FSIS Risk Assessment for Guiding 

Public Health-Based Poultry 

Slaughter Inspection: An 

Implementation Scenario-Based 

Approach

Prepared by:

Risk Assessment and Analytics Staff

Office of Public Health Science

Food Safety and Inspection Service

U.S. Department of Agriculture

July 2014
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

2

TABLE OF CONTENTS

EXECUTIVE SUMMARY .......................................................................................................... 7

Background................................................................................................................................7

Risk Management Questions.....................................................................................................7

Structure and Scope ...................................................................................................................8

Findings....................................................................................................................................15

INTRODUCTION....................................................................................................................... 18

METHODS.................................................................................................................................. 18

Stage 1: Characterizing the Relationship between FSIS Inspection Activities and Product 

Contamination using a Regression Model ...............................................................................19

Stage 1: Regression Model Outputs.........................................................................................22

Stage 2: Model to Predict the Effect of Increased Inspection Procedures...............................26

Stage 2: Implementation Scenarios..........................................................................................31

RESULTS .................................................................................................................................... 32

Estimated Annual Changes in Salmonella and Campylobacter Prevalence in Young Chicken 

Establishments.........................................................................................................................32

Estimated Annual Changes in Salmonella and Campylobacter Prevalence in Young Turkey 

Establishments.........................................................................................................................33

Estimation of Changes in Human Illness.................................................................................33

Sensitivity Analysis .................................................................................................................42

DISCUSSION.............................................................................................................................. 50

REFERENCES............................................................................................................................ 54

APPENDIX.................................................................................................................................. 56

Regression Modeling Methods and Observational Datasets ...................................................56

Regression Model Approach....................................................................................................56

Data Sets ..................................................................................................................................60

Decision variables: Inspection procedures...............................................................................61

Structural variables: Non-inspection procedures.....................................................................62

Final Models ............................................................................................................................64

PRIA Dataset Evaluation .........................................................................................................67
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

3

LIST OF FIGURES

Figure ES-1. Overview of the Risk Assessment........................................................................... 11

Figure 1. Estimated Change in the Annual Salmonella Human Illness Rate when Off-line 

Inspection Procedures are Increased in Young Chicken Establishments. .............................. 38

Figure 2. Estimated Change in the Annual Campylobacter Human Illness Rate when Off-line 

Inspection Procedures are Increased in Young Chicken Establishments. .............................. 39

Figure 3. Uncertainty about the Change in the Annual Salmonella Human Illness Rate when 

Off-line Inspection Procedures are Increased in Young Turkey Establishments. .................. 40

Figure 4. Estimated Change in the Annual Campylobacter Human Illness Rate when Off-line 

Iinspection Procedures are Increased in Young Turkey Establishments. ............................... 41

Figure 5. Relative Contributions to Uncertainty of Estimated Illnesses Avoided........................ 43

Figure 6. Sensitivity Analysis: Young Chicken-Salmonella Model............................................. 46

Figure 7. Sensitivity Analysis: Young Chicken-Campylobacter Model ...................................... 47

Figure 8. Sensitivity Analysis: Young Turkey-Salmonella Model............................................... 48

Figure 9. Sensitivity Analysis: Young Turkey-Campylobacter Model........................................ 49

Figure A-1. ROC Plot of Sensitivity against 1-Specificity with an AUC of 0.792 for the Young 

Chicken Salmonella Predictive Model.................................................................................... 69

Figure A-2. ROC Plot of Sensitivity against 1-Specificity with an AUC of 0.702 for the Young 

Chicken Campylobacter Predictive Model............................................................................. 69

Figure A-3. ROC Plot of Sensitivity against 1-Specificity with an AUC of 0.710 for the Young 

Turkey Salmonella Predictive Model ..................................................................................... 70

Figure A-4. ROC Plot of Sensitivity against 1-Specificity with an AUC of 0.852 for the Young 

Turkey Campylobacter Predictive Model............................................................................... 70
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

4

LIST OF TABLES

Table ES-1. Available Information and Assumptions in the Risk Assessment............................ 12

Table ES-2. Summary of Key Uncertainties in the Risk Assessment .......................................... 14

Table 1. Results of the Regression Analyses for each Decision Variables from Four ProductPathogen Pairs......................................................................................................................... 25

Table 2. Estimated Number of Annual Salmonella and Campylobacter Illnesses, with 

Uncertainty Bounds, from Young Chicken and Young Turkey. ............................................ 30

Table 3. Estimated Reductions in Prevalence of Salmonella and Campylobacter in Young 

Chicken and Young Turkey Slaughter Establishments .......................................................... 33

Table 4. Estimated Human Illnesses Avoided from Monte Carlo Simulations of the 

Indiscriminate Scenario across the Four Product-Pathogen Models ...................................... 35

Table 5. Estimated Human Illnesses Avoided from Monte Carlo Simulations of the Discriminate 

Scenario that Specifically Increases Unscheduled Procedures across the Four ProductPathogen Models..................................................................................................................... 37

Table A-1a. Categorical Time Selection Matrix for Chicken Salmonella Model ........................ 71

Table A-1b. Selection Matrix Variables for the Final Four Prevalence Models.......................... 72

Table A-2. Selection Matrix Variables for Prevalence Model Comparison................................. 73

Table A-3. Inspection System Procedure (ISP) Code Listing of Individual and Summed Codes, 

used as Independent Variable Identifiers for Daily Sums of Procedures Scheduled, 

Performed, Unscheduled, and Non-Compliant in the Binary Logistic Regression Model..... 75

Table A-4. Combinations of Eight Basic Inspection Systems Used in FSIS Inspected Chicken 

Establishments........................................................................................................................ 78

Table A-5. Basic Inspection Systems Used in FSIS Inspected Turkey Establishments............... 79

Table A-6. Parameter Estimates for Young Chicken Salmonella Model Used in Scenario 

Analysis................................................................................................................................... 80

Table A-7. Parameter Estimates for Young Chicken Campylobacter Model Used in Scenario 

Analysis................................................................................................................................... 83

Table A-8. Parameter Estimates for Young Turkey Salmonella Model Used in Scenario Analysis

................................................................................................................................................. 85

Table A-9. Parameter Estimates for Young Turkey Campylobacter Model Used in Scenario 

Analysis................................................................................................................................... 87

Table A-10. Parameter Estimates from the Young Chicken Salmonella Split Datasets .............. 89

Table A-11. Prevalence Estimates from the Young Chicken Salmonella Model for the Mean, 

Rehang, and Post-chill Sample Collection Locations............................................................. 92

Table A-12. Parameter Estimates from the Young Chicken Campylobacter Split Datasets........ 93

Table A-13. Prevalence Estimates from the Young Chicken Campylobacter Model for the Mean, 

Rehang, and Post-chill Sample Collection Locations............................................................. 95

Table A-14. Parameter Estimates from the Young Turkey Salmonella Split Datasets................ 96

Table A-15. Prevalence Estimates from the Young Turkey Salmonella Model for the Mean, 

Rehang, and Post-chill Sample Collection Locations............................................................. 97

Table A-16. Parameter Estimates from the Young Turkey Campylobacter Split Datasets.......... 98

Table A-17. Prevalence Estimates from the Young Turkey Campylobacter Model for the Mean, 

Rehang, and Post-chill Sample Collection Locations............................................................. 99

Table A-18. Regression Coefficients for Unscheduled Procedures (U) by Inspection Element 100

Table A-19. Regression Coefficient for Unscheduled Procedures (U) by ISP Code ................. 103
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

5

Table A-20. Prevalence Estimates for Models Disaggregated by Unscheduled Procedures..... 107

Table A-21. Number of Establishments in the Four Observed Datasets by SBA Size .............. 107

Table A-22. Number of Establishments Expected to adopt the Proposed Inspection System by 

SBA Size according to Preliminary Regulatory Impact Analysis (PRIA) ........................... 108

Table A-23. Number of Observed Establishments Expected to adopt the Proposed Inspection 

System by SBA Size ............................................................................................................. 108

Table A-24. Observed Baseline Datasets and Expected to Shift Baseline Datasets Prevalence 

Estimates............................................................................................................................... 109
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

6

This Page Intentionally Left Blank
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

7

EXECUTIVE SUMMARY

Background

The Food Safety and Inspection Service (FSIS) conducted this risk assessment to help inform 

proposals to change allocation of inspection personnel in poultry slaughter establishments.

FSIS on-line inspectors currently conduct hands-on appraisals of every poultry carcass to ensure 

each carcass is unadulterated, free of feathers, bruises, and defects and disease. FSIS off-line 

inspectors verify that establishments maintain sanitary operations and perform other public 

health -related assignments. However, many of these on-line inspection tasks are in fact related 

to food quality, rather than aligning with the FSIS mission of food safety. This risk assessment 

considers scenarios that might allow FSIS to target resources more efficiently, by allowing FSIS 

inspection personnel more time and flexibility to perform off-line inspection tasks based on 

human health risk factors specific to individual establishments, with the goal of providing net 

public health benefit. 

This risk assessment updates a 2008 risk assessment (FSIS, 2008a), originally presented in 

conjunction with a review by the National Advisory Committee on Meat and Poultry Inspection 

(NACMPI, 2008; FSIS 2011) with more recent data and a modified modeling approach. This 

risk assessment takes into consideration public and stakeholder comments on the 2011 version

[Docket No. FSIS-2011-0012], and has been modified in response to an independent peer review 

completed in August 2012. 

 

Risk Management Questions

 Can FSIS reallocate inspection activities in young chicken slaughter establishments without 

significant negative impact on microbial prevalence in the establishments?

 How will the relocation of on-line inspectors to off-line duties, or other areas within or 

outside the establishment, affect human illness?

 Where within the establishment can relocated inspection activities have the most impact 

toward reducing microbial prevalence and corresponding human illness?

 What is the uncertainty about these effects?
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

8

Structure and Scope

This is a quantitative probabilistic food safety risk assessment. It examines the relationships 

between variations in inspection activities in FSIS-regulated poultry slaughter establishments and 

the prevalence of both Salmonella and Campylobacter on poultry (specifically young chicken 

and young turkey). The model predicts the probability of changes in prevalence of these 

pathogens that would subsequently be directly translated into changes in attributable human 

illnesses. 

Figure ES-1 provides an overview of the two analytical stages conducted as part of this risk 

assessment. In Stage 1, a regression model uses historical data to characterize the relationship 

between specific categories of off-line inspection procedures and contamination of chicken or 

turkey carcasses with either Salmonella or Campylobacter (specifically, four product-pathogen 

pairs: young chicken-Salmonella, young chicken-Campylobacter, young turkey-Salmonella, and 

young turkey-Campylobacter). The four categories of off-line inspection procedures examined in 

the regression model are: 1) scheduled-and-performed procedures [SP]; 2) scheduled-and-notperformed procedures [SNP]; 3) unscheduled procedures [U]; and observed and recorded non 

compliances [NC]. 

The relationship characterized in Stage 1 is then used as input to Stage 2 of the risk assessment, 

the simulation model. The simulation model is designed to explore the effect of various potential 

changes in those same four categories of off-line inspection activities (SP, SNP, U and NC; 

termed the decision variables in the simulation model) on changes in human Salmonella or 

Campylobacter illnesses attributable to the consumption of young chicken and young turkey. In 

that way, scenarios representing different potential changes in those inspection activities that 

FSIS might consider—that is, different values in the decision variables—can be modeled to 

evaluate how relocation of on-line to different off-line duties would change the proportion of 

samples testing positive from poultry carcasses, thereby changing the probability of human 

illnesses.

Two approaches to changing the decision variables are the main focus of this risk assessment. In 

the first approach, referred to as the “indiscriminant scenario,” allows the values of decision 

variables (that is, the percentage increase of each of the four categories of inspection procedures 

input into the model) to vary for each iteration or run of the statistical model within a broad 

range of expected values. This scenario estimates the range of potential effects associated with 

increasing any combination of its inspection procedures. It is important to note that with this 

indiscriminant scenario alternately selects from among the four off-line procedure categories 

identified above, without targeting any one. As such, this scenario includes exploration of the 

effect of an increase of procedures that might conceivably be correlated with an increase in 

contamination that is observed and recorded non compliances [NC]. This indiscriminant scenario 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

9

does, however, allow the sensitivity of the model to the four decision variables to be investigated

to identify the types of offline inspection procedures that could have the greatest public health 

impact. 

The second approach, referred to as the “discriminate scenario,” focuses on the probability of 

changes in prevalence of the pathogens if one specific category of inspection procedures, 

unscheduled procedures, is targeted for increase. To do that, the value of the decision variable for 

unscheduled procedures [U] is increased while the values of the other three decision variables 

[SP, SNP and NC] are kept constant at baseline levels. Unscheduled procedures are increased in 

establishments in the HACCP Inspection Models Project (HIMP); therefore, increasing the 

unscheduled procedures in the simulation model allows estimation of the probability that having 

other establishments increase those procedures would change the prevalence of the pathogens 

that would subsequently be translated into changes in attributable human illnesses. In addition, 

there are data-driven reasons to focus on increasing unscheduled procedures. As discussed later, 

the regression analysis conducted in Stage 1 of this assessment indicated that the unscheduled 

decision variable had the most consistent strong correlation with product contamination. That 

correlation suggests that increasing unscheduled procedures would have the greatest likelihood 

(that is, would likely be the most effective) of decreasing product contamination leading to 

benefits. Focusing on this decision variable, therefore, allowed characterization of the relocation 

of inspection activities most likely to have a beneficial impact.

Data used in the risk assessment include:

1. Inspection activity data recorded in FSIS’ PBIS database, paired with Salmonella and 

Campylobacter prevalence data for the same establishments and timeframes. The sources of 

the pathogen data are:

 Young chicken data: 

o Salmonella and Campylobacter data from FSIS’ Young Chicken Baseline study 

(rehang and post chill samples) (July 2007 through September 2008) (6,558 

samples for each pathogen); and 

o Salmonella data from the FSIS PR/HACCP verification program (post chill 

samples) (July 2007 through September 2010) (16,115 Salmonella samples)1. 

 Young turkey data: 

o Salmonella and Campylobacter data from FSIS’ Young Turkey Baseline study 

(rehang and post chill samples) (August 2008 through July 2009) (2,884 samples 

for each pathogen); and 

o Salmonella data from the PR/HACCP verification program (post chill samples) 

(July 2007 through September 2010) (5,865 Salmonella samples). 

 

1

FSIS’ PR/HACCP verification program did not analyze poultry samples for Campylobacter until July, 2011. 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

10

2. Human illness data, i.e. estimates for the mean number of human Salmonella and human 

Campylobacter illness attributable to young chicken and turkey consumption, based on 

distribution parameters from Centers for Disease Control and Prevention (CDC) total 

foodborne illness and outbreak data (CDC, 2001-2007).

As can be seen above, the number of samples for each product-pathogen pair varies. There is 

greater confidence in the results of the analyses for those product-pathogen pairs with larger 

sample sizes. Greater certainty in model estimates, therefore, should be given to estimates based 

on those larger sample sizes.

The number of inspection procedures also varies among the four procedure categories (i.e., SP, 

SNP, U and NC). There are fewer procedures that are scheduled-and-not-performed [SNP] or 

observed and recorded non compliances [NC] than scheduled-and-performed [SP] or 

unscheduled [U]. That variability affects the confidence in estimates and should also be 

considered when interpreting the model results.

The risk model incorporates the uncertainty about: (i) the initial analyses and data used; (ii) the 

expected changes in off-line inspection activities with the proposed inspection system, and (iii) 

current estimates of human illnesses, into its predictions about the change in human illnesses 

expected to occur as a result of implementation of the proposed inspection system. Table ES-1 

summarizes the information available and assumptions in the risk assessment. Table ES-2 

summarizes key uncertainties in the risk assessment. 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

11

Figure ES-1. Overview of the Risk Assessment.

The figure summarizes the two major stages of the risk assessment of alternative scenarios, and 

the inputs and outputs from those stages.

a The ten specific inspection activities analyzed are: sanitation(01), HACCP (03), wholesomeness/economic 

consumer protection (04), sampling (05), other inspection requirements (06), sanitation performance standards 

(06D01), raw ground (03B), raw not ground (03C), fecal checks (03J), and economic poultry kill (04C04).

b

Evaluation of the HACCP-Based Inspection Models Project (FSIS, 2011a) is available at 

http://www.fsis.usda.gov/shared/PDF/Evaluation_HACCP_HIMP.pdf.

Stage 1: Estimate the Relationship between establishment variations in FSIS-Inspection Activities and 

frequency of Salmonella and Campylobacter positives on Poultry carcasses.

 Conduct a logistic regression analysis to estimate the relationship between off-line inspection procedures 

and contamination.

Stage 2: Explore the Effect of Increasing Various OffLine Inspection Activities Using a Simulation Model and 

the Relationship Estimated in Stage 1

 Predictions are made for scenarios with a range for the 

number of the four different inspection procedures 

(SP, SNP, U and NC)

Coefficients for the Relationship between 

Inspection Activities and Contamination

Regression Model 

Inputs

Regression Model 

Output

Prediction Output

Estimated Annual Number of Illnesses from 

Salmonella and Campylobacter

Human Illness Data

 Estimated mean number of human 

Salmonella and Campylobacter illnesses 

attributable to young chicken and turkey 

consumption:

o Total illnesses estimated by CDC 

(Scallan et al., 2011)

o Independent FSIS analysis to estimate 

attributable shares (2011) 

o Apply attributable shares to 

credibility intervals calculated using 

Scallan et al. (2011).

Simulation Model Inputs

Scenarios

 Develop scenarios for the increased 

percentage of off-line procedures based on 

the number of those procedures performed 

in establishments in the HACCP-Based 

Inspection Models Project (HIMP) 

compared with non-HIMP establishments. 

Data on procedures in HIMP from FSIS 

(2011)

b

 Use those scenarios to model the effect of 

increases in various off-line procedures 

across all FSIS-regulated establishments.

FSIS Microbiological Data

 FSIS Salmonella and Campylobacter data from 

young chicken (July 2007 through September 

2008) and turkey (August 2008 – July 2009) 

baseline studies. 

o Samples collected at post-chill and rehang

 FSIS PR/HACCP chicken and turkey carcass postchill samples from the Salmonella verification 

program results from July 2007 through September 

2010.

Data from FSIS Inspection Activities

 July 2007 through September 2010

 The number of specific inspection activities

a

conducted 

as:

o Scheduled and performed procedures (SP)

o Scheduled and not performed procedures (SNP)

o Unscheduled procedures (U)

o Non-compliances (NC)
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

12

Table ES-1. Available Information and Assumptions in the Risk Assessment

Information 

Required

Available Data Assumptions

Stage 1: Estimate Relationship Between Establishment Variations in FSIS-Inspection Activities and Frequency of 

Salmonella and Campylobacter Proportions Positives on Poultry Carcasses using a Production Volumeweighted logistic regression model.

Inspection Data  FSIS establishment-level data on the number 

of specific inspection activities

a

conducted 

from July 2007 through September 2010, 

stored in PBIS

Data are representative of the young chicken and 

turkey slaughter establishments 

Microbiological 

Data

 FSIS establishment-level Salmonella and 

Campylobacter data from young chicken 

(July 2007 through September 2008) and 

turkey (August 2008 – July 2009) baseline 

studies (post-chill and rehang)

 Establishment-level FSIS PR/HACCP 

chicken and turkey carcass post-chill 

samples from the Salmonella verification 

program results from July 2007 through 

September 2010 (post-chill)

Data are representative of the young chicken and 

turkey slaughter establishments

Production 

Volume Data

 FSIS establishment-level production volume 

data 

Stage 2: Explore the potential implications for risk of Increasing Various Off-Line Inspection Activities Using a 

Simulation Model and the Statistical Relationship Estimated in Stage 1 and the Relationship between 

Salmonella and Campylobacter Contamination and human illness

Estimated mean 

number of human 

Salmonella and 

Campylobacter

illnesses 

attributable to 

young chicken 

and turkey 

consumption

 Independent FSIS analysis to estimate 

attributable shares (2011)b

 The total annual number of Salmonella and 

Campylobacter illnesses in the United States 

is estimated by CDC (Scallan et al., 2011). 

Then apply attributable shares (FSIS, 

2011b)

b

to credibility intervals calculated 

using Scallan et al. (2011)

 Human illnesses can be modeled as a Poisson 

process because in microbial food safety, 

sporadic exposure events are considered 

independent events and chronic exposures to 

pathogens are not considered

Relationship 

between 

Salmonella and 

Campylobacter

on chicken and 

turkey carcasses 

and human 

Salmonella and 

Campylobacter

illnesses

 The relationship between product 

contamination and human illnesses has been 

published previouslyc

 The probability that exposure to a random 

contaminated serving would produce illness is 

constant regardless of changes in the frequency 

of exposure to the pathogen on a per serving 

basis (that is, dose levels at consumption are 

independent of the frequency of contamination)d

Distribution of 

establishments 

that would accept 

the proposed 

inspection system

 No empirical data, therefore, use estimates 

from FSIS’ Preliminary Regulatory Impact 

Analysis (PRIA) of the proposed poultry 

slaughter rule. 

 All establishments perform offline inspection 

procedures after implementation at levels 

equivalent to HIMP establishments within the 

data range used.
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

13

Information 

Required

Available Data Assumptions

Percentage of offline inspection 

procedures that 

would be 

conducted in each 

establishment 

under the 

proposed 

inspection system

 No empirical data available, therefore,

different scenarios were developed on the 

basis of the increased percentage of off-line 

procedures performed in establishments in 

the HIMP compared with non-HIMP 

establishments (FSIS, 2011a)

b

. Those 

scenarios are used to model the effect of 

increased off-line procedures across all 

FSIS-regulated establishments and compared 

to the ‘baseline’ of current establishment 

activities. Assumptions specific to the two 

different scenarios are outlined below. 

 There would be a shift of the majority of on-line 

inspectors to off-line inspection duties while 

leaving one inspector on-line for final carcass 

inspection.

e The proposed increase in off-line 

inspectors is expected to increase scheduled and 

performed procedures.fSimilarly, increased 

availability of off-line inspectors should increase 

unscheduled procedures while reducing 

scheduled but not performed procedures.

g

 An estimate of the distribution for off-line 

inspection activities performed upon 

implementation of the proposed inspection

system would reflect the distribution for off-line 

inspection activities observed in establishments 

currently operating under HIMP

Indiscriminate Scenario

(No assumption of how FSIS might 

emphasize or de-emphasize activities in 

proposed inspection system)

 SP and U procedures: assumed the most likely 

change is an increase of 25%, a minimum of no 

change and a maximum of a 60% increase

 SNP procedures: assumed the most likely 

change is a decrease of 10%, a minimum of no 

change and a maximum of 100% reduction

Discriminate Scenario

(assumes FSIS would emphasize the 

performance of unscheduled procedures in 

proposed inspection system

 Unscheduled procedures increase while other 

three (SNP, SP, NC) are fixed to baseline levels

a The ten specific inspection activities analyzed are: sanitation(01), HACCP (03), wholesomeness/economic 

consumer protection (04), sampling (05), other inspection requirements (06), sanitation performance standards 

(06D01), raw ground (03B), raw not ground (03C), fecal checks (03J), and economic poultry kill (04C04).

b

FSIS (2011b). Potential Public Health Impact of Salmonella and Campylobacter Performance Guidance for Young 

Chickens and Turkeys. Available at: http://www.fsis.usda.gov/wps/wcm/connect/335168ad-ff20-4885-8ee3-

4ad983ae486c/Potential_Public_Health_Impact_Sal_Campy_Performance_Guidance_Broilers_Turkeys_2011.pdf?

MOD=AJPERES. 

c Williams M.S., Ebel, E.D., Vose, D. 2011. Framework for Microbial Food-Safety Risk Assessments Amenable to 

Bayesian Modeling Risk Analysis. Risk Analysis, Vol. 31, no. 4, 548-565.

d This assumption is supported by empiric evidence. FSIS chicken carcass baseline results indicate that the average 

concentration of Salmonella per ml of rinsate had not changed from 1995 in 2007, but the prevalence of positive 

carcasses was different. Similar results have been seen by others (Crouch et al., 2009; Withee et al., 2009) and in

FSIS’ Campylobacter data. 

e This shift in inspectors is from the Preliminary Regulatory Impact Analysis (PRIA) of the proposed poultry 

slaughter rule.

f This assumption follows from the observation that there are fewer scheduled but not performed procedures and 

more unscheduled procedures performed when establishments are fully staffed and off-line inspectors are not 

required to fill line positions

g Based on analysis of the HACCP Inspection Models Project (HIMP) (FSIS, 2011a).

Abbreviations: HIMP, HACCP-Based Inspection Models Project; NC, non-compliances; SNP, scheduled and not 

performed procedures ; SP, scheduled and performed procedures; Unscheduled procedures. 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

14

Table ES-2. Summary of Key Uncertainties in the Risk Assessment

Contributors to 

Uncertainty in the 

Model Symbol Classification

Handling of Uncertainty in the

Model

Relative 

Importance

Regression coefficients βStatistical Modeled as multivariate normal 

distributions

Adjustment parameters 

to reflect the number of 

future off-line inspection 

activities

Ai

Modeling Modeled as Pert uncertainty 

distributions

Most Influential 

uncertainty 

assumptions

Baseline Annual Number 

of Salmonella and 

Campylobacter Illnesses

ill

Modeling Independently calculate a 90% 

credibility interval from Scallan et al.

