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.
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.