FSIS Risk Assessment for Guiding 

Public Health-Based Poultry 

Slaughter Inspection

Prepared by:

Risk Assessment Division

Office of Public Health Science

Food Safety and Inspection Service

U.S. Department of Agriculture

August 2012 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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Contents

EXECUTIVE SUMMARY ....................................................................................... 6

Background ......................................................................................................................................6

Risk Management Questions ...........................................................................................................6

Structure and Scope .........................................................................................................................7

Model Results ..................................................................................................................................8

Conclusions ......................................................................................................................................9

INTRODUCTION ...................................................................................................12

METHODS ..............................................................................................................13

Regression model description ........................................................................................................13

Model to forecast the effect of proposed rule ................................................................................17

DISCUSSION ..........................................................................................................29

REFERENCES.........................................................................................................31

APPENDIX..............................................................................................................32

Regression Modeling Methods and Observational Datasets .........................................................32

Regression Model Approach..........................................................................................................32

Data Sets ........................................................................................................................................34

Decision variables: Inspection procedures.....................................................................................34

Structural variables: Non-inspection procedures ...........................................................................35

Final Models ..................................................................................................................................36

PRIA Dataset Evaluation ...............................................................................................................38
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Table of Figures

Figure 1. Uncertainty about the change in the annual Salmonella human illness rate when offline inspection procedures are intensified in chicken establishments is depicted for the 

indiscriminate scenario, the increased unscheduled procedures scenario and the decreased 

scheduled but not performed procedures scenario. ....................................................................... 25

Figure 2. Uncertainty about the change in the annual Campylobacter human illness rate when 

off-line inspection procedures are intensified in chicken establishments is depicted for the 

indiscriminate scenario, the increased unscheduled procedures scenario and the decreased 

scheduled but not performed procedures scenario. ....................................................................... 26

Figure 3. Uncertainty about the change in the annual Salmonella human illness rate when offline inspection procedures are intensified in turkey establishments is depicted for the 

indiscriminate scenario, the increased unscheduled procedures scenario and the decreased 

scheduled but not performed procedures scenario. ....................................................................... 27

Figure 4. Uncertainty about the change in the annual Campylobacter human illness rate when 

off-line inspection procedures are intensified in turkey establishments is depicted for the 

indiscriminate scenario, the increased unscheduled procedures scenario and the decreased 

scheduled but not performed procedures scenario. ....................................................................... 28

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

Young Chicken Salmonella Predictive Model .............................................................................. 40

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

Young Chicken Campylobacter Predictive Model ....................................................................... 40

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

Young Turkey Salmonella Predictive Model ............................................................................... 40

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

Young Turkey Campylobacter Predictive Model ......................................................................... 41

Table of Tables

Table 1. Decision variable estimates from four regression analyses are shown. ......................... 16

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

Uncertainty Bounds, from Young Chicken and Turkey. .............................................................. 19

Table 3. Summary statistics of changes in establishment prevalence from Monte Carlo 

simulations of the indiscriminate scenario across the four product-pathogen models are shown. 21

Table 4. Summary statistics of changes in establishment prevelance from Monte Carlo 

simulations of the unsceduled procedures scenario across the four product-pathogen models are 

shown. ........................................................................................................................................... 22
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Table 5. Summary statistics for human illnesses avoided from Monte Carlo simulations of the 

indiscriminate scenario across the four product-pathogen models. .............................................. 23

Table 6. Summary statistics for human illnesses avoided from Monte Carlo simulations of an 

alternative scenario that increases unscheduled procedures across the four product-pathogen 

models. .......................................................................................................................................... 24

Appendix Table 1. 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 ........... 41

Appendix Table 2. Parameter Estimates for Young Chicken Salmonella Model Used in Scenario 

Analysis......................................................................................................................................... 43

Appendix Table 3. Parameter Estimates for Young Chicken Campylobacter Model Used in 

Scenario Analysis.......................................................................................................................... 45

Appendix Table 4. Parameter Estimates for Young Turkey Salmonella Model Used in Scenario 

Analysis......................................................................................................................................... 47

Appendix Table 5. Parameter Estimates for Young Turkey Campylobacter Model Used in 

Scenario Analysis.......................................................................................................................... 48

Appendix Table 6. Parameter Estimates from the Young Chicken Salmonella Split Datasets .... 50

Appendix Table 7. Prevalence Estimates from the Young Chicken Salmonella Model for the 

Mean, Rehang, and Post-chill Sample Collection Locations ........................................................ 52

Appendix Table 8. Parameter Estimates from the Young Chicken Campylobacter Split Datasets

....................................................................................................................................................... 52

Appendix Table 9. Prevalence Estimates from the Young Chicken Campylobacter Model for the 

Mean, Rehang, and Post-chill Sample Collection Locations ........................................................ 54

Appendix Table 10. Parameter Estimates from the Young Turkey Salmonella Split Datasets .... 54

Appendix Table 11. Prevalence Estimates from the Young Turkey Salmonella Model for the 

Mean, Rehang, and Post-chill Sample Collection Locations ........................................................ 55

Appendix Table 12. Parameter Estimates from the Young Turkey Campylobacter Split Datasets

....................................................................................................................................................... 55

Appendix Table 13. Prevalence Estimates from the Young Turkey Campylobacter Model for the 

Mean, Rehang, and Post-chill Sample Collection Locations ........................................................ 56

Appendix Table 14. Regression Coefficients for Unscheduled Procedures by Inspection Element

....................................................................................................................................................... 56

Appendix Table 15. Regression Coefficeint for Unscheduled Procedures by ISP Code ............. 57

Appendix Table 16. Prevalence Estimates for Models Disaggregated by Unscheduled 

Procedures ..................................................................................................................................... 58

Appendix Table 17. Number of Establishments in the Four Observed Datasets by SBA Size .... 59

Appendix Table 18. Number of Establishments Expected to adopt the New Inspection System by 

SBA Size ....................................................................................................................................... 59

Appendix Table 19. Number of Observed Establishments Expected to adopt the New Inspection 

System by SBA Size ..................................................................................................................... 59
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Appendix Table 20. Observed Baseline Datasets and Expected to Shift Baseline Datasets 

Prevalence Estimates .................................................................................................................... 59
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EXECUTIVE SUMMARY

 Background 

The Food Safety and Inspection Service is proposing a new inspection system to change 

allocation of inspection personnel in poultry slaughter establishments. Under new inspection 

system guidelines, individual poultry slaughter establishments will decide whether to operate 

under a slightly modified version of the current inspection system (9 CFR § 381.76) or the 

proposed new system. 

The intent of the proposed new inspection system is to allow FSIS resources to be used more 

efficiently. If this efficiency reduces the occurrence of foodborne pathogens such as Salmonella

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

Improved efficiency should occur by allowing more time and flexibility for FSIS personnel to 

perform off-line verification activities based on human health risk factors specific to individual 

establishments. The proposed new system may also drive technological innovation by the 

industry because they will have greater control over carcass sorting and establishing maximum 

line speeds. 

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

it is unadulterated, free of feathers, bruises, and defects and disease, while FSIS off-line 

inspectors verify that establishments maintain sanitary operations and perform other health- and 

safety-related assignments. Many of the on-line inspection tasks are related to food quality rather 

than food safety. 

This risk assessment updates a 2008 risk assessment, originally presented in conjunction with a 

review by the National Advisory Committee on Meat and Poultry Inspection (NACMPI, 1,3), 

with new data and a modified modeling approach. This version of the risk assessment takes into 

consideration public and stakeholder comments [Docket No. FSIS-2011-0012].

The original risk management questions were: 

 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?
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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?

 Structure and Scope

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

variations in personnel assignments and inspection activities in FSIS poultry slaughter facilities 

compared to the prevalence of both Salmonella and Campylobacter on young chicken and young 

turkey and, subsequently, attributable human illness. 

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

procedures and contamination of carcasses with either Salmonella or Campylobacter. A 

stochastic simulation model uses the estimates from the logistic regression to forecast the effect 

of changes in off-line inspection categories on changes in human Salmonella or Campylobacter

illnesses attributable to the consumption of young chicken and young turkey. The simulation 

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

change in off-line inspection activities with the new inspection system, and uncertainty in the 

current estimate of human illnesses, into its forecasts about the change in human illnesses that 

could occur as a result of implementation of the new inspection system. 

Data used in the risk assessment came from several sources. Inspection activities data from 

FSIS’s PBIS database were paired with Salmonella and Campylobacter prevalence data for the 

same establishments and timeframes:

Young chicken data comprise results of the FSIS Young Chicken Baseline study (July 

2007 through September 2008, 8) and PR/HACCP Salmonella verification program (July 

2007 through September 2010). 

