Abstract
Introduction
Ice hockey is a popular global sport with growing participation for boys and girls yet remains a high-risk sport for injury. While the evidence for some risk factors, such as bodychecking policy is well established, other risk factors such as player sex have been understudied. The objectives of this study were to examine factors associated with rates of game-related injury, game-related injury resulting in >7 days of time-loss, and practice-related injury in youth ice hockey.
Materials and methods
Safe2Play was a five-year prospective cohort study (2013–2018). All injuries were identified using validated injury surveillance methodology. Multilevel Poisson regression (adjusting for cluster by team and including multiple imputation of missing covariates) was used to estimate incidence rate ratios (IRRs) for player sex, age group, bodychecking policy, year of play, level of play, weight, previous injury within last 12 months, previous lifetime concussion history, and position.
Results
A total of 4418 male and female ice hockey players (representing 6584 player-seasons) participating in under-13 (ages 11–12), under-15 (ages 13–14) and under-18 (ages 15–17) age groups were recruited. There were 1184 game-related and 182 practice-related injuries. Factors associated with game-related injury included female sex (IRR = 1.57; 95% CI: 1.18–2.08), previous injury (IRR = 1.46; 95% CI: 1.26–1.70) and lifetime concussion history (IRR = 1.41; 95% CI: 1.23–1.62). Goaltenders had a lower rate of injury (IRR = 0.54; 95% CI: 0.40–0.72) relative to forwards, as did players exposed to policy disallowing bodychecking in games (IRR = 0.44; 95% CI: 0.35–0.55). Female sex (IRR = 1.90; 95% CI: 1.10–3.28) and lifetime concussion history were also significantly associated with practice-related injury (IRR = 1.53; 95% CI: 1.08–2.18).
Conclusions
Based on data from a large 5-year Canadian youth ice hockey longitudinal cohort, several factors associated with injury were identified. Future injury prevention strategies should consider age, sex, previous concussion and injury history, and body checking leagues. Future research in female youth ice hockey including female-only leagues should be a priority to inform prevention strategies in this understudied population.
Keywords: Ice hockey, risk factors, injury, adolescent, injury prevention
Introduction
Ice hockey is a popular winter sport and has grown in participation globally, particularly for females, over the past decade [1,2]. There are many known benefits to participating in sport such as improved physical, social and mental health benefits [3]. Unfortunately, participation in ice hockey is associated with a high burden of injury [4–6]. In Canada, ice hockey accounts for over 10% of all youth sport-related injuries [5]. Concussion incidence rates in ice hockey are among the highest in youth sports [4], accounting for between 18 and 66% of all ice hockey-related injuries [7–10].
Previous youth ice hockey studies have identified risk factors for injury such as policy permitting bodychecking [9–16]. This research has informed policy disallowing bodychecking in games in the under-13 age group (ages 11–12) nationally in the USA and Canada (USA Hockey 2011; Hockey Canada 2013), as well as in non-elite levels of play in older age categories (ages 13–17) [7,10,13,14]. These policy changes have prevented thousands of injuries to date and subsequently contributed to considerable health care cost savings [7,10,13,14,17–19]. While the evidence is strong for bodychecking policy and some other risk factors, like previous injury and concussion history, the evidence for other factors such as sex, weight, level of play, and player position is inconsistent or understudied. This may be due in part to differing injury definitions, methodological discrepancies and inconsistent reporting [12]. Further, the literature to date has primarily focused on game-related injury with little attention to practice-related injury.
Safe2Play was a large 5-year prospective cohort study in Canadian youth ice hockey players and provides an ideal opportunity to examine factors associated with injury for games and practices. As such, the purpose of this study was to examine the association between several potential risk factors for game-related injury, game-related injury resulting in >7 days of time-loss, and practice-related injury rates in youth ice hockey players aged 11–17.
Materials and methods
Design
Safe2Play was a 5-year prospective cohort study conducted from 2013/14 to 2017/18 in two western Canada provinces (Alberta and British Columbia). The cohort included male and female players from all levels of play in under-13 (ages 11–12), under-15 (ages 13–14) and under-18 (ages 15–17) age groups. This included players in leagues either allowing or disallowing bodychecking. Eligible females played in either ‘female-only’ leagues, where policy does not permit bodychecking at any age group or level of play, or on primarily male-participant teams where bodychecking policy was league dependent. The cohort utilised an inclusive sampling approach where all regional hockey associations were invited to participate. Individual teams were then contacted from those associations that agreed to participate. Teams were included if they agreed to participate and could identify a person willing to report weekly player participation and any injuries in games and practices. All players from eligible teams could participate if they provided written informed consent (player or parent) and had no previous injury or illness that prevented full participation at the beginning of the playing season. The recruitment process details have been previously published [10,13,14]. As this was a secondary data analysis, there was no a priori sample size calculation. Sample size details from the original study have been previously published [8]. Ethical approval was received from research ethics boards at the University of Calgary (Ethics ID: REB14-0348 and REB14-2209), University of Alberta (Ethics ID: REB Pro00024093) and the University of British Columbia (Ethics ID: CW14-0304/H14-01894).
Procedures
The previously validated youth ice hockey injury surveillance methodology included a preseason baseline questionnaire, recording of weekly playing exposure in games and practices, and injury details [20]. Each team designate (typically the team manager) collected weekly playing exposure and identified players with injuries. Injuries must have resulted in medical attention, the inability to complete a session, or time-loss from playing to be included. A study athletic therapist and/or physiotherapist confirmed the information on all injuries that were reported by the team designate. Further details of the validated injury surveillance system have been previously published [9,10,13,17]. Players with a suspected concussion were referred to a study sport medicine physician within 72 h for diagnosis and management. Concussion definition and a standardised follow-up and return to play protocol based on the 4th (2013–2016) and 5th (2017–2018) International Conferences on Concussion in Sport (Zurich 2013 and Berlin 2016 Consensus Statements) was followed by all study physicians [21,22]. To ensure consistency across diagnosis and return to sport processes as per the latest International Statement on Concussion in Sport [21,22], meetings with study physicians were held at the start of each season. Study therapists validating the injury report forms also completed standardised training at the start of each season.