(2011), and use that interval in a 

putative lognormal distribution to 

reflect uncertainty about attributable 

illnesses

Least Influential 

uncertainty 

assumptions

Fraction of Salmonella

and Campylobacter

Illnesses Attributable to 

Poultry

N/A Modeling Not modeled explicitly, awaiting 

further development of the parameter 

by CDC and other Agencies

N/A

Abbreviations: N/A, not applicable.
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

15

Change scenarios predict how prevalence of both Salmonella and Campylobacter on poultry, and 

ultimately annual human illnesses, might be expected to change based on four categories of 

inspection procedures:

1. scheduled-and-performed procedures [SP] 

2. unscheduled procedures [U] 

3. scheduled-not-performed procedures [SNP] and 

4. observed and recorded non compliances [NC] 

To identify the types of offline inspection procedures that could have the greatest public health 

impact the model ran an “indiscriminate” scenario where the relative frequencies of all four 

categories of these inspection procedures are increased or decreased independently of any other 

information or pre-determined assumption about their relative impact on prevalence of pathogens 

in the establishment. Uncertainty distributions about changes in four categories of inspection 

procedures are developed using information provided in the FSIS HACCP-Based Inspection 

Models Project (HIMP) report (young chickens only)(FSIS, 2011a), which informed the 

development of the new poultry inspection system. The model also ran a “discriminate” scenario, 

where only the unscheduled offline procedures (U) are targeted for increased inspection activity. 

It was observed from the historical data that in HIMP establishments up to 60% more offline 

procedures are performed than in non-HIMP establishments. The model assumes that an estimate 

of the distribution for off-line inspection activities performed upon implementation of the 

alternative inspection scenario would reflect the distribution for off-line inspection activities 

observed in establishments currently operating under HIMP. 

Findings

The risk assessment attempts to model answers to each of the four risk management questions

posed at the outset.

 Can FSIS reallocate inspection activities in young chicken slaughter establishments without 

significant negative impact on microbial prevalence in the establishments?

Depending on how reallocation of inspection activities is implemented, it is likely 

that changes in off-line inspection could result in a decrease in the numbers of 

positive microbial samples in FSIS-regulated young chicken and young turkey 

establishments. Specifically, the discriminate scenario, which only increases 

unscheduled inspection procedures, performs much better than the indiscriminate 

scenario, which does not target specific types of procedures. 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

16

Under the indiscriminate scenario in young chicken establishments, when off-line inspection 

procedures are increased, the model predicts an average decline of 2% in the proportion of 

samples testing Salmonella positive. However, when the substantially less robust Campylobacter

data available at the time of the assessment are used, the model suggests a 0.2% increase in the 

proportion of samples testing Campylobacter positive. This very small increase is likely a 

function of the instability of this model, which is due to the combination of both small sample 

sizes and allowing the option of increasing off-line procedures that do not necessarily directly 

influence contamination rates. Under the discriminate scenario, where only unscheduled 

inspection procedures are targeted for increase, the model in young chicken slaughter 

establishments predicts an average decline of 2% in the proportion of samples testing Salmonella

positive, and an average decline of 0.44% in the proportion of samples testing Campylobacter

positive.

Under the indiscriminate scenario in young turkey slaughter establishments, when off-line 

procedures are increased, the model predicts an average decline of 6% in the proportion of 

samples testing Salmonella positive, and an average decline of 26% in the proportion of samples 

testing Campylobacter positive. Under the discriminate scenario, where only unscheduled 

inspection procedures are targeted for increase, the model in young turkey slaughter 

establishments predicts an average decline of 3% in the proportion of samples testing Salmonella

positive, and an average decline of 19% in the proportion of samples testing Campylobacter

positive.

However, the results under the discriminate scenario indicate that more than 90% confidence that 

the combined annual illnesses avoided under the scenario that increases unscheduled procedures 

exceeds zero (i.e., the 10th percentile of the combined Campylobacter illnesses avoided would be 

30). 

 How will the relocation of on-line inspectors to off-line duties, or other areas within or 

outside the establishment, affect human illness?

We assume that the total annual Salmonella and Campylobacter illnesses rates attributed to 

poultry are about 174,686 and 169,005, respectively (1).

Under the indiscriminate scenario, where no specific category of procedures was targeted for 

increase, the model estimates an average decrease of 4,440 annual Salmonella illnesses 

attributable to young chicken and young turkey establishments combined, or an estimated

prevention of 2.5% of those illnesses per year. The same scenario estimates an average increase 

of 170 annual Campylobacter illnesses (0.1% of illnesses) attributable to young chicken and

turkey establishments combined. 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

17

Under the discriminate scenario, where only unscheduled procedures are targeted for increase, 

the model estimates an average decrease of 3,980 annual Salmonella illnesses (2.3%) attributable 

to young chicken and young turkey establishments, and an average decrease of 840 annual 

Campylobacter illnesses (0.5%) attributable to young chicken and turkey establishments

combined. 

 Where within the establishment can relocated inspection activities have the most impact 

toward reducing microbial prevalence and corresponding human illness?

The modeling and scenario analysis results suggest that increasing unscheduled procedures (i.e, 

the discriminate scenario) would be most effective in reducing pathogen occurrence on carcasses

because of consistency in that decision variable parameter’s effect across all models. The other 

decision variables suggest ambiguous effects from their intended changes when those effects are 

considered across all four pathogen-product models. This seems due to variable model parameter 

precision from variable sample sizes and non-uniform data quality. When targeting unscheduled 

inspection procedures in young chicken establishments, the estimated decrease in prevalence was 

2% for Salmonella and 0.5% for Campylobacter. For young turkey establishments, the 

corresponding decrease in prevalence was 3% and 19% respectively. 

 What is the uncertainty about these effects?

Our modeling approach includes the inherent uncertainty about the relationship between the 

frequency of inspection activities and pathogen prevalence, about the actual change in future 

inspection activities that would likely be observed, and about the representativeness of the rates 

of human Salmonella and Campylobacter illness attributable to poultry. 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

18

INTRODUCTION

The Food Safety and Inspection Service (FSIS) conducted this risk assessment to help inform 

proposals to change allocation of inspection personnel in poultry slaughter establishments. This 

risk assessment considers scenarios that might allow FSIS to target resources more efficiently, by 

allowing FSIS inspection personnel more time and flexibility to perform off-line inspection tasks 

based on human health risk factors specific to individual establishments. If the proposed change 

reduces the occurrence of foodborne pathogens such as Salmonella and Campylobacter on 

finished poultry products, then a net public health benefit may result.

This risk assessment updates a 2008 risk assessment (FSIS, 2008a)—originally presented in 

conjunction with a review by the National Advisory Committee on Meat and Poultry Inspection 

(NACMPI, 2008)—with more recent data and a modified modeling approach. This version of the 

risk assessment takes into consideration public and stakeholder comments and has been modified 

in response to an independent peer review completed in August 2012. 

The risk management questions were:

 Can FSIS reallocate inspection activities in poultry slaughter establishments without 

significant negative impact on microbial prevalence in the establishments?

 How will the relocation of on-line inspectors to off-line duties, or other areas within 

or outside the establishment, affect human illness?

 Where within the establishment can relocated inspection activities have the most 

impact toward reducing microbial prevalence and corresponding human illness?

 What is the uncertainty about these effects?

METHODS

Logistic regression analysis is performed to estimate the relationship between off-line inspection 

procedures (described below) and contamination of young chicken and young turkey carcasses 

with either Salmonella or Campylobacter (Stage 1). Subsequently, a stochastic simulation model 

uses the coefficient estimates from that logistic regression to predict the effect of changes in offline inspection categories on changes in the annual rate of human Salmonella or Campylobacter

illnesses attributable to the consumption of young chicken and young turkey (Stage 2). The 

simulation model incorporates uncertainty about the regression coefficients, the expected change 

in off-line inspection activities associated with the model scenarios and its estimate about the 

current rate of human illnesses, into predictions about the change in human illnesses. The overall 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

19

prediction model for estimation of illnesses avoided is presented here as a two-stage model: the 

regression model and the model to predict the effect of increased inspection procedures.

 

Stage 1: Characterizing the Relationship between FSIS Inspection Activities and 

Product Contamination using a Regression Model 

An overview of the logistic regression model is provided here. More detail about the regression 

model can be found in the Appendix to this report. 

The first stage is a daily production volume-weighted logistic regression model with the 

regression coefficients estimated from the maximum quasi-likelihood equations of the Fisher 

scoring algorithm using standard SAS software2.The regression model predicts the conditional 

likelihood that there would be a change in the proportion of samples testing positive given the 

structural and decision variable input values. The independent variables consist of categorical 

and continuous structural variables that differ in number among the four models (i.e. young 

chicken/Salmonella; young chicken/Campylobacter; Young turkey/Salmonella; young 

turkey/Campylobacter) and four inspection variables (i.e. scheduled-and-performed procedures 

[SP]; unscheduled procedures [U]; scheduled-not-performed procedures [SNP] and; observed 

and recorded non compliances [NC]). The estimating equations predicting prevalence of positive 

samples then enter the second stage of the process.

Prevalence estimates are derived from a weighted average prevalence estimate across all data 

points. Multivariate normal estimating equations averaged across all data points are iterated 

100,000 times; each iteration estimates a population prevalence that becomes an element in the 

prevalence distribution that is transformed in the second stage into a distribution of illnesses 

avoided. 

The model relates occurrences of Salmonella and Campylobacter among poultry carcasses to 

four decision variables—each representing a category or grouping of off-line inspection 

procedures—and several structural variables, which are variables that describe differences in 

plant design, inspection system and other demographic characteristics. In the model structural 

variables are treated as given or constant, and, by intent, do change with changes in modeled

scenarios. 

Young chicken data are comprised of the Salmonella and Campylobacter results of the FSIS 

Young Chicken Baseline study (post-chill and rehang samples) (July 2007 through September 

2008) (FSIS, 2008b) and PR/HACCP Salmonella verification program (post chill samples) (July 

 

2

Proc logistic SAS 9.1.3 Service Pack 1 Copyright (c) 2002-2003 by SAS Institute Inc., Cary, NC, USA
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

20

2007 through September 2010). Young turkey data are comprised of the Salmonella and 

Campylobacter results of the FSIS Young Turkey Baseline study (post-chill and rehang samples) 

(August 2008 through July 2009) (FSIS, 2009) and PR/HACCP Salmonella verification program

(post chill samples) (July 2007 through September 2010) 3. 

The four decision variables categories identified are: 

 

Scheduled and Performed Procedures (SP)

Scheduled procedures are those that follow a random scheduling procedure from 

headquarters and performed or not performed accordingly as time and inspection 

personnel were available. The decision variable Scheduled and Performed (SP) 

procedures is the number of procedures that are scheduled at headquarters and that the 

inspector completes in the specified establishment. 

Scheduled and Not Performed Procedures (SNP) 

Scheduled procedures are those that follow a random scheduling procedure from 

headquarters and performed or not performed accordingly as time and inspection 

personnel were available. The decision variable Scheduled and Not Performed (SP) 

procedures is the number of procedures that are scheduled at headquarters but that the 

inspector does not complete in the specified establishment. 

Unscheduled Procedures (U)

Unscheduled procedures are procedures not on the scheduled list for each establishment 

but that may be performed in response to possible establishment non-compliance with 

regulations or simply an expansion of routine inspection procedures when time and 

personnel are available. More unscheduled procedures are performed when 

establishments are fully staffed and off-line inspectors are not required to fill line 

positions.

Observed and Recorded Non-Compliances (NC)

An NC may be observed and recorded when performing scheduled and unscheduled 

procedures. Unlike the above three categories of inspection activities, which are 

indications of the number of the tasks an inspector is performing, Observed and Recorded 

Non-Compliances (NC) capture the results of the inspection task and the value is a 

function of not only of how frequently FSIS conducts inspection tasks, but also the 

establishment’s food safety practices. That is, they capture when an inspector finds that 

an establishment is not properly implementing its sanitation, food safety processes and

other controls. Because some of those non-compliances are directly related to carcass 

contamination, this decision variable, might be expected to be positively associated with 

an increase in product contamination. That is, the contamination rate of an establishment 

that does not have consistently good food safety practices in place might be expected to 

be increased compared with an establishment with good food safety practices. If such a 

correlation exists, then using that correlation to estimate or predict what would occur if 

 

3

FSIS’ PR/HACCP verification program did not analyze poultry samples for Campylobacter until July, 2011.
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

21

the number of observed and recorded non-compliances were to increase would lead to an 

increase in illnesses.

Those four categories serve to group the six Inspection System Procedure (ISP) Codes into 

mutually exclusive classes. The ISP codes refer to (i) sanitation, (ii) HACCP, (iii) 

wholesomeness/economic consumer protection, (iv) sampling, (v) sanitation performance 

standards, and (vi) emergency procedures. Each ISP code is further delineated into more precise 

activities and most activities are noted as either SP, SNP, U or NC (see Appendix for details). 

The four decision variables represent the sum of activities on each establishment day across the 

various ISP codes as follows (see detailed description in Table A-3 in the Appendix): 

1. SP = scheduled and performed procedures for sanitation (01), HACCP (03), 

wholesomeness/economic consumer protection (04), sampling (05), other inspection 

requirements (06), sanitation performance standards (06D01), raw ground (03B), raw not 

ground (03C), fecal checks (03J), economic poultry kill (04C04)

2. SNP = scheduled not performed procedures for sanitation(01), HACCP (03), 

wholesomeness/economic consumer protection (04), sampling (05), other inspection 

requirements (06), sanitation performance standards (06D01), raw ground (03B), raw not 

ground (03C), fecal checks (03J), economic poultry kill (04C04)

3. U = unscheduled procedures performed for sanitation (01), HACCP (03), 

wholesomeness/economic consumer protection (04), sampling(05), other inspection 

requirements(06), sanitation performance standards (06D01), raw ground (03B), raw not 

ground (03C), fecal checks (03J), economic poultry kill (04C04), emergency procedures (08)

4. NC = observation and reporting by inspection personnel of non-compliant procedures for 

sanitation (01), HACCP (03), wholesomeness/economic consumer protection (04), sampling

(05), other inspection requirements (06), sanitation performance standards (06D01), raw 

ground (03B), raw not ground (03C), fecal checks (03J), economic poultry kill (04C04)

These four defined categories were chosen because the expected/intended effect of the modeled 

scenarios was consistent for inspection procedures within each category. For example, a 

proposed increase in off-line inspectors is expected to increase scheduled and performed 

procedures. Similarly, increased availability of off-line inspectors should increase unscheduled 

procedures while reducing scheduled but not performed procedures. This assumption follows 

from the observation that there are fewer scheduled but not performed procedures and more 

unscheduled procedures performed when establishments are fully staffed and off-line inspectors 

are not required to fill line positions. The model also assumes that – in the long-run – reported 

non-compliances would decrease with more off-line inspectors in slaughter establishments 

because such establishments would attain appropriate process control through increased 

inspection scrutiny and also through likely industry innovation. Although an alternative approach 

that collapsed decision variables according to the six ISP classes of off-line procedures was 

explored, this approach created confusion about the intended effect of the modeled scenarios

within each class. For example, a random variable that summarized HACCP procedures would 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

22

need to increase scheduled and performed procedures (and unscheduled procedures) but also 

decrease scheduled but not performed procedures (and non-compliances).

After considering several alternative sets of decision variables, this treatment of decision 

variables avoids some potential problems with collinearity in the model. It also avoids overinterpretation of specific procedures that simply reflect random associations that can occur with 

over-parameterized models.

Versions of the regression analysis on the extensive dataset including more than 40 decision 

variables representing specific ISP codes were considered but ultimately rejected. The analysis 

of these complicated models was indeterminate and therefore unsuitable because these variables 

could be correlated with each other. Such collinearity made inferences about specific 

coefficients potentially invalid. 

Previous versions also attempted to simplify inferences about specific variables by developing 

submodels that eliminated other variables and isolating the effect of the variable of interest. 

Nevertheless, predictions from submodels required consideration of the implications across all 

submodels such that each submodel would be weighted as part of a whole. Such a weighting 

scheme was deemed too complicated and potentially fraught with error to pursue.

Instead, the regression approach used in this assessment estimates a single regression equation 

for each product-pathogen pair (i.e., young chicken-Salmonella, young chicken-Campylobacter, 

young turkey-Salmonella, and young turkey-Campylobacter). This is a valid approach to making 

predictions from each model. The four decision variables (SP, SNP, U and NC) are included in 

each regression model. For one of those decision variables to be found statistically significant in 

the model, all inspection procedures within the category must be strongly associated with 

pathogen occurrence. Consequently, inferences made about significant variables are stronger, 

but more general, than inferences from previously considered models (see Appendix) with more 

decision variables. 

Stage 1: Regression Model Outputs

Table 1 presents the results of the regression analysis for the four decision variables categories of 

inspection activities (SP, SNP, U and NC) for each of the four product-pathogen pairs. This 

analysis evaluates the correlation between each of those inspection activities and product 

contamination. Estimates of the regression coefficient vary among each of the decision 

variables and among the four product-pathogen pair . 

Looking at SNP procedures, if more off-line personnel are available to complete scheduled 

procedures there should be fewer SNP procedures. The coefficient sign for SNP is positive in 3 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

23

of the 4 product-pathogen pair models (chicken-Salmonella, chicken-Campylobacter and turkeyCampylobacter), suggesting that decreasing occurrences in this category would decrease 

pathogen prevalence in these product classes. In contrast, the sign of the coefficient of the 

significant SNP variable in the turkey-Salmonella model is negative, suggesting, that reducing 

SNP could actually increase Salmonella prevalence in young turkey. However, even though the 

magnitude of the SNP coefficient in each model is large compared to the other coefficients, in 

each model the magnitude of expected change over all establishments is less than one SNP 

procedure per day. In addition, the SNP variable is the most unpredictable of the four decision 

variables because relatively small number of SNP procedures relative to the other three decision 

variables in each model, which leads to an instability in results of increasing Salmonella

prevalence by decreasing scheduled-but-non performed procedures. The smaller number of 

turkey samples also contribute to lower confidence in this result relative to the chickenSalmonella results. 

All four product-pathogen pair models support the expectation that increased unscheduled 

activities (the U category) would reduce pathogen prevalence (i.e., the coefficient sign is 

negative for all pairs). The U variable is highly significant in the chicken-Salmonella model and 

the turkey-Campylobacter models, but not significant (at a p=0.05 significance level) in the other 

two models. Nevertheless, the p-value for these other two models does not entirely reject the 

possibility that the U variable may be importantly associated with pathogen occurrence. 

The regression coefficient for the scheduled and performed procedures (SP) decision variable is 

significant only for turkey-Campylobacter product-pathogen pair model. The other models 

suggest—although the variables are not statistically significant at the p=0.05 level—that 

increasing this variable would increase pathogen prevalence.

As can be seen in Table 1, the regression coefficients for the non-compliance (NC) decision 

variable is positive for both the chicken and the turkey Salmonella product pairs (a significant 

positive correlation for turkey), suggesting that decreasing occurrences in this category would 

decrease pathogen prevalence in these product classes. In contrast, the coefficient for both the 

chicken and the turkey Campylobacter product pairs is negative (a significant negative 

correlation for chicken), suggesting that decreasing occurrences in this category would increase 

pathogen prevalence in these product classes. Both anticipating the effect of changes in NC 

procedures and interpreting the estimated changes to non-compliance (NC) episodes in 

establishments is complicated. The number of non-compliances reported is a function of failures 

in process control and the availability of inspection personnel to detect these failures. As such, 

the non-compliance decision variable is different from the other three decision variables because 

it partly reflects occurrences (i.e., failures) that are not controlled by off-line inspectors. In 

contrast, the other decision variables are directly amenable to change simply by changing 

inspection resources (e.g., unscheduled procedures can increase or decrease directly with the 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

24

number of off-line inspectors – these do not require detection of establishment failures).Taking 

those aspects into account, on the one hand, FSIS expects that increased off-line inspection 

resources would generate improved process control within establishments that adopt the 

proposed change. Improved process control should, in the longer term, result in fewer noncompliance reports from these establishments. On the other hand, these increased off-line 

inspectors would also have more time and opportunity to identify non-compliant activities and 

thereby generate more non-compliance reports, at least in the short term. Differential long-term 

versus short-term effects may be what is reflected in the difference in NC reporting between 

Salmonella and Campylobacter testing. Regardless of whether that is the case, as stated 

previously the number of Salmonella testing results compared with the number Campylobacter

test results at the time of this assessment provide more confidence in the analysis of the 

Salmonella results. On the basis of an analysis of the HACCP Inspection Models Project (HIMP) 

(FSIS, 2011a) it would be anticipated that fewer non-compliances would occur following 

implementation of the proposed change. Nevertheless, the regression model results observed 

here are inconsistent across the four models. 

The mean values for each of the four inspection categories indicate the average number of daily 

instances across the population of all establishments for each category of off-line inspection 

procedures represented in the data (Table 1). For example, the average number of scheduled and 

performed procedures used as explanatory variables in the chicken-Salmonella model is ~13 per 

establishment per day. Similarly, the average number of unscheduled procedures is ~29 per 

establishment per day. Comparing these values with the chicken-Campylobacter data suggests 

similarities (e.g., 31 vs. 29 for U) and differences (e.g., ~7 vs. ~13 for SP). Differences highlight 

the fact that the dataset for chicken-Salmonella is augmented with testing data generated from 

the PR/HACCP testing while the chicken-Campylobacter model only includes data from the 

Young chicken Baseline study. A similar explanation applies to comparisons between the two 

young turkey datasets.
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

25

Table 1. Results of the Regression Analyses for each Decision Variables from Four ProductPathogen Pairs

ProductPathogen PairDecision 

Variable

Regression 

Coefficient 

Estimate

Std 

Error p-value

Variable 

Mean

Variable 

Std Dev

Young chicken –

Salmonella

SP 0.0021 0.0021 0.1587 12.9624 6.0291

SNP 0.0461 0.0093 <0.0001* 0.5536 1.0524

U -0.0032 0.0009 0.0002* 29.1353 20.5648

NC 0.0091 0.0096 0.1716 0.7834 1.1422

Young chicken -

Campylobacter

SP 0.0076 0.0065 0.1212 6.5629 0.8762

SNP 0.0198 0.0107 0.0321* 0.6929 0.2600

U -0.0014 0.0011 0.1016 31.0927 7.3283

NC -0.0157 0.0074 0.0170* 1.3634 0.3212

Young turkey –

Salmonella

SP 0.0054 0.0121 0.3277 10.7622 6.3381

SNP -0.0805 0.0408 0.0243* 0.4945 1.0889

U -0.0208 0.0190 0.1368 6.9431 3.1892

NC 0.0581 0.0223 0.0046* 1.8542 3.6883

Young turkey -

Campylobacter

SP -0.0344 0.0203 0.0451* 10.8187 4.2699

SNP 0.0444 0.0573 0.2192 0.9022 1.3254

U -0.1027 0.0303 0.0004* 8.8464 3.1642

NC -0.0548 0.0801 0.2470 0.5374 1.0612

*Significant difference for two-sided t-test on the regression coefficient

SP = scheduled and performed procedures 

SNP = scheduled not performed procedures 

U = unscheduled procedures performed 

NC = observation and reporting by inspection personnel of non-compliance

Source: FSIS analysis of Agency generated data
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

26

Stage 2: Model to Predict the Effect of Increased Inspection Procedures

Stage 2 of the risk assessment predicts the estimated change in pathogen prevalence for different 

changes in inspection activities. Specifically, it used results of the regression model in Stage 1 to 

predict the probability of a change in pathogen prevalence, and the mathematically maps that 

change in the conditional likelihood of prevalence to the attendant estimated change in the 

annual rate of human illnesses. This mapping directly mirrors the signs and magnitudes of the

conditional regression coefficients from Stage 1, which is reflected in the probability of illness 

reduction estimated from Stage 2.

The modeling framework in Stage 2 stems from the three primary determinants of adverse 

human health outcomes from foodborne pathogens; 1) the frequency of exposure to the 

pathogen; 2) the distribution of pathogens in a random exposure event on a per serving basis; and 

3) the probability that a random exposure event causes the adverse human health outcome (Cox, 

2006; Haas, 1996). In microbial food safety, sporadic exposure events are considered 

independent events and chronic exposures to pathogens are historically not considered. These 

characteristics support modeling the occurrence of human illnesses as a Poisson process.

A prevalence-based model estimates changes in annual illness counts based on changes in the 

frequency of occurrence of the pathogen among food commodities (Williams et al., 2011). The 

basic model is:

P ill P ill exp P exp ( ) ( | ) ( ) 

where

P ill ( )

is the probability of illness from a product-pathogen pairing across a population, 

P ill exp ( | )

is the probability that exposure to a random contaminated serving would produce 

illness4and 

P exp ( )

is the frequency of exposure to the pathogen on a per serving basis5.

This basic model enables a simple estimation of annual illnesses avoided (

λavoided

) resulting 

from an intervention that reduces prevalence. 

A model to predict the effect of the increased off-line poultry inspection procedures is defined as 

follows:

 

4 P ill exp ( | )

is the solution to the integral where 

R D( )

is the dose-response function and the exposure distribution 

of doses (D > 0 organisms) is the probability density

f D( ).

