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.