Epidemiology of Ankle-Related Basketball Injuries Treated in Emergency Departments During 2014-2023

Abstract

Background:

Evidence is mixed and conflicting regarding emergent ankle injuries by sex and age in basketball.

Purpose:

To determine during the time frame of 2014-2023 (1) the prevalence and patterns of basketball-related ankle injuries admitted to emergency departments (EDs) and (2) whether prevalence and mechanisms of emergent ankle-related basketball injuries differ by age and sex.

Study Design:

Descriptive epidemiology study.

Methods:

Patients who sought care in the ED and were part of the National Electronic Injury Surveillance System were categorized by sex and age groups (13-18, 19-24, and 25-35 years). Annual injury national estimate trends were compared over time by age and sex using linear regression analyses. Injury diagnoses, prevalence, mechanisms, settings (practice vs competition), and treatment dispositions were compared by age and sex using chi-square tests. Odds ratios (ORs) were determined using logistic regression.

Results:

There were 20,303 patients included in this analysis (15.3% female). Female players sought ED care more frequently than male players for ankle sprains/strains (OR, 1.33; 95% CI, 1.19-1.47) and less often for fractures (OR, 0.57; 95% CI, 0.48-0.67) and dislocations (OR, 0.46; 95% CI, 0.21-0.99) than male players (all P < .05). Patients aged 19 to 24 years and 25 to 35 years had 31% and 22% greater odds of seeking care for sprains/strains and lower odds for fractures and dislocations than patients aged 13 to 18 years (P < .05). Injuries among male players were associated more often with poor landings than female players (10.0% vs 6.8%); female players had more injuries involving falls (10.8% vs 8.5%), being pushed, and collisions than males (all P < .05). Compared with patients aged 13 to 18 years and 19 to 24 years, those aged 25 to 35 years had fewer ankle injuries involving poor landings and rolling of ankles (P < .05). The 13- to 18-year-old age group had the greatest involvement in falls and collisions. In total, 98.4% of all cases were treated and released, and 0.4% were hospitalized.

Conclusion:

Our study demonstrated that sex- and age-related differences exist among diagnoses and injury mechanisms that involved ED visits. Targeted prevention strategies could include neuromuscular and balance programs for youth, ankle stability and proprioception programs for female players, and dorsiflexion mobility and landing biomechanics programs for male players.

Basketball participation rates have remained consistently high at the high school and collegiate levels over the past 15 years. ¹⁸ Basketball is the second most popular sport in the world for viewership, and an estimated 610 million individuals participate in the sport. ⁷ The physically demanding nature of the sport can predispose players to musculoskeletal injuries, likely caused by repetitive jumps, jump landings, fast accelerations/decelerations, and sudden changes in direction in crowded player conditions. These sport-specific movements all place significant physical stress on the musculoskeletal system, particularly on the lower extremities. ¹ Overall, basketball-related injuries range from 7 to 10 injuries per 1000 athletic exposures, with lower extremities contributing to approximately 58% to 66% of all injuries. ²⁴

Ankle sprains have been shown to be a commonly diagnosed basketball-related injury among male and female players, accounting for 25% of all injuries—from pediatric to collegiate levels. ²⁴ Other common ankle injuries include acute fractures, dislocations, and soft tissue injuries. Although these acute injuries are typically transient, they can increase the likelihood of players developing chronic conditions such as ankle instability, osteoarthritis, and tendinopathy.^22,24,26 Moreover, players with existing ankle injuries experience lower extremity kinetic chain obstruction during jump landings, sharp cutting, and pivoting. This can inevitably lead to compensatory maladaptation of the lower extremities, making athletes more susceptible to devastating injuries, such as anterior cruciate ligament rupture. ¹³ In addition, the severity of the ankle injury may determine if emergency department (ED) visits are needed for treatment. However, current trends of ED visits associated with basketball-related ankle injuries—especially sex differences—are not known.

