Postmarketing surveillance for dog and cat vaccines: new resources in changing times

Approaches to canine health surveillance

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.

Companion animal disease surveillance: a new solution to an old problem?

Infectious disease surveillance in companion animals has a long history. However, it has mostly taken the form of ad hoc surveys, or has focused on adverse reactions to pharmaceuticals. In 2006 a Blue Ribbon Panel was convened by the U.S. White House Office of Science and Technology Policy to discuss the potential utility of a national companion animal health surveillance system. Such a system could provide fundamental information about disease occurrence, transmission and risk factors; and could facilitate industry-supported pharmaco-epidemiological studies and post-market surveillance.

Disease WatchDog, a prospective national disease surveillance project, was officially launched in January 2010 to capture data on diseases in dogs and cats throughout Australia. Participation is encouraged by providing registrants real-time disease maps and material for improved communication between veterinarians and clients.

From January to mid-November 2010, an estimated 31% of veterinary clinics Australia-wide joined the project. Over 1300 disease cases – including Canine Parvovirus (CPV), Canine Distemper, Canine Hepatitis, Feline Calicivirus, Feline Herpesvirus, and Tick Paralysis – were reported. In New South Wales alone, 552 CPV cases in dogs were reported from 89 postcode locations. New South Wales data was scanned using the space–time permutation test. Up to 24 clusters (P < 0.01) were identified, occurring in all months except March. The greatest number of clusters (n = 6) were identified in April. The most likely cluster was identified in western Sydney, where 36 cases of CPV were reported from a postcode in February. Although the project is still in its infancy, already new information on disease distribution has been produced. Disease information generated could facilitate targeted control and prevention programs.