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BACKGROUND: Animal-related injuries pose a significant risk to the veterinary profession. This study aimed to describe the incidence, demographics, context and consequences of animal-related injuries at UK veterinary schools.METHODS: A multicentre audit of accident records (2009-2018) across five UK veterinary schools was performed. Injury rates were stratified by school, demographics and species. The context and cause of the injury were described. Multivariable logistic models explored factors associated with medical treatment, hospital visits and time off work.RESULTS: An annual rate of 2.60 (95% confidence interval 2.48-2.72) injuries per 100 graduating students was calculated, varying between veterinary schools. Injuries were more frequently recorded in staff than students, and there were significant differences between staff and students in the activities performed preceding injury. Cats and dogs were associated with the highest number of reported injuries. However, injuries associated with cattle and horses were the most severe, with significantly higher hospital attendances and more time off work taken.LIMITATIONS: Data were based on reported injuries and likely underestimate the true injury rate. The population at risk was hard to quantify as population size and exposure were variable.CONCLUSION: Further research is recommended to explore the clinical and workplace management, including recording culture, of animal-related injuries among veterinary professionals.

According to the Adult Psychiatric Morbidity Survey (APMS) 2014, one in six (17%) people in England met the criteria for a common mental disorder (CMD), with most mental disorders frequently present in those with poor physical health (McManus et al, 2016). Data from the same survey using the Alcohol Use Disorders Identification Test (AUDIT) showed that nearly 20% of adults drank at a hazardous level that is potentially harmful to health (AUDIT scores 8+) (Drummond et al, 2016). Alcohol has been found to cause numerous physical health conditions (Wholly Alcohol Attributable Conditions, WAACs; Lopez et al, 2014) and to contribute to the increased risk of a large number of chronic and acute diseases (Partially Alcohol Attributable Conditions, PAACs; PHE, 2020). It has been estimated that one in five patients in UK hospitals use alcohol in a harmful way and one in 10 are dependent on alcohol (Roberts et al, 2019). Alcohol use disorders have been linked with a range of mental health difficulties (PHE, 2016b) with 86% of people using alcohol treatment services having a co-occurring mental health difficulty (PHE, 2017). Multimorbidity (MM) is typically defined as the presence of two or more conditions, and research in this area has investigated how different conditions can occur together. For example, Weich et al (2011) used latent class analysis to analyse the APMS 2007 to describe the extent and nature of psychiatric comorbidity. The results revealed four latent classes of MM with the most common conditions of anxiety and depression dominating three of the multimorbid classes (Co-thymia, Addictions, Highly co-morbid) accounting for 18.4% of the participants. Another study with veterans discovered that most individuals with a mental health problem also had multiple medical and psychiatric diagnoses (Bhalla, Robert, & Rosenheck, 2018). Those with more than one mental disorder are different with more complex healthcare needs and thus treatment need to be tailored accordingly. Treating the conditions in isolation is likely to be ineffective, impractical, or even risky (e.g. medical risk can interact with psychiatric treatment) (Bhalla et al, 2018). Existing research focusing on multimorbidity has typically examined which factors are associated with the number of health problems rather than defining clusters of disease. This provides less specific advice for clinicians to act on, but has identified the important role of mental health in predicting multimorbidity (Read et al., 2017). A recent study examining specific clusters of multimorbidity found that a cluster of mental disorders and substance/alcohol use disorders was associated with the greatest risk of premature mortality (Zhu et al, 2020). There is a recognised dearth of research in understanding the complexity of the links between mental health and alcohol use and how this determines an individual’s future physical health. This project will use the APMS 2014 dataset to identify the multimorbidity clusters of physical non-communicable diseases (NCDs) with common and severe mental disorders (CMDs and SMIs). We know that mental health and behavioural issues are associated with the development of numerous socioeconomically patterned physical health conditions (Kivimäki et al, 2020). Our work will allow us to better understand how the health inequalities relating to alcohol attributable disease are greater for individuals with, compared to without, a mental disorder.