Evaluation of differences across age groups in the incidence, severity, and recovery of concussion in adolescent student-athletes from 2009 to 2019

Theodore C. Hannah Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York;

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Roshini Kalagara Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York;

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Muhammad Ali Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York;

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Alexander J. Schupper Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York;

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Adam Y. Li Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York;

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Zachary Spiera Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York;

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Naoum Fares Marayati Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York;

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Addison Quinones Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York;

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Zerubabbel K. Asfaw Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York;

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Vikram Vasan Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York;

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Eugene I. Hrabarchuk Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York;

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Lily McCarthy Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York;

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Alex Gometz Physical Medicine and Rehabilitation, Concussion Management of New York, New York, New York; and

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Mark Lovell Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania

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Tanvir Choudhri Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York;

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Free access

OBJECTIVE

Concussion incidence is known to be highest in children and adolescents; however, there is conflicting evidence about the effect of age on concussion risk and recovery within the adolescent age range. The heterogeneity of results may be partially due to the use of age groupings based on convenience, making comparisons across studies difficult. This study evaluated the independent effect of age on concussion incidence, severity, and recovery in student-athletes aged 12–18 years using cluster analysis to define groupings.

METHODS

Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) scores of 11,403 baseline tests and 4922 postinjury tests were used to calculate the incidence rates for adolescent student-athletes grouped into 3 age bands (12–13, 14–15, and 16–18 years of age) on the basis of clustering analysis. The recently created Severity Index was used to compare concussion severity between groups. Follow-up tests for subjects who sustained a concussion were used to evaluate recovery time. The chi-square test and 1-way ANOVA were used to compare differences in demographic characteristics and concussion incidence, severity, and recovery. Multivariable logistic and linear regressions were used to evaluate the independent effects of age on concussion incidence and severity, respectively. Multivariable Cox hazard regression was used to evaluate differences in recovery time. Further analyses were conducted to directly compare findings across studies on the basis of the age groupings used in prior studies.

RESULTS

Multivariable regression analyses demonstrated that the 14- to 15-year-old age group had a significantly higher concussion incidence than both the 12- to 13-year-old (14- to 15-year-old group vs 12- to 13-year-old group, OR 1.57, 95% CI 1.16–2.17, p = 0.005) and 16- to 18-year-old (16- to 18-year-old group vs 14- to 15-year-old group, OR 0.79, 95% CI 0.69–0.91, p = 0.0008) age groups. There was no difference in incidence between the 12- to 13-year-old and 16- to 18-year-old groups (16- to 18-year group vs 12- to 13-year group, OR 1.26, 95% CI 0.93–1.72, p = 0.15). There were also no differences in concussion severity or recovery between any groups.

CONCLUSIONS

This study found that concussion incidence was higher during mid-adolescence than early and late adolescence, suggesting a U-shaped relationship between age and concussion risk over the course of adolescence. Age had no independent effect on concussion severity or recovery in the 12- to 13-, 14- to 15-, and 16- to 18-year-old groups. Further analysis of the various age groups revealed that results may vary significantly with minor changes to groupings, which may explain the divergent results in the current literature on this topic. Thus, caution should be taken when interpreting the results of this and all similar studies, especially when groupings are based on convenience.

ABBREVIATIONS

ADHD = attention-deficit/hyperactivity disorder; DLD = diagnosed learning disability; ImPACT = Immediate Post-Concussion Assessment and Cognitive Testing; PI1 = postinjury 1; PI2 = postinjury 2; SAC = Standardized Assessment of Concussion; Sdiff = significant deviation; SI = Severity Index.

OBJECTIVE

Concussion incidence is known to be highest in children and adolescents; however, there is conflicting evidence about the effect of age on concussion risk and recovery within the adolescent age range. The heterogeneity of results may be partially due to the use of age groupings based on convenience, making comparisons across studies difficult. This study evaluated the independent effect of age on concussion incidence, severity, and recovery in student-athletes aged 12–18 years using cluster analysis to define groupings.

METHODS

Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) scores of 11,403 baseline tests and 4922 postinjury tests were used to calculate the incidence rates for adolescent student-athletes grouped into 3 age bands (12–13, 14–15, and 16–18 years of age) on the basis of clustering analysis. The recently created Severity Index was used to compare concussion severity between groups. Follow-up tests for subjects who sustained a concussion were used to evaluate recovery time. The chi-square test and 1-way ANOVA were used to compare differences in demographic characteristics and concussion incidence, severity, and recovery. Multivariable logistic and linear regressions were used to evaluate the independent effects of age on concussion incidence and severity, respectively. Multivariable Cox hazard regression was used to evaluate differences in recovery time. Further analyses were conducted to directly compare findings across studies on the basis of the age groupings used in prior studies.

RESULTS

Multivariable regression analyses demonstrated that the 14- to 15-year-old age group had a significantly higher concussion incidence than both the 12- to 13-year-old (14- to 15-year-old group vs 12- to 13-year-old group, OR 1.57, 95% CI 1.16–2.17, p = 0.005) and 16- to 18-year-old (16- to 18-year-old group vs 14- to 15-year-old group, OR 0.79, 95% CI 0.69–0.91, p = 0.0008) age groups. There was no difference in incidence between the 12- to 13-year-old and 16- to 18-year-old groups (16- to 18-year group vs 12- to 13-year group, OR 1.26, 95% CI 0.93–1.72, p = 0.15). There were also no differences in concussion severity or recovery between any groups.

CONCLUSIONS

This study found that concussion incidence was higher during mid-adolescence than early and late adolescence, suggesting a U-shaped relationship between age and concussion risk over the course of adolescence. Age had no independent effect on concussion severity or recovery in the 12- to 13-, 14- to 15-, and 16- to 18-year-old groups. Further analysis of the various age groups revealed that results may vary significantly with minor changes to groupings, which may explain the divergent results in the current literature on this topic. Thus, caution should be taken when interpreting the results of this and all similar studies, especially when groupings are based on convenience.

In Brief

Researchers evaluated the effect of age on concussion incidence, severity, and recovery within the adolescent age range. Student-athletes aged 14-15 years had a higher concussion incidence than 12- to 13-year-old and 16- to 18-year-old student-athletes. No differences in severity or recovery were observed. This study demonstrates the possibility of a U-shaped relationship between age and concussion incidence over the course of adolescence.

