Abstract
Objective
To evaluate sex differences in incidence rates (IRs) of anterior cruciate ligament (ACL) injury by sport type (collision, contact, limited contact, and noncontact).
Data Sources
A systematic review was performed using the electronic databases PubMed (1969–January 20, 2017) and EBSCOhost (CINAHL, SPORTDiscus; 1969–January 20, 2017) and the search terms anterior cruciate ligament AND injury AND (incidence OR prevalence OR epidemiology).
Study Selection
Studies were included if they provided the number of ACL injuries and the number of athlete-exposures (AEs) by sex or enough information to allow the number of ACL injuries by sex to be calculated. Studies were excluded if they were analyses of previously reported data or were not written in English.
Data Extraction
Data on sport classification, number of ACL injuries by sex, person-time in AEs for each sex, year of publication, sport, sport type, and level of play were extracted for analysis.
Data Synthesis
We conducted IR and IR ratio (IRR) meta-analyses, weighted for study size and calculated. Female and male athletes had similar ACL injury IRs for the following sport types: collision (2.10/10 000 versus 1.12/10 000 AEs, IRR = 1.14, P = .63), limited contact (0.71/10 000 versus 0.29/10 000 AEs, IRR = 1.21, P = .77), and noncontact (0.36/10 000 versus 0.21/10 000 AEs, IRR = 1.49, P = .22) sports. For contact sports, female athletes had a greater risk of injury than male athletes did (1.88/10 000 versus 0.87/10 000 AEs, IRR = 3.00, P < .001). Gymnastics and obstacle-course races were outliers with respect to IR, so we created a sport category of fixed-object, high-impact rotational landing (HIRL). For this sport type, female athletes had a greater risk of ACL injury than male athletes did (4.80/10 000 versus 1.75/10 000 AEs, IRR = 5.51, P < .001), and the overall IRs of ACL injury were greater than all IRs in all other sport categories.
Conclusions
Fixed-object HIRL sports had the highest IRs of ACL injury for both sexes. Female athletes were at greater risk of ACL injury than male athletes in contact and fixed-object HIRL sports.
Keywords: epidemiology, knee, sprain, athletes
Anterior cruciate ligament (ACL) injury is a common and debilitating injury among athletes. It can occur from both contact and noncontact mechanisms1,2 and has a relatively high incidence in sports involving deliberate contact.1 The relationship between the amount of inherent contact in a sport and the risk of injury to the ACL is unclear, especially when including sex as a variable. In the United States, collision sports, such as football, rugby, and wrestling, are male dominated. Females play collision sports such as ice hockey and rugby, but contact sports such as soccer and basketball are more commonly cited when comparing ACL injury risk by sex. Whereas the rate of ACL injury in females playing soccer was among the highest, it was also high in limited-contact and noncontact sports, including alpine skiing and gymnastics, respectively.1,3 Hootman et al1 found some of the highest rates of ACL injury among males in collision sports (spring and fall football and wrestling). Conversely, in females, gymnastics (noncontact), followed by soccer and basketball, resulted in the highest rates of ACL injury.1
Deliberate contact during sport is believed to contribute to increased rates of ACL injury.4 However, given that many ACL injuries result from noncontact mechanisms, the role of sport type in ACL injury is uncertain. Moreover, it is unclear if a sex difference in ACL injury incidence exists when stratifying by sport type (eg, collision, full contact, limited contact, and noncontact). Therefore, the purpose of our systematic review and meta-analysis was to compare the incidence rates (IRs) of ACL injury of male and female athletes in each of the following sport types: collision, contact, limited contact, and noncontact.
METHODS
We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses5 (PRISMA) guidelines when conducting and reporting this systematic review and meta-analysis.
Literature Search
A systematic review of the current literature was performed using the electronic databases PubMed (1969–January 20, 2017) and EBSCOhost (CINAHL and SPORTDiscus; 1969–January 20, 2017) and the following search terms: anterior cruciate ligament AND injury AND (incidence OR prevalence OR epidemiology). Results were further limited to peer-reviewed articles written in English.
In addition to the electronic search, we contacted experts in the field for further suggestions and examined references cited in review papers to identify any other relevant articles for potential inclusion. Publication details from all studies identified in the literature search were exported to bibliographic software (Endnote X7; Clarivate Analytics, Philadelphia, PA).
Selection Criteria
Given the large number of identified studies, a single author (A.M.M.) performed the initial screening of articles for inclusion. Any gray areas were discussed with the second author (D.K.S.), and any disagreements were decided by the senior author (G.D.M.). Articles were screened first by title, second by abstract, and third by full text according to the inclusion and exclusion criteria. We included articles in which the total number of ACL injuries and the total number of athlete-exposures (AEs) were reported by sex and the data were provided in such a way that the number of ACL injuries by sex could be calculated. We excluded articles that included further analyses on previously reported prospective studies, were written in languages other than English, or were review papers. Full texts were retrieved when the title or abstract met the selection criteria or when the status could not be determined from the title and abstract alone.