Young turkey data comprise results of the FSIS “Young Turkey Baseline” (August 2008 

through July 2009, 9) and PR/HACCP Salmonella verification program (July 2007 

through September 2010). 

Estimates for the mean number of human Salmonella and human Campylobacter illness 

attributable to young chicken and turkey consumption were based on distribution parameters 

estimated from total foodborne illness and outbreak data from Centers for Disease Control and 

Prevention (1, 10, 12).

Change scenarios predict how prevalence of both Salmonella and Campylobacter and ultimately 

annual human illnesses might change based on 4 categories of decision variables (scheduled-andperformed procedures [SP], unscheduled procedures [U], scheduled-not-performed procedures 

[SNP], and non compliances [NC]). As Agency guidance has heretofore been unspecific about 

the types of offline inspection procedures that could improve from the new inspection system, an 

“indiscriminate” scenario is propagated in which all 4 categories of decision variables are 
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randomly changed. Uncertainty distributions for each of these change decision variables is 

developed using information provided in the FSIS HIMP report (13). We assume that off-line 

inspection activities after the voluntary implementation of the new inspection system will parallel 

off-line inspection activities in current HIMP establishments. 

Model Results

Indiscriminate scenario: These results describe estimated changes in both poultry slaughter 

establishment prevalence and in attributable human illnesses associated with an indiscriminate 

change across all 4 decision variables – based on the premise that unspecified changes (increases 

in terms of procedures performed, decreases in terms of unperformed procedures and noncompliances) might occur across all off-line inspection activities. 

 

Discriminating scenario where unscheduled procedures are targeted for increase: These results 

describe estimated changes in both poultry slaughter establishment prevalence and in attributable 

human illnesses associated with a targeted increase in unscheduled inspection procedures while 

holding other decision variables constant – based on the observation that in HIMP establishments 

up to 60% more unscheduled procedures are performed than in non-HIMP establishments. 

Given that more unscheduled procedures are a likely focus the new inspection system, this 

specific scenario is of interest.

Predicted annual changes in Salmonella and Campylobacter prevalence in chicken 

establishments: When off-line procedures are indiscriminately changed in young chicken 

establishments, the analysis predicts an average decline of 2 percent (mean) (.005, .04) (10th and 

90th percentile, respectively) in the percentage of positive Salmonella samples. The analysis also 

predicts that there could be a small increase -.0002(-.018, .007) in the percentage of positive 

Campylobacter samples. This could be due to the effect that non compliances – a poorly 

understood explanatory variable in this model, have on the predicted estimates for 

Campylobacter. 

If only unscheduled inspection procedures in young chicken slaughter establishments are 

targeted for increase the analysis predicts a average decline of 2 percent (mean) (.008, .038) in 

the percentage of positive Salmonella samples. The analysis also predicts that there is a small 

decline .005(-0, .017) in the percentage of positive Campylobacter samples.

Predicted annual changes in human illnesses attributable to chicken establishments:

There is an 87 percent probability that human illnesses will decline if all off-line inspection 

procedures are changed ‘indiscriminately’ (as described above) in young chicken slaughter 

establishments. Salmonella illnesses are expected to decline by an average of 4,203 (mean) 

(872, 8,089) (10th and 90th percentile, respectively), while Campylobacter illnesses could 

increase by 462 (-2,668, 1,067). However, the modes of these distributions indicate a decline of 

3,181 Salmonella illnesses and 0 Campylobacter illnesses from young chicken. 

If only unscheduled inspection procedures in young chicken slaughter establishments are 

targeted for increase, there is a near 100 percent probability that human illnesses will decline. 

Salmonella illnesses are expected to decline by an average of 4,044 (1,390, 7,301), while 

Campylobacter illnesses could decrease by 868 (0, 2,728). Similarly, the modes of these 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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distributions show declines of 2,483 Salmonella illnesses and 0 Campylobacter illnesses related 

to increases in unscheduled procedures. 

Predicted annual changes in Salmonella and Campylobacter prevalence in turkey establishments:

When off-line procedures are indiscriminately changed in young turkey establishments, the 

analysis predicts an average decline of 4 percent (-.02, .11) in the percentage of positive 

Salmonella samples. The analysis also predicts that there could be a decrease of 17 percent (-

.015, .32) in the percentage of positive Campylobacter samples. 

If only unscheduled inspection procedures in young turkey slaughter establishments are targeted 

for increase the analysis predicts a average decline of 3 percent (-.004, .08) in the percentage of 

positive Salmonella samples. The analysis also predicts a similar decline of 17 percent (.021, 32) 

in the percentage of positive Campylobacter samples.

Predicted annual changes in human illnesses attributable to turkey establishments: 

There is also an 87 percent probability that human illnesses will decline if all off-line inspection 

procedures are changed indiscriminately in young turkey slaughter establishments. Salmonella 

illnesses are expected to decline by an average of 311 (-146, 834), while Campylobacter illnesses 

are expected to decline by 119 (9, 252). The decline in the mode of 161 Salmonella illnesses and 

0 Campylobacter illnesses from young turkey is expected. 

If only unscheduled inspection procedures in young turkey slaughter establishments are targeted 

for increase, there is a 94 percent probability that human illnesses will decline. Salmonella 

illnesses are expected to decline by an average of 242 (-30, 603), while Campylobacter illnesses 

could decrease by 118 (12, 249). The distribution modes indicate a decline of 90 Salmonella

illnesses and 0 Campylobacter illnesses related to increased unscheduled procedures in young 

turkey establishments. 

Conclusions

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? 

In general, the probability that indiscriminate changes in off-line inspection procedures will 

increase the annual rate of human illnesses is small, and there is a greater probability that such 

changes would contribute to no net change or even reductions in human illnesses. Nevertheless, 

this analysis suggests ambiguous effects of the proposed rule with respect to Campylobacter

occurrence on chicken carcasses. The larger probability of increased Campylobacter illnesses 

from contaminated chicken carcasses is primarily driven, however, by the non-compliances 

decision variable. This decision variable is poorly understood and the intended effect of changes 

in this category of procedures is arguable. The frequency of non-compliance reports could 

decrease either because plant performance improves or because incidents of non-compliance are 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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less frequently detected and reported. It is noteworthy that removing the effect of this decision 

variable in alternative scenarios substantially reduces the probability that the human illness rate 

might increase. 

This latter conclusion is further supported by consideration of the HIMP structural variable in the 

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 (13), these 

regression findings suggest that the positive Salmonella implications of that HIMP analysis also 

apply to Campylobacter. While 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? 

Most likely point estimates from a scenario that indiscriminately changes all four decision 

variables in our analysis suggest a net reduction (mode) of 3,342 Salmonella illnesses 

attributable to both young chicken and young turkey establishments. This analysis assumes that 

the total annual Salmonella illnesses rate attributed to poultry is centered about 174,686 (1). 

Therefore, the proposed rule might be expected to prevent 1.9% of these illnesses per year. 

Most likely point estimates from the same scenario suggest that there will be no net change in the 

annual rate of Campylobacter illnesses in either chicken or turkey establishments. 

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

reducing microbial prevalence and corresponding human illness? 

The most reliable implication from the regression models is that increasing unscheduled 

procedures seems to reduce 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. 

What is the uncertainty about these effects? 

Our modeling approach includes uncertainty about regression coefficients that relate the 

frequency of inspection activities to pathogen prevalence, uncertainty about the change in future 

inspection activities, and uncertainty in the baseline annual rates of human Salmonella and 

Campylobacter illness attributable to poultry. These sources of uncertainty translate into 

substantial uncertainty about forecasted changes in illness rates.

This analysis necessarily focuses on the “down-side” potential of the proposed rule, i.e., the 

probability that proposed changes to inspection may cause illness rates might increase. This 

focus seems appropriate for a proposed rule that intends to change inspection processes in 

slaughter establishments. Significantly, however, the uncertainty about changes in illness rates 

includes “up-side” potential that illnesses avoided could be substantially larger than the model 

values cited. 
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The uncertainty that surrounds these forecasts suggests monitoring opportunities for FSIS 

following implementation of the proposed rule. For example, FSIS can periodically assess 

aggregate inspection procedures and compare these to the baseline predictions from this model. 

Such comparisons will empirically measure the changes occurring for the decision variables in 

the model and reduce the current uncertainty about these model inputs. Also, under the proposed 

rule, FSIS will continue to monitor the pathogen prevalence on carcasses among participating 

and non-participating establishments. The pathogen verification testing data can be used to 

assess correspondence with its expectations following implementation of the proposed rule. 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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INTRODUCTION

FSIS is proposing a system to change allocation of inspection personnel in poultry slaughter 

establishments. Under the proposed rule, poultry slaughter establishments will decide whether to 

operate under a modified traditional inspection system (9 CFR § 381.76) or the proposed new 

system. 

