The Epidemiology of Ankle Sprains in US High School Sports, 2011–2012 to 2018–2019 Academic Years

Sample and Sports Included

High School Reporting Information Online relied on a sample of high schools with at least 1 athletic trainer (AT) who had a valid email address. Two data-collection groups were used in HS RIO, both of which were included in this study. The first group involved a random sample of approximately 100 high schools recruited annually from 8 strata based on cross-sections of school population (enrollment ≤1000 or >1000) and US census geographic region (Midwest, Northeast, South, West). If a school dropped out of the system, a replacement from the same stratum was selected. These schools collected data for 9 sports (boys' baseball, basketball, football, soccer, and wrestling and girls' basketball, soccer, softball, and volleyball). The second group was an additional convenience sample of high schools recruited annually. These schools reported data for additional sports (eg, boys' ice hockey, lacrosse, and track and field; girls' field hockey, lacrosse, and track and field; and coed cheerleading) and any of the original 9 sports. The ATs working in high schools in the first group could also provide data for these additional sports. We selected the sports included in the research—boys' baseball, basketball, football, ice hockey, lacrosse, soccer, track and field, and wrestling; girls' basketball, field hockey, lacrosse, soccer, softball, track and field, and volleyball; and coed cheerleading—as data were available across all 8 years of the study period (ie, 2011–2012 to 2018–2019 academic years), although participation varied by sport and year.

Data Collection

The ATs at participating high schools reported injury incidence and athlete-exposure (AE) information on a weekly basis using a secure website. An AE was defined as 1 athlete participating in 1 school-sanctioned practice or competition. Although certain injuries were reported in HS RIO regardless of participation-restriction time, our injury of focus, ankle sprain, was reported only if it resulted in participation-restriction time of ≥24 hours. Thus, the inclusion criteria for ankle sprains in this study were (1) occurring because of participation in a school-sanctioned practice or competition (including performance in cheerleading), (2) requiring medical attention by an AT or physician, (3) being diagnosed as a sprain, (4) occurring to the ankle, and (5) resulting in participation restriction of ≥24 hours.

For each injury, the AT completed a detailed injury report. Characteristics pertinent to this investigation were event type (competition, practice), time in season (preseason, in-season, postseason), participation-restriction time (ie, the number of days that injured athletes were withheld from some form of participation in their sports), injury mechanism, and recurrence (to the currently injured body part: yes or no). Participation-restriction time was categorized into <7 days, 7 to 21 days, or >21 days; participation-restriction time of >21 days was associated with injuries resulting in the athletes being medically disqualified, choosing not to continue, or being released from the team. Injury mechanism was categorized as contact with another person, contact with surface, contact with apparatus, noncontact (including acute or no contact and overuse or chronic), or other (including other, unknown, or missing). Recurrent ankle sprains were further classified as recurrent from the present academic year or recurrent from a previous academic year.

When an AT noted that an ankle sprain had occurred, HS RIO was automated to request additional information regarding the ligament(s) affected. The AT could select from 6 ligaments, which were then recoded into 3 groups, similar to those used in previous injury-surveillance studies^17,18: (1) lateral ligament complex (LLC), consisting of the anterior talofibular, posterior talofibular, and calcaneofibular ligaments; (2) deltoid ligament; and (3) high ankle, consisting of the anterior tibiofibular and posterior tibiofibular ligaments. No formal definitions were provided for terminology beyond injury and AE. Rather, HS RIO relied on the expertise and extensive training of the participating ATs for the detection and diagnosis of ankle sprains and their related characteristics and mechanisms. Internal validity checks conducted by HS RIO staff—which used a 5% random sample of schools to compare data reported in HS RIO with deidentified health logs¹⁹—have consistently identified sensitivity, specificity, positive predictive value, and negative predictive value above 90%.²⁰

Statistical Analyses

Data were analyzed using SAS (version 9.4; SAS Institute). First, we examined ankle sprain frequencies and rates per 10 000 AEs with 95% CIs. Injury rate ratios (IRRs) compared ankle sprain rates by event type. We also determined the frequencies of ankle sprains sustained to multiple ligament areas (eg, LLC and high ankle). Distributions were assessed by participation-restriction time, injury mechanism, and recurrence.