Data Extraction and Analysis
The primary variables extracted were the sport classification, number of ACL injuries for each sex, and person-time in AEs for each sex. Sports were classified as follows: collision (contact with an opponent or object is inherent), contact (contact with an opponent or object is acceptable), limited contact (contact with an opponent or object is discouraged), and noncontact (contact with an opponent or object is unexpected; Table 1). For each sport classification, we calculated the overall ACL injury rate and separate IRs for men and women. The IR ratio (IRR) between men and women was subsequently calculated using only data from studies in which injury-risk data were reported for both men and women to allow direct comparisons. Additional extracted data included year of publication, sport, sport type, and level of play. One author (A.M.M.) recorded all pertinent data from the included articles, and another author (D.K.S.) independently reviewed those data for accuracy and completeness.
Table 1.
Sport Classification Key
| Classification |
Sport |
| Collision | Boxing |
| Boys'/men's lacrosse | |
| Close-quarters combat | |
| Football | |
| Handball | |
| Ice hockey | |
| Rugby | |
| Wrestling | |
| Contact | Basketball |
| Field hockey | |
| Girls'/women's lacrosse | |
| Judo | |
| Soccer | |
| Limited contact | Baseball |
| Cheerleading | |
| Fencing | |
| Flickerball | |
| Floorball | |
| Frisbee | |
| Softball | |
| Volleyball | |
| Noncontact | Alpine skiing |
| Dance/ballet | |
| Running/track | |
| Fixed-object high-impact rotational landing | Gymnastics |
| Indoor obstacle-course test | |
| Obstacle-course race |
The reported person-time unit was not uniform across studies. Therefore, to establish a common metric, we tabulated AEs. When the number of player-hours was reported, the number of AEs was estimated by dividing player-hours by 2. The assumption for converting player-hours to AEs was that each AE (1 game or 1 practice) on average would last about 2 hours. In addition, not all authors reported the number of ACL injuries by sex; instead, they provided IRs by sex. For these studies, the number of AEs and the reported IRs were used to calculate the number of ACL injuries by sex (number of ACL injuries by sex = total AEs by sex × the rate numerator by sex/the rate denominator by sex).6–8 For studies in which the number of ACL injuries by sex could not be estimated, we e-mailed the authors to gather those data. If they did not have access to the information or did not respond, the study was excluded from the meta-analysis.9–15
Risk of Bias Assessment
Included studies were critically appraised independently by 2 authors (A.M.M., D.K.S.). Given that most included articles described observational cohort studies that did not include an intervention, traditional checklists were not appropriate. After a thorough search for tools to appraise observational cohort studies, we decided that the tool best suited to be used quantitatively was the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies.16 This tool, available through the National Institutes of Health (Bethesda, MD), assesses criteria such as participation rate, whether exposure data were collected before the outcome, whether the time frame was sufficient to allow for the outcome to occur, and the number of participants lost to follow-up after baseline. If a criterion was met, the item was scored as 1. If it was absent or not reported, the item was scored as zero. The maximum score possible was 14. Items were scored independently by 2 authors (A.M.M., D.K.S.). These authors discussed any discrepancies in scoring. For discrepancies that could not be resolved, a third author (G.D.M.) was consulted for arbitration. Given that the included studies with interventions were treated as cohort studies in the analyses, they were assessed with the same tool, which allowed for quality comparisons across all included studies.
Statistical Analysis
The number of included studies per analysis varied. For the total IR, any study in which authors reported the rate of either sex was included. For the IR by sex, any study in which the authors reported female or male rates was included for the respective analyses. Only studies that included both female and male athletes were used to calculate ratios. The ACL injury IR in noncontact sports comprised sports with marked differences in ACL injury IRs. Given that several outliers were present, we subdivided the category into sports that did and sports that did not include a fixed-object and high-impact landing. These latter sports were removed from the noncontact category, and a new fixed-object, high-impact rotational-landing (HIRL) category was created. Fixed-object HIRL sports were defined as noncontact sports that included high-impact landings from fixed objects, such as beams, vaults, and obstacles. Injury IRs for the individual studies were summarized in forest plots for the following groups by total, female, and male IRs: collision, contact, limited-contact, noncontact, and fixed-object HIRL sports. These rates were multiplied to calculate ACL injury IRs per 10 000 AEs in each respective group. Incidence rate ratios for women versus men were calculated for each group and summarized in forest plots.
Injury data were analyzed using R (version 3.3.2; R Foundation for Statistical Computing, Vienna, Austria) and the R packages meta and metafor with the functions metarate for IR and metainc for IRR weighted for individual study size. When AEs but no events (ACL injuries) were present, a continuity correction was applied. The default value for the continuity correction, 0.5, was used to calculate individual point estimates and the 95% confidence interval (CI) and to conduct a meta-analysis based on the inverse variance method. We set the α level at .05.
RESULTS
The electronic literature search yielded 3774 abstracts for initial review. After duplicates were removed, a total of 1300 unique titles remained. We screened the titles and abstracts and removed 1155 articles for lack of relevance to the research. The remaining 145 articles were manually cross-referenced, and experts were consulted to identify additional relevant articles, resulting in the inclusion of 17 more articles. Full texts of these 162 articles were obtained and assessed for the inclusion and exclusion criteria. We contacted the corresponding authors of the included articles for additional information as needed. At the end of the search, 36 articles were included in the study.1,6–8,17–48 An outline of the literature review process is presented in Figure 1. The data that were extracted for each analysis and can be used to determine which studies were included in each analysis are shown in Table 2.
