Reporting Multiple Individual Injuries in Studies of Team Ball Sports: A Systematic Review of Current Practice

Gender differences in female and male Australian football concussion injury: A prospective observational study of emergency department presentations

OBJECTIVE

To examine gender differences in Australian football (AF)-related concussion presentations to EDs in regional Australia.

METHODS

A prospective observational study of patients presenting to 1 of the 10 EDs in Western Victoria, Australia, with an AF-related concussion was conducted. Patients were part of a larger study investigating AF injuries over a complete AF season, including pre-season training and practice matches. Information regarding concussion injuries was extracted from patient medical records, including clinical features, concurrent injuries, mechanism and context of injury. Female and male data were compared with chi-squared and Fisher's exact tests. P < 0.05 was considered significant.

RESULTS

From the original cohort of 1635 patients with AF-related injuries (242 female and 1393 male), 231 (14.1%) patients were diagnosed with concussion. Thirty-eight (15.7%) females had concussions versus 193 (13.9%) males (P > 0.05). Females over the age of 16 were more likely to be concussed than males in the same age range (females n = 26, 68.4% vs males n = 94, 48.7%; P = 0.026). Neurosurgically significant head injury was rare (one case). Similar rates of concurrent injury were found between females 15 (39.5%) and males 64 (33.2%), with neck injury the single most common in 24 (10.3%) concussions. Sixty-nine patients (29%) were admitted for observation or to await the results of scans. The majority of concussions occurred in match play (87.9%). Females were more likely injured in contested ball situations (63.2% vs 37.3%; P < 0.05).

CONCLUSION

Concussion rates for community-level AF presentations to regional EDs were similar between genders. Serious head injury was rare, although hospital admission for observation was common. Concurrent injuries were common, with associated neck injury most often identified. Match play accounted for the majority of head injuries.

Dance-related musculoskeletal injury leading to forced time-loss in elite pre professional dancers - a retrospective study

OBJECTIVES

Describe the epidemiology of dance-related musculoskeletal injury leading to forced time-loss in elite pre-professional dancers and provide descriptive data on the prevalence concerning diagnoses, location, and injury type, stratified by gender and skill level.

METHODS

Retrospective cohort, over a 3-year period on a full-time pre-professional Portuguese dance school featuring 70 both gender students with a mean age of 14.87 years. A total of 110 injuries were analyzed. Non-parametric statistics were used.

RESULTS

The most prevalent diagnosis leading to forced time-loss in female dancers were Medial Tibial Stress Syndrome (MTSS), 14.29%, and Posterior Ankle Impingement Syndrome (PAIS), 7.14%, whereas in male dancers these were Hallux Sprain, 17.50%, and Lumbar Spine Joint Injuries, 7.50%. Differences were found in injuries' anatomical location between genders and in Incidence Proportion and Clinical Incidence between skill levels. 72.20% of the chronic injuries and 50.00% of the acute injuries occurred in Level III students.

CONCLUSIONS

With respect to forced time-loss injuries, male dancers were found to sustain more acute traumatic injuries and female dancers overuse, respectively, sprains and MTSS and PAIS. Identifying which injuries are most likely to lead to forced time-loss may be useful for helping health-care professionals in clinical decision-making and in developing more effective injury prevention and management strategies.

Sports-related injuries sustained by officers of the State Fire Service on duty - nationwide 7-year follow-up

Aim

The accident rate in the State Fire Service from 2015 to 2021 related to sports activities was analyzed in relation to the regions of the country per year.

Materials and methods

The study included analysis of data from the SFS Headquarters - Department for Occupational Health and Safety and Preventive Health. Data collected from across the country in the form of an annual analysis of the accident. The reports included such information as: the number of accidents, the cause and circumstances of accident (injury), with a breakdown listing individual and group accidents.

