LEADING ART ICLE
Sports Medicine 12 (I): 8-15, 1991 0112-1642/91/0007-0008/$04.00/0 © Adis International Limited. All rights reserved. SP0130
Predictability of Sports Injuries What is the Epidemiological Evidence? w.H. Meeuwisse University of Calgary Sports Medicine Centre, Calgary, Alberta, Canada
As the science of sport medicine has evolved over the past several decades, interest has grown in the epidemiological study of injury patterns. With the recognition of these patterns, some investigators have wondered if sports injuries were predictable. However, in surveying the literature, a plethora of terminology arises to cloud the issue. There is abundant reference to risk factors, injuryproneness, predisposition, cause (aetiology), predictability and prevention, but it is often unclear exactly what these factors are, and how they relate to each other. In order to resolve this problem, we might consider the following example. A father is approached by his son, who seeks advice regarding his intent to join the high school football team. The father, being concerned for his son's safety, wonders how likely his son is to be hurt. What type of injury might he sustain? Are boys his age, as a group, more prone to certain injuries, or is his son, in particular, at risk of a given problem? Indeed, parents, coaches and health care personnel may well question if an injury could be predicted or if it could be prevented in some way? To answer these questions, we must understand the different terminology used and the interrelationship of the different factors. The purpose of this paper is to explore these issues in order to gain a more clear understanding of their impact on the individual, the clinician and the health care system.
1. Risk Risk has been defined by Fletcher et at. (1982) as 'the likelihood that people who are without a disease, but exposed to certain factors (risk factors), will acquire the disease'. In many situations in life we are acutely aware of risk, and have a good idea of the chance of a given outcome. The risk of athletic injury occurrence, however, is often much less clear. Hershman (1984) stated that 'risk factors for a particular sport are derived by combining the epidemiology of injuries for a particular sport and the predisposing conditions that may lead to injury'. As alluded to in the definition of risk, those entities which contribute to the occurrence of some event, are called risk factors. In this case, the event is an athletic injury. Risk factors may be classified as either internal or external (Lyens et at. I 984b). Other authors have referred to these as intrinsic and extrinsic. The latter are those factors which have an impact on the athlete while they are playing their sport. They include weather, field conditions, rules, position, etc. Internal risk factors are those factors that are a part of the athlete themselves and include biomechanics, fatigue, conditioning, maturational stage and somatotype. We are all well aware that certain injuries occur with certain sports. For example, swimmers may suffer from otitis externa and shoulder ailments, whereas runners can be afflicted with stress fractures. Each sport, due to its unique demands, will
Predictability of Injuries
9
predispose individuals to a given pattern of injury (Koplan et al. 1982). Many authors have studied the presence of risk factors, and a summary appears in table I. The most basic expression of risk is incidence, which is defined as 'the number of new cases of disease arising in a defined population during a given period of time' (Fletcher et al. 1982). However, this is not very useful clinically, as it does not give us an idea of the chance of injury. Since we must have some indication of the population at risk, there are 4 measures that may be employed to gain a more useful indication of risk (attributable, relative, population, and population attributable risk). What they have in common is their ability to express incidence of injury in relation to the people exposed to the risk factor. Studies that are done on a group of injured patients may give an indication of that factor only in those injured, and not in the population of uninjured athletes ex-
posed to the same factor. Knowledge of this population (often referred to as the 'denominator') gives the clinician an appreciation of the true chance of injury. A weakness of several studies that analysed internal (intrinsic) risk factors was that this denominator was not calculated, as the risk factor was only determined in injured athletes (Taunton et al. 1981). Depending on the sample or population that is studied, an appropriate measure of risk should be calculated based on exposure in both noninjured and injUred groups. 1.1 Risk Factors and Study Design The type of design employed may have considerable impact on risk factor identification and on the strength and the validity of an author's claims. As Walter et al. (1985) points out, the bulk of
Table I. Research on risk factors in sports injuries Design
Sport
Risk factor
Findings
Reference
Cohort
Football
Ugament laxity
Nicholas (1970)
Cohort
Football
Practice activity
Uterature review
Variety
Resurfaced field conditions
Cohort Case series
Rugby Variety
Case series survey
Variety
Endomorphic somatotype Functional anatomical features Gender
Retrospective survey
Running
Increased risk of knee ligament Injury Low risk: agility drills, cellisthenics High risk: practice games Lower risk of acute knee Injury Increased risk of Injury Higher risk of stress syndromes Same risk in men and women Higher risk of injury
Case series Case series
Running Running
Uterature review Cohort
Gymnastics Running
Retrospective survey
Running
Women with menstrual Irregularities Women less than 30 ys Children Young females Age, stretching habits running surface, terrain, speed, strength Body composition, age, sex, flexibility, endurance Increased mileage
Higher risk of injury Risk of growth centre overuse injuries Higher risk of bony injury No risk factor for injury
Cahill & Griffith (1979) Halpern at al. (1987) Halpern at al. (1987) Reilly & Hardlker (1981) Taunton at al. (1981) Haycock & Gillette (1978) Lloyd at al. (1988) Clement et al. (1981) Michell & Miohell (1985) Caine & Undner (1985) Blair & Kohl (1987)
All risk factors for injury Increased risk of injury
Koplan et al. (1982)
Sports Medicine 12 (1) 1991
10
aetiological studies done in sports injuries employ a case series design where a group of patients with similar injuries are studied. This methodology offers the advantage of doing a convenient retrospective analysis of characteristics in an injured population. For example, Clement et al. (1981) examined a group of injured athletes to determine the frequency of injury by type, sport, sex, etc. A case series, then, allows identification of the injuries most likely to occur with a given form of exercise (Koplan et a1. 1985). However, the true incidence of injury cannot be calculated, since the defined population is the injured athletes, and not all the athletes involved in the sport. The other difficulty with this type of design is that conclusions cannot be drawn as to the presence of risk factors, since the population at risk is not defined. Therefore, the absolute and relative risk of injury cannot be established (Walter et a1. 1985). Alternatively, an epidemiological design may be employed where the 'denominator', or entire population at risk (exposed), is estimated. Then the proportion of injured athletes with a factor can be compared to the proportion of noninjured athletes with that same factor. If more injured athletes possess the factor, its presence would increase the risk of injury. However, if it is present to a greater extent in the noninjured athletes, it may actually be protective of injury. Case-series studies which only examine factors in injured athletes have no way of knowing if the factor increases or decreases the risk of injury, or if it is completely unrelated to outcome. Walter et al. (1985) suggested that this could be accomplished by studying teams, clubs or other structured settings where both the injured and uninjured athletes could be documented. Additionally, a random sample of the general population may form a control (comparison) group. With a knowledge of athlete exposure to injury, a calculation of risk could be made, and the biases of drawing conclusions from injured athletes alone could be avoided (Walter et al. 1985). Ideally, this can be done in a prospective fashion, which is referred to as a cohort study. In this design, a group or population (cohort) with some-
thing in common is assembled prior to being exposed to injury (i.e. preseason) and then followed longitudinally to study their outcome. With a sound basis for drawing valid research conclusions, the role that risk factors play in the aetiology, prediction and prevention of athletic injuries can be explored.