5 Exposure to a contaminated serving can be defined at any point in the farm-to-table continuum assuming that 

P exp ( )

is proportional to the percent of positive units observed at some point prior to consumption (i.e., these 

measures of occurrence differ by a multiplicative constant). In food safety applications, the best data for measuring 

frequency is usually at the point of commercial production (e.g., retail-ready raw chicken carcasses).
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

27

avoided ill 1

Prev(scenario)

Prev(baseline)

where 

avoided

is the annual rate of product-pathogen illnesses avoided following modeled 

alternative scenarios; 

ill

is the current annual rate of product-pathogen illnesses (i.e., illnesses in 

the baseline); 

Prev(scenario)

is the post-chill prevalence of pathogen-contaminated poultry 

carcasses projected following implementation of a modeled scenario; 

Prev(baseline)

is the postchill prevalence of pathogen-contaminated poultry carcasses projected prior to modeled

changes6.

The advantage of this modeling approach is that it avoids the need to estimate an exposure 

distribution or a dose-response relationship. A critical assumption needed to apply such a 

prevalence-based approach is that dose levels at consumption are independent of the frequency 

of contamination (in other words, the level of contamination is independent of pathogen 

prevalence). This assumption asserts that 

P ill exp ( | )

is constant regardless of changes in

P exp ( ). 

Although the prevalence-based model may not always hold, in this instance the use of the 

prevalence based model is supported, as only small changes in microbial concentration are 

observed despite large changes in prevalence. Furthermore, there is empiric evidence that 

supports the independence of prevalence and contamination levels at the end of the production of 

raw poultry carcasses. For example, in rinse samples of young chicken carcasses that test 

positive, the average concentration of Salmonella per ml of sample rinsate was 0.16 and 0.14 

colony forming units (cfu) in the 1995 and 2007 baseline surveys, respectively (FSIS, 1996; 

FSIS, 2009). Yet, the prevalence of positive carcasses was substantially different (20% vs. 7.5%) 

in those surveys. Similarly, those same surveys found the average concentration of 

Campylobacter per ml of sample rinsate was 21 and 9.1 cfu in 1995 and 2007, respectively; 

despite a dramatic reduction in the prevalence of positive carcasses from 88% to 11%. Other 

studies have drawn similar conclusions with respect to other product-pathogen pairs (Crouch et 

al., 2009; Withee et al., 2009).

The baseline prevalence is defined as

, where 

 

6 Note that 

avoided

might be negative if 

Prev(scenario)

>

Prev(baseline)

. In such cases, the negative sign 

would reflect an increase in that rate parameter (although the negative sign would not directly enter a Poisson 

distribution).

1 1

1 1

... ...

... ...

1 1

j i ij n nj

j i ij n nj

n X X X

j X X X

j

e

Prev(baseline)= w

e
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

28

the variables and coefficients are estimated via the logistic regression models described above, 

n

represents the number of sampling occasions for the specific product-pathogen pair (e.g., 

n 22,671 

for the Young chicken-Salmonella model) and 

wj

is a fractional weight given to 

each sampling occasion to reflect the annual production volume (carcasses slaughtered per year) 

for each sampled establishment. Because the logistic regression model predicts the probability of 

an individual sample being positive (given the 

Xij

values for that sample), this equation is 

summed to calculate prevalence across the entire population of samples. 

The modeled prevalence following implementation of a given scenario is,

1 1

1 1

... ...

... ...

1 1

j i ij i n nj

j i ij i n nj

n X X A X

j X X A X

j

e

Prev(scenario)= w

e

where 

one or more of the random variables are adjusted by 

Ai

to account for a change that occurs 

following modeled scenario implementation. To forecast post-chill prevalence, the rehang 

structural variable in the regression model is adjusted to reflect post-chill testing (i.e., its value is 

set to one) when estimating both

Prev(baseline)

and

Prev(scenario).

There is uncertainty associated with the inputs 

ill

, Prev(baseline), Prev(scenario)and 

Ai

in 

this assessment. To assess the overall uncertainty about 

avoided

, a Monte Carlo model7 was 

developed to propagate those sources of uncertainty to a prediction about the future annual rate 

of illnesses avoided, in this way determining which source of inherent uncertainty had the 

greatest impact on the public health estimate from this model. In this model, uncertainty about 

regression coefficients is modeled as 

~ Normal μ,Σ

where is a vector of mean 

coefficients and is the variance-covariance matrix generated from the regression analysis8; 

uncertainty about 

Ai

is modeled as 

~ ( ) Ai Pert min,mode,max

; uncertainty about

ill

is 

modeled as

~ , ill lognormal

. Because 

avoided

is a function of the ratio of 

Prev(scenario)

and

Prev(baseline)- and these random variables can be reasonably assumed to be correlated – each 

 

7 All Monte Carlo simulations were completed using Palisade’s @Risk software in Microsoft Excel. Each 

simulation comprises 100,000 iterations; this number of iterations produces outputs that change by <1% from one 

simulation to the next.

8

Random values from this multivariate normal distribution are generated using the Cholesky decomposition method 

(Press et al. 2007).
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

29

iteration of a simulation paired the estimates of 

Prev(scenario)

and

Prev(baseline)

such that each 

estimate reflected the same uncertain coefficient values from the regression model (thus the 

baseline and each modeled scenario is run in parallel with each other).

Estimates of 

ill

are needed for all four product-pathogen pairs. Uncertainty about the total 

Salmonella and Campylobacter illnesses per year attributable to young chickens and young 

turkeys is modeled by considering the uncertainty in the total annual domestically acquired 

foodborne illnesses estimated by CDC (Scallan et al., 2011). The mean estimated total cases 

(and 90% credibility interval) for Salmonella and Campylobacter were 1,027,561 (644,786 –

1,679,667) and 845,024 (337,031 – 1,611,083), respectively. 

As presented in Table 2, previous analysis estimated that the fractions of total Salmonella and 

Campylobacter illnesses per year attributable to young chicken as 16.33% (167,831/1,027,561) 

and 19.91% (168,291/845,024), respectively (FSIS, 2011b).9 That analysis also estimated the 

fraction of total Salmonella and Campylobacter illnesses per year attributable to young turkeys 

as 0.67% (6855/1,027,561) and 0.08% (714/845,024), respectively. These attribution fractions 

are applied to the credibility intervals of Scallan et al. (2011) to determine the 5th and 95th

percentiles of a putative lognormal distribution that describes uncertainty about the annual cases 

of these pathogens attributed to each poultry class (Table 2). This treatment, however, does not 

consider uncertainty associated with the fraction of illnesses attributed to each poultry class. 

Consideration of this source of uncertainty awaits further development of this parameter by CDC 

and other food safety agencies. 

 

9

Following completion of this assessment, the Centers for Disease Control published Painter J.A., Hoekstra R.M.,

Ayers T., Tauxe R.V., Braden C.R., Angulo F.J, Griffin P.M. (2013). Attribution of foodborne illnesses, 

hospitalizations, and deaths to food commodities by using outbreak data, United States, 1998–2008. Emerging 

Infectious Diseases, 19(3):407-415. http://dx.doi.org/10.3201/eid1903.111866.. Our assumed attribution for 

Salmonella-poultry is within the range estimated by Painter et al. (2013), but our Campylobacter-poultry attribution 

(19.71%) is substantially larger than reported by Painter et al. (2013) 7.6%. Painter et al. (2013) explain that 

outbreak data under-represent poultry as a source of Campylobacter spp. infection and further note that studies of 

sporadic infections implicate consumption of poultry as a more significant risk factor. 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

30

Table 2. Estimated Number of Annual Salmonella and Campylobacter Illnesses, with 

Uncertainty Bounds, from Young Chicken and Young Turkey.

ProductPathogen

Estimated attributed annual illnesses1 Lognormal distribution 

parameters2

Mean 5

th percentile 95th

percentile Mu Sigma

Young chicken -

Salmonella 167,831 105,313 274,340 12.043 0.291

Young chicken -

Campylobacter 168,291 66,413 317,473 11.886 0.476

Young turkey -

Salmonella 6,855 4,320 11,254 8.850 0.292

Young turkey -

Campylobacter 714 283 1,353 6.428 0.477

Source: FSIS analysis of data from Scallan et al., 2011.

1These distribution parameters are estimated from total illness data and attribution fractions for Salmonella and 

Campylobacter (Scallan et al., 2011; FSIS, 2011). 

2 This parameterization assumes ln(annual illnesses) ~ Normal (Mu, Sigma). The lognormal distribution parameters 

were estimated using a percentile fitting algorithm: 

0.95

ln(95 % ) ln(5 % ) ln(95 % )

,

2

th ile th ile th ile

Z

where 

Z0.95

is the 95th percentile of a standard 

Normal distribution. 

One objective of this risk assessment is to estimate the adjustment parameters

Ai

that reflect the 

expected change in the decision variables following implementation of the proposed change. To 

establish baseline prevalence estimates, it is assumed that each decision variable simply reflects 

the data used to estimate the regression models. For the scenarios it is assumed that the data for

each random decision variable would be adjusted as follows:

 Scheduled and performed (SP) and unscheduled procedures (U) in an establishment could 

either increase, decrease, or stay the same, once an establishment adopts the inspection 

system in the proposed change. FSIS inspection records in HIMP establishments are 

considered a good indicator of what the proposed FSIS inspection system might look like 

under the proposed change. On average, FSIS inspectors performed 14,136 offline 

verification inspections per HIMP establishment in CY2010 versus an average of 8,724 

offline verification inspections per non-HIMP establishment. This varied from 1.6 times 

more offline verification inspection procedures in HIMP establishments than in non-HIMP 

establishments to 3.2 times more HACCP verification inspection procedures. Because a 

fraction of establishments already participate in HIMP and another fraction of establishments 

would choose not to adjust in response to the proposed change, it is assumed that a most 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

31

likely value of a 25% increase in SP and U procedures in our modeled scenario. At a 

minimum, it is assumed that there would be no change and at a maximum that there would be 

a 60% increase in these procedures. Therefore, for the SP and U decision variables, 

~ (1.0,1.25,1.6) Ai Pert

is modeled. Note: although conceptually there is no reason why SP 

and U procedures could not decrease, because they are “decision variables” and it is not 

anticipated, operationally, that the number of those inspection activities would decrease, it 

was elected to truncate the Pert distributions at a lower limit of 1; assuming a worst case 

scenario of “no increase’ in either of these off-line inspection procedures.

 Scheduled-but-not performed procedures would most likely decline under the proposed

inspection system, as the primary reason for SNPs in an establishment is limited personnel to 

complete the offline procedure. Because the proposed inspection system may result in fewer 

scheduled procedures, it is difficult to compare current HIMP data on SNP procedures. It 

was assumed that these SNP procedures would most likely be reduced by 10%, but could be 

reduced by 100% or not change at all. Therefore, for the SNP decision variable,

~ (0.0,0.9,1.0) Ai Pert

is modeled. Note: a minimum value of 0.5 for this change variable

was also considered, but the results were not significantly altered and only the above 

distribution is used in the final analysis.

The discussion above examines the uncertainty in how recorded non-compliances might change 

in establishments under different inspection scenarios. Data from HIMP establishments were 

used to model that change (FSIS, 2011a). On average, HIMP broiler establishments have 26%

fewer reported public health-related non-compliances than do non-HIMP broiler establishments. 

Nevertheless, it remains possible that under the modeled scenario that non-compliances may be 

reduced by 100% or not change at all. Therefore, for the NC decision variable, 

~ (0.0,0.74,1.0) Ai Pert

is modeled. In this case an alternative minimum value of 0.74, and a 

mean value of 0.9 for this change variable were also considered, but the results again were not 

significantly altered and only the above distribution is used in the final analysis.

Stage 2: Implementation Scenarios

To predict how annual illness rates might change following implementation of the proposed 

change, it is assumed that the four decision variables (that is the inspection categories SP, SNP, 

U and NC) would all change according to the assumptions outlined above. Those assumptions 

were then modeled using two different implementation scenarios.

An “indiscriminate scenario” is used to identify the types of offline inspection procedures that 

could have the greatest public health impact and to determine the sensitivity of the model to the 

different decision variables. In this scenario, no preference is provided for how FSIS might 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

32

emphasize or de-emphasize particular decision variables in the regression models. In other 

words, this scenario does not specify the implementation strategy employed, just that some 

combination of the four categories of inspection procedures would increase, with no inspection 

category more heavily emphasized than others. Note that it is not clear whether increasing NCs 

would actually be expected to increase illness. 

A second scenario, termed the “discriminate scenario”, models an increase in unscheduled 

procedures (i.e. Increase U) similar to what currently occurs in HIMP establishments. This model 

estimates how human illness might change if this particular category of inspection procedures 

(i.e., decision variable), is altered while leaving other decision variables unchanged. This 

scenario is modeled such that the Ai parameter for the U decision variable is the same as 

explained above while the Ai parameter values for the other decision variables are fixed at a 

value of one, to indicate no change from the baseline in the other inspection activities. The 

decision to consider this alternative scenario is based, in part, on the output of Stage 1 of this risk 

assessment. In that stage the outputs of the regression model for the unscheduled procedures 

decision variable was the most consistently negatively correlated with contamination across all 

four product-pathogen models, with that correlation being statistically significant in one chicken 

and one turkey model. That suggests that this decision variable is the most likely to decrease 

prevalence and, subsequently, human illnesses. In addition, operationally there is an assumption 

that FSIS would alter performance of the equivalent of unscheduled procedures. 

RESULTS

Estimated Annual Changes in Salmonella and Campylobacter Prevalence in Young 

Chicken Establishments:

Under the indiscriminate scenario, this risk model predicts an average decline of 2 % in the 

proportion of positive Salmonella samples and an increase of 0.2% in the proportion of positive 

Campylobacter samples. However, recall that the Campylobacter models are substantially less 

precise due to sample sizes, and the indiscriminate model includes procedures that, as discussed 

previously, would not always be expected to reduce contamination (i.e., NC activities) and 

categories of procedures that are infrequent (i.e., SNP).

Under the discriminate scenario, when only unscheduled inspection procedures in young 

chicken slaughter establishments are targeted for increase, the risk model predicts an average

decline of 2% in the proportion of positive Salmonella samples, and a decline of 0.44% in the 

proportion of positive Campylobacter samples if (Table 3).
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

33

Estimated Annual Changes in Salmonella and Campylobacter Prevalence in Young 

Turkey Establishments:

Under the indiscriminate scenario, this risk model predicts an average decline of 6% in the 

proportion of positive Salmonella samples and a decline of 26% in the proportion of positive 

Campylobacter samples when off-line procedures are changed in young turkey slaughter 

establishments. 

Under the discriminate scenario, when only unscheduled inspection procedures in young turkey 

slaughter establishments are targeted for increase, the risk model predicts an average decline of 

3% in the proportion of positive Salmonella samples and a decline of 19% in the proportion of 

positive Campylobacter samples (Table 3).

Table 3. Estimated Reductions in Prevalence of Salmonella and Campylobacter in Young 

Chicken and Young Turkey Slaughter Establishments 

Estimated Reduction in prevalence: mean (10th percentile, 90th percentile)

Indiscriminate Scenario Discriminate Scenario

Salmonella Campylobacter Salmonella Campylobacter

Young chicken 

establishments

0.020

(0.006,0.041)

-0.002

(-0.010,0.006)

0.0210

(0.008,0.036)

0.004

(-0.0001,0.0099)

Young turkey 

establishments

0.060

(-0.024,0.141)

0.260

(0.137,0.386)

0.030

(-0.005,0.079)

0.190

(0.080,0.310)

Source: FSIS analysis of Agency generated data

Summary statistics derived using Monte Carlo simulations of the indiscriminate and Increase Unscheduled 

procedures scenarios across the four product-pathogen models. 

Estimation of Changes in Human Illness 

The estimated changes in human illness are summarized in Figures 1-4 and Tables 4 and 5. The 

figures depict the cumulative probability plots for the indiscriminate and alternative scenarios 

across the four product-pathogen pairs. 

The first focus is on the indiscriminate scenarios, where any combination of the four inspection 

categories is increased without consideration or targeting of those that would be expected to 

decrease illnesses. The model predicts that implementation of the indiscriminate scenario would 

net a small decrease in illnesses. Mean estimates from the indiscriminate scenario suggest an 

estimated reduction of 4,020 in annual Salmonella illnesses and 350 additional annual 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

34

Campylobacter illnesses in young chicken establishments. The mean estimates for young turkey 

establishments under the indiscriminate scenario show an annual decline of 420 and 180 

Salmonella and Campylobacter illnesses, respectively.

The model also includes an estimate of the probability (percentage) that there will be an increase 

in illnesses and the probability that there will be a decrease in illnesses with the implementation 

of the indiscriminate scenario. That percentage represents the likelihood of having a positive 

change in illnesses, without consideration of the magnitude of that change (in other words, it 

indicates the likelihood of an increase of any size, even one illness). Under the indiscriminate 

scenario, the models (with the exception of the young chicken-Campylobacter prediction) 

suggest a high probability that the scenario modeled might result in a decrease in human 

illnesses. The probability that illnesses might increase by at least one illness (i.e., a negative 

value for illnesses avoided) is 0.04, 0.63, 0.18, 0.01 for the young chicken-Salmonella, young 

chicken-Campylobacter, young turkey-Salmonella and young turkey-Campylobacter models, 

respectively (Table 3). Stated a different way, there is a 96%, 37%, 82%, and 99% chance that 

illnesses would not increase by even one illness for the young chicken-Salmonella, young 

chicken-Campylobacter, young turkey-Salmonella and young turkey-Campylobacter models, 

respectively. 

It is important to note that the young chicken-Campylobacter model results are ambiguous under 

the indiscriminate scenario. The possibility of a predicted increase in Campylobacter illnesses is 

primarily driven by the SP decision variable and by the statistically significant NC decision 

variable. For both of these variables, the expected changes serve to increase prevalence, and 

their effects tend to overwhelm the prevalence-decreasing effects of expected changes to the 

SNP and U decision variables. It would only take a slight change in any of these decision 

variables to impact illness predictions because, compared to the young chicken-Salmonella

model, the Campylobacter model is based on a considerably smaller sample, leading to less 

precision. Therefore, we have less confidence in the Campylobacter model results. In addition, 

as discussed previously, the effect of increasing non-compliances procedures on illness rates, 

particularly in the short term, is poorly understood and decreases the confidence in the model 

results that adjust that variable.

The Monte Carlo simulation results reflect the aggregate estimated change in total illnesses 

across young chicken and young turkey slaughter industries. To estimate this aggregate value, 

the 

avoided

values for the young chicken-Salmonella and young turkey-Salmonella models were 

summed for each iteration of a Monte Carlo simulation. This same approach was used for the 

Campylobacter models (Table 4). These results imply a 97% probability that aggregate human 

Salmonella illnesses would be unchanged or decrease following a change to inspection 

procedures under the indiscriminate scenarios described. However, the indiscriminate scenario 

suggests a 54% probability that Campylobacter illnesses associated with both young chicken and 

young turkey establishments might increase, even by less than one illness, if such an

implementation strategy were to be adopted.
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

35

Table 4. Estimated Human Illnesses Avoided from Monte Carlo Simulations of the 

Indiscriminate Scenario across the Four Product-Pathogen Modelsa,b

Statistic

Attributable to Young Chicken 

Establishments

Attributable to Young Turkey 

Establishments

Combined Illnesses Avoided

Salmonella 

illnesses 

avoided

Campylobacter

illnesses 

avoided

Salmonella 

illnesses 

avoided

Campylobacter

illnesses 

avoided

Salmonella 

illnesses 

avoided

Campylobacter

illnesses avoided

Mean 4,020 -350 420 180 4,440 -170

Median 3,650 -260 390 150 4,090 -90

Mode 3,200 -310 250 130 3,580 -150

Std 

Deviation 2,800 1,180 520 120 2,850 1,180

10th 

percentile 920 -1,690 -160 70 1,260 -1,520

90th 

percentile 7,640 860 1,060 330 8,120 1,060

Probability 

of any 

increase in

illnesses c

4.23% 63.28%b17.81% 0.12% 3.11% 54.30% b

Source: FSIS analysis of Agency generated data

a

Predicted changes in illness rounded to the nearest 10.

bThis indiscriminate scenario does not target any inspection activity category to increase. It shows the range of 

illnesses avoided if any combination of inspection activity category is increased. 

cThis percentage represents the probability that an increase in illness of any size, even one illness, will occur. In 

other words, it is the likelihood that the decrease in illnesses will be negative.

d

Sensitivity analyses indicate that much of the probability of seeing an increase in illnesses in the indiscriminate 

scenario is driven by the NC decision variable. The underlying reason for seeing what might be thought of as a 

counterintuitive results is, at least in part, a results of the statistical model and the unclear relationship between non 

compliances and product contamination. As discussed previously, an increase in the value of that variable would not 

necessarily be expected to be associated with a decrease in illnesses. In fact, the results from the regression analysis 

in Stage 1 of this risk assessment show either positive or negative correlations between NCs and contamination 

depending on the product-pathogen model. Furthermore, increasing the number of FSIS inspection tasks conducted

would not, automatically, increase the number of NCs because the true value is a function of not only of how 

frequently FSIS conducts inspection tasks, but also the establishment’s food safety practices. Those factors make the 

effect of increasing NCs poorly understood and uncertain, and could contribute to increase in estimated illnesses. 

The fact that indiscriminately increasing the decision variables results in some probability of increasing illnesses 

supports exploring the effect of targeting specific procedures for increase that logically, and according to the Stage 1 

regression analysis, are more likely to be associated with decreased illnesses, specifically unscheduled procedures. 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

36

These results can be compared with those associated with the discriminate scenario in which 

only unscheduled procedures are increased. Table 5 shows that under the discriminate scenario, 

that combined mean Salmonella illnesses could be reduced by as much as 3,980 annually (3,740 

attributable to young chicken establishments and 240 attributable to young turkey 

establishments)(Table 5). Similarly, combined mean Campylobacter illnesses could be reduced 

by 840 annually (710 attributable to young chicken establishments and 130 attributable to young 

turkey establishments).

The discriminate model also minimizes the probability (i.e., the likelihood) that illnesses would

increase by even one illness. In the young chicken-Salmonella model, the probability that the 

Salmonella illness rate would increase is down from 4.23% under the indiscriminate scenario to 

0.013% for the discriminate scenario. Furthermore, the similarity of the uncertainty distributions 

for the “indiscriminate” and “discriminate” scenario results reinforce the importance of the “U” 

decision variable in the indiscriminate scenario (Figure 1). 

Of great interest is the fact that under the discriminate scenario, run on the young chickenCampylobacter data, the probability that the Campylobacter illness rate would increase is 

significantly lower than under the indiscriminate scenario (i.e., it drops from 63% to 10%). That 

is because the discriminate scenario does not include the effect of decreasing non-compliances. 

In fact, the discriminate scenario suggests the potential for avoiding substantially more 

Campylobacter illnesses if FSIS emphasizes increased unscheduled procedures.

When compared to the indiscriminate scenario, the discriminate scenario, in which the 

unscheduled procedures are specifically targeted for increase, the young turkey-Salmonella

model suggests a small reduction in the probability that the Salmonella illness rate would

increase (i.e., from 18% to 14%). The alternative scenario in the young turkey-Campylobacter

model suggests only a minor change relative to the indiscriminate analysis. 

For the discriminate scenario, the combined illnesses avoided results show a substantially lower

probability that illnesses might increase (i.e., down from 3% to 0.03% for Salmonella and down

from 54% to 6% for Campylobacter). These results suggest that aggregate human illnesses 

would be unchanged or decrease—with approximately 100% and 94% probabilities for 

Salmonella and Campylobacter respectively—if FSIS emphasized increases in unscheduled 

procedures in inspection resource allocation.
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

37

Table 5. Estimated Human Illnesses Avoided from Monte Carlo Simulations of the 

Discriminate Scenario that Specifically Increases Unscheduled Procedures across the Four 

Product-Pathogen Modelsa.

Statistic

Attributable to Young 

Chicken Establishments

Attributable to Young 

Turkey Establishments

Combined Illnesses 

Avoided

Salmonella 

illnesses 

avoided

Campylobacter

illnesses 

avoided

Salmonella 

illnesses 

avoided

Campylobacter

illnesses 

avoided

Salmonella 

illnesses 

avoided

Campylobacter

illnesses 

avoided

Mean 3,740 710 240 130 3,980 840

Median 3,300 510 200 110 3,550 650

Mode 2,550 240 120 80 2,350 300

Std 

Deviation 2,260 850 280 100 2,280 850

10th 

percentile 1,300 -10 -30 40 1,510 100

90th 

percentile 6,690 1,720 590 250 6,960 1,860

Probability 

of any 

increase in

illnessesb

0.013% 10.45% 13.68% 0.04% 0.03% 6.21%

Source: FSIS analysis of Agency generated data

a

Predicted changes in illness rounded to the nearest 10.

bThis percentage represents the probability that an increase in illness of any size, even one illness, will occur. In 

other words, it is the likelihood that the decrease in illnesses will be negative.
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

38

Figure 1. Estimated Change in the Annual Salmonella Human Illness Rate when Off-line 

Inspection Procedures are Increased in Young Chicken Establishments. 

