of 563 workers completed the questionnaire (response rate, 32.8%). Adverse life events and additional severe occupational injuries that occurred within the follow-up period, and decreased salary after the injury were significant factors for low scores in all domains of the WHOQOL-BREF. In addition, unmarried participants had low scores in the social relationship domain. Workers with family members requiring care scored low in the physical and environment domains. Workers whose injuries had major effects on their physical appearance had low scores in the physical and psychological domains. Workers with unstable employment had low scores in physical, psychological, and environment domains. At 6years after occupational injury, workers' HRQOL was poor among those whose salaries decreased after the injury, after adjustment for other factors.

Association of Poultry Processing Industry Exposures With Reports of Occupational Finger Amputations: Results of an Analysis of OSHA Severe Injury Report (SIR) Data.

This analysis was conducted to identify industry exposures strongly associated with reports of finger amputation. Reports of severe occupational injuries in the Occupational Safety and Health Administration (OSHA) Severe Injury Report (SIR) database were analyzed in relation to U.S. Census Bureau industry employment data. Industries with significantly elevated reporting odds ratios (RORs) and relative reporting risks (RRRs) were identified. Multiple population association measures including population attributable fraction (PAF) were calculated by industry. Among industries with statistically significant RRR and ROR, the poultry processing industry (RRR = 12.60, ROR = 3.37) accounted for the highest PAF by RRR (2.34%) and ROR (1.65%) CONCLUSION:: The results of this analysis identify the poultry processing industry as a leading source of reports of occupational finger amputations and substantiate the need for further collaboration with this industry.

Occupant-level injury severity analyses for taxis in Hong Kong: A Bayesian space-time logistic model

This study aimed to identify the factors affecting the crash-related severity level of injuries in taxis and quantify the associations between these factors and taxi occupant injury severity. Casualties resulting from taxi crashes from 2004 to 2013 in Hong Kong were divided into four categories: taxi drivers, taxi passengers, private car drivers and private car passengers. To avoid any biased interpretation caused by unobserved spatial and temporal effects, a Bayesian hierarchical logistic modeling approach with conditional autoregressive priors was applied, and four different model forms were tested. For taxi drivers and passengers, the model with space-time interaction was proven to most properly address the unobserved heterogeneity effects. The results indicated that time of week, number of vehicles involved, weather, point of impact and driver age were closely associated with taxi drivers’ injury severity level in a crash. For taxi passengers’ injury severity an additional factor, taxi service area, was influential. To investigate the differences between taxis and other traffic, similar models were established for private car drivers and passengers. The results revealed that although location in the network and driver gender significantly influenced private car drivers’ injury severity, they did not influence taxi drivers’ injury severity. Compared with taxi passengers, the injury severity of private car passengers was more sensitive to average speed and whether seat belts were worn. Older drivers, urban taxis and fatigued driving were identified as factors that increased taxi occupant injury severity in Hong Kong.

Elderly road collision injury outcomes associated with seat positions and seatbelt use in a rapidly aging society-A case study in South Korea.

IntroductionAging has long been regarded as one of the most critical factors affecting crash injury outcomes. In South Korea, where the elderly population is projected to reach 35.9% by 2050, the implications of an increasing number of elderly vehicle users on road safety are evident. In this research, the confounding effect of occupant age in a vehicle in terms of seat position and seatbelt use was investigated. In addition, elderly occupants were divided into a younger-old group aged between 65 and 74 years and an older-old group aged 75 years and older in an effort to assess whether the conventional elderly age standard of 65 years should be reconsidered.MethodsA multinomial logit framework was adopted to predict two-level injury severity using collision data between 2008 and 2015. Predictor variables included gender, age group, seat position, seatbelt, road type, road slope, road surface, road line, and type of vehicle. Five models, a base model with no interactions and four interaction models which were combinations of age group, seatbelt use and seat position, were devised and evaluated.ResultsWith no interacting term, age was the most prominent predictor. Elderly occupants were most likely to suffer from severe injury without a seatbelt in all seat positions, and the use of a seatbelt reduced this likelihood the most in the elderly group as well. Front passenger seats had the highest risk to elderly occupants, while the driver seat was statistically insignificant. When the elderly group was divided into the younger-old group and the older-old group, the older-olds were found to be much more vulnerable compared to the younger-olds. In particular, older drivers were five times more likely to suffer a severe injury without a seatbelt.ConclusionsThe degree of injury severity of elderly occupants was reduced the most with the use of a seatbelt, demonstrating the importance of using seat restraints. The sharp increase in the risk of injury of the older-old group suggests that the age standard of 65 years as the elderly group with regard to traffic safety may require reconsideration due to the growing number of elderly vehicle users on the road. Our results provide practical evidence with which to formulate new safety policies, including mandatory seatbelt use, driving age limits and insurance pricing.