Figure 1.
Flow chart of the literature review process.
Table 2.
Data Extracted From Each Included Study Continued on Next Page
| Article (y) |
Sport |
Classification |
Level |
Anterior Cruciate Ligament Injuries |
Athlete-Exposures |
|||
| Female |
Male |
Total |
Female |
Male |
||||
| Agel et al38 (2016) | Football | Collision | Collegiate | 0 | 513 | 513 | 0 | 3 017 647 |
| Agel et al38 (2016) | Ice hockey | Collision | Collegiate | 3 | 15 | 18 | 150 000 | 500 000 |
| Agel et al38 (2016) | Lacrosse | Collision | Collegiate | 0 | 46 | 46 | 0 | 353 846 |
| Agel et al38 (2016) | Wrestling | Collision | Collegiate | 0 | 34 | 34 | 0 | 226 667 |
| Beynnon et al17 (2014) | Lacrosse | Collision | High school | 0 | 7 | 7 | 0 | 121 583 |
| Beynnon et al17 (2014) | Lacrosse | Collision | Collegiate | 0 | 6 | 6 | 0 | 71 731 |
| Beynnon et al17 (2014) | Football | Collision | High school | 0 | 8 | 8 | 0 | 144 233 |
| Beynnon et al17 (2014) | Football | Collision | Collegiate | 0 | 3 | 3 | 0 | 18 417 |
| Beynnon et al17 (2014) | Rugby | Collision | Collegiate | 6 | 3 | 9 | 14 723 | 17 886 |
| Brooks et al6 (2005) | Rugby | Collision | Professional | 0 | 2 | 2 | 0 | 98 205 |
| Dallalana et al18 (2007) | Rugby | Collision | Professional | 0 | 9 | 9 | 0 | 108 920 |
| Dragoo et al19 (2012) | Football | Collision | Collegiate | 0 | 318 | 318 | 0 | 2 222 155 |
| Gwinn et al22 (2000) | Rugby | Collision | Collegiate | 3 | 4 | 7 | 8475 | 22 788 |
| Gwinn et al22 (2000) | Instructional wrestling | Collision | Amateur | 1 | 2 | 3 | 1306 | 10 582 |
| Hootman et al1 (2007) | Football | Collision | Collegiate | 0 | 2538 | 2538 | 0 | 13 142 929 |
| Hootman et al1 (2007) | Ice hockey | Collision | Collegiate | 3 | 78 | 81 | 100 000 | 1 300 000 |
| Hootman et al1 (2007) | Lacrosse | Collision | Collegiate | 0 | 131 | 131 | 0 | 1 091 667 |
| Hootman et al1 (2007) | Wrestling | Collision | Collegiate | 0 | 147 | 147 | 0 | 1 336 364 |
| Joseph et al23 (2013) | Football | Collision | High school | 0 | 286 | 286 | 0 | 2 580 637 |
| Joseph et al23 (2013) | Wrestling | Collision | High school | 0 | 27 | 27 | 0 | 809 430 |
| Levy et al26 (1997) | Rugby | Collision | Collegiate | 21 | 0 | 21 | 58 296 | 0 |
| Mountcastle et al29 (2007) | Ice hockey | Collision | Collegiate | 0 | 2 | 2 | 0 | 39 587 |
| Mountcastle et al29 (2007) | Lacrosse | Collision | Collegiate | 0 | 8 | 8 | 0 | 39 204 |
| Mountcastle et al29 (2007) | Football | Collision | Collegiate | 0 | 52 | 52 | 0 | 223 307 |
| Mountcastle et al29 (2007) | Football | Collision | Amateur | 1 | 52 | 53 | 1828 | 129 956 |
| Mountcastle et al29 (2007) | Wrestling | Collision | Collegiate | 0 | 6 | 6 | 0 | 47 039 |
| Mountcastle et al29 (2007) | Wrestling | Collision | Amateur | 0 | 10 | 10 | 0 | 149 022 |
| Mountcastle et al29 (2007) | Wrestling | Collision | Amateur | 0 | 4 | 4 | 0 | 48 203 |
| Mountcastle et al29 (2007) | Close-quarters combat | Collision | Amateur | 0 | 2 | 2 | 37 184 | 150 606 |
| Mountcastle et al29 (2007) | Boxing | Collision | Amateur | 0 | 1 | 1 | 0 | 165 376 |
| Mountcastle et al29 (2007) | Boxing | Collision | Amateur | 0 | 2 | 2 | 0 | 41 270 |
| Mountcastle et al29 (2007) | Handball | Collision | Amateur | 4 | 4 | 8 | 25 090 | 25 090 |
| Mountcastle et al29 (2007) | Handball | Collision | Amateur | 0 | 2 | 2 | 13 564 | 39 348 |
| Mountcastle et al29 (2007) | Rugby | Collision | Amateur | 0 | 13 | 13 | 770 | 95 200 |
| Mountcastle et al29 (2007) | Rugby | Collision | Amateur | 0 | 31 | 31 | 0 | 62 785 |
| Myklebust et al44 (2003) | Handball | Collision | Elite, subelite | 69 | 0 | 69 | 104 468 | 0 |
