1

Risk Assessment of the Potential 

Human Health Effect of Applying 

Continuous Inspection to Catfish

Prepared by the

Risk Assessment Division

Office of Public Health Science

Food Safety and Inspection Service

United States Department of Agriculture

July, 2012
2

Contributors to the Risk Assessment of the Potential 

Human Health Effect of Applying Continuous Inspection to 

Catfish

Nathan Bauer2

Victor Cook1

Terry Disney2

Eric Ebel2

Chuanfa Guo2

John Johnston2

David LaBarre2

Joy Lee3

Erica McCoy3

Jamie Morrison4

Wayne Schlosser2

Michael Williams2

 

1 Microbiology Division, Office of Public Health Science, Food Safety and Inspection Service, U.S. 

Department of Agriculture, Washington, DC 20250

2 Risk Assessment Division, Office of Public Health Science, Food Safety and Inspection Service, U.S. 

Department of Agriculture, Washington, DC 20250

3 American Association for the Advancement of Science, Washington, DC 20005

4 College of Veterinary Medicine, Michigan State University, East Lansing MI 48824
3

Acknowledgements

Completion of this risk assessment was facilitated by constructive comments, 

suggestions, and data provided by experts from the following agencies and academic 

institutions: the U.S. Department of Agriculture, Food Safety Inspection Service; the 

U.S. Department of Agriculture Office of Budget and Program Analysis; the U.S. 

Department of Agriculture, Office of Risk Assessment and Cost-Benefit Analysis; the 

U.S. Department of Agriculture, Agricultural Research Service; the U.S. Department of 

Agriculture, Agricultural Marketing Service; the U.S. Department of Agriculture, Office 

of Catfish Inspection Programs; the U.S. Department of Health and Human Services, 

Food and Drug Administration, Center for Food Safety and Applied Nutrition; the U.S. 

Department of Health and Human Services, Food and Drug Administration, Center for 

Veterinary Medicine; the U.S. Department of Health and Human Services, Centers for 

Disease Control and Prevention; the U.S. Department of Commerce, National Oceanic 

and Atmospheric Administration; the Environmental Protection Agency; the Minnesota 

State Health Department; the University of Arkansas, Pine Bluff; Colorado State 

University; Johns Hopkins University; Mississippi State University, and Texas A&M 

University.

We are grateful to the following individuals: Linda Abbot,5 Cade Akers,6Patricia 

Bennett,7 Quita Bowman Blackwell,7Sid Clemans,8 Kerry Dearfield,7Philip Derfler,7

Carole R. Engle,9 Denise Eblen,2Emilio Esteban,7 David Goldman,7Elisabeth Hagen,7 

John Hicks,7Larry D’Hoostelaere,10 Janell Kause,2 Kelly Kovich,25 Heejeong Latimer2

Carol Maczka,7Patrick McCaskey,7 Otis Miller7 William Milton, Jr.,7 Doritza PaganRodriguez2 Mark Powell,7 George Salem,10 Carl J. Sciacchitano,

10

James Schaub,5 Carl 

 

5 Office of Risk Assessment and Cost-Benefit Analysis, U.S. Department of Agriculture, Washington, DC 20250

6 Mississippi State University, Mississippi State, Mississippi 39762

7

Food Safety and Inspection Service, U.S. Department of Agriculture, Washington, DC 20250

8 Office of Budget and Program Analysis, U.S. Department of Agriculture, Washington, DC 20250

9 University of Arkansas Pine Bluff; Pine Bluff , Arkansas 71601

10 Food and Drug Administration, Department of Health and Human Services, Rockville, Maryland 20857
4

Schroeder,

7 William Shaw7

, Zachary Shirley,

11 Juan Silva,12 Alice Thaler,7

James 

Wilkus,7 Charles Williams,7 Ray Yang.13

We acknowledge and thank: Dare Akingbade,2 Tracy Ayers,14 Mark Briggs,15

Mary Carson,10 Lynn Cruickshank,7 Thaddeus Graczyk,16 Tim Hansen,17 Karen Herman,14

Joseph Hill,7 Kristin Holt,7 Martha Lamont,18 Beth Leopold,7 Neal Golden,2 Patricia 

McCann,15 Margaret O'Keefe,2Julia Oriani,10 Fran Pell,10 Maritza Quinn,7 Rachel 

Edelstein,7 Kirk Smith,15Jay Vodela,7 Harry Walker,7James Withee,2 Penny Zervos,7 and 

Martin Zhu.7

We also thank Peter Bridgeman,7 Diana Haynes,18 Sherri Johnson,7 Mike Kelley,7

Davonna Koebrick,19 Jo (Dyer) Kraemer,18 Andrew Maccabe,14 Gregory McMillon,20

Michael P. Masser,21 David Soderberg,22 Christopher Sommers,23 Isaac Gene Sterling,18

Granvil Treece,21 Rasika Tripathy,7Sherri B Turnipseed,10 Patricia W. Varner,21 and 

Steven Wilson,17 and Peter Woods.24

Notwithstanding the considerable help and valuable expertise provided by the 

abovementioned, responsibility for the content of this report rests solely with the U.S. 

Department of Agriculture’ Food Safety and Inspection Service.

 

11 University of Texas at Austin, Austin, Texas 78712

12 Mississippi State University, Mississippi State, MS 39762

13 Colorado State University, Fort Collins, Colorado 80523

14 Centers for Disease Control and Prevention, Atlanta, Georgia 30333

15 Minnesota Department of Health, St. Paul, Minnesota 55155

16 Johns Hopkins University, Baltimore, MD 21205

17 Seafood Inspection Program, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 

U.S. Department Of Commerce, Silver Spring, Maryland 20910

18 Agricultural Marketing Service, U.S. Department of Agriculture, Washington, DC 20250

19 Texas Department of State Health Services, Austin, Texas 78756

20 Farm Catch Catfish Processors, Inc, Hughes Springs, Texas 75656

21 Texas A&M University, College of Agriculture and Life Sciences, Wildlife and Fisheries Extension Unit, College 

Station, Texas 77843

22 U.S. Environmental Protection Agency, Washington, DC 20460

23 Agricultural Research Service, U.S. Department of Agriculture, Wyndmoor, Pennsylvania 19038

24 Texas AgriLife Extension Service, Department of Wildlife and Fisheries Science, Bay City, Texas 7741

25 University of Minnesota Duluth, MN 55812
5

Table of Contents

Acknowledgements............................................................................................................. 3

Table of Figures.................................................................Error! Bookmark not defined.

Table of Tables ................................................................................................................... 7

Executive Summary............................................................................................................ 9

1. Introduction .................................................................................................... 13

2. Model overview.............................................................................................. 15

3. Exposure Assessment ..................................................................................... 20

3.1 Catfish-associated Hazard Concentration (

, , and X prev prev domestic imports

)..... 20

3.2 Storage and Cooking Effect (

, , and G C C Baked Fried

)........................................ 24

3.3 Catfish Consumption (

, , , , Bread Nonbread Bread Baked imports S S f f f

and

Nservings

).......... 28

4. Hazard Characterization (Dose-Response)..................................................... 32

5. Risk characterization ...................................................................................... 33

5.1 Default estimation of numbers of Salmonella illnesses per year.................... 34

5.2 Comparison of illnesses per year using surveillance data .............................. 36

5.3 Modeling program effectiveness.................................................................... 38

5.4 Program effectiveness estimates..................................................................... 42

5.5 Sensitivity of default illnesses estimates to changes in model inputs ............ 53

5.6 Uncertainty scenario analyses ........................................................................ 57

6. Summary......................................................................................................... 65

7. References ...................................................................................................... 67

8. Appendix: Hazard Identification .................................................................... 73

8.1 Prioritization of Microbial Hazards................................................................ 73

8.2 Identification of Chemical Hazards................................................................ 80

8.3 Selected Chemical Residues Detected in Catfish ........................................ 104
6

Table of Figures

Figure 1. Inputs to number of Salmonella illnesses among U.S. consumers per year...... 15