A Review of General Aviation Safety (1984-2017).

General aviation includes all civilian aviation apart from operations involving paid passenger transport. Unfortunately, this category of aviation holds a lackluster safety record, accounting for 94% of civil aviation fatalities. In 2014, of 1143 general aviation accidents, 20% were fatal compared with 0 of 29 airline mishaps in the United States. Herein, research findings over the past 30 yr will be reviewed. Accident risk factors (e.g., adverse weather, geographical region, post-impact fire, gender differences) will be discussed. The review will also summarize the development and implementation of stringent crashworthiness designs with multi-axis dynamic testing and head-injury protection and its impact on mitigating occupant injury severity. The benefits and drawbacks of new technology and human factor considerations associated with increased general aviation automation will be debated. Data on the safety of the aging general aviation population and increased drug usage will also be described. Finally, areas in which general aviation occupant survival could be improved and injury severity mitigated will be discussed with the view of equipping aircraft with 1) crash-resistant fuel tanks to reduce post-impact conflagration; 2) after-market ballistic parachutes for older aircraft; and 3) current generation electronic locator beacons to hasten site access by first responders.Boyd DD. A review of general aviation safety (1984-2017). Aerosp Med Hum Perform. 2017; 88(7):657-664.

A Modified Rank Ordered Logit model to analyze injury severity of occupants in multivehicle crashes

The current study developed a simultaneous model of injury severity outcomes of all occupants in multi-vehicle crashes including all the drivers and the passengers of all vehicles involved in a crash. Specifically, a Modified Rank Ordered Logit (MROL) methodology that can predict the relative order of occupant injury severity as well as the actual injury severity was developed. The final model captures the effects of several key occupant, vehicle, and accident level variables on four possible levels of injury severity. The results indicate the presence of accident-specific unobserved factors that influence the severity outcomes of all people involved in the crash as well as unobserved heterogeneity in the effect of key covariates including occupant’s gender and speed limit. The performance of the MROL model was compared with the traditional mixed multinomial logit (MMNL) model that is the most commonly used model for injury severity analysis. Overall, the results demonstrate superior predictive ability of the MROL model in comparison to the MMNL model. The traditional MMNL model performed satisfactory in terms of replicating the simple shares of different injury severity levels across all occupants. However, the performance of the MMNL model dropped significantly when the observed and predicted shares were compared for combinations of injury severity levels among crashes involving multiple occupants. Lastly, elasticity effects were computed to demonstrate considerably different policy implications of the MROL and MMNL models.

Multinomial Logit Framework to Evaluate the Impact of Seating Position on Senior Occupant Injury Severity in Traffic Accidents

Multinomial Logit Framework to Evaluate the Impact of Seating Position on Senior Occupant Injury Severity in Traffic Accidents

Investigation on occupant injury severity in rear-end crashes involving trucks as the front vehicle in Beijing area, China