The outcomes of interest for this study were the incidence of game-related injury, game-related injury that resulted in >7 days of time-loss, and all practice-related injuries. The seven-day cut-point for injury has been supported in the literature to differentiate between more severe injury and allows for comparison with previous youth ice hockey studies [7,9,10,13,15,16,20,23,24]. Since most concussions typically resolve within 4 weeks in youth [21,22], a sensitivity analysis was also conducted restricting injury that resulted in >7 days of time loss to musculoskeletal injury only (i.e. excluding concussion).
Statistical analyses
The baseline characteristics of the cohort were described by age group and injury status. When weekly playing exposure was missing, an estimation approach based on weekly means was performed within participant, team, or city and division of play. This technique is a recommended method for dealing with missing playing exposure in youth ice hockey and is supported by several previous studies [7,10,13,25]. Crude rates of game-related injury, game-related injury resulting in >7 days of time-loss, and all practice-related injuries (offset for game-hours for game-related injury and practice-hours for practice-related injury) were estimated for each age group with 95% Poisson confidence intervals adjusted for cluster by team. For each age group, crude game and practice-related injury rates were calculated by injury location and type with either exact Poisson 95% CIs (when injury numbers <10) or adjusted for clustering by team (numbers ≥10).
Separate multiple multilevel Poisson regression models were used to evaluate potential risk factors associated with each injury outcome. Each model was adjusted for the following important covariates as suggested by previous youth ice hockey studies [7,8,10,12–16]: age group (under-13, under-15, under-18), bodychecking policy (permitted, not permitted), year of play (1st, 2nd or 3rd), level of play (elite divisions of play (top 20–30% by division of play depending on age group), sub-elite (lower 70–80%)), sex (male, female), player weight (kg), previous injury in the last 12 months (yes, no), previous lifetime concussion history (yes, no) and player position (forward, defence, goalie). Non-linearity of weight was explored by itself and with an interaction term within each age group (α = 0.05). Player exposure hours were used as an offset in each model (game-hours for game-related outcomes and practice-hours for the practice outcome), and a random effect at the team level was examined to account for clustering. Missing covariate data were imputed using multiple imputation using chained equations with 30 imputations completed. All analyses were completed using Stata MP (release 17; with copyright license) [26].
Results
The Safe2Play cohort recruited a total of 4418 players. Of those, 1344 participated in more than one season of play and resulted in a combined 6584 player-seasons. The median number of players recruited per team was 14 (range: 2–20). There were also an additional 1027 individual players (representing 1308 player-seasons) that were the only members of a team to participate. The median number of weeks players were followed was 20 (range: 2–31) and varied depending on when they were recruited during the playing season. Most players (84.6%; 5565/6581) had at least one week of game exposure estimated. The median number of game weeks imputed per player was 3 (first quartile: 1, third quartile: 14).
Table 1 summarises the baseline characteristics of the player-seasons by age group and injury status. Of interest, ‘female-only’ leagues had a higher proportion of injured players (119/431; 27.6%) than females playing on predominantly male-based teams (22/168; 13.1%).
Table 1.
Baseline characteristics for under-13, under-15 and under-18 youth ice hockey players over five seasons (2013–2018).
| Under-13 |
Under-15 |
Under-18 |
||||
|---|---|---|---|---|---|---|
| Injured (nc = 159) | No injury (nc = 1410) | Injured (nc = 591) | No injury (nc = 2486) | Injured (nc = 439) | No injury (nc = 1499) | |
| City, n (%) | ||||||
| Calgary | 150 (94.34) | 1292 (91.63) | 392 (66.33) | 1549 (62.31) | 276 (62.87) | 832 (55.50) |
| Calgary surrounding area | 1 (0.63) | 29 (2.06) | 13 (2.20) | 57 (2.29) | 24 (5.47) | 45 (3.00) |
| Edmonton | 8 (5.03) | 88 (6.24) | 142 (24.03) | 520 (20.92) | 105 (23.92) | 291 (19.41) |
| Vancouver | 0 (0) | 0 (0) | 42 (7.11) | 321 (12.91) | 34 (7.74) | 331 (22.08) |
| Okanagan region | 0 (0) | 1 (0.07) | 2 (0.34) | 39 (1.57) | 0 (0) | 0 (0) |
| Year, n (%) | ||||||
| 2013–2014 | 80 (50.31) | 652 (46.24) | 19 (3.21) | 71 (2.86) | 0 (0) | 0 (0) |
| 2014–2015 | 48 (30.19) | 340 (24.11) | 180 (30.46) | 830 (33.39) | 0 (0) | 0 (0) |
| 2015–2016 | 11 (6.92) | 83 (5.89) | 198 (33.50) | 702 (28.24) | 199 (45.33) | 709 (47.30) |
| 2016–2017 | 13 (8.18) | 172 (12.20) | 101 (17.09) | 490 (19.71) | 133 (30.30) | 410 (27.35) |