Figure 2. Inputs to probability of illness per contaminated serving ................................. 17

Figure 3. Log reductions of Salmonella due to baking catfish. ........................................ 26

Figure 4. Log reductions of Salmonella due to frying catfish. ......................................... 27

Figure 5. The cumulative empirical distribution for serving size..................................... 30

Figure 6. Uncertainty in the effectiveness of regulation on the annual number of 

Salmonella illnesses avoided over 10-yrs following FSIS regulation of catfish. These 

values assume a 5-yr timeframe and the Siluriformes definition of catfish. .................... 44

Figure 7. Uncertainty in the effectiveness of regulation on the annual number of 

Salmonella illnesses avoided over 10-yrs following FSIS regulation of catfish. These 

values assume a 5-yr timeframe and the Ictaluridae definition of catfish. ....................... 49

Figure 8. Tornado diagram describing the elasticity of the model’s annual illness 

estimates to various model inputs. The x-axis here is in elasticity units. ........................ 56

Figure 9. Cumulative reduction in the number of illnesses for combined lower bound 

scenarios............................................................................................................................ 61

Figure 10. Cumulative increase in the number of illnesses for combined upper bound 

scenarios............................................................................................................................ 62
7

Table of Tables

Table 1. Summary of Salmonella concentrations in enumerated positive broiler carcass 

rinse samples (USDA-FSIS Nationwide Broiler Chicken Microbiological Baseline Data 

Collection Program, 1994-1995)...................................................................................... 23

Table 2. The estimated distribution of Salmonella per gram of contaminated catfish 

carcass (X). ....................................................................................................................... 24

Table 3. Parameters for cooking and growth inputs......................................................... 25

Table 4. The distribution of growth effect multiplier per serving (G) estimated by the 

model is shown. ................................................................................................................ 26

Table 5. Kilograms of varieties of catfish available for consumption in the United States, 

2008................................................................................................................................... 31

Table 6. Mean Estimates of Catfish Servings................................................................... 31

Table 7. Model outputs for the probability of illness per contaminated serving for the 

combinations of cooking and breading effects. ................................................................ 35

Table 8. The estimated annual numbers of Salmonella illnesses for each definition of 

catfish................................................................................................................................ 35

Table 9. Salmonella Foodborne Outbreaks from 1990 through 2007. ............................. 37

Table 10. Estimated baseline Salmonella illnesses per year............................................. 43

Table 11. Estimated Number of Salmonella illnesses avoided due to FSIS regulation 

assuming the Siluriformes definition of catfish and a 2-year to effectiveness timeframe.46

Table 12. Estimated Number of Salmonella illnesses avoided due to FSIS regulation 

assuming the Siluriformes definition of catfish and a 10-year to effectiveness timeframe.

........................................................................................................................................... 47

Table 13. Estimated Number of Salmonella illnesses avoided due to FSIS regulation 

assuming the Siluriformes definition of catfish and a 15-year to effectiveness timeframe.

........................................................................................................................................... 48

Table 14. Estimated Number of Salmonella illnesses avoided due to FSIS regulation 

assuming the Ictaluridae definition of catfish and a 2-year to effectiveness timeframe... 50

Table 15. Estimated Number of Salmonella illnesses avoided due to FSIS regulation 

assuming the Ictaluridae definition of catfish and a 10-year to effectiveness timeframe. 51
8

Table 16. Estimated Number of Salmonella illnesses avoided due to FSIS regulation 

assuming the Ictaluridae definition of catfish and a 15-year to effectiveness timeframe. 52

Table 17. An outline of potential lower and upper bound values for various model inputs 

is shown. Symbols are used to identify changes in Figure x and y. ................................ 59

Table 18. Estimated Number of Salmonella illnesses avoided by FSIS regulation of 

catfish using the Siluriformes definition of catfish; assuming a 5-year timeframe and 50% 

effectiveness of FSIS inspection....................................................................................... 64

Table 19. FDA Violation Codes for Catfish Refusals ...................................................... 75

Table 20. Summary of Recent Catfish Residue Data .................................................... 105
9

Executive Summary

The Food, Conservation, and Energy Act of 2008 (Public Law 110-246, §10016(b)), 

known as the 2008 Farm Bill, amended the Federal Meat Inspection Act (FMIA) to 

provide that "catfish, as defined by the Secretary,” is an amenable species under the 

FMIA. The 1994 Federal Crop Insurance Reform and Department of Agriculture 

Reorganization Act (Title III, § 304, 7 U.S.C. 6901), requires the United States 

Department of Agriculture (USDA) to ensure that all “major regulations” – a regulation 

estimated to have an annual impact on the economy of at least $100 million in 1994 

dollars – involving human health, safety, or environment are supported by a sound risk 

assessment and benefit-cost analysis.

In accordance with this mandate, the USDA Food Safety and Inspection Service 

(FSIS) considers it important to assess the food safety risk associated with consuming 

farm-raised catfish in the United States. However, limited information on the distribution 

of microbial contamination and chemical residues on catfish limit our ability to make 

strong statements about the baseline risk. Furthermore, the lack of experience with 

implementing continuous inspection programs in the context of aquaculture makes 

estimating the impact of such a program on risk difficult. As such, the risk assessment 

FSIS presents here simply provides insight into the risk reductions that might accompany 

the implementation of the type of continuous inspection program now required for catfish 

under the FMIA. The illustrative risk assessment presented here focuses on exposure to 

Salmonella because a broad hazard identification study identified Salmonella as a

potential concern in catfish. We are particularly interested in Salmonella because the 

general burden of illness from this pathogen in the U.S. remains a concern and there is 

evidence that at least one outbreak of human salmonellosis may have been related to 

catfish consumption.25 Although catfish-specific information is unavailable, FSIS has 

empirical evidence describing the effectiveness of an FSIS continuous inspection 

program for Salmonella control in other regulated species (i.e., poultry). The Appendix of 

this report identifies other potential chemical and microbial hazards. Once implemented, 

 

25 For purposes of convenience, this risk assessment simply uses the term “catfish” to refer to all fish 

classified within the order of Siluriformes. The use of this term is not with prejudice to what fish FSIS will 

ultimately determine to be “catfish” for purposes of the final rule. 
10

the continuous inspection program will generate data on chemical and microbial hazards 

that can be used to develop better estimates of risk.

The objectives of this illustrative risk assessment for Salmonella are:

1) To estimate the annual numbers of human salmonellosis cases from catfish

2) To estimate the potential number of cases that might be avoided following 

implementation of an FSIS continuous inspection program

3) To compare these estimates with those based on to public health surveillance 

evidence

4) To explore the sensitivity of these estimates to different modeling assumptions

5) To characterize some aspects of the uncertainty surrounding these risk assessment 

model estimates.

The risk assessment model uses Monte Carlo techniques to combine the random 

variables that estimate exposures for four different exposure classes, which include the 

most common ways catfish is prepared in the United States. There are four key 

assumptions underlying the risk assessment model: 

1) Estimates of the current level of Salmonella contamination on catfish

Salmonella contamination data from poultry were used as surrogates for 

catfish contamination

The same data (from poultry) were used for both import and domestic 

catfish

Catfish handling during retail and home storage was considered 

independent of the initial Salmonella concentration on the catfish carcass.

2) Estimated amount of catfish consumption in the US

Each catfish serving was derived from a single catfish carcass.

3) Modeled estimates of illness incidence

Incidence of salmonellosis cases was estimated using WHO/FAO’s doseresponse relationship

4) Potential levels of effectiveness associated with the FSIS continuous inspection 

program 

Empirical data on program effectiveness for FSIS poultry inspection (i.e. 

broilers) was used.

With regard to the risk assessment for catfish, FSIS continues to evaluate the hazards, 

particularly Salmonella, associated with this fish. FSIS invites all interested stakeholders 

to submit additional data and scientific evidence specific to catfish food safety. FSIS will 
11

consider this information and other data in the development of a final risk assessment in 

this proceeding. 