2. Cause In attempting to apply risk factor analysis to a clinical situation, the researcher often seeks to establish a causal relationship. However, the fact that a risk factor is correlated with a given injury does not necessarily mean that a causal relationship exists. The correlation may be due to some as yet undetermined variable (confounder) that the risk factor and the injury have in common. For example, Lloyd et a1. (1986) conducted a retrospective survey on a population of female 10km race participants. He found that premenopausal women who had irregular menses while engaged in vigorous exercise had an increased risk of sustaining a musculoskeletal injury. However, a causal relationship might not necessarily exist since both the irregular menses and the injury occurrence could be due to a third factor such as overtraining. The theoretical presence of a third factor, or confounding variable, could affect the ability to imply a causal relationship. However, as Fletcher et al. (1982) points out, this does not diminish the value of the risk factor as a way of predicting injury if the factors are known to occur together in a reliable fashion. However, removing it (in this case by regulating the menstrual cycle hormonally) might not remove the excess risk associated with it. The value of a risk factor, then, is in its ability to mark outcome, whether it is the cause or merely confounded with a causal factor (Fletcher et a1. 1982).
3. The Predictability
0/ Sports Injuries
The essential difference between risk factor and predictor variables is that a risk factor has not been subjected to statistical tests for correlation or eval-
Predictability of Injuries
uated for predictive value. Again, it is vital to have an appropriate expression of risk as a starting point in studying correlation and predictability. It is the strength of association between a risk factor and outcome that is used to predict injury. We must be aware that there are some fundamental differences between prediction within a group, and predicting outcome in a single individual.
11
typically attempt to identify some unique marker, or risk factor, that will identify those in the injured group. As mentioned earlier, a risk factor can be used to mark outcome, even if it is not a causal factor (Fletcher et al. 1982). The method used in prediction differs depending upon whether single or multiple factors are used in the analysis.
3.2.1 Single Factor Analysis 3.1 Group Predictability Prediction for a group is the prediction of future incidence of injury (Fletcher et al. 1982). If an incidence of injury is known to exist in a given population, and the risk factors remain unchanged, then the same incidence can be expected to recur. For example, if a varsity hockey team is found to have a player injury rate of 70% annually, and no intervention occurs, the same rate would be expected the following year. Likewise, the data on incidence can be extrapolated to reflect future incidence in a similar population, where the strength of the prediction depends upon the similarity of the populations. While this is intuitively obvious, there has been little research done to objectively assess the recurrence of injury on a longitudinal basis. Two studies done several years apart in the same athletic injuries clinic did document very similar rates of injury, but have not been analysed for statistical significance (Fowler 1983; Meeuwisse & Fowler 1988). However, Richards et al. (1984) were able to predict the number of casualties in fun-runs based on the previous number of injuries, when weather conditions and the number of entries were taken into account. 3.2 Individual Prediction While injury occurrence can be predicted rather well in this way for a group or population, it is not possible to be precise about which individual in particular is at risk of injury (Fletcher et al. 1982). If the chance of injury is 80% in a population, an individual has no way of knowing which group (injured vs noninjured) they will fall into. Researchers
Much as a diagnostic test is used, the presence of a risk factor may be analysed for sensitivity, specificity, and its predictive value (table II). The results can be analysed for statistical significance using methods such as x2 analysis. In discussing this method, Fletcher et al. (1982) noted that predictive value is influenced by prevalence. As the prevalence increases, the predictive value increases, and vice versa. In other words, the predictive value depends upon the population to whom the risk factor analysis is applied. If there is no incidence of injury within the group being studied, there will still likely be a risk factor present in some individuals. All of these will then be false positives, and the predictive value of a positive test will be zero. Likewise, if the prevalence is very high, the predictive value will be falsely elevated, and results not generalisable to other populations. Therefore, it is important to apply the test (or risk factor analysis) to a population in whom the prevalence of injury reflects the group to whom one may wish to generalise. This means investigating different teams separately, as opposed to doing a risk factor analysis across a variety of teams within a sporting population. The dilutional effect of athletes without injury will lower the prevalence, and with it, the positive predictive value. This fact was illustrated in a recent study by this author (Meeuwisse & Fowler 1988). When the presence of posterior compartment tenderness was tested for in a population of over 700 varsity athletes, the predictive value of the finding was negligible. However, if the group of female track runners was examined, a positive predictive value of 88% was found. Unfortunately, the strength of association only had minimal clinical value since the risk factor might well have been an early manifes-
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Sports Medicine 12 (1) 1991
Table II. The use of a single risk factor to predict injury Injured
Non-injured
Risk factor present
True positive A
False positive B
Predictive value positive =
Risk factor not present
False negative C
True negative 0
Predictive value negative =
Sensitivity
A A+C
Specificity
tation of the injury rather than a marker of future occurrence. One author claimed an ability to predict ankle injury based on the risk factor of functional ankle instability, but an exact value was not determined (Tropp et al. 1984). 3.2.2 Multiple Factor Analysis One can also calculate the correlation using multiple regression analysis. In this method a variety of factors enter into a predictive equation in a stepwise fashion, depending upon the strength they add to the correlation. Warren and Jones (1987) attempted to use discriminant functional analysis to predict plantar fasciitis in runners using anatomical, biomechanical and history variables. However, they were not able to correctly predict membership in the presently or formerly injured groups. Warren and Davis (1988) also used 9 factors to predict running related pain without success. In contrast, a study on female gymnasts by Steele & White (1986) used multiple regression analysis to find a multiple correlation coefficient (R) of 0.840, which was considered to be predictive of injury proneness. The factors used in the evaluation were postinjury scores on flexibility, spinal posture and anthropometry. The difficulty with this study is that no measures were made in noninjured athletes to see if they were different from the injured group. This would affect the ability to predict injury in healthy athletes, as a normal range was not established. Sec-