| 2017–2018 | 7 (4.40) | 163 (11.56) | 93 (15.74) | 393 (15.81) | 107 (24.37) | 380 (25.35) |
| Sex, n (%) | ||||||
| Male | 141 (88.68) | 1295 (91.84) | 519 (87.82) | 2252 (90.59) | 388 (88.38) | 1390 (92.73) |
| Female | 18 (11.32) | 115 (8.16) | 72 (12.18) | 234 (9.41) | 51 (11.62) | 109 (7.27) |
| Sex within league, n (%) | ||||||
| Males in mainly male leagues | 141 (88.68) | 1295 (91.84) | 519 (87.82) | 2252 (90.59) | 388 (88.38) | 1390 (92.73) |
| Females in ‘female-only’ | 10 (6.29) | 38 (2.70) | 62 (10.49) | 183 (7.36) | 47 (10.71) | 91 (6.07) |
| Females in mainly male-based leagues | 8 (5.03) | 77 (5.46) | 10 (1.69) | 51 (2.05) | 4 (0.91) | 18 (1.20) |
| Anthropometrics | ||||||
| Height, cm, median (Q1, Q3) | 152.4 (147.0, 158.3) | 152.2 (147.0, 157.5) | 165.1 (157.6, 172.0) | 165.3 (159.0, 172.5) | 177.8 (171.2, 180.3) | 177.0 (171.40, 182.9) |
| Missing, n (%) | 20 (12.58) | 74 (5.25) | 187 (31.64) | 875 (35.20) | 116 (26.42) | 512 (34.16) |
| Weight, kg, median (Q1, Q3) | 42.4 (35.6, 48.8) | 41.4 (36.5, 47.0) | 54.4 (46.5, 62.5) | 54.0 (46.7, 61.2) | 68.0 (61.2, 74.8) | 68.0 (61.2, 76.0) |
| Missing, n (%) | 21 (13.21) | 55 (3.90) | 192 (32.49) | 838 (33.71) | 118 (26.88) | 499 (33.29) |
| Level of play, n (%) | ||||||
| Elite (top 20–30%) | 52 (32.70) | 470 (33.33) | 193 (32.66) | 551 (22.16) | 165 (37.59) | 425 (28.35) |
| Sub-elite (lower 70–80%) | 95 (59.75) | 848 (60.14) | 376 (63.62) | 1806 (72.65) | 262 (59.68) | 1022 (68.18) |
| Missing | 12 (7.55) | 92 (6.52) | 22 (3.72) | 129 (5.19) | 12 (2.73) | 52 (3.47) |
| Bodychecking policy, n (%) | ||||||
| Allowed | NA | NA | 443 (74.96) | 1512 (60.82) | 332 (75.63) | 897 (59.84) |
| Prohibited | 159 (100) | 1410 (100) | 132 (22.34) | 853 (34.31) | 99 (22.55) | 561 (37.42) |
| Missing | 0 (0) | 0 (0) | 16 (2.71) | 121 (4.87) | 8 (1.82) | 41 (2.74) |
| Year of play, n (%) | ||||||
| First | 69 (43.40) | 649 (46.03) | 277 (46.87) | 1196 (48.11) | 172 (39.18) | 618 (41.23) |
| Second | 90 (56.60) | 755 (53.55) | 289 (48.90) | 1176 (47.30) | 128 (29.16) | 520 (34.69) |
| Third | NA | NA | NA | NA | 69 (15.72) | 208 (13.88) |
| Missing | 0 (0) | 6 (0.43) | 25 (4.23) | 114 (4.59) | 70 (15.95) | 153 (10.21) |
| Position, n (%) | ||||||
| Forward | 82 (51.57) | 740 (52.48) | 319 (53.98) | 1242 (49.96) | 226 (51.48) | 713 (47.57) |
| Defence | 57 (35.85) | 441 (31.28) | 186 (31.47) | 726 (29.20) | 128 (29.16) | 407 (27.15) |
| Goalie | 9 (5.66) | 133 (9.43) | 29 (4.91) | 216 (8.69) | 27 (6.15) | 153 (10.21) |
| Missing | 11 (6.92) | 96 (6.81) | 57 (9.64) | 302 (12.15) | 58 (13.21) | 226 (15.08) |
| Previous injurya | ||||||
| No | 69 (43.40) | 884 (62.70) | 229 (38.75) | 1236 (49.72) | 141 (32.12) | 707 (47.16) |
| Yes | 60 (37.74) | 329 (23.33) | 238 (40.27) | 752 (30.25) | 219 (49.89) | 547 (36.49) |
| Missing | 30 (18.87) | 197 (13.97) | 124 (20.98) | 498 (20.03) | 79 (18.00) | 245 (16.34) |
| Previous concussionb | ||||||
| No | 91 (57.23) | 975 (69.15) | 287 (48.56) | 1509 (60.70) | 154 (35.08) | 735 (49.03) |
| Yes | 65 (40.88) | 415 (29.43) | 270 (45.69) | 875 (35.20) | 255 (58.09) | 678 (45.23) |
| Missing | 3 (1.89) | 20 (1.42) | 34 (5.75) | 102 (4.10) | 30 (6.83) | 86 (5.74) |
Q1: first quartile; Q3: third quartile.
Previous injury or concussion 12 months prior to baseline test.
Previous concussion lifetime.
Sum of n is 6584, given that it is player-seasons (1344 players participated in more than one season).
Table 2 summarises the injury outcomes for each age group. The proportion of game-related injuries that resulted in >7 days of time-loss was similar for those under-13 (79/138; 57.2%), under-15 (359/594; 60.4%) and under-18 (304/452; 67.3%). The rates of game-related injury and game-related injury resulting in >7 days of time-loss was highest in the under-18 age group followed by under-15 and lowest in under-13. The rates of practice-related injury were markedly lower compared with game-related rates but still highest in the under-18 age group.
Table 2.
Summary of injury outcomes for under-13, under-15 and under-18 youth ice hockey players.
| Under-13 | Under-15 | Under-18 | |
|---|---|---|---|
| Number of game-related injuries | 138 | 594 | 452 |
| Number of game-related injuries with >7 days of time-loss | 79 | 359 | 304 |
| Player game participation hours | 59891.21 | 128321.3 | 80505.3 |
| Game-related injury rate, injuries per 1000 player game-hours (95% CI)a | 2.30 (1.91, 2.78) | 4.63 (4.12, 5.20) | 5.61 (4.86, 6.48) |
| Game-related injury rate with >7 days of time-loss, injuries per 1000 player game-hours (95% CI)a | 1.32 (1.07, 1.63) | 2.80 (2.45, 3.20) | 3.78 (3.25, 4.39) |
| Number of practice-related injuries | 35 | 79 | 68 |
| Player practice participation hours | 53765.45 | 120814.4 | 75368.29 |
| Practice-related injury rate, injuries per 1000 player practice-hours (95% CI)a | 0.65 (0.47, 0.91) | 0.65 (0.51, 0.84) | 0.90 (0.68, 1.20) |
Crude rates with 95% Poisson CIs adjusted for cluster by team (offset by game or practice hours).