The range of risk estimates is dependent on uncertainty about the model form and

inputs. The assumptions listed above and the quality of available data both contribute to 

the level of uncertainty in risk assessment model outputs. Such input uncertainties 

translate into substantial uncertainty about the estimated baseline number of 

salmonellosis cases attributable to catfish consumption. Consideration of modeled lower 

and upper bound scenarios suggest estimates between 100 and 6,200 salmonellosis cases 

might be associated with catfish consumption annually, depending upon how many fish 

are actually inspected. 

The eventual determination regarding how catfish will be defined in this regulation 

directly effects the estimated reduction in salmonellosis cases following introduction of 

an FSIS catfish inspection program. One potential definition of catfish includes all fish in 

the order Siluriformes, whereas another potential definition includes only fish within the 

Ictaluridae family. This risk assessment estimates a current annual average of 2,308 

salmonellosis cases under the potential Siluriformes definition of catfish and a current 

annual average of 1,764 salmonellosis cases under the potential Ictaluridae definition. 

These estimates seem consistent with an independent estimate based on the limited data 

available from the Department of Health and Human Services’ Centers for Disease 

Control and Prevention (CDC) regarding outbreaks that may have been associated with 

catfish. Regardless of the definition of catfish, the model estimates an average

probability of illness of 1.5 × 10-6salmonellosis cases per serving, though this number 

could be substantially lower because our baseline information on the rate of 

contamination is extremely limited. This probability incorporates the prevalence of 

contaminated servings and suggests that Salmonella illness from catfish is an uncommon 

event.

There is substantial uncertainty regarding the effectiveness of a future FSIS 

inspection program aimed at reducing the prevalence of Salmonella-contaminated catfish, 

and different levels of effectiveness yield different levels of benefit. To better serve as a 

decision tool, this risk assessment models a range of assumptions – from 10% to 90% 
12

inspection program effectiveness – to estimate public health benefit outcomes. For 

example, if an FSIS inspection program is 50% effective within a 5-year timeframe, our 

model estimates that between 50 and approximately 3,100 Salmonella illnesses 

(Siluriformes definition) or between 38 and approximately 2,353 Salmonella illnesses 

(Ictaluridae definition) might be prevented annually. In the scenario of a 2-year time 

frame and a range of 10% to 90% effectiveness, the model estimates that between 176 

and 1,587 Salmonella illnesses per year might be prevented if catfish is defined as 

Ictaluridae. This is about 24% fewer salmonellosis cases prevented per year compared to 

when catfish are defined as Siluriformes. 

This risk assessment’s outputs are subject to substantial uncertainty regarding both

the estimated baseline number of salmonellosis cases attributable to catfish consumption

and the extent to which the experience associated with controlling salmonella in poultry 

is applicable to controlling salmonella in catfish. Once the FSIS inspection program is in 

place, however, the data generated will allow the Agency to further address the effect of 

inspection on chemical hazards and other microbial hazards (in addition to Salmonella)

that can cause adverse human health outcomes associated with the consumption of farm 

raised catfish.
13

1. Introduction

This risk assessment provides an estimate of the differential effect of introducing

an FSIS continuous catfish inspection program on the potential number of human 

Salmonella illnesses from farm-raised catfish consumption each year. The Appendix 

provides a comprehensive catalog of other chemical and microbiological hazards that 

have been associated with seafood or aquaculture and, thus, have the potential to be 

present in catfish. Additionally, the Appendix provides information on current testing 

data for chemical hazards. The main body of this risk assessment report, however,

describes the quantitative modeling approach used for the analysis of the high priority 

microbial hazard Salmonella. The identification of Salmonella as a microbial hazard of 

potential concern includes reasons outlined in the executive summary as well as the 

Hazard Identification section in the Appendix.Appendix: Hazard Identification.

This risk assessment model estimates the potential public health effect of 

implementing an FSIS continuous inspection program for farm-raised catfish. 

Incorporated into the model was the consideration of two different potential definitions of 

catfish. The definition of catfish that is consistent with the 2002 Farm Bill26 is specific to 

the family Ictaluridae, native to North America. However, there are additional families of 

fish on the commercial market in the order Siluriformes, where the North American 

family Ictaluridae resides. Therefore, an evaluation of public health effect of using the 

Ictaluridae definition of “catfish” represents a different range of exposure to that of the 

Siluriformes definition for “catfish”. This risk assessment includes an evaluation of 

public health risks resulting from exposure to Salmonella for both Siluriformes and 

Ictaluridae.

This report consists of four sections. An overview section explains the conceptual 

model and its mathematical structure. The exposure assessment section explains the 

modeling inputs used to estimate exposures of humans to Salmonella in catfish servings. 

The hazard characterization section introduces the dose-response relationship used to 

estimate the probability of illness per Salmonella-contaminated catfish serving. The risk 

 

26 Farm Security and Rural Investment Act of 2002, known as the 2002 Farm Bill, amended the Federal 

Food, Drug, and Cosmetic Act (21 United States Code §§ 321d(a), 343(t); Public Law 107-171, Title 

X, §10806, 116 Statute 526).
14

characterization section combines the exposure assessment with the hazard 

characterization to:

estimate numbers of human Salmonella illnesses occurring per year

estimate the potential number of human Salmonella illnesses that might be 

avoided following implementation of an FSIS inspection program

determine the consistency of these estimates relative to public health surveillance 

evidence

examine the sensitivity of these estimates to different modeling assumptions

characterize the uncertainty surrounding these risk assessment model estimates. 
15

2. Model overview

Estimating annual human salmonellosis cases potentially resulting from consuming 

contaminated catfish comprises two basic steps. First, the number of contaminated catfish 

servings consumed each year is based on domestic catfish production and import data, 

and estimates of the prevalence of Salmonella contamination of catfish (Figure 1). 

Second, the estimated average probability of illness across all contaminated catfish 

servings is determined by modeling contaminated servings of catfish from the point of 

production through consumption (Figure 2). The product of these two steps estimates the 

annual number of human salmonellosis cases in the United States.

Figure 1. Inputs to number of Salmonella illnesses among U.S. consumers per year

Number of contaminated

servings per year

Domestic prevalence 

of contaminated catfish

Import prevalence

of contaminated catfish

Total servings of catfish consumed

in United States per year

Import share of catfish

consumed per year

Domestic share of catfish

consumed per year

Number of Salmonella illnesses

among U.S. consumers per year 

Probability of illness per

contaminated serving

From Figure 2
16

Mathematically, the first step is a simple algebraic calculation of the annual 

number of Salmonella-contaminated catfish servings.

Equation 1

 = 

(1 ) N f prev f prev servings imports imports imports domestic

# contaminated servings / yr

where 

Nservings

is the total number of servings of catfish consumed in the U.S. per year, 

import f

is the fraction (share) of all servings generated by imported catfish, 

domestic prev

and 

import prev

are the proportions of domestic and imported catfish contaminated with some 

level of Salmonella. This modeling approach assumes that each catfish carcass produces 

roughly the same average number of servings regardless of its contamination status. The 

values for these inputs are described in the Exposure Assessment section.

The model assumes each catfish serving derives from a single catfish carcass (i.e., 

servings are not mixtures of multiple carcasses). The model considers the average 

concentration of Salmonella (per gram of a catfish carcass) for contaminated catfish only. 

This concentration randomly varies among contaminated catfish. It is assumed that the 

average concentration of Salmonella per gram of a contaminated catfish is independent of 

the size of serving generated from a catfish (i.e., the grams of catfish in a consumed 

serving does not depend on the amount of Salmonella per gram on the contaminated 

carcass). Furthermore, it is assumed that handling of catfish during retail and home 

storage (and cooking of catfish serving prior to consumption) is independent of the 

concentration of Salmonella initially on the carcass. (Because consumers will not be 

aware of the concentration of Salmonella on any particular catfish carcass, these 

assumptions seem reasonable.) 
17

Salmonella concentration on contaminated

catfish carcasses post-processing 

[Salmonella per gram] 

Serving size

(grams per 

serving)

Salmonella per serving =

Salmonella per gram x Serving size

Growth per serving

Cooking effect;

baked or fried

(decimal reduction)

Baked or fried 

temperature

D-value

Baked or fried 

cook time

Exposure per contaminated serving=

Salmonella per serving x Growth x Cook effect

Dose-response function

(Beta-Poisson)

Probability of illness per 

contaminated serving

(averaged across all 

contaminated servings) 

Breading effect.