Next, rates were compared between sex-comparable sports (ie, basketball, baseball and softball, soccer, track and field). Lacrosse was not considered a sex-comparable sport as the rules regarding contact vary between the boys' and girls' sports. Also, injury proportion ratios (IPRs) compared proportions between sex-comparable sports that were selected a priori, including by participation-restriction time (<7 days, >21 days, etc), injury mechanism (contact with another person, noncontact, etc), and recurrence (recurrent, nonrecurrent ankle sprains). When we found overall sex-comparable sport differences, we repeated the analysis for each pairing of sex-comparable sports.

Finally, yearly rates were computed and graphed. Given that annual changes were expected to be nonlinear in nature, in order to statistically evaluate the change in ankle sprain rates across the study period, we used IRRs to compare the rates in 2011–2012 and 2018–2019. Comparisons of interest were selected a priori and consisted of comparisons overall and by event type (competition, practice), specific ligament complexes (LLC, deltoid, high ankle, etc), participation-restriction time (<7 days, >21 days, etc), injury mechanism (contact with another person, noncontact, etc), and recurrence (recurrent, nonrecurrent).

Upon completion of all a priori analyses, we conducted additional analyses to compare the 2011–2012 and 2018–2019 rates overall in the 9 original sports used in HS RIO (boys' baseball, basketball, football, soccer, and wrestling and girls' basketball, soccer, softball, and volleyball) and the proportions of ankle sprains resulting in <7 days and >21 days between recurrent versus nonrecurrent ankle sprains across all sports combined. The IRRs and IPRs with 95% CIs excluding 1.00 were considered statistically significant. An example of an IRR comparing ankle sprain rates in competition and practice is \(\def\upalpha{\unicode[Times]{x3B1}}\)\(\def\upbeta{\unicode[Times]{x3B2}}\)\(\def\upgamma{\unicode[Times]{x3B3}}\)\(\def\updelta{\unicode[Times]{x3B4}}\)\(\def\upvarepsilon{\unicode[Times]{x3B5}}\)\(\def\upzeta{\unicode[Times]{x3B6}}\)\(\def\upeta{\unicode[Times]{x3B7}}\)\(\def\uptheta{\unicode[Times]{x3B8}}\)\(\def\upiota{\unicode[Times]{x3B9}}\)\(\def\upkappa{\unicode[Times]{x3BA}}\)\(\def\uplambda{\unicode[Times]{x3BB}}\)\(\def\upmu{\unicode[Times]{x3BC}}\)\(\def\upnu{\unicode[Times]{x3BD}}\)\(\def\upxi{\unicode[Times]{x3BE}}\)\(\def\upomicron{\unicode[Times]{x3BF}}\)\(\def\uppi{\unicode[Times]{x3C0}}\)\(\def\uprho{\unicode[Times