Surveillance data and systematic reviews show mixed results concerning overall ankle injury incidence between the sexes. Some data show that female basketball players have higher injury rates, ¹⁹ but other studies show a higher incidence among males. ¹⁴ Other data show no sex differences. ³⁰ Furthermore, it is unclear whether the specific mechanisms underlying ankle injuries differ by sex. Many injuries are not emergent and may not involve loss of playing time. While injury surveillance commonly involves the capture of all injuries, injury severity, and time lost, the use of ED visits is not often reported. Determining specific ankle injury patterns among male and female players across age brackets will help identify subgroups at greatest risk for ankle injuries requiring ED care.

Therefore, this study investigated (1) the prevalence and patterns of basketball-related ankle injuries admitted to EDs for both male and female players during 2014-2023 and (2) whether the prevalence and potential mechanisms of ankle-related basketball injuries differ among high school (13-18 years old), collegiate (19-24 years old), and postcollegiate (25-35 years old) basketball athletes. We hypothesized that ankle sprains would be the most common ankle injury, regardless of sex or age differences, and that fractures and dislocations would be more common among male than female players. In addition, we expect injury mechanism patterns to differ by sex and age.

Methods

Data Source

The study team leveraged data from the National Electronic Injury Surveillance System (NEISS). NEISS is a publicly available database maintained by the US Consumer Product Safety Commission (CPSC). The NEISS data for this study comprise a stratified probability sample of US hospital EDs. It does not include urgent-care clinics, athletic-training rooms, or outpatient practices. Throughout, we report weighted national estimates (NEs) of ED presentations, not exposure-based incidence. This system collects data on injuries treated in a statistically representative sample of EDs across the United States, enabling researchers to generate NEs. NEISS cases are abstracted by trained coders using standardized manuals; anatomic locations undergo routine auditing.

To produce NEs, the data from the sample hospitals were weighted based on factors such as hospital size and geographic location, allowing them to represent the broader population. Data from this sample can be used to estimate the total number of similar injuries occurring nationwide, not just within the participating hospitals. The hospitals were grouped into 5 strata: 4 strata represent hospital EDs of different sizes, and 1 represents EDs from children's hospitals. When a hospital is removed from the sampling frame, the highest-ranked hospital within the same stratum is invited to replace it. Weights are recalibrated each year so that longitudinal analyses of NEs can occur even with a dynamic sampling frame. On an annual basis, the previous year's data are made available through the CPSC website. Hospital weights are equal to the inverse of the probability of selection at the stratum level, which are then adjusted for nonresponse or hospital mergers. The data are anonymized, and therefore, this study was deemed exempt from institutional review board approval at this institution. NEISS characterizes injuries presenting to US EDs rather than exposure-based risk. Consequently, sex distributions reflect ED care-seeking and coding patterns, not general athlete-exposure statistics.

Patients

The entire NEISS database was queried retrospectively for all injuries related to basketball from January 1, 2014, to December 31, 2023, using the product code 1205. Data extracted from NEISS initially included patients aged 10 to 65 years; there were 91,680 cases of total injuries related to basketball. To more closely address the study aims, the data set was reduced to male and female players in age brackets, including high school (13-18 years), college (19-24 years), and young adult (25-35 years), who incurred ankle injuries. Data were organized by age and injured body part; all cases that involved individuals outside the age ranges were removed.

Data Extraction and Coding

The specific ankle injury diagnosis was also extracted, including fracture, sprain/strain, and “other” (most often described in the text as “pain,”“trauma,”“swelling,” or just “injury”). The patient's demographic information (age, race, and sex), ankle injury type, and a brief ED narrative were also extracted. Hospital disposition and the environmental site where the injury occurred (school, sports facility, public space, and other, not specified) were provided by NEISS and extracted for analysis.

