Comparison of Statistical Methods for Identifying Risk Factors for Salmonella Contamination of Whole Chicken Carcasses

Abstract

The complexity of the food system makes analyzing microbiological data from food studies challenging because many of the assumptions (e.g., linear relationship between independent and dependent variable and independence of observations) associated with common analytical approaches (e.g., analysis of variance) are violated. Repeated sampling within an establishment introduces longitudinal correlation that must be accounted for during analyses. In this study, statistical methods for clustered or correlated data were used to determine how correlation impacts conclusions and to compare how assumptions associated with statistical methods impact the appropriateness of these methods within the context of food safety. Risk factor analyses for Salmonella contamination of whole chicken carcasses were conducted as a case study with regulatory data collected by the U.S. Department of Agriculture Food Safety and Inspection Service between May 2015 and December 2019 from 203 regulated establishments. Three models, generalized estimating equation, random effects, and logistic, were fit to Salmonella presence or absence data with establishment demographics and inspection history included as potential covariates. Beta parameter estimates and their standard errors and odds ratios and their 95% confidence intervals were compared across models. Conclusions drawn from the three models differed with respect to geographic region, whether the chicken establishment also slaughters turkeys, and establishment noncompliance with 9 CFR §417.4 (hazard analysis critical control point system validation, verification, and reassessment) in the 84 days leading up to sample collection. The results of this study reveal the need to consider clustering and correlation when analyzing food microbiological data, provide context for selecting a statistical method, and suggest that generalized estimating equation and random effects models are preferrable over logistic regression when analyzing correlated food data. These results support a renewed focus on statistical methodology in food safety.