United States Department of Agriculture

Food Safety and Inspection Service

August 27, 2008

Carol Maczka, PhD

Assistant Administrato

Office of Food Defense and Emerge

FSIS Data Infrastructure Impr

Public Health Information Sys
United States Department of Agriculture

Food Safety and Inspection Service

Data Infrastructure Impr

• Actively strengthening our pub

data infrastructure to improve fand food defense.

• OIG December 2007 audit ide

number of areas for improvemdata infrastructure.

– OIG has agreed to all 35 of FSIS

to its recommendations.
United States Department of Agriculture

Food Safety and Inspection Service

Public Health Inform

System (PHIS)

• Improved information infrastruc

– Integrate FSIS data streams – Support a data driven approach 

inspection, auditing, and schedu

– Facilitate greater information sha

external agencies
United States Department of Agriculture

Food Safety and Inspection Service

PHIS Modules 

• Predictive Analytics• Domestic Inspection• Import • Export
United States Department of Agriculture

Food Safety and Inspection Service

Predictive Analyt

• Will allow analysts to carry out ad hoc da

using multiple FSIS data sources in ordetrends and anomalies

• Example: Relationship between Salmonella te

inspection findings

• Will monitor establishment data in real tim

built-in alerts for anomalies

Examples: • High rates of noncompliance for SRM in an est• Positive pathogen test result in an establishme

ground beef, Listeria monocytogenes in ready-

• Large number of inspection activities not comp

establishment
United States Department of Agriculture

Food Safety and Inspection Service

Predictive Analyt

• Will have automated algorithms for consi

and scheduling of inspection activities

Examples: • Performance measure calculation• Risk-based sampling algorithms (Salmonella, E

• Scheduling of Food Safety Assessments based 

process control

• District Activity Reports• Foreign establishment audit ranking

• Will have automated self-learning algorith

by Carnegie Mellon University that analyzcreate models to detect patterns in dispa

• Example: Analysis of relationship between pul

from CDC’s PulseNet data on human illnesses data on product sampling
United States Department of Agriculture

Food Safety and Inspection Service

Predictive Analyt

Methods Developm

• Question One

– Can we use our methods, data, and too

establishment risk factors that can be uresources based on public health impac

• Question Two

– Can we use our methods, data, and too

patterns in our data that might indicate 

• Question Three

– Can we use our methods, data, and too

investigations and trace back?
United States Department of Agriculture

Food Safety and Inspection Service

Microbial Sampling Data(M2K)

Regulatory Inspection data(PBIS, eADRS)

Est. info,and volume (PBIS, eADRS)

PulseNet Human PFGE Data

PHLIS Serotype Data

eLEXNET Food Safety Data 

Positive samples

FSIS 

CDC 

Legen

Salmonella Data Sources
United States Department of Agriculture

Food Safety and Inspection Service

Question 1: Can we use our methods, datidentify establishment risk factors that can ballocate resources based on public health imAnalysis: Cross over FSIS/ARS Serotype PHLIS serotype data to determine:

- Which serotypes in FSIS products caugreatest amount of human illness. - What percentage of Salmonella positi

regulated establishments are resistant to an
United States Department of Agriculture

Food Safety and Inspection Service

The top serotypes of Sacausing human illness asame as those found min our establishments.

CDC Top 10 FSIS Top – Not in CDC List

Analysis 1: Serotypes causing the greate

amount of human illness.
United States Department of Agriculture

Food Safety and Inspection Service

This establishment has the

highest count of positives

But this one has a much greater

fraction of resistant isolates

Analysis 2: What percent of Salmonella positiveregulated establishments are resistant to antib
United States Department of Agriculture

Food Safety and Inspection Service

Question 1 SummaryWe have identified refined risk mcan be incorporated into the publranking algorithm: -- serotypes of public health conc-- resistance to antibiotics A similar approach can be used tvariables of the public health risk algorithm.
United States Department of Agriculture

Food Safety and Inspection Service

Predictive Analyt

Methods Developm

• Question Two: Can we use our methods, da

to discover new patterns in our data that migproblems?

– Analysis: Examined geographic and temporal tre

antibiotic resistance using FSIS/ARS serotype datPHLIS serotype data . 

• Question Three: Can we use our methods, 

to support recall investigations and trace bac

– Analysis: Combined FSIS/ARS data and CDC da

temporal and geographic relationships between S

pulsetypes in FSIS products and those known to hhuman illness. 
United States Department of Agriculture

Food Safety and Inspection Service

PHIS Component S

Update

• Currently in Design Phase• System full production readines
United States Department of Agriculture

Food Safety and Inspection Service

• Automated capability to capture an

information on FSAs

• Ability to collect detailed informatio

verifications, compliance, and otherelated activities 

• Automated laboratory sample sche• Secure data entry via internet• Ability to pull information into our sy

the inspection force, and from otheexternal sources

Domestic Inspect
United States Department of Agriculture

Food Safety and Inspection Service

Imports

• More secure and timely transmissio

health certificates

– Ability to receive electronic certif

three trading partners, provide adand foreign government certificaof US-destined product

• Integration with Customs & Border 

ACE/ITDS

• Ability to schedule, track, and store 

foreign audits
United States Department of Agriculture

Food Safety and Inspection Service

• Automates manual processes 

printed/handwritten forms to ele

• Automates edit checks to verify 

with foreign import requiremen

• Standardizes product descriptio

coding for products certified for 

• Provides ability for exporters to 

pay fees

Exports