Approaches to canine health surveillance

Abstract

Effective canine health surveillance systems can be used to monitor disease in the general population, prioritise disorders for strategic control and focus clinical research, and to evaluate the success of these measures. The key attributes for optimal data collection systems that support canine disease surveillance are representativeness of the general population, validity of disorder data and sustainability. Limitations in these areas present as selection bias, misclassification bias and discontinuation of the system respectively. Canine health data sources are reviewed to identify their strengths and weaknesses for supporting effective canine health surveillance. Insurance data benefit from large and well-defined denominator populations but are limited by selection bias relating to the clinical events claimed and animals covered. Veterinary referral clinical data offer good reliability for diagnoses but are limited by referral bias for the disorders and animals included. Primary-care practice data have the advantage of excellent representation of the general dog population and recording at the point of care by veterinary professionals but may encounter misclassification problems and technical difficulties related to management and analysis of large datasets. Questionnaire surveys offer speed and low cost but may suffer from low response rates, poor data validation, recall bias and ill-defined denominator population information. Canine health scheme data benefit from well-characterised disorder and animal data but reflect selection bias during the voluntary submissions process. Formal UK passive surveillance systems are limited by chronic under-reporting and selection bias. It is concluded that active collection systems using secondary health data provide the optimal resource for canine health surveillance.

Epidemiology of hyperadrenocorticism among 210,824 dogs attending primary‐care veterinary practices in the UK from 2009 to 2014

“…This complicated the calculation of prevalence estimates but should not have affected the overall findings of the study because of the sizeable number of cases that were still verified and because a randomised process was used to select the subset of candidate animals that were manually verified. As previously described for other published VetCompass studies, these data were not recorded primarily for research purposes and thus were limited by both missing or incomplete data and potential misdiagnoses (O'Neill et al, 2014a, O'Neill et al, 2014bO′Neill et al, 2014). Many cases did not undergo testing to differentiate between PTHAC and ATHAC, and consequently we have analysed the data by combining PTHAC and ATHAC cases as a general hyperadrenocorticism diagnosis.…”

Use of large‐scale veterinary data for the investigation of antimicrobial prescribing practices in equine medicine

“…Interrogation of medical records datasets is expedited and validated thanks to widespread use of embedded standardised clinical terminologies (e.g. Both of these issues are currently being tackled by a variety of organisations and collaborations [8][9][10][11][12], but extraction of useful clinical information from existing datasets relies, in most cases, upon bespoke methods of collection and analysis of free text. In recent years, the use of practice management software in veterinary surgeries for the entry and curation of medical records has become commonplace.…”

Evidence in Practice – A Pilot Study Leveraging Companion Animal and Equine Health Data from Primary Care Veterinary Clinics in New Zealand

“…An approach to surveillance has been evolving that utilizes computerized veterinary practice management systems to capture data from individual primary-care veterinary practices and generate population-level information on animal health and welfare (1). While the concept of using veterinary clinical data and electronic medical records for animal health surveillance is not new (2), considerable advances have recently been made to improve compliance, for example, through the development of web-based data collection systems (3).…”

“…While the concept of using veterinary clinical data and electronic medical records for animal health surveillance is not new (2), considerable advances have recently been made to improve compliance, for example, through the development of web-based data collection systems (3). An automated approach to collecting, sharing, and analyzing veterinary primary care data to understand disorders and improve the welfare of animals was initially pioneered for companion animals by the Royal Veterinary College in the UK (VetCompass 1 ) and similar systems or adaptations are now emerging in different institutions and countries (1, 4, 5). The advantage of this approach is that it can be fully integrated in the veterinary workflow and captures information directly from the practice management software, thus minimizing time-consuming additional data entry and the use of linked software or websites.…”