Data depicted for the indiscriminate scenario and the discriminate scenario that increased 

unscheduled procedures (labeled above as “Increase U”). Note that model uncertainty and data 

variability cannot statistically distinguish the two distributions.

Source: FSIS analysis of Agency generated data.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-10,000 -8,000 -6,000 -4,000 -2,000 0 2,000 4,000 6,000 8,000 10,000

Cumulative Probability

Annual Illnesses Avoided (negative implies increased illnesses)

Indiscriminate

increase U

No change
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

39

Figure 2. Estimated Change in the Annual Campylobacter Human Illness Rate when Off-line 

Inspection Procedures are Increased in Young Chicken Establishments.

Data depicted for the indiscriminate scenario and the discriminate scenario that increased 

unscheduled procedures (labeled above as “Increase U”). Note that the two distributions are 

distinguishable but that the model precision and data quality are not comparable to the young 

chicken-Salmonella model estimates.

Source: FSIS analysis of Agency generated data.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-5,000 -4,000 -3,000 -2,000 -1,000 0 1,000 2,000 3,000 4,000 5,000

Cumulative Probability

Annual Illnesses Avoided (negative implies increased illnesses)

Indiscriminate

increase U

No change
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

40

Figure 3. Uncertainty about the Change in the Annual Salmonella Human Illness Rate when 

Off-line Inspection Procedures are Increased in Young Turkey Establishments.

Data depicted for the indiscriminate scenario and the discriminate scenario that increased 

unscheduled procedures (labeled above as “Increase U”). Note that model uncertainty and data 

variability cannot statistically distinguish the two distributions.

Source: FSIS analysis of Agency generated data.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-4,000 -3,000 -2,000 -1,000 0 1,000 2,000 3,000 4,000

Cumulative Probability

Annual Illnesses Avoided (negative implies increased illnesses)

Indiscriminate

increase U

No change
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

41

Figure 4. Estimated Change in the Annual Campylobacter Human Illness Rate when Off-line 

Iinspection Procedures are Increased in Young Turkey Establishments.

Data depicted for the indiscriminate scenario and the discriminate scenario that increased 

unscheduled procedures (labeled above as “Increase U”). Note that model uncertainty and data 

variability cannot statistically distinguish the two distributions.

Source: FSIS analysis of Agency generated data.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

-1,000 -800 -600 -400 -200 0 200 400 600 800 1,000

Cumulative Probability

Annual Illnesses Avoided (negative implies increased illnesses)

Indiscriminate

increase U

No change
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

42

Sensitivity Analysis

This sensitivity analysis examined how the final model output (

avoided

) is influenced by changes 

in the model inputs. First, the analysis examined the relative influence of the main stochastic 

inputs on the final uncertainty distribution for illnesses avoided. Second, the analysis examined 

the partial derivative of 

avoided

for insight about the effect of alternative input values. 

Three main stochastic inputs contribute uncertainty to the final distribution of 

avoided

: (i) 

ill

(lambda) that is modeled as a lognormal distribution; (ii)

Ai

that are modeled as Pert 

distributions; and (iii) 

i

(beta) coefficients that are modeled in a multivariate Normal 

distribution. The analysis examined how each of these inputs influences uncertainty about 

avoided

by simulating the model with only one stochastic input at a time. The variability from a 

simulation with just one stochastic input is compared to the simulation results when all inputs are 

stochastic.

Because the indiscriminate scenario model for chicken-Salmonella has the largest standard 

deviation for 

avoided

, this model was assessed for the relative influence of the main stochastic 

inputs on the uncertainty about 

avoided

(Figure 5). These results demonstrate that the 

uncertainties about the 

Ai

inputs are the largest contributor to uncertainty about 

avoided

. In other 

words, when the model only included the uncertainty about the 

Ai

inputs – while inputs for

ill

and 

i

are fixed at their expected values – the resulting 

avoided

distribution was only slightly less 

uncertain when compared to the 

avoided

distribution where all inputs were stochastic (“All 

variability” distribution). The uncertainty about

ill

was least influential as demonstrated by the 

narrowest 

avoided

distribution resulting when that was the only stochastic input. The influence of 

uncertainty about the 

i

inputs on the 

avoided

distribution is in between that of the

Ai

and 

ill

inputs.
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

43

Figure 5. Relative Contributions to Uncertainty of Estimated Illnesses Avoided.

Source: FSIS analysis of Agency generated data.
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

44

Results of analysis of the relative contribution of uncertainty about 

avoided

are shown in Figure 5. 

The indiscriminate scenario for chicken-Salmonella was simulated with all of the three main 

stochastic inputs (

ill

[lambda], 

Ai

and 

i

[beta]); the uncertainty about 

avoided

is shown as the 

“All variability” distribution. Alternatively, the same model was simulated with just one of these 

uncertain inputs (while holding the other two at their expected values); the resulting distributions 

for 

avoided

are labeled as “Only A variability”, “Only beta variability” and “Only lambda 

variability”. These results demonstrate that the “Only A variability” distribution comes closest 

to replicating the “All variability” distribution. Therefore, uncertainty about

Ai

contributes most 

to total uncertainty about 

avoided

. Conversely, uncertainty about

ill

contributes least to total 

uncertainty about 

avoided

. A simulation where all three inputs are fixed at their expected values 

(“No variability”) is included to demonstrate that the model simply returns an expected value for 

avoided

.

To assess the effect of changing inputs on this model’s output, the fundamental equation of the 

model is considered as

1 1

1 1

... ...

... ...

1

1

1

i i n n

i i i n n

X X X

avoided ill X A X X

e

e

that takes advantage of the fact that 

1 1

1 1 1 1

... ...

... ... ... ...

1

1

i i n n

i i n n i i n n

X X X

X X X X X X

e

p =

e 1+e

and 

ignores the weighting of data used in the actual model. Note that 

avoided

is simply a linear 

function of 

ill

, so any change in

ill

would change 

avoided

proportionally. Therefore, the main 

interest is how 

avoided

changes with respect to the elements of the logistic regression. The 

partial derivative of the illnesses avoided function with respect to the logistic beta coefficients 

(while ignoring 

ill

) is determined as:

1 1 1 1 1 1 1 1

1 1

... ... ... ... ... ... ... ...

2

... ...

1 1

1

i i n n i i n n i i n n i i i n n

i i i n n

X X X X X X X X X X A X X

i i avoided

X A X X

i

AX e e X e e d

d e
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

45

From this partial derivative it is clear that if 

1 Ai

(no change modeled, that is the traditional 

scenario), then there is no change in illnesses avoided (i.e., the partial derivative is 

zero). Therefore, the only coefficients in the illnesses avoided equation that would generate a 

change in illnesses are those influenced by the alternative scenario modeled.

The analysis calculates this partial derivative for each of the four decision variables (SP, SNP, U 

and NC), and for each of the four product-pathogen models (Figures 6-9). In the young chickenSalmonella model, that determination of the number of illnesses avoided is most sensitive to the 

U decision variable (Figure 6). This variable is contributing to more illnesses avoided because 

the partial derivative implies that illnesses avoided would increase with an increase in the value 

of the U decision variable. A similar pattern is observed for the young chicken-Campylobacter

model (Figure 7). In the young turkey-Salmonella model (Figure 8), the estimate of illnesses 

avoided is most sensitive to the SP decision variable, but the effect of increasing SP is to 

decrease illnesses avoided. In contrast, the effect of increasing SP in the young turkeyCampylobacter model is to increase illnesses avoided (Figure 9). For both young turkey models, 

the estimate of illnesses avoided is also sensitive to increasing U. Nevertheless, these partial 

derivatives are based on mean coefficient values and do not account for any uncertainty about 

those values. Consequently, the SP coefficient in the turkey-Salmonella model is highly 

influential based on its mean value, but its effect may be reversed if its value changes signs – and 

this can happen because of the large standard error associated with this coefficient (i.e., this 

coefficient is not statistically significant). Therefore, interpreting these partial derivatives is only 

appropriate when considering their mean values. These figures, which represent a standard 

sensitivity analysis, further substantiate focusing in the discriminate scenario on unscheduled 

inspection activities. 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

46

Figure 6. Sensitivity Analysis: Young Chicken-Salmonella Model.

This tornado graph illustrates the partial derivatives of 

avoided

with respect to the non-compliance 

(NC), unscheduled procedures (U), scheduled not preformed procedures (SNP) and scheduled 

performed procedures (SP) logistic model coefficients. 

Source: FSIS analysis of Agency generated data.

-4 -2 0 2 4 6 8

SNP

NC

SP

U

Partial derivative: change in illnesses avoided 

for change in logistic beta coefficients
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

47

Figure 7. Sensitivity Analysis: Young Chicken-Campylobacter Model 

This tornado graph illustrates the partial derivatives of 

avoided

with respect to the noncompliance (NC), unscheduled procedures (U), scheduled not performed procedures (SNP) and 

scheduled performed procedures (SP) logistic model coefficients. 

Source: FSIS analysis of Agency generated data.

-1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5

SNP

NC

SP

U

Partial derivative: change in illnesses avoided 

for change in logistic beta coefficients
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

48

Figure 8. Sensitivity Analysis: Young Turkey-Salmonella Model.

This tornado graph illustrates the partial derivatives of 

avoided

with respect to the noncompliance (NC), unscheduled procedures (U), scheduled not performed procedures (SNP) and 

scheduled performed procedures (SP) logistic model coefficients. 

Source: FSIS analysis of Agency generated data.

-3 -2 -1 0 1 2

SNP

NC

U

SP

Partial derivative: change in illnesses avoided 

for change in logistic beta coefficients
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

49

Figure 9. Sensitivity Analysis: Young Turkey-Campylobacter Model. 

This tornado graph illustrates the partial derivatives of 

avoided

with respect to the noncompliance (NC), unscheduled procedures (U), scheduled not performed procedures (SNP) and 

scheduled performed procedures (SP) logistic model coefficients. 

Source: FSIS analysis of Agency generated data.

-0.5 0 0.5 1 1.5 2 2.5

SNP

NC

U

SP

Partial derivative: change in illnesses avoided 

for change in logistic beta coefficients
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

50

DISCUSSION

The model and analyses presented here examine available data to describe the quantitative 

relationship between observed Salmonella- and Campylobacter-positive samples and FSIS

inspection activities taking place in young chicken and young turkey slaughter establishments.

Each of the four product-pathogen pairs are modeled using the same decision variables with both 

an indiscriminate scenario and with a discriminate scenario. It is assumed that the association is 

predictive of the underlying relationship. It is further assumed that there is a probable 

relationship between observed Salmonella and Campylobacter positive samples in young 

chicken and young turkey slaughter establishments and attributable human illnesses from 

chicken and turkey consumption. A great deal of the quantitative portion of this risk assessment 

focuses on these two relationships. 

Although more complicated models to relate occurrences of microbial pathogens to human 

illnesses might be conceived, the approach taken here makes the best use of available data to 

examine the potential public health effects of changes to inspection activities. It uses available 

inspection activity and pathogen testing data to assess the influence of those activities on the

conditional likelihood of positive samples. This takes into account the differences in precision of 

the sampling data due to varying number of samples among the product-pathogen pairs for the 

data set available at the time of the assessment, and the complex empirical correlation structure 

between decision variable coefficients in the model’s Stage 1 and their direct relationship to the 

estimated illnesses prevented in the model’s Stage 2. It uses available human illness data to 

model the effect of changes in the likelihood of positive samples on numbers of human illnesses 

avoided. The methods used here have been applied extensively in other risk assessments

(Bartholomew et al., 2005; Williams and Ebel 2012; Ebel et al., 2012; Withee et al., 2009). The 

risk assessment provides answers to each of the four risk management questions.

 Can FSIS reallocate inspection activities in young chicken slaughter establishments 

without significant negative impact on microbial prevalence in the establishments?

Depending on how reallocation of inspection activities is implemented, it is likely 

that changes in off-line inspection could result in a decrease in the numbers of 

positive microbial samples in FSIS-regulated young chicken and young turkey 

establishments. The results indicate that under some product-pathogen scenarios, the 

null hypothesis of no change in prevalence (or illness) would not be rejected at the 

80% confidence level. In such cases, any purported public health benefits are 

dubious. Specifically, the discriminate scenario, which only increases unscheduled 

inspection procedures, performs much better than the indiscriminate scenario, which 

does not target specific types of procedures, and the results suggest a reasonable 

degree of confidence that the discriminate scenario would do no harm. 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

51

The results reported for combined illnesses avoided are based on the assumption that the 

commodity-pathogen uncertainty distributions are independent. There is no empirical basis for 

accepting or rejecting this assumption. However, even if the uncertainty distributions were 

perfectly correlated (such that the percentiles simply summed), there would still be more than 

90% confidence that the combined illnesses avoided under the scenario that increases 

unscheduled procedures exceeds zero (i.e., the 10th percentile of the combined Campylobacter

illnesses avoided would be 30). The performance of both the HIMP and non-HIMP plants 

improved and converged over time.

This risk model results suggest that the discriminate scenario of increased off-line 

inspection could decrease the number of positive Salmonella and Campylobacter

samples in young chicken and young turkey establishments with high probability. 

Under the indiscriminate scenario in young chicken establishments, when off-line inspection 

procedures are increased, the model predicts an average decline of 2% in the proportion of 

samples testing Salmonella positive. However, when the substantially less robust Campylobacter

data available at the time of the assessment are used, the model suggests a 0.2% increase in the 

proportion of samples testing Campylobacter positive. This very small increase is likely a 

function of the instability of this model, which is due to the combination of both small sample 

sizes and allowing the option of increasing off-line procedures that do not necessarily directly 

influence contamination rates. Under the discriminate scenario, where only unscheduled 

inspection procedures are targeted for increase, the model in young chicken slaughter 

establishments predicts an average decline of 2% in the proportion of samples testing Salmonella

positive, and an average decline of 0.44% in the proportion of samples testing Campylobacter

positive. Removing the effect of this decision variable substantially reduces the probability that 

Campylobacter prevalence could increase.

Under the indiscriminate scenario in young turkey slaughter establishments, when off-line 

procedures are increased, the model predicts an average decline of 6% in the proportion of 

samples testing Salmonella positive, and an average decline of 26% in the proportion of samples 

testing Campylobacter positive. Under the discriminate scenario, where only unscheduled 

inspection procedures are targeted for increase, the model in young turkey slaughter 

establishments predicts an average decline of 3% in the proportion of samples testing Salmonella

positive, and an average decline of 19% in the proportion of samples testing Campylobacter

positive.

However, the results under the discriminate scenario indicate that more than 90% confidence that 

the combined annual illnesses avoided under the scenario that increases unscheduled procedures 

exceeds zero (i.e., the 10th percentile of the combined Campylobacter illnesses avoided would be 

30). 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

52

The results of the indiscriminant model suggest ambiguous effects of changes in the 

prevalence of inspection activities with respect to Campylobacter occurrence among

young chicken establishments, with the possibility of increased illnesses occurring 

under some scenarios. Data supporting this model, however, was limited the smaller 

number of Campylobacter samples compared with Salmonella samples. Furthermore, 

this effect is primarily driven by the non-compliances decision variable. It is 

important to note that an increase in this variable might be expected to be associated 

with an increased prevalence of contamination, because when an inspector observes 

and records a non compliances in an establishment it is an indication that the 

establishment might not good food safety practices in place. That lack of good food 

safety practices could lead to an increase in positive results on product pathogen 

testing. Indeed the intended effect of changes in this category of procedures is 

arguable due to the diverse nature of potential reasons for non-compliances being 

issued in poultry slaughter establishments. Removing the effect of this decision 

variable in the alternative scenario substantially reduces the probability that 

Campylobacter prevalence could increase.

This latter conclusion is further supported by consideration of the HIMP structural 

variable in the young chicken-Campylobacter regression model (see Appendix). That 

model suggests that participation in HIMP was associated with a reduced prevalence of 

Campylobacter. Although Campylobacter occurrence was not considered in an analysis 

of HIMP establishments (FSIS, 2011a), these regression findings suggest that the positive 

Salmonella implications of that HIMP analysis also apply to Campylobacter. 

In fact, the HIMP structural variable was (highly) statistically significant in all four 

regression models. In each case, that variable implied participation in HIMP was 

associated with reduced pathogen prevalence. While that regression model is not a focus 

of this risk assessment, the regression model’s implication about HIMP establishments 

should provide some measure of confidence about the effects of the proposed rule –

which intends to replicate HIMP across a wider swath of the poultry industry.

 How will the relocation of on-line inspectors to off-line duties, or other areas within 

or outside the establishment, affect human illness?

We assume that the total annual Salmonella and Campylobacter illnesses rates attributed to 

poultry are about 174,686 and 169,005, respectively (1).

Under the indiscriminate scenario, where no specific category of procedures was targeted for

increase, the model estimates an average decrease of 4,440 annual Salmonella illnesses 

attributable to young chicken and young turkey establishments combined, or an estimated

prevention of 2.5% of those illnesses per year. The same scenario estimates an average increase 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

53

of 170 annual Campylobacter illnesses (0.1% of illnesses) attributable to young chicken and

turkey establishments combined. 

Under the discriminate scenario, where only unscheduled procedures are targeted for increase, 

the model estimates an average decrease of 3,980 annual Salmonella illnesses (2.3%) attributable 

to young chicken and young turkey establishments, and an average decrease of 840 annual 

Campylobacter illnesses (0.5%) attributable to young chicken and turkey establishments

combined. 

 Where within the establishment can relocated inspection activities have the most 

impact toward reducing microbial prevalence and corresponding human illness?

The modeling results suggest that increasing unscheduled procedures would be most 

effective in reducing pathogen occurrence on carcasses. The other decision variables 

suggest ambiguous effects from their intended changes when those effects are considered 

across all four pathogen-product models. When targeting unscheduled inspection 

procedures in young chicken establishments, the estimated decrease in prevalence was 

2% for Salmonella and 0.5% for Campylobacter. For young turkey establishments, the 

corresponding decrease in prevalence was 3% and 19% respectively. 

 What is the uncertainty about these effects?

Our modeling approach includes the inherent uncertainty about the relationship between the 

frequency of inspection activities and pathogen prevalence, about the actual change in future 

inspection activities that would likely be observed, and about the representativeness of the rates 

of human Salmonella and Campylobacter illness attributable to poultry. 

It is also advisable when encountering the results of the models might appear counterintuitive or 

inconsistent, when interpreting those results it is important to consider how model uncertainty, 

variability in the number of samples among the product-pathogen pairs, and the number of 

instances of a decision variable might affect the results. For example, among the productpathogen pairs, there are more sample results for Salmonella than for Campylobacter, and more 

for chicken than turkey. The model’s precision and correlation estimates and predicted illness 

reductions are highly dependent on variable sample size and assumptions of uniform data 

quality.
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

54

REFERENCES

Bartholomew M.J., Vose, D.J., Tollefson, L.R., and Travis C.C. (2005). “A Linear Model for Managing 

the Risk of Antimicrobial Resistance Originating in Food Animals”. Risk Analysis 25(1).

CDC, 2001-2007. OutbreakNet: Foodborne Outbreak Online Database, 

http://wwwn.cdc.gov/foodborneoutbreaks/.

Cox, L.A. (2006) Quantitative Health Risk Analysis Methods: Modeling the Human Health Impacts of 

Antibiotics Used in Food Animals. New York: Springer.

Crouch, E.A., LaBarre, D., Golden, N.J., Kause, J.R., and Dearfield, K.L. (2009). Application of 

quantitative microbial risk assessments for estimation of risk management metrics: Clostridium 

perfringens in ready-to-eat and partially cooked meat and poultry products as an example. Journal of 

Food Protection, 72(10):2151–2161.

Ebel, E. D., Williams M. S., Golden, N. J., and Marks, H. (2012). Simplified framework for predicting 

changes in public health from performance standards applied in slaughter establishments. Food 

Control 28:250-257.

FSIS (1996) Nationwide Young Chicken Microbiological Baseline Data

http://www.fsis.usda.gov/OPHS/baseline/broilers_1994-1995.pdf

FSIS (2008a) Risk Assessment for Guiding Public Health-Based Poultry Slaughter Inspection 

http://www.fsis.usda.gov/PDF/Poultry_Slaughter_Risk_Assess_Jan2008.pdf

FSIS (2008b) Nationwide Young Chicken Microbiological Baseline Data 

http://www.fsis.usda.gov/PDF/Baseline_Data_Young_Chicken_2007-2008.pdf

FSIS (2009) Nationwide Microbiological Baseline Data Collection Program Young Turkey 

http://www.fsis.usda.gov/PDF/Baseline_Data_Young_Turkey_2008-2009.pdf

FSIS (2011a) Evaluation of HACCP Inspection Models Project (HIMP), United States Department of 

Agriculture Food Safety and Inspection Service. 

http://www.fsis.usda.gov/PDF/Evaluation_HACCP_HIMP.pdf

FSIS (2011b) Potential Public Health Impact of Salmonella and Campylobacter Performance Guidance 

for Young Chickens and Turkeys.

http://www.fsis.usda.gov/PDF/Potential_Public_Health_Impact_Sal_Campy_Performance_Guidance_Br

oilers_Turkeys_2011.pdf

Haas, C.N., Rose, J.B., and Gerba C.P. (1996). Quantitative Microbial Risk Assessment. New York: John 

Wiley and Sons.

Hosmer, D.W., and Lemeshow, S. (2000) Applied Logistic Regression, Wiley, New York,

National Advisory Committee on Meat and Poultry Inspection (2008) February Meeting 

http://www.fsis.usda.gov/Regulations_&_Policies/National_Advisory_Committee_on_Meat_&_Poult

ry/index.asp#2008

Painter J.A., Hoekstra R.M., Ayers T., Tauxe R.V., Braden C.R., Angulo F.J, and Griffin P.M. (2013).

Attribution of foodborne illnesses, hospitalizations, and deaths to food commodities by using 

outbreak data, United States, 1998–2008. Emerging Infectious Diseases, 19(3):407-415.

http://dx.doi.org/10.3201/eid1903.111866.

Picard RR, and Berk KN, Data Splitting. The American Statistician Vol. 44, no. 2, May 1990.

Press W.H., Teukolsky S.A., Vetterling W.T., and Flannery B.P. (2007) Numerical Recipes 3rd Edition: 

The Art of Scientific computing. Cambridge University Press.
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

55

Scallan, E. Hoekstra, R.M., Angulo, F.J., Tauxe, R.V., Widdowson, M-A, Roy, S.L., Jones, J.L. and

Griffin, P.M. (2011) Foodborne Illness Acquired in the United States—Major Pathogens. Emerging 

Infectious Disease.17 no. 1, 7-15.

Williams M.S., and Ebel, E.D., Vose, D. (2011) Framework for Microbial Food-Safety Risk Assessments 

Amenable to Bayesian Modeling Risk Analysis. Risk Analysis, Vol. 31, no. 4, 548-565.

Williams M. S., and Ebel, E.D. (2012). Estimating Changes in Public Health Following Implementation 

of HACCP in the United States Broiler Slaughter Industry. Foodborne Pathogens and Disease 9:59-

67.

Withee, J., Williams, M., Disney, T., Schlosser, W., Bauer, N., and Ebel, E. (2009). Streamlined analysis 

for evaluating the use of preharvest interventions intended to prevent Escherichia coli O157:H7 

illness in humans. Foodborne Pathogens and Disease, 6(7):1-9.
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

56

APPENDIX 

Regression Modeling Methods and Observational Datasets

This appendix explains the results of regression modeling that are the foundation of this risk 

assessment. It is here that evidence about the occurrence of pathogens on poultry carcasses is 

statistically linked to evidence about possible explanatory variables. Based on these findings, the 

body of this report estimates human illnesses avoided following implementation of the poultry 

slaughter rule.

The proposed rule intends to shift some on-line inspectors to off-line inspection duties. It is 

assumed that the increased off-line inspection work force would– because of apparent 

correlations between performance of inspection procedures and occurrence of pathogens on 

carcasses – influence public health exposures to these foodborne pathogens. 

Regression models were developed to assess the strength of relationships between the 

performance of off-line inspection procedures and the prevalence of Salmonella and 

Campylobacter on young chicken and young turkey carcasses. A binary logistic regression with 

coefficients that are weighted by slaughter volume were estimated. 

Previously, the basic modeling approach was peer reviewed and revised in the 2008 version of 

this risk assessment. In this version, the number of samples and variables evaluated were 

increased. Prior experience with the logistic regression modeling of FSIS poultry slaughter 

sampling verification methods – and inspector procedure data – were used to update the model. 

Also, this version included modifications in response to comments from the National Advisory 

Committee on Meat and Poultry Inspection (NACMPI) after release of the 2008 FSIS Risk 

Assessment (FSIS, 2008a). 

 

Regression Model Approach 

Four basic regression models are estimated to account for the two target pathogens (Salmonella

and Campylobacter) and two major poultry classes (young chickens and young turkeys). For 

each product-pathogen pair, a multivariate logistic model is fit. Each model accounts for 

slaughter volume and the clustered (and correlated) nature of the data available from slaughter 

establishments. Each model uses pseudo-likelihood estimation and employs a correction for 

over-dispersion. 