If breaded,

then a reduction 

in serving size

Figure 2. Inputs to probability of illness per contaminated serving 

Mathematically, the average exposure dose of Salmonella27 consumed in a 

random contaminated catfish serving is modeled as; 

Equation 2 

D X S G C

where

D

is one instance of an average exposure dose of Salmonella consumed,

X

 is one 

instance of an average Salmonella concentration per gram of a contaminated catfish 

carcass,

S

 is one instance of a catfish serving size (in grams consumed),

G

is one instance 

 

27 Average dose of Salmonella is modeled because the beta-Poisson dose-response relationship is based on 

an average number of organisms in a serving. For example, if a value for average Salmonella dose of 0.2 

CFU is used in the beta-Poisson, the function determines the probability that a serving will contain one or 

more CFU’s (based on Poisson probabilities), as well as the probability that each integer unit Salmonella 

dose will result in illness (based on beta probabilities). Ignoring this aspect may lead to incorrectly 

including a Poisson function to determine integer Salmonella doses consumed in the exposure assessment; 

this would essentially ‘double-count’ the Poisson effect once the beta-Poisson relationship was included. 
18

of the growth of Salmonella on a catfish carcass (to account for handling and storage 

between processing and consumption), and

C

is one instance of the expected reduction of

Salmonella in a serving of catfish caused by the effects of cooking. The inputs to this 

calculation (

X S G C , , ,

) are random variables. The inputs for catfish serving size and the 

effect of cooking, however, are somewhat complicated.

The amount of catfish in a serving depends on whether the serving was breaded or 

not. Breaded servings contain a smaller amount of catfish, on average, than non-breaded 

catfish servings. Therefore, there are actually two variables for the servings of catfish –

one for breaded servings (

Bread S

) and one for non-breaded servings (

Nonbread S

). 

The effect of cooking depends on the method used. Baking tends to involve 

longer cook times than frying. Therefore, there are two variables to differentiate the type 

of cooking used for catfish – one for baked servings (

CBaked

) and one for fried servings (

CFried

). 

A dose-response relationship is used to predict the probability of salmonellosis for 

each serving of catfish contaminated with Salmonella. There are four categories of catfish 

exposures assessed:

1. Breaded and baked (

DBread Baked ,

);

2. Breaded and fried (

DBread Fried ,

);

3. Non-breaded and baked(

DNonbread Baked ,

); and

4. Non-breaded and fried (

DNonbread Fried ,

).

For each category of catfish consumed, the average probability of salmonellosis 

across all contaminated servings within the class is determined as:

Equation 3

,

,

1

1

( ) 1 1 i

n

b c

b c

i

D

P ill

n

where 

n

is the number of iterations of the Monte Carlo model, and

b

and

c

symbolize the 

breading and cooking indexes, respectively. This calculation is a numeric integration 

assuming a beta-Poisson dose-response function with parameters and .
19

Given the preceding discussion, the annual number of human salmonellosis cases 

from catfish exposure is estimated as:

Equation 4

,

,

,

,

=

( )

1 ( )

1 ( )

1 1 ( )

Bread Baked Bread Baked

Bread Baked Bread Fried

Bread Baked NonBread Baked

Bread Baked NonBread Fired

Number_ill/yr

f f P ill

f f P ill

#contaminated servings/yr

f f P ill

f f P ill

where

Bread f

is the fraction of catfish servings that are breaded and

Baked f

is the fraction of 

catfish servings that are baked.

The risk assessment model uses Monte Carlo techniques to convolve the random 

variables (

X S G C , , ,

) that predict exposures for each of the four exposure classes and 

complete the numeric integration step described in Equation 3. The model is currently 

developed in the R software package (http://www.r-project.org/ Version 2.9.1), but is 

equivalently solvable in any software that supports Monte Carlo simulation. Each 

simulation of the model comprises three million iterations. Each model iteration 

represents a different contaminated serving of catfish across all four exposure pathways.
20

3. Exposure Assessment

The exposure assessment estimates the annual exposures to Salmonella from

catfish consumed in the U.S. Model inputs for the exposure assessment are explained in 

the following sections. The catfish-associated hazard concentration section describes the 

development of the inputs

X , prevdomestic

, and

imports prev

. The storage and cooking effect 

section considers the effects of

G , CBaked

, and 

CFried

. The section on catfish consumption 

describes the development of the inputs

Bread S , Nonbread S , Breaded f , Baked f , 

imports f

, and 

Nservings

.

3.1 Catfish-associated Hazard Concentration (

, , and X prev prev domestic imports

)

No empiric evidence regarding concentrations of Salmonella on processed catfish 

carcasses was available and limited evidence was available regarding the prevalence of 

Salmonella contaminated catfish. One U.S. study collected 220 catfish fillets from 

August 1994 through May 1995 (McCaskey et al., 1998). That study found 5 (2.3%) 

positive samples. This evidence was used to represent the default prevalence of 

Salmonella contamination of catfish (

prevdomestic

) in the model.

Although the Food and Drug Administration’s (FDA) Office of Regulatory 

Affairs/Division of Field Science collects some samples of imported catfish, those 

samples are pooled samples of multiple catfish homogenized for regulatory testing. 

Furthermore, the samples are intentionally targeted towards imported shipments thought 

to have a higher probability of testing positive. The pooled and biased nature of these 

catfish data would likely over-estimate the prevalence of Salmonella contamination of 

catfish consumed in the U.S. Furthermore, these inherent sampling and Salmonella 

testing biases make reasonable inferences about Salmonella prevalence among imported 

catfish from FDA data nearly impossible. Lacking any other evidence regarding 

Salmonella prevalence on imported catfish, the catfish risk assessment assumed that the 

prevalence of Salmonella on imported catfish is the same as the prevalence of Salmonella 

found on domestic catfish (i.e., a default assumption that

imports domestic prev prev

).
21

While there is limited data on the prevalence of Salmonella-contaminated catfish, 

there is no data on the amount (concentration) of Salmonella per gram of catfish. 

Therefore, the concentration of Salmonella on contaminated catfish, model input 

X

, is 

assumed to be reflected by available Salmonella enumeration results from FSIS poultry 

(i.e. broiler) testing programs. There are a variety of limitations with the use of poultry 

Salmonella testing data as a surrogate that effects what we might see after implementing 

a continuous inspection program for catfish. On the other hand, we note that it might be 

the best option available for such an analysis because:

1. Of the species FSIS currently regulates, poultry represent a surface area to mass 

ratio that most closely approximates this ratio for catfish.

2. Salmonella testing methods for poultry would more nearly approximate those 

used for catfish (i.e., both methods use whole carcass rinsing) than testing results 

for other species that FSIS regulates. Also, the enumeration of Salmonella

concentrations on poultry using these methods makes extrapolation to Salmonella

per carcass more intuitive compared to cattle or hog carcass sampling techniques 

that do not involve rinse sampling of the entire carcass surface area.

3. Poultry processing typically involves a carcass chilling step that requires 

submersion of carcasses in water that might reflect the potential crosscontamination that can occur in the aquatic environment of catfish.

Although use of poultry concentration data as a surrogate for catfish concentration 

data is arguable, the concept that Salmonella contamination levels on catfish are variable 

is crucial to assessing risk. Ignoring this variability in the risk assessment would 

potentially undervalue the risk posed to consumers because catfish servings with larger 

concentrations of Salmonella might not be considered. 

