Yuhuan Chen, Sherri Dennis, and Sherri McGarry Food and Drug Administration Current Approach to FSMA Section 204: Designating High-Risk Foods for Tracing

Outline FSMA section 204 requirements for High-Risk Foods designation specific to Product Tracing FDA’s draft approach to HRF designation Characteristics of draft HRF risk ranking model Data and data challenges Example areas and issues to be addressed 2

Section 204. Enhancing tracking and tracing of food and record keeping 204.(d) “In order to rapidly and effectively identify recipients of a food to prevent or mitigate a foodborne illness outbreak and to address credible threats of serious adverse health consequences or death to humans or animals as a result of such food being adulterated under section 402 of the Federal Food, Drug, and Cosmetic Act or misbranded under section 403(w) of such Act, not later than 2 years after the date of enactment of this Act, the Secretary shall publish a notice of proposed rulemaking to establish recordkeeping requirements, in addition to the requirements under section 414 of the Federal Food, Drug, and Cosmetic Act (21 U.S.C. 350c) and subpart J of part 1 of title 21, Code of Federal Regulations (or any successor regulations), for facilities that manufacture, process, pack, or hold foods that the Secretary designates under paragraph (2) as high-risk foods …” 3

FSMA Requirements under section 204.(d)2(A) HRFs designation shall be based on-- (i) the known safety risks of a particular food, including the history and severity of foodborne illness outbreaks attributed to such food; (ii) the likelihood that a particular food has a high potential risk for microbiological or chemical contamination or would support the growth of pathogenic microorganisms due to the nature of the food or the processes used to produce such food; (iii) the point in the manufacturing process of the food where contamination is most likely to occur; 4

FSMA Requirements cont. (iv) the likelihood of contamination and steps taken during the manufacturing process to reduce the possibility of contamination; (v) the likelihood that consuming a particular food will result in a foodborne illness due to contamination of the food; and (vi) the likely or known severity, including health and economic impacts, of a foodborne illness attributed to a particular food. 5

Procedures for Designating High-Risk Foods Develop draft model approach to designating HRF based on FSMA requirements Data-driven, predictive, risk-informed Gather input from various stakeholders on how best to approach HRF designation Conduct expert elicitation to address data gaps Operationalize the HRF model approach to calculate scores … Designate a list of high-risk foods 6

Invited comments on specific issues: Alternative approaches to HRF designation ‒ Additional criteria that should be considered within the factors mandated by Congress ‒ Should equal or different weights be assigned to different criteria? Food categorization scheme, representative foods to be evaluated Requested data and information on: Prevalence and levels of contaminants Typical steps and control measures Impact of acute or chronic exposures to allergens and chemical contaminants 7 Issued FRN to Help Refine the Draft HRF Approach

Draft Approach to HRF Designation Accounts for both the characteristics of the food and the hazard Accounts for both human and animal foods and their manufacturing processes both microbial and chemical hazards (including undeclared allergens) 8

Draft Approach to HRF Designation Although section 204.(d) of FSMA requires FDA to designate a list of “high-risk foods,” in order to apply the FSMA factors it is necessary to first take into account both the characteristics of foods and known or reasonably foreseeable hazards, i.e., food-hazard pairs. This is not anticipated to be a food-hazard list but rather a food list 9

Classification of Foods and Food Categories Align as best possible with the data to identify a comprehensive list of food-hazard pairs as candidates Based on Reportable Food Registry (RFR) definitions, considering both food characteristics and manufacturing processes (e.g., LACF, fresh produce) Select representative foods 10

Characteristics of draft HRF risk ranking model 11

Draft HRF Risk Ranking Model A semi-quantitative model scoring for seven criteria: C1. Frequency of outbreaks and occurrence of illnesses C2. Severity of illness, taking into account illness duration, hospitalization and mortality C3. Likelihood of contamination C4. Growth potential/shelf life C5. Manufacturing process contamination probability/intervention C6. Consumption C7. Economic impact 12 HRF model similar to the produce risk ranking model published by Anderson et al. (2011)