Results

During the observation period, about 30,000 officers were on duty in the SFS, of which about 20% were on daily (8-h) duty, and 80% were on shift (24-h) duty. Between 2015 and 2021, there were N = 11,332 (Mean: 1617.4; SD: 284.1) accidents in SFS. Total accident covers individual and mass accidents. The number of sports injuries was N = 4,254 (Mean: 532.2; SD: 137.9).

Conclusion

There is a need for comprehensive approach to physical training in the firefighter population. Physical activity should be continuous and systematic strengthening of the whole body. Sports activities should begin with performing thorough warm-ups. It is necessary to maintain facilities, premises, equipment and technical devices in a condition that sets the ground for doing sports safely and in a hygienic manner. Most of the sports injuries sustained by firefighters are related to team sports.

Predictors of multiple injuries in individual distance runners: A retrospective study of 75,401 entrants in 4 annual races-SAFER XX

BACKGROUND

There are limited data on factors that predict an increased risk of multiple injuries among distance runners. The objective of this study was to determine risk factors that are predictive of individual runners with a high annual multiple injury risk (MIR).

METHODS

A retrospective, cross-sectional study at 4 annual (2012-2015) Two Oceans 21.1 km and 56.0 km races in South Africa with 75,401 consenting race entrants. Running-related injury data were collected retrospectively through an online pre-race medical screening questionnaire. The average number of injuries for each runner every year was calculated by taking a runner's race entry history and injury history into account and categorizing entrants into 4 MIR categories (high, intermediate, low, and very low (reference)). Multiple logistic regression modeling (odds ratios) was used to determine whether the following factors were predictive of a high MIR (average > 1 injury/year): demographics, training and racing, chronic-disease history (composite chronic disease score (CCDS)), and history of allergies.

RESULTS

Of all entrants, 9.2% reported at least 1 injury, and 0.4% of entrants were in the high MIR category; the incidence rate was 2.5 injuries per 10 runner-years (95% confidence interval (95%CI): 2.4-2.7). Significant factors predictive of runners in the high MIR category were: running for > 20 years: OR = 2.0 (95%CI: 1.3-3.1; p = 0.0010); a higher CCDS: OR = 2.2 (95%CI: 2.0-2.4; p < 0.0001); and a history of allergies: OR = 2.8 (95%CI: 2.0-3.8; p < 0.0001).

CONCLUSION

Runners who have been running recreationally for > 20 years and those with multiple chronic diseases or a history of allergies were at higher risk of multiple running-related injuries. This high-risk group can be targeted for further study and possible injury-prevention interventions.

Injury patterns differ with age in male youth football: a four-season prospective study of 1111 time-loss injuries in an elite national academy

OBJECTIVES

To describe age group patterns for injury incidence, severity and burden in elite male youth football.

METHODS

Prospective cohort study capturing data on individual exposure and time-loss injuries from training and matches over four seasons (2016/2017 through 2019/2020) at a national football academy (U13-U18; age range: 11-18 years). Injury incidence was calculated as the number of injuries per 1000 hours, injury severity as the median number of days lost and injury burden as the number of days lost per 1000 hours.

RESULTS

We included 301 players (591 player-seasons) and recorded 1111 time-loss injuries. Overall incidence was 12.0 per 1000 hours (95% CI 11.3 to 12.7) and burden was 255 days lost per 1000 hours (252 to 259). The mean incidence for overall injuries was higher in the older age groups (7.8 to 18.6 injuries per 1000 hours), while the greatest burden was observed in the U16 age group (425 days; 415 to 435). In older age groups, incidence and burden were higher for muscle injuries and lower for physis injuries. Incidence of joint sprains and bone stress injuries was greatest for players in the U16, U17 and U18 age groups, with the largest burden observed for U16 players. No clear age group trend was observed for fractures.

CONCLUSION

Injury patterns differed with age; tailoring prevention programmes may be possible.

Association of Skeletal Maturity and Injury Risk in Elite Youth Soccer Players: A 4-Season Prospective Study With Survival Analysis

BACKGROUND

The association between injury risk and skeletal maturity in youth soccer has received little attention.