Each model evaluates pathogen prevalence in relation to four off-line inspection procedure 

categories: (i) scheduled and performed; (ii) scheduled but not performed; (iii) unscheduled; and 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

57

(iv) non-compliances. These four categories of inspection procedures encompass the totality of 

procedure elements across six classes of standard off-line procedures completed by FSIS 

personnel: (i) sanitation; (ii) HACCP; (iii) wholesomeness/economic consumer protection; (iv)

sampling; (v) sanitation performance standards; and (vi) emergency procedures. 

The four defined categories were chosen because the expected/intended effect of the modeled 

alternative scenario was consistent for procedures within each category. For example, the 

proposed increase in off-line inspectors is expected to increase scheduled and performed 

procedures. Similarly, increased availability of off-line inspectors should increase unscheduled 

procedures while reducing scheduled but not performed procedures. It is also assumed that – in 

the long-run – reported non-compliances would decrease with more off-line inspectors in 

slaughter establishments because such establishments would attain appropriate process control. 

Although an alternative approach was explored that collapsed decision variables according to the 

six classes of off-line procedures, this approach created confusion about the intended effect of 

the alternative scenario modeled within each class. For example, a random variable that 

summarized all HACCP procedures would need to increase scheduled and performed procedures 

(and unscheduled procedures) but also decrease scheduled but not performed procedures (and 

non-compliances). Thus, some components of the aggregate HACCP procedures variable 

would be going up (positive signs) while others would be going down (negative signs) – leaving 

any interpretation of effects on pathogen prevalence in establishments as, at the least complex, 

and most likely intractable. 

Because of the observational nature of the data, a set of structural variables were used to control 

confounding. These structural variables pertained to non-inspection activities but included 

consideration of establishment size, temporal, spatial and other establishment factors. 

The regressions are estimated using SAS Proc Logistic version 9.1 software. The logit link 

function is used for the dependent variable and quasi-maximum likelihood estimates of the 

structural and decision variable regression coefficients are obtained using the Fisher scoring 

algorithm. Wald statistics are calculated for assessing the significance of regression coefficients. 

The general form of the binary model relating unconditional probabilities (p) to the regression 

coefficients (bi) in standardized form with Xi’s as the regressors is:

 p = exp(b0 + b1 X1 + b2 X2 + ... + bn Xn) / (1 + exp(b0 + b1 X1 + b2 X2 + ... + bn Xn) )

The logit link function relating the natural log of the odds ratio (p/(1-p)) to the standardized 

regression coefficients is:

 log(p/(1-p)) = b0 + b1 X1 + b2 X2 + ... + bn Xn

A single estimate of the linear component in the prevalence prediction equations is η which is 

equal to the logit or log((p)/(1-p)). 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

58

 η = b0 + b1 X1 + b2 X2 + ... + bp Xp ,

The scalar quantity, η, is simplified to:

 η = BX

in the tables below where B and X are vectors of the b coefficients and the X means combined as 

a linear composition

The estimate of the η vector over all data points is a vector equation. Each vector element 

represents a data point from the X matrix of n data points and p variables plus the intercept.

 η(n,1) = X(n,p+1)b(p+1,1)

At each iteration of the multivariate normal distribution of regression coefficients in the 

simulation model first stage a b* vector is produced.

 b*(n,1) = b + z C, 

where C’C = S, the variance-covariance matrix taken from the SAS model output and C is the 

upper triangular Cholesky factor of S. The result is that for each iteration of b* a new set of 

multivariate normal regression coefficients is estimated. The coefficient vector, b, has the initial 

quasi-likelihood regression coefficient estimates and z is a vector of random normal deviates. So, 

at each iteration the vector, η* is produced.

 η*(n,1) = X(n,p+1)b*(p+1,1)

The equation for estimating a single prevalence for a single η estimate is the inverse logistic 

equation.

 p = 1/(1+exp(-η)) 

The equation for estimating the prevalence vector over all data points is the vectorized inverse 

logistic equation.

 p(n,1)= 1/(1+exp(-η(n,1))) 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

59

At each of the 100,000 iterations of the model the weighted average of the p vector is taken and 

then divided by the baseline prevalence. The weighted prevalence of the p vector is the weighted 

average.

1 1 /

n n

p w p w ave i i i  

The ratio of the average weighted prevalence to the baseline prevalence is the simple ratio of pave

to pbaseline. The baseline prevalence is estimated from the single prevalence estimating equation 

where η is calculated with the b’s taken at their maximum quasi-likelihood estimates. Because 

multiple variables were identified as possible contributors to each logistic regression model the

SAS forward selection procedure in proc logistic was used to include structural variables in the 

model with the largest data set (chicken-Salmonella with n=22,761). This method proved 

adequate for identifying structural variables to include in the model except for the time variable 

which was problematic. The selection of the appropriate time variable as a categorical variable 

representing each month of the time series represented by each of the four models was decided 

by the matrix of AIC, BIC, R-squared (Nagelkerke corrected), Hosmer-Lemeshow test p-value, 

AUC (area under the curve) as the c coefficient, and the validation statistic. Each of these 

statistics was captured from the SAS proc logistic output. This same matrix is used below for the 

evaluation of the final four models. The best categorical time variable combination is identified 

by the smallest AIC and BIC, the largest R-squared, a p-value for the Hosmer-Lemeshow test 

greater than 0.05, a significant c coefficient representing the area under the ROC curve, and a 

decreasing validation statistic. The validation statistic, v, is calculated as the average sum of 

squares of the predicted prevalence minus the cross-validated prevalence (using N-1 deletion in 

proc logistic) divided by (1-leverage(h)). 

 

2

1

(( ) / (1 )) / nv p pcv h N     i i i

The relationship between the validation statistic and R-squared provide evidence that the model 

is not over-parameterized if the Nagelkerke parameter corrected R-squared is increasing but the 

validation statistic is not increasing which means for the sample size the increasing R-squared 

that naturally increases with increasing parameters in the model is balanced by increasing 

information in the model. The point at which R-squared and v increase together is where too 

many parameters have been added to the model even though they may be significant. The 

categorical time selection matrix is shown in Table A-1a for the chicken Salmonella model. 

Since it is well known that Salmonella prevalence is seasonal several categorical time variable 

groups were evaluated that encompassed broad and narrow seasonal definitions. These groups 

were four quarters, 12 months, and the total months of the study which varied among models. 

Because of the unusual data structure that required rehang and post-chill prevalence from the 

baselines in the first 12 months and only post-chill prevalence in the last three 12-month cycles,
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

60

categorical time variables with interaction components were added as additional categorical 

variables for the quarterly (8 components) and 12 month variables (24 components). Due to the 

unusual data structure for a binary logistic model the 39 monthly component variable was 

selected on the basis of the smallest AIC and BIC and the largest R-squared, Hosmer-Lemeshow 

p-value, and c statistic. The validation statistics were all similar in magnitude. This same analysis 

was carried out for each of the four prevalence models. The selection matrix for the four final 

regression models is shown in Table A-1b.

Each binary logistic regression model was evaluated for lack of fit to the data using the standard 

Hosmer-Lemeshow test. All models are required to pass this test for fit to the logistic 

distribution. Model over-dispersion was evaluated with the Pearson chi-square divided by the 

degrees of freedom. The dispersion parameter statistic indicating over-dispersion requires 

multiplication of the covariance matrix to correct for the over-dispersion when greater than 1.05. 

This adjustment converts the regression coefficient estimates to quasi-likelihoods and 

appropriately decreases the regression coefficient significance by increasing the standard errors 

of the estimates effectively converting the model dispersion parameter to unity. 

Unconditional maximum likelihood estimates are used because the total sample size in the data 

structure is sufficiently large. A conditional analysis was assessed, but offered no advantage. 

The conditional analysis shows an advantage when the total sample size is small (in the hundreds 

or less). The expected requirements for a valid unconditional maximum likelihood analysis are 

met for both the Salmonella and Campylobacter datasets.

Data Sets

The core data come from the FSIS “Young Chicken Baseline” (July 2007 through September 

2008) and the FSIS “Young Turkey Baseline” (August 2008 through July 2009). Both baselines 

provide data for Salmonella and Campylobacter sampling at rehang and post-chill locations. 

These data are supplemented with young chicken and young turkey data from the FSIS 

PR/HACCP Salmonella verification program (July 2007 through September 2010). 

Data from 189 young chicken slaughter establishments provided 6,558 Baseline results for 

Salmonella and Campylobacter, with an additional 16,115 PR/HACCP post chill results added to 

the Salmonella dataset. In the Baseline data there were 3,379 samples taken at rehang and 3,278 

taken at post chill. There are 2,790 positive Salmonella results out of 22,671 total results, and 

4,809 positive Campylobacter results out of 6,558 total results.

For young turkeys, there were 65 establishments in the Salmonella dataset and 58 establishments 

in the Campylobacter dataset. The Salmonella dataset had 8,749 samples (2,884 baseline and 

5,865 regulatory) of which 638 (7.29%) were positive and the Campylobacter dataset had 2,884 

samples of which 343 (11.89%) were positive.
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

61

Decision variables: Inspection procedures

There are six general inspection system procedure (ISP) code activity categories captured in the 

FSIS database (Table A-3). Sums of daily scheduled and unscheduled procedures performed –

as well as unperformed procedures and non-compliance reports – for individual establishments 

were matched with same-day positive and negative Salmonella or Campylobacter results. 

The ISP codes from the FSIS database were tabulated daily for all scheduled procedures, 

unscheduled procedures, uncompleted procedures, non-compliances, and total procedures 

performed for each establishment. Scheduled procedures are assigned to each establishment’s 

shift according to a systematic process by an automated Performance-Based Inspection System. 

Unscheduled procedures are performed according to in-establishment inspector availability that 

goes beyond the time allocated for performing scheduled procedures; they typically involve 

regulatory inspection activities such as fecal checks for zero-tolerance beyond the requirement of 

twice per line per shift or other procedures not regularly scheduled or performed. Unscheduled 

procedures are also performed in response to unforeseen hazards such as metal or plastic in 

product which are identified during operations and were not previously seen at this stage in 

operations, or unsanitary conditions arising from Sanitation Standard Operating Procedures 

(SSOP) failures, and PR/HACCP corrective actions. 

Among the six general ISP procedure activities, 47 specific ISP procedure codes were used. 

These included five Sanitation (01) codes, 17 PR/HACCP (03) codes, 11 

Wholesomeness/Economic Consumer Protection (04) codes, six Sampling (05) codes, four Other 

Inspection Requirements (06) codes and four Emergency Activity (08) codes (Table A-3). 

Ultimately, these specific codes were designated in the database as scheduled and performed 

(SP), scheduled and not performed (SNP), unscheduled (U) and non-compliance (NC). The 

inspection procedures used in the model are shown in Table A-3. The code sum variable denotes 

the summed procedure elements on each sample day while the detail sum variable gives specific 

details of each inspection procedure element included in the daily sums.

The total activity for each of these four categories was calculated as the sum across all codes for 

that category. The categories are repetitive such that all are the same except for unscheduled 

procedure which include the extra emergency procedures (08) elements. The four categories are 

sub-categorized with the common name for the procedure followed in parenthesis by the 

procedure element code: 

SP = scheduled and performed procedures for sanitation(01), HACCP(03), 

wholesomeness/economic consumer protection(04), sampling(05), other inspection 

requirements(06), sanitation performance standards (06D01), raw ground (03B), raw not ground 

(03C), fecal check (03J), economic poultry kill (04C04)
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

62

SNP = scheduled not performed procedures for sanitation(01), HACCP (03), 

wholesomeness/economic consumer protection(04), sampling (05), other inspection 

requirements (06), sanitation performance standards(06D01), raw ground (03B), raw not ground 

(03C), fecal check(03J), economic poultry kill (04C04)

U = unscheduled procedures performed for sanitation(01), HACCP(03), 

wholesomeness/economic consumer protection(04), sampling(05), other inspection 

requirements(06), sanitation performance standards(06D01), raw ground(03B), raw not 

ground(03C), fecal check (03J), economic poultry kill (04C04), emergency procedures (08)

NC = non-compliant procedures for sanitation(01), HACCP(03), wholesomeness/economic 

consumer protection(04), sampling(05), other inspection requirements(06), sanitation 

performance standards(06D01), raw ground(03B), raw not ground(03C), fecal check(03J), 

economic poultry kill(04C04).

Structural variables: Non-inspection procedures 

A minimal set of structural variables were found to contribute most to reducing the model 

deviance, controlling confounding and providing the best overall model fit to the data as assessed 

by the Hosmer-Lemeshow test. Structural variables were selected using forward regression in the 

SAS logistic procedure with the probability to enter the model taken as 0.05. Twelve of nineteen 

tested structural variables provided the best model10 (i.e., the inclusion of these structural 

variables significantly reduces the model deviance). These structural variables are: 

1. The re-hang variable distinguishes between locations of sample collection (where 1 

signifies post-chill samples and 0 signifies re-hang samples). 

 

10 Variables that were considered but are excluded because of less contribution or overlapping contribution to the 

model fit to the data are HACCP size (Large, Small, and Very Small according to the Small Business 

Administration definition), production area, inspector positions (numbers of supervisors, on-line inspectors, and offline inspectors), time in weeks (52), time in months (12), time in quarters (4 and 12), time in years (4), and time 

from grant of inspection date.

8 Line speed is defined as the maximum rated speed allowable by regulation for the establishment 

inspection/evisceration system(s) in use at the time of sampling. The units are “birds per minute” or “bpm” and 

refers to the maximum number of carcasses that passes a given point on the line every minute. FSIS does not record 

ongoing line speed in poultry establishments, but is allowed to evaluate line speed with a stop watch. It is evaluated 

as a fixed categorical variable in the models because there is no day-to-day data available.

9 Number of establishment inspectors is specific for each establishment. Therefore it is not an average and varies by 

establishment depending on the number of slaughter lines and the size of the establishment. The larger the 

establishment and the more processing activities the greater the size of the inspection staff required

10 Line count is the number of slaughter lines in the establishment.
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

63

2. The categorical month variable breaks down the time dependency into 39 consecutive 

months. The last study month in 2010 is used as reference. In the case of Campylobacter

this variable was shortened to 12 months due to only one year of data being available.

3. The categorical district variable differentiates the 15 districts. District 90 is used as the 

reference. 

4. Linespeed8(carcasses per minute) - the maximum rated speed allowable by regulation 

for the establishment inspection/evisceration system(s) in use at the time of sampling.

5. Number of establishment inspectors9, 

6. Linecount10 (number of processing lines)

7. The categorical inspection system variable identifies 22 inspection type combinations 

(Table A-7) from the eight basic types (MAESTRO, NELS, Nu-Tech, Nuova, SIS, 

HIMP, Traditional, and Religious Slaughter). Traditional inspection is used as the 

reference. (Table A-4 shows these categories for young chicken while Table A-5 shows 

the shorter list for young turkey)

8. The binary HACCP Inspection Models Project (HIMP) variable appears separately in 

the young chicken models and examines the HIMP establishment model contribution. 

Non-HIMP establishments are used as the reference. 

9. Septicemia-toxemia the number of condemnations of carcasses as the daily total, 

10. Contamination the number of contaminated carcasses condemned for contamination 

(fecal, ingesta, body fluids, etc.) as the daily total 

11. Air sacculitis the number of cases among carcasses as the daily total

12. Synovitis the number of cases among carcasses as the daily total (only a relevant disease 

to the turkey slaughter).
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

64

Final Models 

Tables A-6 and A-7 list the estimated regression coefficients, standard errors, the means and the 

standard deviations for all decision and structural variables in the young chicken models. Tables 

A-8 and A-9 show these estimates for young turkey. The same structural variables were used in 

each of the models to compensate for confounding. Some coefficients have non-significant 

contributions according to a 0.05 significance assumption but were retained in the model for 

consistency across all four models. 

Among the four decision variables, a common finding across all four models was that the 

coefficient for unscheduled procedures was consistently negative. This finding suggests that 

increasing these procedures (while holding other variables constant) would decrease the 

prevalence of Salmonella and Campylobacter. Nevertheless, the unscheduled procedures U 

variable is only statistically significant in the chicken-Salmonella and turkey-Campylobacter

models. 

Among structural variables, a common finding was the (statistically significant) negative 

coefficient for HIMP participation across all four models. The HIMP participation variable is a 

separate structural variable in the chicken models, but it is incorporated into an inspection system 

variable in the turkey models. This finding suggests that when this variable is assigned a value 

of one (indicating participation in HIMP), the prevalence of Salmonella and Campylobacter

predicted by the model is lower than when the alternative (non-HIMP) participation value is 

assigned11. 

The baseline post-chill prevalence predictions from each model are derived by setting the rehang 

structural variable to one. Comparing these predictions to production-volume weighted 

prevalence values from the data suggests that the model reasonably reflects the empiric evidence. 

For example, the chicken-Salmonella model predicts a post-chill prevalence of 0.058 versus a 

weighted average of 0.053 from the raw data. The chicken-Campylobacter model predicts a 

post-chill prevalence of 0.63 versus a weighted average of 0.61 from the raw data. The turkeySalmonella model predicts a post-chill prevalence of 0.046 versus a weighted average of 0.069 

from the raw data. The turkey-Campylobacter model predicts a post-chill prevalence of 0.009 

versus a weighted average of 0.008 from the raw data. Differences between predicted and raw 

values generally reflect the additional weighting for other structural factors (e.g., temporal 

factors, spatial factors, line speed, HIMP participation, etc.) included in the predicted values (but 

not included in the simple weighting of the raw data prevalence levels). 

 

11 This alternative value is -1 for the chicken-Salmonella model and zero for the other models.
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

65

Alternative models were assessed by using 43 and 21 decision variables. Models were compared 

with respect to three statistics: the Akaike Information Criterion (AIC); the Bayesian Information 

Criterion (BIC-Schwartz); and the coefficient of determination (R-squared). For the young 

chicken-Salmonella model, the four decision variable model was best according to all statistics

(Table A-2). For the young chicken-Campylobacter model, the BIC and R-squared statistics 

indicated the four decision variable model was best, although the AIC suggested the 21-variable 

model was preferred. For the young turkey models (Salmonella and Campylobacter), only the 

BIC statistic supported the four variable model while the other models were each preferred by 

different statistics. Nevertheless, to maintain consistency when estimating effects of the

expected model scenarios, the four decision variable model for each product-pathogen pairing

was selected. The R-square values for these chicken-Salmonella, chicken-Campylobacter, 

turkey-Salmonella and turkey-Campylobacter models are 0.2707, 0.4124, 0.0966, and 0.3328 

respectively.

For model evaluation and validation, the datasets used in model development were randomly 

split, the regression coefficients for each subset of data were re-estimated and the stability of the 

prevalence estimates were assessed (Picard et al. 1990). 

Tables A-10 and A-11 show the results of splitting the young chicken datasets for Salmonella. 

Table A-8 shows the parameter estimates for the un-split data model estimates and also for the 

two split halves of data. Table A-11 shows the prevalence estimates from each of the models 

compared to the unadjusted prevalence estimates from the full dataset. The model appears to be 

stable when splitting the data since all estimates for the mean, rehang, and post-chill prevalence 

are in close agreement. Also, the post-chill prevalence is within the sampling error of the postchill prevalence found in the FSIS HIMP report (FSIS 2011a). The only matter of concern is the 

estimation of the mean prevalence which is lower than the unweighted overall prevalence. This 

is likely due to the model weighting compensating from the relatively high prevalence at re-hang 

and the low prevalence at post-chill.

Similarly, the results for splitting the young chicken Campylobacter dataset are shown in Tables 

A-12 and A-13. The parameter estimates from Table A-10 are used to calculate the prevalence 

estimates in Table A-13. The BX element as described above equal to η in Table A-11 is the sum 

of cross products of the B regression parameter and the mean variable components in the model. 

By back transforming BX through the inverse logit function the estimated prevalence is obtained. 

The prevalence estimates for the mean, rehang, and post-chill are consistent within the sampling 

error across the dataset splits. There is no external comparison data for Campylobacter.

Tables A-14 and A-15 show the dataset splitting results for young turkey Salmonella. All the 

prevalence estimates are consistent with sampling error across the splits of data and agree with 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

66

the full dataset estimates. The estimates are in agreement with the high unweighted Salmonella

prevalence. 

Tables A-16 and A-17 show the dataset splitting results for young turkey Campylobacter. This 

model has the smallest number of observations and the expectation with split datasets is that 

there would be some variability not seen with the larger datasets. This is in fact the case. For 

although the rehang and post-chill estimates are in relatively close agreement there is variation 

with the mean estimates which tend to be lower than the unweighted prevalence estimate. Since 

this is a concern further model evaluation is warranted.

Figures A-1through A-4 show the Receiver Operating Characteristic (ROC) plots for the four 

models. The interpretation of these plots is that the model is more predictive the greater the 

distance the curve is away from the imaginary diagonal dividing the figure in halves. The best 

predictors are the closest to the 100% sensitivity and 0% 1-Specifity corner point. Sensitivity is 

defined as the number of positives (taken as the number of positives with a given cut point) 

divided by the total positives (taken as the number of FSIS positive tests) . The false positive rate 

is defined as 1 – Specificity. Where the specificity is the number of negatives (taken as the 

number of negatives with the same cut point) divided by the total negatives (taken as the number 

of FSIS negative tests). The curve described by ROC plot follows the various cut points dividing 

the positives and negatives from the total positives and total negatives thus producing 

corresponding pairs of sensitivity and 1-specificity on the ROC curve. A standard method for 

ROC curve evaluation is to estimate the area under the curve (AUC). This can be done using the 

SAS logistic procedure output for binary response models. The c-statistic is equivalent to the 

area under the curve (AUC). Each c-statistic was evaluated for significance against the c=0.5 

non-significant alternative and passed the z-test with p<0.05 (Hosmer and Lemeshow, 2000). 

The predictive order of c coefficients across the four models is 0.702, 0.710, 0.792, and 0.852, 

making the young chicken Campylobacter the least predictive, young turkey Salmonella

somewhat more predictive, young chicken Salmonella still more predictive, and the young turkey 

Campylobacter model the most predictive. However, all models are sufficiently predictive with 

areas under the curve all greater than 0.7. 

Because the analysis so far shows that the unscheduled procedures regression coefficients are 

consistent in sign and generally significant across all four models, curiosity about what the four 

model sets expanded for only unscheduled procedures might look like was undertaken. Because 

the turkey-Salmonella model does not have a significant aggregate coefficient only the three 

remaining models were considered. Therefore, the previously aggregated sets of sanitation, 

HACCP, wholesomeness/economic consumer protection, sampling, other inspection 

requirements, and emergency procedures were disaggregated and put into each of the models 

with their respective structural variables. Table A-18 shows the results for the four models. The 

results are mixed between significant negative coefficient signs for decreased prevalence and 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

67

significant positive coefficient signs for increased prevalence. Because of the aggregate 

significant negative sign coefficients for two of the four models, focusing on the same type of 

significant negative coefficient in the disaggregated models seemed justified. The HACCP(03), 

wholesomeness/economic consumer protection (04), and other inspection requirements (06)

procedure elements have this characteristic in the chicken-Salmonella model and the 

wholesomeness/economic consumer protection (04) and sampling(05) procedures elements 

behave similarly in the chicken-Campylobacter model with the HACCP(03) element almost 

significant. The turkey-Campylobacter model has the HACCP(03) and other inspection 

requirements (06) elements significant. It is not clear why the sampling(05) and other inspection 

requirements (06) coefficients have significant positive signs in the chicken models. Table A-19

shows the results for further disaggregated models. It becomes clear that the fecal check(03J)

procedures are the drivers decreasing prevalence for HACCP in the chicken-Campylobacter

model and the sanitation performance standards (06D01) procedures are drivers for other 

inspection requirements in the chicken-Salmonella and turkey-Campylobacter models. The 

prevalence estimates from these models shown in Table A-20 indicate the same consistent 

predictability and validity associated with the subset models that was verified with the same 

collinearity analysis.

PRIA Dataset Evaluation

Because the original observational dataset used to develop the four models for scenario analysis 

excluded some of the establishments that are expected to adopt the proposed inspection system a 

presumptive dataset including all poultry slaughter establishments was created. The overarching assumption required a shift of the majority of on-line inspectors to off-line inspection 

duties while leaving one inspector on-line for final carcass inspection according to the 

Preliminary Regulatory Impact Analysis (PRIA) of the proposed poultry slaughter rule. A

simulated dataset corresponding to all establishments expected to adopt the proposed inspection 

system was created. Looking at the establishment breakdown by the small business 

administration (SBA) size classification of large, small, and very small establishments (L, S, V) 

it was noticed that there is an imperfect match between the observed establishment size and the 

total number of establishments available. Additionally none of the very small establishments in 

the observational dataset are expected to adopt the proposed inspection system. This is an 

assumption taken from the PRIA where very small establishments are not expected to accept the 

proposed system due to economic burden. Table A-21 shows the breakdown for SBA size for the 

observational study and Table A-22 shows the expected size breakdown for establishments that 

would adopt the proposed inspection system according to the PRIA. Therefore, four simulated 

datasets were constructed based on the known characteristics studied in the observational 

analysis and using substituted known values according to matched establishment characteristics 

based on the list of establishments expected to adopt the proposed inspection system. Repeated 

random selection of establishments with matching characteristics created an averaged (expected 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

68

value) dataset corresponding to the characteristics of the establishment distribution of 

establishments expected to adopt the proposed inspection system. 

