Recency-Weighted Statistical Modeling Approach to Attribute Illnesses Caused by 4 Pathogens to Food Sources Using Outbreak Data, United States.

Foodborne illness source attribution is foundational to a risk-based food safety system. We describe a method for attributing US foodborne illnesses caused by nontyphoidal Salmonella enterica, Escherichia coli O157, Listeria monocytogenes, and Campylobacter to 17 food categories using statistical modeling of outbreak data. This method adjusts for epidemiologic factors associated with outbreak size, down-weights older outbreaks, and estimates credibility intervals.

The impact of local environmental health capacity on foodborne illness morbidity in Maryland.

Objectives: We evaluated the relationship between local food protection capacity and service provision in Maryland's 24 local food protection programs (FPPs) and incidence of foodborne illness at the county level.

Methods: We conducted regression analyses to determine the relationship between foodborne illness and local FPP characteristics. We used the Centers for Disease Control and Prevention's FoodNet and Maryland Department of Health and Mental Hygiene outbreak data set, along with data on Maryland's local FPP capacity (workforce size and experience levels, budget) and service provision (food service facility inspections, public notification programs).