PURPOSE

To prospectively investigate injury patterns and incidence in relation to skeletal maturity in elite youth academy soccer players and to determine the injury risks associated with the skeletal maturity status, both overall and to the lower limb apophysis.

STUDY DESIGN

Descriptive epidemiology study.

METHODS

All injuries that required medical attention and led to time loss were recorded prospectively during 4 consecutive seasons in 283 unique soccer players from U-13 (12 years of age) to U-19 (18 years). The skeletal age (SA) was assessed in 454 player-seasons using the Fels method, and skeletal maturity status (SA minus chronological age) was classified as follows: late, SA >1 year behind chronological age; normal, SA ±1 year of chronological age; early, SA >1 year ahead of chronological age; and mature, SA = 18 years. An adjusted Cox regression model was used to analyze the injury risk.

RESULTS

A total of 1565 injuries were recorded; 60% were time-loss injuries, resulting in 17,772 days lost. Adjusted injury-free survival analysis showed a significantly greater hazard ratio (HR) for different status of skeletal maturity: early vs normal (HR = 1.26 [95% CI, 1.11-1.42]; P < .001) and early vs mature (HR = 1.35 [95% CI, 1.17-1.56]; P < .001). Players who were skeletally mature at the wrist had a substantially decreased risk of lower extremity apophyseal injuries (by 45%-61%) compared with late (P < .05), normal (P < .05), and early (P < .001) maturers.

CONCLUSION

Musculoskeletal injury patterns and injury risks varied depending on the players' skeletal maturity status. Early maturers had the greatest overall adjusted injury risk. Players who were already skeletally mature at the wrist had the lowest risk of lower extremity apophyseal injuries but were still vulnerable for hip and pelvis apophyseal injuries.

Recurrent and Subsequent Injuries in Professional and Elite Sport: a Systematic Review

BACKGROUND

Injury surveillance in professional sport categorises injuries as either "new" or "recurrent". In an attempt to make categorisation more specific, subsequent injury categorisation models have been developed, but it is not known how often these models are used. The aim was to assess how recurrent and subsequent injuries are reported within professional and elite sport.

METHODS

Online databases were searched using a search strategy. Studies needed to prospectively report injury rates within professional or elite sports that have published consensus statements for injury surveillance.

RESULTS

A total of 1322 titles and abstract were identified and screened. One hundred and ninety-nine studies were screened at full text resulting in 81 eligible studies. Thirty studies did not report recurrent injuries and were excluded from data extraction. Within the studies that reported recurrent injuries, 21 reported the number and percentage; 13 reported only the proportion within all injuries; three reported only the number; five reported the number, percentage and incidence; and two only reported the incidence. Seven studies used subsequent injury terminology, with three reporting subsequent injury following concussion, one using an amended subsequent injury model and three using specific subsequent injury categorisation models. The majority of subsequent injuries (ranging from 51 to 80%) were categorised as different and unrelated to the index injury. The proportion of recurrent injuries (exact same body area and nature related to index injury) ranged from 5 to 21%.

CONCLUSIONS

Reporting recurrent or subsequent injuries remains inconsistent, and few studies have utilised subsequent injury models. There is limited understanding of subsequent injury risk, which may affect the development of injury prevention strategies.

TRIAL REGISTRATION

CRD42019119264.

International Olympic Committee consensus statement: methods for recording and reporting of epidemiological data on injury and illness in sport 2020 (including STROBE Extension for Sport Injury and Illness Surveillance (STROBE-SIIS))