D B+D
Prevalence =
A A+B 0 C+D
injured
A+C
total
A+B+C+D
ondly, since measures were taken after the injury occurred, there is a possibility that the injury itself caused the observed difference (i.e. lower flexibility with higher injury) which could lead to a misleadingly high statistical correlation. This difference would again have little predictive value in gymnasts who have not yet sustained an injury. 3.3 Predictability and Clinical Correlation A review of the literature revealed the results that are summarised in table III. There are several reasons why most of these studies have found no predictability. Firstly, a well controlled study with a large number of homogeneous subjects must be conducted to find statistically significant correlations. This is difficult to do in practical terms in most athletic populations. Even an 8-year study by Godshall (1975) failed to find predictability in football players. Secondly, the statistical problems are compounded by the large variability of both injury severity, and individual differences including functional anatomy, somatotype and personality. Lyens et al. (1984b) felt that 'a fortuitous element will always be present in injury statistics, which means that even the most sophisticated battery of predictor indices will never account for anywhere near the total amount of variance'. This heterogeneity of predisposing and outcome factors often makes the correlation not only statistically nonsignificant, but also clinically insignificant.
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Predictability of Injuries
While most studies on individual prediction have not met with success, sports injuries may be predictable if a study is conducted with appropriate identification of risk factors, and analysis is applied to a specific group. Ideally this should be done with a cohort design in a prospective fashion. If a risk factor is identified that could predict injury occurrence with a degree of certainty, then the individual would know in advance that he/she might likely sustain a form of injury and could seek some way of preventing its occurrence.
4. Prevention Injury is something that everyone seeks to avoid, and athletes are certainly no exception. Prevention is an element of health provision that is not only ideal from the point of view of the patient but is
often touted as being the most preferred and costeffective method of providing health care. There are two strategies that can be adopted to prevent injury. First, as alluded to earlier, if a risk factor is known to cause an injury, removal of the risk factor can prevent the injury. Fletcher et al. (1982) noted that this is true whether or not the mechanism of the injury is known. Secondly, if an injury is predictable based on several factors, one could seek to modify those factors in an attempt to reduce injury. For example, Pashby (1979) documented the occurrence of eye injuries in ice hockey in Canada. Based on his report, the mandatory use of full face masks was implemented, resulting in dramatic decrease in injury. Several other studies have outlined programmes for prevention of injury (Hershman 1984; McKeag 1985). These programmes were designed to iden-
Table III. Literature on athletic injury predictability Group sport Group predictability 10km running race
Predictor variable
Predicted event
Results
Reference
Number of partiCipants and weather conditions
Number of casualties
Good predictability
Richards et al. (1984)
Knee ligament injury Knee ligament injury Knee ligament injury Any injury Any injury Any injury
No No No No No No
Moretz et al. (1982) Clark et al. (1971) Godshall (1975) Jackson et al. (1978) Grana & Moretz (1978) Meeuwisse & Fowler
Individual predictability Football Ligamentous laxity Ligamentous laxity Football Ligamentous laxity Football Joint flexibility and laxity Football Ligamentous laxity Football, basketball Variety Previous injury, flexibility, range of motion, strength Variety Height, weight, somatotype fitness, previous injury, flexibility, laxity, alignment, personality Variety Joint flexibility and laxity Running History, anatomical factors Running History, anatomical factors Soccer Ankle joint instability
Any injury
No prediction
Any injury Plantar fascilitis Running related pain Ankle injury
Jackson et al. (1978) Warren & Jones (1987) Warren & Davis (1988) Tropp et al. (1984)
Gymnastics
Flexibility, spinal posture, anthropometry , hypermobility
Any injury
No prediction No prediction No prediction Increased risk of injury Good predictability
Track runners
Posterior compartment
Shin splints
Positive prediction
Meeuwisse & Fowler
tenderness
prediction prediction prediction prediction prediction prediction
(1988) Lyens et al. (1984a)
Steele & White (1986)
(1988)
Sports Medicine 12 (1) 1991
14
tify and treat the target areas of weakness and inflexibility on the assumption that they would be a risk factor for injury. However, no assessment of their actual preventative value was conducted. In analysing aetiological factors in a case series of running injuries McKenzie et al. (1986) outlined a guide for prevention of overuse running injuries. The assumption was that if the treatment variables which were effective in the rehabilitation of injuries were applied to the general running population, injuries could be prevented. While this certainly is good general advice, no analyses were done to confirm the suspicion. Ideally, a second prospective study could be done with a control group to determine the effectiveness of such a programme. Some other reports which offer preventative strategies likewise outline factors normally used in the treatment of injuries (Jobe & Bradley 1988; Reid 1988). While all these recommendations may have some merit, they should undergo further study before being considered truly preventative. Although these studies have some weaknesses, the institution of a preventative strategy on the basis of intuition may still prevent injury. For example, Cahill & Griffith (1978) found a decrease in knee ligament injuries during high school football participation following preseason conditioning. This illustrates the importance of assessing the effectiveness of prevention (intervention) programmes prior to recommending their general implementation.