The most common game-related injury location was the head/face for each age group (Table 3). The shoulder/clavicle was the next most common region of injury for players under-18 and under-15, and these rates were markedly higher compared with those under-13. Concussion was the most common game-related injury type for each age group. The rates of more severe injury types such as joint sprains/dislocations and fractures were also notably higher in under-18 and under-15 groups compared with those under-13. When practice-related injury details were known, the head/face was the most common region of injury for all age groups and concussion was the most common injury type (Table 4).
Table 3.
Number and rates of game-related injuries per 1000 player game-hours among under-13, under-15 and under-18 youth ice hockey players by injury type and location.
| Under-13 |
Under-15 |
Under-18 |
||||
|---|---|---|---|---|---|---|
| Number (/138) | Ratea (95% CI) | Number (/594) | Ratea (95% CI) | Number (/452) | Ratea (95% CI) | |
| Injury location | ||||||
| Head/face | 88 | 1.47 (1.17, 1.85) | 290 | 2.26 (1.96, 2.61) | 185 | 2.30 (1.95, 2.71) |
| Neck/throat | 3 | 0.05 (0.01, 0.15) | 18 | 0.14 (0.09, 0.22) | 8 | 0.10 (0.04, 0.20) |
| Shoulder/clavicle | 4 | 0.07 (0.02, 0.17) | 55 | 0.43 (0.32, 0.58) | 64 | 0.80 (0.61, 1.04) |
| Upper arm/elbow/forearm | 6 | 0.10 (0.04, 0.22) | 20 | 0.16 (0.10, 0.25) | 11 | 0.14 (0.07, 0.26) |
| Wrist/hand | 4 | 0.07 (0.02, 0.17) | 42 | 0.33 (0.23, 0.46) | 38 | 0.47 (0.33, 0.67) |
| Back/side | 4 | 0.07 (0.02, 0.17) | 17 | 0.13 (0.08, 0.23) | 17 | 0.21 (0.13, 0.35) |
| Chest/ribs/abdomen | 3 | 0.05 (0.01, 0.15) | 13 | 0.10 (0.06, 0.18) | 6 | 0.07 (0.03, 0.19) |
| Pelvis/hips/groin/upper leg | 4 | 0.07 (0.02, 0.17) | 25 | 0.19 (0.12, 0.31) | 26 | 0.32 (0.18, 0.57) |
| Knee | 7 | 0.12 (0.05, 0.24) | 32 | 0.25 (0.17, 0.36) | 36 | 0.45 (0.30, 0.66) |
| Lower leg/ankle/foot | 9 | 0.15 (0.07, 0.29) | 25 | 0.19 (0.13, 0.29) | 19 | 0.24 (0.14, 0.39) |
| Other | 1 | 0.02 (0.00, 0.09) | 4 | 0.03 (0.01, 0.08) | 2 | 0.02 (0.00, 0.09) |
| Missing/unknown | 5 | 0.08 (0.03, 0.19) | 53 | 0.41 (0.29, 0.58) | 40 | 0.50 (0.35, 0.70) |
| Injury type | ||||||
| Contusion | 19 | 0.32 (0.19, 0.53) | 47 | 0.37 (0.27, 0.50) | 33 | 0.41 (0.27, 0.63) |
| Concussion | 86 | 1.44 (1.14, 1.81) | 287 | 2.24 (1.94, 2.58) | 181 | 2.25 (1.90, 2.66) |
| Joint/ligament sprain/dislocation | 12 | 0.20 (0.11, 0.36) | 60 | 0.47 (0.35, 0.62) | 76 | 0.94 (0.71, 1.25) |
| Fracture | 5 | 0.08 (0.03, 0.19) | 75 | 0.58 (0.45, 0.76) | 56 | 0.70 (0.50, 0.96) |
| Muscle strain/tendinitis | 7 | 0.12 (0.05, 0.24) | 53 | 0.41 (0.30, 0.57) | 40 | 0.50 (0.36, 0.69) |
| Abrasion/bleeding/burn/cut/blister | 1 | 0.02 (0.00, 0.09) | 3 | 0.02 (0.00, 0.07) | 5 | 0.06 (0.02, 0.14) |
| Other | 0 | 0.00 (0.00, 0.06) | 7 | 0.05 (0.02, 0.11) | 6 | 0.07 (0.03, 0.16) |
| Missing/unknown | 8 | 0.13 (0.06, 0.26) | 62 | 0.48 (0.36, 0.65) | 55 | 0.68 (0.51, 0.91) |
Crude rates per 1000 game-hours with 95% CIs adjusted for cluster by team. Exact CIs were calculated when number was less than 10.
Table 4.
Number and rates of practice-related injuries per 1000 player practice-hours among under-13, under-15 and under-18 youth ice hockey players by injury type and location.
