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Obtaining accurate estimates of the levels of a disease in the national dairy herd is challenging, not least due to difficulties in recruiting truly random samples. Such data are increasingly important as the dairy industry strives to identify, benchmark and reduce the levels of key endemic diseases. Accurately quantifying population disease levels has proved particularly difficult for lameness because both incidence and prevalence data are problematic, as described in a previous review.1 Most existing information is based on non-random and potentially biased data sets (eg, farms contracted to aligned milk supply groups or members of farm assurance schemes) and lameness is often recorded by partial observers (eg, farm staff).
Point prevalence based on a visual assessment of gait by a trained observer is currently the most widely accepted method for quantifying herd levels of lameness. While being practical to use on-farm, this method has limitations including within and between observer variability2 3 and being impacted by factors such as season, breed and underfoot conditions.4 5 In addition, high prevalence can reflect a range of disease situations (eg, high incidence with rapid resolution through to low incidence with prolonged recovery). The four-point Agriculture and Horticulture Development Board (AHDB) Dairy mobility score is widely accepted as the UK industry standard lameness scoring system.
The aim of this study was to record lameness prevalence across a random sample of dairy farms in the Midlands region of the UK. A list …
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