It was found that each of the four observed datasets could be recast to resemble the distribution 

of establishments expected to adopt the proposed inspection system as shown in Table A-23. The 

19 establishments in the “other” category were placed in either the young chicken or the young

turkey datasets according to size and predominant production characteristics. The 19 “other” 

establishments accounted for all the very small establishments in the expected datasets. 

However, upon further inspection it became apparent that all but the small establishments in the 

Salmonella and Campylobacter young chicken datasets were subsets of the original four 

observed datasets. This meant that 4% and 10% of the small plants from these two datasets 

would have to be reused in recasting the expected distributions for the young chicken Salmonella 

and Campylobacter models. This was not a problem when all four datasets were recast as 

expected datasets for logistic regression analysis and the four expected dataset prevalence 

estimates were found to be within the prevalence error of each the observed datasets (Table A24). It is therefore assumed that the results of the four observed dataset models contain the 

results of the four expected dataset models and that no further analysis is required because the 

conclusions of the risk assessment contain the same conclusions that can be drawn from the 

expected datasets.
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

69

Figure A-1. ROC Plot of Sensitivity against 1-Specificity with an AUC of 0.792 for the Young 

Chicken Salmonella Predictive Model 

Figure A-2. ROC Plot of Sensitivity against 1-Specificity with an AUC of 0.702 for the Young 

Chicken Campylobacter Predictive Model 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

70

Figure A-3. ROC Plot of Sensitivity against 1-Specificity with an AUC of 0.710 for the Young 

Turkey Salmonella Predictive Model 

Figure A-4. ROC Plot of Sensitivity against 1-Specificity with an AUC of 0.852 for the Young 

Turkey Campylobacter Predictive Model 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

71

APPENDIX TABLES

Table A-1a. Categorical Time Selection Matrix for Chicken Salmonella Model

Statistic month391quarter42 month123quarter84 month245

AIC668,942.52 69,317.84 69,124.08 69,312.80 69,090.97

BIC769,633.00 69,727.31 69,597.78 69,746.36 69,652.99

R-Sq80.2707 0.2645 0.2674 0.2645 0.5535

p(H-L)90.4537 0.0065 0.0005 0.0034 0.2137

c

10 0.792 0.784 0.787 0.784 0.787

Validation11 0.0002 0.0002 0.0011 0.0002 0.0002

Parameters12 85 50 58 53 69

N 22,671 22,671 22,671 22,671 22,671

1 Model with 39 month categorical time scale

2 Model with 4 quarter categorical time scale

3 Model with 12 month categorical time scale

4 Model with 4 quarters and 4 interactions

5 Model with 12 months and 12 interactions

6 Akaike information criterion- smaller is better

7 Bayesian information criterion (Schwarz) – smaller is better

8 Adjusted R-Square (Nagelkerke) – larger is better

9

p-value for Hosmer-Lemeshow test – greater than 0.05 is better

10 

c-statistic – greater than 0.7 is better

11 Validation statistic- smaller is better

12 Number of parameters in model minus intercept
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

72

Table A-1b. Selection Matrix Variables for the Final Four Prevalence Models

Statistic CS1 CC2TS3TC4

AIC568,942.52 36,661.69 4,438.87 1,283.20

BIC669,633.00 37,055.42 4,913.01 1,521.88

R-Sq70.2707 0.4124 0.0966 0.3328

p(H-L)80.4537 0.7585 0.981 0.5631

c

9

0.792 0.702 0.71 0.852

Validation10 0.0002 0.0003 0.0002 0.0011

Parameters11 85 57 66 39

N 22,671 6,558 8,749 2,884

1 Chicken Salmonella Model

2 Chicken Campylobacter Model

3 Turkey Salmonella Model

4 Turkey Campylobacter Model

5 Akaike information criterion- smaller is better

6 Bayesian information criterion (Schwarz) – smaller is better

7 Adjusted R-Square (Nagelkerke) – larger is better

8

p-value for Hosmer-Lemeshow test – greater than 0.05 is better

9

c-statistic – greater than 0.7 is better

10 Validation statistic- smaller is better

11 Number of parameters in model minus intercept
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

73

Table A-2. Selection Matrix Variables for Prevalence Model Comparison

Model Criterion Value model Criterion Value

CS-431 AIC713730.6 CC-434 AIC 38935.89

CS-43 SC814734.2 CC-43 SC 39354.04

CS-43 -2 Log L913480.6 CC-43 -2 Log L 38812.69

CS-43 R-Square10 0.2673 CC-43 R-Square 0.4380

CS-212 AIC 13719.5 CC-215 AIC 37681.16

CS-21 SC 14538.4 CC-21 SC 38085.84

CS-21 -2 Log L 13515.5 CC-21 -2 Log L 37561.93

CS-21 R-Square 0.2645 CC-21 R-Square 0.4239

CS-4

3 AIC 13535.3 CC-46 AIC 36661.69

CS-4 SC 14225.8 CC-4 SC 37055.42

CS-4 -2 Log L 13363.3 CC-4 -2 Log L 36545.69

CS-4 R-Square 0.2707 CC-4 R-Square 0.4124

1 Chicken Salmonella model- 43 decision parameters

2 Chicken Salmonella model- 21 decision parameters

3 Chicken Salmonella model- 4 decision parameters

4 Chicken Campylobacter model- 43 decision parameters

5 Chicken Campylobacter model- 21 decision parameters

6 Chicken Campylobacter model- 4 decision parameters

7 Akaike information criterion – smaller is better

8 Bayesian information criterion (Schwartz) – smaller is better

9 

-2 Log Likelihood – smaller is better

10 R-Square (Nagelkerke) – larger is better
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

74

Table A-2. (continued)

Model Criterion Value model Criterion Value

TS-4311 AIC75141.39 TC-4314 AIC 3576.64

TS-43 SC86022.06 TC-43 SC 4755.80

TS-43 -2 Log L94892.52 TC-43 -2 Log L 3243.41

TS-43 R-Square10 0.1134 TC-43 R-Square 0.5407

TS-2112 AIC 4679.51 TC-2115 AIC 1282.97

TS-21 SC 5295.49 TC-21 SC 1628.31

TS-21 -2 Log L 4505.44 TC-21 -2 Log L 1167.20

TS-21 R-Square 0.1013 TC-21 R-Square 0.3440

TS-4

13 AIC 4438.87 TC-416 AIC 1283.20

TS-4 SC 4913.01 TC-4 SC 1521.88

TS-4 -2 Log L 4304.87 TC-4 -2 Log L 1203.20

TS-4 R-Square 0.0966 TC-4 R-Square 0.3328

7 Akaike information criterion – smaller is better

8 Bayesian information criterion (Schwartz) – smaller is better

9

-2 Log Likelihood – smaller is better

10 R-Square (Nagelkerke) – larger is better

11 Turkey Salmonella model- 43 decision parameters

12 Turkey Salmonella model- 21 decision parameters

13 Turkey Salmonella model- 4 decision parameters

14 Turkey Campylobacter model- 43 decision parameters

15 Turkey Campylobacter model- 21 decision parameters

16 Turkey Campylobacter model- 4 decision parameters
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

75

Table A-3. Inspection System Procedure (ISP) Code Listing of Individual and Summed Codes, 

used as Independent Variable Identifiers for Daily Sums of Procedures Scheduled, Performed, 

Unscheduled, and Non-Compliant in the Binary Logistic Regression Model

No. Code 

Sum*

Activity Detail

Sum**

Elements No. ISP Code Procedures

1 sum01 sanitation sum01A verification 24 01A01 sanitation SOP

2 sum01 sanitation sum01B preoperational 25 01B01 m/v/r/ca/fu4

3 sum01 sanitation sum01B preoperational 26 01B02

01B01 

verification

4 sum01 sanitation sum01C operational 27 01C01 m/v/r/ca/fu4

5 sum01 sanitation sum01C operational 28 01C02

01C01 

verification

6 sum03 HACCP sum03A verification 29 03A01 HACCP plan 

7 sum03 HACCP sum03B raw ground 30 03B01 m/v/r/ca/fu4

8 sum03 HACCP sum03B raw ground 31 03B02

03B01 

verification

9 sum03 HACCP sum03C raw not ground 32 03C01 m/v/r/ca/fu4
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

76

Table A-3. (continued)

No. Code 

Sum*

Activity Detail

Sum**

Elements No. ISP Code Procedures

10 sum03 HACCP sum03C raw not ground 33 03C02

03C01 

verification

11 sum03 HACCP sum03E not heat treated-shelf stable 34 ‘03E01 m/v/r/ca/fu4

12 sum03 HACCP sum03F not heat treated-shelf stable 35 ‘03E02

03E01 

verification

13 sum03 HACCP sum03F heat treated-shelf stable 36 03F01 m/v/r/ca/fu4

14 sum03 HACCP sum03F heat treated-shelf stable 37 03F02

03F01 

verification

15 sum03 HACCP sum03G fully cooked-not shelf stable 38 03G01 m/v/r/ca/fu4

16 sum03 HACCP sum03G fully cooked-not shelf stable 39 03G02

03G01 

verification

17 sum03 HACCP sum03H heat treated-not fully cooked 40 03H01 m/v/r/ca/fu4

18 sum03 HACCP sum03H heat treated-not fully cooked 41 03H02

03H01 

verification

19 sum03 HACCP sum03I secondary inhibitors-not shelf stable 42 03I01 m/v/r/ca/fu4

20 sum03 HACCP sum03I secondary inhibitors-not shelf stable 43 03I02 03I01 verification

21 sum03 HACCP sum03J slaughter/fecal check 44 03J01 m/v/r/ca/fu4

22 sum03 HACCP sum03J slaughter/fecal check 45 03J02

03J01 

verification

23 sum04 W/ECP1sum04A01 yield/shrink 46 04A01 m/v/r/ca/fu4

47 sum04 W/ECP1sum04A02 product solution formulation 71 04A02 m/v/r/ca/fu4

48 sum04 W/ECP1sum04A03 comminuted/mechanically separated 72 04A03 m/v/r/ca/fu4

49 sum04 W/ECP1sum04A04 battered products 73 04A04 m/v/r/ca/fu4

50 sum04 W/ECP1sum04B01 product meets standard 74 04B01 m/v/r/ca/fu4

51 sum04 W/ECP1sum04B02 packaging/labeling standards 75 04B02 m/v/r/ca/fu4

52 sum04 W/ECP1sum04B03 stated label net weight 76 04B03 m/v/r/ca/fu4

53 sum04 W/ECP1sum04B04 product identification 77 04B04 m/v/r/ca/fu4

54 sum04 W/ECP1sum04C02 humane slaughter requirements 78 04C02 m/v/r/ca/fu4

55 sum04 W/ECP1sum04C03 non-food safety product req. 79 04C03 m/v/r/ca/fu4

56 sum04 W/ECP1sum04C04 poultry humane slaughter 

(economic) 80 04C04 m/v/r/ca/fu4

57 sum05 sampling sum05A01 generic E. coli record plan 81 05A01 verification

58 sum05 sampling sum05A02 generic E. coli record review 82 05A02 m/v/r/ca/fu4

59 sum05 sampling sum05A03 Salmonella in raw products 83 05A03 sample collection
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

77

Table A-3. (continued)

No. Code 

Sum*

Activity Detail

Sum**

Elements No. ISP Code Procedures

60 sum05 sampling sum05B01 random product sample 84 05B01 sample 

collection

61 sum05 sampling sum05B02 CS/DO/headquarters request585 05B02 sample 

collection

62 sum05 sampling sum05C01 random residue sample 86 05C01 sample 

collection

63 sum06 OIR/SPS2sum06A01 export regulation compliance 87 06A01 m/v/r/ca/fu4

64 sum06 OIR/SPS2sum06B01

custom exempt retail 

compliance

88 06B01 m/v/r/ca/fu4

65 sum06 OIR/SPS2sum06D01 sanit. performance standards 89 06D01 m/v/r/ca/fu4

66 sum06 OIR/SPS2sum06D02 facility sanitation compliance 90 06D02 m/v/r/ca/fu4

67 sum08 emergency3sum08S14 water systems 91 08S14

unscheduled 

check

68 sum08 emergency3sum08S15 processing/manufacture 92 08S15

unscheduled 

check

69 sum08 emergency3sum08S16 storage areas 93 08S16

unscheduled 

check

70 sum08 emergency3sum08S17 shipping/receiving 94 08S17

unscheduled 

check

* Code Sum refers to the ISP code summation variable that contains the Detail Sum elements

** Detail Sum refers to the procedure summed within given code summed ISP elements with their descriptions

1W/ECP = wholesomeness/economic consumer protection

2 OIR/SPS = other inspection requirements/sanitation performance standards

3 emergency procedures performed under homeland security requirements

4 m/v/r/ca/fu = monitoring/verification/records checks/corrective action to non-compliance/follow up reassessment 

to corrective action 

5 Circuit supervisor, district office, or headquarters request
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

78

Table A-4. Combinations of Eight Basic Inspection Systems Used in FSIS Inspected Chicken 

Establishments

number Inspection System line speed1

1 MAESTRO.2140 bpm

2 MAESTRO, Nu-Tech 140 bpm, 140 bpm

3 MAESTRO, Religious 140 bpm, 14-35 bpm

4 MAESTRO, SIS 140 bpm

5 NELS391 bpm

6 NELS, MAESTRO 91 bpm, 140 bpm

7 NELS, Nu-Tech, MAESTRO 91 bpm, 140 bpm, 140 bpm

8 NELS, Nu-Tech 91 bpm, 140 bpm

9 NELS, Nu-Tech, Religious 91 bpm, 140 bpm, 14-35 bpm

10 NELS, Religious 91 bpm, 14-35 bpm

11 NELS, SIS 91 bpm, 70 bpm

12 NELS, SIS, Religious 91 bpm, 70 bpm, 14-35 bpm

13 Nuova4140 bpm

14 Nu-Tech5142 bpm

15 Nu-Tech, Religious 142 bpm, 14-35 bpm

16 SIS670 bpm

17 SIS, MAESTRO 70 bpm, 140 bpm

18 SIS, MAESTRO, Religious 70bpm, 140 bpm, 14-35 bpm

19 SIS, Religious 70 bpm, 14-35 bpm

20 SIS- NuOva 70 bpm, 140 bpm

21 SIS, Nu-Tech 70 bpm, 142 bpm

22 Traditional735 bpm

23 Religious Slaughter814-35 bpm

24 HIMP9170 bpm10

1 maximum regulatory line speed in birds per minute (bpm) for each inspection type taken as the average over the 

number of lines per establishment

2 MAESTRO (Meyn Poultry, Gainesville, GA)

3 New Enhanced Line Speed (NELS) various manufacturers

4 Nuova (Marel Stork Poultry Processing, Gainesville, Ga)

5 Nu_Tech (Stork Gamco, Gainesville, Ga)

6 Streamlined Inspection System (SIS) various manufacturers

7 Traditional Inspection System

8 Religious Slaughter (Buddhist, Kosher, Islamic)

9 HACCP Inspection Models Project (HIMP)

10 HIMP establishments can exceed this limit
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

79

Table A-5. Basic Inspection Systems Used in FSIS Inspected Turkey Establishments

number Inspection System line speed1

1 HIMP, NTIS no maximum

2 NTIS-1 or 2 J or Bar-Type1

3 NTIS-1 or 2 

(modified)

Bar-Type2

4 NTIS - Other not specific

1 J-type - one inspector 32 bpm (<16#), 30 bpm (>16#)

 J-type - two inspectors 51 bpm (<16#), 41 bpm (>16#)

 Bar-type - one inspector 25 bpm (<16#), 21 bpm (>16#)

 Bar-type two inspectors 45 bpm (<16#), 35 bpm (>16#)

2 Bar-type one inspector 32 bpm (<16#), 30 bpm (>16#)
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

80

Table A-6. Parameter Estimates for Young Chicken Salmonella Model Used in Scenario 

Analysis

Parameter Estimate Std Error p-value Mean Std Dev

Intercept -1.9647 0.3123 <0.0001* 1 0

rehang -1.1699 0.0162 <0.0001* 0.7107 0.7035

loglinespeed 0.4675 0.1553 0.0013* 2.0266 0.1786

logInspectors -0.2878 0.0823 0.0002* 1.2820 0.2675

lines -0.0866 0.0184 <0.0001* 2.1464 1.0877

Himp -0.068 0.0267 0.0054* 0.7518 0.6594

month1 0.3558 0.0846 <0.0001* -0.0110 0.1598

month2 0.0076 0.0537 0.4437 0.0047 0.2035

month3 0.4576 0.0473 <0.0001* 0.0090 0.2137

month4 0.2492 0.0493 <0.0001* 0.0076 0.2103

month5 0.302 0.0479 <0.0001* 0.0094 0.2145

month6 0.2414 0.0502 <0.0001* 0.0067 0.2082

month7 0.6349 0.0485 <0.0001* 0.0063 0.2073

month8 0.0956 0.0522 0.0335* 0.0056 0.2057

month9 0.1752 0.0499 0.0002* 0.0078 0.2107

month10 0.2302 0.0494 <0.0001* 0.0080 0.2112

month11 -0.1409 0.0525 0.0036* 0.0075 0.2102

month12 0.1534 0.0504 0.0012* 0.0073 0.2097

month13 0.0988 0.0704 0.0803 0.0100 0.2159

month14 0.0228 0.0669 0.3666 0.0152 0.2273

month15 0.0969 0.0753 0.0991 0.0049 0.2040

month16 -0.2017 0.1055 0.0280* -0.0043 0.1799

month17 -0.7525 0.1801 <0.0001* -0.0108 0.1606

month18 0.0571 0.0707 0.2097 0.0082 0.2116

month19 0.3435 0.059 <0.0001* 0.0133 0.2232

month20 0.2108 0.0685 0.0010* 0.0075 0.2100

month21 -0.5773 0.1134 <0.0001* 0.0000 0.1916

month22 -0.4173 0.0776 <0.0001* 0.0157 0.2285

month23 -0.4668 0.0770 <0.0001* 0.0184 0.2341

month24 -0.3467 0.0821 <0.0001* 0.0099 0.2156

month25 0.0985 0.0731 0.0889 0.0065 0.2077

month26 -0.1432 0.0748 0.0278* 0.0105 0.2169

month27 -0.2187 0.0751 0.0018* 0.0113 0.2189
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

81

Table A-6. (continued)

Parameter Estimate Std Error p-value Mean Std Dev

month28 -0.0124 0.0846 0.4417 0.0014 0.1952

month29 0.2626 0.0865 0.0012* -0.0026 0.1845

month30 0.0750 0.1045 0.2365 -0.0056 0.1763

month31 0.6006 0.1286 <0.0001* -0.0130 0.1535

month32 -0.2403 0.1991 0.1137 -0.0142 0.1492

month33 -0.2092 0.0766 0.0032* 0.0095 0.2147

month34 -0.1156 0.0544 0.0168* 0.0363 0.2678

month35 -0.5026 0.0634 <0.0001* 0.0380 0.2706

month36 -0.3344 0.064 <0.0001* 0.0298 0.2562

month37 -0.0387 0.0698 0.2896 0.0134 0.2235

month38 0.0351 0.0775 0.3253 0.0061 0.2069

District1 -0.3544 0.1256 0.0024* -0.2177 0.4311

District2 -0.5096 0.0977 <0.0001* -0.2097 0.4440

District3 0.3047 0.0815 <0.0001* -0.2113 0.4416

District4 0.3918 0.1251 0.0009* -0.2174 0.4315

District5 -0.1139 0.0561 0.0212* -0.1793 0.4894

District6 -0.0603 0.0388 0.0601 -0.0857 0.5982

District7 -0.0185 0.0491 0.3532 -0.1513 0.5260

District8 -1.2824 0.2123 <0.0001* -0.2219 0.4240

District9 0.5377 0.0469 <0.0001* -0.1615 0.5131

District10 0.2689 0.056 <0.0001* -0.1828 0.4845

District11 0.5986 0.1054 <0.0001* -0.2130 0.4388

District12 0.3913 0.0449 <0.0001* -0.1440 0.5350

District13 -0.0510 0.0381 0.0904 -0.0781 0.6056

District14 0.0505 0.0392 0.0988 -0.1080 0.5756

InspSysMAESTRO -0.1228 0.0392 0.0008* 0.3088 0.5336

InspSysMAESTRO,Nu-Tech -0.1219 0.0777 0.0583 -0.0144 0.2381

InspSysMAESTRO,Religio 0.0269 0.0716 0.3536 -0.0106 0.2461

InspSysMAESTRO-SIS -0.5622 0.1875 0.0014* -0.0315 0.1968

InspSysNELS 0.0633 0.0414 0.0631 0.0670 0.3658

InspSysNELS,MAESTRO 0.5052 0.0851 <0.0001* -0.0236 0.2171

InspSysNELS,NTIS,MAEST 0.7756 0.1451 <0.0001* -0.0325 0.1942

InspSysNELS,Nu-Tech -0.3414 0.1383 0.0068 -0.0267 0.2095
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

82

Table A-6. (continued)

Parameter Estimate Std Error p-value Mean Std Dev

InspSysNELS,Nu-Tech,Re 0.6381 0.1179 <0.0001* -0.0304 0.1998

InspSysNELS,Religious 0.3605 0.0696 <0.0001* -0.0080 0.2515

InspSysNELS,SIS 0.2929 0.0967 0.0013* -0.0220 0.2209

InspSysNELS,SIS,Religi -0.2293 0.1551 0.0697 -0.0296 0.2020

InspSysNu-Ova -0.8808 0.3005 0.0017* -0.0333 0.1919

InspSysNu-Tech -0.1878 0.0477 <0.0001* 0.0886 0.3899

InspSysNu-Tech,Religio -0.4308 0.1088 <0.0001* -0.0286 0.2047

InspSysSIS -0.0361 0.0401 0.1840 0.1452 0.4420

InspSysSIS,MAESTRO 0.3542 0.0586 <0.0001* 0.0011 0.2690

InspSysSIS,MAESTRO,Rel 0.2889 0.1318 0.0142* -0.0292 0.2031

InspSysSIS,Religious S -0.3865 0.1259 0.0011* -0.0255 0.2123

InspSysSIS-Nu-Tech 0.066 0.0898 0.2312 -0.0198 0.2260

InspSysSIS-NuOva -0.8173 0.1442 <0.0001* -0.0289 0.2037

Sep_Tox 0.0001 0.000001 <0.0001* 258.0830 282.0689

Contam 0.0005 0.0001 <0.0001* 34.1020 84.5970

AirSac 0.0000 0.0001 0.496 134.3891 1101.8910

sum_SP 0.0021 0.0021 0.1587 12.9624 6.0291

sum_SNP 0.0461 0.0093 <0.0001* 0.5536 1.0524

sum_U -0.0032 0.0009 0.0002* 29.1353 20.5648

sum_NC 0.0091 0.0096 0.1716 0.7834 1.1422

*Significant difference for two-sided t-test on the regression coefficient
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

83

Table A-7. Parameter Estimates for Young Chicken Campylobacter Model Used in Scenario 

Analysis

Parameter Estimate Std Error p-value Mean Std Dev

Intercept 0.3286 5.8184 0.4775 1 0

Rehang -0.6359 0.0134 <0.0001* -0.0003 1.0001

loglinespeed 1.2788 0.2047 <0.0001* 2.0428 0.1626

logInspectors -0.9754 0.1212 <0.0001* 1.3214 0.2366

lines 0.0497 0.0237 0.0180* 2.1751 1.0420

Himp -0.4332 0.0689 <0.0001* 0.1327 0.3392

month1 -0.1895 0.0713 0.0039* -0.063 0.3316

month2 -0.0734 0.0429 0.0436* -0.0085 0.4102

month3 0.5022 0.0444 <0.0001* 0.0063 0.4279

month4 0.2178 0.0427 <0.0001* 0.0012 0.4221

month5 0.2193 0.0418 <0.0001* 0.0075 0.4293

month6 0.1160 0.0430 0.0035* -0.0018 0.4184

month7 -0.1053 0.0416 0.0057* -0.0032 0.4168

month8 -0.0817 0.0424 0.0270* -0.0055 0.4140

month9 0.1315 0.0423 0.0009* 0.0018 0.4228

month10 -0.3165 0.0392 <0.0001* 0.0026 0.4237

month11 -0.2484 0.0400 <0.0001* 0.0014 0.4222

District1 -0.3553 0.1548 0.0109* -0.2318 0.4404

District2 -0.3201 0.1995 0.0543 -0.2353 0.4345

District3 -0.5514 0.1431 <0.0001* -0.2315 0.4409

District4 -0.3275 0.2135 0.0625 -0.2351 0.4348

District5 0.1098 0.0755 0.0730 -0.1991 0.4906

District6 -0.0589 0.0505 0.1218 -0.0820 0.6251

District7 0.2839 0.0656 <0.0001* -0.1700 0.5296

District8 -0.6106 0.1520 <0.0001* -0.2336 0.4373

District9 0.4256 0.0920 <0.0001* -0.2052 0.4817

District10 0.1869 0.0889 0.0178* -0.2072 0.4788

District11 1.5979 0.2689 <0.0001* -0.2321 0.4399

District12 -0.2427 0.0589 <0.0001* -0.1475 0.5570

District13 -0.3898 0.0510 <0.0001* -0.0907 0.6169

District14 0.3007 0.0520 <0.0001* -0.0944 0.6133

InspSysMAESTRO -0.8593 5.8054 0.4412 0.3385 0.5116

InspSysMAESTRO,Nu-Tech -0.4422 5.8058 0.4696 0.0127 0.2243
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