Children and adolescents account for half of the 4 million annual recreational and sports-related concussions that occur in the United States.1,2 Whether the brain is more susceptible to injury and prolonged recovery as the adolescent progresses through the various stages of central nervous system development remains unclear. However, as concussion incidence continues to increase within this high-risk population, determination of the independent effect of age on concussion incidence, severity, and recovery within the pediatric age range is becoming increasingly important.

Previous studies that investigated concussion incidence and recovery in children and adolescents divided their age groups in a variety of manners, including as a continuous variable or on the basis of convenience, developmental stages, current grade of education, or age-based sport level.3 Across these classifications, adolescent athletes in particular have demonstrated greater neuropsychological sequelae, more frequent hospital visits, longer recovery time, and longer time to return to school than younger children.47 Young adults and college athletes also reportedly have higher concussion incidence but faster recovery than adolescent high school athletes across various sports.810 However, other studies found dissimilar results, often noting no differences across age groups; thus, the evidence for age-related differences in concussion remains inconclusive.7,1117 Age as a risk factor of concussion over the first 2 decades of life requires further study.

Using a large heterogeneous cohort, we evaluated the effect of age on concussion incidence, severity, and recovery in adolescent student-athletes aged 12–18 years. Despite the amount of research conducted on differences in concussion outcomes among adolescents, meaningful comparisons are often difficult to make due to the lack of standardized age groupings and the fact that often only 1 aspect of concussion management (e.g., incidence, severity, or recovery) is evaluated. The current study attempted to address these issues by evaluating concussion incidence, severity, and recovery within a single study and providing the same analyses for a range of age groupings that can be used to make direct comparisons with prior studies.

Methods

Data Collection

Data from 25,815 Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) neurocognitive tests administered between 2009 and 2019 to subjects aged 12–22 years were obtained through a research agreement with ImPACT Applications, Inc. The data came from athletic organizations in Florida and Colorado that also have research agreements with ImPACT Applications to conduct ongoing research. ImPACT Applications is an FDA-approved data repository that obtains and stores test data for the contracted organizations on a secure, password-protected server. ImPACT Applications must obtain consent from the athletic organizations to share de-identified data with third parties for research purposes. The athletic organizations obtain and administer the ImPACT tests via contractual arrangements with ImPACT Applications and are responsible for obtaining informed consent from participants. There is no cost to subjects or organizations for sharing data with ImPACT Applications beyond the normal cost of administering the tests. Individual test data are interpreted by local healthcare professionals associated with the athletic organization. ImPACT Applications does not provide clinical consultation. This was completed by neuropsychologists, physicians, and other healthcare professional who work directly with the athletes and their organizations.

The data used in this study were obtained from 11,563 initial baseline tests along with 5216 postinjury 1 tests (PI1) that were conducted after a head injury. Of these PI1 tests, 1864 deviated significantly from baseline and met the ImPACT criteria for concussion (described below). Of the PI1 tests, 1296 both met the ImPACT concussion criteria and the subject had at least 1 follow-up (postinjury 2 [PI2]) test, which can be used to assess neurocognitive recovery. Approximately 70% of subjects were seen for follow-up testing, which consisted of the ImPACT test. IRB approval was obtained for this study, and the requirement for informed consent was waived.

Subjects

Only subjects aged 12–18 years were included in this study. No further exclusions were made. The subjects were all members of athletic organizations in Florida or Colorado that used ImPACT testing for concussion evaluation and management and followed ImPACT-designed evaluation protocols (described below).

PI1 Testing, Head Injury, and Concussion

According to the protocol for data collection, athletes who suffered a head injury on the field of play were evaluated immediately on the sideline by a coach, athletic trainer, or other school faculty member. If a concussion was suspected, athletes were referred for PI1 ImPACT testing. Therefore, in this study, any PI1 test was considered a "head injury." The ImPACT test was used to evaluate whether neurocognitive deficits were significantly deviated from baseline. The ImPACT test evaluates performance on a battery of neurocognitive tasks in order to assess concussion symptoms after head trauma. Changes from baseline were measured with 5 composite scores: Verbal Memory, Visual Memory, Processing Speed, Reaction Time, and Symptom Score. ImPACT defines significant neurocognitive deficit as any PI1 test that shows significant deviation (Sdiff) from baseline for at least 2 of the 5 composite scores. Sdiff from baseline was defined as any adverse change greater than the standard error of difference at the 80% confidence interval for healthy control subjects, as previously described.18

Age Cohorts

Clustering analysis was performed using R (The R Foundation) and the average values of the 5 main composite and impulse control scores for each age between 12 and 18 years (Fig. 1). Based on the results of this analysis, subjects were divided into 3 cohorts: 12- to 13-year-olds, 14- to 15-year-olds, and 16- to 18-year-olds.

FIG. 1.
FIG. 1.

Age groupings. A clustered heatmap analysis of the baseline neurocognitive metrics was conducted for each age between 12 and 18 years to determine which ages were most similar. The results shown suggest that subjects should be grouped into 3 groups: 12–13 years of age, 14–15 years, and 16–18 years. IC = Impulse Control; PS = Processing Speed; RT = Reaction Time; S = Symptom Score; VeM = Verbal Memory; ViM = Visual Memory. Figure is available in color online only.

Incidence and Person-Years Calculations

Incidence was calculated as the number of events (head injury or concussion) per person-year. The event was credited to the cohort on the basis of the age of the subject at the time of the event (e.g., at PI1). Because the subjects were followed for multiple years, many subjects changed age groups during the study. In order to account for this in the calculation of person-years, all patients were assumed to be halfway between their reported integer age and the next integer age, and their person-years were then assigned to the correct cohort. For example, if a subject’s age was 13 years at the time of their baseline test and they were followed for 2 years, they were assumed to be 13.5 years old at their baseline; thus the 12- to 13-year-old group received credit for 0.5 person-years, whereas the remaining 1.5 person-years were credited to the 14- to 15-year-old group. Baseline ImPACT tests are considered valid for as long as 2 years. Subjects lost to follow-up were given 1 person-year for the corresponding baseline, in accordance with Centers for Disease Control and Prevention guidelines.19

Severity Index

Injury severity was assessed with the previously defined Severity Index (SI).20 To calculate SI, the difference between the baseline and PI1 composite scores was measured and compared with Sdiff. If the difference was greater than Sdiff and in the direction of adverse neurocognition, then the difference was divided by Sdiff to measure the number of deviations from baseline; otherwise, we assumed no difference between scores. The SI is the sum of deviations from baseline of all significant composite scores. For all severity analyses, the age of the subject at the time of their injury was used to determine to which age cohort they belonged.