The narratives of the remaining cases were used to obtain additional information on the mechanisms related to the injury and play conditions. Language coding was used to identify mechanisms of injury in Excel, and keywords included “rolled ankle,”“fell” (and “falling” and “fell down”; due to being pushed or tripping), “collision” (“colli,”“collided,”“hit another player,”“ran into,”“crashed”), “poor landings” (“bad landing,”“landed badly”: after rebounds, layups, jump shots, vying for a ball, landing on another player's foot), and “was pushed” (“pushed,”“push,”“shoved”). Where available, play conditions were coded as competition (during games, tournaments) or noncompetition (practice, pick-up games at school, or on a public court). Second, each of these 20,500 cases was reviewed by 2 members of the study team (H.K.V. and D.Z.) to verify appropriate inclusion into the data set before statistical analysis. Cases were excluded if the injury did not involve the ankle or if it was not sustained directly while playing basketball (commonly excluded were injuries sustained by jumping off bleachers at a basketball court, being hit by a basketball or player as a spectator, or tripping and falling on a basketball in the home environment). A total of 71,377 cases were removed, and a final data set of 20,303 cases met all the inclusion criteria in this analysis. A final audit check of a 50-case randomly chosen subset was performed by 1 team member (H.K.V.) to confirm the quality of the data and appropriateness for inclusion.

Statistical Considerations

Statistics were obtained from IBM SPSS Statistics version 29.0 (SPSS, Inc) for data analysis. Descriptive statistics were reported in 2 ways: as raw numbers reflecting the number of cases and as NEs that were calculated using statistical weights provided by the CPSC. Chi-square tests were used to test for differences in the distribution of ankle injuries and hospital disposition between sex (female, male) and by age bracket (13-18 years, 19-24 years, and 25-35 years). Linear regression analysis was implemented to determine annual trends in ankle injuries during 2014-2023. The year of injury was the independent variable, and the frequency of basketball-related ankle injuries was the dependent variable. Logistic regression was applied to determine the odds ratio (ORs) of specific diagnoses by sex and age bracket; β-coefficients and 95% confidence intervals were calculated for each comparison. The α level was established at .05 a priori.

Results

Patient Characteristics

Table 1 presents the demographic characteristics of ankle-related injuries sustained during basketball. Male players accounted for most cases (84.7% of the cohort; NE, 629,048). Most ankle injuries occurred among players aged 13 to 18 years. With respect to the location of the injury, ankle injuries most often occurred in sports facilities. The available data indicated that 352 injuries (1.7%) occurred during practice, whereas 2208 injuries (10.9%) occurred during competition (games and tournaments).

Table 1.

Characteristics of Patients Treated for Ankle-Related Injuries During Basketball ^a

Characteristic	NEISS Cases (N = 20,303), No.	NE, No.	% of Cohort
Sex
Male	18,221	629,048	84.7
Female	3201	110,351	15.3
Age, y
13-18	14,189	465,028	65.9
19-24	4651	173,989	21.6
25-35	2682	100,278	12.5
Race
White	6010	250,395	27.9
Black/African American	7025	194,796	32.6
Other	1049	33,995	4.9
Asian	384	10,474	1.8
American Indian/Alaska Native	60	3287	0.3
Native Hawaiian/Pacific Islander	30	1015	0.1
Not specified	6964	245,439	32.4
Injured at which location
School	2214	95,200	2.6
Sports facility	11,557	364,120	53.7
Public and other	896	38,791	11.9
Not specified	6855	241,286	31.8

^aNE, national estimate.

Trends in Ankle Injury ED Admissions by Age and Sex

Figure 1 shows the time trends for the whole patient cohort and by sex and age, respectively. Figure 1A shows the NE of annual trends in basketball-related ankle injuries treated in EDs. The regression analysis was significant by time (model F = 12.660, P < .001). Sex was a significant contributor (F = 8.126, P < .001). Figure 1B shows that age bracket was also a significant contributor to ankle injury NE (model F = 52.333, P < .001).

Figure 1.

(A) National estimates (NEs) of annual trends in basketball-related ankle injuries treated in emergency departments by sex. (B) NEs of annual trends in ankle injuries treated in emergency departments by age bracket.

Ankle Injury Diagnosis by Sex and Age

Table 2 shows the ankle injury diagnoses by sex and age. Female players had 4.7% more ankle sprains/strains, 3.3% fewer ankle fractures, and 0.4% fewer ankle dislocations than male players (P < .001). Among the 3 age groups, the 13- to 18-year-old group had 1.4% to 4.6% more ankle sprains/strains than the other 19- to 24-year-old and 25- to 35-year-old groups. However, the 25- to 35-year-old group had 0.3% to 1.0% more dislocations and 1.3% to 2.9% more “other” ankle injuries than the other 2 age groups (P < .001).

