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Topics in dose-response modeling
1Office of Public Health and Science, U.S. Department of Agriculture, Washington, D.C. 20250-3700, USA. peg.coleman@usda.gov
Abstract:
Great uncertainty exists in conducting dose-response assessment for microbial pathogens. The data to support quantitative modeling of dose-response relationships are meager. Our philosophy in developing methodology to conduct microbial risk assessments has been to rely on data analysis and formal inferencing from the available data in constructing dose-response and exposure models. The probability of illness is a complex function of factors associated with the disease triangle: the host, the pathogen, and the environment including the food vehicle and indigenous microbial competitors. The epidemiological triangle and interactions between the components of the triangle are used to illustrate key issues in dose-response modeling that impact the estimation of risk and attendant uncertainty. Distinguishing between uncertainty (what is unknown) and variability (heterogeneity) is crucial in risk assessment. Uncertainty includes components that are associated with (i) parameter estimation for a given assumed model, and (ii) the unknown "true" model form among many plausible alternatives such as the exponential, Beta-Poisson, probit, logistic, and Gompertz. Uncertainty may be grossly understated if plausible alternative models are not tested in the analysis. Examples are presented of the impact of variability and uncertainty on species, strain, or serotype of microbial pathogens; variability in human response to administered doses of pathogens; and effects of threshold and nonthreshold models. Some discussion of the usefulness and limitations of epidemiological data is presented. Criteria for development of surrogate dose-response models are proposed for pathogens for which human data are lacking. Alternative dose-response models which consider biological plausibility are presented for predicting the probability of illness.
Insights
Quantitative microbial risk assessment faces challenges due to limited data for dose-response modeling. This study explores methods to address uncertainty and variability in predicting illness probability from microbial pathogens.
Area of Science:
- Microbiology
- Risk Assessment
- Epidemiology
Background:
- Conducting dose-response assessments for microbial pathogens is challenging due to limited supporting data for quantitative modeling.
- The probability of illness is influenced by complex interactions between host, pathogen, and environmental factors (disease triangle).
Purpose of the Study:
- To develop and present methodologies for microbial risk assessment, focusing on dose-response and exposure modeling.
- To illustrate key issues in dose-response modeling impacting risk estimation and uncertainty analysis.
- To propose criteria for developing surrogate dose-response models for pathogens lacking human data.
Main Methods:
- Utilizing data analysis and formal inferencing to construct dose-response and exposure models.
- Applying the epidemiological triangle to understand host-pathogen-environment interactions in modeling.
- Evaluating various dose-response model forms (e.g., exponential, Beta-Poisson, probit, logistic, Gompertz) and distinguishing uncertainty from variability.
Main Results:
- Demonstrating the impact of variability and uncertainty on pathogen characteristics and human response.
- Highlighting the risk of underestimating uncertainty if alternative models are not considered.
- Presenting biologically plausible alternative dose-response models for illness prediction.
Conclusions:
- Distinguishing between uncertainty and variability is critical for accurate microbial risk assessment.
- Testing plausible alternative models is essential to avoid understating uncertainty.
- Alternative dose-response models can improve predictions, especially when human data are scarce.