Recovery Assessment

For subjects who met the criteria for concussion at PI1, SI at their next follow-up ImPACT assessment (PI2) was calculated and used for comparison between cohorts. Kaplan-Meier curves were also used to analyze recovery. The event in these analyses was recovery from concussion. Recovery was defined as fewer than 2 of 5 composite scores significantly deviated from baseline. All PI2 tests conducted within 30 days of PI1 were included.

Statistical Analysis

Statistical analyses were performed with Prism 8.0 (GraphPad Software) and MATLAB (MathWorks). The chi-square test was used to compare categorical variables including incidence rates, the log-rank test was used to compare Kaplan-Meier curves, and 1-way ANOVA was used to compare the mean values of all continuous variables.

Multivariable logistic and linear regression analyses were used to evaluate the independent effect of age on concussion incidence and severity, respectively. These regressions controlled for the following self-reported variables: gender, diagnosed learning disability (DLD), attention-deficit/hyperactivity disorder (ADHD), prior concussion history, and a binary variable for whether the subject played football. Multivariable Cox hazard regression analyses were used to evaluate for differences in concussion recovery time between the age groupings. In addition to the variables controlled for in the linear and logistic regressions, concussion severity at PI1 and latency between PI1 and PI2 were also controlled for in the Cox hazard regressions. For all analyses, α = 0.05.

Results

Demographic Characteristics

The mean age of the 12- to 13-year-old group was 12.6 years, 14.6 years for the 14- to 15-year-old group, and 16.6 years for the 16- to 18-year-old group (p < 0.0001). The average age difference between each consecutive group was approximately 2 years. The number of females did not differ significantly between groups (35.1% vs 34.2% vs 33.7%, p = 0.66). The distributions of sports played by the subjects in each group differed significantly (p < 0.0001), although in each group the sport played most often was football (40.7% vs 43.0% vs 36.6%, p < 0.0001). The 16- to 18-year-old group had a significantly higher percentage of subjects with a history of 2 or more past concussions (6.4% vs 5.0% vs 12.0%, p < 0.0001). DLDs (1.1% vs 2.1% vs 3.7%, p < 0.0001) and ADHD (3.6% vs 4.8% vs 5.7%, p = 0.008) were more prevalent in the 16- to 18-year-old group. At baseline, the 16- to 18-year-old group had better scores on the Verbal Memory (80.7 vs 82.0 vs 83.3, p < 0.0001), Visual Memory (70.6 vs 71.5 vs 72.9, p < 0.0001), Processing Speed (30.0 vs 34.3 vs 37.0, p < 0.0001), and Reaction Time (0.698 vs 0.636 vs 0.621, p < 0.0001) composite scores. The 12- to 13-year-old group had the lowest symptom burden at baseline (5.00 vs 6.03 vs 5.59, p = 0.002) (Table 1).

TABLE 1.

Baseline cohort demographic characteristics

CharacteristicAge Groupp Value
12–13 yrs (n = 1132)14–15 yrs (n = 5622)16–18 yrs (n = 5294)
Female gender397 (35.1)1923 (34.2)1784 (33.7)0.66
Age, yrs12.6 ± 0.0214.6 ± 0.0116.6 ± 0.01<0.0001
ADHD41 (3.6)271 (4.8)300 (5.7)0.008
DLD12 (1.1)119 (2.1)196 (3.7)<0.0001
History of ≥2 concussions73 (6.4)285 (5.0)635 (12.0)<0.0001
Sport<0.0001
 Football461 (40.7)2416 (43.0)1936 (36.6)
 Soccer77 (6.8)704 (12.5)737 (13.9)
 Basketball167 (14.8)458 (8.1)420 (7.9)
 Volleyball137 (12.1)415 (7.4)254 (4.8)
 Lacrosse18 (1.6)277 (4.9)449 (8.4)
 Softball23 (2.0)206 (3.7)194 (3.7)
 Baseball18 (1.6)188 (3.3)194 (3.7)
 Wrestling35 (3.1)173 (3.1)225 (4.3)
 Ice hockey3 (0.2)13 (0.2)5 (0.1)
 Other193 (17.0)772 (13.7)880 (16.6)
Baseline scores
 Symptom Score5.00 (4.50–5.50)6.03 (5.77–6.28)5.59 (5.33–5.86)0.002
 Verbal Memory80.7 (80.1–81.3)82.0 (81.7–82.2)83.3 (83.1–83.6)<0.0001
 Visual Memory70.6 (69.8–71.4)71.5 (71.2–71.9)72.9 (72.5–73.3)<0.0001
 Processing Speed30.0 (29.6–30.4)34.3 (34.2–34.5)37.0 (36.8–37.2)<0.0001
 Reaction Time0.698 (0.691–0.704)0.636 (0.633–0.638)0.621 (0.618–0.624)<0.0001

Values are shown as number (%), mean ± SEM, or mean (95% CI) unless indicated otherwise. Boldface type indicates statistical significance (p < 0.05).

Head Injury Incidence and Severity

The incidence of PI1 testing for head injury was significantly higher in the 14- to 15-year-old group than the 16- to 18-year-old and 12- to 13-year-old groups (12- to 13-year-old group 0.34/person-year, 14- to 15-year-old group 0.55/person-year, and 16- to 18-year-old group 0.48/person-year; p < 0.0001). The SI for head injuries was not significantly different between groups (3.39 vs 3.78 vs 3.54, p = 0.26). The 16- to 18-year-old group had significantly lower deviations from baseline for both the Verbal Memory (0.80 vs 0.79 vs 0.65, p = 0.0004) and Visual Memory (0.45 vs 0.45 vs 0.38, p = 0.02) composite scores. There were no differences in the magnitudes of deviation for Processing Speed (0.28 vs 0.41 vs 0.36, p = 0.06), Reaction Time (0.87 vs 0.98 vs 0.96, p = 0.72), or Symptom Score (1.00 vs 1.14 vs 1.19, p = 0.29) (Table 2).

TABLE 2.