5. Conclusion Much of the study predictability is based on risk factor analysis. An appropriate expression of injury risk where the entire population exposed to injury is identified is vital as a starting point for further analysis. Essentially, a risk factor can be used to predict injury even if it is a confounding variable, since its value is in being a marker. However, removal of the risk factor cannot be used to prevent injury unless a causal relationship exists. One might expect that sports injuries are predictable to the extent that if rules, equipment, play-
ing conditions, and other factors remain constant, the same number and type of injuries would likely occur on an annual basis in a predictable fashion. On average, there are a certain number of knee injuries in skiing each year, and a given number of catastrophic cervical spine injuries annually in football. While this overall predictability may hold true, individual injury prediction is much more difficult. There has been abundant research to identify the existence of risk factors for injury. But, a survey of the literature demonstrates that there is little epidemiological evidence to date for predicting individual injury. Prediction is possible through single or multiple factor analysis, but one problem in finding significant results appears to be due to the wide individual variability in predisposing (risk) factors. The next generation of research in this area should select a study population with a representative prevalence of injury, and examine the presence of risk factors in both injured and noninjured athletes. In this way, a meaningful conclusion can be made with respect to the value of the factor in predicting injury. At present, with a few exceptions, an individual who is about to participate in a sport cannot know with certainty that they will be hurt. Regardless of the overall risk of injury, the individual has no way ofknowing if they will fall into the injured or noninjured group, unless some marker has been identified that is unique to them. However, if they are aware of the overall risk of injury, they can make an informed decision whether or not to participate. 'After all', as Fletcher et al. (1982) pointed out, 'weather forecasts are not always accurate either, but they do help us to decide whether to carry an umbrella'.
References Blair SN, Kohl HW, Goodyear NN. Rates and risks for running and exercise populations: studies in three populations. Research Quarterly for Exercise and Sport 58: 221-228, 1987 Cahill BR, Griffith EH. Effect of preseason conditioning on the incidence and severity of high school football knee injuries. American Journal of Sports Medicine 6: 180-184, 1978 Cahill BR, Griffith EH. Exposure to injury in major college football. American Journal of Sports Medicine 7: 183-185, 1979 Caine DJ, Lindner KJ. Overuse injuries of growing bones: the
Predictability of Injuries
young female gymnast at risk? Physician and Sportsmedicine 13: 51-64, 1985 Clark JL, Challop RS, McCabe EB. Predicting lower-extremity injuries in high school football players. Journal of the American Medical Association 217: 1552-1553, 1971 Oement DB, Taunton JE, Smart GW, McNichol KL. A survey of overuse running injuries. Physician and Sportsmedicine 9: 47-58, 1981 Fletcher RH, Fletcher SW, Wagner EH. Oinical epidemiology the essentials. Williams and Wilkins, London, 1982 Fowler PJ. Injuries to university athletes - a challenge for us all. FISU Sports Medicine Conference, Edmonton, Alberta, 1983 Godshall RW. The predictability of athletic injuries: an eight year study. Journal of Sports Medicine 3: 50-54, 1975 Grana WA, Moretz AI. Ligamentous laxity in secondary school athletes. Journal of the American Medical Association 240: 1975-1976, 1978 Helpern B, Thompson N, Curl WW, Andrews JR, Hunter SC, et al. High school football injuries: identifying the risk factors. American Journal of Sports Medicine 15: SI13-S117, 1987 Haycock C, Gillette JV. Susceptibility of women athletes to injury. Journal of the American Medical Association 236: 163165, 1976 Hershman E. The profile for prevention of musculoskeletal injury. Clinics in Sports Medicine 3: 65-84, 1984 Jackson DW, Jarrett H, Bailey D, Kausek J, Swanson J, Powell JW. Injury prediction in the young athlete: a preliminary report. American Journal of Sports Medicine 6: 6-14,1978 Jobe FW, Bradley JP. Rotator cuff injuries in baseball: prevention and rehabilitation. Sports Medicine 6: 378-387, 1988 Koplan JP, Powell KE, Sikes RK, Shirley RW, Campbell Cc. An epidemiological study of the benefits and risks of running. Journal of the American Medical Association 248: 3118-3121, 1982 Koplan JP, Siscovick DS, Goldbaum GM. The risks of exercise: public health view of injuries and hazards. Public Health Reports 100: 189-195, 1985 Lloyd T, Triantafyllou SJ, Baker ER, Houts PS, Whiteside JA, et aI. Women athletes with menstrual irregularity have increased musculoskeletal injury. Medicine and Science in Sports and Exercise 18: 374-379, 1986 Lyens R, Lefevre J, Renson L, Ostyn M. The predictability of sports injuries: a preliminary report. International Journal of Sports Medicine 5: 153-155, 1984a Lyens R, Steverlynck A, van den Auweele Y, Lefevre J, Renson
