title: The Beta Poisson dose-response model is not a single-hit model.
abstract: The choice of a dose-response model is decisive for the outcome of quantitative risk assessment. Single-hit models have played a prominent role in dose-response assessment for pathogenic microorganisms, since their introduction. Hit theory models are based on a few simple concepts that are attractive for their clarity and plausibility. These models, in particular the Beta Poisson model, are used for extrapolation of experimental dose-response data to low doses, as are often present in drinking water or food products. Unfortunately, the Beta Poisson model, as it is used throughout the microbial risk literature, is an approximation whose validity is not widely known. The exact functional relation is numerically complex, especially for use in optimization or uncertainty analysis. Here it is shown that although the discrepancy between the Beta Poisson formula and the exact function is not very large for many data sets, the differences are greatest at low doses--the region of interest for many risk applications. Errors may become very large, however, in the results of uncertainty analysis, or when the data contain little low-dose information. One striking property of the exact single-hit model is that it has a maximum risk curve, limiting the upper confidence level of the dose-response relation. This is due to the fact that the risk cannot exceed the probability of exposure, a property that is not retained in the Beta Poisson approximation. This maximum possible response curve is important for uncertainty analysis, and for risk assessment of pathogens with unknown properties.
authors.affiliation: National Institute of Public Health and The Environment, Bilthoven, The Netherlands. Peter.Teunis@rivm.nl
authors.forename: P F
authors.initials: PF
authors.lastname: Teunis
authors.forename: A H
authors.initials: AH
authors.lastname: Havelaar
mesh_terms: D000465:Algorithms; D002169:Campylobacter Infections; D016123:Campylobacter jejuni; D002771:Cholera; D016001:Confidence Intervals; D005516:Food Microbiology; D006801:Humans; D007239:Infections; D016013:Likelihood Functions; D008954:Models, Biological; D015233:Models, Statistical; D016012:Poisson Distribution; D011336:Probability; D018570:Risk Assessment; D012400:Rotavirus Infections; D014871:Water Microbiology; D014881:Water Supply