Incidence and severity of head injury and concussion

CharacteristicAge Groupp Value
12–13 yrs14–15 yrs16–18 yrs
Head injury
 Person-yrs66934445901
 Incidence rate0.34 (0.31–0.38)0.55 (0.53–0.57)0.48 (0.47–0.49)<0.0001
 Deviation from baseline, no.23018872805
  SI3.39 (2.77–4.02)3.78 (3.53–4.02)3.54 (3.35–3.74)0.26
  Symptom Score1.00 (0.77–1.22)1.14 (1.06–1.23)1.19 (1.12–1.26)0.29
  Verbal Memory0.80 (0.62–0.97)0.79 (0.73–0.85)0.65 (0.60–0.69)0.0004
  Visual Memory0.45 (0.35–0.56)0.45 (0.41–0.49)0.38 (0.35–0.41)0.02
  Processing Speed0.28 (0.17–0.38)0.41 (0.37–0.45)0.36 (0.33–0.40)0.06
  Reaction Time0.87 (0.61–1.12)0.98 (0.89–1.08)0.96 (0.88–1.04)0.72
Concussion
 Person-yrs66934445901
 Incidence rate0.13 (0.11–0.16)0.21 (0.19–0.22)0.17 (0.16–0.18)<0.0001
 Deviation from baseline, no.90708997
  SI7.54 (6.41–8.68)8.63 (8.17–9.08)8.45 (8.07–8.82)0.06
  Symptom Score2.28 (1.83–2.73)2.51 (2.35–2.67)2.71 (2.57–2.85)0.09
  Verbal Memory1.77 (1.42–2.12)1.83 (1.71–1.95)1.56 (1.47–1.66)0.002
  Visual Memory0.95 (0.74–1.16)1.03 (0.95–1.12)0.92 (0.86–0.99)0.13
  Processing Speed0.69 (0.44–0.93)1.02 (0.93–1.12)0.96 (0.88–1.04)0.06
  Reaction Time1.86 (1.28–2.43)2.23 (2.00–2.45)2.29 (2.11–2.48)0.41

Values are shown as mean (95% CI) unless indicated otherwise. Boldface type indicates statistical significance (p < 0.05).

Concussion Incidence and Severity

The incidence of concussion was significantly higher in the 14- to 15-year-old group than both the 12- to 13-year-old and 16- to 18-year-old groups (12- to 13-year-old group 0.13/person-year, 14- to 15-year-old group 0.21/person-year, and 16- to 18-year-old group 0.17/person-year; p < 0.0001). There was no significant difference in severity of concussion between any groups (7.54 vs 8.63 vs 8.45, p = 0.06). The 16- to 18-year-old group had significantly lower deviation from baseline in Verbal Memory (1.77 vs 1.83 vs 1.56, p = 0.002). There were no significant differences between groups in terms of Visual Memory (0.95 vs 1.03 vs 0.92, p = 0.13), Processing Speed (0.69 vs 1.02 vs 0.96, p = 0.06), Reaction Time (1.86 vs 2.23 vs 2.29, p = 0.41), or Symptom Score (2.28 vs 2.51 vs 0.271, p = 0.09) (Table 2).

Concussion Recovery

All 3 cohorts had similar percentages of patients who returned for a follow-up PI2 test (70.0% vs 70.3% vs 69.1%, p = 0.86) and similar percentages of subjects with persistent concussion at PI2 (25.4% vs 27.5% vs 25.6%, p = 0.61). For those subjects with persistent concussions at PI2, the 16- to 18-year-old group had significantly lower SI than the other groups (7.85 vs 8.07 vs 6.60, p = 0.05), although deviations between individual composite scores were not different between groups. There were no significant differences in the mean number of days between PI1 and PI2 (9.33 vs 8.80 vs 8.16 days, p = 0.08) (Table 3).

TABLE 3.

Comparisons of concussion recovery at follow-up

CharacteristicAge Groupp Value
12–13 yrs14–15 yrs16–18 yrs 
Recovery
 No. of patients63498595
 Patients w/ PI263/90 (70.0)498/708 (70.3)689/997 (69.1)0.86
 Patients still concussed at PI216 (25.4)137 (27.5)177 (25.6)0.61
 Days btwn tests9.33 (8.04–10.6)8.80 (8.29–9.32)8.16 (7.73–8.58)0.08
 Recovery time, days10 (6.00–14.0)8.00 (5.00–11.0)8.00 (5.00–11.0)0.09
Concussion at PI2
 Deviation from baseline, no.16137177
  SI7.85 (5.04–10.7)8.07 (7.07–9.08)6.60 (5.90–7.30)0.05
  Symptom Score1.92 (0.66–3.18)1.62 (1.26–1.97)1.31 (1.06–1.56)0.23
  Verbal Memory1.89 (1.05–2.72)1.87 (1.60–2.14)1.54 (1.32–1.77)0.16
  Visual Memory0.81 (0.27–1.34)1.26 (1.08–1.45)1.02 (0.88–1.17)0.06
  Processing Speed1.03 (0.29–1.77)0.97 (0.74–1.20)0.73 (0.57–0.89)0.19
  Reaction Time2.21 (1.13–3.28)2.35 (1.87–2.83)1.99 (1.59–2.39)0.50

Values are shown as number (%) or mean (95% CI) unless indicated otherwise. Boldface type indicates statistical significance (p < 0.05).

The log-rank test of the Kaplan-Meier survival curves showed a significantly longer recovery time for the 14- to 15-year-old group than the 16- to 18-year-old group (p = 0.04). There were no significant differences in recovery time between the other groups. The median recovery time was 10 days for the 12- to 13-year-old group and 8 days for both the 14- to 15-year-old and 16- to 18-year-old groups (Fig. 2).

FIG. 2.
FIG. 2.

Concussion recovery. Kaplan-Meier curves of concussion recovery over the first 30 days after PI1 for the 3 age groupings. The dotted lines indicate the 95% CIs. The p values determined with the log-rank test for comparison of the survival curves of the different age groups are reported in the table. Figure is available in color online only.

Multivariable Analyses

Multivariable regression analysis demonstrated that the 14- to 15-year group had a significantly higher incidence of concussion than both the 12- to 13-year-old group (14- to 15-year-old group vs 12- to 13-year-old group, OR 1.57, 95% CI 1.16–2.17, p = 0.005) and the 16- to 18-year-old group (16- to 18-year-old group vs 14- to 15-year-old group, OR 0.79, 95% CI 0.69–0.91, p = 0.0008). Concussion incidence did not differ between the 12- to 13-year-old and 16- to 18-year-old groups (OR 1.26, 95% CI 0.93–1.72, p = 0.15). There was no independent effect of age on concussion severity or recovery between any groups (Table 4).

