Epidemiology of occupational acute traumatic hand injuries: A literature review
Abstract
The purpose of this review was to summarize the literature on occupational, acute, traumatic hand injury and suggest directions for future research. In 1996, the leading occupational injury treated in United States' hospital emergency departments was an acute hand injury (e.g. laceration, crush or fracture). These injuries affected 30% of an estimated 3.3 million injured workers (990,000). Cuts and lacerations of the fingers ranked third after back and leg strains in the number of lost workday cases in the USA in 1994. The incidence rate of hand injuries studied in seven manufacturing environments around the world ranged from 4 to 11 per 100 workers per year. Workers aged 24 years or less had the highest risk of hand injury. Men had higher rates of severe hand injury than women.Despite the high frequency and significant amount of lost work time associated with these injuries, they are poorly understood from an etiological perspective. There is only one case-control study of occupational hand injury in the literature. That study suggested an important role for both fixed (age) and transient risk factors (doing an unusual task) at the time of the injury. More analytic epidemiological research is needed to identify potentially modifiable risk or protective factors (e.g. glove use) for acute hand injuries. In this regard, the case-crossover design, a relatively new epidemiological approach using cases as their own controls, could prove an efficient method for determining transient, modifiable risk factors for acute, occupational hand injury.
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- ReferencesReferences64
- These injuries are frequent work-related and are also one of the most costly injury types [1][2][3][4]. Hand and wrist injuries can occur during a wide variety of activities at home, during recreation, in traffic and at work [5][6][7]. Therefore, to define target areas for prevention, and to reduce costs, it is important to study the underlying causes. Research has already provided some insight into the costs of upper extremity injuries and injuries to the hand and arm and hand [8][9][10][11], but an analysis of the most important causes of the costs of hand and wrist injuries is lacking.
[Show abstract] [Hide abstract] ABSTRACT: Background: Hand and wrist injuries are very common at the Emergency Departments (ED), and among the most costly injury types in the working population. The purpose of this study was to explore the causes of non-trivial hand and wrist injuries (i.e., hand fractures, wrist fractures and complex soft-tissue injuries) in working-age adults in order to identify target areas for prevention. Methods: Data were extracted from the Dutch Injury Surveillance System, from the National Hospital Discharge Registry and from a patient follow-up survey in working-age adults (aged 20-64 years) in the period 2008-2012. An incidence-based cost model was used to estimate healthcare costs, and an absenteeism model for estimating the productivity costs. Total costs were calculated by external cause, subdivided in their main categories (home, sports, work, traffic and violence) and their most important subclasses. Results: Total costs of these injuries in The Netherlands were US $410 million per year, of which 75% (US $307 million) productivity costs. Males represented 66% (US $271 million) of the total costs. Within the male group, the group 35-49 years had the highest contribution to total costs (US $112 million), as well as the highest costs per case (US $10,675). Work-related injuries showed the highest costs per case (US $11,797), however, only 25% of the total costs were work-related. The top five causes in terms of total costs were: accidents at home (falls 23%, contact with an object 17%), traffic (cycling 9%) and work (industrial work 4%, and construction work 4%). Conclusion: Hand and wrist injuries are a major cause of healthcare and productivity costs in working-age adults. To reduce the costs to society, prevention initiatives should be targeted at major contributing causes, that are mainly related to activities at home (falls, contact with an object) and accidents at the road (cycling).- Hand injuries account for up to 20% of all treated injuries in an Emergency Department.1) Furthermore, acute hand injuries are the most common occupational injury evaluated in emergency departments within the United State.11) The spectrum of traumatic hand injuries includes minor soft tissue injuries and fractures to complex injuries requiring nerve, tendon, or artery repair.