| Under-13 |
Under-15 |
Under-18 |
||||
|---|---|---|---|---|---|---|
| Number (/35) | Ratea (95% CI) | Number (/79) | Ratea (95% CI) | Number (/68) | Ratea (95% CI) | |
| Injury location | ||||||
| Head/face | 20 | 0.37 (0.25, 0.56) | 35 | 0.29 (0.21, 0.40) | 10 | 0.13 (0.07, 0.26) |
| Neck/throat | 1 | 0.02 (0.00, 0.10) | 0 | 0.00 (0.00, 0.03) | 0 | 0.00 (0.00, 0.05) |
| Shoulder/clavicle | 0 | 0.00 (0.00, 0.07) | 4 | 0.03 (0.01, 0.08) | 9 | 0.12 (0.05, 0.23) |
| Upper arm/elbow/forearm | 0 | 0.00 (0.00, 0.07) | 1 | 0.01 (0.00, 0.05) | 2 | 0.03 (0.00, 0.10) |
| Wrist/hand | 2 | 0.04 (0.00, 0.13) | 8 | 0.07 (0.03, 0.13) | 9 | 0.12 (0.05, 0.23) |
| Back/side | 0 | 0.00 (0.00, 0.07) | 0 | 0.00 (0.00, 0.03) | 3 | 0.04 (0.01, 0.12) |
| Chest/ribs/abdomen | 0 | 0.00 (0.00, 0.07) | 0 | 0.00 (0.00, 0.03) | 2 | 0.03 (0.00, 0.10) |
| Pelvis/hips/groin/upper leg | 6 | 0.11 (0.04, 0.24) | 2 | 0.07 (0.00, 0.06) | 5 | 0.07 (0.02, 0.15) |
| Knee | 2 | 0.04 (0.00, 0.13) | 6 | 0.05 (0.02, 0.11) | 6 | 0.08 (0.03, 0.17) |
| Lower leg/ankle/foot | 1 | 0.02 (0.00, 0.10) | 3 | 0.02 (0.01, 0.07) | 6 | 0.08 (0.03, 0.17) |
| Other | 0 | 0.00 (0.00, 0.07) | 0 | 0.00 (0.00, 0.03) | 0 | 0.00 (0.00, 0.05) |
| Missing/unknown | 3 | 0.06 (0.01, 0.16) | 20 | 0.17 (0.10, 0.28) | 16 | 0.21 (0.10, 0.45) |
| Injury type | ||||||
| Contusion | 5 | 0.09 (0.03, 0.22) | 6 | 0.05 (0.02, 0.11) | 5 | 0.07 (0.02, 0.15) |
| Concussion | 20 | 0.37 (0.25, 0.56) | 33 | 0.27 (0.20, 0.38) | 10 | 0.13 (0.07, 0.26) |
| Joint/ligament sprain/dislocation | 0 | 0.00 (0.00, 0.07) | 10 | 0.08 (0.04, 0.18) | 14 | 0.19 (0.10, 0.35) |
| Fracture | 1 | 0.02 (0.00, 0.10) | 5 | 0.04 (0.01, 0.10) | 9 | 0.12 (0.05, 0.23) |
| Muscle strain/tendinitis | 7 | 0.13 (0.05, 0.27) | 3 | 0.02 (0.01, 0.07) | 8 | 0.11 (0.05, 0.21) |
| Abrasion/bleeding/burn/cut/blister | 0 | 0.00 (0.00, 0.07) | 1 | 0.01 (0.00, 0.05) | 1 | 0.01 (0.00, 0.07) |
| Other | 0 | 0.00 (0.00, 0.07) | 1 | 0.01 (0.00, 0.05) | 0 | 0.00 (0.00, 0.05) |
| Missing/unknown | 2 | 0.04 (0.00, 0.13) | 20 | 0.17 (0.10, 0.28) | 21 | 0.28 (0.14, 0.54) |
Crude rates per 1000 practice-hours with 95% CIs adjusted for cluster by team. Exact CIs were calculated when number was less than 10.
The results of the multiple multilevel Poisson regression models evaluating each outcome are summarised in Table 5. There was no evidence of non-linearity with weight by itself or within any age group. Female players had a significantly higher rate of injury (incidence rate ratio (IRR) = 1.57; 95% CI: 1.18–2.08) and injury resulting in >7 days of time-loss (IRR = 1.66; 95% CI: 1.19–2.32) than male players (despite 431/599 female players in leagues disallowing body checking). Policy not permitting bodychecking was associated with a 56% lower rate of injury (IRR = 0.44; 95% CI: 0.35–0.55) and a 59% lower rate of injury resulting in >7 days of time-loss (IRR = 0.41; 95% CI: 0.31–0.54). Having a previous injury within the last 12 months and lifetime concussion history were both associated with higher rates of injury and injury with >7 days of time-loss. Relative to forwards, goaltenders had a lower rate of injury (IRR = 0.54; 95% CI: 0.40–0.72) and injury with >7 days of time-loss (IRR = 0.63; 95% CI: 0.45–0.89), while there was no difference in injury rates between forwards and defensive players. Relative to the under-13 age group, under-18 age groups had higher rates of game-related injury (IRR = 1.23; 95% CI: 0.89–1.70) and injury resulting in >7 days of time-loss (IRR = 1.45; 95% CI: 0.99–2.13), but these results were not statistically significant. Year of play, level of play, and player weight were not associated with either game-related outcomes. Female sex (IRR = 1.90; 95% CI: 1.10–3.28) and lifetime concussion history (IRR = 1.55; 95% CI: 1.09–2.20) were the only factors significantly associated with a higher rate of practice-related injury. When comparing the estimates of the models between all injury resulting in >7 days of time-loss with the sensitivity analysis restricting to only musculoskeletal injury with >7 days of time loss (Table S1 in supplemental content), the results were generally consistent. Of note in the musculoskeletal restricted model, under-18 players had significantly higher rates relative to under-13 players, while the effects for females (relative to males) and those with a previous concussion history were no longer statistically significant (but point estimates remained higher for those groups). The point estimate for sub-elite players was below 1 (suggesting lower rates of more severe musculoskeletal injury) but remained non-significant.
Table 5.