Injury and illness surveillance, and epidemiological studies, are fundamental elements of concerted efforts to protect the health of the athlete. To encourage consistency in the definitions and methodology used, and to enable data across studies to be compared, research groups have published 11 sport-specific or setting-specific consensus statements on sports injury (and, eventually, illness) epidemiology to date. Our objective was to further strengthen consistency in data collection, injury definitions and research reporting through an updated set of recommendations for sports injury and illness studies, including a new Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist extension. The IOC invited a working group of international experts to review relevant literature and provide recommendations. The procedure included an open online survey, several stages of text drafting and consultation by working groups and a 3-day consensus meeting in October 2019. This statement includes recommendations for data collection and research reporting covering key components: defining and classifying health problems; severity of health problems; capturing and reporting athlete exposure; expressing risk; burden of health problems; study population characteristics and data collection methods. Based on these, we also developed a new reporting guideline as a STROBE Extension-the STROBE Sports Injury and Illness Surveillance (STROBE-SIIS). The IOC encourages ongoing in- and out-of-competition surveillance programmes and studies to describe injury and illness trends and patterns, understand their causes and develop measures to protect the health of the athlete. Implementation of the methods outlined in this statement will advance consistency in data collection and research reporting.

International Olympic Committee Consensus Statement: Methods for Recording and Reporting of Epidemiological Data on Injury and Illness in Sports 2020 (Including the STROBE Extension for Sports Injury and Illness Surveillance (STROBE-SIIS))

BACKGROUND

Injury and illness surveillance, and epidemiological studies, are fundamental elements of concerted efforts to protect the health of the athlete. To encourage consistency in the definitions and methodology used, and to enable data across studies to be compared, research groups have published 11 sport- or setting-specific consensus statements on sports injury (and, eventually, illnesses) epidemiology to date.

OBJECTIVE

To further strengthen consistency in data collection, injury definitions, and research reporting through an updated set of recommendations for sports injury and illness studies, including a new Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist extension.

STUDY DESIGN

Consensus statement of the International Olympic Committee (IOC).

METHODS

The IOC invited a working group of international experts to review relevant literature and provide recommendations. The procedure included an open online survey, several stages of text drafting and consultation by working groups, and a 3-day consensus meeting in October 2019.

RESULTS

This statement includes recommendations for data collection and research reporting covering key components: defining and classifying health problems, severity of health problems, capturing and reporting athlete exposure, expressing risk, burden of health problems, study population characteristics, and data collection methods. Based on these, we also developed a new reporting guideline as a STROBE extension-the STROBE Sports Injury and Illness Surveillance (STROBE-SIIS).

CONCLUSION

The IOC encourages ongoing in- and out-of-competition surveillance programs and studies to describe injury and illness trends and patterns, understand their causes, and develop measures to protect the health of the athlete. The implementation of the methods outlined in this statement will advance consistency in data collection and research reporting.

A 2-Year Prospective Study of Injury Epidemiology in Elite Australian Rugby Sevens: Exploration of Incidence Rates, Severity, Injury Type, and Subsequent Injury in Men and Women

BACKGROUND

Injuries are common in rugby sevens, but studies to date have been limited to short, noncontinuous periods and reporting of match injuries only.

PURPOSE

To report the injury incidence rate (IIR), severity, and burden of injuries sustained by men and women in the Australian rugby sevens program and to provide the first longitudinal investigation of subsequent injury occurrence in rugby sevens looking beyond tournament injuries only.

STUDY DESIGN

Descriptive epidemiology study.

METHODS

Ninety international rugby sevens players (55 men and 35 women) were prospectively followed over 2 consecutive seasons (2015-2016 and 2016-2017). All medical attention injuries were reported irrespective of time loss. Individual exposure in terms of minutes, distance, and high-speed distance was captured for each player for matches and on-field training, with the use of global positioning system devices. The IIR and injury burden (IIR × days lost to injury) were calculated per 1000 player-hours, and descriptive analyses were performed.

RESULTS

Seventy-three players (81.1%) sustained 365 injuries at an IIR of 43.2 per 1000 player-hours (95% CI, 43.0-43.3). As compared with male players, female players experienced a lower IIR (incidence rate ratio, 0.91; 95% CI, 0.90-0.91). Female players also sustained a higher proportion of injuries to the trunk region (relative risk, 1.75; 95% CI, 1.28-2.40) but a lower number to the head/neck region (relative risk, 0.58; 95% CI, 0.37-0.93; P = .011). The majority (80.7%) of subsequent injuries were of a different site and nature than previous injuries. A trend toward a reduced number of days, participation time, distance, and high-speed distance completed before the next injury was observed after successive injury occurrence.