[Show abstract] [Hide abstract] ABSTRACT: Background Acute traumatic tendon injuries of the hand and wrist are commonly encountered in the emergency department. Despite the frequency, few studies have examined the true incidence of acute traumatic tendon injuries in the hand and wrist or compared the incidences of both extensor and flexor tendon injuries. Methods We performed a retrospective population-based cohort study of all acute traumatic tendon injuries of the hand and wrist in a mixed urban and rural Midwest county in the United States between 2001-2010. A regional epidemiologic database and medical codes were used to identify index cases. Epidemiologic information including occupation, year of injury, mechanism of injury and the injured tendon and zone were recorded. Results During the 10-year study period there was an incidence rate of 33.2 injuries per 100,000 person-years. There was a decreasing rate of injury during the study period. Highest incidence of injury occurred at 20-29 years of age. There was significant association between injury rate and age, and males had a higher incidence than females. The majority of cases involved a single tendon, with extensor tendon injuries occurring more frequently than flexor tendons. Typically, extensor tendon injuries involved zone three of the index finger, while flexor tendons involved zone two of the index finger. Work-related injuries accounted for 24.9% of acute traumatic tendon injuries. The occupations of work-related injuries were assigned to major groups defined by the 2010 Standard Occupational Classification structure. After assigning these patients' occupations to respective major groups, the most common groups work-related injuries occurred in construction and extraction occupations (44.2%), food preparation and serving related occupations (14.4%), and transportation and material moving occupations (12.5%). Conclusions Epidemiology data enhances our knowledge of injury patterns and may play a role in the prevention and treatment of future injuries, with an end result of reducing lost work time and economic burden.- To date, descriptive and analytical parametric modelling procedures such as descriptive statistical methods and regression analysis have often been used. A great number of studies describe the distribution of injuries (numbers, rates, frequency index) usually in terms of person, place and workplace characteristics and are useful for identifying hazardous industries, occupations and work situations (Armell et al., 2002; Biddle and Marsh, 2002; Larsson and Field, 2002; Salminen, 2005; Sorock et al., 2001; Trontin and Bejean, 2004). Another primary statistical tool often adopted in occupational injury studies is regression analysis which may be used to evaluate the relationship between the injury frequency index and one or more covariates or predictor variables (Ciarapica and Giaccheta, 2008).
[Show abstract] [Hide abstract] ABSTRACT: In this research an adaptive neuro-fuzzy inference system (ANFIS) has been applied to study the effect of working conditions on occupational injury using data of professional accidents assembled by ship repair yards. The data were statistically processed in order to select the most important parameters. These parameters were day and time, specialty, type of incident, dangerous situations and dangerous actions involved in the incident. The selected parameters proved, due to statistical processing, to be correlated to the observed frequency of four injury categories, namely negligible wounding, slight wounding, severe wounding and death. For each parameter a Gravity Factor (GF) was calculated based on the percentage of injury categories resulting to the incident each of the above mentioned parameter was involved. These GF values and the resulting risk value based on the accident data were used as input values to train the ANFIS model. Trapezoidal and Gauss membership functions were used for the training of the data. The model combined the modeling function of fuzzy inference with the learning ability of artificial neural networks. A set of rules has been generated directly from the statistically processed reported data. The model’s predictions were compared with a number of recorded data for verifying the approach.- Type of damage in 48.3 percents of patients was sharp, in 23.2 blunt and in 25.6 sharp and blunt and 10 percent had amputation. Other studies have confirmed this issue (25). Average percentage of whole person impairment (WPI), was approximately 22% and patients, on average, examined within 9 months after injury.
