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This study aimed to examine thirteen seasons of flat racing starts (n = 388,964) in the context of an ecological system and identify metrics that describe the inherent characteristics and constraints of the New Zealand Thoroughbred racing industry. During the thirteen years examined, there was a 2–3% per year reduction in the number of races, starts and number of horses. There was a significant shift in the racing population with a greater number of fillies (aged 2–4 years) having a race start, and subsequent longer racing careers due to the inclusion of one more racing preparation post 2008 (p < 0.05). Additionally, there was an increasingly ageing population of racehorses. These changes resulted in more race starts in a career, but possibly because of biological constraints, there was no change in the number of race starts per season, starts per preparation, or days spelling between preparations (p < 0.05). There was no change in the proportion of horses having just one race start (14% of new entrants), indicating that the screening for suitability for a racing career remained consistent. These data identify key industry parameters which provide a basis for future modelling of intervention strategies to improve economic performance and reduce horse injury. Consideration of the racing industry as a bio-economic or ecological model provides framework to test how the industry may respond to intervention strategies and signal where changes in system dynamics may alter existing risk factors for injury.

The objective of this study was to describe the incident and non-incident reports of Thoroughbred flat racing in New Zealand. Retrospective stipendiary stewards’ reports of race day events during the 2015/2016 and 2016/2017 racing season were examined. The primary injury and reporting outcomes were analysed to assess the horse- and race-level risk factors associated with the occurrence of incident and non-incident reports. The number of incident and non-incident events and binomial exact 95% confidence intervals were calculated per 1000 horse starts. Most reports were for non-incidents and examinations were requested for poor performance (10.3 per 1000 races, 95% CI = 9.5–11.1). Horses running in open-class races had greater odds of having an incident than horses in lower-rating classes. The incidence of musculoskeletal injuries (1.3 per 1000 races, 95% CI = 1.13–1.40) and fractures (0.6 per 1000 races, 95% CI = 0.39–0.74) were low and similar to previous New Zealand reports. There was a low incidence of epistaxis (0.8 per 1000 races, 95% CI = 0.69–0.92) possibly due to trainers screening susceptible horses before entering them in races, due to the regulatory consequences of an episode of epistaxis during a race.

Race day fatalities as a consequence of catastrophic musculoskeletal injury and cardiac failure are both a welfare concern and provide a challenge for the social perceptions of equine welfare within the racing industry. To reduce race day fatalities, the risk factors under New Zealand racing conditions need to be identified. The aim of this study was to examine race and horse-level risk factors for fatalities in New Zealand Thoroughbred flat racing using retrospective race day data from the 2011/12–2021/22 racing seasons. Horse and race-level factors associated with a suspected cardiac failure and fatal fracture were identified by merging fatality data with the master race dataset for the corresponding seasons. Most fatalities were associated with fatal fracture (0.4 per 1000 starts, 95% CI 0.4–0.5). Horses which raced over distances > 1600 m were 1.7 times (95% CI 1.2–2.5) more likely to sustain a fatal fracture than horses racing ≤ 1600 m. Male horses and firmer track conditions were also associated with an increase in the risk of fatal fracture. Horses aged 5 years and older were 2.1 (95% CI 1.1–4.6) times more likely to suffer a suspected cardiac failure than younger horses. Changes in the industry reporting system improved the level of detail provided for fatalities, enabling the identification of specific risk factors.

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