Adjusted incidence rate ratios for injury outcomes in youth ice hockey players.
| Incidence rate ratio (95% CI) |
|||
|---|---|---|---|
| Potential risk factor | Game-related injury | Game-related injury >7 days of time-loss | Practice-related injury |
| Age group | |||
| Under-13 | 1 (reference) | 1 (reference) | 1 (reference) |
| Under-15 | 1.06 (0.78, 1.41) | 1.07 (0.76, 1.51) | 0.81 (0.46, 1.43) |
| Under-18 | 1.23 (0.89, 1.70) | 1.45 (0.99, 2.13) | 0.90 (0.45, 1.78) |
| Bodychecking policy | |||
| Permitted | 1 (reference) | 1 (reference) | 1 (reference) |
| Not permitted | 0.44 (0.35, 0.55)† | 0.41 (0.31, 0.54)† | 0.84 (0.51, 1.40) |
| Year of play | |||
| First | 1 (reference) | 1 (reference) | 1 (reference) |
| Second | 1.00 (0.87, 1.15) | 1.07 (0.90, 1.27) | 1.06 (0.76, 1.49) |
| Third | 1.01 (0.75, 1.36) | 1.05 (0.74, 1.49) | 1.28 (0.66, 2.48) |
| Level of play | |||
| Elite (top 20–30%) | 1 (reference) | 1 (reference) | 1 (reference) |
| Sub-elite (lower 70–80%) | 1.07 (0.90, 1.29) | 1.15 (0.94, 1.41) | 1.19 (0.82, 1.73) |
| Sex | |||
| Male | 1 (reference) | 1 (reference) | 1 (reference) |
| Female | 1.57 (1.18, 2.08)† | 1.66 (1.19, 2.32)† | 1.90 (1.10, 3.28)† |
| Player weight | 1.00 (0.99, 1.00) | 0.99 (0.98, 1.00) | 1.00 (0.98, 1.02) |
| Previous injury in the last yeara | |||
| No | 1 (reference) | 1 (reference) | 1 (reference) |
| Yes | 1.46 (1.26, 1.70)† | 1.44 (1.18, 1.75)† | 1.26 (0.87, 1.82) |
| Previous concussionb | |||
| No | 1 (reference) | 1 (reference) | 1 (reference) |
| Yes | 1.41 (1.23, 1.62)† | 1.50 (1.27, 1.78)† | 1.55 (1.09, 2.20)† |
| Position | |||
| Forward | 1 (reference) | 1 (reference) | 1 (reference) |
| Defense | 1.00 (0.87, 1.14) | 1.05 (0.89, 1.25) | 0.93 (0.65, 1.34) |
| Goalie | 0.54 (0.40, 0.72)† | 0.63 (0.45, 0.89)† | 1.09 (0.65, 1.83) |
The covariate ‘previous injury in the last year’ includes any concussion that occurred in the previous 12 months.
The covariate ‘previous concussion’ includes any concussion without a date limit.
Statistically significant at p < .05.
Discussion
This study investigated potential risk factors associated with rates of game and practice-related injury in Canadian youth ice hockey players using data from a large, prospective cohort. Consistent with other studies, our results suggest that one of the strongest factors associated with game-related injury rates is policy permitting bodychecking [7,10–14,27]. Collectively, the substantial evidence demonstrating lower game-related injury rates following policy disallowing bodychecking suggests significant public health impact. Research examining injury risk after policy restricting bodychecking has shown no overall unintended injury consequences related to fewer years of bodychecking experience for adolescent players in leagues permitting bodychecking [9,15,16]. This counters the argument made in the ice hockey community that bodychecking should be implemented earlier to protect players from injury when they are older.
Our results suggest that females had a 57% greater rate of game-related injury, a 66% greater rate of game-related injury resulting in >7 days of time-loss, and a 90% greater rate of practice-related injury relative to male players. A higher proportion of females who participated in ‘female-only’ leagues were injured relative to females playing in leagues on predominately male-based teams. Potentially, differences may exist between the sociocultural context of females playing in male-dominated leagues and females in ‘female-only’ leagues related to injury reporting or differences within gameplay affecting risk of injury [28]. While ‘female-only’ leagues do not permit bodychecking at any age or level of play, these leagues have been noted to have high levels of body contact (i.e. positional play that includes angling players into the boards) which, when combined with the fast speeds of play, may result in inadvertent collisions and opportunities for injury [29]. Future studies are needed to better understand why females are at greater risk of injury to inform prevention programs [28].
Players with a previous injury history in the last 12 months and a lifetime history of concussion had significantly higher rates of game-related injury and injury with >7 days of time-loss. This finding is consistent with several previous youth ice hockey studies as well as research in other sports [7–10,15,16,22,30]. Also of note was that players with a lifetime concussion history had a significant 55% higher rate of practice-related injury. Goalies had a significantly lower rate of both game-related injury outcomes relative to forwards, similar to the results of prior studies [7,8,10,12–14,16]. While under-18 leagues did not have significantly higher rates of injury than under-13 leagues, the trend of higher injury rates in increasingly older age groups is consistent with previous studies [8,10,13,14].
Year of play, level of play, and player weight were not related with any injury outcomes in the current study. First year players in under-13 leagues have been suggested to have a higher risk of injury relative to second year players [8,10], but year of play was not significantly associated in older age groups [13,14]. Studies have varied on whether level of play is associated with injury risk and for which levels [7,9,10,12,15,16,20]. This may be related to different populations between studies and categorisations of level of play. Previous studies suggest that injury rates were highest for players in the lowest weight category [7,12], while other studies suggest an increased risk in heavier players [13]. For the under-15 age group, higher point estimates for all injury and concussion outcomes were seen for lighter and heavier categories relative to the middle category [16], but this relationship was not seen in under-18 players [15]. These conflicting findings may be due to different populations of interest and different categorisations or analysis of weight (i.e. examined continuously or categorically).
The estimates from the model that was restricted to only musculoskeletal injury with >7 days of time loss were largely consistent with the model examining all injury with >7 days of time loss (i.e. including concussion). These findings are also generally consistent with concussion specific outcomes in this cohort; however, sub-elite players were associated with higher rates of concussion outcomes [31], but not injury rates in the current study. Players in the under-18 age group had a significantly higher rate of musculoskeletal injury with >7 days of time loss than under-13 and under-15 (IRR = 1.60; 95% CI: 1.13–2.26, as obtained from linear combinations from the restricted model), but age group was not related to game-related concussion outcomes [31]. Factors related to other specific injury types or locations remain an opportunity for further evaluation.