PurposeRear-end crashes attribute to a large portion of total crashes in China, which lead to many casualties and property damage, especially when involving commercial vehicles. This paper aims to investigate the critical factors for occupant injury severity in the specific rear-end crash type involving trucks as the front vehicle (FV). MethodsThis paper investigated crashes occurred from 2011 to 2013 in Beijing area, China and selected 100 qualified cases i.e., rear-end crashes involving trucks as the FV. The crash data were supplemented with interviews from police officers and vehicle inspection. A binary logistic regression model was used to build the relationship between occupant injury severity and corresponding affecting factors. Moreover, a multinomial logistic model was used to predict the likelihood of fatal or severe injury or no injury in a rear-end crash. ResultsThe results provided insights on the characteristics of driver, vehicle and environment, and the corresponding influences on the likelihood of a rear-end crash. The binary logistic model showed that drivers' age, weight difference between vehicles, visibility condition and lane number of road significantly increased the likelihood for severe injury of rear-end crash. The multinomial logistic model and the average direct pseudo-elasticity of variables showed that night time, weekdays, drivers from other provinces and passenger vehicles as rear vehicles significantly increased the likelihood of rear drivers being fatal. ConclusionAll the abovementioned significant factors should be improved, such as the conditions of lighting and the layout of lanes on roads. Two of the most common driver factors are drivers' age and drivers' original residence. Young drivers and outsiders have a higher injury severity. Therefore it is imperative to enhance the safety education and management on the young drivers who steer heavy duty truck from other cities to Beijing on weekdays.

Investigating the interplay between the attributes of at-fault and not-at-fault drivers and the associated impacts on crash injury occurrence and severity level

ABSTRACTThe article proposes a two-staged modelling approach to identify the association between one vehicle's attributes, as well as roadway engineering, environmental and crash characteristics, and the injury severity of occupants in the partnering vehicle in two-vehicle crashes. The modelling approach uses a bivariate binary probit model, and crash data for Toronto, to first determine the probability of injury and no injury occurring, followed by the use of a bivariate ordered probit model to investigate the conditional probability of the specific severity level. Vehicles in two-vehicle crashes are categorized as “not-at-fault” (NAF) or “at-fault” (AF) and their occupants also categorized as such. The findings demonstrate that the modelling approach used in this study can reveal meaningful insights by improving understanding of how the same attribute could behave differently for NAF and AF vehicles. For example, factors found to be associated with increased probability of more severe injuries of NAF vehicle occupants are inattentive driving, left-turn movement, heavy vehicle type of the AF vehicle, and angle and rear-end impact type; conversely, for AF vehicles, their probability of more severe injury is positively associated with inattentive driving and heavy vehicle type of the NAF vehicle, and angle and approaching impact type.

Health, work, and development: agribusiness and accidents in a state in the Brazilian Amazon region

Abstract Introduction Mato Grosso is one of the Brazilian states with the highest agribusiness growth, which is accompanied by deforestation and an increase in occupational injuries. Objective To assess some factors associated with agribusiness-related severe occupational injuries in Mato Grosso. Method Occupational injury notifications were provided by the System of Information of Notifiable Injuries (SINAN). The production sectors, considered the dependent variables, were classified as forestry/wood industry, animal husbandry, and plant husbandry. Some sociodemographic, care, and type of injury-related variables were selected and considered the independent variables. We conducted a spatial analysis of the density of occupational injury. The associations were identified by calculating the proportion ratio (PR) by the chi-square test and considered significant when p-value &lt; 0.05. Results and conclusion The highest rates of severe occupational injuries occurred in municipalities with intense plant and animal husbandry, and forestry/wood industry activities. In plant husbandry severe occupational injuries were more common in men, adults, non-whites, illiterates, and rural areas. However, in animal husbandry and forestry/wood industry, severe occupational injuries were more common in adults, and full recoveries were less frequent.

Prevalence and associated factors of occupational injuries among municipal solid waste collectors in four zones of Amhara region, Northwest Ethiopia.