| Petersen et al31 (2005) | Handball | Collision | Semiprofessional, amateur | 6 | 0 | 6 | 11 905 | 0 |
| Stanley et al39 (2016) | Lacrosse | Collision | High school | 0 | 22 | 22 | 0 | 166 667 |
| Agel et al38 (2016) | Basketball | Contact | Collegiate | 162 | 70 | 232 | 736 364 | 875 000 |
| Agel et al38 (2016) | Field hockey | Contact | Collegiate | 20 | 0 | 20 | 181 818 | 0 |
| Agel et al38 (2016) | Lacrosse | Contact | Collegiate | 59 | 0 | 59 | 256 522 | 0 |
| Agel et al38 (2016) | Soccer | Contact | Collegiate | 71 | 26 | 97 | 710 000 | 650 000 |
| Beynnon et al17 (2014) | Basketball | Contact | High school | 6 | 4 | 10 | 98 296 | 108 622 |
| Beynnon et al17 (2014) | Basketball | Contact | Collegiate | 5 | 2 | 7 | 34 882 | 38 927 |
| Beynnon et al17 (2014) | Soccer | Contact | High school | 15 | 3 | 18 | 114 077 | 117 140 |
| Beynnon et al17 (2014) | Soccer | Contact | Collegiate | 11 | 6 | 17 | 28 115 | 30 241 |
| Beynnon et al17 (2014) | Field hockey | Contact | Collegiate | 1 | 0 | 1 | 25 993 | 0 |
| Beynnon et al17 (2014) | Field hockey | Contact | High school | 4 | 0 | 4 | 82 946 | 0 |
| Beynnon et al17 (2014) | Lacrosse | Contact | High school | 6 | 0 | 6 | 86 160 | 0 |
| Beynnon et al17 (2014) | Lacrosse | Contact | Collegiate | 4 | 0 | 4 | 37 567 | 0 |
| Faude et al40 (2005) | Soccer | Contact | Elite | 11 | 0 | 11 | 17 655 | 0 |
| Gilchrist et al20 (2008) | Soccer | Contact | Collegiate | 25 | 0 | 25 | 88 139 | 0 |
| Giza et al48 (2005) | Soccer | Contact | Professional | 8 | 0 | 8 | 177 778 | 0 |
| Gomez et al21 (1996) | Basketball | Contact | High school | 11 | 0 | 11 | 60 376 | 0 |
| Gwinn et al22 (2000) | Basketball | Contact | Collegiate | 5 | 1 | 6 | 10 452 | 11 282 |
| Gwinn et al22 (2000) | Soccer | Contact | Collegiate | 5 | 1 | 6 | 6508 | 12 408 |
| Gwinn et al22 (2000) | Basketball | Contact | Amateur | 0 | 5 | 5 | 1360 | 33 866 |
| Gwinn et al22 (2000) | Soccer | Contact | Amateur | 2 | 10 | 12 | 742 | 25 462 |
| Hägglund et al41 (2009) | Soccer | Contact | Elite | 8 | 8 | 16 | 27 078 | 35 681 |
| Hootman et al1 (2007) | Basketball | Contact | Collegiate | 498 | 167 | 665 | 2 165 217 | 2 385 714 |
| Hootman et al1 (2007) | Field hockey | Contact | Collegiate | 53 | 0 | 53 | 757 143 | 0 |
| Hootman et al1 (2007) | Lacrosse | Contact | Collegiate | 145 | 0 | 145 | 852 941 | 0 |
| Hootman et al1 (2007) | Soccer | Contact | Collegiate | 411 | 168 | 579 | 1 467 857 | 1 866 667 |
| Joseph et al23 (2013) | Soccer | Contact | High school | 96 | 44 | 140 | 643 206 | 914 551 |
| Joseph et al23 (2013) | Basketball | Contact | High school | 92 | 25 | 117 | 894 391 | 1 106 060 |
| Kiani et al24 (2010) | Soccer | Contact | Amateur | 5 | 0 | 5 | 66 505 | 0 |
| Krutsch et al42 (2016) | Soccer | Contact | Professional and amateur | 0 | 16 | 16 | 0 | 75 312 |
| LaBella et al25 (2011) | Soccer, basketball | Contact | High school | 12 | 0 | 12 | 22 925 | 0 |
| Le Gall et al43 (2008) | Soccer | Contact | Elite, youth | 12 | 0 | 12 | 48 359 | 0 |
| Mandelbaum et al7 (2005) | Soccer | Contact | Amateur | 73 | 0 | 73 | 205 308 | 0 |
| Messina et al28 (1999) | Basketball | Contact | High school | 0 | 4 | 4 | 0 | 84 943 |
| Mountcastle et al29 (2007) | Basketball | Contact | Collegiate | 6 | 0 | 6 | 15 300 | 14 273 |
| Mountcastle et al29 (2007) | Basketball | Contact | Amateur | 1 | 2 | 3 | 3438 | 19 483 |
| Mountcastle et al29 (2007) | Basketball | Contact | Amateur | 2 | 12 | 14 | 16 896 | 100 409 |
| Mountcastle et al29 (2007) | Soccer | Contact | Collegiate | 4 | 5 | 9 | 23 080 | 34 192 |