CONCLUSION

Female players have a lower IIR than male players, with variation of injury profiles observed between sexes. With a surveillance period of 2 years, subsequent injuries account for the majority of injuries sustained in rugby sevens, and they are typically different from previous types of sustained injuries. After each successive injury, the risk profile for future injury occurrence appears to be altered, which warrants further investigation to inform injury prevention strategies in rugby sevens.

Comparison of subsequent injury categorisation (SIC) models and their application in a sporting population

Background

The original subsequent injury categorisation (SIC-1.0) model aimed to classify relationships between chronological injury sequences to provide insight into the complexity and causation of subsequent injury occurrence. An updated model has recently been published. Comparison of the data coded according to the original and revised subsequent injury categorisation (SIC-1.0 and SIC-2.0) models has yet been formally compared.

Methods

Medical attention injury data was prospectively collected for 42 elite water polo players over an 8 month surveillance period. The SIC-1.0 and SIC-2.0 models were retrospectively applied to the injury data. The injury categorisation from the two models was compared using descriptive statistics.

Results

Seventy-four injuries were sustained by the 42 players (median = 2, range = 0–5), of which 32 injuries (43.2%) occurred subsequent to a previous injury. The majority of subsequent injuries were coded as occurring at a different site and being of a different nature, while also being considered clinically unrelated to the previous injury (SIC-1.0 category 10 = 57.9%; SIC-2.0 clinical category 16 = 54.4%). Application of the SIC-2.0 model resulted in a greater distribution of category allocation compared to the SIC-1.0 model that reflects a greater precision in the SIC-2.0 model.

Conclusions

Subsequent injury categorisation of sport injury data can be undertaken using either the original (SIC-1.0) or the revised (SIC-2.0) model to obtain similar results. However, the SIC-2.0 model offers the ability to identify a larger number of mutually exclusive categories, while not relying on clinical adjudication for category allocation. The increased precision of SIC-2.0 is advantageous for clinical application and consideration of injury relationships.

Electronic supplementary material

The online version of this article (10.1186/s40621-019-0183-1) contains supplementary material, which is available to authorized users.

The role of prediagnostic data in injury epidemiology in preprofessional dancers

INTRODUCTION

preprofessional dance training starts at very early ages, on a highly demanding environment placing students at significant risk for injury. Injury management and prevention are a matter of concern. Given the constant interchangeability of risk factors, identifying injury patterns may prove to be equally as important. Data looking back from the time of injury through context-specific approaches are missing.

OBJECTIVES

To identify activity-related injury patterns based on prediagnostic data.

METHODS

Prospective, non-randomized, observational study, over a 3 years period on a full-time preprofessional dance school featuring both gender students aged 9-21 years old. Non-parametric statistics were used.

RESULTS

A total of 625 dance injury records from 209 students, n = 68 males and n = 141 females, were analyzed. Season injury risk probability was identified, proving different for each skill level (SkL). Multiple individual injuries revealed a trend toward prevalence rates in advanced level, while index injuries incidence becomes more noticeable in entry level students. Overall incidence rates had no significant differences within SkL. Anatomical location was in line with previous research, although differences were found between gender and SkL. Most injuries occurred in classes, with jumps standing out as the main motor action associated with injury symptoms of gradual onset mechanism.

CONCLUSION

Dance injuries happen because of dance practice. Knowing the context of injury history from the injured dancer perspective is determinant for management and prevention. prediagnostic data are an umbrella term encompassing several aspects of injury background and represents fertile ground for research. Context-specific methodological approaches are recommended.