BackgroundRefuse collectors are at a high risk for fatal and non-fatal occupational accidents. This is more intensified in developing countries, like Ethiopia, due to physically demanding nature of the job. However, information on occupational injuries and related factors are almost non-existent in Ethiopia. Thus, the aim of this study was to assess the prevalence of occupational injuries and its associated factors.MethodsA cross-sectional study was conducted among municipal solid waste collectors in four zones of Amhara region from February to May 2015. Computer generated simple random sampling technique was used to select the samples. Interviewer administrated questionnaires were used for the data collection process. Binary logistic regression was used to assess the association between outcome variables and explanatory variables.ResultsIn this study, the annual prevalence of at least one occupational injury among solid waste workers was 34.3 % (95 % CI: 29.52, 39.10). Of these, 50.7 % of them were visited health facility to receive health care. The independent predictors of at least one occupational injury were shorter service years, low monthly salary, history of job related stress, and sleeping disturbance related to the job. Being illiterate, having lower monthly income, and those who reported sleeping disturbance were significantly and positively associated with severe occupational injuries of solid waste collectors.ConclusionThe magnitude of occupational injuries among municipal solid waste collectors is lower than other similar studies conducted in Ethiopia. Based on the finding of this and other studies, job rotation among work components, improvement of employees’ income, job specific guideline regarding maximum production limits, and replacement of bags and bins with wheeled containers are an interventions expected to cope with the problem. There is also a need of specific periodic health surveillance (PHS) for refuse collectors to detect early signs of work related complaints and to monitor work ability.

Analysis of occupant injury severity in winter weather crashes: A fully Bayesian multivariate approach

Multivariate injury severity models that consider the cross-group heterogeneity in the crash data where individuals or occupants are nested within vehicles and vehicles are nested within crashes are limited in the literature. Most previous studies on crash injury severity were conducted at the crash level ignoring the potential correlation in severity for the vehicles involved in the same crashes or individuals involved in the same vehicles. Ignoring these correlation and dependence effects might result in underestimation of standard errors and erroneous inferences. The objective of this paper is to correctly determine the factors affecting occupant injury severity in winter seasons by addressing the within-crash and between-crash correlation of injury severity. To achieve this, fully Bayesian hierarchical multinomial logit models were developed for estimating occupant injury severity in weather-related crashes, non weather-related crashes, and all crashes. These models were developed using disaggregate crash data with occupants nested within crashes for four winter seasons in Iowa. Significant factors affecting occupant injury severity included factors related to occupants (gender, seating position, occupant trapped status, ejection status, and occupant protection used), as well as crash-level factors (road junction type, first harmful event and major cause of crash). Weather-related variables, such as visibility, pavement and air temperature, were also significant factors in winter weather crashes. Interaction effects involving crash-level variables and occupant-level variables were also found significant. Overall, the model diagnostics suggested significant within-crash correlation in the study dataset justifying the use of a multivariate model specification that addresses multivariate error term correlation issues.

Modeling of Individual and Organizational Factors Affecting Traumatic Occupational Injuries Based on the Structural Equation Modeling: A Case Study in Large Construction Industries.

BackgroundIndividual and organizational factors are the factors influencing traumatic occupational injuries.ObjectivesThe aim of the present study was the short path analysis of the severity of occupational injuries based on individual and organizational factors.Materials and MethodsThe present cross-sectional analytical study was implemented on traumatic occupational injuries within a ten-year timeframe in 13 large Iranian construction industries. Modeling and data analysis were done using the structural equation modeling (SEM) approach and the IBM SPSS AMOS statistical software version 22.0, respectively.ResultsThe mean age and working experience of the injured workers were 28.03 ± 5.33 and 4.53 ± 3.82 years, respectively. The portions of construction and installation activities of traumatic occupational injuries were 64.4% and 18.1%, respectively. The SEM findings showed that the individual, organizational and accident type factors significantly were considered as effective factors on occupational injuries’ severity (P < 0.05).ConclusionsPath analysis of occupational injuries based on the SEM reveals that individual and organizational factors and their indicator variables are very influential on the severity of traumatic occupational injuries. So, these should be considered to reduce occupational accidents’ severity in large construction industries.

Analysis and Modeling of Threatening Factors of Workforce's Health in Large-Scale Workplaces: Comparison of Four-Fitting Methods to select optimum technique.