The intent of the proposed new inspection is to allow FSIS resources to be used more efficiently

and to lead to industry innovations in operations and processing. Improved efficiency should 

occur by allowing more time and flexibility for FSIS personnel to perform off-line verification 

activities based on risk factors specific to individual establishments. The proposed new system 

may also drive technological innovation by the industry because they will have greater control 

over carcass sorting and establishing maximum line speeds. It is anticipated that greater control 

by industry will encourage slaughter establishments to adopt new procedures, equipment, and 

processing techniques. Consequently, the industry will be responsible for designing its own 

process control tasks, which will incorporate new and improved procedures, equipment, and 

processes as appropriate. This should result in the efficient production of poultry products. If the 

proposed rule 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 (2), originally presented in conjunction with 

a review by the National Advisory Committee on Meat and Poultry Inspection (NACMPI) (3), 

with new data and a modified modeling approach. This version of the risk assessment takes into 

consideration public and stakeholder comments [Docket No. FSIS-2011-0012].

The original 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?

This updated risk assessment reexamines these questions using a methodology similar to

the 2008 risk assessment but augmented with additional data. 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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METHODS

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

procedures and contamination of carcasses with either Salmonella or Campylobacter. A 

stochastic simulation model uses the coefficient estimates from the logistic regression to forecast 

the effect of changes in off-line inspection categories on changes in human Salmonella or 

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

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

change in off-line inspection activities following implementation of the proposed rule, and the 

current estimate of human illnesses into its forecasts about the change in human illnesses. 

 Regression model description

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

can be found in the Appendix to this report. 

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. Young chicken data 

comprise results of the FSIS Young Chicken Baseline study (July 2007 through September 2008) 

and PR/HACCP Salmonella verification program (July 2007 through September 2010). Young 

turkey data comprise results of the FSIS Young Turkey Baseline study (August 2008 through 

July 2009) and PR/HACCP Salmonella verification program (July 2007 through September 

2010). 

The four decision variables are Scheduled and Performed procedures (SP), Scheduled and Not 

Performed procedures (SNP), Unscheduled procedures (U), and Non-Compliances (NC). These 

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. The four decision variables 

represent the sum of activities on each establishment day across the various ISP codes as 

follows: 

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), fecals (03J), economic poultry kill (04C04) 

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

wholesomeness/economic consumer protection(04), sampling (05), other inspection 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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

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

We chose the four defined categories because the expected/intended effect of the proposed 

policy 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. We also assume that – in the long-run – 

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

because such establishments will attain appropriate process control through increased inspection 

scrutiny and also through likely industry innovation. Although we explored an alternative 

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

this approach created confusion about the intended effect of the proposed policy within each 

class. For example, a random variable that summarized HACCP procedures would 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 might simply reflect random associations that can occur 

with over-parameterized models.

Rejected versions of the regression analysis on the extensive dataset included more than 40 

decision variables representing specific ISP codes. The analysis of these complicated models 

was indeterminate 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 current regression approach estimates a single regression equation for each productpathogen pair (i.e., young chicken-Salmonella, young chicken-Campylobacter, young turkeySalmonella, and young turkey-Campylobacter). This is a valid approach to making predictions 

from each model. The four decision variables are included in each regression model. For one of 

these decision variables to be found statistically significant in the model, the totality of its 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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inspection procedures must be strongly associated with pathogen occurrence. Consequently, 

inferences made about significant variables are stronger, but more general, than inferences from 

previous models with more decision variables. 

Estimates of the decision variables for the four regression models suggest inconsistent effects 

(Table 1). The proposed rule should result in fewer SNP procedures because more off-line 

personnel will be available to complete scheduled procedures. Nevertheless, the sign of the 

coefficient of the significant SNP variable in the turkey-Salmonella model suggests that reducing 

SNP will actually increase Salmonella prevalence in turkey. In contrast, the coefficient sign for 

SNP in the other models (i.e., a positive sign) suggests that decreasing occurrences in this 

category will decrease pathogen prevalence in these product classes. 

All four models support the expectation that increased activity in the U category will reduce 

pathogen prevalence (i.e., the coefficient sign is negative). 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 random variable may be importantly

associated with pathogen occurrence. 

The only model in which scheduled and performed procedures (SP) are significant decision 

variables is that for turkey-Campylobacter. The other models suggest – although the variables 

are not statistically significant at the p=0.05 level - that increasing this random variable will 

increase pathogen prevalence.

Interpreting the direction of intended changes to non-compliance (NC) episodes in 

establishments is problematic. On the one hand, FSIS expects that increased off-line inspection 

resources will generate improved process control within establishments that adopt the proposed 

rule. Improved process control should – in the longer term – result in fewer non-compliance 

reports from these establishments. On the other hand, these increased off-line inspectors will 

also be able to identify non-compliant activities and thereby generate more reports (at least in the 

short term). As these two perspectives imply, reported non-compliances are 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 

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

Based on analysis of the HACCP Inspection Models Project (HIMP) (13), we assume that fewer

non-compliances are intended to occur following implementation of the proposed rule. 

Nevertheless, the regression model results are inconsistent across the four models. The two 

models in which the NC variable is statistically significant have opposite signs for this 

coefficient (i.e., the chicken-Campylobacter model suggests prevalence will increase if NC 

decreases while the turkey-Salmonella model suggests the prevalence will decrease if NC 

decreases). Similarly, in the cases where NC is not significant, the chicken-Salmonella model 

suggests prevalence will decrease while the turkey-Campylobacter model suggests it will 

increase. 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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Table 1. Decision variable estimates from four regression analyses are shown. 

Product-Pathogen

Decision 

Variable

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 0.26

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 0.019 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 0.247 0.5374 1.0612

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

The mean values for the decision variables 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 

Chicken Baseline study. A similar explanation applies to comparisons between the two turkey 

datasets. 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

17 

 Model to forecast the effect of proposed rule 

To address the risk management questions, we develop a method for forecasting the effects of 

the proposed rule on public health. This method examines the change in pathogen prevalence as 

predicted by a regression model and mathematically maps the change in prevalence to a change 

in the annual rate of human illnesses. 

A simple prevalence-based risk assessment method was assumed based on Williams et al. (11). 

The general approach assumes annual illnesses can be modeled as a Poisson process. Therefore, 

we use the standard notation of the Greek letter lambda    to reflect the rate parameter of a 

Poisson distribution in the following model description. 

We define a model to forecast the effect of the proposed poultry slaughter rule as follows:

1 avoided ill

Prev(policy)

Prev(baseline)

             

where avoided is the annual rate of product-pathogen illnesses avoided following policy 

implementation; ill is the current annual rate of product-pathogen illnesses (i.e., before policy 

implementation); Prev(policy)is the post-chill prevalence of pathogen-contaminated poultry 

carcasses projected following policy implementation; Prev(baseline) is the post-chill prevalence 

of pathogen-contaminated poultry carcasses projected prior to policy implementation1. 

The baseline prevalence is defined as

1 1

1 1

... ...

... ... 1

ii nn

ii nn

XX X

XX X

e Prev(baseline)=

e

     

       , where the 

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

The prevalence following policy implementation is 

1 1

1 1

... ...

... ... 1

i ii n n

i ii n n

XX X

XX X

e Prev(policy)=

e

     

      

A

A , 

where one or more of the random variables are adjusted by Ai to account for a change that occurs 

following policy implementation. Because we want to forecast post-chill prevalence, the rehang 

structural random 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(policy). 

The inputs ill , Prev(baseline), Prev(policy) and Ai are all uncertain variables in this 

assessment. To assess the uncertainty about avoided , a Monte Carlo model2 was developed to 

 

1 Note that avoided might be negative if Prev(policy)> 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). 2 All Monte Carlo simulations were completed using Palisade’s @Risk software in Microsoft Excel. Each 

simulation comprises 100,000 iterations; this number of iterations was deemed to produce sufficiently stable 

forecasts.
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

18 

propagate these sources of uncertainty to a forecast about the annual rate of illnesses avoided. In 

this model, uncertainty about regression coefficients3 was modeled as 

  ˆ ˆ ~ , ()   i ii Normal stderror ; uncertainty about Ai was modeled as 

~( ) Ai Pert min,mode,max ; uncertainty about ill was modeled as ~ log ,   ill  normal   . 

Because avoided is a function of the ratio of Prev(policy)and Prev(baseline)- and these random 

variables can be reasonably assumed to be correlated – each iteration of a simulation paired the 

estimates of Prev(policy) and Prev(baseline)such that each estimate reflected the same uncertain 

coefficient values from the regression model. 

Estimates of ill are needed for all four product-pathogen pairs. We model uncertainty about the 

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

turkeys by considering the uncertainty in the total annual domestically acquired foodborne 

illnesses estimated by CDC (Scallan et al., 2011, 12). 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. 