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L, et aI. The predictability of sports injuries. Sports Medicine I: 6-10, 1984b McKeag DB. Preseason physical examination for the prevention of sports injuries. Sports Medicine 2: 413-431,1985 McKenzie DC, Taunton JE, Clement DB. The prevention of running injuries. Australian Joumal of Science and Medicine in Sport 18: 7-8, 1986 Meeuwisse WH, Fowler PJ. Frequency and predictability of sports injuries in intercollegiate athletes. Canadian Journal of Sports Sciences 13: 35-42, 1988 Micheli U, Micheli ER. Children's running: special risks? Annals of Sports Medicine 2: 61-63, 1985 Moretz AI, Walters R, Smith L. Flexibility as a predictor of knee injuries in college football players. Physician and Sportsmedicine 10: 93-97, 1982 Nicholas JA. Injuries to knee ligaments. Journal ofthe American Medical Association 212: 2236-2239, 1970 Pashby TJ. Eye injuries in Canadian amateur-hockey. American Journal of Sports Medicine 7: 254-257, 1979 Reid DC. Prevention of hip and pelvis injuries in ballet dancers. Sports Medicine 6: 295-307, 1988 Reilly T, Hardiker R. Somatotype and injuries in adult student rugby football. Journal of Sports Medicine 21: 186-191, 1981 Richards R, Richards D, Whittaker R. Method of predicting the number of casualties in the Sydney City-ta-Surf fun runs. Medical Journal of Australia 141: 805-808, 1984 Steele VA, White JA. Injury prediction in female gymnasts. British Journal of Sports Medicine 20: 31-33, 1986 Taunton JE, Oement DB, Webber D. Lower extremity stress fractures in athletes. Physician and Sportsmedicine 9: 77-86, 1981 Tropp H, Ekstrand J, GilIquist J. Stabilometry in functional instability of the ankle and its value in predicting injury. Medicine and Science in Sports and Exercise 16: 64-66, 1984 Walter SD, Sutton JR, McIntosh JM, Connolly C. The aetiology of sports injuries: a review of methodologies. Sports Medicine 2: 47-58, 1985 Warren BL, Davis V. Determining predictor variables for running related pain. Physical Therapy 68: 647-651, 1988 Warren BL, Jones CJ. Predicting plantar fasciitis in runners. Medicine and Science in Sports and Exercise 19: 71-73, 1987
Correspondence and reprints: W.H. Meeuwisse, University of Calgary Sports Medicine Centre, 2500 University Dr. N.W., Calgary, Alberta T2N IN4, Canada.
Sports Medicine 12 (I): 8-15, 1991 0112-1642/91/0007-0008/$04.00/0 © Adis International Limited. All rights reserved. SP0130
Predictability of Sports Injuries What is the Epidemiological Evidence? w.H. Meeuwisse University of Calgary Sports Medicine Centre, Calgary, Alberta, Canada
As the science of sport medicine has evolved over the past several decades, interest has grown in the epidemiological study of injury patterns. With the recognition of these patterns, some investigators have wondered if sports injuries were predictable. However, in surveying the literature, a plethora of terminology arises to cloud the issue. There is abundant reference to risk factors, injuryproneness, predisposition, cause (aetiology), predictability and prevention, but it is often unclear exactly what these factors are, and how they relate to each other. In order to resolve this problem, we might consider the following example. A father is approached by his son, who seeks advice regarding his intent to join the high school football team. The father, being concerned for his son's safety, wonders how likely his son is to be hurt. What type of injury might he sustain? Are boys his age, as a group, more prone to certain injuries, or is his son, in particular, at risk of a given problem? Indeed, parents, coaches and health care personnel may well question if an injury could be predicted or if it could be prevented in some way? To answer these questions, we must understand the different terminology used and the interrelationship of the different factors. The purpose of this paper is to explore these issues in order to gain a more clear understanding of their impact on the individual, the clinician and the health care system.