| Mountcastle et al29 (2007) | Soccer | Contact | Amateur | 0 | 1 | 1 | 1810 | 10 261 |
| Mountcastle et al29 (2007) | Soccer | Contact | Amateur | 1 | 13 | 14 | 14 382 | 80 124 |
| Mountcastle et al29 (2007) | Judo | Contact | Amateur | 1 | 5 | 6 | 4600 | 29 900 |
| Nagano et al8 (2011) | Basketball | Contact | Elite | 23 | 0 | 23 | 254 831 | 0 |
| Östenberg and Roos45 (2000) | Soccer | Contact | Elite | 3 | 0 | 3 | 4839 | 0 |
| Pfeiffer et al32 (2006) | Basketball | Contact | High school | 5 | 0 | 5 | 24 378 | 0 |
| Pfeiffer et al32 (2006) | Soccer | Contact | High school | 1 | 0 | 1 | 15 270 | 0 |
| Söderman et al46 (2000) | Soccer | Contact | Elite | 5 | 0 | 5 | 7017 | 0 |
| Stanley et al39 (2016) | Basketball | Contact | High school | 35 | 12 | 47 | 289 256 | 363 636 |
| Stanley et al39 (2016) | Lacrosse | Contact | High school | 32 | 0 | 32 | 101 266 | 0 |
| Stanley et al39 (2016) | Soccer | Contact | High school | 31 | 19 | 50 | 173 184 | 208 791 |
| Steffen et al33 (2008) | Soccer | Contact | Amateur | 9 | 0 | 9 | 66 574 | 0 |
| Tegnander et al34 (2008) | Soccer | Contact | Elite | 2 | 0 | 2 | 14 810 | 0 |
| Trojian and Collins35 (2006) | Basketball | Contact | Professional | 9 | 0 | 9 | 45 036 | 0 |
| Waldén et al37(2012) | Soccer | Contact | Amateur | 21 | 0 | 21 | 139 149 | 0 |
| Waldén et al47 (2011) | Soccer | Contact | Professional | 15 | 20 | 35 | 52 389 | 164 923 |
| Agel et al38 (2016) | Baseball | Limited contact | Collegiate | 0 | 12 | 12 | 0 | 600 000 |
| Agel et al38 (2016) | Softball | Limited contact | Collegiate | 33 | 0 | 33 | 550 000 | 0 |
| Agel et al38 (2016) | Volleyball | Limited contact | Collegiate | 30 | 0 | 30 | 500 000 | 0 |
| Beynnon et al17 (2014) | Volleyball | Limited contact | Collegiate | 1 | 0 | 1 | 2237 | 0 |
| Hootman et al1 (2007) | Baseball | Limited contact | Collegiate | 0 | 56 | 56 | 0 | 2 800 000 |
| Hootman et al1 (2007) | Softball | Limited contact | Collegiate | 129 | 0 | 129 | 1 612 500 | 0 |
| Hootman et al1 (2007) | Volleyball | Limited contact | Collegiate | 142 | 0 | 142 | 1 577 778 | 0 |
| Joseph et al23 (2013) | Volleyball | Limited contact | High school | 20 | 0 | 20 | 841 608 | 0 |
| Joseph et al23 (2013) | Baseball | Limited contact | High school | 0 | 6 | 6 | 0 | 861 964 |
| Joseph et al23 (2013) | Softball | Limited contact | High school | 21 | 0 | 21 | 657 246 | 0 |
| Mountcastle et al29 (2007) | Baseball | Limited contact | Collegiate | 0 | 1 | 1 | 0 | 27 674 |
| Mountcastle et al29 (2007) | Volleyball | Limited contact | Collegiate | 2 | 0 | 2 | 19 357 | 0 |
| Mountcastle et al29 (2007) | Volleyball | Limited contact | Amateur | 0 | 2 | 2 | 6856 | 38 849 |
| Mountcastle et al29 (2007) | Fencing | Limited contact | Amateur | 0 | 1 | 1 | 12 148 | 16 964 |
| Mountcastle et al29 (2007) | Cheerleading | Limited contact | Amateur | 2 | 2 | 4 | 16 780 | 16 780 |
| Mountcastle et al29 (2007) | Flickerball | Limited contact | Amateur | 0 | 2 | 2 | 5845 | 31 896 |
| Mountcastle et al29 (2007) | Frisbee | Limited contact | Amateur | 0 | 1 | 1 | 925 | 4829 |
| Pasanen et al30 (2008) | Floorball | Limited contact | Elite | 10 | 0 | 10 | 28 679 | 0 |
| Stanley et al39 (2016) | Softball | Limited contact | High school | 1 | 0 | 1 | 142 857 | 0 |
| Stanley et al39 (2016) | Baseball | Limited contact | High school | 0 | 5 | 5 | 0 | 208 333 |
| Liederbach et al27 (2008) | Dance | Noncontact | Elite | 10 | 2 | 12 | 873 067 | 545 266 |
| Mountcastle et al29 (2007) | Track | Noncontact | Collegiate | 2 | 0 | 2 | 76 542 | 114 409 |
