Identification of Microchip Implantation Events for Dogs and Cats in the VetCompass Australia Database.
Paul McGreevySophie MastersLeonie RichardsRicardo J Soares MagalhaesAnne E PeastonMartin CombsPeter J IrwinJanice K F LloydCatriona CrotonClaire WylieBethany WilsonPublished in: Animals : an open access journal from MDPI (2019)
In Australia, compulsory microchipping legislation requires that animals are microchipped before sale or prior to 3 months in the Australian Capital Territory, New South Wales, Queensland and Victoria, and by 6 months in Western Australia and Tasmania. Describing the implementation of microchipping in animals allows the data guardians to identify individual animals presenting to differing veterinary practices over their lifetimes, and to evaluate compliance with legislation. VetCompass Australia (VCA) collates electronic patient records from primary care veterinary practices into a database for epidemiological studies. VCA is the largest companion animal clinical data repository of its kind in Australia, and is therefore the ideal resource to analyse microchip data as a permanent unique identifier of an animal. The current study examined the free-text 'examination record' field in the electronic patient records of 1000 randomly selected dogs and cats in the VCA database. This field may allow identification of the date of microchip implantation, enabling comparison with other date fields in the database, such as date of birth. The study revealed that the median age at implantation for dogs presented as individual patients, rather than among litters, was 74.4 days, significantly lower than for cats (127.0 days, p = 0.003). Further exploration into reasons for later microchipping in cats may be useful in aligning common practice with legislative requirements.
Keyphrases
- primary care
- healthcare
- electronic health record
- case report
- adverse drug
- big data
- end stage renal disease
- ejection fraction
- emergency department
- single cell
- machine learning
- general practice
- prognostic factors
- south africa
- pregnant women
- artificial intelligence
- data analysis
- preterm birth
- deep learning
- patient reported