Statistical models of health risk due to microbial contamination of foods

Ralph L. Kodell, Seung Ho Kang, James J. Chen

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Between 6 million and 33 million cases of food-related illness are estimated to occur in the United States each year, with about 5000 episodes resulting in death. Growing concerns about the safety of food prompted the National Food Safety Initiative of 1997, the goal of which is to reduce the incidence of illness caused by food-borne pathogens. A key component of the food safety initiative is the improvement of farm-to-table risk assessment capabilities, including the development of improved dose-response models for estimating risk. When sufficient data are available, allowable contamination levels of specific micro-organisms in food are established using dose-response models to predict risk at very low doses based on experimental data at much higher doses. This necessitates having reliable models for setting allowable exposures to food-borne pathogens. While only limited data on relatively few micro-organisms that occur in food are available at present for dose-response modeling and risk estimation, still none of the two-parameter models proposed so far, including the popular Beta-Poisson (BP) model, appears to be completely satisfactory for describing and fitting all of the present data (Holcomb et al., 1999). The Weibull-Gamma (WG) model is the only three-parameter model that has been proposed to date. In this paper, new competitive three- parameter models are derived, using a formulation that can be parameterized to represent statistical variation with respect to the dose of micro-organism received by the host and the host's susceptibility to infection. Parameters of the models are estimated using the maximum likelihood method. Experimental data on several common microbial contaminants in food are used to illustrate the methodology.

Original languageEnglish
Pages (from-to)259-271
Number of pages13
JournalEnvironmental and Ecological Statistics
Volume9
Issue number3
DOIs
Publication statusPublished - 2002 Sep

Bibliographical note

Funding Information:
The authors are grateful to an assoc iate editor and two anonymous referees for muc h helpful guidance on improving this paper. One referee, especially, made a substantial contribution. This research was supported in part by Dr Seung-Ho Kang's appointment to the Postgraduate Research Program at the National Center for Toxicological Research administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the United States DOE and the United States FDA.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Environmental Science(all)
  • Statistics, Probability and Uncertainty

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