An Updated Subsequent Injury Categorisation Model (SIC-2.0): Data-Driven Categorisation of Subsequent Injuries in Sport

BACKGROUND

Accounting for subsequent injuries is critical for sports injury epidemiology. The subsequent injury categorisation (SIC-1.0) model was developed to create a framework for accurate categorisation of subsequent injuries but its operationalisation has been challenging.

OBJECTIVES

The objective of this study was to update the subsequent injury categorisation (SIC-1.0 to SIC-2.0) model to improve its utility and application to sports injury datasets, and to test its applicability to a sports injury dataset.

METHODS

The SIC-1.0 model was expanded to include two levels of categorisation describing how previous injuries relate to subsequent events. A data-driven classification level was established containing eight discrete injury categories identifiable without clinical input. A sequential classification level that sub-categorised the data-driven categories according to their level of clinical relatedness has 16 distinct subsequent injury types. Manual and automated SIC-2.0 model categorisation were applied to a prospective injury dataset collected for elite rugby sevens players over a 2-year period. Absolute agreement between the two coding methods was assessed.

RESULTS

An automated script for automatic data-driven categorisation and a flowchart for manual coding were developed for the SIC-2.0 model. The SIC-2.0 model was applied to 246 injuries sustained by 55 players (median four injuries, range 1-12), 46 (83.6%) of whom experienced more than one injury. The majority of subsequent injuries (78.7%) were sustained to a different site and were of a different nature. Absolute agreement between the manual coding and automated statistical script category allocation was 100%.

CONCLUSIONS

The updated SIC-2.0 model provides a simple flowchart and automated electronic script to allow both an accurate and efficient method of categorising subsequent injury data in sport.

Time-to-event analysis for sports injury research part 2: time-varying outcomes

BACKGROUND

Time-to-event modelling is underutilised in sports injury research. Still, sports injury researchers have been encouraged to consider time-to-event analyses as a powerful alternative to other statistical methods. Therefore, it is important to shed light on statistical approaches suitable for analysing training load related key-questions within the sports injury domain.

CONTENT

In the present article, we illuminate: (i) the possibilities of including time-varying outcomes in time-to-event analyses, (ii) how to deal with a situation where different types of sports injuries are included in the analyses (ie, competing risks), and (iii) how to deal with the situation where multiple subsequent injuries occur in the same athlete.

CONCLUSION

Time-to-event analyses can handle time-varying outcomes, competing risk and multiple subsequent injuries. Although powerful, time-to-event has important requirements: researchers are encouraged to carefully consider prior to any data collection that five injuries per exposure state or transition is needed to avoid conducting statistical analyses on time-to-event data leading to biased results. This requirement becomes particularly difficult to accommodate when a stratified analysis is required as the number of variables increases exponentially for each additional strata included. In future sports injury research, we need stratified analyses if the target of our research is to respond to the question: 'how much change in training load is too much before injury is sustained, among athletes with different characteristics?' Responding to this question using multiple time-varying exposures (and outcomes) requires millions of injuries. This should not be a barrier for future research, but collaborations across borders to collecting the amount of data needed seems to be an important step forward.

Sports Biostatistician: a critical member of all sports science and medicine teams for injury prevention

Sports science and medicine need specialists to solve the challenges that arise with injury data. In the sports injury field, it is important to be able to optimise injury data to quantify injury occurrences, understand their aetiology and most importantly, prevent them. One of these specialty professions is that of Sports Biostatistician. The aim of this paper is to describe the emergent field of Sports Biostatistics and its relevance to injury prevention. A number of important issues regarding this profession and the science of sports injury prevention are highlighted. There is a clear need for more multidisciplinary teams that incorporate biostatistics, epidemiology and public health in the sports injury area.

