American Journal of Epidemiology Advance Access originally published online on September 29, 2006
American Journal of Epidemiology 2006 164(12):1209-1221; doi:10.1093/aje/kwj337
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ORIGINAL CONTRIBUTIONS |
A Prospective Study of Injury Incidence among North Carolina High School Athletes
1 Department of Epidemiology, School of Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC
2 The University of North Carolina Injury Prevention Research Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC
3 Department of Orthopedics, School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC
4 Department of Health Behavior and Health Education, School of Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC
5 Department of Community and Behavioral Health, College of Public Health, The University of Iowa, Iowa City, IA
6 Department of Behavioral Science and Health Education, School of Public Health, Saint Louis University, St. Louis, MO
7 Department of Biostatistics, School of Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC
8 Department of Exercise and Sport Science, The University of North Carolina at Chapel Hill, Chapel Hill, NC
Correspondence to Dr. Sarah Knowles, Palo Alto Medical Foundation Research Institute, 795 El Camino Real, Ames Building, Palo Alto, CA 94301 (e-mail: knowless{at}pamfri.org).
Received for publication February 24, 2006. Accepted for publication May 8, 2006.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Sports-related injuries are an issue of concern in high school sports athletes. A prospective cohort study of injury risk factors was conducted from 1996 to 1999 among varsity high school athletes in 12 sports in 100 North Carolina high schools. Data were collected by trained school personnel. Unadjusted and adjusted incidence rates and rate ratios were estimated using Poisson regression models. The overall rate of injury was 2.08 per 1,000 athlete-exposures (95% confidence interval (CI): 1.79, 2.41). At 3.54 per 1,000 athlete-exposures (95% CI: 2.87, 4.37), football had the highest rate of injury of all sports. The adjusted rate ratio for athletes with a history of injury, compared with those without a prior injury, was 1.94 (95% CI: 1.69, 2.22). The injury rate rose with each year of playing experience (rate ratio = 1.06, 95% CI: 1.01, 1.12). In a subanalysis restricted to gender-comparable sports, boys had a higher rate of injury than did girls (rate ratio = 1.33, 95% CI: 0.99, 1.79). All other factors did not appear to be independent predictors of the injury rate. The influence of prior injury suggests that proper rehabilitation and primary prevention of the initial injury are important strategies for injury control.
athletic injuries; incidence; prospective studies; risk; sports; wounds and injuries
Abbreviations: CI, confidence interval; NCHSAA, North Carolina High School Athletic Association; RR, incidence rate ratio
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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More than seven million students in the United States participated in interscholastic high school athletics during the 2004–2005 school year, representing 53.4 percent of all high school students (1, 2). Historically, participation is at the highest level since record keeping began more than 30 years ago (1, 2).
There are numerous health benefits associated with participation in sports (3, 4); however, injury is a potential outcome of participation and an important public health problem (5). Sport- and recreation-related injuries account for 46.4 percent of emergency department visits among adolescents aged 15–19 years (6). Additionally, an unknown number of sports injuries are treated in medical settings other than the emergency department (school health providers, dentists, private physicians).
Several studies have observed an association between injury incidence and risk factors, such as type of sport, gender, and history of injury, in both high school populations (7–10) and other athletic populations (10–12). In addition, low physical fitness has been identified as a risk factor in other physically active populations, such as the military (13, 14). However, the role of other risk factors, such as age, body mass index (weight (kg)/height (m)2), and coaching factors, is less clear. The purpose of this paper was to estimate the injury incidence and to identify risk factors for injury in organized high school varsity athletics in North Carolina.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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Design overview
The North Carolina High School Athletic Injury Study was a prospective cohort study conducted from 1996 to 1999. An extensive description of the study methods has been published previously (15). In brief, injury and risk factor data were collected from a sample of high school varsity athletes from 100 public high schools in North Carolina. Twelve sports were included: boys' and girls' soccer, boys' and girls' track, boys' and girls' basketball, baseball, softball, wrestling, volleyball, cheerleading, and football. For each of the selected schools, six sports were studied, and all varsity athletes playing any of these selected sports were included as participants. A student that participated in two sports is represented as two athletes in the data set.
