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Results from our model suggest that eating Salmonella enterica serovar Enteritidis–contaminated shell eggs caused 182,060 illnesses in the United States during 2000. Uncertainty about the estimate ranged from 81,535 (5th percentile) to 276,500 illnesses (95th percentile). Our model provides 1 approach for estimating foodborne illness and quantifying estimate uncertainty.

The prevalence and concentration of many foodborne pathogens exhibit seasonal patterns at different stages of the farm-to-table continuum. Escherichia coli O157:H7 is one such pathogen. While numerous studies have described the seasonal trend of E. coli O157:H7 in live cattle, ground beef, and human cases, it is difficult to relate the results from these different studies and determine the interrelationships that drive the seasonal pattern of beef-related human illnesses. This study uses a common modeling approach, which facilitates the comparisons across data sets, to relate prevalence in live cattle to raw ground beef and human illness. The results support an intuitive model where a seasonal rise of E. coli O157:H7 in cattle drives increased ground beef prevalence and a corresponding rise in the human case rate. We also demonstrate the use of these models to assess the public health impact of consumer behaviors. We present an example that suggests that the probability of illness, associated with summertime cooking and handling practices, is not substantially higher than the baseline probability associated with more conventional cooking and handling practices during the remainder of the year.

Concerns over declining farm numbers, shifts in farm size distribution, and associated infrastructural problems have led to a heightened awareness of structural considerations within policy making circles. Future policy decisions will have substantial structural consequences for the agricultural industry. Often, however, the indirect effects of grain pricing policies on the livestock sector have been overlooked in these policy decisions. The incorporation of price effects into a Markov chain analysis of pork farm size distributions and the simulation of those projections to the year 2000 under various price scenarios should provide some insight into the future structure of livestock farming in the South.

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