Criteria in Draft HRF Model and Factors Required by FSMA 13

Example Scoring Matrix – Criterion 3 Likelihood of Contamination of the Hazard in Food a Assign 0 = No known detection of a microbial hazard, or No known detection of a chemical hazard above an action level or allowable level 14 Score = 0Score = 1Score = 3Score = 9 No known occurrence a Low (≤1%)Medium (1-5%)High (>5%) No recalls; or no RFR reports; other indicators ≤ 5 recalls/yr; and ≤ 5 RFR reports/yr; other indicators >5-10 recalls/yr; and >5-10 RFR reports/yr; other indicators >10 recalls/yr; or >10 RFR reports/yr; other indicators

Scoring for Food-Hazard Pairs For each food-hazard pair Where quantitative data are available, e.g., frequency of outbreaks, number of cases, hospitalization rate, prevalence of pathogen in a food, the data would be used for scoring. Where data are not available, alternatives such as qualitative descriptions and scoring methods based on subject matter expert opinions would be employed. For each of the seven criteria Data and information grouped into scoring bins, defined and assigned a numerical value from 0 to 9. 15

Example Scoring for Food-Hazard Pair: Summing of scores for seven criteria 16 Food-HazardC1C2C3C4C5C6C7Total Score Food A- Pathogen A Food B- Pathogen A Food B- Pathogen B Food C- Chemical C Food D- Chemical D

Challenges and Issues 1. What is the granularity of food classification needed and supportable by data? 2. What approaches to consider to combine data and expert opinions in scoring and ranking of food-hazard pairs? 3. Should we assign individual weights to each criterion? If so, which criteria should receive more weight and how should those weights be assigned? 4. How do we aggregate scores for food-hazard pairs to scores for foods/food commodities? 5. … 17

Food Granularity Example 1 Seafood (one of 28 RFR categories) Finfish (example commodity in RFR seafood category) Representative foods 18 k

Food Granularity Example 2 Produce – Raw Ag. Commodities (an RFR category) Fresh fruits (example commodity) Tropical fruits Banana – Norovirus (outbreak, 2003) Mango – Salmonella spp. (outbreak, 2003) Mamey – Salmonella spp. (outbreak, 1998) Mamey – Salmonella Typhi (recall data) Papaya – Salmonella spp. (outbreak, 2011) Tropical fruits – Listeria monocytogenes (potential) 19

Data and Data Challenges 20

HRF Risk Ranking Model Data Needs 21

Data Sources 22 Published literature Government surveys and investigations Commissioned studies Expert elicitation Data calls via Federal Register Notice Industry provided data

Obtain Contamination Data for Criterion 3 Conduct comprehensive literature search data specific to food-hazard pairs Determine likelihood of contamination weighted percent contamination rate for microbial hazards weighted percent positive above action levels or above allowable levels for chemical hazards Note: where data not available for scoring, use indicators such as RFR reports and FDA recalls expert opinions 23

Example Available Data from FDA Regulatory Sampling Programs Ongoing surveillance and monitoring TDS data for contaminants Compliance programs sampling assignments Domestic sampling data Import sampling data “For cause” sampling such as in outbreak investigations 24

Data Challenges to be Addressed How to combine data from different studies differences in the number of samples, study year and study location How to incorporate recall and RFR data How to incorporate compliance sampling data and for-cause sampling data programs not designed to determine likelihood of contamination How to combine data and expert opinions 25

Data Timeliness Issue What time frame to use for: outbreak data contamination data (e.g., 2003 Listeria in RTE Foods survey) food surveillance assignments or studies, etc… In the absence of more recent data or evidence in change in practices? 26

Survey data: L. monocytogenes contamination in RTE foods 27 * Preliminary results from phase I FDA/ARS Survey* (2013) (0.27%) (4.31%) (0.76%) (4.70%) (0.049%) (1.25%) (1.04%)(2.36%)

Available Methodology to Address Data Timeliness Issue Weighting of contamination data: sample size, geographic location, and study date Study Weight = n * gw * dw n, the total number of samples in the study gw, the geographic weight dw, weight for the date of the study (FDA/FSIS Lm QRA, 2003) 28

29 Food-HazardC1C2C3C4C5C6C7Total Score Food A- Pathogen A Food B- Pathogen A Food B- Pathogen B Food C- Chemical C Food D- Chemical D Weighting Criteria and Aggregating Scores

Summary: FDA’s Draft Approach for Designating High-Risk Foods 30

Acknowledgements FDA Project Advisory Group (PAG) for HRF Expert panel and subject matter experts 31