A 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 19.71% 

(168,291/845,024), respectively (FSIS, 2011, 1). 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. (12) 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). 

Nevertheless, this treatment ignores 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. 

 

3 We assume independence in the errors among the independent variables (i.e., we do not include covariance terms 

between these variables). The calculated standard error from the regression is somewhat smaller than the value as 

we have simulated it; this result suggests that the aggregate effect of any non-zero covariance terms is to reduce 

uncertainty in modeled forecasts. Therefore, our simple treatment increases uncertainty and is deemed conservative 

for that reason.
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

19 

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

Young Chicken and Turkey.

ProductPathogen

Estimated attributed annual illnesses1 Lognormal distribution 

parameters2

Mean 5th 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 0.291

Young turkey -

Campylobacter 714 283 1,353 6.428 0.476 1

These distribution parameters are estimated from total illness data (12) and attribution fractions for Salmonella and 

Campylobacter (1). 

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 % ) , 2th ile th ile th ile

Z

      where Z0.95 is the 95th percentile of a standard 

Normal distribution. The fitting algorithm obtains a mean of 177,329 for chicken – Salmonella. 

which is a reasonable approximation of the intended uncertainty distribution. 

We also need estimates of the adjustment parameters Ai that reflect the expected change in the 

decision variables following implementation of the proposed rule. To establish baseline 

prevalence estimates, we assume each decision variable equals the mean from data used to 

estimate the regression models. In the policy scenarios, we assume the mean of each random 

decision variable will be adjusted as follows:

Scheduled and performed and unscheduled procedures in an establishment could either 

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

system in the proposed rule. FSIS inspection records in HIMP establishment are 

considered to be a good indicator of what a new FSIS inspection system might look like 

under the proposed rule. 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 nonHIMP establishments to 3.2 times more HACCP verification inspection procedures (13). 

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

establishments will not adjust in response to the proposed rule, we assumed a most likely 

value of a 25% increase in SP and U procedures in our policy scenario. At a minimum, 

we assumed no change and we assumed a maximum 60% increase in these procedures. 

Therefore, for the SP and U decision variables, we model ~ (1.0,1.25,1.6) Ai Pert . 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

20 

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

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

personnel to complete the offline procedure. Because the new inspection system requires 

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

procedures. We conservatively assume that these SNP procedures will be reduced by a 

most likely10%, but could be reduced by 100% or not change at all. Therefore, for the 

SNP decision variable, we model ~ (0.0,0.9,1.0) Ai Pert . Note: to test the sensitivity of 

this assumption we also looked at a minimum value of 0.5 for this change variable, but 

the results were not significantly altered and we only used the above distribution in the 

final analysis.

We are uncertain as to how recorded non-compliances might change in establishments 

under the new FSIS inspection system, for reasons discussed above. Current FSIS 

inspection records in HIMP establishment are considered a good indication of what a new 

FSIS inspection system might look like. On average, the current data suggests that HIMP 

broiler establishments have 26 percent fewer reported health-related non-compliances 

than do non-HIMP broiler establishments (a simple average reduction across all 

inspection categories from table 3-6 of the 2011 HIMP report) (13). Nevertheless, noncompliances may be reduced by 100% or not change at all. Therefore, for the NC 

decision variable, we model ~ (0.0,0.74,1.0) Ai Pert . In this case we also tested the 

sensitivity of this assumption , by modeling a minimum value of 0.74, and a most likely 

value of 0.9 for this change variable, but the results again were not significantly altered 

and we only used the above distribution in the final analysis. 

Implementation scenarios 

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

rule, we initially assumed that the four decision variables would all change according to the 

assumptions outlined above. We term this forecast an “indiscriminate” scenario because its 

adjustments make no further assumptions about how FSIS might emphasize or de-emphasize 

particular decision variables in the regression models with the new inspection system. 

An alternative scenario (Increase U) considers how human illness forecasts might change by 

emphasizing changes to the unscheduled procedures (U) decision variable while leaving other 

decision variables unchanged. This alternative 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 on the 

consistency of this decision variable’s sign (i.e., negative) across all four product-pathogen 

models; its statistical significance in one chicken and one turkey model; and the assumption that 

FSIS will particularly emphasize performance of the equivalent of unscheduled procedures in the 

new inspection system. 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

21 

RESULTS

Predicted annual changes in Salmonella and Campylobacter prevalence in turkey establishments:

Table 3 shows that when off-line procedures are indiscriminately changed in young chicken 

establishments, the analysis predicts a average decline of 4 percent (-.02, .11) in the percentage 

of positive Salmonella samples. The analysis also predicts that there could be a decrease of 17 

percent (-.015, .32) in the percentage of positive Campylobacter samples. 

If only unscheduled inspection procedures in young chicken slaughter establishments are 

targeted for increase Table 4 sows that the analysis predicts a average decline of 3 percent (-

.004, .08) in the percentage of positive Salmonella samples. The analysis also predicts a similar 

decline of 17 percent(.021, 32) in the percentage of positive Campylobacter samples.

Table 3. Summary statistics of changes in establishment prevalence from Monte Carlo simulations of the 

indiscriminate scenario across the four product-pathogen models are shown.

Change in prevalence mean(10th 

percentile, 90th percentile)

Salmonella Campylobacter

young chicken 

establishments .02(.006,.038) -.002(-.018,.007) 

young turkey 

establishments .04(-.02,.11) .17(.015,.32) 

Predicted annual changes in Salmonella and Campylobacter prevalence in turkey establishments:

Table 3 shows that when off-line procedures are indiscriminately changed in young chicken 

establishments, the analysis predicts a average decline of 4 percent (-.02, .11) in the percentage 

of positive Salmonella samples. The analysis also predicts that there could be a decrease of 17 

percent (-.015, .32) in the percentage of positive Campylobacter samples. 

If only unscheduled inspection procedures in young chicken slaughter establishments are 

targeted for increase Table 4 shows that the analysis predicts a average decline of 3 percent (-

.004, .08) in the percentage of positive Salmonella samples. The analysis also predicts a similar 

decline of 17 percent(.021, 32) in the percentage of positive Campylobacter samples.
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

22 

Table 4. Summary statistics of changes in establishment prevelance from Monte Carlo simulations of the 

unsceduled procedures scenario across the four product-pathogen models are shown.

Unscheduled 

procedures 

Scenario

Change in prevalence 

mean(10th percentile, 90th

percentile)

Salmonella Campylobacter

young chicken 

establishments .02(.008,.038) .005(-0,.017)

young turkey 

establishments .03(-.004,.08) .17(.015,.32)

Predicted changes in human illness: The results for predicted changes in human illness are 

graphically summarized in Figures 1-4, depicting the cumulative probability plots for the 

indiscriminate and alternative scenarios across the four product-pathogen pairs. We first focus 

on the indiscriminate scenarios. 

The analysis indicates that we might expect (with high probability) a small net benefit of 

decreased illnesses, but that there is a small probability of a net increase. Except for the chickenCampylobacter forecast, the indiscriminate analyses suggest a high probability that the proposed 

policy might result in a decrease in human illnesses (Table 5). This means there is a 95%, a 37%, 

an 80%, and a 99% chance that illnesses will not increase for the chicken-Salmonella, chickenCampylobacter, turkey-Salmonella and turkey-Campylobacter models, respectively Stated a 

different way, the probability that illnesses might increase (i.e., a negative value for illnesses 

avoided) is 0.05, 0.63, 0.20, 0..01 for the chicken-Salmonella, chicken-Campylobacter, turkeySalmonella and turkey-Campylobacter models, respectively. 

Of the measures of central tendency (mean, median and mode), the modal (most likely) value 

represents the least change from the baseline across the four models in the aggregate (i.e., 

summing net illness rate changes) (Table 5). This is consistent with the intuition that by 

themselves, the changes in inspection activities being considered are most likely to have no large 

effect in either direction. The modes from the indiscriminate scenario suggest a net reduction of 

3,342 in the annual rate of Salmonella illnesses and no change in the annual rate of 

Campylobacter illnesses. 

At best, the chicken-Campylobacter model results are ambiguous as to the effect of an 

indiscriminate implementation of the proposed rule. The forecasted increase in Campylobacter

illnesses is primarily driven by the SP decision variable and 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. 

The simulation results in Table 3 also reflect the aggregate change in total illnesses (i.e., 

Salmonella + Campylobacter) across chicken and turkey slaughter industries. To estimate this 

value, the avoided values for the chicken-Salmonella and chicken-Campylobacter models were 

summed on each iteration of a Monte Carlo simulation. This same approach was used for the 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

23 

turkey models. It should be noted that the adjustments to the U decision variable were the same 

for both pathogen models (i.e., the draws from the random variable Ai were perfectly correlated 

between the two chicken models and the two turkey models). 