Injury definition
A reportable injury was one that resulted from participation in a high school sport and either limited the student's full participation in the sport the day following the injury or required medical attention by a medical professional (athletic trainer, physician, nurse, emergency medical technician, emergency room personnel, or dentist) (15). Additionally, concussions, fractures, or eye injuries were reportable regardless of whether they resulted in lost participation or required medical attention.
Sampling methods
The study sample was selected using a stratified two-stage probability proportional-to-size cluster sampling technique (15, 16). The first stage involved the selection of 100 schools among members of the North Carolina High School Athletic Association (NCHSAA), an organization representing all public high schools in the state. The probability of any school's selection at stage 1 was proportional to the number of active eligible teams listed with the NCHSAA. In the second stage of sampling, six sports teams from each school were randomly selected during the first year of the study (1996–1997 school year). The probability of selection at stage 2 was inversely proportional to the total number of NCHSAA-sanctioned teams in that school. Prior to selection, the sample was stratified by the presence of a certified athletic trainer, competition division, geographic region, and average school attendance (15). Poststratification weights were used to account for nonresponse and the sampling scheme.
Most school contacts were the athletic trainer (69.0 percent), with the remainder being athletic directors (31.0 percent). At the sport and athlete levels, the response rate was 76.7 percent and 75.5 percent, respectively, resulting in an overall response rate of 57.9 percent among participating athletes at participating schools (15). Data collectors were trained to administer the data collection instruments using a specifically developed videotape, routine phone calls, and visits to each school.
Data collection
Data were collected using four instruments: an athlete's demographic questionnaire, a coach's demographic questionnaire, an injury report form, and a weekly participation form. The athlete's and coach's questionnaires were completed at the start of each season. The athlete's survey collected information about gender, grade, age, race, previous injuries, and participation in additional sports. The coach's survey collected information about faculty position, duration and level of playing experience (that sport and others), duration and level of coaching experience (that sport and others), education, and first aid or cardiopulmonary resuscitation certification. The injury report form gathered information about the type of injury, time lost from participation, and type of medical attention received, along with sport-specific questions about the circumstances of the injury, including the athlete's and team's activity at the time of the injury. An injury report form was completed for each sustained injury, so an athlete who suffered multiple injuries during one incident had multiple corresponding injury reports. Finally, the weekly participation form assessed exposure by tracking the number of games and practices per week for each participant in each sport throughout the season.
Athlete-exposures
Athlete-exposures were computed by summing the number of practices and competitions during the preseason and regular season for each athlete. These exposures represent any opportunity for an athlete to be injured and can be subdivided into game and practice exposures. Missing exposure data were estimated using single imputation.
Risk factors
Based on a review of the literature, data were collected on the following potential risk factors: sport, gender, grade, multisport participation, years of playing experience (in both middle school and high school), prior injury (includes ankle, knee, wrist, elbow, shoulder, concussion, fracture, or heat stroke), age, body mass index for age, competition division, and coaching experience, qualifications, and training.
Coaching experience, qualifications, and training was a composite variable based on coaches' answers to five yes/no questions, categorized into low (from zero to two positive answers), medium (three positive answers), and high (four or five positive answers) (17, 18). Competition division (divisions 1A–4A) is based on school enrollment. At the time of this study, division 1A schools had less than 668 students, division 2A had 668–957 students, division 3A had 967–1,308 students, and division 4A had 1,314–2,600 students. The variable, body mass index for age, was based on gender- and age-specific body mass index percentile categories commonly used in adolescent populations (19), defined as underweight (
5th body mass index percentile); normal weight (>5th–<85th percentile); at risk of overweight (
85th–<95th percentile); and overweight (95th percentile).
Data analysis
Incidence rates were estimated as the number of injuries divided by the sum of athlete-exposures. Unadjusted and adjusted incidence rates and rate ratios associated with each risk factor were estimated using Poisson regression. Because of the difficulty in measuring exposure during the postseason, the analysis of incidence rates included only injuries and exposure time during the preseason and regular season. For each risk factor, separate unadjusted Poisson regression models were used to estimate the incidence rate and rate ratio associated with different levels of the risk factor. A multivariate Poisson regression model was then used to estimate the incidence rate and rate ratio associated with each risk factor, controlling for all other risk factors.
