Abstract
Summary Reasons for performing the study The National Equine Database (NED) contains information on the size and distribution of the horse population, but the data quality remains unknown. These data could assist with surveillance, research and contingency planning for equine infectious disease outbreaks. Objectives 1) To assess the extent of obsolete and missing data from NED, 2) evaluate the extent of spatial separation between horse and owner location and 3) identify relationships between spatial separation and land use. Methods Two questionnaires were used to assess data accuracy in NED utilising local authority passport inspections and distribution of questionnaires to 11,000 horse owners. A subset of 1010 questionnaires was used to assess horse–owner geographic separation. Results During 2005–2010, 17,048 passports were checked through local authority inspections. Of these, 1558 passports (9.1%; 95% confidence interval [CI] 8.7–9.5%) were noncompliant, with 963 (5.6%; 95% CI 5.3–6.0%) containing inaccurate information and 595 (3.5%; 95% CI 3.2–3.8%) classified as missing. Of 1382 questionnaires completed by horse owners, 380 passports were obsolete (27.5%; 95% CI 25.2–29.9%), with 162 (11.7%; 95% CI 10.0–13.4%) being retained for deceased horses and 218 (15.8%; 95% CI 13.9–17.7%) having incorrect ownership details. Fifty-three per cent (95% CI 49.9–56.1%) of owners kept their horse(s) at home and 92% (95% CI 90.3–93.7%) of horses resided within 10 km of their owners. Conclusions and potential relevance Data from a small sample survey suggest the majority of data on NED are accurate but a proportion of inaccuracies exist that may cause delay in locating horses and contacting owners during a disease outbreak. The probability that horses are located in the same postcode sector as the owner's home address is larger in rural areas. Appropriate adjustment for population size, horse–owner spatial separation and land usage would facilitate meaningful use of the national horse population derived from NED for risk modelling of incursions of equine diseases into Great Britain.