TABLE 4.

Multivariable analyses of the effect of age on concussion incidence, severity, and recovery

OutcomeEstimatep Value
14- to 15-yr-olds vs 12- to 13-yr-olds
 Concussion incidence, OR (95% CI)*1.57 (1.16 to 2.17)0.05
 Severity at PI1, β (95% CI)*1.52 (–0.71 to 2.22)0.07
 Recovery at PI2, HR (95% CI)0.95 (0.63 to 1.51)0.83
16- to 18-yr-olds vs 12- to 13-yr-olds
 Concussion incidence, OR (95% CI)*1.26 (0.93 to 1.72)0.15
 Severity at PI1, β (95% CI)*1.55 (–0.18 to 3.28)0.08
 Recovery at PI2, HR (95% CI)1.08 (0.49 to 2.39)0.85
16- to 18-yr-olds vs 14- to 15-yr-olds
 Concussion incidence, OR (95% CI)*0.79 (0.69 to 0.91)0.0008
 Severity at PI1, β (95% CI)*0.09 (–0.65 to 0.84)0.81
 Recovery at PI2, HR (95% CI)0.93 (0.66 to 1.33)0.71

Boldface type indicates statistical significance (p < 0.05).

Covariates included binary indicators for gender, DLD, ADHD, previous concussion history, and sport (football vs non-football).

Covariates included concussion severity at PI1, latency to PI2, and binary indicators for gender, DLD, ADHD, previous concussion history, and sport (football vs non-football).

Discussion

The present study evaluated the effect of age on concussion incidence, severity, and recovery in a large group of adolescent student-athletes. The initial univariate analysis demonstrated that the 14- to 15-year-old group had a higher concussion incidence than the other groups and slightly prolonged recovery in comparison with the 16- to 18-year-old group, with no differences in concussion severity between any groups. The increased incidence of concussion in the 14- to 15-year-old group was robust to multivariable regression. There were no differences in concussion severity or recovery time.

This is the first study of the effect of age on concussion outcomes to use the SI to calculate initial concussion severity and to control for it with concussion recovery analyses. It is also the only study to investigate concussion incidence, recovery, and severity within the same group in a single study. In order to make direct comparisons across studies, we performed supplemental analyses with various age groupings. These analyses demonstrated that small differences in age groupings can significantly alter results and may partly explain the variability in findings throughout the literature.

Incidence

The present study found that concussion incidence ranged from 0.13 to 0.21 per person-year in subjects aged 12–18 years, with incidence peaking in middle adolescence (14–15 years of age). However, within the adolescent age range, the findings concerning the effect of age on concussion incidence have been mixed. For example, multiple studies have reported that younger adolescents have higher risk of concussion than older adolescents.2124 Specifically, Mc Fie et al. found that adolescents in the 14- to 16-year age range had significantly higher incidence of concussion than 17- to 18-year-old adolescents, which is consistent with the results of our study.21 Additionally, Kontos et al. also found that 12- to 14-year-old hockey players were more likely to sustain a concussion than 15- to 18-year-old players.24 It was difficult to make direct comparisons based on the age groupings in the present study; however, when we used those exact age groupings (Table S1), we found no differences in concussion incidence. Conversely, other studies found that older adolescents are at increased risk of concussion in comparison with younger adolescents.25,26 For example, consistent with our results, Tsushima et al. found that student-athletes aged 15, 17, and 18 years all had higher concussion risk than 13-year-old student-athletes.25 However, again, when we performed an identical analysis with the exact age groupings (Table S2), we did not find that any single age had increased incidence of concussion in comparison with 13-year-old subjects.

Importantly, this study provides an explanation for the mixed results. As we have demonstrated, the effect of age on concussion incidence can differ with minor changes in groupings. Thus, future researchers must refrain from basing their groupings on convenience, as the grouping method may have a serious impact on study conclusions and clinical recommendations. However, it should also be noted that apparent discrepancies between studies may also be due to a U-shaped, rather than linear, relationship between age and concussion incidence during adolescence. For example, the results of the present study may be consistent with those of Mc Fie et al. and Tsushima et al. if concussion incidence peaks around 15 years of age. Thus, researchers should endeavor to compare more than 2 groupings within the adolescent age range in future studies.

Severity

In general, our current understanding of concussion severity is limited. This is the first study to use the recently designed SI to evaluate for differences in concussion severity. The SI is the only current marker of concussion severity that combines objective neurocognitive performance and subjective symptom reporting into a single metric that can be calculated at the time of injury and followed over time. SI has also demonstrated utility in predicting recovery length.20

No differences in SI were found in univariate or multivariable analyses. Similarly, Howell et al. found no effect of age on symptom burden when comparing children (ages 7–12 years) to adolescents (13–18 years) and adults (19–30 years).11 Conversely, when Grubenhoff et al. used the Standardized Assessment of Concussion (SAC) and compared performance between case-patients and controls in 4 groupings, they found that the 12- to 14-year-old group had greater change in and lower overall SAC scores between concussed patients and controls when compared with the 15- to 18-year-old group.27 Similarly, Majerske et al. found that younger adolescents (13- to 15-year-olds) had worse Verbal Memory and Visual Memory performance after concussion than older adolescents (16- to 18-year-olds).28 No differences in concussion severity between the age groupings used by both Grubenhoff et al. and Majerske et al. were revealed in our analyses (Tables S1–S3). In fact, across almost every supplemental analysis, SI was not significantly different across age groups. This may indicate that other factors that have been previously demonstrated to affect SI, such as gender and prior concussion history, have greater influence on concussion severity than age.29,30

Recovery

The effect of age on concussion recovery has been studied extensively, but little consensus has been generated. In a systematic review, Iverson et al. discussed the possibility of an age gradient for protracted recovery, wherein risk for protracted recovery increases gradually from elementary school through high school before decreasing in college age, as well as for professional athletes.31 However, many studies provide evidence that contradicts an age gradient for recovery, and as pointed out by Iverson et al., multiple confounding factors may generate this gradient that are not so much age-related as they are related to differences in motivation level to return to play, psychosocial dynamics, or ability to verbalize symptoms.31,32 Although the present study found an age gradient for concussion incidence, it did not reveal any evidence of an age gradient for recovery time within the adolescent age range.