]{x3C1}}\)\(\def\upsigma{\unicode[Times]{x3C3}}\)\(\def\uptau{\unicode[Times]{x3C4}}\)\(\def\upupsilon{\unicode[Times]{x3C5}}\)\(\def\upphi{\unicode[Times]{x3C6}}\)\(\def\upchi{\unicode[Times]{x3C7}}\)\(\def\uppsy{\unicode[Times]{x3C8}}\)\(\def\upomega{\unicode[Times]{x3C9}}\)\(\def\bialpha{\boldsymbol{\alpha}}\)\(\def\bibeta{\boldsymbol{\beta}}\)\(\def\bigamma{\boldsymbol{\gamma}}\)\(\def\bidelta{\boldsymbol{\delta}}\)\(\def\bivarepsilon{\boldsymbol{\varepsilon}}\)\(\def\bizeta{\boldsymbol{\zeta}}\)\(\def\bieta{\boldsymbol{\eta}}\)\(\def\bitheta{\boldsymbol{\theta}}\)\(\def\biiota{\boldsymbol{\iota}}\)\(\def\bikappa{\boldsymbol{\kappa}}\)\(\def\bilambda{\boldsymbol{\lambda}}\)\(\def\bimu{\boldsymbol{\mu}}\)\(\def\binu{\boldsymbol{\nu}}\)\(\def\bixi{\boldsymbol{\xi}}\)\(\def\biomicron{\boldsymbol{\micron}}\)\(\def\bipi{\boldsymbol{\pi}}\)\(\def\birho{\boldsymbol{\rho}}\)\(\def\bisigma{\boldsymbol{\sigma}}\)\(\def\bitau{\boldsymbol{\tau}}\)\(\def\biupsilon{\boldsymbol{\upsilon}}\)\(\def\biphi{\boldsymbol{\phi}}\)\(\def\bichi{\boldsymbol{\chi}}\)\(\def\bipsy{\boldsymbol{\psy}}\)\(\def\biomega{\boldsymbol{\omega}}\)\(\def\bupalpha{\bf{\alpha}}\)\(\def\bupbeta{\bf{\beta}}\)\(\def\bupgamma{\bf{\gamma}}\)\(\def\bupdelta{\bf{\delta}}\)\(\def\bupvarepsilon{\bf{\varepsilon}}\)\(\def\bupzeta{\bf{\zeta}}\)\(\def\bupeta{\bf{\eta}}\)\(\def\buptheta{\bf{\theta}}\)\(\def\bupiota{\bf{\iota}}\)\(\def\bupkappa{\bf{\kappa}}\)\(\def\buplambda{\bf{\lambda}}\)\(\def\bupmu{\bf{\mu}}\)\(\def\bupnu{\bf{\nu}}\)\(\def\bupxi{\bf{\xi}}\)\(\def\bupomicron{\bf{\micron}}\)\(\def\buppi{\bf{\pi}}\)\(\def\buprho{\bf{\rho}}\)\(\def\bupsigma{\bf{\sigma}}\)\(\def\buptau{\bf{\tau}}\)\(\def\bupupsilon{\bf{\upsilon}}\)\(\def\bupphi{\bf{\phi}}\)\(\def\bupchi{\bf{\chi}}\)\(\def\buppsy{\bf{\psy}}\)\(\def\bupomega{\bf{\omega}}\)\(\def\bGamma{\bf{\Gamma}}\)\(\def\bDelta{\bf{\Delta}}\)\(\def\bTheta{\bf{\Theta}}\)\(\def\bLambda{\bf{\Lambda}}\)\(\def\bXi{\bf{\Xi}}\)\(\def\bPi{\bf{\Pi}}\)\(\def\bSigma{\bf{\Sigma}}\)\(\def\bPhi{\bf{\Phi}}\)\(\def\bPsi{\bf{\Psi}}\)\(\def\bOmega{\bf{\Omega}}\)\begin{equation}{\rm{IRR}} = {{\left( {{{{\rm{Total\ Number\ of\ Competition\ Ankle\ Sprains}}} \over {\sum\nolimits_{i = 1}^j {{\rm{Number\ of\ Participating\ Athletes}}} }}} \right)} \over {\left( {{{{\rm{Total\ Number\ of\ Practice\ Ankle\ Sprains}}} \over {\sum\nolimits_{i = 1}^k {{\rm{Number\ of\ Participating\ Athletes}}} }}} \right)}}\end{equation}where j denotes all reported competition exposure events and k denotes all reported practice exposure events.