The combined illnesses avoided results suggest the probability that illnesses associated with both 

young chicken and turkey establishments might increase is ~0.13. This result suggests with 

approximately 87% confidence that aggregate human illnesses will be unchanged or decrease 

following an indiscriminate implementation of the proposed poultry rule. 

Alternative scenarios considered the effect of only increasing unscheduled procedures (Table 6). 

In each of these alternative scenarios, the other decision variables were assumed to not change, 

although uncertainty in their regression coefficients was still included in the simulations. 

In the chicken-Salmonella model, the alternative scenario suggests a minor reduction in the 

probability that the Salmonella illness rate will increase. Furthermore, the similarity of the 

uncertainty distributions for the “Increase U” and “Indiscriminate” scenario results suggests the 

importance of the U decision variable in that indiscriminate scenario (Figure 1). 

The alternative scenario in the chicken-Campylobacter model suggests a substantial reduction in 

the probability that the Campylobacter illness rate will increase (i.e., from 0.63 to 0.10). This

scenario avoids increased illnesses mostly because it does not include the effect of decreasing 

non-compliances. In fact, this alternative scenario suggests the potential for avoiding 

substantially more Campylobacter illnesses if FSIS emphasizes increased unscheduled 

procedures – and de-emphasizes reducing the frequency of non-compliance reports – in the 

implementation of the proposed rule. 

The alternative scenario in the turkey-Salmonella model suggests a small reduction in the 

probability that the Salmonella illness rate will increase (i.e., from 0.20 to 0.14). The alternative 

scenario in the turkey-Campylobacter model suggests only a minor change relative to the 

indiscriminate analysis. 

For the alternative scenario, the combined illnesses avoided results demonstrate a substantial 

decrease in the probability that illnesses might increase for young chicken establishments (i.e., 

from 0.13 to 0.0009). This result for young turkey establishments is less dramatic (i.e., from 

0.13 to 0.06). These results suggest that aggregate human illnesses will be unchanged - or 

decrease - with approximately 100% and 94% confidence among young chicken and young 

turkey establishments, respectively, if increasing unscheduled procedures is emphasized in the 

proposed rule. 

Table 5. Summary statistics for human illnesses avoided from Monte Carlo simulations of the indiscriminate 

scenario across the four product-pathogen models.

Attributable to Young 

Chicken Establishments

Attributable to Young Turkey 

Establishments

Combined Illnesses 

Avoided

Statistic

Salmonella 

illnesses 

avoided

Campylobacter

illnesses 

avoided

Salmonella 

illnesses 

avoided

Campylobacter

illnesses 

avoided

Salmonella 

illnesses 

avoided

Campylobacter

illnesses 

avoided
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

24 

Mean 4,203 -462 311 119 4513 -341

Median 3,806 -3 270 95 4109 51

Mode 3,181 0 161 0 3059 1

Std 

Deviation 3,018 2,216 423 106 3110 2230

10th 

percentile 872 -2,668 -146 9 1075 -2590

90th 

percentile 8,089 1,067 834 252 8534 1222

Probability 

of 

increased 

illnesses

0.0465 0.6268 0.198 0.0086 .0407 .4000 

Table 6. Summary statistics for human illnesses avoided from Monte Carlo simulations of an alternative 

scenario that increases unscheduled procedures across the four product-pathogen models.

Attributable to Young 

Chicken Establishments

Attributable to Young Turkey 

Establishments

Combined Illnesses 

Avoided

Statistic

Salmonella 

illnesses 

avoided

Campylobacter

illnesses 

avoided

Salmonella 

illnesses 

avoided

Campylobacter

illnesses 

avoided

Salmonella 

illnesses 

avoided

Campylobacter

illnesses 

avoided

Mean 4,044 868 242 118 4286 986

Median 3,567 174 187 95 3804 326

Mode 2,483 0 90 0 2995 1

Std 

Deviation 2,463 1,626 285 104 2548 1620

10th 

percentile 1,390 0 -30 12 1514 26

90th 

percentile 7,301 2,728 603 249 7682 2865

Probability 

of 

increased 

illnesses

0.0001 0.1044 0.1368 0.0004 .0058 .0501 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

25 

Figure 1. Uncertainty about the change in the annual Salmonella human illness rate when off-line inspection 

procedures are intensified in chicken establishments is depicted for the indiscriminate scenario, the increased 

unscheduled procedures scenario and the decreased scheduled but not performed procedures scenario. 

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
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

26 

Figure 2. Uncertainty about the change in the annual Campylobacter human illness rate when off-line inspection 

procedures are intensified in chicken establishments is depicted for the indiscriminate scenario, the increased 

unscheduled procedures scenario and the decreased scheduled but not performed procedures scenario.

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
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

27 

Figure 3. Uncertainty about the change in the annual Salmonella human illness rate when off-line inspection 

procedures are intensified in turkey establishments is depicted for the indiscriminate scenario, the increased 

unscheduled procedures scenario and the decreased scheduled but not performed procedures scenario.

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
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

28 

Figure 4. Uncertainty about the change in the annual Campylobacter human illness rate when off-line inspection 

procedures are intensified in turkey establishments is depicted for the indiscriminate scenario, the increased 

unscheduled procedures scenario and the decreased scheduled but not performed procedures scenario.

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
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

29 

DISCUSSION

In the model and analyses presented here we examine available data to establish quantitative

relationships between observed Salmonella and Campylobacter positive samples and Agency 

inspection activities taking place in young chicken and young turkey slaughter establishments. 

While this does not establish a cause-and-effect relationship, we can draw inferences from these 

observed associations. We further assume that there is a 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 (11). A 

great deal of the quantitative portion of this risk assessment focuses on these two relationships. 

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? 

In general, this analysis suggests that the proposed change in off-line 

inspection will decrease the net annual human illness rate with high 

probability and that the probability of proposed changes in off-line inspection 

procedures increasing the annual rate of human illnesses is small. and 

dependent on how the rule is implemented in practice. 

This analysis also suggests ambiguous effects with respect to Campylobacter

occurrence among chicken establishments. The larger probability of increased 

Campylobacter illnesses from contaminated chicken carcasses is primarily 

driven, however, by the non-compliances decision variable. This decision 

variable is poorly understood and the intended effect of changes in this 

category of procedures is arguable. Removing the effect of this decision 

variable in alternative scenarios substantially reduces the probability that the 

human illness rate might increase. 

This latter conclusion is further supported by consideration of the HIMP 

structural variable in the 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 (13), 

these regression findings suggest that the positive Salmonella implications of 

that HIMP analysis also apply to Campylobacter. 

The HIMP structural variable in all four regression models implied 

participation in HIMP was associated with reduced pathogen prevalence. 

While 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. 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

30 

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

within or outside the establishment, affect human illness? 

Most likely point estimates from a scenario that indiscriminately changes all four 

decision variables in our analysis suggest a net reduction of 3,342 (3,181+161, 

from Table 3) in the annual rate of Salmonella illnesses. This analysis assumes 

that the total annual Salmonella illnesses rate attributed to poultry is centered 

about 174,686 (167,831+6,855, from Table 2). Therefore, an increase in off-line 

inspection activities might be expected to prevent ~1.9% of these illnesses per 

year. 

Most likely point estimates from the same scenario suggest that there will be no 

net change in the annual rate of Campylobacter illnesses in either chicken or 

turkey establishments. 

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

impact toward reducing microbial prevalence and corresponding human illness? 

The most reliable implication from the regression models is that increasing 

unscheduled procedures seems to reduce 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. 

 What is the uncertainty about these effects? 

Our modeling approach includes uncertainty about regression coefficients, uncertainty as 

to the effective change in future inspection activities , and uncertainty in the baseline 

annual rates of human Salmonella and Campylobacter illness attributable to poultry. 

These sources of uncertainty translate into substantial uncertainty about forecasted 

changes in illness rates.

The focus of this analysis is on the “down-side” potential of the proposed rule (i.e., the 

probability that illness rates might increase). This focus seems appropriate for a proposed 

rule that intends to change inspection processes in slaughter establishments. 

Nevertheless, the uncertainty about changes in illness rates includes “up-side” potential 

that a public health benefit in the form of illnesses avoided could be substantially larger 

than the model values cited. 