The results of this study are consistent with those of Corwin et al., who found no differences in time to complete symptom resolution after concussion in 13- to 14-year-old, 15- to 16-year-old, and 17- to 18-year-old age groupings.7 Numerous other studies have demonstrated no effect of age on concussion recovery in subjects ranging in age from 7 to 27 years.13,1517,33,34 Conversely, a number of studies have found differences in recovery based on age.28,35,36 For example, Majerske et al. demonstrated that subjects aged 13–15 years performed worse on Verbal Memory and Visual Memory tasks during concussion recovery than those aged 16–18 years.28 However, even when we performed recovery analyses on identical age groupings (Table S3), we found no difference in overall recovery to baseline.

Significant differences in recovery definitions and study design between the present study and those studies that found an effect of age on concussion recovery may also contribute to divergent results. Median recovery in our study was less than 10 days, whereas median recovery in another study was more than 30 days, thereby limiting meaningful comparison of results.36 Moreover, none of these other studies controlled for initial concussion severity, which may have been a confounding factor in the assessment of recovery time.

Limitations

There were a number of limitations to this study. First, we did not have information about date of birth because the data were de-identified, which limited our ability to accurately ascribe person-years to the correct group in our incidence analyses. Incidence calculations were also limited by the fact that it was unclear for many subjects how long they had participated in sports after their baseline test. ImPACT test performance was used to determine whether a subject had sustained a concussion, but the ImPACT test is susceptible to falsely classifying subjects as concussed or healthy, thereby limiting the validity of our findings. It was also unclear how much exercise or mental exertion was performed by the subjects between their initial injury and follow-up testing, which may have impacted recovery. The recovery analyses were also limited because time held out from competition, time between initial injury and follow-up testing, and treatment types were not standardized. Additionally, not all subjects were followed until resolution of their injury. Our estimates of recovery time are likely overestimates of the actual number of days it took for subjects to recover because subjects were not tested every day.

Conclusions

This study investigated the independent effect of age on concussion incidence, severity, and recovery in adolescent student-athletes. After controlling for relevant covariates, we determined that concussion incidence was higher during mid-adolescence than early and late adolescence, suggesting a U-shaped relationship between age and concussion risk over the course of adolescence. There was no independent effect of age on concussion severity or recovery between the 12- to 13-year-old, 14- to 15-year-old, and 16- to 18-year-old age groups. Further analysis of the various age groupings revealed that results may vary significantly on the basis of minor changes to the groupings. These changes, in combination with a nonlinear relationship between age and concussion incidence, may explain the divergent results in the current literature on this topic.

Disclosures

Dr. Lovell is the co-founder of Impact Applications, Inc.; he is no longer involved with the company.

Author Contributions

Conception and design: Hannah, Kalagara, Ali, Gometz, Lovell, Choudhri. Acquisition of data: Gometz, Lovell, Choudhri. Analysis and interpretation of data: Hannah, Kalagara, Ali, Schupper, Li, Choudhri. Drafting the article: Hannah, Kalagara, Ali, Schupper, Li, Spiera, Marayati. Critically revising the article: all authors. Reviewed submitted version of manuscript: all authors. Approved the final version of the manuscript on behalf of all authors: Hannah. Statistical analysis: Hannah. Administrative/technical/material support: Kalagara, Gometz, Lovell, Choudhri. Study supervision: Lovell, Choudhri.

Supplemental Information

Online-Only Content

Supplemental material is available with the online version of the article.

References

  • 1

    Crowe L, Babl F, Anderson V, Catroppa C. The epidemiology of paediatric head injuries: data from a referral centre in Victoria, Australia. J Paediatr Child Health. 2009;45(6):346350.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2

    Lyttle MD, Crowe L, Oakley E, Dunning J, Babl FE. Comparing CATCH. CHALICE and PECARN clinical decision rules for paediatric head injuries. Emerg Med J. 2012;29(10):785794.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3

    Moser RS, Davis GA, Schatz P. The age variable in childhood concussion management: a systematic review. Arch Clin Neuropsychol. 2018;33(4):417426.

  • 4

    Baillargeon A, Lassonde M, Leclerc S, Ellemberg D. Neuropsychological and neurophysiological assessment of sport concussion in children, adolescents and adults. Brain Inj. 2012;26(3):211220.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5

    Bakhos LL, Lockhart GR, Myers R, Linakis JG. Emergency department visits for concussion in young child athletes. Pediatrics. 2010;126(3):e550e556.

  • 6

    Eisenberg MA, Andrea J, Meehan W, Mannix R. Time interval between concussions and symptom duration. Pediatrics. 2013;132(1):817.

  • 7

    Corwin DJ, Zonfrillo MR, Master CL, et al. Characteristics of prolonged concussion recovery in a pediatric subspecialty referral population. J Pediatr. 2014;165(6):12071215.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8

    Gessel LM, Fields SK, Collins CL, Dick RW, Comstock RD. Concussions among United States high school and collegiate athletes. J Athl Train. 2007;42(4):495503.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    Lincoln AE, Hinton RY, Almquist JL, Lager SL, Dick RW. Head, face, and eye injuries in scholastic and collegiate lacrosse: a 4-year prospective study. Am J Sports Med. 2007;35(2):207215.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    Shields BJ, Smith GA. Cheerleading-related injuries in the United States: a prospective surveillance study. J Athl Train. 2009;44(6):567577.

  • 11

    Howell DR, Kriz P, Mannix RC, Kirchberg T, Master CL, Meehan WP III. Concussion symptom profiles among child, adolescent, and young adult athletes. Clin J Sport Med. 2019;29(5):391397.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12

    Emery CA, Meeuwisse WH. Injury rates, risk factors, and mechanisms of injury in minor hockey. Am J Sports Med. 2006;34(12):19601969.

  • 13

    Morgan CD, Zuckerman SL, Lee YM, et al. Predictors of postconcussion syndrome after sports-related concussion in young athletes: a matched case-control study. J Neurosurg Pediatr. 2015;15(6):589598.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14

    Askow AT, Erickson JL, Jagim AR. Recent trends in youth concussions: a brief report. J Prim Care Community Health. 2020;11:2150132720985058.

  • 15

    Elbin RJ, Sufrinko A, Schatz P, et al. Removal from play after concussion and recovery time. Pediatrics. 2016;138(3):e20160910.

  • 16

    Chrisman SP, Rivara FP, Schiff MA, Zhou C, Comstock RD. Risk factors for concussive symptoms 1 week or longer in high school athletes. Brain Inj. 2013;27(1):19.