The FSIS nationwide broiler chicken microbiological baseline data from 1994-1995 

were used to estimate Salmonella concentrations on catfish (USDA-FSIS, 1996). This 

survey was chosen because it represented a snapshot of the poultry industry prior to the 

formal implementation of a new FSIS inspection program (HACCP). The current 

regulatory decision for the catfish industry is similar to the decision made in the mid1990’s for broiler poultry and other FSIS-regulated species in that a new FSIS program 
22

will be implemented. The existing regulatory program under FDA serves as the baseline 

protection of food safety, upon which the new FSIS inspection program will be built. 

Because this risk assessment will ultimately predict human illnesses avoided following 

implementation of a new FSIS-style regulatory system within the catfish industry, it 

seems appropriate to consider the status of the broiler poultry industry prior to the 

implementation of HACCP by FSIS.

The broiler poultry data imply that most contaminated carcasses have low 

Salmonella concentrations (Table 1). For this analysis, positive broiler poultry samples 

with a Most Probable Number per milliliter (MPN/ml) values <0.03 (i.e., below the limit 

of enumeration) were assumed to be uniformly distributed between 0.0025 MPN/ml (i.e., 

the assumed absolute lower limit of qualitative detection in a 400 ml chicken rinse 

sample) and 0.03 MPN/ml. For Salmonella concentrations greater than 0.03 MPN/ml, 

values are randomly distributed according to the data summarized in Table 128. To adjust 

these data to units of Salmonella per gram of catfish, the following calculation was 

completed:

Equation 5

1

400

1500

MPN Salmonella ml

gram ml g

This calculation indicates that the Salmonella concentration per ml of rinse is 

expanded by the 400 ml rinse volume to estimate total MPN per carcass; this total is then 

divided by 1,500 grams to account for the average weight of a broiler poultry carcass.

Broiler poultry rinse samples typically come from skin-on carcasses. Evidence 

suggests that pathogen concentrations are 0.4 to 0.9 logs less for skinless poultry 

carcasses (Davis and Conner, 2007; Berrang et al., 2002). Because catfish are generally 

sold skinless, broiler poultry concentrations are translated to catfish concentrations by 

adjusting for the skinless catfish carcass. As a default, it is assumed that catfish 

Salmonella concentrations are 0.65 logs (midway between 0.4 and 0.9) less than poultry 

concentrations. This step is modeled by multiplying the value obtained in Equation 5 by 

0.22 (

0.65 10 0.22

).

 

28 Initial attempts to fit these data to parametric distributions suggested a poor fit. Therefore, this risk 

assessment uses an empirical cumulative distribution based on the data in Table 1 to model variability in 

Salmonella concentration.
23

Equation 6

1

400 0.22

NoSkin 1500

MPN Salmonella ml

gram ml g

One last adjustment truncates the distribution of Salmonella concentration on 

catfish at a minimum of 1 colony forming unit (CFU) per 330 gram to represent the 

average weight of a catfish carcass (Morris, 1993)29. For a default assumption, any 

concentration resulting from the adjusted broiler poultry data that is less than 0.003 

CFU/g is assumed to equal 0.003 CFU/g. Nevertheless, in scenarios exploring 

uncertainty, the risk assessment model randomly redistributes concentrations less than 

0.003 CFU/g to values above that threshold.

Table 1. Summary of Salmonella concentrations in enumerated positive broiler carcass rinse samples 

(USDA-FSIS Nationwide Broiler Chicken Microbiological Baseline Data Collection Program, 1994-

1995)

Range, MPN/ml Number of samples Cumulative percent

<0.03 109 41.9

0.03 – 0.30 118 87.3

0.301 - 3.0 24 96.5

3.01 - 30.0 6 98.8

>30.0 3 100.0

Total 260

The resulting random variable 

X

(average Salmonella per gram of contaminated 

catfish carcass) estimates that 64% of contaminated carcasses have Salmonella

concentrations of 0.003 per gram (the theoretic minimum level) (Table 2). Because a 

serving of catfish generally represents something less than the entire carcass weight, the 

amount of Salmonella actually in a serving of catfish could be less than one Salmonella

bacterium. The maximum concentration of Salmonella on a catfish carcass is estimated to 

 

29It is assumed that the average catfish marketed in the U.S. is 1.44 pounds 

(http://usda.mannlib.cornell.edu/usda/nass/CatfProd//2000s/2009/CatfProd-01-30-2009_revision.pdf) and 

an average dressing percentage of 50% (Morris, 1993).
24

be 16.7 bacteria per gram. For comparison, the maximum concentration for broiler 

poultry was ~75 Salmonella per gram30. 

Table 2. The estimated distribution of Salmonella per gram of contaminated catfish carcass (X).

Cumulative 

frequency

Salmonella per 

gram of catfish 

carcass

Average 

Salmonella per 

carcass*

0% 0.003 1

64% 0.003 1

68% 0.005 2

75% 0.006 2

76% 0.007 2

80% 0.010 3

81% 0.013 4

82% 0.014 5

87% 0.016 5

90% 0.029 10

91% 0.030 10

92% 0.039 13

95% 0.064 21

96% 0.157 52

97% 0.417 139

98% 1.006 335

99% 2.106 701

100% 16.715 5,566

* Average Salmonella per carcass is estimated by assuming each carcass weighs 333 grams

3.2 Storage and Cooking Effect (

, , and G C C Baked Fried

)

Salmonella concentrations on raw processed catfish at the point of consumption 

are adjusted to account for concentration changes associated with both storage and 

cooking of catfish. Microbial hazard concentrations may increase during storage and 

preparation and typically decrease during cooking. Specific modifying factors for 

Salmonella were calculated based on cooking style (e.g. baked versus fried). Because 

 

30 The maximum Salmonella MPN/ml from the USDA-FSIS 1994-1995 baseline study was 280. The 

maximum implied a Salmonella concentration per gram of chicken of: 

280 400 74.7 /

1500

MPN ml MPN g

ml g

.
25

specific evidence regarding Salmonella growth and cooking effects is not available for 

catfish, modeling techniques from a published risk assessment regarding Salmonella in 

chicken were used for these factors (Oscar, 2004). 

Variability of growth multiplication and cooking decimal reductions was based on 

Pert (min, most likely, max) distributions for log growth, cooking time, and cooking 

temperature (Table 3). The Salmonella growth model and parameters were adopted 

directly from predictive microbial models for chicken developed by Oscar (2004); that

model includes the assumption that growth could only occur among 0.02% of servings. 

The cooking model was also based on Oscar (2004), but time and temperature parameters 

for baking or frying catfish were based on expert opinion and review of several on-line 

cooking recommendations.

Table 3. Parameters for cooking and growth inputs

The random variable for growth effect (

G

) estimates 99.98% of servings are 

unchanged between processing and consumption (Table 4). The remaining small fraction 

of contaminated servings in which growth occurs experience a 10% to 40% increase in 

the number of Salmonella within the serving.

Factor Equation Cooking 

type

Parameters

Min Most 

likely Max

Cooking

1

min max most likely 8.7344- 0.1316*Pert , , temp temp temp

min max most likely Pert , , time time time

10

C=

10

Baked 

(minutes) 12.00 13.50 15.00

Baked 

(°C) 58.75 64.20 69.70

Fried 

(minutes) 6.00 9.00 12.00

Fried 

(°C) 58.75 64.17 69.70

Growth

10Triangle min, most likely,max G

All 

methods 0 0.04 0.15
26

Table 4. The distribution of growth effect multiplier per serving (G) estimated by the model is shown. 

Cumulative 

frequency

Growth effect 

multiplier

0.000% 1

99.970% 1

99.980% 1.1

99.992% 1.2

99.999% 1.3

100.000% 1.4

The reduction in Salmonella per serving caused by baking (

CBaked

) extends from 

nearly 1.2 logs to nearly 40 logs (Figure 3). The logs of the median and mean reductions 

are about 7 and 3, respectively.

Figure 3. Log reductions of Salmonella due to baking catfish.

The reduction in Salmonella per catfish serving caused by frying (

CFried

) extends 

from less than 1 log to nearly 30 logs (Figure 4). The logs of the median and mean 

reductions are about 4.5 and 2, respectively. 