The effect of gender is fully subsumed by sport, since there are no sports in which boys and girls customarily compete on the same team. Thus, to assess the effect of gender, two sets of regression models were developed: one for all 12 sports and a second restricted to eight gender-comparable sports (boys' and girls' soccer, boys' and girls' basketball, boys' and girls' track, and baseball and softball).
Study year, grade, years of playing experience, age, body mass index, and coaching experience, qualifications, and training met the assumption of linearity in the log rate and were coded as continuous variables in the regression models. Therefore, the rate ratio for this coding represents the change in the injury rate associated with a one-unit change in the continuous risk factor. For illustrative purposes, continuous variables were also categorized, and the incidence rates and rate ratios associated with each category of the risk factor were also calculated. Competition division and sport were included as indicator variables, and gender, prior injury, and multisport participation were included as dichotomous variables. All analyses were performed using SUDAAN, version 8.0, software (20).
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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A total of 15,038 athletes, 19,977 athlete-seasons, and 1,032,117 athlete-exposures were observed for all 12 sports over the 3-year study period. During the entire study period, there were 2,559 injured athletes and 2,990 injuries, an average of 997 injuries per year in participating schools. Based on these data, there is an estimated average of 10,531 injuries per year statewide.
Study population
Male athletes comprised 61.4 percent of the study cohort (table 1); 36.0 percent of all male athletes played football. Most study athletes participated in more than one sport (68.6 percent), self-reported no prior injury at study baseline (55.8 percent), and had played the sport they participated in for at least 3 years (66.8 percent). Approximately 60.3 percent of the cohort played in a division 3A or 4A school.
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Overall injury incidence
For the study period, the unadjusted overall injury incidence rate was 2.08 injuries per 1,000 athlete-exposures (95 percent confidence interval (CI): 1.79, 2.41). Among injured athletes, 72.8 percent were boys and 41.6 percent played football. For all sports, more than half of the injuries occurred during games (56.4 percent). The overall rate of injury during games was 5.00 per 1,000 athlete-exposures (95 percent CI: 4.31, 5.80), whereas the overall rate of injury during practices was 1.26 per 1,000 athlete-exposures (95 percent CI: 1.06, 1.49).
Extrinsic risk factors
Risk factors external to the athlete were termed "extrinsic" and included sport, grade, study year, competition division, and coaching experience, qualifications, and training (table 2). Study year was also included in the fully adjusted model to control for year-to-year changes in incidence. Football had the highest injury incidence, followed by boys' and girls' soccer (figure 1).
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With the exception of sport, the extrinsic risk factors were not associated with the injury rate after adjustment for other risk factors (table 2). In a multivariate Poisson regression model, the rate ratios associated with competition division, grade, and coaching experience, qualifications, and training decreased relative to the unadjusted rate ratios. Compared with division 1A, there was a slight protective effect of division 3A schools. In the unadjusted model, there was a 27 percent increase in the rate of injury for every one-unit increase in grade. This effect substantially decreased after adjustment for other risk factors. Similarly, when the risk factor coaching experience, qualifications, and training was examined adjusting for all risk factors, there was no effect of increasing this variable on the injury rate.
Intrinsic risk factors
Risk factors internal to the athlete were classified as "intrinsic" and included gender, age, body mass index, prior injury, participation in multiple sports, and years of playing experience (table 2). For gender, in the unadjusted model including all 12 sports, the rate of injury for boys was 1.80 times the rate for girls (95 percent CI: 1.42, 2.28) (table 3). However, when the multivariable model was restricted to the eight gender-comparable sports, the elevated rate for boys compared with girls greatly diminished (incidence rate ratio (RR) = 1.33, 95 percent CI: 0.99, 1.79). Even so, there was still a modestly increased injury rate for boys after controlling for all other risk factors.
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For body mass index, the unadjusted model indicated an increased injury rate with increasing body mass index. This effect was not present after adjustment for other risk factors. Similarly, playing multiple sports was predictive of injury incidence in the unadjusted model, but the association diminished after adjustment for other risk factors.