84

Table A-7. (continued)

Parameter Estimate Std Error p-value Mean Std Dev

InspSysMAESTRO,Religio -0.0386 5.8061 0.4973 0.0041 0.2048

InspSysMAESTRO-SIS -0.5936 5.8080 0.4593 -0.0131 0.1566

InspSysNELS -0.7700 5.8054 0.4472 0.0718 0.3233

InspSysNELS,MAESTRO -0.4104 5.8060 0.4718 0.0008 0.1964

InspSysNELS,NTIS,MAEST -1.8641 5.8112 0.3742 -0.0168 0.1441

InspSysNELS,Nu-Tech 10.621 116.1000 0.4636 -0.0177 0.1408

InspSysNELS,Nu-Tech,Re -0.7159 5.8065 0.4509 -0.0087 0.1705

InspSysNELS,Religious -0.9813 5.8061 0.4329 0.0035 0.2033

InspSysNELS,SIS -0.4999 5.8065 0.4657 -0.0055 0.1797

InspSysNELS,SIS,Religi -0.1027 5.8079 0.4929 -0.0128 0.1576

InspSysNu-Tech -0.8998 5.8055 0.4384 0.1136 0.3722

InspSysNu-Tech,Religio -0.2656 5.8062 0.4818 -0.0029 0.1869

InspSysSIS -0.5426 5.8054 0.4628 0.1629 0.4173

InspSysSIS,MAESTRO -0.8898 5.8056 0.4391 0.0178 0.2353

InspSysSIS,MAESTRO,Rel 0.4083 5.8089 0.4720 -0.0134 0.1556

InspSysSIS,Religious S -1.1934 5.8100 0.4186 -0.0131 0.1566

InspSysSIS-Nu-Tech -0.0369 5.8069 0.4975 -0.0069 0.1758

InspSysSIS-NuOva -0.1944 5.8075 0.4866 -0.0119 0.1606

Sep_Tox 0.0005 0.0001 <0.0001* 295.9538 265.3369

Contam -0.0003 0.0001 0.0014* 49.3667 98.6220

AirSac 0.0000 0.0000 0.1587 237.9061 2006.1750

sum_SP 0.0076 0.0065 0.1212 6.5629 0.8762

sum_SNP 0.0198 0.0107 0.0321* 0.6929 0.2600

sum_U -0.0014 0.0011 0.1016 31.0927 7.3283

sum_NC -0.0157 0.0074 0.0170* 1.3634 0.3212

*Significant difference for two-sided t-test on the regression coefficient
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

85

Table A-8. Parameter Estimates for Young Turkey Salmonella Model Used in Scenario Analysis

Parameter Estimate Std Error p-value Mean Std Dev

Intercept -3.5814 1.0534 0.0003* 1 0

rehang -0.4599 0.0622 <0.0001* 0.6704 0.7421

loglinespeed -0.2945 0.8881 0.3701 1.4698 0.1246

logInspectors 1.5612 0.5439 0.0020* 0.9141 0.1980

lines -0.1717 0.2275 0.2252 1.2725 0.4453

month1 0.7670 0.2418 0.0008* 0.0025 0.2149

month2 0.8158 0.2844 0.0021* -0.0057 0.1947

month3 0.9719 0.3408 0.0022* -0.0129 0.1749

month4 0.4361 0.3146 0.0829 -0.0064 0.1929

month5 0.6889 0.3059 0.0121* -0.0081 0.1884

month6 1.1158 0.2472 <0.0001* -0.0048 0.1971

month7 0.0318 0.3150 0.4598 -0.0053 0.1959

month8 -0.2106 0.3494 0.2733 -0.0077 0.1896

month9 0.0922 0.3317 0.3905 -0.0071 0.1911

month10 0.4242 0.3176 0.0909 -0.0082 0.1881

month11 0.3148 0.3469 0.1821 -0.0119 0.1779

month12 0.5751 0.4077 0.0792 -0.0154 0.1673

month13 -0.0699 0.5346 0.4480 -0.017 0.1623

month14 0.1461 0.2439 0.2746 0.0066 0.2242

month15 0.1761 0.2183 0.2099 0.0186 0.2489

month16 -0.0216 0.2318 0.4629 0.0200 0.2515

month17 -0.5254 0.2975 0.0387* 0.0134 0.2385

month18 -0.4990 0.2798 0.0373* 0.0158 0.2433

month19 -0.1435 0.2746 0.3006 0.0117 0.2349

month20 0.0301 0.2551 0.4530 0.0114 0.2345

month21 -0.2562 0.2700 0.1714 0.0121 0.2359

month22 -0.1792 0.2304 0.2184 0.0369 0.2815

month23 -0.3559 0.2287 0.0599 0.0554 0.3099

month24 0.3405 0.1880 0.0351* 0.0610 0.3178

month25 0.2955 0.2031 0.0729 0.0395 0.2858

month26 0.5999 0.3965 0.0652 -0.0122 0.1769

month27 -3.2689 2.8210 0.1233 -0.0138 0.1722

month28 -0.6259 0.6202 0.1565 -0.0090 0.1859
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

86

Table A-8. (continued)

Parameter Estimate Std Error p-value Mean Std Dev

month29 -3.4238 2.8103 0.1116 -0.0117 0.1785

month30 -0.0102 0.5741 0.4929 -0.0128 0.1752

month31 0.0199 0.4202 0.4811 -0.0086 0.1871

month32 0.5131 0.3604 0.0773 -0.0099 0.1834

month33 -1.4332 0.6777 0.0172* -0.0046 0.1977

month34 0.1280 0.3056 0.3377 0.0053 0.2211

month35 -0.4092 0.3445 0.1175 0.0142 0.2401

month36 0.0642 0.2774 0.4085 0.0184 0.2485

month37 0.5597 0.2781 0.0221* 0.0033 0.2167

month38 0.9966 0.2835 0.0002 -0.0045 0.198

district1 -0.0841 0.1910 0.3299 0.1021 0.3295

district2 0.1486 0.2300 0.2591 0.0354 0.2261

district3 0.5899 0.1464 <0.0001* 0.1605 0.3894

district4 0.3528 0.1979 0.0373* 0.0794 0.3001

district5 -1.3221 0.4326 0.0011* 0.0350 0.2251

district6 0.0284 0.1970 0.4427 0.0769 0.2965

district7 -1.3599 0.6720 0.0215* 0.0158 0.1801

district8 0.3582 0.2027 0.0386* 0.0552 0.2628

district9 0.5694 0.1552 0.0001* 0.1005 0.3276

district10 -0.1438 0.2189 0.2556 0.0655 0.2795

district11 0.4412 0.8227 0.2959 -0.0046 0.1110

district12 -0.0660 0.2531 0.3971 0.0501 0.2539

district13 0.5190 0.1709 0.0012* 0.1098 0.3387

InspSysHIMP -0.4680 0.2356 0.0235* 0.0507 0.3450

InspSysNTIS -0.1056 0.1150 0.1793 0.7058 0.5278

InspSysOtherNTIS 0.7860 0.2182 0.0002* 0.1017 0.4028

sep_tox 0.0011 0.0005 0.0139* 60.1749 75.9333

contam 0.0053 0.0034 0.0595 3.7394 9.3027

airsac 0.0016 0.0009 0.0377* 8.5823 30.7198

synovitis 0.0012 0.0019 0.2638 5.5832 21.0532

sum_SP 0.0054 0.0121 0.3277 10.7622 6.3381

sum_SNP -0.0805 0.0408 0.0243* 0.4945 1.0889

sum_U -0.0208 0.0190 0.1368 6.9431 3.1892

sum_NC 0.0581 0.0223 0.0046* 1.8542 3.6883

*Significant difference for two-sided t-test on the regression coefficient
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

87

Table A-9. Parameter Estimates for Young Turkey Campylobacter Model Used in Scenario 

Analysis

Parameter Estimate Std Error p-value Mean Std Dev

Intercept -13.1301 3.2288 <0.0001* 1 0

rehang -1.7619 0.1523 <0.0001* -0.6770 1.0002

loglinespeed 7.4946 2.6152 0.0021* 1.4706 0.1266

logemployees 3.6115 1.0235 0.0002* 0.9212 0.1865

lines -2.7200 0.6853 <0.0001* 1.2420 0.4284

month14 -0.3209 0.4314 0.2285 0.0583 0.2372

month15 0.7339 0.3665 0.0227* 0.0943 0.2947

month16 0.6898 0.3639 0.0291* 0.1040 0.3076

month17 0.3507 0.3764 0.1758 0.0929 0.2928

month18 0.2939 0.3756 0.2170 0.0874 0.2849

month19 0.5901 0.3813 0.0609 0.0693 0.2568

month20 0.5215 0.3819 0.0861 0.0721 0.2614

month21 0.1840 0.3819 0.3150 0.0818 0.2767

month22 -1.5164 0.4950 0.0011* 0.0867 0.2839

month23 -0.8771 0.4473 0.0250* 0.0777 0.2703

month24 -0.5709 0.4238 0.0890 0.0798 0.2735

month25 -0.2184 0.3940 0.2897 0.0867 0.2839

district1 0.4785 0.3196 0.0672 -0.0576 0.4639

district2 -0.6647 1.0543 0.2642 -0.1290 0.3611

district3 0.3415 0.2636 0.0976 0.0069 0.5320

district4 0.9143 0.3496 0.0045* -0.0596 0.4614

district5 0.0481 0.3594 0.4468 -0.0673 0.4520

district6 0.3492 0.2922 0.1161 -0.0368 0.4878

district7 -1.5516 0.6421 0.0079* -0.1047 0.4005

district8 -0.6302 0.3867 0.0516 -0.077 0.4395

district9 -0.2110 0.2587 0.2074 -0.0132 0.5126

district10 0.8127 0.2975 0.0032* -0.0617 0.4589

district11 -0.9561 1.2489 0.2220 -0.1269 0.3647

district12 1.0358 0.4560 0.0116* -0.0673 0.4520

InspSysHIMP -1.6265 0.5348 0.0012* 0.1179 0.4359

InspSysNTIS 0.1801 0.1804 0.1591 0.6845 0.5496

InspSysOtherNTIS 0.7410 0.3786 0.0252* 0.0257 0.3332

sep_tox 0.0015 0.0011 0.0864 63.1945 81.9786
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

88

Table A-9. (continued)

Parameter Estimate Std Error p-value Mean Std Dev

contam 0.0023 0.0046 0.3086 3.3797 10.4619

airsac 0.0011 0.0015 0.2317 9.9397 47.0573

synovitis -0.0067 0.0065 0.1514 4.8176 23.6373

sum_SP -0.0344 0.0203 0.0451* 10.8187 4.2699

sum_SNP 0.0444 0.0573 0.2192 0.9022 1.3254

sum_U -0.1027 0.0303 0.0004* 8.8464 3.1642

sum_NC -0.0548 0.0801 0.2470 0.5374 1.0612

*Significant difference for two-sided t-test on the regression coefficient
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

89

Table A-10. Parameter Estimates from the Young Chicken Salmonella Split Datasets

Parameter B mean B split1 mean B split2 mean

Intercept -1.8967 1.0000 -3.0788 1.0000 -0.8715 1.0000

rehang -1.1699 0.7107 -1.2067 0.7105 -1.1434 0.7110

loglinespeed 0.4675 2.0266 1.1160 2.0265 -0.1595 2.0266

logemployees -0.2878 1.2820 -0.3838 1.2809 -0.1754 1.2830

lines -0.0866 2.1464 -0.1059 2.1380 -0.0753 2.1549

Himp -0.0680 0.7518 -0.0001 0.7532 -0.1444 0.7505

month1 0.3558 -0.0110 0.4367 -0.0129 0.2887 -0.0092

month2 0.0076 0.0047 -0.0618 0.0034 0.0957 0.0060

month3 0.4576 0.0090 0.5183 0.0081 0.4234 0.0100

month4 0.2492 0.0076 0.0373 0.0055 0.4472 0.0097

month5 0.3020 0.0094 0.2938 0.0067 0.3088 0.0121

month6 0.2414 0.0067 0.0869 0.0049 0.4057 0.0085

month7 0.6349 0.0063 0.6008 0.0044 0.6795 0.0082

month8 0.0956 0.0056 -0.0525 0.0043 0.2246 0.0070

month9 0.1752 0.0078 0.0918 0.0083 0.2367 0.0072

month10 0.2302 0.0080 0.1706 0.0086 0.3204 0.0073

month11 -0.1409 0.0075 -0.1608 0.0064 -0.0815 0.0086

month12 0.1534 0.0073 0.1047 0.0068 0.2069 0.0079

month13 0.0988 0.0100 0.2928 0.0075 -0.0415 0.0125

month14 0.0228 0.0152 -0.1733 0.0143 0.2070 0.0161

month15 0.0969 0.0049 0.0846 0.0023 0.1151 0.0076

month16 -0.2017 -0.0043 -0.4168 -0.0056 -0.0051 -0.0030

month17 -0.7525 -0.0108 -0.5376 -0.0114 -0.9929 -0.0102

month18 0.0571 0.0082 0.0748 0.0052 0.0803 0.0111

month19 0.3435 0.0133 0.3778 0.0144 0.2915 0.0122

month20 0.2108 0.0075 0.4840 0.0073 -0.1315 0.0076

month21 -0.5773 0.0000 -0.5580 -0.0020 -0.5348 0.0020

month22 -0.4173 0.0157 -0.2626 0.0149 -0.5369 0.0166

month23 -0.4668 0.0184 -0.4863 0.0160 -0.4385 0.0209

month24 -0.3467 0.0099 -0.1900 0.0102 -0.5007 0.0095

month25 0.0985 0.0065 -0.0428 0.0055 0.2650 0.0075

month26 -0.1432 0.0105 -0.1998 0.0099 -0.0297 0.0110

month27 -0.2187 0.0113 -0.2753 0.0100 -0.1581 0.0127
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

90

Table A-10. (continued)

Parameter B mean B split1 mean B split2 mean

month28 -0.0124 0.0014 -0.2503 0.0005 0.1983 0.0023

month29 0.2626 -0.0026 0.5159 -0.0040 0.0380 -0.0013

month30 0.0750 -0.0056 -0.6091 -0.0061 0.5400 -0.0051

month31 0.6006 -0.0130 0.7536 -0.0136 0.4727 -0.0124

month32 -0.2403 -0.0142 0.0421 -0.0154 -0.4760 -0.0131

month33 -0.2092 0.0095 -0.4207 0.0077 -0.0174 0.0113

month34 -0.1156 0.0363 0.0468 0.0352 -0.2828 0.0375

month35 -0.5026 0.0380 -0.5960 0.0376 -0.3811 0.0384

month36 -0.3344 0.0298 -0.2227 0.0296 -0.4242 0.0300

month37 -0.0387 0.0134 0.3686 0.0123 -0.6047 0.0146

month38 0.0351 0.0061 0.0119 0.0061 0.1033 0.0062

District1 -0.3544 -0.2177 -0.2458 -0.2149 -0.5112 -0.2204

District2 -0.5096 -0.2097 -0.4804 -0.2083 -0.5441 -0.2112

District3 0.3047 -0.2113 0.5023 -0.2088 0.1484 -0.2137

District4 0.3918 -0.2174 0.2477 -0.2156 0.4641 -0.2192

District5 -0.1139 -0.1793 0.0366 -0.1758 -0.2511 -0.1827

District6 -0.0603 -0.0857 -0.0672 -0.0814 -0.0422 -0.0900

District7 -0.0185 -0.1513 0.0342 -0.1479 -0.0494 -0.1548

District8 -1.2824 -0.2219 -1.2668 -0.2199 -1.2299 -0.2238

District9 0.5377 -0.1615 0.4967 -0.1577 0.5982 -0.1653

District10 0.2689 -0.1828 0.2931 -0.1808 0.2465 -0.1848

District11 0.5986 -0.2130 0.2874 -0.2102 0.8852 -0.2158

District12 0.3913 -0.1440 0.4247 -0.1444 0.3592 -0.1435

District13 -0.0510 -0.0781 -0.1031 -0.0783 -0.0033 -0.0779

District14 0.0505 -0.1080 0.1461 -0.1052 -0.0654 -0.1107

InspSysMAESTRO -0.1228 0.3088 -0.1436 0.3079 -0.0138 0.3096

InspSysMAESTRO,Nu-Tech -0.1219 -0.0144 -0.0504 -0.0150 -0.0640 -0.0138

InspSysMAESTRO,Religio 0.0269 -0.0106 -0.1947 -0.0126 0.2813 -0.0086

InspSysMAESTRO-SIS -0.5622 -0.0315 -1.8466 -0.0330 0.1943 -0.0301

InspSysNELS 0.0633 0.0670 -0.0188 0.0656 0.2369 0.0684

InspSysNELS,MAESTRO 0.5052 -0.0236 0.6424 -0.0248 0.4402 -0.0224

InspSysNELS,NTIS,MAEST 0.7756 -0.0325 0.8684 -0.0335 0.8551 -0.0315

InspSysNELS,Nu-Tech -0.3414 -0.0267 0.1567 -0.0279 -0.7383 -0.0255
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

91

Table A-10. (continued)

Parameter B mean B split1 mean B split2 mean

InspSysNELS,Nu-Tech,Re 0.6381 -0.0304 0.7337 -0.0311 0.7066 -0.0297

InspSysNELS,Religious 0.3605 -0.0080 0.5837 -0.0108 0.2934 -0.0052

InspSysNELS,SIS 0.2929 -0.0220 0.1642 -0.0227 0.5175 -0.0213

InspSysNELS,SIS,Religi -0.2293 -0.0296 0.0992 -0.0310 -0.4378 -0.0282

InspSysNu-Ova -0.8808 -0.0333 -0.3615 -0.0342 -3.0147 -0.0325

InspSysNu-Tech -0.1878 0.0886 -0.3631 0.0903 0.0876 0.0870

InspSysNu-Tech,Religio -0.4308 -0.0286 -0.3161 -0.0291 -0.4191 -0.0281

InspSysSIS -0.0361 0.1452 0.0259 0.1401 0.0137 0.1502

InspSysSIS,MAESTRO 0.3542 0.0011 0.2914 0.0007 0.5088 0.0015

InspSysSIS,MAESTRO,Rel 0.2889 -0.0292 0.2840 -0.0311 0.3791 -0.0273

InspSysSIS,Religious S -0.3865 -0.0255 -0.4129 -0.027 -0.2581 -0.0241

InspSysSIS-Nu-Tech 0.0660 -0.0198 -0.0234 -0.0218 0.2237 -0.0178

InspSysSIS-NuOva -0.8173 -0.0289 -0.9757 -0.0303 -0.5229 -0.0276

Sep_Tox 0.0001 258.0830 0.0000 257.031 0.0002 259.1350

Contam 0.0005 34.102 0.0005 33.4667 0.0006 34.7371

AirSac 0.0000 134.389 0.0000 142.77 0.0001 126.009

sum_SP 0.0021 12.9624 0.0024 12.9508 0.0019 12.9740

sum_SNP 0.0461 0.5536 0.0451 0.5580 0.0491 0.5493

sum_U -0.0032 29.1353 -0.001 29.0843 -0.0056 29.1864

sum_NC 0.0091 0.7834 0.0025 0.7869 0.0196 0.7798
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

92

Table A-11. Prevalence Estimates from the Young Chicken Salmonella Model for the Mean, 

Rehang, and Post-chill Sample Collection Locations

Estimates unsplit split1 split2

BX (rehang= mean)1-2.3905 -2.4041 -2.4069

BX (rehang= 1) post-chill2-2.7290 -2.7535 -2.7373

BX (rehang= -1) rehang30.3376 1.5320 -0.7224

Prevalence (rehang= mean)40.0839 0.0829 0.0826

Prevalence (rehang= 1) post-chill50.0613 0.0599 0.0608

Prevalence (rehang= -1) rehang60.4039 0.4158 0.3892

Prevalence unweighted70.1231 0.1226 0.1235

1 BX is the linear combination equal to η where the rehang variable for x equals the mean

2 BX is the liner combination equal to η where the rehang variable for x equals +1

3 BX is the liner combination equal to η where the rehang variable for x equals -1

4 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at the mean

5 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at +1

6 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at -1

7 Crude Prevalence 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

93

Table A-12. Parameter Estimates from the Young Chicken Campylobacter Split Datasets

Parameter B unsplit mean B split1 mean B split2 mean

Intercept 0.3286 1 0.2875 1 0.4175 1

Rehang -0.6359 -0.0003 -0.6443 0.0259 -0.6463 -0.0265

loglinespeed 1.2788 2.0428 1.2441 2.0428 1.2848 2.0429

logInspectors -0.9754 1.3214 -0.8820 1.3222 -1.0994 1.3206

lines 0.0497 2.1751 0.0694 2.1799 0.0305 2.1702

Himp -0.4332 0.1327 -0.4044 0.1330 -0.4538 0.1324

month1 -0.1895 -0.0630 -0.6428 -0.0403 1.5155 -0.0857

month2 -0.0734 -0.0085 -0.071 0.0021 -0.0911 -0.0192

month3 0.5022 0.0063 0.4724 0.0162 0.5727 -0.0037

month4 0.2178 0.0012 0.1247 0.0088 0.4277 -0.0064

month5 0.2193 0.0075 0.0787 0.0195 0.4805 -0.0046

month6 0.1160 -0.0018 -0.1132 0.0052 0.1816 -0.0088

month7 -0.1053 -0.0032 -0.1489 0.0095 -0.0387 -0.0159

month8 -0.0817 -0.0055 -0.1046 0.0037 -0.0457 -0.0146

month9 0.1315 0.0018 -0.2289 0.0091 -0.2282 -0.0055

month10 -0.3165 0.0026 -0.3073 0.0113 -0.2962 -0.0061

month11 -0.2484 0.0014 -0.2782 0.0107 -0.1985 -0.0079

District1 -0.3553 -0.2318 -0.3006 -0.2315 -0.4227 -0.2321

District2 -0.3201 -0.2353 -0.369 -0.2339 -0.3255 -0.2367

District3 -0.5514 -0.2315 -0.7509 -0.2306 -0.3496 -0.2324

District4 -0.3275 -0.2351 -0.4915 -0.2339 -0.1427 -0.2364

District5 0.1098 -0.1991 0.1810 -0.1979 0.0378 -0.2004

District6 -0.0589 -0.082 0.0159 -0.0808 -0.1348 -0.0833

District7 0.2839 -0.1700 0.3628 -0.1677 0.2042 -0.1723

District8 -0.6106 -0.2336 -0.6771 -0.233 -0.5854 -0.2342

District9 0.4256 -0.2052 0.5492 -0.2025 0.3060 -0.2080

District10 0.1869 -0.2072 0.2304 -0.2059 0.1410 -0.2086

District11 1.5979 -0.2321 1.3490 -0.2309 1.9126 -0.2333

District12 -0.2427 -0.1475 -0.1381 -0.1464 -0.3443 -0.1485

District13 -0.3898 -0.0907 -0.3474 -0.0887 -0.4343 -0.0927

District14 0.3007 -0.0944 0.2965 -0.0936 0.3164 -0.0952

InspSysMAESTRO -0.8593 0.3385 -0.9787 0.3388 -0.7395 0.3382

InspSysMAESTRO,Nu-Tech -0.4422 0.0127 -0.6049 0.0128 -0.2462 0.0125
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

94

Table A-12. (continued)

Parameter B unsplit mean B split1 mean B split2 mean

InspSysMAESTRO,Religio -0.0386 0.0041 0.0216 0.0046 -0.1066 0.0037

InspSysMAESTRO-SIS -0.5936 -0.0131 -0.5830 -0.0128 -0.5870 -0.0134

InspSysNELS -0.7700 0.0718 -0.8675 0.0717 -0.6704 0.0720

InspSysNELS,MAESTRO -0.4104 0.0008 -0.6552 0.0006 -0.1678 0.0009

InspSysNELS,NTIS,MAEST -1.8641 -0.0168 -1.8537 -0.0165 -1.9102 -0.0171

InspSysNELS,Nu-Tech 10.6210 -0.0177 10.7515 -0.0168 9.9638 -0.0186

InspSysNELS,Nu-Tech,Re -0.7159 -0.0087 -0.9883 -0.0082 -0.4274 -0.0091

InspSysNELS,Religious -0.9813 0.0035 -0.8961 0.0040 -1.0827 0.0030

InspSysNELS,SIS -0.4999 -0.0055 -0.7386 -0.0052 -0.2767 -0.0058

InspSysNELS,SIS,Religi -0.1027 -0.0128 -0.1361 -0.0128 -0.0791 -0.0128

InspSysNu-Tech -0.8998 0.1136 -0.9766 0.1134 -0.8265 0.1138

InspSysNu-Tech,Religio -0.2656 -0.0029 -1.2466 -0.0024 0.7596 -0.0034

InspSysSIS -0.5426 0.1629 -0.6646 0.1641 -0.4284 0.1616

InspSysSIS,MAESTRO -0.8898 0.0178 -0.9271 0.0186 -0.8542 0.0171

InspSysSIS,MAESTRO,Rel 0.4083 -0.0134 0.8663 -0.0131 0.2159 -0.0137

InspSysSIS,Religious S -1.1934 -0.0131 -1.0458 -0.0128 -1.4245 -0.0134

InspSysSIS-Nu-Tech -0.0369 -0.0069 -0.0588 -0.0067 -0.0235 -0.0070

InspSysSIS-NuOva -0.1944 -0.0119 1.4957 -0.0119 -2.0137 -0.0119

Sep_Tox 0.0005 295.953 0.0005 297.638 0.0006 294.260

Contam -0.0003 49.3667 -0.0005 48.8615 -0.0001 49.872

AirSac -1E-5 237.906 -3E-5 229.711 1.3E-5 246.1000

sum_SP 0.0076 6.5629 0.0118 6.5784 0.0039 6.5474

sum_SNP 0.0198 0.6929 0.0183 0.6879 0.0210 0.6979

sum_U -0.0014 31.092 -0.0022 31.1031 -0.0006 31.0820

sum_NC -0.0157 1.3634 -0.0078 1.3617 -0.0220 1.3652
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