IntroductionWorkforce is one of the pillars of development in any country. Therefore, the workforce’s health is very important, and analyzing its threatening factors is one of the fundamental steps for health planning. This study was the first part of a comprehensive study aimed at comparing the fitting methods to analyze and model the factors threatening health in occupational injuries.MethodsIn this study, 980 human occupational injuries in 10 Iranian large-scale workplaces within 10 years (2005–2014) were analyzed and modeled based on the four fitting methods: linear regression, regression analysis, generalized linear model, and artificial neural networks (ANN) using IBM SPSS Modeler 14.2.ResultsAccident Severity Rate (ASR) of occupational injuries was 557.47 ± 397.87. The results showed that the mean of age and work experience of injured workers were 27.82 ± 5.23 and 4.39 ± 3.65 years, respectively. Analysis of health-threatening factors showed that some factors, including age, quality of provided H&S training, number of workers, hazard identification (HAZID), and periodic risk assessment, and periodic H&S training were important factors that affected ASR. In addition, the results of comparison of the four fitting methods showed that the correlation coefficient of ANN (R = 0.968) and the relative error (R.E) of ANN (R.E = 0.063) were the highest and lowest, respectively, among other fitting methods.ConclusionThe findings of the present study indicated that, despite the suitability and effectiveness of all fitting methods in analyzing severity of occupational injuries, ANN is the best fitting method for modeling of the threatening factors of a workforce’s health. Furthermore, all fitting methods, especially ANN, should be considered more in analyzing and modeling of occupational injuries and health-threatening factors as well as planning to provide and improve the workforce’s health.

Occupant Injury Severity and Accident Causes in Helicopter Emergency Medical Services (1983-2014).

Helicopter emergency medical services (HEMS) transport critically ill patients to/between emergency care facilities and operate in a hazardous environment: the destination site is often encumbered with obstacles, difficult to visualize at night, and lack instrument approaches for degraded visibility. The study objectives were to determine 1) HEMS accident rates and causes; 2) occupant injury severity profiles; and 3) whether accident aircraft were certified to the more stringent crashworthiness standards implemented two decades ago. The National Transportation Safety Board (NTSB) aviation accident database was used to identify HEMS mishaps for the years spanning 1983-2014. Contingency tables (Pearson Chi-square or Fisher's exact test) were used to determine differences in proportions. A generalized linear model (Poisson distribution) was used to determine if accident rates differed over time. While the HEMS accident rate decreased by 71% across the study period, the fraction of fatal accidents (36-50%) and the injury severity profiles were unchanged. None of the accident aircraft fully satisfied the current crashworthiness standards. Failure to clear obstacles and visual-to-instrument flight, the most frequent accident causes (37 and 26%, respectively), showed a downward trend, whereas accidents ascribed to aircraft malfunction showed an upward trend over time. HEMS operators should consider updating their fleet to the current, more stringent crashworthiness standards in an attempt to reduce injury severity. Additionally, toward further mitigating accidents ascribed to inadvertent visual-to-instrument conditions, HEMS aircraft should be avionics-equipped for instrument flight rules flight.

Use of Artificial Neural Networks (ANNs) for the Analysis and Modeling of Factors That Affect Occupational Injuries in Large Construction Industries.

IntroductionOccupational injuries as a workforce’s health problem are very important in large-scale workplaces. Analysis and modeling the health-threatening factors are good ways to promote the workforce’s health and a fundamental step in developing health programs. The purpose of this study was ANN modeling of the severity of occupational injuries to determine the health-threatening factors and to introduce a model to predict the severity of occupational injuries.MethodsThis analytical chain study was conducted in 10 large construction industries during a 10-year period (2005–2014). Nine hundred sixty occupational injuries were analyzed and modeled based on feature weighting by the rough set theory and artificial neural networks (ANNs). Two analytical software programs, i.e., RSES and MATLAB 2014 were used in the study.ResultsThe severity of occupational injuries was calculated as 557.47 ± 397.87 days. The findings of both models showed that the injuries’ severity as a health problem resulted in various factors, including individual, organizational, health and safety (H&S) training, and risk management factors, which could be considered as causal and predictive factors of accident severity rate (ASR).ConclusionThe results indicated that ANNs were a reliable tool that can be used to analyze and model the severity of occupational injuries as one of the important health problems in large-scale workplaces. Additionally, the combination of rough set and ANNs is a good and proper chain approach to modeling the factors that threaten the health of workforces and other H&S problems.