Establishment and maintenance of a longitudinal study of bovine spongiform encephalopathy (the ULiSES scheme)

Dogslife: a web-based longitudinal study of Labrador Retriever health in the UK

BACKGROUND

Dogslife is the first large-scale internet-based longitudinal study of canine health. The study has been designed to examine how environmental and genetic factors influence the health and development of a birth cohort of UK-based pedigree Labrador Retrievers.

RESULTS

In the first 12 months of the study 1,407 Kennel Club (KC) registered eligible dogs were recruited, at a mean age of 119 days of age (SD 69 days, range 3 days - 504 days). Recruitment rates varied depending upon the study team's ability to contact owners. Where owners authorised the provision of contact details 8.4% of dogs were recruited compared to 1.3% where no direct contact was possible. The proportion of dogs recruited was higher for owners who transferred the registration of their puppy from the breeder to themselves with the KC, and for owners who were sent an e-mail or postcard requesting participation in the project. Compliance with monthly updates was highly variable. For the 280 dogs that were aged 400 days or more on the 30th June 2011, we estimated between 39% and 45% of owners were still actively involved in the project. Initial evaluation suggests that the cohort is representative of the general population of the KC registered Labrador Retrievers eligible to enrol with the project. Clinical signs of illnesses were reported in 44.3% of Labrador Retrievers registered with Dogslife (median age of first illness 138 days), although only 44.1% of these resulted in a veterinary presentation (median age 316 days).

CONCLUSIONS

The web-based platform has enabled the recruitment of a representative population of KC registered Labrador Retrievers, providing the first large-scale longitudinal population-based study of dog health. The use of multiple different methods (e-mail, post and telephone) of contact with dog owners was essential to maximise recruitment and retention of the cohort.

A "shotgun" method for tracing the birth locations of sheep from flock tags, applied to scrapie surveillance in Great Britain

Movement records are often used to identify animal sample provenance by retracing the movements of individuals. Here we present an alternative method, which uses the same identity tags and movement records as are used to retrace movements, but ignores individual movement paths. The first step uses a simple query to identify the most likely birth holding for every identity tag included in a database recording departures from agricultural holdings. The second step rejects a proportion of the birth holding locations to leave a list of birth holding locations that are relatively reliable. The method was used to trace the birth locations of sheep sampled for scrapie in abattoirs, or on farm as fallen stock. Over 82% of the sheep sampled in the fallen stock survey died at the holding of birth. This lack of movement may be an important constraint on scrapie transmission. These static sheep provided relatively reliable birth locations, which were used to define criteria for selecting reliable traces. The criteria rejected 16.8% of fallen stock traces and 11.9% of abattoir survey traces. Two tests provided estimates that selection reduced error in fallen stock traces from 11.3% to 3.2%, and in abattoir survey traces from 8.1% to 1.8%. This method generated 14,591 accepted traces of fallen stock from samples taken during 2002-2005 and 83,136 accepted traces from abattoir samples. The absence or ambiguity of flock tag records at the time of slaughter prevented the tracing of 16-24% of abattoir samples during 2002-2004, although flock tag records improved in 2005. The use of internal scoring to generate and evaluate results from the database query, and the confirmation of results by comparison with other database fields, are analogous to methods used in web search engines. Such methods may have wide application in tracing samples and in adding value to biological datasets.