An example of an IPR comparing the proportion of recurrent ankle sprains in girls and boys is \begin{equation}{\rm{IPR}} = {{\left( {{{{\rm{Number\ of\ Recurrent\ Ankle\ Sprains\ in\ Girls}}} \over {{\rm{Number\ of\ Ankle\ Sprains\ in\ Girls}}}}} \right)} \over {\left( {{{{\rm{Number\ of\ Recurrent\ Ankle\ Sprains\ in\ Boys}}} \over {{\rm{Number\ of\ Ankle\ Sprains\ in\ Boys}}}}} \right)}}\end{equation}

Ankle Sprain Counts and Rates

During the 2011–2012 through 2018–2019 academic years, 9320 ankle sprains occurred across 31 548 302 AEs, for an overall injury rate of 2.95/10 000 AEs (Table 1). The largest proportion of ankle sprains came from boys' football (n = 2777/9320), but the highest rate was in girls' basketball (5.32/10 000 AEs), followed by boys' basketball (5.13/10 000 AEs), girls' soccer (4.96/10 000 AEs), and boys' football (4.55/10 000 AEs). Most ankle sprains occurred in-season (75.1%), followed by the preseason (21.0%), and postseason (3.9%); an additional 65 had missing information.

Table 1 Ankle Sprain Counts and Rates Among High School Student-Athletes in 16 Sports, 2011–2012 to 2018–2019 Academic Years

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The majority of sprains were reported in competition (54.3%). Sports with the highest ankle sprain rates in competition were boys' football (15.33/10 000 AEs) and girls' soccer (10.77/10 000 AEs; Table 1). Sports with the highest ankle sprain rates in practice were boys' basketball (3.73/10 000 AEs) and girls' basketball (3.38/10 000 AEs). Across the 16 sports, the competition rate was higher than the practice rate (6.02 [competition] versus 1.84 [practice] per 10 000 AEs; IRR = 3.27; 95% CI = 3.14, 3.40); this was observed in each sport except cheerleading (IRR = 0.83; 95% CI = 0.56, 1.25).

Of the 9320 ankle sprains, 85.2% involved the LLC, followed by 22.2% to the high ankle and 6.9% to the deltoid (Table 2). Also, 15.6% of all ankle sprains affected more than 1 group of ligaments. The most common combination was the LLC and high ankle (12.6% of all injury events), followed by the LLC and deltoid (1.8%). As with overall rates, the highest LLC rates were in girls' basketball (4.79/10 000 AEs), boys' basketball (4.54/10 000 AEs), and girls' soccer (4.30/10 000 AEs; Table 2). For deltoid ligament ankle sprains, boys' football had the highest rate (0.41/10 000 AEs). For high ankle sprains, the highest rates were in boys' basketball (1.16/10 000 AEs), boys' football (1.15/10 000 AEs), and girls' basketball (1.11/10 000 AEs).

Table 2 Counts and Rates of Ankle Sprains, by Specific Ligament Areas Affected, Among High School Student-Athletes in 16 Sports, 2011–2012 Through 2018–2019 Academic Years

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Participation-Restriction Time

Overall, 44.4% of ankle sprains resulted in <7 days of participation-restriction time (Table 3). In addition, 5.8% resulted in >21 days of participation-restriction time; included in these 538 ankle sprains were 72 resulting in medical disqualification, 61 in the athlete choosing not to continue, and 12 in the athlete being released from the team. Distributions of participation-restriction time varied by sport and by ligament injured. For example, compared with other types of ankle sprains, high ankle sprains had a lower proportion resulting in <7 days of participation-restriction time (33.5%) and a higher proportion resulting in >21 days of participation restriction time (9.5%).

Table 3 Counts of Ankle Sprains, by Participation-Restriction Time, Among High School Student-Athletes in 16 Sports, 2011–2012 Through 2018–2019 Academic Years

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Injury Mechanism

Overall, 39.5% and 35.0% of all ankle sprains were due to contact with another person and noncontact, respectively (Table 4). Sports with larger proportions of ankle sprains due to noncontact mechanisms were boys' lacrosse (62.8%), girls' lacrosse (60.3%), and girls' field hockey (68.9%). Sport-specific mechanisms and activities highlighted a variety of manners in which ankle sprains occurred (Table 5). For example, foot inversion was a common mechanism overall in boys' and girls' soccer, girls' field hockey, boys' and girls' lacrosse, and boys' and girls' track and field.