0

0.02

0.04

0.06

0.08

0.1

0.12

-4 0 -3 0 -2 0 -1 0 0

Relative 

frequency

Log10 reduction from baking
27

Figure 4. Log reductions of Salmonella due to frying catfish.

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

-3 0 -2 5 -2 0 -1 5 -1 0 -5 0

Relative 

frequency

Log10 reduction from frying
28

3.3 Catfish Consumption (

, , , , Bread Nonbread Bread Baked imports S S f f f

and

Nservings

)

Data on the consumption of catfish in the U.S. were obtained from the National 

Health and Nutrition Examination Survey (NHANES). Four 2-year consumption survey 

data sets (1999-2006) of total size 41,474 were combined to create an 8-year data file of 

single 24-hour consumption recall estimates and a combined 4-year file of two 2-year 

datasets (2003-2006) of total size 20,471, with first and second day 24-hour recall 

estimates. The 8-year file of consumption data was taken from the 1-day mobile 

examination center (MEC) face-to-face interviews and used 8-year MEC weights 

permitting standardization of the sample results to the 2000 U.S. census. Estimates were 

made for the U.S. population at the midpoint of the 8-year survey period using SASCallable SUDAAN software (version 10, Research Triangle Institute International, 

Research Triangle Park, NC). Similarly, the 4-year dataset used the MEC examination 

and interview results for the first 24-hour recall and a second telephone interview 24-hour 

recall within 3 to 10 days of the first interview. The 2-day NHANES weights, corrected 

for post stratification and non-response, were used in the 4-year dataset analysis. Each 

dataset was validated for completeness and 2122 subjects were eliminated from the 

calculations leaving 39,352 validated subjects in the 8-year dataset, 18,382 validated 

subjects in the first day of the 4-year dataset, and 16,781 validated subjects in the second 

day of the 4-year dataset.

The 8-year dataset provided 249 consumers of catfish, while the 4-year one and 

two day combined datasets provided 125 and 110 consumers respectively. In the latter 

study, only 8 subjects were validated for both first and second day interviews. The 8-year 

dataset provided the estimate for grams of catfish consumed per day, which did not differ 

significantly from the combined 4-year dataset estimate. The estimates for the fractions 

of baked and breaded catfish consumed and annual percent catfish consumers were made 

from the combined averaged data over all survey years and interview days. 

Two datasets were evaluated because of the low frequency of catfish consumers 

in the survey population and the motivation to find the best dataset. The NHANES 

priority was to over-sample women, children, and minorities requiring a multi-stage 

sampling design for estimation of U.S. population mean and standard error of the total 

daily grams of catfish consumption and the fractions of baked and breaded catfish 
29

consumed. Because the second day data did not sufficiently provide within subject 

variability estimates due to only eight persons actually validated for both first and second 

day interviews, a correction for the averaged first and second day responses was assessed 

by simulation using the SUDAAN “HOTDECK” procedure to produce estimates of 

between and within subject error providing the necessary factors for reducing increased 

variance bias using the National Research Council (1986) recommendation for bias 

correction. Additionally, the recommended procedure for estimating nutrient intake from 

complex survey data was employed (Nusser et al., 1996). 

Due to the smaller sample size, the mean, variance, and percentile estimates for 

daily grams catfish consumed for the combined 4-year dataset were not significantly 

different from the 8-year dataset using standard T-tests for the mean, F-tests for variance, 

and the Kolmogorov-Smirnov two-sample test for distribution shape at 95% confidence. 

The standard Taylor series linearized estimates from the SUDAAN DESCRIPT 

procedure provided the smallest error estimates compared with the internal validation 

methods employed. The validation methods were Jackknife (N-1) (SUDAAN proc 

DESCRIPT), balanced repeated replication (Fay’s modified BRR in WesVar, Westat, 

Inc. Rockville, MD 2008), and the Rao-Wu-Yue bootstrap (Rao et al., 1992). Each of the 

validation methods provided mean and percentile estimates that were within the 95% 

confidence intervals of the Taylor series estimates. However, the variance estimates 

showed significantly more variability and were each contained only within the 99% 

confidence interval. This type of variability was expected and did not affect the risk 

model since the mean estimate for grams catfish consumed per day was used which was 

shown to be stable.

The mean serving size determined from the 8-year dataset analysis was 122.28 

grams per eating occasion. Given the low frequency of catfish consumption, this analysis 

assumed the quantity consumed in one day represented a single catfish serving. The 

serving size random variable ranges from 5 grams to over 500 grams (1st and 99th

percentiles) (Figure 5). This random variable is modeled as an empiric distribution 

because attempts to fit the data to parametric distributions did not demonstrate adequate 

goodness of fit. 
30

Figure 5. The cumulative empirical distribution for serving size.

The estimates for fraction baked and fraction breaded were taken from both the 

one and two day datasets as independent estimates using the SUDAAN CROSSTAB 

procedure and averaged. Six catfish food codes were used to ascertain the fraction baked 

(versus fried) and the fraction breaded as proportions of the weighted U.S. population 

catfish consumer estimates. The population-adjusted estimates were 

0.79 Bread f

and

0.24 Baked f

. Breading is assumed to represent between 20% and 30% of total serving 

weight (TAES, 1989). Therefore, serving size for breaded servings is multiplied by a 

randomly selected value between 0.7 and 0.8 (i.e. 

Bread effect Uniform _ (0.7,0.8)

) to 

adjust for the amount of catfish in such servings.

Data from 2008 regarding total sales of catfish products were summarized 

(USDA-NASS, 2009). These data were adjusted slightly to estimate the proportion of 

amount sold that was catfish meat. For example, whole dressed and steak cuts were 

assumed to be 67.5% and 80% edible meat, respectively. In contrast, fillets and nuggets 

were assumed to represent 100% edible meat. 

The combination of total domestic sales and total imported catfish in 2008 was 

assumed to represent the total quantity of catfish consumed annually (Table 5). Imported 

catfish were reported by type; Ictalurus, Pangasius and other Siluriformes. Imports 

constitute a smaller fraction of total Ictalurus

10,470,953 9%

116,150,192 imports f

than total 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 100 200 300 400 500 600

Cumulative

frequency

Serving size (grams) consumed per eating occasion 
31

Siluriformes 

46, 276, 651 30%

151,955,889 imports f

. Also, total Ictaluridae catfish available for 

consumption represent about 76% 

116,150,192

151,955,889

of all fish in the order Siluriformes. 

Table 5. Kilograms of varieties of catfish available for consumption in the United States, 2008.

Origin Ictalurus Pangasius Other 

Siluriformes Totals

Imported 10,470,953 35,748,529 57,169 46,276,651

Domestic 105,679,239 0 0 105,679,239

Total 116,150,192 35,748,529 57,169 151,955,889

Given the estimates for average serving size and total catfish consumed, the total 

number of catfish servings 

Nservings

is estimated (Table 6). Nevertheless, average 

serving size is adjusted to account for the fraction of servings that are breaded (i.e., 

serving size reported by consumers includes the breading material, while catfish sales 

does not include breading). The average serving size of catfish (adjusted for breading 

material) is calculated as follows:

 = 1 _ 98.13 Avg. serving size Serving_size f f Bread effe NHANES Bread Bread ct grams

Table 6. Mean Estimates of Catfish Servings

Catfish definitions

Mean 

Serving 

Size (g)1

Annual U.S. Catfish 

Consumption (kg)2

Annual U.S. 

Catfish 

Servings3

Siluriformes 98.13 151,955,889 1,548,519,606

Ictaluridae 98.13 116,150,192 1,183,638,553

1 Calculated from NHANES data 

2 Domestic and import catfish production data 

3 Annual U.S. Catfish Consumption divided by Mean Serving Size 
32

4. Hazard Characterization (Dose-Response)

The dose-response equation described by the World Health Organization and Food 

and Agriculture Organization (WHO/FAO, 2002) was used to estimate the probability of 

illness resulting from exposure to Salmonella in a single serving of catfish. The doseresponse function is:

Equation 7

| 1 1 DP ill exp

with parameters (

0.1324 , 51.45

).