| Mountcastle et al29 (2007) | Skiing | Noncontact | Amateur | 1 | 1 | 2 | 3586 | 20 361 |
| Mountcastle et al29 (2007) | Parachute | Noncontact | Amateur | 0 | 2 | 2 | 8402 | 42 300 |
| Viola et al36 (1999) | Alpine skiing | Noncontact | Professional | 10 | 21 | 31 | 227 766 | 499 070 |
| Agel et al38 (2016) | Gymnastics | High-impact rotational landing | Collegiate | 24 | 0 | 24 | 100 000 | 0 |
| Gwinn et al22 (2000) | Obstacle-course race | High-impact rotational landing | Amateur | 4 | 3 | 7 | 650 | 5289 |
| Hootman et al1 (2007) | Gymnastics | High-impact rotational landing | Collegiate | 134 | 0 | 134 | 406 061 | 0 |
| Mountcastle et al29 (2007) | Gymnastics | High-impact rotational landing | Collegiate | 1 | 0 | 1 | 14 317 | 0 |
| Mountcastle et al29 (2007) | Gymnastics | High-impact rotational landing | Amateur | 7 | 7 | 14 | 29 304 | 166 054 |
| Mountcastle et al29 (2007) | Obstacle-course race | High-impact rotational landing | Amateur | 5 | 9 | 14 | 5323 | 35 630 |
Incidence Rates for Collision Sports by Sex
In collision sports, the total IR of ACL injury among female and male athletes combined was 1.29/10 000 AEs (95% CI = 1.07, 1.54; P < .01, I2 = 95.0%; Figure 2). The injury IR among female athletes was 2.10/10 000 AEs (95% CI = 1.12, 3.96; P < .01, I2 = 84.0%; Figure 3) and among male athletes was 1.12/10 000 AEs (95% CI = 0.94, 1.33; P < .01, I2 = 93.0%; see Supplemental Figure 1 (197.2KB, zip) , available online at http://dx.doi.org/10.4085/1062-6050-407-16.S1). We observed no difference between sexes for the ACL injury IR (IRR = 1.14; 95% CI = 0.68, 1.92, P = .63; I2 = 0%; see Supplemental Figure 2 (197.2KB, zip) ).
Figure 2.
Forest plot for the total incidence rate of anterior cruciate ligament injury in male and female collision-sport athletes combined. a Sports are provided in Table 2. Abbreviation: CI, confidence interval.
Figure 3.
Forest plot for the incidence rate of anterior cruciate ligament injury in female collision-sport athletes. a Sports are provided in Table 2. b We substituted 0.1 for 0 to estimate an extremely low rate that could be used in the analysis. Abbreviation: CI, confidence interval.
Incidence Rates for Contact Sports by Sex
The total IR of ACL injury in contact sports was 1.51/10 000 AEs (95% CI = 1.31, 1.75; P < .01, I2 = 90.0%; see Supplemental Figure 3 (197.2KB, zip) ). The injury IR was greater among female (1.88/10 000 AEs; 95% CI = 1.61, 2.20; P < .01, I2 = 88.0%; see Supplemental Figure 4 (197.2KB, zip) ) than among male (0.87/10 000 AEs; 95% CI = 0.69, 1.11; P < .01, I2 = 84.0%; see Supplemental Figure 5 (197.2KB, zip) ) athletes. We observed a difference between sexes for the ACL injury IR (IRR = 3.00; 95% CI = 2.70, 3.34; P < .001, I2 = 4.0%; see Supplemental Figure 6 (197.2KB, zip) ).
Incidence Rates for Limited-Contact Sports by Sex
In limited-contact sports, the total IR of ACL injury was 0.48/10 000 AEs (95% CI = 0.33, 0.70; P < .01, I2 = 91.0%; see Supplemental Figure 7 (197.2KB, zip) ). The injury IR in female athletes was 0.71/10 000 AEs (95% CI = 0.50, 1.01; P < .01, I2 = 84.0%; see Supplemental Figure 8 (197.2KB, zip) ) and in male athletes was 0.29/10 000 AEs (95% CI = 0.18, 0.48; P < .01, I2 = 63.0%; see Supplemental Figure 9 (197.2KB, zip) ). The IRR was calculated using only data from Mountcastle et al,29 as data comparing injury rates among women and men in this sport type were not available. We observed no difference between sexes for the ACL injury IR (IRR = 1.21; 95% CI = 0.35, 4.20; P = .77, I2 = 0%; see Supplemental Figure 10 (197.2KB, zip) ).