Limitations of the Safe2Play cohort include self-reporting of covariate data, which may change over the course of the season (e.g. position of play). As previously suggested, several factors may affect the return to play decision after an injury such as importance of the game, motivation, player personality characteristics and parental influence [13,14]. Each of these may affect the precision of the IRR estimates of the injury outcome based on time-loss. Survivor bias is possible in sport-related longitudinal cohorts when players stop their sport participation after an injury leading to loss to follow-up in the cohort. The players that continue to participate may have a lower underlying risk profile than those that stopped playing and this remains an area for future research [9,15]. Selection bias is possible based on teams not willing to participate; however, the main reason for non-participation was the inability to identify someone on the team willing to record playing exposure and injury details. As such, it is unlikely that non-participation would be related to any exposure variable and injury outcomes. The multiple multilevel models included a random effect at a team level to account for clustering effects but did not include a random effect at the individual level. While models accounting for both levels of random effects were attempted, these crossed-effects are known for their prohibitively long computational times when adjusting for several covariates [32].
Conclusions
Based on data from a large 5-year longitudinal cohort in Canadian youth ice hockey, several factors were associated with game-related injury and injury resulting in >7 days of time-loss. The factors associated with higher injury rates included being female, playing in older age groups, having a history of injury within the previous 12 months, and a lifetime history of concussion. Factors associated with a lower rate of injury included the goaltender position and playing in leagues where policy disallowed bodychecking. Bodychecking policy continues to be the most reported modifiable risk factor in youth ice hockey and policy disallowing bodychecking can have the greatest public health impact in reducing the burden of injury. Future research in ice hockey should target females and female-specific injury prevention strategies.
Supplementary Material
Acknowledgements
The Sport Injury Prevention Research Centre (one of the International Research Centres for Prevention of Injury and Protection of Athlete Health supported by the International Olympic Committee). Carolyn Emery holds a Canada Research Chair (Tier 1). This research was possible due to the support from Hockey Calgary, Airdrie Minor Hockey Association, Hockey Edmonton, BC Hockey, Hockey Alberta and Hockey Canada. This research could not have been completed without the support of many players, parents, coaches, team designates, research assistants, physiotherapists, athletic therapists, student therapists and study physicians.
Funding Statement
The funding for this study was provided by Alberta Innovates Health Solutions (Collaborative Research and Innovation Opportunities Programme) under Grant # (3685), the Canadian Institutes of Health Research (Institute of Neuroscience, Mental Health and Addictions) under Grant (#293332) and the Hotchkiss Brain Institute (University of Calgary).
Author contributions
PHE, J-MG, KJS and CAE contributed to the data collection, entry or data cleaning. PHE, J-MG, StB, BEH and CAE contributed to the data analysis and interpretation of study results. ShB and MM contributed to all aspects of data collection at study sites outside of Calgary. CAE, KJS and BEH contributed to acquisition of funding, study design, and CAE led all aspects of the cohort. All authors critically reviewed and edited the manuscript before submission.
Consent form
All participants provided either written assent with parental consent or informed player consent.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
All data relevant to the study are included in the article or uploaded as supplemental information.
References
- 1.International Ice Hockey Federation . Survey of players; 2023. [cited 2023 Jan 5]. Available from: https://www.iihf.com/en/static/5324/survey-of-players
- 2.Hockey Canada . 2019–2020 annual report; 2023. [cited 2023 Jan 5]. Available from: https://cdn.hockeycanada.ca/hockey-canada/Corporate/About/Downloads/2019-20-hockey-canada-annual-report-e.pdf
- 3.World Health Organization . Global action plan on physical activity 2018–2030: more active people for a healthier world; 2018. [Google Scholar]
- 4.Pfister T, Pfister K, Hagel B, et al. The incidence of concussion in youth sports: a systematic review and meta-analysis. Br J Sports Med. 2016;50(5):292–297. doi: 10.1136/bjsports-2015-094978. [DOI] [PubMed] [Google Scholar]
- 5.Emery CA, Meeuwisse WH, McAllister JR.. Survey of sport participation and sport injury in Calgary and area high schools. Clin J Sport Med. 2006;16(1):20–26. doi: 10.1097/01.jsm.0000184638.72075.b7. [DOI] [PubMed] [Google Scholar]
- 6.Black AM, Meeuwisse DW, Eliason PH, et al. Sport participation and injury rates in high school students: a Canadian survey of 2029 adolescents. J Safety Res. 2021;78:314–321. doi: 10.1016/j.jsr.2021.06.008. [DOI] [PubMed] [Google Scholar]
- 7.Emery C, Kang J, Shrier I, et al. Risk of injury associated with body checking among youth ice hockey players. JAMA. 2010;303(22):2265–2272. doi: 10.1001/jama.2010.755. [DOI] [PubMed] [Google Scholar]
- 8.Black AM, Macpherson AK, Hagel BE, et al. Policy change eliminating body checking in non-elite ice hockey leads to a threefold reduction in injury and concussion risk in 11- and 12-year-old players. Br J Sports Med. 2016;50(1):55–61. doi: 10.1136/bjsports-2015-095103. [DOI] [PubMed] [Google Scholar]
