Using time-series analysis techniques to enhance the understanding of musculoskeletal injury in Thoroughbred racehorses.

Abstract

Many racing jurisdictions monitor race-day musculoskeletal injury (MSI) but fail to evaluate injuries occurring during training. Additionally, previous risk factor analyses have failed to explore temporal trends in injury occurrence. To use time-series analysis techniques to identify trends, cyclicity and peaks in MSI incidence, in racehorses training and racing at the Hong Kong Jockey Club (HKJC) from July 2010 to June 2018. Retrospective longitudinal study. The monthly incidence of all MSI, superficial digital flexor tendon (SDFT) injury, suspensory ligament injury and appendicular skeletal fracture occurring in training and racing were collected from veterinary records. The number of horses in training was collated monthly from trainer records. Time-series analysis techniques were used to describe trends and cyclical patterns for injury types. For each injury, incidence risks above the 90th percentile were identified as peaks in incidence. A total of 1471 injuries were recorded over eight racing seasons; 605 fractures (41.1%), 550 SDFT injuries (37.4%), and 316 suspensory ligament injuries (21.5%). Evidence of seasonality was detected in fracture incidence risk; increasing from October (median 0.25 per 1000 horses) until May (median 0.71 per 1000 horses), coinciding with the racing season (ending mid-July). Elevated incidence of MSI occurred throughout 2012; however, the greatest incidence risks of SDFT (14.8 per 1000 horses) and fracture (1.3 per 1000 horses) occurred since 2017. Monthly (opposed to daily) incidence risk of injury reduced the resolution of the data. Additionally, fracture was not described according to bone or fracture type, which may have confounded overall trends. Evidence for seasonal variation in the incidence of fracture occurrence has been demonstrated. Based on using time-series techniques, further epidemiological studies, retrospectively targeting periods of high peaks in injury incidence risk could be used to aid identification of risk factors for injury.