Incidence Rates for Noncontact Sports by Sex
The total IR of ACL injury in noncontact sports was 0.25/10 000 AEs (95% CI = 0.10, 0.65; P < .01, I2 = 85.0%; see Supplemental Figure 11 (197.2KB, zip) ). The ACL injury IR among female athletes was 0.36/10 000 AEs (95% CI = 0.14, 0.96; P < .01, I2 = 74.0%; see Supplemental Figure 12 (197.2KB, zip) ) and among male athletes was 0.21/10 000 AEs (95% CI = 0.07, 0.62; P < .01, I2 = 70.0%; see Supplemental Figure 13 (197.2KB, zip) ). We observed no difference between sexes (IRR = 1.49; 95% CI = 0.79, 2.79; P = .22, I2 = 0%; see Supplemental Figure 14 (197.2KB, zip) ).
Incidence Rates for Fixed-Object HIRL Sports by Sex
In fixed-object HIRL sports, the total IR of ACL injury was 2.62/10 000 AEs (95% CI = 1.44, 4.75; P < .01, I2 = 89.0%; see Supplemental Figure 15 (197.2KB, zip) ). The ACL injury IR among female athletes was 4.80/10 000 AEs (95% CI = 2.37, 9.70; P < .01, I2 = 89.0%; see Supplemental Figure 16 (197.2KB, zip) ) and among male athletes was 1.75/10 000 AEs (95% CI = 0.41, 7.48; P < .01, I2 = 89.0%; see Supplemental Figure 17 (197.2KB, zip) ). We observed a difference between sexes (IRR = 5.51; 95% CI = 2.80, 10.82; P < .001, I2 = 0%; see Supplemental Figure 18 (197.2KB, zip) ).
Risk of Bias Assessment
Most studies were of moderate quality (Table 3). Three studies fulfilled 75% or more of the criteria, and 33 studies fulfilled 50% or more of the criteria. The remaining 3 studies fulfilled fewer than 50% of the criteria and were deemed to be of low quality. Studies may have received lower scores for lack of reporting information, such as the total number of eligible individuals, how outcomes were measured, and attrition. Overall, the risk of bias was deemed to be moderate.
Table 3.
Results of Risk of Bias Assessment Using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studiesa
| Study (y) |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
Total Present |
| Agel et al38 (2016) | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 9 |
| Beynnon et al17 (2014) | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 9 |
| Brooks et al6 (2005) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 9 |
| Dallalana et al18 (2007) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 9 |
| Dragoo et al19 (2012) | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
| Faude et al40 (2005) | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 8 |
| Gilchrist et al20 (2008) | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 10 |
| Giza et al48 (2005) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
| Gomez et al21 (1996) | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
| Gwinn et al22 (2000) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 9 |
| Hägglund et al41 (2009) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 9 |
| Hootman et al1 (2007) | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 8 |
| Joseph et al23 (2013) | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 8 |
| Kiani et al24 (2010) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 12 |
| Krutsch et al42 (2016) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 8 |
| LaBella et al25 (2011) | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 10 |
| Le Gall et al43 (2008) | 1 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 6 |
| Levy et al26 (1997) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 0 | 9 |
| Liederbach et al27 (2008) | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 8 |
| Mandelbaum et al7 (2005) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 10 |
| Messina et al28 (1999) | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
| Mountcastle et al29 (2007) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 9 |
| Myklebust et al44 (2003) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 9 |
| Nagano et al8 (2011) | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
| Östenberg and Roos45 (2000) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 8 |
| Pasanen et al30 (2008) | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 0 | 10 |
| Petersen et al31 (2005) | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 9 |
| Pfeiffer et al32 (2006) | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 9 |
| Söderman et al46 (2000) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
| Stanley et al39 (2016) | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 8 |
| Steffen et al33 (2008) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 13 |
| Tegnander et al34 (2008) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
| Trojian and Collins35 (2006) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 9 |
| Viola et al36 (1999) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
| Waldén et al37 (2012) | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 12 |
| Waldén et al47 (2011) | 1 | 1 | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
0 = criterion was absent or not reported; 1 = criterion was present.
DISCUSSION
The purpose of our study was to quantify sex differences in ACL injury risk for sports with various amounts of contact. Female athletes participating in contact and fixed-object HIRL sports had greater ACL injury IRs than their male counterparts. In contrast, the ACL injury IRs for collision, limited-contact, and noncontact sports did not differ between sexes. The findings from this meta-analysis support a previous report4 indicating that the amount of athlete-to-athlete contact inherent to a sport was correlated with the rate of ACL injury in both male and female athletes. However, adding the fixed-object HIRL category suggested that sports such as gymnastics and obstacle-course races may result in the highest rates of ACL injury.
Identifying the ACL injury IR associated with fixed-object HIRL sports is especially relevant as it pertains to military training and activities. Over a 7-year period, the IR of ACL injury in US military members of all services was 3.09/1000 person-years for men and 2.29/1000 person-years for women.49 Investigators49 noted that service members were at 10 times greater risk of ACL injury than the general population. This increased risk may be partially explained by participation in fixed-object HIRL activities. In contrast to our findings, Owens et al49 did not find women to be at greater risk of ACL injury than men; however, they reported person-years because they did not have exposure information. In addition, men outnumbered women in their study49 and, thus, had higher rates of ACL injury. Military service members, especially those participating in regular training that includes fixed-object HIRLs, may benefit from integrative neuromuscular training to mitigate their risk of ACL injury.