The Influence of Playing Experience and Position on Injury Risk in NCAA Division I College Football Players

Purpose: American football is widely played by college student-athletes throughout the United States; however, the associated injury risk is greater than in other team sports. Numerous factors likely contribute to this risk, yet research identifying these risk factors is limited. The present study sought to explore the relationship between playing experience and position on injury risk in NCAA Division I college football players. Methods: Seventy-six male college student-athletes in the football program of an American NCAA Division I university participated. Injuries were recorded over 2 consecutive seasons. Players were characterized based on college year (freshman, sophomore, junior, or senior) and playing position. The effect of playing experience and position on injury incidence rates was analyzed using a generalized linear mixed-effects model, with a Poisson distribution, log-linear link function, and offset for hours of training exposure or number of in-game plays (for training and game injuries, respectively). Results: The overall rates of non-time-loss and time-loss game-related injuries were 2.1 (90% CI: 1.8–2.5) and 0.6 (90% CI: 0.4–0.8) per 1000 plays, respectively. The overall rates of non-time-loss and time-loss training-related injuries were 26.0 (90% CI: 22.6–29.9) and 7.1 (90% CI: 5.9–8.5) per 1000 h, respectively. During training, seniors and running backs displayed the greatest risk. During games, sophomores, juniors, and wide receivers were at greatest risk. Conclusions: Being aware of the elevated injury risk experienced by certain player groups may help coaches make considered decisions related to training design and player selection.

Subsequent Injuries Are More Common Than Injury Recurrences: An Analysis of 1 Season of Prospectively Collected Injuries in Professional Australian Football

BACKGROUND

It is known that some people can, and do, sustain >1 injury over a playing season. However, there is currently little high-quality epidemiological evidence about the risk of, and relationships between, multiple and subsequent injuries.

PURPOSE

To describe the subsequent injuries sustained by Australian Football League (AFL) players over 1 season, including their most common injury diagnoses.

STUDY DESIGN

Cohort study; Level of evidence, 3.

METHODS

Within-player linked injury data on all date-ordered match-loss injuries sustained by AFL players during 1 full season were obtained. The total number of injuries per player was determined, and in those with >1 injury, the Subsequent Injury Classification (SIC) model was used to code all subsequent injuries based on their Orchard Sports Injury Classification System (OSICS) codes and the dates of injury.

RESULTS

There were 860 newly recorded injuries in 543 players; 247 players (45.5%) sustained ≥1 subsequent injuries after an earlier injury, with 317 subsequent injuries (36.9% of all injuries) recorded overall. A subsequent injury generally occurred to a different body region and was therefore superficially unrelated to an index injury. However, 32.2% of all subsequent injuries were related to a previous injury in the same season. Hamstring injuries were the most common subsequent injury. The mean time between injuries decreased with an increasing number of subsequent injuries.

CONCLUSION

When relationships between injuries are taken into account, there is a high level of subsequent (and multiple) injuries leading to missed games in an elite athlete group.

How should we categorise self-reported data on subsequent injuries?

Classifying subsequent injuries is of high importance in injury epidemiology since a previous injury has been reported to increase the risk of a new injury or increase the risk of a more severe injury. Multiple reports have shown that self-reported data provide an extensive view of an injury problem and add valuable information to the understanding of the athlete's health. The purpose of this study was to display a method that can be used to facilitate classification of subsequent injuries and to discuss challenges faced when categorising subsequent injuries based on self-reported data. The suitability of a new model for Subsequent Injuries Adjusted for Self-reported data (SIAS model) was demonstrated with sport injury data from a cohort of 101 adolescent elite track & field athletes, followed over 52 weeks. A total number of 71 subsequent injuries were identified. Of all subsequent injuries, recurrent injuries represented 69.0% (n = 49) and 31.0% (n = 22) were classified as new injuries. The majority of subsequent injuries (n = 60, 84.5%) occurred after athletes had recovered from a previous injury. Of all subsequent injuries, 15.5% (n = 11) represented injuries where athletes had not fully recovered from a previous injury. Application of the SIAS model allows for classification of subsequent injuries based on self-reported data on the recovery level of the athletes, the injury onset and injury type. The developed SIAS model follows the consensus recommendations of injury definition, injury classification and is an attempt to increase the understanding of the complex relationship of subsequent injuries in self-reported data sets.