The uncertainty that surrounds these forecasts suggests further monitoring opportunities 

for FSIS.. For example, FSIS can periodically assess aggregate inspection procedures 

and compare these to the baseline predictions from this model. Such comparisons will 

empirically measure the changes occurring for the decision variables in the model and 

reduce the current uncertainty about these model inputs. Also, FSIS will continue to

monitor the pathogen prevalence on carcasses among participating and non-participating 

establishments. The pathogen verification testin data can be used to assess 

correspondence with its expectations.. 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

31 

REFERENCES

1. FSIS (2011) 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_Guid

ance_Broilers_Turkeys_2011.pdf

2. FSIS (2008) Risk Assessment for Guiding Public Health-Based Poultry Slaughter Inspection 

http://www.fsis.usda.gov/PDF/Poultry_Slaughter_Risk_Assess_Jan2008.pdf

3. National Advisory Committee on Meat and Poultry Inspection (2008) February Meeting 

http://www.fsis.usda.gov/Regulations_&_Policies/National_Advisory_Committee_on_Meat_&_

Poultry/index.asp#2008

4. Proc logistic SAS 9.1.3 Service Pack 1 Copyright (c) 2002-2003 by SAS Institute Inc., Cary, 

NC, USA. 

5. Hosmer, D.W., Lemeshow, S. (2000) Applied Logistic Regression, Wiley, New York, 

6. Hilbe, J. (2009) Logistic Regression Models, Chapter 9 Overdispersion, CRC Press/Chapman 

Hall, Boca Raton, Fl. 

7. Hardin, JW, Hilbe, JM. (2003) Generalized Estimating Equations, Chapter 2 Model 

Construction and Estimating Equations, CRC Press/Chapman Hall, Boca Raton, Fl. 

8. FSIS (2008) Nationwide Young Chicken Microbiological Baseline Data 

http://www.fsis.usda.gov/PDF/Baseline_Data_Young_Chicken_2007-2008.pdf

9. FSIS (2009) Nationwide Microbiological Baseline Data Collection Program Young Turkey Survey 

August 2008- July 2009 

http://www.fsis.usda.gov/PDF/Baseline_Data_Young_Turkey_2008-2009.pdf

10. CDC, 2001-2007. Outbreaknet: Foodborne Outbreak Online Database, 

http://wwwn.cdc.gov/foodborneoutbreaks/. 

11. Williams M.S., Ebel, E.D., Vose, D. (2011) Framework for Microbial Food-Safety Risk 

Assessments Amenable to Bayesian Modeling Risk Analysis. 31, Risk Analysis, Vol. 31, no. 4, 

548-565, April 2011. Available online under DOI: 10.1111/j.1539-6924.2010.01532.x 

12. Scallan, E. Hoekstra, R.M., Angulo, F.J., Tauxe, R.V., Widdowson, M-A, Roy, S.L., Jones, J.L. 

Griffin, P.M. (2011) Foodborne Illness Acquired in the United States—Major Pathogens. Emerging 

Infectious Disease.17 no. 1, 7-15, January 2011, DOI: 10.3201/eid1701.P11101 

13. FSIS (2011) Evaluation of HACCP Inspection Models Project (HIMP), United States 

Department of Agriculture Food Safety and Inspection Service, August 2011.
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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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 forecasts 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. We 

assume that the increased off-line inspection work force will – because of apparent correlations 

between performance of inspection procedures and occurrence of pathogens on carcasses – 

influence public health exposures to these foodborne pathogens. 

We developed regression models 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. We estimated a binary logistic regression with coefficients 

that are weighted by slaughter volume. 

Previously, the basic modeling approach was peer reviewed and revised (2). In this version, we 

increased the number of samples and variables evaluated. We used our prior experience with the 

logistic regression modeling of FSIS poultry slaughter sampling verification methods – and 

inspector procedure data – 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 a 2008 FSIS Risk Assessment. 

 

 Regression Model Approach 

Four basic regression models are estimated to account for the two target pathogens (Salmonella 

sp. and Campylobacter sp.) 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 

(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. 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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We chose the four defined categories because the expected/intended effect of the proposed 

policy 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. We also assume that – in the long-run – 

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

because such establishments will attain appropriate process control. Although we explored an 

alternative approach that collapsed decision variables according to the six classes of off-line 

procedures, this approach created confusion about the intended effect of the proposed policy 

within each class. For example, a random variable that summarized HACCP procedures would 

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

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

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 (4). The logit link 

function is used for the dependent variable and pseudo-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 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

Each binary logistic regression model was evaluated for lack of fit to the data using the standard 

Hosmer-Lemeshow test (5). 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 

(6). 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 (7). 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.
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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 Data Sets 

The core data come from the FSIS “Young Chicken Baseline” (July 2007 through September 

2008, 8) and the FSIS “Young Turkey Baseline” (August 2008 through July 2009, 9). 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. 

 Decision variables: Inspection procedures

 

There are six general inspection system procedure (ISP) code activity categories captured in the 

FSIS database (Table 1). 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 needs; they 

typically involve regulatory inspection activities such as fecal checks for zero-tolerance. 

Unscheduled procedures are also performed in response to unforeseen hazards, 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 codes, 17 PR/HACCP codes, 11 Wholesomeness/Economic 

Consumer Protection codes, six Sampling codes, four Other Inspection Requirements codes and 

four Emergency Activity codes (Table 1). 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 total activity for each of these four categories was calculated as the sum across all codes for 

that category: 

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

wholesomeness/economic consumer protection(04), sampling(05), other inspection 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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

(03C), fecals (03J), economic poultry kill (04C04) 

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(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), fecals (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), fecals(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 model4

 (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). 

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. Line-speed, 

5. Number of establishment inspectors, 

6. Line count 

7. The categorical inspection system variable identifies 22 inspection type combinations 

(Table 5) from the eight basic types (MAESTRO, NELS, Nu-Tech, Nuova, SIS, HIMP, 

Traditional, and Religious Slaughter). Traditional inspection is used as the reference. 

 

4 Variables that were considered but are excluded because of less contribution or overlapping contribution to the 

model fit to the data are HACCP size, production area, inspector positions, 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.
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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(Table 8 shows these categories for young chicken while Table 9 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 condemnations of carcasses, 

10. contamination (fecal, ingesta, body fluids, etc.) of carcasses, and 

11. air sacculitis cases among carcasses 

12. synovitis cases among carcasses (only a relevant disease to the turkey slaughter).

 Final Models 

Tables 2 and 3 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 

4 and 5 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) will decrease the prevalence 

of Salmonella and Campylobacter. Nevertheless, the U procedures 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 

assigned5

. 

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 

 

5 This alternative value is -1 for the chicken-Salmonella model and zero for the other models.
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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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). 

Alternative models were assessed by using 43 and 21 decision variables. These alternatives 

represented the 43 non-emergency procedures listed in Table 1 and a collapsing of these to 21 

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. 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 

forecasting effects of the proposed policy, we selected the four decision variable model for each 

product-pathogen pairing. The R-square values for these chicken-Salmonella, chickenCampylobacter, turkey-Salmonella and turkey-Campylobacter models are 0.27, 0.09, 0.10 and 

0.33, respectively. 

For model evaluation and validation, we randomly split the datasets used in model development, 

re-estimated the regression coefficients for each subset of data and assessed the stability of the 

prevalence estimates. 

Tables 6 and 7 show the results of splitting the young chicken datasets for Salmonella. Table 6 

shows the parameter estimates for the un-split data model estimates and also for the two split 

halves of data. Table 7 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 post-chill 

prevalence found in the FSIS HIMP report (13). The only matter of concern is the prediction 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 

8 and 9. The parameter estimates from Table 8 are used to calculate the prevalence estimates in 

Table 9. The BX element in Table 9 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 10 and 11 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 

the full dataset estimates. The estimates are in agreement with the high unweighted Salmonella

prevalence. 

Tables 12 and 13 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 will 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

38 

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 1-4 show the Receiver Operating Characteristic (ROC) plots for the four models. The 

interpretation of these plots is that the model is more predictive the farther away 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. A standard method for 

evaluation is to estimate the area under the curve. 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). 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 14 shows the results for the three models. The 

results are mixed between significant negative coefficient signs for decreased prevalence and 

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 03, 04, and 06 

procedure elements have this characteristic in the chicken-Salmonella model and the 04 and 05 

procedures elements behave similarly in the chicken-Campylobacter model with the 03 element 

almost significant. The turkey-Campylobacter model has the 03 and 06 elements significant. It is 

not clear why the 05 and 06 coefficients have significant positive signs in the chicken models. 