Table 2.

Basketball-Related Ankle Injuries Treated in the Emergency Department by Diagnoses by Sex and Age ^a

Sex
Characteristic	All, No. (%)	NE, No.	Male, No. (%)	Female, No. (%)
Sprain/strain	17,573 (81.7)	599,765	14,748 (80.9)	2825 (85.6) b
Fracture	1642 (7.6)	53,743	1484 (8.1)	158 (4.8) b
Dislocation	122 (0.6)	4432	115 (0.6)	7 (0.2) b
Soft tissue	125 (0.6)	4013	106 (0.6)	19 (0.6)
Other	2060 (9.6)	77,446	1768 (9.7)	292 (8.8)
Age, No. (%)
Characteristic	13-18		19-24	25-35
Sprain/strain	11,709 (82.5)		3374 (81.1)	2090 (77.9) b
Fracture	1094 (7.7)		322 (6.9)	226 (8.4)
Dislocation	40 (0.3)		47 (1.0)	35 (1.3) b
Soft tissue	90 (0.6)		21 (0.5)	14 (0.5)
Other	1256 (8.9)		487 (10.5)	217 (11.8) b

^aSoft tissue injury = contusions/abrasions, hematoma, laceration, crushing, avulsions. Other = generic “injury” or “ankle pain”. NE, National Estimate.

^bDenotes difference between the groups at P < .05.

OR for Ankle Injury Diagnosis

Table 3 presents the OR for reporting to the ED with specific types of ankle injuries. Male players were the designated reference group for sex comparisons, and the 13- to 18-year-old age group was designated as the reference for age group comparisons. Female players had a 33% greater odds of presenting to the ED with ankle sprains/strains, but a 43% and 54% lower odds of presenting with ankle fractures and dislocations than male players, respectively. Compared with the 13- to 18-year-old age group, 19- to 24-year-olds and 25- to 35-year-olds had 1% and 22% higher odds, respectively, of presenting with ankle sprain/strains. Additionally, the 25- to 35-year age cohort had a 19% lower odds of incurring ankle fractures than 13- to 18-year-olds (P < .05). The 19- to 24-year-old group was 77% less likely to present to the ED with ankle dislocations than 13- to 18-year-olds (P < .05). Finally, the 19- to 24-year-old group was 29% less likely to present with “other” ankle injuries than 13- to 18-year-olds (P < .05).

Table 3.

Odds Ratio (95% CI) of Incurring a Specific Type of Ankle Injury During Basketball Play by Sex and Age Bracket

Characteristic	Sprain/Strain	Fracture	Dislocation	Soft Tissue	Other
Sex
Male (reference)	1.0	1.0	1.0	1.0	1.0
Female	1.33 (1.19-1.47) a	0.57 (0.48-0.67) a	0.46 (0.21-0.99) a	0.95 (0.58-1.57)	0.98 (0.85-1.11)
Age bracket
13-18 (reference)	1.0	1.0	1.0	1.0	1.0
19-24	1.31 (1.19-1.46) a	0.97 (0.83-1.13)	0.23 (0.14-0.36) a	1.22 (0.69-2.15)	0.71 (0.62-0.8)
25-35	1.22 (1.08-1.37) a	0.81 (0.68-0.96) a	0.77 (0.49-1.19)	0.87 (0.44-1.71)	0.88 (0.76-1.02)

^aDifferent compared with the reference group, P < .05.

Injury Mechanisms

Table 4 highlights the mechanisms involved in ankle injuries during basketball activity. Unfortunately, 14,005 cases did not provide any details regarding their mechanisms of injury. Female players were documented to have 2.3% more injuries involving falls, 0.5% more injuries involving being pushed, and 0.2% more injuries involving collisions with another player compared with male players. Male players had 3.2% more ankle injuries caused by poor landings (P < .001). Among the 3 age groups, the 25- to 35-year-olds had fewer mechanisms of ankle rolling and poor landings than the younger groups, while the 19- to 24-year-old group had fewer injuries related to falls and being pushed (P < .05). The 13- to 18-year-old group had the highest proportion of injuries related to player collisions (P = .013).

