Marrying economic and epidemiological data and methods to enhance knowledge about the causes of foodborne illness

Information about the food sources of foodborne illness provides the foundation for targeting interventions to prevent foodborne illness under the new Food Safety Modernization Act. Current foodborne illness source attribution estimates are based on outbreak investigations, yet outbreaks account for less than 5% of total foodborne illnesses in the U.S. Case control studies suggest that attribution estimates from outbreak data do not reflect the role of different foods in causing sporadic foodborne illnesses equally well for all pathogens. FoodNet active surveillance data captures sporadic illness, but historically has not directly linked these illnesses to foods.

This study weds epidemiological and economics data and research methods to provide an entirely new approach to foodborne source attribution that focuses on sporadic foodborne illness and food consumption. The work is a collaboration between the CDC, the USDA Economic Research Service and the University of California, Berkeley Dept of Agricultural and Resource Economics. It uses time series modeling methods developed in economics, FoodNet surveillance data, and Neilsen HomeScan consumption data to estimate multipliers between food consumption and sporadic illness. The method uses lag structure, seasonality, and geographic variability as well as exogenous controls to identify and estimate the relationship between campylobacteriosis and illness from non-O157 STEC and consumption of different foods from 2000 to 2008. This presentation will focus on the methodological links between epidemiology and economics and on the development of successful interdisciplinary research collaborations.