Submitted to: Risk Analysis
Publication Type: Peer reviewed journal
Publication Acceptance Date: 6/18/2003
Publication Date: 1/1/2004
Citation: Oscar, T.P. 2004. Dose-response model for thirteen strains of salmonella. Risk Analysis. 24(1):41-49. Interpretive Summary: The number of Salmonella that must be ingested with food to cause illness ranges from less than 100 cells to one billion cells. This large variation in illness dose reflects differences among strains of Salmonella in the ability to cause human illness, differences in resistance to Salmonella among humans and the interacting effect of food. Knowledge of how the aforementioned factors interact to influence the risk and severity of salmonellosis is limited. Consequently, data from a large clinical trial conducted over 50 years ago in which healthy men were fed different doses of Salmonella in eggnog were used to develop a dose-response model for predicting the rate of salmonellosis as a function of the variation in the ability to cause human illness among thirteen strains of Salmonella. Although the predictions were only relevant for the strains and doses of Salmonella used to develop the model and for eggnog and healthy men, the study was successful in that it resulted in the development of new methods for modeling this type of dose-response data and it resulted in a strategy for the design and analysis of future dose-response studies that could result in models that provide more relevant predictions of the risk of human illness from food contaminated with Salmonella.
Technical Abstract: Data from a human feeding trial with healthy men were used to develop a dose-response model for thirteen strains of Salmonella and to determine effects of strain variation on the shape of the dose-response curve. Dose-response data for individual strains were fit to a three-phase linear model to determine minimum, median and maximum illness doses, which were used to define pert distributions in a computer simulation model. Pert distributions for illness dose of individual strains were combined in an Excel spreadsheet using a discrete distribution to model strain prevalence. In addition, a discrete distribution was used to model dose-groups and thus, create a model that simulated human feeding trials. During simulation of the model with @Risk, an illness dose and a dose consumed were randomly assigned to each consumption event in the simulated feeding trial and if the illness dose was greater than the dose consumed then the model predicted no illness, otherwise the model predicted that an illness would occur. To verify the dose-response model predictions, the original feeding trial was simulated. The dose-response model predicted a median of 69 (range of 43 to 101) illnesses compared to 74 in the original trial. Thus, its predictions were in agreement with the data used to develop it. However, predictions of the model are only valid for eggnog, healthy men and the strains and doses of Salmonella used to develop it. When multiple strains of Salmonella were simulated together, the predicted dose-response curves were irregular in shape. Thus, the sigmoid shape of dose-response curves in feeding trials with one strain of Salmonella may not accurately reflect dose-response in naturally contaminated food where multiple strains may be present.