Table 15 shows the results for further disaggregated models. It becomes clear that the 03J 

procedures are the drivers decreasing prevalence for HACCP in the chicken-Campylobacter

model and the 06D01 procedures are drivers for other inspection requirements in the chickenSalmonella and turkey-Campylobacter models. The prevalence estimates from these models 

shown in Table16 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 predicted to adopt the new inspection system 

requiring 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, we decided to create a simulated dataset 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

39 

corresponding to all establishments expected to adopt the new inspection system. Looking at the 

establishment breakdown by the small business administration (SBA) size classification of large, 

small, and very small establishments (L, S, V) we noticed that there is an imperfect match and 

additionally none of the very small establishments in the observational dataset are expected to 

adopt the new inspection system. Table 17 shows the breakdown for SBA size for the 

observational study and Table 18 shows the expected size breakdown for establishments that will 

adopt the new 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 new inspection system. Repeated random selection of 

establishments with matching characteristics created an averaged dataset corresponding to the 

characteristics of the establishment distribution of establishments expected to adopt the new 

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 new inspection system as shown in Table 19. The 19 

establishments in the “other” category were placed in either the chicken or the 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 20). 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.
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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

Salmonella Predictive Model 

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

Campylobacter Predictive Model 

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

Salmonella Predictive Model 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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

Campylobacter Predictive Model 

TABLES

Appendix Table 1. 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 NonCompliant in the Binary Logistic Regression Model

 Code 

Sum Activity Other Sum Elements 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

10 sum03 HACCP sum03C raw not ground 33 03C02 03C01 verification

11 sum03 HACCP sum03E not heat treated-shelf stable 34 3.00E+01 m/v/r/ca/fu4

12 sum03 HACCP sum03F not heat treated-shelf stable 35 3.00E+02 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
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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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 44 03J01 m/v/r/ca/fu4

22 sum03 HACCP sum03J slaughter/fecal 45 03J02 03J01 verification

23 sum04 W/ECP1 sum04A01 yield/shrink 46 04A01 m/v/r/ca/fu4

47 sum04 W/ECP1 sum04A02 product solution formulation 71 04A02 m/v/r/ca/fu4

48 sum04 W/ECP1 sum04A03 comminuted/mechanically 

separated 72 04A03 m/v/r/ca/fu4

49 sum04 W/ECP1 sum04A04 battered products 73 04A04 m/v/r/ca/fu4

50 sum04 W/ECP1 sum04B01 product meets standard 74 04B01 m/v/r/ca/fu4

51 sum04 W/ECP1 sum04B02 packaging/labeling standards 75 04B02 m/v/r/ca/fu4

52 sum04 W/ECP1 sum04B03 stated label net weight 76 04B03 m/v/r/ca/fu4

53 sum04 W/ECP1 sum04B04 product identification 77 04B04 m/v/r/ca/fu4

54 sum04 W/ECP1 sum04C02 humane slaughter requirements 78 04C02 m/v/r/ca/fu4

55 sum04 W/ECP1 sum04C03 non-food safety product req. 79 04C03 m/v/r/ca/fu4

56 sum04 W/ECP1 sum04C04 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

60 sum05 sampling sum05B01 random product sample 84 05B01 sample collection

61 sum05 sampling sum05B02 CS/DO/headquarters request 85 05B02 sample collection

62 sum05 sampling sum05C01 random residue sample 86 05C01 sample collection

63 sum06 OIR/SPS2 sum06A01 export regulation compliance 87 06A01 m/v/r/ca/fu4

64 sum06 OIR/SPS2 sum06B01 custom exempt retail 

compliance 88 06B01 m/v/r/ca/fu4

65 sum06 OIR/SPS2 sum06D01 sanit. performance standards 89 06D01 m/v/r/ca/fu4

66 sum06 OIR/SPS2 sum06D02 facility sanitation compliance 90 06D02 m/v/r/ca/fu4

67 sum08 emergency3 sum08S14 water systems 91 08S14 unscheduled check

68 sum08 emergency3 sum08S15 processing/manufacture 92 08S15 unscheduled check

69 sum08 emergency3 sum08S16 storage areas 93 08S16 unscheduled check

70 sum08 emergency3 sum08S17 shipping/receiving 94 08S17 unscheduled check

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 
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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Appendix Table 2. Parameter Estimates for Young Chicken Salmonella Model Used in Scenario Analysis

Parameter Estimate Std Error p-value Mean Std Dev

Intercept -1.8967 0.3123 <0.0001* 1.0000 0.0000

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 0.077 <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

month28 -0.0124 0.0846 0.4417 0.0014 0.1952

month29 0.2626 0.0865 0.0012* -0.0026 0.1845
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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month30 0.075 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 -0.051 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

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
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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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 0.4960 134.3891 1101.8907

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

Appendix Table 3. 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 1.0000 0.0000

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 1.042

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 0.116 0.043 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 0.414

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 0.04 <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
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

46 

District6 -0.0589 0.0505 0.1218 -0.082 0.6251

District7 0.2839 0.0656 <0.0001* -0.17 0.5296

District8 -0.6106 0.152 <0.0001* -0.2336 0.4373

District9 0.4256 0.092 <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 0.557

District13 -0.3898 0.051 <0.0001* -0.0907 0.6169

District14 0.3007 0.052 <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

InspSysMAESTRO,Religio -0.0386 5.8061 0.4973 0.0041 0.2048

InspSysMAESTRO-SIS -0.5936 5.808 0.4593 -0.0131 0.1566

InspSysNELS -0.77 5.8054 0.4472 0.0718 0.3233

InspSysNELS,MAESTRO -0.4104 5.806 0.4718 0.0008 0.1964

InspSysNELS,NTIS,MAEST -1.8641 5.8112 0.3742 -0.0168 0.1441

InspSysNELS,Nu-Tech 10.621 116.1 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 5.81 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 98.622

AirSac 0.0000 0.0000 0.1587 237.9061 2006.175

sum_SP 0.0076 0.0065 0.1212 6.5629 0.8762

sum_SNP 0.0198 0.0107 0.0321* 0.6929 0.26

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
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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Appendix Table 4. 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* 1.0000 0.0000

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 0.198

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 0.02 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* 0.061 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 -0.009 0.1859

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
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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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* 0.035 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 0.111

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 0.345

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

Appendix Table 5. 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* 1.0000 0.0000

rehang -1.7619 0.1523 <0.0001* -0.677 1.0002

loglinespeed 7.4946 2.6152 0.0021* 1.4706 0.1266
August 2012 Public Health-Based Poultry Slaughter Inspection Risk Assessment

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logemployees 3.6115 1.0235 0.0002* 0.9212 0.1865

lines -2.7200 0.6853 <0.0001* 1.242 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* 0.104 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 -0.129 0.3611

district3 0.3415 0.2636 0.0976 0.0069 0.532

district4 0.9143 0.3496 0.0045* -0.0596 0.4614

district5 0.0481 0.3594 0.4468 -0.0673 0.452

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 0.452

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

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
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Appendix Table 6. 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

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
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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

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 -0.0270 -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 257.0309 0.0002 259.1351
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Contam 0.0005 34.1020 0.0005 33.4667 0.0006 34.7371

AirSac 0.0000 134.3891 0.0000 142.7701 0.0001 126.0088

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 -0.0010 29.0843 -0.0056 29.1864

sum_NC 0.0091 0.7834 0.0025 0.7869 0.0196 0.7798

Appendix Table 7. 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) -2.3905 -2.4041 -2.4069

BX (rehang= 1) post-chill -2.7290 -2.7535 -2.7373

BX (rehang= -1) rehang 0.3376 1.5320 -0.7224

Prevalence (rehang= mean) 0.0839 0.0829 0.0826

Prevalence (rehang= 1) post-chill 0.0613 0.0599 0.0608

Prevalence (rehang= -1) rehang 0.4039 0.4158 0.3892

Prevalence unweighted 0.1231 0.1226 0.1235

Appendix Table 8. Parameter Estimates from the Young Chicken Campylobacter Split Datasets

Parameter B unsplit mean B split1 mean B split2 mean

Intercept 0.3286 1.0000 0.2875 1.0000 0.4175 1.0000

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 -0.0710 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 -0.3690 -0.2339 -0.3255 -0.2367

District3 -0.5514 -0.2315 -0.7509 -0.2306 -0.3496 -0.2324
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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 -0.0820 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 -0.2330 -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

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 294.26

Contam -0.0003 49.3667 -0.0005 48.8615 -0.0001 49.872

AirSac -1.00E-05 237.906 -3.00E-05 229.711 1.30E-05 246.10

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 31.082

sum_NC -0.0157 1.3634 -0.0078 1.3617 -0.0220 1.3652
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Appendix Table 9. 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) 1.1615 1.1755 0.9760

BX (rehang= 1) post-chill 0.5254 0.5479 0.3125

BX (rehang= -1) rehang 1.7972 1.8365 1.6052

Prevalence (rehang= mean) 0.7616 0.7641 0.7263

Prevalence (rehang= 1) post-chill 0.6284 0.6336 0.5775

Prevalence (rehang= -1) rehang 0.8578 0.8625 0.8327

Prevalence Unweighted 0.7333 0.7310 0.7356

Appendix Table 10. Parameter Estimates from the Young Turkey Salmonella Split Datasets