[Show abstract] [Hide abstract] ABSTRACT: Background: Severe upper extremity injuries can affect the quality of life in patients and cause multi-factorial and long-term costs of disease. The aim of this study was to assess quality of life in patients with upper extremity injuries caused by work-related accidents. Methods: In this study cross-sectional method was used in patients referred to the Occupational medicine Clinic of Rasoul Akram Hospital to determine their impairments. Patient's information including demographic variables, calculation of the impairment rate based on AMA Guide book (in terms of WPI), returning to work, and location of injury, work experience and type of injury. Then the quality of their life was assessed and interpreted using SF36 questionnaire. Results: 203 patients were evaluated. Different aspects of the patients’ life were not associated with age, gender and education of patients based on The SF-36 questionnaire. There was an inverse relationship between the percentage of patients’ impairment and different aspects of life quality; there were also a significance correlation between impairment rate and physical performance of patients (p<0.001, r= -0.26), social performance of patients (p= 0.001, r= -0.24), pain (p= 0.005, r= -0.2), emotional health of patients (p= 0.006, r= -0.29), energy / fatigue in patients (p<0.001, r= -0.29) and the patient's general health (p<0.001, r= -0.27). Conclusion: This study shows that upper extremity impairment due to occupational injuries has an inverse and significant association with various aspects of quality of life.- To date, descriptive and analytical parametric modeling procedures such as descriptive statistical methods and regression analysis have often been used. A great number of studies describe the distribution of injuries (numbers, rates, frequency index) usually in terms of person, place and workplace characteristics and are useful for identifying hazardous industries, occupations and work situations (Armell et al., 2002; Biddle and Marsh, 2002; Larsson and Field, 2002; Salminen, 2005; Sorock et al., 2001; Trontin and Bejean, 2004). Another primary statistical tool often adopted in occupational injury studies is regression analysis which may be used to evaluate the relationship between the injury frequency index and one or more covariates or predictor variables (Ciarapica and Giacchetta, 2008 E-mail addresses: nikolasf2002@yahoo.gr
[Show abstract] [Hide abstract] ABSTRACT: In this research an adaptive neuro-fuzzy inference system (ANFIS) has been applied to study the effect of working conditions on occupational injury using data of professional accidents assembled by ship repair yards. The data were statistically processed in order to select the most important parameters. These parameters were day and time, specialty, type of incident, dangerous situations and dangerous actions involved in the incident. The selected parameters proved, due to statistical processing, to be correlated to the observed frequency of four injury categories, namely negligible wounding, slight wounding, severe wounding and death. For each parameter a Gravity Factor (GF) was calculated based on the percentage of injury categories resulting to the incident each of the above mentioned parameter was involved. These GF values and the resulting risk value based on the accident data were used as input values to train the ANFIS model. Trapezoidal and Gauss membership functions were used for the training of the data. The model combined the modeling function of fuzzy inference with the learning ability of artificial neural networks. A set of rules has been generated directly from the statistically processed reported data. The model’s predictions were compared with a number of recorded data for verifying the approach.- Work-Related injuries represent significant rates of acute hand injuries seen in emergency services [2,11,12] . Therefore, the concept of hand injury severity assessment is focused on Work-Related injuries.
[Show abstract] [Hide abstract] ABSTRACT: Introduction: Work-Related Hand Injuries (WRHIs) may result in disability and diminished productivity and cause economic impacts not only to the individual, worker’s families and industries, but to the local community as well. Objectives: To determine the prevalence of severe Work-Related Hand Injuries (WRHIs) and factors associated at a tertiary hospital. Methods: A pre-tested validated questionnaire was used to obtain data. All patients 18 years and above with WRHIs seen at a tertiary hospital between January 2010 and June 2010 were included in the study. Data was analysed using SPSS version 18. Results: Out of the 297 industrial accidents, 74 (24.9%) were WRHIs. Among those with WRHIs, (47.3%) of them had severe hand injuries. The overall mean age of the respondents was 30.36 (± 9.54 SD) years. Majority (82.5%) of the injuries occurred between Mondays to Friday. Majority (70.1%) of hand injuries were caused by machine and 48.6% of the hand injuries occurred when the hand was caught in the operating part of the machine. Majority (62.1%) of the respondents had fingers’ injuries and 32.4% had open fracture. Bivariate analysis showed that there was significant association between severity of WRHIs and locations of injury, mechanisms of injury, sources of injury and sectors of industry (p < 0.05). Logistic regression analysis showed that WRHIs was significantly associated with source of injury and sector of industry. Respondents with hand injury resulted while operating on mechanical machine was 26 times more likely to report severe WRHIs than those with other sources of their hand injury like (sharp tool, heavy door, and wet floor). Respondents working in metal-machinery industries were eight times more likely to report severe WRHIs than those who working in other sectors of industry like (wood-furniture, constriction, food preparing, service and automotive). Conclusions: WRHIs contributed to 24.9% of all industrial accidents seen at the emergency department and orthopaedic clinic and 47.3% of the respondents with WRHIs had severe hand injuries. Severity of WRHIs was significantly associated with sources of injury and sectors of industry.
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