  • 17

    Nelson LD, Guskiewicz KM, Barr WB, et al. Age differences in recovery after sport-related concussion: a comparison of high school and collegiate athletes. J Athl Train. 2016;51(2):142152.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18

    Iverson GL, Lovell MR, Collins MW. Interpreting change on ImPACT following sport concussion. Clin Neuropsychol. 2003;17(4):460467.

  • 19

    Centers for Disease Control and Prevention. Principles of Epidemiology in Public Health Practice, 3rd edition: An Introduction to Applied Epidemiology and Biostatistics. Lesson 3: Measures of Risk. Section 2: Morbidity Frequency Measures. Accessed June 29, 2022. https://www.cdc.gov/csels/dsepd/ss1978/lesson3/section2.html

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20

    Hannah T, Dreher N, Li AY, et al. Assessing the predictive value of primary evaluation with the Immediate Post-Concussion Assessment and Cognitive Test following head injury. J Neurosurg Pediatr. 2020;26(2):171178.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21

    Mc Fie S, Brown J, Hendricks S, et al. Incidence and factors associated with concussion injuries at the 2011 to 2014 South African rugby union youth week tournaments. Clin J Sport Med. 2016;26(5):398404.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22

    Brett BL, Kuhn AW, Yengo-Kahn AM, Solomon GS, Zuckerman SL. Risk factors associated with sustaining a sport-related concussion: an initial synthesis study of 12,320 student-athletes. Arch Clin Neuropsychol. 2018;33(8):984992.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23

    Koh JO, Cassidy JD. Incidence study of head blows and concussions in competition taekwondo. Clin J Sport Med. 2004;14(2):7279.

  • 24

    Kontos AP, Elbin RJ, Sufrinko A, et al. Incidence of concussion in youth ice hockey players. Pediatrics. 2016;137(2):e20151633.

  • 25

    Tsushima WT, Siu AM, Ahn HJ, Chang BL, Murata NM. Incidence and risk of concussions in youth athletes: comparisons of age, sex, concussion history, sport, and football position. Arch Clin Neuropsychol. 2019;34(1):6069.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26

    Zhang AL, Sing DC, Rugg CM, Feeley BT, Senter C. The rise of concussions in the adolescent population. Orthop J Sports Med. 2016;4(8):2325967116662458.

  • 27

    Grubenhoff JA, Kirkwood M, Gao D, Deakyne S, Wathen J. Evaluation of the standardized assessment of concussion in a pediatric emergency department. Pediatrics. 2010;126(4):688695.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28

    Majerske CW, Mihalik JP, Ren D, et al. Concussion in sports: postconcussive activity levels, symptoms, and neurocognitive performance. J Athl Train. 2008;43(3):265274.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29

    Hannah TC, Li AY, Spiera Z, et al. Sex-related differences in the incidence, severity, and recovery of concussion in adolescent student-athletes between 2009 and 2019. Am J Sports Med. 2021;49(7):19291937.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30

    Hannah TC, Spiera Z, Li AY, et al. Effects of recurrent mild traumatic brain injuries on incidence, severity, and recovery of concussion in young student-athletes. J Head Trauma Rehabil. 2021;36(4):293301.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31

    Iverson GL, Gardner AJ, Terry DP, et al. Predictors of clinical recovery from concussion: a systematic review. Br J Sports Med. 2017;51(12):941948.

  • 32

    Jeckell AS, Fontana RS. Psychosocial aspects of sport-related concussion in youth. Psychiatr Clin North Am. 2021;44(3):469480.

  • 33

    Kriz PK, Stein C, Kent J, et al. Physical maturity and concussion symptom duration among adolescent ice hockey players. J Pediatr. 2016;171:234-239.e12.

  • 34

    Hang B, Babcock L, Hornung R, Ho M, Pomerantz WJ. Can computerized neuropsychological testing in the emergency department predict recovery for young athletes with concussions? Pediatr Emerg Care. 2015;31(10):688693.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35

    Zuckerman SL, Lee YM, Odom MJ, Solomon GS, Forbes JA, Sills AK. Recovery from sports-related concussion: days to return to neurocognitive baseline in adolescents versus young adults. Surg Neurol Int. 2012;3:130.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36

    Terwilliger VK, Pratson L, Vaughan CG, Gioia GA. Additional post-concussion impact exposure may affect recovery in adolescent athletes. J Neurotrauma. 2016;33(8):761765.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • Collapse
  • Expand

Image from Tran et al. (pp 394–399).

  • FIG. 1.

    Age groupings. A clustered heatmap analysis of the baseline neurocognitive metrics was conducted for each age between 12 and 18 years to determine which ages were most similar. The results shown suggest that subjects should be grouped into 3 groups: 12–13 years of age, 14–15 years, and 16–18 years. IC = Impulse Control; PS = Processing Speed; RT = Reaction Time; S = Symptom Score; VeM = Verbal Memory; ViM = Visual Memory. Figure is available in color online only.

  • FIG. 2.

    Concussion recovery. Kaplan-Meier curves of concussion recovery over the first 30 days after PI1 for the 3 age groupings. The dotted lines indicate the 95% CIs. The p values determined with the log-rank test for comparison of the survival curves of the different age groups are reported in the table. Figure is available in color online only.

  • 1

    Crowe L, Babl F, Anderson V, Catroppa C. The epidemiology of paediatric head injuries: data from a referral centre in Victoria, Australia. J Paediatr Child Health. 2009;45(6):346350.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2

    Lyttle MD, Crowe L, Oakley E, Dunning J, Babl FE. Comparing CATCH. CHALICE and PECARN clinical decision rules for paediatric head injuries. Emerg Med J. 2012;29(10):785794.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3

    Moser RS, Davis GA, Schatz P. The age variable in childhood concussion management: a systematic review. Arch Clin Neuropsychol. 2018;33(4):417426.

  • 4

    Baillargeon A, Lassonde M, Leclerc S, Ellemberg D. Neuropsychological and neurophysiological assessment of sport concussion in children, adolescents and adults. Brain Inj. 2012;26(3):211220.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5

    Bakhos LL, Lockhart GR, Myers R, Linakis JG. Emergency department visits for concussion in young child athletes. Pediatrics. 2010;126(3):e550e556.

  • 6

    Eisenberg MA, Andrea J, Meehan W, Mannix R. Time interval between concussions and symptom duration. Pediatrics. 2013;132(1):817.