95

Table A-13. Prevalence Estimates from the Young Chicken Campylobacter Model for the Mean, 

Rehang, and Post-chill Sample Collection Locations

Estimates unsplit split1 split2

BX (rehang= mean)11.1615 1.1755 0.9760

BX (rehang= 1) post-chill20.5254 0.5479 0.3125

BX (rehang= -1) rehang31.7972 1.8365 1.6052

Prevalence (rehang= mean)40.7616 0.7641 0.7263

Prevalence (rehang= 1) post-chill50.6284 0.6336 0.5775

Prevalence (rehang= -1) rehang60.8578 0.8625 0.8327

Prevalence Unweighted70.7333 0.7310 0.7356

1 BX is the linear combination equal to η where the rehang variable for x equals the mean

2 BX is the liner combination equal to η where the rehang variable for x equals +1

3 BX is the liner combination equal to η where the rehang variable for x equals -1

4 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at the mean

5 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at +1

6 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at -1

7 Crude Prevalence 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

96

Table A-14. Parameter Estimates from the Young Turkey Salmonella Split Datasets

Parameter B unsplit mean B split1 mean B split2 mean

Intercept -13.1301 1 -13.7398 1 -11.0424 1

rehang -1.7619 -0.6770 -1.7406 -0.1678 -1.7728 0.1678

loglinespeed 7.4946 1.4706 8.1873 1.4706 5.4553 1.4706

logemployees 3.6115 0.9212 3.0640 0.9212 5.0195 0.9212

lines -2.7200 1.2420 -2.8184 1.242 -2.502 1.2420

month14 -0.3209 0.0583 -0.1256 0.0583 -0.7948 0.0583

month15 0.7339 0.0943 0.6582 0.0943 0.7898 0.0943

month16 0.6898 0.1040 0.7230 0.1040 0.6085 0.1040

month17 0.3507 0.0929 0.2723 0.0929 0.4151 0.0929

month18 0.2939 0.0874 0.1584 0.0874 0.4748 0.0874

month19 0.5901 0.0693 0.4681 0.0693 0.6994 0.0693

month20 0.5215 0.0721 0.4142 0.0721 0.5889 0.0721

month21 0.184 0.0818 0.0628 0.0818 0.2895 0.0818

month22 -1.5164 0.0867 -1.0174 0.0867 -5.0329 0.0867

month23 -0.8771 0.0777 -0.8407 0.0777 -1.0566 0.0777

month24 -0.5709 0.0798 -0.9067 0.0798 -0.3064 0.0798

month25 -0.2184 0.0867 -0.4303 0.0867 -0.0354 0.0867

district1 0.4785 -0.0576 0.7216 -0.0576 0.0289 -0.0576

district2 -0.6647 -0.1290 -0.6636 -0.1290 -0.4711 -0.129

district3 0.3415 0.0069 0.6031 0.0069 -0.0099 0.0069

district4 0.9143 -0.0596 0.9601 -0.0596 1.1142 -0.0596

district5 0.0481 -0.0673 0.4158 -0.0673 -0.0823 -0.0673

district6 0.3492 -0.0368 0.5643 -0.0368 0.1535 -0.0368

district7 -1.5516 -0.1047 -1.2849 -0.1047 -1.8853 -0.1047

district8 -0.6302 -0.0770 -0.3543 -0.0770 -0.8661 -0.0770
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

97

Table A-14. (continued)

Parameter B unsplit mean B split1 mean B split2 mean

district9 -0.2110 -0.0132 -0.1346 -0.0132 -0.4063 -0.0132

district10 0.8127 -0.0617 0.8419 -0.0617 0.8601 -0.0617

district11 -0.9561 -0.1269 -2.7293 -0.1269 0.6884 -0.1269

district12 1.0358 -0.0673 1.1407 -0.0673 0.7464 -0.0673

InspSysHIMP -1.6265 0.1179 -1.5988 0.1158 -1.4969 0.1200

InspSysNTIS 0.1801 0.6845 0.1829 0.6803 0.2115 0.6886

InspSysOtherNTIS 0.7410 0.0257 0.8485 0.0236 0.6748 0.0277

sep_tox 0.0015 63.1945 0.0007 63.3731 0.0036 63.0160

contam 0.0023 3.3797 0.0026 3.9619 -0.0791 2.7975

airsac 0.0011 9.9397 0.0015 10.7621 -0.0026 9.1172

synovitis -0.0067 4.8176 -0.0022 4.8904 -0.0118 4.7448

sum_SP -0.0344 10.8187 -0.0268 10.8308 -0.0445 10.8065

sum_SNP 0.0444 0.9022 0.0681 0.8988 0.0182 0.9057

sum_U -0.1027 8.8464 -0.0894 8.8405 -0.1056 8.8523

sum_NC -0.0548 0.5374 -0.0479 0.5270 -0.0589 0.5479

Table A-15. Prevalence Estimates from the Young Turkey Salmonella Model for the Mean, 

Rehang, and Post-chill Sample Collection Locations

Estimates unsplit split1 split2

BX (rehang= mean)1-2.8464 -2.8534 -2.8557

BX (rehang= 1) post-chill2-2.9980 -2.9823 -2.9792

BX (rehang= -1) rehang3-2.0782 -2.2187 -2.2496

Prevalence (rehang= mean)40.0549 0.0545 0.0544

Prevalence (rehang= 1) postchill5

0.0475 0.0482 0.0484

Prevalence (rehang= -1) rehang60.1112 0.0981 0.0954

Prevalence Unweighted70.0729 0.0729 0.0715

1 BX is the linear combination equal to η where the rehang variable for x equals the mean

2 BX is the liner combination equal to η where the rehang variable for x equals +1

3 BX is the liner combination equal to η where the rehang variable for x equals -1

4 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at the mean

5 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at +1

6 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at -1

7 Crude Prevalence 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

98

Table A-16. Parameter Estimates from the Young Turkey Campylobacter Split Datasets

Parameter B unsplit mean B split1 mean B split2 mean

Intercept -13.1300 1 -13.7400 1 -11.0420 1

rehang -1.7619 -0.6770 -1.7406 -0.1678 -1.7728 0.1678

loglinespeed 7.4946 1.4706 8.1873 1.4706 5.4553 1.4706

logemployees 3.6115 0.9212 3.064 0.9212 5.0195 0.9212

lines -2.7200 1.2420 -2.8184 1.2420 -2.5020 1.2420

month14 -0.3209 0.0583 -0.1256 0.0583 -0.7948 0.0583

month15 0.7339 0.0943 0.6582 0.0943 0.7898 0.0943

month16 0.6898 0.1040 0.7230 0.1040 0.6085 0.1040

month17 0.3507 0.0929 0.2723 0.0929 0.4151 0.0929

month18 0.2939 0.0874 0.1584 0.0874 0.4748 0.0874

month19 0.5901 0.0693 0.4681 0.0693 0.6994 0.0693

month20 0.5215 0.0721 0.4142 0.0721 0.5889 0.0721

month21 0.1840 0.0818 0.0628 0.0818 0.2895 0.0818

month22 -1.5164 0.0867 -1.0174 0.0867 -5.0329 0.0867

month23 -0.8771 0.0777 -0.8407 0.0777 -1.0566 0.0777

month24 -0.5709 0.0798 -0.9067 0.0798 -0.3064 0.0798

month25 -0.2184 0.0867 -0.4303 0.0867 -0.0354 0.0867

district1 0.4785 -0.0576 0.7216 -0.0576 0.0289 -0.0576

district2 -0.6647 -0.1290 -0.6636 -0.1290 -0.4711 -0.129

district3 0.3415 0.0069 0.6031 0.0069 -0.0099 0.0069

district4 0.9143 -0.0596 0.9601 -0.0596 1.1142 -0.0596

district5 0.0481 -0.0673 0.4158 -0.0673 -0.0823 -0.0673

district6 0.3492 -0.0368 0.5643 -0.0368 0.1535 -0.0368

district7 -1.5516 -0.1047 -1.2849 -0.1047 -1.8853 -0.1047

district8 -0.6302 -0.077 -0.3543 -0.0770 -0.8661 -0.077

district9 -0.211 -0.0132 -0.1346 -0.0132 -0.4063 -0.0132

district10 0.8127 -0.0617 0.8419 -0.0617 0.8601 -0.0617

district11 -0.9561 -0.1269 -2.7293 -0.1269 0.6884 -0.1269

district12 1.0358 -0.0673 1.1407 -0.0673 0.7464 -0.0673

InspSysHIMP -1.6265 0.1179 -1.5988 0.1158 -1.4969 0.1200

InspSysNTIS 0.1801 0.6845 0.1829 0.6803 0.2115 0.6886

InspSysOtherNTIS 0.7410 0.0257 0.8485 0.0236 0.6748 0.0277

sep_tox 0.0015 63.1945 0.0007 63.3731 0.0036 63.016
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

99

Table A-16. (continued)

Parameter B unsplit mean B split1 mean B split2 mean

contam 0.0023 3.3797 0.0026 3.9619 -0.0791 2.7975

airsac 0.0011 9.9397 0.0015 10.7621 -0.0026 9.1172

synovitis -0.0067 4.8176 -0.0022 4.8904 -0.0118 4.7448

sum_SP -0.0344 10.8187 -0.0268 10.8308 -0.0445 10.8065

sum_SNP 0.0444 0.9022 0.0681 0.8988 0.0182 0.9057

sum_U -0.1027 8.8464 -0.0894 8.8405 -0.1056 8.8523

sum_NC -0.0548 0.5374 -0.0479 0.5270 -0.0589 0.5479

Table A-17. Prevalence Estimates from the Young Turkey Campylobacter Model for the Mean, 

Rehang, and Post-chill Sample Collection Locations

Estimates unsplit split1 split2

BX (rehang= mean)1-1.9928 -2.8116 -3.5105

BX (rehang= 1) post-chill2-4.9475 -4.8444 -4.9858

BX (rehang= -1) rehang3-1.4237 -1.3632 -1.4402

Prevalence (rehang= mean)40.1200 0.0567 0.0290

Prevalence (rehang= 1) postchill5

0.0071 0.0078 0.0068

Prevalence (rehang= -1) rehang60.1941 0.2037 0.1915

Prevalence Unweighted70.1189 0.1401 0.0978

1 BX is the linear combination equal to η where the rehang variable for x equals the mean

2 BX is the liner combination equal to η where the rehang variable for x equals +1

3 BX is the liner combination equal to η where the rehang variable for x equals -1

4 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at the mean

5 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at +1

6 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at -1

7 Crude Prevalence
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

100

Table A-18. Regression Coefficients for Unscheduled Procedures (U) by Inspection Element

Young Chicken - Salmonella

ISP Element B sB p-value Mean Std Dev

sum01_U1-0.0020 0.0150 0.8966 0.3741 0.7482

sum03_U2-0.0030 0.0016 0.0500* 13.2204 14.3555

sum04_U3-0.0035 0.0015 0.0237* 12.1161 10.3950

sum05_U40.0845 0.0159 <.0001* 0.8947 0.6132

sum06_U5-0.0146 0.0053 0.0058* 1.7249 2.6899

sum08_U60.0059 0.0212 0.7813 0.8051 0.6211

1 Unscheduled sanitation procedures

2 Unscheduled HACCP procedures

3 Unscheduled wholesomeness/other consumer protection procedures

4 Unscheduled sampling procedures

5 Unscheduled sanitation performance standard procedures

6 Emergency procedures are always unscheduled
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

101

Table A-18. (continued)

Young Chicken -

Campylobacter

ISP Element B sB p-value Mean Std Dev

sum01_U10.0065 0.0205 0.7528 0.3741 0.7482

sum03_U2-0.0264 0.0146 0.0715 13.2204 14.3555

sum04_U3-0.0780 0.0280 0.0053* 12.1161 10.395

sum05_U4-0.1099 0.0183 <.0001* 0.8947 0.6132

sum06_U50.0128 0.0063 0.0435* 1.7249 2.6899

sum08_U60.0043 0.0277 0.8775 0.8051 0.6211

1 Unscheduled sanitation procedures

2 Unscheduled HACCP procedures

3 Unscheduled wholesomeness/other consumer protection procedures

4 Unscheduled sampling procedures

5 Unscheduled sanitation performance standard procedures

6 Emergency procedures are always unscheduled

Table A-18. (continued)

Young Turkey - Salmonella

ISP Element B sB p-value Mean Std Dev

sum01_U1-0.1060 0.1077 0.3252 0.1845 0.4483

sum03_U20.0283 0.0285 0.3213 2.4450 1.8068

sum04_U3-0.0169 0.0731 0.8166 1.6907 0.8603

sum05_U4-0.1286 0.0785 0.1015 0.9444 0.6137

sum06_U5-0.0638 0.0690 0.3555 0.8843 0.8492

sum08_U60.0557 0.0992 0.5744 0.7939 0.5501

1 Unscheduled sanitation procedures

2 Unscheduled HACCP procedures

3 Unscheduled wholesomeness/other consumer protection procedures

4 Unscheduled sampling procedures

5 Unscheduled sanitation performance standard procedures

6 Emergency procedures are always unscheduled
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

102

Table A-18. (continued)

Young Turkey - Campylobacter

ISP Element B sB p-value Mean Std Dev

sum01_U1-0.0994 0.1244 0.4242 0.2510251 0.6869

sum03_U2-0.1031 0.0492 0.0363* 2.6741 1.7617

sum04_U3-0.0818 0.0860 0.3412 2.8266 1.0534

sum05_U4-0.0559 0.1252 0.6556 0.9917 0.6807

sum06_U5-0.1675 0.0808 0.0381* 1.1390 1.1582

sum08_U6-0.2074 0.2018 0.3040304 0.9639 0.3763

1 Unscheduled sanitation procedures

2 Unscheduled HACCP procedures

3 Unscheduled wholesomeness/other consumer protection procedures

4 Unscheduled sampling procedures

5 Unscheduled sanitation performance standard procedures

6 Emergency procedures are always unscheduled
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

103

Table A-19. Regression Coefficient for Unscheduled Procedures (U) by ISP Code

Chicken-Salmonella

ISP Code B sB p-value Mean Std Dev

sum01B_U10.0143 0.0468 0.7596 0.0768 0.2763

sum01C_U20.0022 0.0184 0.9055 0.2886 0.6435

sum01_Uother3-0.2239 0.1081 0.0383* 0.0087 0.1038

sum03B_U40.0200 0.0561 0.7216 0.0356 0.2071

sum03C_U5-0.1036 0.0294 0.0004* 0.3627 1.2117

sum03J_U6-0.0026 0.0017 0.1133 12.3816 13.8886

sum03_Uother70.1119 0.0272 <.0001* 0.4405 1.3024

sum04_U8-0.0034 0.0015 0.028* 12.1161 10.395

sum05_U90.0799 0.0159 <.0001* 0.8947 0.6132

sum06D01_U10-0.1247 0.0181 <.0001* 0.3250 0.621

sum06_Uother11-0.0076 0.0055 0.1652 1.400 2.6579

sum08_U12 0.0036 0.0212 0.8644 0.8051 0.6211

1 Preoperational sanitation

2 Operational sanitation

3 Other 01 sanitation

4 HACCP raw not ground product inspection

5 HACCP raw ground product inspection

6 HACCP fecal checks in slaughter area

7 Other HACCP procedures

8 Wholesomeness/other consumer protection inspection

9 Sampling

10 Sanitation performance standard inspection

11 Other 06 sanitation inspection

12 Emergency inspection 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

104

Table A-19. (continued)

Chicken-Campylobacter

ISP Code B sB p-value Mean Std Dev

sum01B_U10.0259 0.0551 0.6383 0.0627 0.2522

sum01C_U2-0.0015 0.0240 0.9501 0.2849 0.6290

sum03B_U30.1078 0.0459 0.0190* 0.0610 0.3019

sum03C_U40.0771 0.0402 0.0554 0.3054 0.9130

sum03J_U5-0.0097 0.0020 <.0001* 13.8051 15.3436

sum03_Uother6-0.0940 0.0357 0.0085* 0.3814 1.0212

sum04_U70.0060 0.0019 0.0020* 11.6642 9.6596

sum05_U8-0.1072 0.0184 <.0001* 0.7620 0.7275

sum06D01_U90.0488 0.0223 0.0286* 0.3667 0.6456

sum06_Uother10 0.0065 0.0066 0.3249 1.8606 2.8507

sum08_U11 0.0145 0.0281 0.6066 1.1757 0.5389

1 Preoperational sanitation

2 Operational sanitation

3 Other 01 sanitation

4 HACCP raw not ground product inspection

5 HACCP raw ground product inspection

6 HACCP fecal checks in slaughter area

7 Other HACCP procedures

8 Wholesomeness/other consumer protection inspection

9 Sampling

10 Sanitation performance standard inspection

11 Other 06 sanitation inspection

12 Emergency inspection 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

105

Table A-19. (continued)

Turkey-Salmonella

ISP Code B sB p-value mean Std Dev

sum01B_U10.1305 0.1787 0.4653 0.0664 0.2558

sum01C_U2-0.2639 0.1534 0.0854 0.1106 0.3188

sum01_Uother3-0.7034 0.6871 0.3059 0.0075 0.0865

sum03B_U40.0697 0.2012 0.7290 0.1193 0.3758

sum03C_U5-0.3255 0.1997 0.1030 0.1437 0.4072

sum03J_U6-0.5469 0.1918 0.0043* 0.9244 0.4632

sum03_Uother70.2725 0.1411 0.0535 1.2576 0.9609

sum04_U80.0292 0.0757 0.6996 1.6907 0.8603

sum05_U9-0.1134 0.0801 0.1567 0.9445 0.6137

sum06D01_U10-0.0686 0.1396 0.6232 0.1589 0.3712

sum06_Uother11-0.0521 0.0849 0.5391 0.7255 0.7601

sum08_U12 0.0866 0.1013 0.3926 0.7939 0.5501

1 Preoperational sanitation

2 Operational sanitation

3 Other 01 sanitation

4 HACCP raw not ground product inspection

5 HACCP raw ground product inspection

6 HACCP fecal checks in slaughter area

7 Other HACCP procedures

8 Wholesomeness/other consumer protection inspection

9 Sampling

10 Sanitation performance standard inspection

11 Other 06 sanitation inspection

12 Emergency inspection 
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

106

Table A-19. (continued)

Turkey-Campylobacter

ISP Code B sB pvalue Mean Std 

Dev

sum01B_U10.1405 0.3308 0.6709 0.0659 0.2591

sum01C_U2-0.4178 0.2833 0.1403 0.1342 0.3480

sum01_Uother30.0636 0.3130 0.8390 0.0510 0.3136

sum03B_U4-0.2212 0.4389 0.6143 0.1120 0.3701

sum03C_U5-0.0018 0.4102 0.9965 0.1449 0.4170

sum03J_U60.2225 0.3607 0.5372 1.0482 0.4321

sum03_Uother7-0.1558 0.3077 0.6127 1.3689 0.9374

sum04_U8-0.1010 0.0905 0.2643 2.8266 1.0534

sum05_U9-0.0678 0.1306 0.6036 0.9917 0.6807

1 Preoperational sanitation

2 Operational sanitation

3 Other 01 sanitation

4 HACCP raw not ground product inspection

5 HACCP raw ground product inspection

6 HACCP fecal checks in slaughter area

7 Other HACCP procedures

8 Wholesomeness/other consumer protection inspection

9 Sampling
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

107

Table A-20. Prevalence Estimates for Models Disaggregated by Unscheduled Procedures

a CS chicken-Salmonella model 

b CC chicken-Campylobacter model

c

TS turkey-Salmonella model 

d

TC turkey-Campylobacter model

1 BX is the linear combination equal to η where the rehang variable for x equals the mean

2 BX is the liner combination equal to η where the rehang variable for x equals +1

3 BX is the liner combination equal to η where the rehang variable for x equals -1

4 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at the mean

5 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at +1

6 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at -1

6 Crude Prevalence

Table A-21. Number of Establishments in the Four Observed Datasets by SBA Size

Pathogen Species L S V total

Salmonella chicken 133 48 8 189

Campylobacter chicken 130 45 5 180

Salmonella turkey 26 26 13 65

Campylobacter turkey 24 22 12 58

total 313 141 38 492

Variable (6) CSa CCbTScTCd

BX (rehang= mean)1-2.3906 1.1632 -2.8368 -3.1793

BX (rehang= 1) post-chill2-2.7291 0.5257 -2.9746 -4.9373

BX (rehang= -1) rehang3-0.3889 1.8003 -2.1386 -1.4213

Prevalence (rehang= mean)40.0839 0.7619 0.0554 0.0400

Prevalence (rehang= 1) post-chill50.0613 0.6285 0.0486 0.0071

Prevalence (rehang= -1) rehang60.4040 0.8582 0.1054 0.1945

Variable (10-12) CSa CCbTScTCd

BX (rehang= mean)1-2.3928 1.1645 -2.8510 -3.2059

BX (rehang= 1) post-chill2-2.7317 0.5267 -2.9934 -4.9695

BX (rehang= -1) rehang3-0.3885 1.8019 -2.1292 -1.4423

Prevalence (rehang= mean)40.0837 0.7622 0.0546 0.0389

Prevalence (rehang= 1) post-chill50.0611 0.6287 0.0477 0.0069

Prevalence (rehang= -1) rehang60.4041 0.8584 0.1063 0.1912
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

108

Table A-22. Number of Establishments Expected to adopt the Proposed Inspection System by 

SBA Size according to Preliminary Regulatory Impact Analysis (PRIA)

Species Switch1L S V total

chicken 170 127 43 0 170

turkey 30 20 10 0 30

subtotal 200 147 53 0 200

other 19 2 14 3 19

total 219 147 72 3 219

1

Switch indicates the number of establishments expected to adopt the proposed inspection system according to the 

PRIA

Table A-23. Number of Observed Establishments Expected to adopt the Proposed Inspection 

System by SBA Size

Pathogen Species L S V total

Salmonella1

chicken 128 50 2 180

Campylobacter2

chicken 128 50 2 180

Salmonella3

turkey 21 17 1 39

Campylobacter4

turkey 21 17 1 39

Total5298 134 6 438

1 Expected values for chicken slaughter Salmonella dataset establishments

2 Expected values for chicken slaughter Campylobacter dataset establishments

3 Expected values for turkey slaughter Salmonella dataset establishments

4 Expected values for turkey slaughter Campylobacter dataset establishments

5

Summed totals reflect pathogen sampling overlap in the same establishments
July 2014 Public Health-Based Poultry Slaughter Inspection Risk Assessment

109

Table A-24. Observed Baseline Datasets and Expected to Shift Baseline Datasets Prevalence 

Estimates

Dataset Prevalence Estimates

Young Chicken

Salmonella Campylobacter

Estimates observed expected observed expected

BX (rehang= mean)1-2.3905 -2.3940 1.1615 1.1657

BX (rehang= 1) post-chill2-2.7290 -2.7289 0.5254 0.5351

BX (rehang= -1) rehang30.3376 -0.4119 1.7972 1.7961

Prevalence (rehang= mean)40.0839 0.08363 0.7616 0.7623

Prevalence (rehang= 1) post-chill50.0613 0.0612 0.6284 0.6306

Prevalence (rehang= -1) rehang60.4039 0.3984 0.8578 0.8576

Prevalence Unweighted70.1231 0.7333

Dataset Prevalence Estimates

Young Turkey

Salmonella Campylobacter

Estimates observed expected observed expected

BX (rehang= mean) -2.8464 -2.8625 -1.9928 -2.0155

BX (rehang= 1) post-chill -2.998 -3.0221 -4.9475 -5.1080

BX (rehang= -1) rehang -2.0782 -2.0233 -1.4237 -1.3690

Prevalence (rehang= mean) 0.0549 0.05404 0.1200 0.11759

Prevalence (rehang= 1) post-chill 0.0475 0.04644 0.0071 0.00601

Prevalence (rehang= -1) rehang 0.1112 0.11678 0.1941 0.20278

Prevalence Unweighted 0.0729 0.1189

1 BX is the linear combination equal to η where the rehang variable for x equals the mean

2 BX is the liner combination equal to η where the rehang variable for x equals +1

3 BX is the liner combination equal to η where the rehang variable for x equals -1

4 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at the mean

5 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at +1

6 Prevalence equals the inverse logistic function with the exponent BX equal to rehang at -1

7 Crude Prevalence for observed data