In addition to the military application, our findings related to fixed-object HIRL sports are also relevant considering the advent of recreational obstacle-course races (eg, Tough Mudder, Spartan, BattleFrog). These races are based on military training obstacle courses. Currently, no information about the rates of ACL injury associated with these races is available, but our results suggest that participants should exercise caution. For gymnastics, our findings indicated that the unique demands of the sport, including both implement-based activity and high-impact landings after full-body rotation, distinguish the sport from other noncontact sports regarding the ACL injury risk. Hootman et al1 found that football, a collision sport, resulted in the greatest incidence of ACL injuries in collegiate male athletes. Our findings indicated that fixed-object HIRL sports resulted in ACL injury IRs that were similar to those of collision sports in men (1.75/10 000 versus 1.12/10 000 AEs). The ACL injury IR was more than 3 times greater among women than among men for fixed-object HIRL sports. Considering the likely mechanisms of injury (landing with rotation, stiff-legged landing), this disparity highlights the neuromuscular deficits typically demonstrated by female athletes.50 Therefore, female athletes participating in fixed-object HIRL sports may benefit the most from preventive strategies.
We also found that female athletes participating in contact sports sustained ACL injuries at 3 times the rate of male athletes in these same sports (IRR = 3.00). These findings are similar to IRRs previously reported4 for male and female collegiate basketball and soccer players, which were approximately 3.6 and 2.8, respectively. However, ACL injury IRs did not differ between women and men for collision sports. The lack of a difference in ACL injury IRs between women and men in collision sports and between women in collision and contact sports may be partially explained by the lack of collision-sport participation by women. When participation was equal among women and men (contact sports), the greater ACL injury IR among women was evident. It is possible that not enough studies were available in which researchers investigated ACL injury incidence among both female and male collision athletes to detect a difference in the rates where one truly exists (ie, low statistical power).
In contrast, the ACL injury IRs for men in collision and contact sports differed (1.12/10 000 and 0.87/10 000 AEs, respectively). The sports included in these categories are similar because they require cutting and pivoting, which are dynamic maneuvers known to contribute to noncontact ACL injury mechanisms. Again, these combined findings further support the idea that neuromuscular deficits may contribute to the greater ACL injury IR among women. Although speculative, it was also possible that the men's decreased IR in collision sports compared with contact sports was due to direct-contact blows to the knee based on the nature of the sports.
Whereas our research may provide a robust estimate of sex differences in ACL injury IRs among sport types, it had limitations. The common metric of AE had to be estimated in some cases when exposure was provided in player-hours. This was necessary to include the maximum amount of data possible. As mentioned, we assumed that 2 player-hours were approximately equal to 1 AE, and we used this assumption to generate estimates of AEs. This assumption may have resulted in the overestimation or underestimation of exposure, depending on the sport. We used broad inclusion criteria to capture the greatest amount of information for generating these estimates. The included articles ranged in study quality, and the estimates are only as strong as the evidence on which they are based. However, we believed it was important to capture a wider range of studies to obtain a truer, more robust picture of ACL injury incidence among athletes. In addition, heterogeneity was relatively high (>75%) for the point estimates, indicating that populations that were grouped together may actually have differed. However, this was expected, as different sports have different demands that change the risk of sustaining an ACL injury. Moreover, heterogeneity for the rate ratios was low, and in some cases was 0%, indicating that the results were consistent and potentially generalizable. Given that female participation in collision sports was less prevalent than male participation, we included relatively few studies in which differences in ACL injury IRs between sexes were investigated. We could not control for variables known to contribute to ACL injury, including surface type, anticipation (anticipated event versus unanticipated event), or mechanism of injury (contact versus noncontact) because of a lack of information. Finally, we did not stratify by age or level of play, as those were not aims of this study.
To address these limitations, future researchers should report their findings in the most accurate units possible (player-hours) or should make both player-hours and AEs available to provide the opportunity for meta-analysis. Given that prospective designs allow for real-time data capture, investigators conducting future research in injury epidemiology should use prospective designs. Developing a standard and comprehensive checklist for criteria that should be met when performing or designing a prospective observational cohort study would provide a guide for researchers to achieve maximum study quality. This meta-analysis should be repeated in the future when more ACL injury data are available to permit comparisons of incidence rates among female and male athletes participating in collision and limited-contact sports. Finally, researchers should establish ACL injury IRs within each sport type while controlling for confounding variables, including age and level of play.
CONCLUSIONS
The incidence of ACL injury is associated with the nature of player-to-player contact inherent in the sport. Female athletes had greater ACL injury IRs than male athletes in contact and fixed-object HIRL sports. The latter sports category had the highest ACL injury IRs for both sexes, which might suggest the need for a new sport type to identify athletes at the highest risk of ACL injury. Future strategies aimed at reducing the risk of ACL injury may benefit from considering and integrating sport-related perturbation that mimics contact exposure to better equip athletes with preprepatory and avoidance techniques.
Supplementary Material
SUPPLEMENTAL MATERIAL
Supplemental Figures (197.2KB, zip) . Series of forest plots for incidence rate ratios.
Found at DOI: http://dx.doi.org/10.4085/1062-6050-407-16.S1
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Associated Data
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