In this study we sought to adapt an attribution model for foodborne salmonellosis (Hald et al
) to use with U.S. data. This adaptation of the model retained much of the original methodology and represents a potential opportunity to inform food safety efforts and develop a common understanding of foodborne disease attribution in multiple countries. The high estimated consumption of chicken relative to the other modeled commodities during the period of study, as well as the distribution of positive Salmonella
samples from chicken at the point of processing, resulted in the highest model-estimated proportion of illnesses being attributed to this commodity. Thus, the model seems to provide reasonable relative attribution estimates for included commodities based upon domestic consumption and the probability of Salmonella
presence in food sources. Although data availability limited the number of food commodities that were included in the model, the estimated relative proportions of Salmonella
illness attribution across the commodities were robust to changes in data inputs and model constraints. This is an important feature for future applications, as shell eggs were initially estimated to be the most risky food vehicle per pound of consumption, but inclusion of this data led to model instability and could not be used to attribute Salmonella
illnesses to commodities. Removal of the shell egg commodity resulted in a shift to egg products and improved stability of the food-source-dependent factor estimate for this commodity. This suggests that it may be possible to include specific food commodities that lack robust data by using data from an alternative model commodity in a “what if” exploration of foodborne disease attribution.
Differences between the two countries' data inputs result in distinct interpretations of the estimated values for food-source-dependent (aj
) and bacteria-dependent (qi
) factors. All of our data on Salmonella
presence in food commodities were obtained from the point of processing, whereas both preharvest surveillance data and end-product samples were used in the Hald model. Because the estimated food-source parameter reflects the cumulative effect of all processes in the food chain between the point of observed Salmonella
presence in the food source and consumption, our variable estimates reflect a smaller range of food production-specific factors that may influence Salmonella
presence at the point of human consumption. In addition, our model did not incorporate Salmonella
phage typing data; we also included serotypes found in many food commodities that have been sources of human infection but were not included in our final attribution model, such as shell eggs, produce, milk, and fish (Lynch et al
), as well as sources of infection not associated with food, such as direct contact with animals (Sato et al
; NASPHV, 2005
; Milstone et al
) and household environmental exposures (Barker and Bloomfield, 2000
). The modeled bacteria-dependent parameters are dependent upon the assumption that the food commodities included in the model are the only reservoirs of human infection for the included serotypes. Consequently, the observed clustering of estimated serotype qi
values for serotypes such as Javiana and Newport toward the upper limit of the specified prior distribution likely reflects the presence of additional exposure pathways not included in our model rather than a higher intrinsic likelihood of the serotype causing disease.
All of the model iterations in our study involved the estimation of more parameters than the originally described model and subsequent adaptations (Hald et al
; Mullner et al
; Little et al
). Estimating a larger number of parameters likely contributed to some of our difficulties with model convergence. Mullner et al
) noted that a major limitation of this attribution model is the high number of estimated food-source-dependent and bacteria-dependent factors compared with the limited number of data points used to generate these, and used a hierarchical approach to generate random values from a hypothetical distribution of bacteria-dependent factors (Mullner et al
). While this approach simplifies the parameterization of the model and improves its convergence properties, it makes the comparison of bacteria-dependent factors among Salmonella
types problematic. This limitation may not be desirable when considering the potential value of these outputs to food safety programs.
Incomplete human illness data limit the validity of the estimated attribution outputs. Laboratory data from three states had to be excluded from the model because of incomplete reporting. Exclusion of these cases may have resulted in an underestimation of the burden of human salmonellosis attributable to the modeled commodities. In addition, the geographic distribution of serotypes included in this model is not uniform (CDC, 2003
); thus, the inclusion of cases from these states might have had significant impacts on estimated bacteria-dependent factors if the serotype distributions in these populations were known. Our model also excluded all model-estimated outbreak-related cases of Salmonella
infection from attribution. Since our study used epidemiologic data reported to FoodNet in 2004 to estimate the total number of outbreak-associated cases among the 1998–2003 NSSS cases, we were not able to identify individual outbreaks in our study. Consequently, we chose to exclude all model-estimated outbreak cases to avoid the introduction of a highly uncertain estimated fraction in our attribution. The annual frequency and size of reported outbreaks, as well as the availability of data that distinguish outbreak and sporadic illnesses, drive the choice of how outbreak cases are used in source attribution. As the 2010 multi-state outbreak of Salmonella
Enteritidis highlights (CDC, 2010
), a single, large foodborne disease outbreak can double the burden of illness associated with a single serotype within a specific time frame and significantly impact the annual attribution to an individual commodity. Likewise, the number of submitted isolates that are directly attributable to the outbreak source is unknown. With additional data, regional differences in attribution as well as the potential role of outbreaks on the number of annually reported Salmonella
cases may be evaluated, and allow us to better represent the overall disease process.
Another major limitation of our model was the absence of data for Salmonella
presence among shell egg samples—a commodity well known to be a source of human infection. Because of the currently very low incidence of Salmonella
contamination of eggs (estimates suggest that somewhere between 1 in 10,000 to 1 in 20,000 eggs are contaminated [Schlosser et al
; Ebel and Schlosser, 2000
]), it is difficult to generate statistically robust sampling data for Salmonella
in eggs. Given the importance of shell eggs as vehicles of Salmonella
infection, a new, nationally representative survey of Salmonella
in and on shell eggs should be considered to inform future attribution models.
Collectively, foodborne disease attribution efforts raise the question of how much human salmonellosis is due to animal product food sources. Hald et al. attribute ~75% of estimated sporadic, domestic cases to animal product food sources. Mullner et al. attributed all estimated cases of salmonellosis to six food animal commodity categories. We also attributed all of the 106,000 cases estimated by our model to be domestically acquired and sporadic to our six animal product food commodities. This approach likely over-estimates the burden of foodborne salmonellosis attributed to the commodities included in the model.