Table 4 Ankle Sprain Injuries, by Injury Mechanism, Among High School Student-Athletes in 16 Sports, 2011–2012 Through 2018–2019 Academic Years

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Table 5 Most Reported Specific Injury Mechanism and Activity Combinations for Ankle Sprain Injuries Among High School Student-Athletes in 16 Sports, 2011–2012 Through 2018–2019 Academic Years

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Comparisons Between Sex-Comparable Sports

Annual Changes Across Study Period

Overall and by Event Type

Across the academic years included in the study period, ankle sprain rates generally increased overall, although this appeared to be mostly attributable to competition injuries (Figure, part A). Rates in the 2018–2019 academic year were higher overall compared with rates in the 2011–2012 academic year (3.21 versus 2.64, respectively, per 10 000 AEs; IRR = 1.22; 95% CI = 1.11, 1.33) and in competitions (6.74 versus 5.05, respectively, per 10 000 AEs; IRR = 1.34; 95% CI = 1.18, 1.51) but not in practices (1.90 versus 1.77, respectively, per 10 000 AEs; IRR = 1.07; 95% CI = 0.94, 1.22). Also, when considering only the 9 original sports from which data were collected in HS RIO (boys' baseball, basketball, football, soccer, and wrestling and girls' basketball, soccer, softball, and volleyball), overall ankle sprain rates increased 24% across the study period (3.95 [2018–2019 academic year] versus 3.18 [2011–2012 academic year] per 10 000 AEs; IRR = 1.24; 95% CI = 1.13, 1.36).

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When examined by specific ligament complexes injured, ankle sprain rates generally increased (Figure, part B). Compared with rates in the 2011–2012 academic year, rates in the 2018–2019 academic year were higher for LLC injuries (2.77 versus 2.26, respectively, per 10 000 AEs; IRR = 1.23; 95% CI = 1.12, 1.35) and high ankle sprains (0.63 versus 0.50, respectively, per 10 000 AEs; IRR = 1.25; 95% CI = 1.02, 1.53) but not for deltoid ligament complex injuries (0.21 versus 0.18, respectively, per 10 000 AEs; IRR = 1.16; 95% CI = 0.83, 1.63).

Limitations

First, only data from high schools with ATs were included. Hence, the study findings may not be generalizable to all high schools, particularly those without AT access.²⁹ Second, we could not account for underreporting, misdiagnosis, or misclassification of ankle sprains and their characteristics. However, the injuries were assessed and documented by ATs, who are trained to accurately detect and diagnose injuries. Further, previous researchers³⁰ found that ATs displayed high agreement with physicians in diagnosing injuries. Nonetheless, although injury-surveillance programs such as HS RIO strive for standardization through their definitions and codebook descriptions, such data were vulnerable to variability among ATs in reporting. Third, HS RIO did not collect data on non–time-loss ankle sprains and thus may have underestimated the true incidence of ankle sprains occurring in high school athletics. Fourth, we did not consider additional injuries that may have occurred concurrently with the reported ankle sprains (eg, 5th metatarsal fracture), which may have affected injury-related outcomes such as participation-restriction time. Fifth, this study could not account for additional factors that may be associated with the incidence and distribution of ankle sprains, such as variations in the implementation of injury-prevention programs at each high school and in the characteristics of participating athletes, such as maturation status and biomechanics. Sixth, it is also important to recognize that some comparisons were conducted with smaller sample sizes, which may have hindered our ability to detect statistically significant differences. Finally, we calculated at-risk exposure using AEs, as opposed to potentially more precise metrics such as minutes and hours. Nonetheless, time-based exposure data may be too laborious and burdensome for ATs in high school athletics, particularly when the number of medical staff available to cover all school-sanctioned sports is limited.