TY - JOUR AU - Batz, Michael AU - Richardson, LaTonia AU - Bazaco, Michael AU - Parker, Cary Chen AU - Chirtel, Stuart AU - Cole, Dana AU - Golden, Neal AU - Griffin, Patricia AU - Gu, Weidong AU - Schmitt, Susan AU - Wolpert, Beverly AU - Kufel, Joanna S. Zablotsky AU - Hoekstra, R. Michael T1 - Recency-Weighted Statistical Modeling Approach to Attribute Illnesses Caused by 4 Pathogens to Food Sources Using Outbreak Data, United States T2 - Emerging Infectious Disease journal PY - 2021 VL - 27 IS - 1 SP - 214 SN - 1080-6059 AB - 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. On the basis of 952 reported outbreaks and 32,802 illnesses during 1998–2012, we attribute 77% of foodborne Salmonella illnesses to 7 food categories (seeded vegetables, eggs, chicken, other produce, pork, beef, and fruits), 82% of E. coli O157 illnesses to beef and vegetable row crops, 81% of L. monocytogenes illnesses to fruits and dairy, and 74% of Campylobacter illnesses to dairy and chicken. However, because Campylobacter outbreaks probably overrepresent dairy as a source of nonoutbreak campylobacteriosis, we caution against using these Campylobacter attribution estimates without further adjustment. KW - foodborne diseases KW - disease outbreaks KW - food safety KW - Salmonella KW - Escherichia coli 0157 KW - Listeria KW - Campylobacter KW - risk factors KW - models KW - statistical KW - analysis of variance DO - 10.3201/eid2701.203832 UR - https://wwwnc.cdc.gov/eid/article/27/1/20-3832_article ER - End of Reference