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ARS Home » Midwest Area » Ames, Iowa » National Animal Disease Center » Food Safety and Enteric Pathogens Research » Research » Publications at this Location » Publication #101591
Title: THE EFFECT OF INTERMITTENT SHEDDING ON PREVALENCE ESTIMATION IN POPULATIONS

Author
item Hurd, Howard
item SCHLOSSER, W - USDA-FSIS-OPHS
item EBEL, E - USDA-FSIS-OPHS

Submitted to: International Symposium on Epidemiology and Control of Salmonella in Pork
Publication Type: Abstract Only
Publication Acceptance Date: 8/7/1999
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: The objective of this study was to evaluate standard sample size recommendations for the prevalence estimation of intermittently shed and clustered infections, such as Salmonella. We developed a simple stochastic computer simulation model of 500 animals. Every iteration, each individual had a random chance of infection based on a set mean true prevalence (TP). The mean TP and its skewness were set at levels currently observed in U.S. finish hogs. Additionally, the possibility of clustering was included. Clustering reflects the expectation that infection is not homogeneously distributed among a population of 500 finish hogs. Instead, it may be grouped among pen mates or adjoining pens. We evaluated different clustering scenarios for each mean TP. At the beginning of the simulation, the sample size was set according to standard formulas. Each iteration, the model randomly tested a sample of animals. The model then estimated the apparent prevalence (AP) for that iteration. Test sensitivity and specificity were set to be 100%. Multiple simulations of 1,000 iterations were run for each scenario. Comparison of the TP and AP distributions suggest that skewness in the prevalence distribution can substantially affect the likelihood of accurately estimating Salmonella's true prevalence from a sample. For clustering scenarios, this comparison suggests the AP tends to underestimate the TP. The distribution of AP possibilities exceeds the predetermined acceptable precision boundaries. Therefore, the sample size determination is heavily affected by a priori estimates of the true prevalence and its distribution.