  • 7

    Corwin DJ, Zonfrillo MR, Master CL, et al. Characteristics of prolonged concussion recovery in a pediatric subspecialty referral population. J Pediatr. 2014;165(6):12071215.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8

    Gessel LM, Fields SK, Collins CL, Dick RW, Comstock RD. Concussions among United States high school and collegiate athletes. J Athl Train. 2007;42(4):495503.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    Lincoln AE, Hinton RY, Almquist JL, Lager SL, Dick RW. Head, face, and eye injuries in scholastic and collegiate lacrosse: a 4-year prospective study. Am J Sports Med. 2007;35(2):207215.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    Shields BJ, Smith GA. Cheerleading-related injuries in the United States: a prospective surveillance study. J Athl Train. 2009;44(6):567577.

  • 11

    Howell DR, Kriz P, Mannix RC, Kirchberg T, Master CL, Meehan WP III. Concussion symptom profiles among child, adolescent, and young adult athletes. Clin J Sport Med. 2019;29(5):391397.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12

    Emery CA, Meeuwisse WH. Injury rates, risk factors, and mechanisms of injury in minor hockey. Am J Sports Med. 2006;34(12):19601969.

  • 13

    Morgan CD, Zuckerman SL, Lee YM, et al. Predictors of postconcussion syndrome after sports-related concussion in young athletes: a matched case-control study. J Neurosurg Pediatr. 2015;15(6):589598.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14

    Askow AT, Erickson JL, Jagim AR. Recent trends in youth concussions: a brief report. J Prim Care Community Health. 2020;11:2150132720985058.

  • 15

    Elbin RJ, Sufrinko A, Schatz P, et al. Removal from play after concussion and recovery time. Pediatrics. 2016;138(3):e20160910.

  • 16

    Chrisman SP, Rivara FP, Schiff MA, Zhou C, Comstock RD. Risk factors for concussive symptoms 1 week or longer in high school athletes. Brain Inj. 2013;27(1):19.

  • 17

    Nelson LD, Guskiewicz KM, Barr WB, et al. Age differences in recovery after sport-related concussion: a comparison of high school and collegiate athletes. J Athl Train. 2016;51(2):142152.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18

    Iverson GL, Lovell MR, Collins MW. Interpreting change on ImPACT following sport concussion. Clin Neuropsychol. 2003;17(4):460467.

  • 19

    Centers for Disease Control and Prevention. Principles of Epidemiology in Public Health Practice, 3rd edition: An Introduction to Applied Epidemiology and Biostatistics. Lesson 3: Measures of Risk. Section 2: Morbidity Frequency Measures. Accessed June 29, 2022. https://www.cdc.gov/csels/dsepd/ss1978/lesson3/section2.html

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20

    Hannah T, Dreher N, Li AY, et al. Assessing the predictive value of primary evaluation with the Immediate Post-Concussion Assessment and Cognitive Test following head injury. J Neurosurg Pediatr. 2020;26(2):171178.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21

    Mc Fie S, Brown J, Hendricks S, et al. Incidence and factors associated with concussion injuries at the 2011 to 2014 South African rugby union youth week tournaments. Clin J Sport Med. 2016;26(5):398404.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22

    Brett BL, Kuhn AW, Yengo-Kahn AM, Solomon GS, Zuckerman SL. Risk factors associated with sustaining a sport-related concussion: an initial synthesis study of 12,320 student-athletes. Arch Clin Neuropsychol. 2018;33(8):984992.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23

    Koh JO, Cassidy JD. Incidence study of head blows and concussions in competition taekwondo. Clin J Sport Med. 2004;14(2):7279.

  • 24

    Kontos AP, Elbin RJ, Sufrinko A, et al. Incidence of concussion in youth ice hockey players. Pediatrics. 2016;137(2):e20151633.

  • 25

    Tsushima WT, Siu AM, Ahn HJ, Chang BL, Murata NM. Incidence and risk of concussions in youth athletes: comparisons of age, sex, concussion history, sport, and football position. Arch Clin Neuropsychol. 2019;34(1):6069.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26

    Zhang AL, Sing DC, Rugg CM, Feeley BT, Senter C. The rise of concussions in the adolescent population. Orthop J Sports Med. 2016;4(8):2325967116662458.

  • 27

    Grubenhoff JA, Kirkwood M, Gao D, Deakyne S, Wathen J. Evaluation of the standardized assessment of concussion in a pediatric emergency department. Pediatrics. 2010;126(4):688695.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28

    Majerske CW, Mihalik JP, Ren D, et al. Concussion in sports: postconcussive activity levels, symptoms, and neurocognitive performance. J Athl Train. 2008;43(3):265274.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 29

    Hannah TC, Li AY, Spiera Z, et al. Sex-related differences in the incidence, severity, and recovery of concussion in adolescent student-athletes between 2009 and 2019. Am J Sports Med. 2021;49(7):19291937.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30

    Hannah TC, Spiera Z, Li AY, et al. Effects of recurrent mild traumatic brain injuries on incidence, severity, and recovery of concussion in young student-athletes. J Head Trauma Rehabil. 2021;36(4):293301.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31

    Iverson GL, Gardner AJ, Terry DP, et al. Predictors of clinical recovery from concussion: a systematic review. Br J Sports Med. 2017;51(12):941948.

  • 32

    Jeckell AS, Fontana RS. Psychosocial aspects of sport-related concussion in youth. Psychiatr Clin North Am. 2021;44(3):469480.

  • 33

    Kriz PK, Stein C, Kent J, et al. Physical maturity and concussion symptom duration among adolescent ice hockey players. J Pediatr. 2016;171:234-239.e12.

  • 34

    Hang B, Babcock L, Hornung R, Ho M, Pomerantz WJ. Can computerized neuropsychological testing in the emergency department predict recovery for young athletes with concussions? Pediatr Emerg Care. 2015;31(10):688693.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35

    Zuckerman SL, Lee YM, Odom MJ, Solomon GS, Forbes JA, Sills AK. Recovery from sports-related concussion: days to return to neurocognitive baseline in adolescents versus young adults. Surg Neurol Int. 2012;3:130.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 36

    Terwilliger VK, Pratson L, Vaughan CG, Gioia GA. Additional post-concussion impact exposure may affect recovery in adolescent athletes. J Neurotrauma. 2016;33(8):761765.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation

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