1. Risk Risk has been defined by Fletcher et at. (1982) as 'the likelihood that people who are without a disease, but exposed to certain factors (risk factors), will acquire the disease'. In many situations in life we are acutely aware of risk, and have a good idea of the chance of a given outcome. The risk of athletic injury occurrence, however, is often much less clear. Hershman (1984) stated that 'risk factors for a particular sport are derived by combining the epidemiology of injuries for a particular sport and the predisposing conditions that may lead to injury'. As alluded to in the definition of risk, those entities which contribute to the occurrence of some event, are called risk factors. In this case, the event is an athletic injury. Risk factors may be classified as either internal or external (Lyens et at. I 984b). Other authors have referred to these as intrinsic and extrinsic. The latter are those factors which have an impact on the athlete while they are playing their sport. They include weather, field conditions, rules, position, etc. Internal risk factors are those factors that are a part of the athlete themselves and include biomechanics, fatigue, conditioning, maturational stage and somatotype. We are all well aware that certain injuries occur with certain sports. For example, swimmers may suffer from otitis externa and shoulder ailments, whereas runners can be afflicted with stress fractures. Each sport, due to its unique demands, will
Predictability of Injuries
9
predispose individuals to a given pattern of injury (Koplan et al. 1982). Many authors have studied the presence of risk factors, and a summary appears in table I. The most basic expression of risk is incidence, which is defined as 'the number of new cases of disease arising in a defined population during a given period of time' (Fletcher et al. 1982). However, this is not very useful clinically, as it does not give us an idea of the chance of injury. Since we must have some indication of the population at risk, there are 4 measures that may be employed to gain a more useful indication of risk (attributable, relative, population, and population attributable risk). What they have in common is their ability to express incidence of injury in relation to the people exposed to the risk factor. Studies that are done on a group of injured patients may give an indication of that factor only in those injured, and not in the population of uninjured athletes ex-
posed to the same factor. Knowledge of this population (often referred to as the 'denominator') gives the clinician an appreciation of the true chance of injury. A weakness of several studies that analysed internal (intrinsic) risk factors was that this denominator was not calculated, as the risk factor was only determined in injured athletes (Taunton et al. 1981). Depending on the sample or population that is studied, an appropriate measure of risk should be calculated based on exposure in both noninjured and injUred groups. 1.1 Risk Factors and Study Design The type of design employed may have considerable impact on risk factor identification and on the strength and the validity of an author's claims. As Walter et al. (1985) points out, the bulk of
Table I. Research on risk factors in sports injuries Design
Sport
Risk factor
Findings
Reference
Cohort
Football
Ugament laxity
Nicholas (1970)
Cohort
Football
Practice activity
Uterature review
Variety
Resurfaced field conditions
Cohort Case series
Rugby Variety
Case series survey
Variety
Endomorphic somatotype Functional anatomical features Gender
Retrospective survey
Running
Increased risk of knee ligament Injury Low risk: agility drills, cellisthenics High risk: practice games Lower risk of acute knee Injury Increased risk of Injury Higher risk of stress syndromes Same risk in men and women Higher risk of injury
Case series Case series
Running Running
Uterature review Cohort
Gymnastics Running
Retrospective survey
Running
Women with menstrual Irregularities Women less than 30 ys Children Young females Age, stretching habits running surface, terrain, speed, strength Body composition, age, sex, flexibility, endurance Increased mileage
Higher risk of injury Risk of growth centre overuse injuries Higher risk of bony injury No risk factor for injury
Cahill & Griffith (1979) Halpern at al. (1987) Halpern at al. (1987) Reilly & Hardlker (1981) Taunton at al. (1981) Haycock & Gillette (1978) Lloyd at al. (1988) Clement et al. (1981) Michell & Miohell (1985) Caine & Undner (1985) Blair & Kohl (1987)
All risk factors for injury Increased risk of injury
Koplan et al. (1982)
Sports Medicine 12 (1) 1991
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aetiological studies done in sports injuries employ a case series design where a group of patients with similar injuries are studied. This methodology offers the advantage of doing a convenient retrospective analysis of characteristics in an injured population. For example, Clement et al. (1981) examined a group of injured athletes to determine the frequency of injury by type, sport, sex, etc. A case series, then, allows identification of the injuries most likely to occur with a given form of exercise (Koplan et a1. 1985). However, the true incidence of injury cannot be calculated, since the defined population is the injured athletes, and not all the athletes involved in the sport. The other difficulty with this type of design is that conclusions cannot be drawn as to the presence of risk factors, since the population at risk is not defined. Therefore, the absolute and relative risk of injury cannot be established (Walter et a1. 1985). Alternatively, an epidemiological design may be employed where the 'denominator', or entire population at risk (exposed), is estimated. Then the proportion of injured athletes with a factor can be compared to the proportion of noninjured athletes with that same factor. If more injured athletes possess the factor, its presence would increase the risk of injury. However, if it is present to a greater extent in the noninjured athletes, it may actually be protective of injury. Case-series studies which only examine factors in injured athletes have no way of knowing if the factor increases or decreases the risk of injury, or if it is completely unrelated to outcome. Walter et al. (1985) suggested that this could be accomplished by studying teams, clubs or other structured settings where both the injured and uninjured athletes could be documented. Additionally, a random sample of the general population may form a control (comparison) group. With a knowledge of athlete exposure to injury, a calculation of risk could be made, and the biases of drawing conclusions from injured athletes alone could be avoided (Walter et al. 1985). Ideally, this can be done in a prospective fashion, which is referred to as a cohort study. In this design, a group or population (cohort) with some-
thing in common is assembled prior to being exposed to injury (i.e. preseason) and then followed longitudinally to study their outcome. With a sound basis for drawing valid research conclusions, the role that risk factors play in the aetiology, prediction and prevention of athletic injuries can be explored.
2. Cause In attempting to apply risk factor analysis to a clinical situation, the researcher often seeks to establish a causal relationship. However, the fact that a risk factor is correlated with a given injury does not necessarily mean that a causal relationship exists. The correlation may be due to some as yet undetermined variable (confounder) that the risk factor and the injury have in common. For example, Lloyd et a1. (1986) conducted a retrospective survey on a population of female 10km race participants. He found that premenopausal women who had irregular menses while engaged in vigorous exercise had an increased risk of sustaining a musculoskeletal injury. However, a causal relationship might not necessarily exist since both the irregular menses and the injury occurrence could be due to a third factor such as overtraining. The theoretical presence of a third factor, or confounding variable, could affect the ability to imply a causal relationship. However, as Fletcher et al. (1982) points out, this does not diminish the value of the risk factor as a way of predicting injury if the factors are known to occur together in a reliable fashion. However, removing it (in this case by regulating the menstrual cycle hormonally) might not remove the excess risk associated with it. The value of a risk factor, then, is in its ability to mark outcome, whether it is the cause or merely confounded with a causal factor (Fletcher et a1. 1982).
3. The Predictability
0/ Sports Injuries
The essential difference between risk factor and predictor variables is that a risk factor has not been subjected to statistical tests for correlation or eval-
Predictability of Injuries
uated for predictive value. Again, it is vital to have an appropriate expression of risk as a starting point in studying correlation and predictability. It is the strength of association between a risk factor and outcome that is used to predict injury. We must be aware that there are some fundamental differences between prediction within a group, and predicting outcome in a single individual.