This dose-response relationship is assumed to be the same for all humans exposed 

(i.e., regardless of age, sex or susceptibility) and all Salmonella strains. Although such 

assumptions are arguable, this relationship represents an international guideline that is 

assumed adequate for estimating effects across whole populations of consumers. 

Nevertheless, assessing the risk for specific consumers or classes of consumers might 

benefit from adjustments to this dose-response relationship.
33

5. Risk characterization

The risk of Salmonella illness potentially associated with the estimated catfish 

consumption distribution is characterized by combining the exposure assessment with the 

hazard characterization. Equations 1 – 4 outline the mathematics of this process.

This section will present the default model estimation for the annual number of 

human cases of salmonellosis potentially associated with catfish consumption. This 

estimation stems from the number of exposures generated from contaminated catfish and 

the average probability of illness among those exposures.

The analysis also compared the modelled estimation of annual Salmonella illnesses 

associated with catfish with the available public health surveillance (i.e., 

epidemiological) evidence. Estimates of total annual cases of human salmonellosis from 

all sources are adjusted by the fraction of cases attributable to catfish.

Because the ultimate purpose of this risk assessment is to inform regulatory 

decision-making, this report focuses on the approach taken to model the effectiveness of 

an FSIS inspection program in reducing the annual burden of human illness. This section 

of risk characterization provides a qualitative description of how FSIS seeks to improve 

food safety for catfish, as well as describing the mathematics, available evidence and 

uncertainty associated with modeling the effectiveness of an FSIS catfish inspection 

program.

Estimates of the potential effectiveness of an FSIS catfish inspection program are 

presented relative to the default annual number of Salmonella illnesses estimated to be 

currently associated with catfish. Even allowing for uncertainty about the default number 

of annual illnesses, substantial uncertainty remains about the level of effectiveness that 

can be achieved by FSIS inspection and the rate at which the effectiveness can be 

achieved. 

The final sections of risk characterization examine the sensitivity of the estimated 

number of Salmonella illnesses associated with catfish to some changes in the risk 

assessment model inputs and the effects of uncertainty about inputs on model estimates.

Several scenarios are examined to explore the potential error in predictions caused by 

influential and highly uncertain risk assessment model inputs.
34

5.1 Default estimation of numbers of Salmonella illnesses per year

The computer model comprises 3 million iterations. By running the model 

multiple times, it was determined that the estimated number of illnesses per year 

stabilized such that there was a 95% confidence that any simulation was within ~2.5% of 

the average estimate calculated across the multiple simulations of 3 million iterations 

each.

A seed value was used for sensitivity and scenario analyses that generated an 

annual number of illnesses equivalent to the mean across multiple simulations. This 

approach allowed a direct comparison between the estimated numbers of illnesses using 

the model’s default settings and alternative settings.

For the default model settings, the number of contaminated servings per year was 

30,970,392 using the Siluriformes definition of catfish. This number determines the 

servings generated from Salmonella-contaminated catfish; it represents the annual 

potential exposures to Salmonella. If catfish are defined as Ictaluridae, then 23,673,768 

contaminated servings per year are estimated. The difference in contaminated servings is 

determined solely by the share of Ictaluridae among Siluriformes.

Exposures from contaminated servings are determined using the average 

Salmonella dose per exposure. A dose-response function determines the probability of 

illness for each exposure dose. The probability of illness per contaminated serving for

each combination of cooking and breading is generally very small (Table 7) with, for 

example, the 75th percentiles suggesting the probability of illness is less than 1.6 in one 

million for contaminated servings. The maximum probability of illness across all 

exposures is 0.35; for the default beta-Poisson dose-response function, this probability of 

illness corresponds to a maximum dose of 1,280 Salmonella in a serving. The mean 

probability of illness is slightly larger for non-breaded servings than breaded servings 

because serving size is larger for non-breaded servings. The mean probability of illness is 

somewhat larger for fried than baked servings because cooking time for frying is 

generally less than baking; therefore, baked servings usually involve more reduction of 

Salmonella prior to consumption than fried servings.
35

Table 7. Model outputs for the probability of illness per contaminated serving for the combinations 

of cooking and breading effects.

The estimated numbers of Salmonella illnesses per year using the Siluriformes

and Ictaluridae definitions are 2,308 and 1,764, respectively. Because Salmonella

prevalence among domestic and imported catfish is assumed to be equal in the default 

model settings, the number of Ictaluridae-associated annual illnesses is simply 76% of the 

number of Siluriformes-associated illnesses. Therefore, there is no significant difference 

between Siluriformes and Ictaluridae in the risk of salmonellosis per serving; rather the 

amount of total illnesses depends on the total volume of catfish consumed in the U.S. 

(Table 8).

Table 8. The estimated annual numbers of Salmonella illnesses for each definition of catfish.

Definition of 

catfish

Number of 

contaminated servings 

(exposures)

Average

probability of 

illness per 

contaminated 

serving

Estimated 

number of 

Salmonella

illnesses per year

Siluriformes 30,970,392 7.452E-05 2,308

Ictaluridae 23,673,768 7.452E-05 1,764

On the basis of total servings of catfish consumed, the average probability of illness per 

serving is 

2308 illnesses/yr 6

1.5 10

 1,548,519,606 servings/yr

. This probability incorporates the 

Probability of 

Salmonella illness per 

contaminated serving

Minimum 25th

percentile

Median Mean 75th 

percentile

Maximum Standard. 

deviation

Baked and non-Breaded 0.0E+00 8.2E-15 1.2E-10 2.1E-05 5.3E08 2.2E-01 7.9E-04

Fried and non-Breaded 0.0E+00 4.3E-11 2.8E-08 1.1E-04 1.6E-06 3.5E-01 2.5E-03

Baked and Breaded 0.0E+00 6.2E-15 9.3E-11 1.6E-05 4.0E-08 2.0E-01 6.4E-04

Fried and Breaded 0.0E+00 3.2E-11 2.1E-08 8.8E-05

1.2E-06 3.2E-01 2.1E-03
36

prevalence of contaminated servings and suggests that Salmonella illness resulting from 

consuming a serving of catfish is an uncommon event.

5.2 Comparison of illnesses per year using surveillance data

Salmonella illnesses attributable to catfish are rare. In the past 20 years there has 

been only one suspected outbreak reported (10 illnesses in May 1991 associated with a 

restaurant in New Jersey). Furthermore, catfish consumption has not been identified as a 

factor in epidemiological (specifically, case–control) studies of salmonellosis. 

Nevertheless, it is possible that there is a low level of sporadic cases of salmonellosis 

associated with catfish occurring in the U.S. which are not detected with current levels of 

surveillance.

The Centers for Disease Control and Prevention (CDC) lists annual reports of 

foodborne outbreaks from 1990 through 2007 (U.S. CDC, 2009). These reports show 

there are a little more than 100 outbreaks of salmonellosis in the U.S. each year. 

Approximately 60% of these outbreaks have a vehicle identified (Table 9). These data are 

used to determine an alternative estimate for the number of catfish-related Salmonella

illnesses each year. This estimation multiplies the fraction of foodborne outbreaks with 

identified vehicles that were attributed to catfish by the estimated total Salmonella

illnesses per year.
37

Table 9. Salmonella Foodborne Outbreaks from 1990 through 2007.

Year Salmonella

Outbreaks

Vehicles 

Identified %

1990 138 82 59.4%

1991 123 64 52.0%

1992 80 48 60.0%

1993 95 62 65.3%

1994 88 52 59.1%

1995 94 51 54.3%

1996 80 47 58.8%

1997 80 54 67.5%

1998 124 62 50.0%

1999 113 77 68.1%

2000 112 76 67.9%

2001 112 78 69.6%

2002 109 74 67.9%

2003 108 70 64.8%

2004 123 64 52.0%

2005 94 67 71.3%

2006 116 62 53.4%

2007 135 69 51.1%

Total 1,924 1,159 60.2%

If it is assumed that the proportion of all Salmonella illnesses caused by a vehicle 

is equivalent to the proportion of outbreaks caused by that vehicle, then the expected 

proportion for any given year (

pt

) using the evidence from the previous year would be 

Equation 8

1

1 1

1

2

t

t

t t

s

p

n s

.

where 

pt

is the mean of a beta distribution (Vose, 2000), 

t 1

s

is the number of outbreaks 

caused by a particular vehicle in the previous year and 

t 1 n

is the number outbreaks in the 

previous year.