Table 4.

Circumstances and Mechanisms Involved in the Ankle Injuries Sustained During Basketball Play

Sex
Characteristic	All (N = 21,522), No. (%)	Male (n = 18,221), No. (%)	Female (n = 3301), No. (%)	P Value
Fell/involved falling	1904	1547 (8.5)	357 (10.8) a	<.001
Was pushed	57	33 (0.2)	24 (0.7) a	<.001
Collision, player	53	39 (0.2)	14 (0.4) a	.025
Poor landing	2052	1826 (10.0)	226 (6.8) a	<.001
Rolled ankle	3451	2927 (16.1)	524 (15.9)	.266
Not specified	14,005	11,849 (65.0)	2156 (65.0)	—
Age
Characteristic	13-18 (n = 14,189), No. (%)	19-24 (n = 4651), No. (%)	25-35 (n = 2682), No. (%)	P Value
Fell/involved falling	1369 (9.6)	318 (6.8)	217 (8.1) a	<.001
Was pushed	45 (0.3)	3 (0.1)	9 (0.3) a	.011
Collision, player	45 (0.3)	6 (0.1)	2 (0.1) a	.013
Poor landing	1430 (10.1)	434 (9.3)	188 (7.0) a	<.001
Rolled ankle	2312 (16.3)	783 (16.8)	356 (13.3) a	<.001
Not specified	7619 (53.7)	3107 (66.8)	1910 (71.2)	—

^aDenotes difference between the groups at P < .05.

Treatment Disposition

Most patient cases in the whole population were treated and released from the ED, and only 0.4% were admitted and hospitalized. When compared by sex, more female players were treated and released than male players, and more male players left without being seen than female players (P = .042). When compared by age bracket, a higher proportion of patients in the 25- to 35-year-old group was admitted compared with the 13- to 18-year-old and 19- to 24-year-old groups, and more patients in the 25- to 35-year-old group also left the ED without being seen than the 13- to 18-year-old and 19- to 24-year-old groups (P < .001). For all patients who were admitted to the hospital, 49.3% involved falls and 21.8% involved poor landings, and resultant diagnoses were fractures and dislocations. Figure 2 provides the breakdown of diagnoses involving hospital admissions; there were no sex differences in the proportions of admissions by diagnoses (Figure 2A), but patients aged 13 to 18 years were admitted with higher proportions of ankle fractures and dislocations and lower proportions of “other” diagnoses (Figure 2B; P < .001).

Figure 2.

(A) Proportions of patients admitted to the hospital for specific diagnoses by sex. (B) Proportions of patients admitted to the hospital for specific diagnoses by age bracket; there was a significantly greater proportion of players aged 13 to 18 years who were admitted for fracture injuries and dislocations than the other age brackets (P < .001).

Discussion

The key findings from this study demonstrated that ankle sprains/strains were the most commonly diagnosed injury, regardless of sex or age groups, with female players having 33% higher odds of sustaining these injuries. Conversely, male players had 43% and 54% higher odds of presenting with ankle fractures and dislocations, respectively. Male players had 3.2% more injuries due to poor landings than female players, whereas female players had 2.3% more injuries involving falls, being pushed, and collisions than male players. Patients who were 19 to 35 years old had a 31% greater odds of presenting with ankle sprains/strains but were 12% to 33% less likely to have fractures, dislocations, and “other” injuries than adolescents. Adolescents were 3 times more likely to be injured in player collisions than other age groups. Injuries that required hospital admissions and resources were primarily fractures and dislocations, which occurred largely from poor landings and falls.