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typically attempt to identify some unique marker, or risk factor, that will identify those in the injured group. As mentioned earlier, a risk factor can be used to mark outcome, even if it is not a causal factor (Fletcher et al. 1982). The method used in prediction differs depending upon whether single or multiple factors are used in the analysis.
3.2.1 Single Factor Analysis 3.1 Group Predictability Prediction for a group is the prediction of future incidence of injury (Fletcher et al. 1982). If an incidence of injury is known to exist in a given population, and the risk factors remain unchanged, then the same incidence can be expected to recur. For example, if a varsity hockey team is found to have a player injury rate of 70% annually, and no intervention occurs, the same rate would be expected the following year. Likewise, the data on incidence can be extrapolated to reflect future incidence in a similar population, where the strength of the prediction depends upon the similarity of the populations. While this is intuitively obvious, there has been little research done to objectively assess the recurrence of injury on a longitudinal basis. Two studies done several years apart in the same athletic injuries clinic did document very similar rates of injury, but have not been analysed for statistical significance (Fowler 1983; Meeuwisse & Fowler 1988). However, Richards et al. (1984) were able to predict the number of casualties in fun-runs based on the previous number of injuries, when weather conditions and the number of entries were taken into account. 3.2 Individual Prediction While injury occurrence can be predicted rather well in this way for a group or population, it is not possible to be precise about which individual in particular is at risk of injury (Fletcher et al. 1982). If the chance of injury is 80% in a population, an individual has no way of knowing which group (injured vs noninjured) they will fall into. Researchers
Much as a diagnostic test is used, the presence of a risk factor may be analysed for sensitivity, specificity, and its predictive value (table II). The results can be analysed for statistical significance using methods such as x2 analysis. In discussing this method, Fletcher et al. (1982) noted that predictive value is influenced by prevalence. As the prevalence increases, the predictive value increases, and vice versa. In other words, the predictive value depends upon the population to whom the risk factor analysis is applied. If there is no incidence of injury within the group being studied, there will still likely be a risk factor present in some individuals. All of these will then be false positives, and the predictive value of a positive test will be zero. Likewise, if the prevalence is very high, the predictive value will be falsely elevated, and results not generalisable to other populations. Therefore, it is important to apply the test (or risk factor analysis) to a population in whom the prevalence of injury reflects the group to whom one may wish to generalise. This means investigating different teams separately, as opposed to doing a risk factor analysis across a variety of teams within a sporting population. The dilutional effect of athletes without injury will lower the prevalence, and with it, the positive predictive value. This fact was illustrated in a recent study by this author (Meeuwisse & Fowler 1988). When the presence of posterior compartment tenderness was tested for in a population of over 700 varsity athletes, the predictive value of the finding was negligible. However, if the group of female track runners was examined, a positive predictive value of 88% was found. Unfortunately, the strength of association only had minimal clinical value since the risk factor might well have been an early manifes-
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Sports Medicine 12 (1) 1991
Table II. The use of a single risk factor to predict injury Injured
Non-injured
Risk factor present
True positive A
False positive B
Predictive value positive =
Risk factor not present
False negative C
True negative 0
Predictive value negative =
Sensitivity
A A+C
Specificity
tation of the injury rather than a marker of future occurrence. One author claimed an ability to predict ankle injury based on the risk factor of functional ankle instability, but an exact value was not determined (Tropp et al. 1984). 3.2.2 Multiple Factor Analysis One can also calculate the correlation using multiple regression analysis. In this method a variety of factors enter into a predictive equation in a stepwise fashion, depending upon the strength they add to the correlation. Warren and Jones (1987) attempted to use discriminant functional analysis to predict plantar fasciitis in runners using anatomical, biomechanical and history variables. However, they were not able to correctly predict membership in the presently or formerly injured groups. Warren and Davis (1988) also used 9 factors to predict running related pain without success. In contrast, a study on female gymnasts by Steele & White (1986) used multiple regression analysis to find a multiple correlation coefficient (R) of 0.840, which was considered to be predictive of injury proneness. The factors used in the evaluation were postinjury scores on flexibility, spinal posture and anthropometry. The difficulty with this study is that no measures were made in noninjured athletes to see if they were different from the injured group. This would affect the ability to predict injury in healthy athletes, as a normal range was not established. Sec-
D B+D
Prevalence =
A A+B 0 C+D
injured
A+C
total
A+B+C+D
ondly, since measures were taken after the injury occurred, there is a possibility that the injury itself caused the observed difference (i.e. lower flexibility with higher injury) which could lead to a misleadingly high statistical correlation. This difference would again have little predictive value in gymnasts who have not yet sustained an injury. 3.3 Predictability and Clinical Correlation A review of the literature revealed the results that are summarised in table III. There are several reasons why most of these studies have found no predictability. Firstly, a well controlled study with a large number of homogeneous subjects must be conducted to find statistically significant correlations. This is difficult to do in practical terms in most athletic populations. Even an 8-year study by Godshall (1975) failed to find predictability in football players. Secondly, the statistical problems are compounded by the large variability of both injury severity, and individual differences including functional anatomy, somatotype and personality. Lyens et al. (1984b) felt that 'a fortuitous element will always be present in injury statistics, which means that even the most sophisticated battery of predictor indices will never account for anywhere near the total amount of variance'. This heterogeneity of predisposing and outcome factors often makes the correlation not only statistically nonsignificant, but also clinically insignificant.