Parameter

B 

unsplit mean B split1 mean B split2 mean

Intercept

-

13.1301 1.0000

-

13.7398 1.0000

-

11.0424 1.0000

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 1.2420 -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 -0.1290

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
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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

Appendix Table 11. 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) -2.8464 -2.8534 -2.8557

BX (rehang= 1) post-chill -2.9980 -2.9823 -2.9792

BX (rehang= -1) rehang -2.0782 -2.2187 -2.2496

Prevalence (rehang= mean) 0.0549 0.0545 0.0544

Prevalence (rehang= 1) post-chill 0.0475 0.0482 0.0484

Prevalence (rehang= -1) rehang 0.1112 0.0981 0.0954

Prevalence Unweighted 0.0729 0.0729 0.0715

Appendix Table 12. Parameter Estimates from the Young Turkey Campylobacter Split Datasets

Parameter B unsplit mean B split1 mean B split2 mean

Intercept -13.1301 1.0000 -13.7398 1.0000 -11.0424 1.0000

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 1.2420 -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
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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 -0.1290

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

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

Appendix Table 13. 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) -1.9928 -2.8116 -3.5105

BX (rehang= 1) post-chill -4.9475 -4.8444 -4.9858

BX (rehang= -1) rehang -1.4237 -1.3632 -1.4402

Prevalence (rehang= mean) 0.1200 0.0567 0.0290

Prevalence (rehang= 1) post-chill 0.0071 0.0078 0.0068

Prevalence (rehang= -1) rehang 0.1941 0.2037 0.1915

Prevalence Unweighted 0.1189 0.1401 0.0978

Appendix Table 14. Regression Coefficients for Unscheduled Procedures by Inspection Element
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Young Chicken - Salmonella

ISP 

Element B Std Error p-value Mean Std Dev

sum01_U -0.0020 0.0150 0.8966 0.3741 0.7482

sum03_U -0.0030 0.0016 0.0500* 13.2204 14.3555

sum04_U -0.0035 0.0015 0.0237* 12.1161 10.3950

sum05_U 0.0845 0.0159 <.0001* 0.8947 0.6132

sum06_U -0.0146 0.0053 0.0058* 1.7249 2.6899

sum08_U 0.0059 0.0212 0.7813 0.8051 0.6211

Young Chicken - Campylobacter

ISP 

Element B Std Error p-value Mean Std Dev

sum01_U 0.0065 0.0205 0.7528 0.3741 0.7482

sum03_U -0.0264 0.0146 0.0715 13.2204 14.3555

sum04_U -0.0780 0.0280 0.0053* 12.1161 10.3950

sum05_U -0.1099 0.0183 <.0001* 0.8947 0.6132

sum06_U 0.0128 0.0063 0.0435* 1.7249 2.6899

sum08_U 0.0043 0.0277 0.8775 0.8051 0.6211

Young Turkey - Campylobacter

ISP 

Element B Std Error p-value Mean Std Dev

sum01_U -0.0994 0.1244 0.4242 0.2510 0.6869

sum03_U -0.1031 0.0492 0.0363* 2.6741 1.7617

sum04_U -0.0818 0.0860 0.3412 2.8266 1.0534

sum05_U -0.0559 0.1252 0.6556 0.9917 0.6807

sum06_U -0.1675 0.0808 0.0381* 1.1390 1.1582

sum08_U -0.2074 0.2018 0.3040 0.9639 0.3763

Appendix Table 15. Regression Coefficeint for Unscheduled Procedures by ISP Code

Chicken-Salmonella

ISP Code B Std Error p-value Mean Std Dev

sum01B_U 0.0143 0.0468 0.7596 0.0768 0.2763

sum01C_U 0.0022 0.0184 0.9055 0.2886 0.6435

sum01_Uother -0.2239 0.1081 0.0383* 0.0087 0.1038

sum03B_U 0.0200 0.0561 0.7216 0.0356 0.2071

sum03C_U -0.1036 0.0294 0.0004* 0.3627 1.2117

sum03J_U -0.0026 0.0017 0.1133 12.3816 13.8886

sum03_Uother 0.1119 0.0272 <.0001* 0.4405 1.3024

sum04_U -0.0034 0.0015 0.028* 12.1161 10.3950

sum05_U 0.0799 0.0159 <.0001* 0.8947 0.6132

sum06D01_U -0.1247 0.0181 <.0001* 0.3250 0.6210
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sum06_Uother -0.0076 0.0055 0.1652 1.4000 2.6579

sum08_U 0.0036 0.0212 0.8644 0.8051 0.6211

Chicken-Campylobacter

ISP Code B Std Error p-value Mean Std Dev

sum01B_U 0.0259 0.0551 0.6383 0.0627 0.2522

sum01C_U -0.0015 0.0240 0.9501 0.2849 0.6290

sum03B_U 0.1078 0.0459 0.0190* 0.0610 0.3019

sum03C_U 0.0771 0.0402 0.0554 0.3054 0.9130

sum03J_U -0.0097 0.0020 <.0001* 13.8051 15.3436

sum03_Uother -0.0940 0.0357 0.0085* 0.3814 1.0212

sum04_U 0.0060 0.0019 0.0020* 11.6642 9.6596

sum05_U -0.1072 0.0184 <.0001* 0.7620 0.7275

sum06D01_U 0.0488 0.0223 0.0286* 0.3667 0.6456

sum06_Uother 0.0065 0.0066 0.3249 1.8606 2.8507

sum08_U 0.0145 0.0281 0.6066 1.1757 0.5389

Turkey-Campylobacter

ISP Code B Std Error p-value Mean Std Dev

sum01B_U 0.1405 0.3308 0.6709 0.0659 0.2591

sum01C_U -0.4178 0.2833 0.1403 0.1342 0.3480

sum01_Uother 0.0636 0.3130 0.8390 0.0510 0.3136

sum03B_U -0.2212 0.4389 0.6143 0.1120 0.3701

sum03C_U -0.0018 0.4102 0.9965 0.1449 0.4170

sum03J_U 0.2225 0.3607 0.5372 1.0482 0.4321

sum03_Uother -0.1558 0.3077 0.6127 1.3689 0.9374

sum04_U -0.1010 0.0905 0.2643 2.8266 1.0534

sum05_U -0.0678 0.1306 0.6036 0.9917 0.6807

Appendix Table 16. Prevalence Estimates for Models Disaggregated by Unscheduled Procedures

Variable (6) CS1 CC2 TS3 TC4

BX (rehang= mean) -2.3906 1.1632 -2.8368 -3.1793

BX (rehang= 1) post-chill -2.7291 0.5257 -2.9746 -4.9373

BX (rehang= -1) rehang -0.3889 1.8003 -2.1386 -1.4213

Prevalence (rehang= mean) 0.0839 0.7619 0.0554 0.0400

Prevalence (rehang= 1) post-chill 0.0613 0.6285 0.0486 0.0071

Prevalence (rehang= -1) rehang 0.4040 0.8582 0.1054 0.1945

Variable (10-12) CS CC TC

BX (rehang= mean) -2.3928 1.1645 -3.2059

BX (rehang= 1) post-chill -2.7317 0.5267 -4.9695

BX (rehang= -1) rehang -0.3885 1.8019 -1.4423

Prevalence (rehang= mean) 0.0837 0.7622 0.0389

Prevalence (rehang= 1) post-chill 0.0611 0.6287 0.0069

Prevalence (rehang= -1) rehang 0.4041 0.8584 0.1912
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1 CS chicken-Salmonella model 2 CC chicken-Campylobacter model

3 TS turkey-Salmonella model 4 TC turkey-Campylobacter model

Appendix Table 17. 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

Appendix Table 18. Number of Establishments Expected to adopt the New Inspection System by SBA Size

Species switch L 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 

Appendix Table 19. Number of Observed Establishments Expected to adopt the New Inspection System by SBA 

Size

Pathogen Species L S V total

Salmonella chicken 128 50 2 180

Campylobacter chicken 128 50 2 180

Salmonella turkey 21 17 1 39

Campylobacter turkey 21 17 1 39

total 298 134 6 438

Appendix Table 20. 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) -2.3905 -2.394 1.1615 1.16579

BX (rehang= 1) post-chill -2.729 -2.7289 0.5254 0.53519

BX (rehang= -1) rehang 0.3376 -0.4119 1.7972 1.79619

Prevalence (rehang= mean) 0.0839 0.08363 0.7616 0.76238

Prevalence (rehang= 1) post-chill 0.0613 0.06129 0.6284 0.63069

Prevalence (rehang= -1) rehang 0.4039 0.39846 0.8578 0.85768
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Prevalence Unweighted 0.1231 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 -5.108

BX (rehang= -1) rehang -2.0782 -2.0233 -1.4237 -1.369

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