Age-Related Considerations

The male-dense and higher fracture demographic likely reflects triage/severity thresholds for ED admissions, whereas many mild sprains are likely treated via other medical avenues, such as outpatient clinics or on-site care. Therefore, this analysis characterizes only the subset of ankle injuries severe enough to warrant ED evaluation. Many injury mechanisms among adolescents involve direct contact with an opponent, ²² such as landing on another player's foot. ³ Adolescents typically have less developed weightbearing ankle complex proprioceptive acuity relative to young adults, ²⁹ have less playing experience, and play with reduced levels of caution during competition than during practice. ⁵ Some studies suggest that puberty and growth spurts among adolescents can produce physical changes that increase the risk of basketball injuries, including ankle sprains.^20,31 During maturation, adolescents may experience disproportionate muscle strength relative to bone length, increased muscle tendon tightness, and variability in bone mineralization. ³¹ Peak bone accrual is typically achieved during early adulthood rather than adolescence. ⁴ Moreover, compared with pediatric players, adolescent athletes tend to play with heightened risk-taking behavior during basketball play, such as increased physicality, speed, and intensity, ³¹ particularly at higher competition levels. While unclear at this time, adolescents may take more risks, especially when crowded near other players (during rebounding, drives to the basket, beginning a fast break, scrambling for out-of-bounds). Pushes, collisions, and falls ensue, which ultimately heighten the risk for fractures and dislocations requiring hospital admissions. Adolescents are more prone to forceful movements and ground/collision impacts that could cause ankle injuries.^5,31 Thus, suboptimal neuromotor control related to quick directional changes and/or jump landings/falls are underlying issues to address for ankle injury among developing juvenile players. Additionally, the 25- to 35-year-old and 19- to 24-year-old age groups demonstrated a lower OR of presenting with bone fractures and dislocations. Older athletes may adopt more refined techniques and strategies that minimize the risk of severe injuries, like fractures.

Sex-Related Considerations

Compared with other general injury surveillance data, the data reported here were composed of a much lower percentage of female players relative to total basketball participation. We consider this an important point illustrating potential differences in the sex risks for injury and where female players may seek care for ankle injuries. High school surveillance data report similar ankle injury rates and proportions of individuals with severe injury among boys and girls. ¹⁰ European data show that sports injuries in the ED setting are predominantly in male players (82% male players, 18% female players; higher proportion of male players with basketball ankle injuries than female players). ¹⁵ Other epidemiologic data of ankle injuries treated in the ED setting found a very similar sex distribution to the present NEISS data set (22.5% female players, 77.5% male players). ²⁷ Female players may be seeking care at sites or from professionals other than at the ED for their ankle injuries.

While female players had a greater OR for sprains/strains than male players, the OR for severe injuries like fractures and dislocations was lower. The most common mechanism of injury for both sexes was related to “rolled ankles.” Male and female basketball players may absorb external workloads differently across their lower extremities and trunk segments during accelerations, curvilinear running, and jump landings. ⁸ In semiprofessional basketball players, men tend to exhibit higher workload absorption in the scapula-lumbar (upper trunk) and knee-ankle (lower leg) segments, while women have greater workload absorption in the lumbar-knee segment. ⁸ Men's higher knee-ankle workload absorption may translate into greater forces directed toward the ankle joint during explosive or high-impact basketball plays such as rebounding, layups, dunks, or forceful landings. In contrast, women may absorb load in the lumbar-knee region more effectively than men. ⁸ Additionally, women are more effective at attenuating peak force during drop jumps than men. ¹² One could speculate that this load-bearing strategy may potentially reduce forces at the ankle in female players, but it may consequently increase susceptibility to repetitive sprains or strains, particularly in the presence of altered landing biomechanics and neuromuscular control.

Potential sex-related differences in intrinsic risk factors for ankle injury include ligament laxity, hip abductor strength, body anthropometrics and center of mass (COM) position, and ankle range of motion. Collegiate-aged female athletes have a 199% greater combined mean ankle joint laxity than collegiate-aged male athletes. ²⁸ We surmise that laxity and reduced ankle stability during high-intensity movements like jumping and cutting may contribute to higher ankle sprain risk. This increased joint laxity may lead to greater reliance on dynamic ankle stabilizers, such as the anterior tibialis, extensor digitorum longus, extensor digitorum brevis, and peroneus tertius, to maintain ankle integrity during high-intensity or high-speed movement. Hip abductor strength may be a factor in injury risk ⁹ as male players generally have greater normalized hip abductor strength compared with female players. ¹³ Additionally, male players with chronic ankle instability have greater hip abductor strength than female players ¹³ ; this is potentially a contributing factor to the lower prevalence of ankle sprains. Male players, who have a higher COM, experience greater challenges with stabilization during dynamic movements.^6,25 When cutting, pivoting, or landing, a higher COM can challenge players’ balance, which we can speculate could increase the risk of ankle dislocations or fractures due to improper foot placement or excessive lateral sway upon landing. Concerning ankle range of motion, greater dorsiflexion is associated with a smaller vertical ground-reaction force loading rate during landings. ¹⁶ Limited ankle dorsiflexion may help explain why fractures and dislocations are more common in male players, and increasing ankle dorsiflexion through preventative training may help dissipate lower limb loads.

