Theoretical example

Imagine that you want to compare game-time injury rates between a University's soccer team and hockey team. For illustrative purposes, we assume that the soccer games are all played for 90 min with 11 players on the field, and 5 players on the bench who play for a small portion of the game as replacements (16 players on the team participate). The hockey games are played for 60 min with six players on the ice and 10 players on the bench (again, 16 players participate). We also assume that teams play with a constant number of players (ie, no penalties); each sport has a 20 game season and each team has four injuries.

Athletes at risk method

One method to estimate team-level exposure time uses the number of players on the field multiplied by either the number of games or the number of game-hours that the team has played.2 ,4 ,5 ,8,–,14 Using the soccer scenario above, there are 11 athletes at risk (AAR) on the field at any one moment during the games. With four injuries over the 20 game season, the game injury rate for soccer is 4/(11×20)=0.0182 injuries per athlete-game (usually expressed as 18.2 injuries per 1000 athlete-games). For hockey, there are six AAR, that is, on the ice, at any one moment. With four injuries, the hockey game injury rate is 33.3 injuries per 1000 athlete-games, that is, 1.8 times that of soccer. If one were interested in the injury rate per hour of game exposure, the injury rate for soccer would be 12.1 per 1000 game-hours because the games last 1.5 h. For hockey, the injury rate would be 33.3 per 1000 game-hours because the games last 1 h. When games are played with a consistent and predictable number of players and do not go into overtime, then this method replicates the results obtained from summing the actual game minutes played at the team level. However, this method will overestimate exposure time in games where penalties reduce the number of players on the field for part of the game (eg, red cards in soccer and penalties in hockey), and underestimate exposure time if games go into overtime and this extra time is not counted.

Athlete participation method

Another method published in high-quality journals uses ‘athlete exposures’,15 which refers to ‘one athlete participating in one practice or game’,16 or some close variation. Within this definition, ‘participation’ is sometimes defined as those who actually played in the games,16,–,18 and sometimes as those on the game roster whether they played or not.19,–,23 We therefore will use the term ‘athlete participation (AP)’, and not replicate the term ‘exposure’. Here, all participating athletes are counted equally even though exposure time ranges from full to partial to, potentially, none. The result is that the AP method consistently overestimates total athlete exposure time at risk, and hence underestimates injury rates. With this method, the magnitude of the bias will depend upon the ratio of the number of athletes on the field at any one time (ie, the athletes at risk), to the number of athletes who ‘participated’ in the game.

Returning to our theoretical example, soccer and hockey had 16 players who participated for some portion of the 20 games and had four injuries. Thus, analysing the players who ‘participated’ as a closed cohort, the calculated injury rate per 1000 athlete-games would be (1000×4)/(16×20)=12.5 injuries per 1000 athlete-games for both sports. This represents a 31.3% underestimate for soccer (recall, soccer had a rate of 18.2) and a 62.5% underestimate for hockey (hockey had a rate of 33.3). Injury rates expressed per 1000 game-hours would have the same proportional bias as per 1000 athlete-games because the estimates of exposure time in the unbiased (using individual-level exposure time, or AAR under specific conditions) and biased (using AP) methods in soccer are divided 1.5 (ie, 1.5 h per game), and those for hockey are divided by 1 (ie, 1 h per game).

Caveats

We have assumed that studies involve entire teams. If team recruitment is incomplete (ie, if less than 100% of the team members participate in the study), then bias will occur if enrolment is associated with playing time or risk-taking behaviours or both. For sports played without a clock (eg, baseball, volleyball and gymnastics), the choice of the denominator (eg, per 1000 innings, per 1000 games, per 1000 events) depends on the research question. We defined ‘at risk’ as being on the field during game time. We consider an injury during warm-ups of a game to be more consistent with a practice-related injury. However, others might view this as a game-related injury. It is possible, albeit rare, for athletes to be injured in a game when not on the field. Although the AP method captures these athletes in the denominator and the AAR method does not, injuries on the bench are not a major focus of sports injury prevention. Some may be interested in questions such as ‘what is the probability of injury to an athlete on a team during a game?’ In this case, the answer is a proportion (sometimes restricted to the word ‘risk’), and not a ‘rate’ (which includes a component of time).3 Here, the AP method using all the players on the game roster most accurately defines exposure as the focus is ‘an athlete on a team during a game’ and not the risk of playing the sport during a game. However, the AP method would still have serious limitations for comparisons between teams, as teams within sports may have different numbers of members.

The analysis of individual-level risk factors for injury (eg, previous injury) is severely limited with the AAR and AP methods. Although data from the AAR method could be used to assess time-independent risk factors (eg, rate ratios for forward vs defense in hockey) one at a time, multiple regression is only possible using an ecological analysis (ie, risk factor variables are recorded as proportion of team members having certain characteristics). Although multiple regression analysis from the AP method could be applied if the data are gathered and stored appropriately (eg, each row representing one athlete with total number of exposures, total number of injuries and columns for each risk factor characteristic), the yes/no categorisation of exposure for a particular game makes interpretation very difficult. Therefore, we strongly recommend that any multiple regression analysis for individual-level risk factor analysis be restricted to data that includes individual-level exposure time data. We recognise that collection of individual-level data is too cumbersome and costly for many studies of sports injury surveillance. However, there is simply too much risk in making inappropriate interpretations based on incomplete data.

The sport of US football raises unique questions, especially if compared with other sports. Although it is played with a clock, most of the time is spent between plays, and the number of plays differs greatly between teams of different playing styles. Thus, unlike other sports, time of play may be a less interesting denominator than number of plays. Unlike other team sports, in US football each position plays for approximately half the game. A game consists of essentially two contests: one between a team's offense (n=11) and the other team's defense (n=11), and vice versa. Full play at one position is only partial game exposure time. In this context, the AAR method can calculate unbiased overall injury rates, and compare offensive versus defensive injury rates as long as data are collected for offense and defense in each game. This is because there are always an equal number of offensive and defensive athletes on the field at any one time, and therefore no bias is introduced. However, if data were not collected on both teams within each game, then one would need to know the time (or number of plays) on offense or defense per game. If one were interested in comparing injury rates per position, one needs additional information on exposure time per position (eg the number of offensive wide receivers on the field changes from play to play). These issues related to risk factor analysis based on position may be pertinent to many sports, but likely more so with football given the large number of positions, and the fact that teams change the number of players at different positions often.

Finally, our discussion has focused on issues related to exposure time for sports injury epidemiology. Readers are referred elsewhere for a more complete discussion of other important issues applicable to all of injury epidemiology such as defining injury and measurement error,7 sufficient and component causes,7 or analytical issues such as proportions of injuries with numerator only data,6 multiplicity6 ,7 and heterogeneity.6

Summary

Recording exact amounts of exposure time in sport events is overly cumbersome for many sports injury surveillance projects. When individual-level data are unavailable, sports injury epidemiologists currently use one of two main methods to assess overall exposure time. The AAR method includes only the number of athletes at risk on the field during the respective sporting events. This method closely replicates the results of the individual-level exposure time calculations if studies have full team enrolment and games are played with a consistent number of players. In contrast, the AP method applies a full unit of exposure time to everyone who plays in a game, however briefly, or to everyone on the game roster whether they participate or not. Consequently, the AP method underestimates game injury rates (because it overestimates exposure time) with a magnitude related to the proportion of players on the field (ie, those at risk) divided by the number of players on the team who are considered to have participated. Recognising this is necessary to properly assess risk factors for injuries in team sport events, appropriately target injury prevention efforts and accurately combine studies using different methods in systematic reviews or meta-analyses.