- 9.Emery C, Kang J, Shrier I, et al. Risk of injury associated with bodychecking experience among youth hockey players. CMAJ. 2011;183(11):1249–1256. doi: 10.1503/cmaj.101540. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Black AM, Hagel BE, Palacios-Derflingher L, et al. The risk of injury associated with body checking among Pee Wee ice hockey players: an evaluation of Hockey Canada’s national body checking policy change. Br J Sports Med. 2017;51(24):1767–1772. doi: 10.1136/bjsports-2016-097392. [DOI] [PubMed] [Google Scholar]
- 11.Emery CA, Black AM, Kolstad A, et al. What strategies can be used to effectively reduce the risk of concussion in sport? A systematic review. Br J Sports Med. 2017;51(12):978–984. doi: 10.1136/bjsports-2016-097452. [DOI] [PubMed] [Google Scholar]
- 12.Emery C, Hagel B, Decloe M, et al. Risk factors for injury and severe injury in youth ice hockey: a systematic review of the literature. Inj Prev. 2010;16(2):113–118. doi: 10.1136/ip.2009.022764. [DOI] [PubMed] [Google Scholar]
- 13.Emery C, Palacios-Derflingher L, Black AM, et al. Does disallowing body checking in non-elite 13- to 14-year-old ice hockey leagues reduce rates of injury and concussion? A cohort study in two Canadian provinces. Br J Sports Med. 2020;54(7):414–420. doi: 10.1136/bjsports-2019-101092. [DOI] [PubMed] [Google Scholar]
- 14.Emery CA, Eliason P, Warriyar V, et al. Body checking in non-elite adolescent ice hockey leagues: it is never too late for policy change aiming to protect the health of adolescents. Br J Sports Med. 2022;56(1):12–17. doi: 10.1136/bjsports-2020-103757. [DOI] [PubMed] [Google Scholar]
- 15.Eliason PH, Hagel BE, Palacios-Derflingher L, et al. Bodychecking experience and rates of injury among ice hockey players aged 15–17 years. CMAJ. 2022;194(24):E834–E842. doi: 10.1503/cmaj.211718. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Eliason P, Hagel BE, Palacios-Derflingher L, et al. No association found between body checking experience and injury or concussion rates in adolescent ice hockey players. Br J Sports Med. 2022;56(23):1337–1344. doi: 10.1136/bjsports-2021-104691. [DOI] [PubMed] [Google Scholar]
- 17.Lacny S, Marshall DA, Currie G, et al. Reality check: the cost-effectiveness of removing body checking from youth ice hockey. Br J Sports Med. 2014;48(17):1299–1305. doi: 10.1136/bjsports-2014-093493. [DOI] [PubMed] [Google Scholar]
- 18.Currie GR, Lee R, Black AM, et al. An economic evaluation of disallowing body checking in 11- to 12-year-old ice hockey leagues. Sports Health. 2022;14(2):292–298. doi: 10.1177/19417381211021551. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Currie GR, Lee R, Palacios-Derflingher L, et al. Reality check 2: the cost-effectiveness of policy disallowing body checking in non-elite 13- to 14-year-old ice hockey players. Int J Environ Res Public Health. 2021;18(12):6322. doi: 10.3390/ijerph18126322. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Emery C, Meeuwisse W.. Injury rates, risk factors, and mechanisms of injury in minor hockey. Am J Sports Med. 2006;34(12):1960–1969. doi: 10.1177/0363546506290061. [DOI] [PubMed] [Google Scholar]
- 21.McCrory P, Meeuwisse WH, Aubry M, et al. Consensus statement on concussion in sport: the 4th International Conference on Concussion in Sport held in Zurich, November 2012. Br J Sports Med. 2013;47(5):250–258. doi: 10.1136/bjsports-2013-092313. [DOI] [PubMed] [Google Scholar]
- 22.McCrory P, Meeuwisse W, Dvorak J, et al. Consensus statement on concussion in sport—the 5th international conference on concussion in sport held in Berlin, October 2016. Br J Sports Med. 2017;51(11):838–847. [DOI] [PubMed] [Google Scholar]
- 23.Fuller CW, Ekstrand J, Junge A, et al. Consensus statement on injury definitions and data collection procedures in studies of football (soccer) injuries. Scand J Med Sci Sports. 2006;16(2):83–92. doi: 10.1111/j.1600-0838.2006.00528.x. [DOI] [PubMed] [Google Scholar]
- 24.Bahr R, Clarsen B, Derman W, et al. International Olympic Committee consensus statement: methods for recording and reporting of epidemiological data on injury and illness in sport 2020 (including STROBE Extension for Sport Injury and Illness Surveillance (STROBE-SIIS)). Br J Sports Med. 2020;54(7):372–389. doi: 10.1136/bjsports-2019-101969. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Kang J, Yuan Y, Emery C.. Assessing remedies for missing weekly individual exposure in sport injury studies. Inj Prev. 2014;20(3):177–182. doi: 10.1136/injuryprev-2012-040537. [DOI] [PubMed] [Google Scholar]
- 26.StataCorp . Stata Statistical Software: release 13. College Station (TX): StataCorp LP; 2013. [Google Scholar]
- 27.Emery CA, Black AM.. Are rule changes the low-hanging fruit for concussion prevention in youth sport? JAMA Pediatr. 2019;173(4):309–310. doi: 10.1001/jamapediatrics.2018.5498. [DOI] [PubMed] [Google Scholar]
- 28.Eliason P, Galarneau J-M, Schneider K, et al. Higher hazard of concussion combined with recurrent concussion in female youth ice hockey compared with males: an opportunity for concussion prevention. Sports Orthopaed Traumatol. 2023;39(2):113–123. doi: 10.1016/j.orthtr.2023.04.005. [DOI] [Google Scholar]
- 29.Heming EE, Sobry AJ, Cairo AL, et al. Higher rates of head contacts, body checking, and suspected injuries in ringette than female ice hockey: time to ring in opportunities for prevention. Clin J Sport Med. 2022;33(2):151–156. doi: 10.1097/JSM.0000000000001089. [DOI] [PubMed] [Google Scholar]
- 30.Krolikowski MP, Black AM, Palacios-Derflingher L, et al. The effect of the "zero tolerance for head contact" rule change on the risk of concussions in youth ice hockey players. Am J Sports Med. 2016;45:468–473. [DOI] [PubMed] [Google Scholar]
- 31.Eliason P, Galarneau J-M, Shill I, et al. Factors associated with concussion rates in youth ice hockey players: data from the largest longitudinal cohort study in Canadian youth ice hockey. Clin J Sport Med. 2023;33(5):497–504. doi: 10.1097/JSM.0000000000001177. [DOI] [PubMed] [Google Scholar]
- 32.Rabe-Hesketh S, Skrondal A. Multilevel and longitudinal modeling using Stata. College Station, Texas: STATA Press; 2008. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data relevant to the study are included in the article or uploaded as supplemental information.