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Predictability of Injuries
While most studies on individual prediction have not met with success, sports injuries may be predictable if a study is conducted with appropriate identification of risk factors, and analysis is applied to a specific group. Ideally this should be done with a cohort design in a prospective fashion. If a risk factor is identified that could predict injury occurrence with a degree of certainty, then the individual would know in advance that he/she might likely sustain a form of injury and could seek some way of preventing its occurrence.
4. Prevention Injury is something that everyone seeks to avoid, and athletes are certainly no exception. Prevention is an element of health provision that is not only ideal from the point of view of the patient but is
often touted as being the most preferred and costeffective method of providing health care. There are two strategies that can be adopted to prevent injury. First, as alluded to earlier, if a risk factor is known to cause an injury, removal of the risk factor can prevent the injury. Fletcher et al. (1982) noted that this is true whether or not the mechanism of the injury is known. Secondly, if an injury is predictable based on several factors, one could seek to modify those factors in an attempt to reduce injury. For example, Pashby (1979) documented the occurrence of eye injuries in ice hockey in Canada. Based on his report, the mandatory use of full face masks was implemented, resulting in dramatic decrease in injury. Several other studies have outlined programmes for prevention of injury (Hershman 1984; McKeag 1985). These programmes were designed to iden-
Table III. Literature on athletic injury predictability Group sport Group predictability 10km running race
Predictor variable
Predicted event
Results
Reference
Number of partiCipants and weather conditions
Number of casualties
Good predictability
Richards et al. (1984)
Knee ligament injury Knee ligament injury Knee ligament injury Any injury Any injury Any injury
No No No No No No
Moretz et al. (1982) Clark et al. (1971) Godshall (1975) Jackson et al. (1978) Grana & Moretz (1978) Meeuwisse & Fowler
Individual predictability Football Ligamentous laxity Ligamentous laxity Football Ligamentous laxity Football Joint flexibility and laxity Football Ligamentous laxity Football, basketball Variety Previous injury, flexibility, range of motion, strength Variety Height, weight, somatotype fitness, previous injury, flexibility, laxity, alignment, personality Variety Joint flexibility and laxity Running History, anatomical factors Running History, anatomical factors Soccer Ankle joint instability
Any injury
No prediction
Any injury Plantar fascilitis Running related pain Ankle injury
Jackson et al. (1978) Warren & Jones (1987) Warren & Davis (1988) Tropp et al. (1984)
Gymnastics
Flexibility, spinal posture, anthropometry , hypermobility
Any injury
No prediction No prediction No prediction Increased risk of injury Good predictability
Track runners
Posterior compartment
Shin splints
Positive prediction
Meeuwisse & Fowler
tenderness
prediction prediction prediction prediction prediction prediction
(1988) Lyens et al. (1984a)
Steele & White (1986)
(1988)
Sports Medicine 12 (1) 1991
14
tify and treat the target areas of weakness and inflexibility on the assumption that they would be a risk factor for injury. However, no assessment of their actual preventative value was conducted. In analysing aetiological factors in a case series of running injuries McKenzie et al. (1986) outlined a guide for prevention of overuse running injuries. The assumption was that if the treatment variables which were effective in the rehabilitation of injuries were applied to the general running population, injuries could be prevented. While this certainly is good general advice, no analyses were done to confirm the suspicion. Ideally, a second prospective study could be done with a control group to determine the effectiveness of such a programme. Some other reports which offer preventative strategies likewise outline factors normally used in the treatment of injuries (Jobe & Bradley 1988; Reid 1988). While all these recommendations may have some merit, they should undergo further study before being considered truly preventative. Although these studies have some weaknesses, the institution of a preventative strategy on the basis of intuition may still prevent injury. For example, Cahill & Griffith (1978) found a decrease in knee ligament injuries during high school football participation following preseason conditioning. This illustrates the importance of assessing the effectiveness of prevention (intervention) programmes prior to recommending their general implementation.
5. Conclusion Much of the study predictability is based on risk factor analysis. An appropriate expression of injury risk where the entire population exposed to injury is identified is vital as a starting point for further analysis. Essentially, a risk factor can be used to predict injury even if it is a confounding variable, since its value is in being a marker. However, removal of the risk factor cannot be used to prevent injury unless a causal relationship exists. One might expect that sports injuries are predictable to the extent that if rules, equipment, play-
ing conditions, and other factors remain constant, the same number and type of injuries would likely occur on an annual basis in a predictable fashion. On average, there are a certain number of knee injuries in skiing each year, and a given number of catastrophic cervical spine injuries annually in football. While this overall predictability may hold true, individual injury prediction is much more difficult. There has been abundant research to identify the existence of risk factors for injury. But, a survey of the literature demonstrates that there is little epidemiological evidence to date for predicting individual injury. Prediction is possible through single or multiple factor analysis, but one problem in finding significant results appears to be due to the wide individual variability in predisposing (risk) factors. The next generation of research in this area should select a study population with a representative prevalence of injury, and examine the presence of risk factors in both injured and noninjured athletes. In this way, a meaningful conclusion can be made with respect to the value of the factor in predicting injury. At present, with a few exceptions, an individual who is about to participate in a sport cannot know with certainty that they will be hurt. Regardless of the overall risk of injury, the individual has no way ofknowing if they will fall into the injured or noninjured group, unless some marker has been identified that is unique to them. However, if they are aware of the overall risk of injury, they can make an informed decision whether or not to participate. 'After all', as Fletcher et al. (1982) pointed out, 'weather forecasts are not always accurate either, but they do help us to decide whether to carry an umbrella'.
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Predictability of Injuries
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Correspondence and reprints: W.H. Meeuwisse, University of Calgary Sports Medicine Centre, 2500 University Dr. N.W., Calgary, Alberta T2N IN4, Canada.
