BACKGROUND: The aim of this study was to assess the sensitivity of the four major bluetongue surveillance components implemented in Belgium in 2007 for farmed animals and prescribed by the European Union regulation; winter serological screening, sentinel system, passive clinical surveillance, export testing. Scenario tree methodology was used to evaluate the relative sensitivity of detection and targeted approach of each component in terms of early detection and freedom of infection substantiation. Field data collected from the previous year's outbreaks in Belgium were used to determine the risk groups to be considered. RESULTS: The best sensitivities at herd level, taking into account the diagnostic test sensitivity, design prevalence and the number of animals tested within a herd were obtained with the winter screening and sentinel component. The sensitivities at risk group level, taking into account the obtained herd sensitivity, effective probabilities of infection and number of herds tested were high in all components, except for the export component. Component sensitivities ranged between 0.77 and 1 for all components except for the export component with a mean value of 0.22 (0.17-0.26). In terms of early detection, the probability of detection was best using the passive clinical component or the sentinel component. Sensitivity analysis showed that the passive clinical component sensitivity was mostly affected by the diagnostic process and the number of herds sampled. The sentinel and export components sensitivity were mainly affected by the relative risk estimates whereas the winter screening component was mainly affected by the assumptions about the design prevalence. CONCLUSIONS: This study revealed interesting features regarding the sensitivity of detection and early detection of infection in the different surveillance components and their risk based approach as requested by the international standards.
BACKGROUND: The aim of this study was to assess the sensitivity of the four major bluetongue surveillance components implemented in Belgium in 2007 for farmed animals and prescribed by the European Union regulation; winter serological screening, sentinel system, passive clinical surveillance, export testing. Scenario tree methodology was used to evaluate the relative sensitivity of detection and targeted approach of each component in terms of early detection and freedom of infection substantiation. Field data collected from the previous year's outbreaks in Belgium were used to determine the risk groups to be considered. RESULTS: The best sensitivities at herd level, taking into account the diagnostic test sensitivity, design prevalence and the number of animals tested within a herd were obtained with the winter screening and sentinel component. The sensitivities at risk group level, taking into account the obtained herd sensitivity, effective probabilities of infection and number of herds tested were high in all components, except for the export component. Component sensitivities ranged between 0.77 and 1 for all components except for the export component with a mean value of 0.22 (0.17-0.26). In terms of early detection, the probability of detection was best using the passive clinical component or the sentinel component. Sensitivity analysis showed that the passive clinical component sensitivity was mostly affected by the diagnostic process and the number of herds sampled. The sentinel and export components sensitivity were mainly affected by the relative risk estimates whereas the winter screening component was mainly affected by the assumptions about the design prevalence. CONCLUSIONS: This study revealed interesting features regarding the sensitivity of detection and early detection of infection in the different surveillance components and their risk based approach as requested by the international standards.
Authors: B Bisdorff; B Schauer; N Taylor; V Rodríguez-Prieto; A Comin; A Brouwer; F Dórea; J Drewe; L Hoinville; A Lindberg; M Martinez Avilés; B Martínez-López; M Peyre; J Pinto Ferreira; J Rushton; G VAN Schaik; K D C Stärk; C Staubach; M Vicente-Rubiano; G Witteveen; D Pfeiffer; B Häsler Journal: Epidemiol Infect Date: 2016-12-12 Impact factor: 4.434