Clinical Importance

Our findings show that ED admissions for ankle injuries are more prevalent among adolescents for fractures and dislocations compared with older players. Medical readiness on-site for these injuries in high school settings could include improved processes for injury recognition and protocols for handling them by coaches and onsite care staff, both of which may facilitate faster care to the ED setting. A multifactorial approach involving screening assessment and training programs may be needed to reduce the prevalence of severe injuries. Ankle-specific tests can be seamlessly integrated into preseason or practice sessions and can identify underlying movement deficits/asymmetries and risk factors that predispose players to ankle injuries. ²³ Some tests assess balance and proprioceptive abilities and can screen for chronic ankle instability. ¹¹ Tests that challenge dynamic balance and postural stability—such as the Star Excursion Balance Test in the anteromedial, medial, and posteromedial directions—are shown to be most effective at identifying those with chronic ankle instability. ²¹ Second, physical therapists and conditioning specialists could implement neuromuscular training programs that incorporate agility, proprioception, and strengthening, which can reduce ankle sprain rates by up to 60%. ²⁴ Single-leg stance drills and wobble board training can help prevent recurrent ankle injuries. ² For physical therapists and athletic trainers, developing the strength and muscular activation of the tibialis anterior and peroneus brevis may be important for preventing ankle instability and ankle injuries during dynamic basketball play. Third, external ankle joint stabilization, such as prophylactic ankle bracing that athletic trainers can do on-site, has also been shown to reduce ankle injury occurrence by 50% to 85%. This type of bracing offers more ankle stabilization than conventional athletic taping. ¹⁷

Study Limitations

Our study is not without limitations. Data collected from NEISS are limited to patients who presented to the ED in the United States. Thus, many patients who did not seek medical treatment or those who sought care from clinics or care centers outside of the ED for their injuries were omitted. Attribution of sport context relies on ED narratives and may be less reliable than anatomic coding. The level of detail provided in the database is limited to the information included by NEISS coders and vague narratives provided in the ED documentation. These narratives did not provide a consistent level of detail and specificity. The narratives were particularly sparse in key areas that could provide insight regarding injury risk factors, such as competitive environment, mechanisms of the incident (ie, involving another player, collisions, tripping, offensive or defensive maneuvers), and if any protective measures were used (bracing, taping, exercise injury programs). Moreover, injury narratives did not include details such as injury history and level of experience, which may have added further depth to our analysis. Sex differences in how sports-related injuries are attributed in ED records, along with different patterns in seeking medical care, may help explain why we observe fewer female patients in ED surveillance data. Female athletes may be underrepresented if they seek care in settings other than EDs. The attribution of injuries to sports contexts based on narrative descriptions may also lack consistency, potentially leading to sex-specific errors during data classification. While we found no evidence of systematic differences in missing data between sexes in our data set, undetected classification errors remain a possibility. Importantly, ED surveillance can be influenced by care-seeking and triage patterns, which in the data seem to favor male patients, meaning our results reflect the burden of injuries managed in EDs rather than true exposure-based risk in the population. These findings from ED settings should be interpreted alongside data from athletic trainers and outpatient care settings when considering age-specific prevention strategies and assessing true population risk.

Conclusion

In summary, targeted prevention strategies could include neuromuscular and balance programs for youth, ankle-stability and proprioception programs for female players, and dorsiflexion mobility and landing biomechanics programs for male players.