Having had a prior injury was associated with nearly a twofold increase in the injury rate compared with no prior injury, after adjustment for other risk factors. The effect of prior injury was even stronger in subanalyses restricted to the specific body sites at highest risk of injury (knee, ankle, shoulder, and wrist) and to fractures (table 4).
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Competitive setting of games versus practices
For coaching experience, qualifications, and training, competition division, body mass index, and multisport participation, there did not appear to be a difference in the effect (or else was no effect) of the risk factors on injury incidence in either games or practices (table 5). Grade and prior injury were associated with higher rate ratios for practices compared with games, and gender (data not shown), sport, years of playing experience, and age had higher rate ratios for games compared with practices.
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For grades 10–12 versus grade 9, there was a slightly higher rate of injury for practices (RR = 1.14, 95 percent CI: 0.98, 1.34) compared with games (RR = 0.98, 95 percent CI: 0.83, 1.17). This may be partially explained by the small number of ninth graders participating in high school varsity sports. Furthermore, there were a greater number of injuries to those in higher grades, including twice as many practice injuries in athletes in higher grades compared with ninth graders.
Prior injury was predictive of both the game injury rate and the practice injury rate, although the rate ratio was greater in practices (RR = 2.25, 95 percent CI: 1.79, 2.82). When the analysis was restricted to gender-comparable sports, boys had a higher injury rate than did girls in both games (RR = 1.26, 95 percent CI: 0.94, 1.70) and practices (RR = 1.24, 95 percent CI: 0.80, 1.92) (data not shown).
Football was associated with a substantially higher rate of game injury compared with other sport competitions. Additionally, compared with practices, there was an increasing rate of game injury associated with both increasing age and increasing years of playing experience (8 percent and 17 percent, respectively).
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONReferences |
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A multivariate Poisson regression was used to simultaneously evaluate several risk factors for injury among athletes in 12 high school varsity sports. Of the risk factors examined, sport (football), gender (male), and having had a prior injury were strongly related to injury incidence. Other risk factors (grade, multisport participation, coaching experience, qualifications, and training, age, years of playing experience, body mass index, and competition division) appeared to be predictive of injury incidence in the unadjusted models, but their rate ratios greatly diminished in the multivariate analysis. Sport was a powerful determinant of the injury rate, with participants in girls' basketball and softball having one third of the rate of injury of football participants. Although most of the excess injury rate in boys disappeared when we restricted the analysis to eight gender-comparable sports, a moderate gender effect remained.
A previous study of 150 high schools with certified athletic trainers reported that football had the highest injury rate among 10 high school sports at 8.1 per 1,000 athlete-exposures (7). That study's rate is nearly twice as large as the football injury rate observed in this analysis. The differential in incidence may be due in part to this study's more restrictive injury definition. Our results are similar to those reported from a study of eight high schools that reported an overall injury rate of 3.20 per 1,000 athlete-exposures (21). That study defined injury as an event that resulted in at least one missed game or practice or a head injury that resulted in the athlete's leaving the game or practice (21). The authors concluded that injury history and playing experience were significant predictors of injury, whereas physical characteristics were not. Our findings were consistent with their results, as body mass index was not a strong predictor of injury incidence, and playing experience was associated with a modest increase in injury risk.
Role of prior injury
After sport, the strongest association was observed between prior injury and injury incidence. Prior injury has been noted in other high school sports injury studies as a risk factor for both additional injury and reinjury to the same site (22–26). Unlike previous studies that were smaller in size, the North Carolina High School Athletic Injury Study allowed for the examination of the influence of prior injury on subsequent injury in a novel approach. For every type of prior injury in this study, there was an elevated rate of injury at that site compared with the rate among athletes with no prior injury. These findings suggest that the rate of subsequent injury, including recurrent and new injuries, among young athletes is an important consideration. Additional epidemiologic research is needed to explore the role that rehabilitation, decisions about return to play, and the types of injuries (i.e., bone or muscle/tendon) play as risk factors for subsequent injury. Epidemiologic studies of effective rehabilitation programs may provide insight about how to minimize musculoskeletal consequences to young injured athletes and to provide a basis for return-to-play guidelines for injured athletes and their coaches. Additionally, the strong rate ratios underscore the importance of identifying the risk factors and developing strategies to prevent the initial injury.
