RESUMEN
Statistical source attribution approaches of food-related zoonoses can generally be based on reported diagnosed human cases and surveillance results from different food sources or reservoirs of bacteria. The attribution model, or probabilistic classifier, can thus be based on the (sub)typing information enabling comparison between human infections and samples derived from source surveillance. Having time series of both data allows analyzing temporal patterns over time providing a repeated natural experiment. A Bayesian approach combining both sources of information over a long time series is presented in the case of Campylobacter in Finland and Norway. The full model is transparently presented and derived from the Bayes theorem. Previous statistical source attribution approaches are here advanced (1) by explicit modeling of the cases not associated with any of the sources under surveillance over time, (2) by modeling uncertain prevalence in a food source by bacteria type over time, and (3) by implementing formal model fit assessment using posterior predictive discrepancy functions. Large proportion of all campylobacteriosis can be attributed to broiler, but considerable uncertainty remains over time. The source attribution is inherently incomplete if only the sources under surveillance are included in the model. All statistical source attribution approaches should include a model fit assessment for judgment of model performance with respect to relevant quantities of interest. It is especially relevant when the model aims at a synthesis of several incomplete information sources under significant uncertainty of explanatory variables.
Asunto(s)
Infecciones por Campylobacter/diagnóstico , Infecciones por Campylobacter/epidemiología , Microbiología de Alimentos , Medición de Riesgo/métodos , Teorema de Bayes , Finlandia , Contaminación de Alimentos , Humanos , Incidencia , Modelos Estadísticos , Noruega , Prevalencia , Probabilidad , Factores de TiempoRESUMEN
The aim of this study was to investigate the effects of ampicillin treatment on selection and diversity of ampicillin-resistant intestinal Escherichia coli in beagles treated with ampicillin, ampicillin + beta-lactamase (targeted to degrade antibiotic residues in the gut) or placebo. Selected faecal (n = 339) and jejunal (n = 63) E. coli isolates with known resistance patterns were typed using pulsed-field gel electrophoresis (PFGE). Among the 25 detected PFGE types, ampicillin resistance was detected in 6, none of which was dominant over others among the dogs. The resistant types increased especially in the ampicillin group, whilst beta-lactamase inhibited their emergence. Selection of genetically unrelated resistant strains rather than emerging resistance among previously susceptible strains accounts for increasing resistance rates during ampicillin treatment.
Asunto(s)
Resistencia a la Ampicilina , Escherichia coli/efectos de los fármacos , Escherichia coli/fisiología , Ampicilina/farmacología , Animales , Perros , Electroforesis en Gel de Campo Pulsado , Escherichia coli/aislamiento & purificación , Heces/microbiología , Yeyuno/microbiología , Selección GenéticaRESUMEN
Biosecurity is important in order to prevent disease transmission between animals on farms as well as from farm to farm. Personal biosecurity routines such as hand washing and the use of protective clothing and footwear are measures that should be used at all farms. Other measures are for example related to purchasing new animals to the farm. A questionnaire-based survey was undertaken to study the frequency of use of different biosecurity measures on cattle, pig and sheep farms in Finland. Information about which biosecurity measures are in use is needed for contingency planning of emerging diseases or when combating endemic diseases. Knowledge about the level of biosecurity of a farm is also needed in order to assess if and where improvement is needed. Information regarding biosecurity levels may benefit future animal disease risk assessments. A total of 2242 farmers responded to the questionnaire resulting in a response rate of 45%. The implementation frequencies of different biosecurity measures are reported. The results revealed differences between species: large pig farms had a better biosecurity level than small cattle farms. There were also differences between production types such as dairy farming versus beef cattle farming, but these were not as remarkable. Sheep farming in Finland is sparse and the large number of hobby farmers keeps the biosecurity level low on sheep farms. This might represent a risk for the entire sheep farming industry. The Finnish farmers were satisfied with their on-farm biosecurity. Eighty percent of the farmers report that they were satisfied even though the biosecurity level was not particularly high. The implementation of biosecurity measures could be further improved. Even though the disease situation in Finland is good today, one must be prepared for possible epidemics of threatening diseases.