Comparing Characteristics of Sporadic and Outbreak-Associated Foodborne Illnesses, United States, 2004-2011

Abstract

Outbreak data have been used to estimate the proportion of illnesses attributable to different foods. Applying outbreak-based attribution estimates to nonoutbreak foodborne illnesses requires an assumption of similar exposure pathways for outbreak and sporadic illnesses. This assumption cannot be tested, but other comparisons can assess its veracity. Our study compares demographic, clinical, temporal, and geographic characteristics of outbreak and sporadic illnesses from Campylobacter, Escherichia coli O157, Listeria, and Salmonella bacteria ascertained by the Foodborne Diseases Active Surveillance Network (FoodNet). Differences among FoodNet sites in outbreak and sporadic illnesses might reflect differences in surveillance practices. For Campylobacter, Listeria, and Escherichia coli O157, outbreak and sporadic illnesses are similar for severity, sex, and age. For Salmonella, outbreak and sporadic illnesses are similar for severity and sex. Nevertheless, the percentage of outbreak illnesses in the youngest age category was lower. Therefore, we do not reject the assumption that outbreak and sporadic illnesses are similar.

Keywords: Campylobacter; Escherichia coli O157; FoodNet; Foodborne Diseases Active Surveillance Network; Listeria; Salmonella; bacteria; disease outbreaks; enteric infections; foodborne diseases; sporadic.

Figure 1

Quintile categorization of season and age for persons with foodborne illness included in the analysis of Foodborne Diseases Active Surveillance Network (FoodNet) data, United States, 2004–2011.

Figure 2

Patterns of the Bayesian information criterion (BIC) statistic as a function of the number of model parameters are shown for the four pathogens included in the analysis of Foodborne Diseases Active Surveillance Network (FoodNet) data, United States, 2004–2011. A) Campylobacter; B) Escherichia coli O157; C) Listeria; D) Salmonella. The BIC decreases to a minimum value and then increases as model complexity (as measured by the number of model parameters) increases.

Figure 3

Residual plots relative to fitted estimates of outbreak-associated case frequency for the best-fitting models used in the analysis of Foodborne Diseases Active Surveillance Network (FoodNet) data, United States, 2004–2011. A) Campylobacter; B) Escherichia coli O157; C) Listeria; D) Salmonella. Generally, all 4 pathogen models demonstrate reasonable fit because the studentized residuals ([observed frequency – predicted frequency of outbreak-associated cases]/SE of predicted frequency) are mostly within 3 SD of the predicted mean frequency of outbreak-associated cases. The state variable is the only factor in the Campylobacter model, whereas year is included in the E. coli O157 and Listeria models. The Salmonella model includes state, year, season, age, and interaction terms.

Figure 4

Interaction plots from the best-fitting Salmonella logistic regression model used in the analysis of Foodborne Diseases Active Surveillance Network (FoodNet) data, United States, 2004–2011. A) Year versus state; B) season versus state; C) year versus season; D) year versus age. The y-axis is the proportion of outbreak-associated cases. Crossing lines indicate interactions between 2 factors for the proportion of outbreak-associated case.