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Prev Vet Med ; 96(3-4): 252-62, 2010 Sep 01.
Article in English | MEDLINE | ID: mdl-20580109

ABSTRACT

In order for policy-makers to formulate effective disease control measures they require accurate estimates of the extent and prevalence of the disease. On occasion, obtaining these data can be difficult, as farmers may perceive relevant information as being sensitive to divulge. Consequently, underestimation of disease prevalence may occur due to farmer reticence about admitting to behaviours and outcomes that are considered professionally transgressive. To overcome such weaknesses, this study used a novel social science survey method known as the Randomized Response Technique in order to obtain population level estimations of transgressionary behavior and disease prevalence for the case of sheep scab in Wales. Since deregulation in 1992, sheep scab has become widespread throughout the UK. Previous estimates of sheep scab in the UK have employed survey techniques which do not adequately protect the anonymity of the farmers' answers other than via verbal or institution backed promise. In this study, farmers at several agricultural shows in Wales were asked to complete questionnaires according to the Randomized Response Technique during the summer of 2009. The survey results suggest that 30% of farmers did not routinely treat their sheep for scab. A further 36.5% of the surveyed farmers admitted to having sheep scab in their flocks in the past 5 years. These estimates are both higher than obtained by previous surveys which used more traditional questionnaire structures. Survey responses revealed a strong willingness to reintroduce compulsory treatment measures to control or eradicate the disease. This is the first study to demonstrate the value of the Randomized Response Technique in the context of animal health.


Subject(s)
Animal Welfare , Mite Infestations/veterinary , Psoroptidae , Sentinel Surveillance/veterinary , Sheep Diseases/epidemiology , Animals , Bias , Confidentiality , Female , Humans , Male , Mite Infestations/epidemiology , Models, Biological , Prevalence , Sheep , Wales/epidemiology
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