Coaching factors
There was no observed association between coaching factors and injury incidence after adjustment for other risk factors. One reason for the lack of protective effect may be the disproportionate representation of highly experienced coaches in sports with a higher number of injuries (e.g., football and boys' and girls' basketball). Although the coaching characteristics that comprised the index of coaching experience, qualifications, and training did not influence overall injury incidence in the way that we expected, within specific sports there may still be an effect associated with increased coaching experience and qualifications (27).
Competitive setting of games versus practices
Another important observation in this analysis was the modification of rate ratios for some risk factors depending on the setting. There were clear differences in the game and practice injury rates between football and other sports. The full contact nature of football and the intensity of play at least partially explain the elevated rates of injury, although wrestling, which is also a full contact sport, had a higher rate of practice injury compared with football (RR = 1.11, 95 percent CI: 0.64, 1.92). One explanation may be that, although some football practices in this population were noncontact, all wrestling practices remained full contact. Other risk factors, such as age and years of playing experience, were more strongly associated with the game injury rate than the practice injury rate. A priori, we hypothesized that these risk factors may be intervening variables in that they were likely associated with time at risk, whereby older athletes with more years of playing experience would spend more time at risk, particularly during games. The North Carolina High School Athletic Injury Study used athlete-exposures as the incidence rate denominator, so variations between the number of games and practices that athletes participated in were controlled for. However, there may still be residual variation in the average amount of time that players participated (i.e., hours or minutes), particularly during games. This may partly account for the observed elevation of injury incidence among older players with more playing experience.
Strengths and limitations
This study has several limitations. The overall response rate of 57.9 percent means that selection bias may be present in this study. For this bias to affect the rate ratios presented here, a risk factor would have to be associated with the probability of selection and the probability of underreporting an injury. Second, the staff of the North Carolina High School Athletic Injury Study regularly contacted schools to obtain information on games and practices and notable athlete absences, but missing data were problematic for some risk factors, such as body mass index (19.8 percent), years of playing experience (15.7 percent), grade (14.1 percent), and age (12.4 percent). We examined whether the observations with missing data, which were automatically excluded from the multivariable regression analysis, accounted for the attenuation of rate ratios observed between the unadjusted and adjusted models. We estimated unadjusted rate ratios for all risk factors among observations with no missing data and compared them with the unadjusted rate ratios for all risk factors among all observations. There was little difference between the unadjusted rate ratios in the restricted and unrestricted analyses, indicating that the observed attenuation in the adjusted rate ratios was not an artifact of missing data. Finally, other studies have identified a low fitness level as a risk factor for injury. Unfortunately, no information about level of fitness was collected as part of this study.
This study also had several strengths, including the sampling methodology, inclusion of 12 diverse sports, and the use of a denominator that reflected exposure (athlete-exposures). Many studies have examined specific high school sports (21, 27–31), but few have examined multiple sports simultaneously (7, 32, 33). Although the limitations of these exposure data have been noted, the collection of athlete-exposure data is critical in sports injury research, because there are between-sport and within-sport variations in the time at risk that are unobservable if researchers use the number of athletes, rather than the number of athlete-exposures, as the denominator.
Comparisons of the univariate and multivariate Poisson models in this analysis underscore the importance of simultaneous adjustment of multiple risk factors. In the univariate model, several risk factors, including body mass index, years of playing experience, grade, coaching experience, qualifications, and training, and competition division, were associated with an increase in the injury rates. However, when adjusted for all other risk factors, these associations were greatly reduced. To date, use of multivariate models has been relatively uncommon in the sports medicine literature.
Conclusion
Of the risk factors examined in this study, sport, prior injury, and gender were important predictors of high school sports injuries. Their effect may vary depending on the competitive setting. Of particular note, the influence of prior injury on the injury rate suggests that prevention of initial injury and proper treatment and rehabilitation following injury are important strategies for injury control in this population.
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ACKNOWLEDGMENTS |
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Conflict of interest: none declared.
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