For 2007, given 69 outbreaks in which the vehicle was identified to not be catfish, 

the value of pt would be 0.014. It would be reasonable, however, to include evidence 

from earlier years. The expected proportion for any given year using the evidence from m

previous years would be 
38

Equation 9

1

1

1

2

m

t i

i

t m

t i t i

i

s

p

n s

.

If the entire set of reports from CDC is used, then there is one outbreak in which 

catfish is identified as the vehicle and 1158 outbreaks in which other vehicles were 

identified. The expected value for pt is 2/1160 = 0.0017. The 95% confidence limits are 

from 0.0002 to 0.0048.

Mead et al. (1999) estimates there are about 1.4 million illnesses due to 

Salmonella annually. If the proportions calculated above are multiplied by 1.4 million, 

the estimated number of annual human illnesses is 2,400, with a lower limit of 280 and an 

upper limit of 6,700.

5.3 Modeling program effectiveness

Traditionally, FSIS has monitored the food safety performance of an industry 

based on a reduction in the prevalence of a pathogen on specific food products. Although 

monitoring the number (concentration) of pathogens on carcasses is an alternative and 

advantageous approach, laboratory enumeration of pathogen levels on carcasses is 

cumbersome and generally non-routine.

In the risk assessment model, it is assumed that the effect of FSIS inspection will 

be some reduction in the prevalence of Salmonella-contaminated catfish carcasses. 

Although FSIS inspection may also influence the number (concentration) of Salmonella 

on contaminated carcasses, that effect is not modeled. 

It is further assumed that the effect of FSIS inspection will be equivalent for 

domestic and imported catfish. This assumption is reasonable because import regulations 

intend to establish equivalency in food safety risk between imports and products of 

domestic origin.

Given these assumptions, predicting the number of human salmonellosis illnesses 

associated with catfish avoided each year is straightforward, given Equation 1 and 

Equation 4. The effectiveness of FSIS inspection may be some fraction, 

FSIS g

, of the 
39

default prevalence of contaminated carcasses. For example, if 

0.1 FSIS g

, then a 10% 

reduction in prevalence of contaminated carcasses following implementation of FSIS 

inspection is expected. Similarly, the new Salmonella prevalence of contaminated catfish 

carcasses can be modeled by multiplying the default prevalence (i.e., 2%) by 

1 0.9 gFSIS

. Because the number of contaminated servings per year is linear with 

respect to 

FSIS g , predicting the annual illnesses avoided is a simple calculation; 

,

,

,

,

=

( )

1 ( )

1 ( )

1 1 ( )

Bread Baked Bread Bakedd

Bread Baked Bread Fried

Bread Baked Nonbread Baked

Bread Baked Nonbread Fried

Number_ill / yr

f f P ill

f f P ill

#contaminated servings / yr

f f P ill

f f P ill

avoided

avoided

with

 = 

(1 ) servings imports FSIS imports imports FSIS domestic

# contaminated servings / yr

N f g prev f g prev

avoided

Or, given a default number of human salmonellosis cases associated with catfish 

estimated to occur prior to an FSIS inspection program (

Number_ill / yr

), the illnesses 

avoided by an FSIS cafish inspection is simply;

Equation 10

 / = Number_ill yr g Number_ill / yr FSIS avoided

Given the nature of Equation 10, it is reasonable to imagine the annual number of 

human salmonellosis cases from catfish as a binomial process. In other words, there are a 

number of Salmonella-contaminated catfish servings (i.e., number of trials, n) and each 

serving has an independent probability of resulting in human illness (i.e., a probability of 

“success”, p). Furthermore, given a large number of catfish servings (trials) and a small 

probability of human illness (defined as a “success” in terms of mathematical 

probabilities), this binomial process can be approximated as a Poisson process with rate 

parameter 

n p

(Vose, 2000). This development allows an appreciation of the nature 
40

of the model’s estimated annual cases. These estimated cases can be assumed to be the

rate parameter to a Poisson distribution; so year-to-year variability in the number of cases 

could be modeled using this distribution. For the most part, this variability is ignored in 

this risk characterization because it represents a change of only about 5% around the 

estimated annual rate. Compared to the substantial uncertainty surrounding the 

effectiveness of FSIS inspection, this amount of variability is minor.

The true effectiveness of FSIS inspection for reducing catfish-associated human 

illnesses is unknown. Also, the rate at which FSIS inspection will achieve its ultimate 

reductions is unknown. Consequently, the model incorporates substantial uncertainty 

about program effectiveness. A plausible range might be from more than 90% to less than 

10% effectiveness, and so the risk assessment model includes an evaluation of possible 

effectiveness levels – 10%, 50% and 90% – to provide a range of predictions. Similarly, 

the model evaluates the public health effect if the FSIS catfish inspection program 

achieves peak effectiveness in 2, 5, 10 or 15 years following its implementation (note: 

linear interpolation is used to predict illnesses avoided in years prior to achieving peak 

effectivness).

Given its mission, FSIS expects to provide assurance of industry control and 

reduction of foodborne hazards to protect U.S. consumers. FSIS has maintained 

continuous inspection of poultry and meat carcasses for several decades. In the mid1990s FSIS further implemented a HACCP-based regulatory system for all products it

regulates. FSIS inspectors comprise veterinarians and other food safety scientists whose 

responsibilities extend from overseeing humane handling to sanitation, slaughter dressing 

and final approval of carcasses. All of their activities contribute to assuring proper food 

safety controls to prevent or mitigate foodborne hazards in all FSIS-regulated products.

Although FSIS does not have experience with catfish slaughter establishments, it 

does have substantial experience with regulatory oversight of other animal slaughter and 

corresponding processing industries. The process of converting live catfish to retail-ready 

meat products is not unlike the process of slaughtering poultry, turkeys, cattle or hogs. 

Catfish are eviscerated and usually skinned and deheaded to produce fillets and other 

edible products (Wyatt et al., 1979). At each step of the process, the potential for crosscontamination of the carcass exists. This contamination can occur from the skin of catfish 
41

directly to the meat surface, via processing workers’ hands, or via the processing 

environment. 

The role of hide or skin contamination is substantial in sanitary carcass dressing. 

These sources of pathogens are recognized as possibly more important than the role of 

intenstinal colonization in the contamination of poultry, turkey, beef or hog carcasses. 

Although intestinal colonization of catfish with Salmonella may be uncommon 

(McCaskey 1998; Wyatt et al. 1979; Pao et al. 2008), environmental contamination of 

catfish skin may have some similities to poultry and other animal contaminations. The 

catfish pond environment is potentially exposed to amphibians, reptiles and mammals 

that may carry and shed Salmonella. 

The role of daily FSIS inspection of catfish processing establishments in reducing 

potential contamination events is expected to be important. The presence of FSIS 

inspectors should serve to enforce compliance with regulations; FSIS personnel observe 

the daily operations in processing facilities and require that immediate corrective actions

are taken if deficiencies are noted. Inherent in the expected importance of FSIS 

inspection is the assumption that enhanced sanitation is correlated with reduced pathogen 

contamination. FSIS also conducts periodic testing of products it inspects for the 

presence of pathogens and chemicals.

5.3.1 Need for data after program implementation

Predicting the effectiveness of FSIS inspection for the reduction of illness from 

catfish consumption is uncertain because data are currently unavailable. Once the FSIS 

catfish inspection program is implemented, substantial amounts of pathogen and 

chemical hazard testing data for catfish will become available. Over time, it is expected 

that testing data will show a progressively lower occurrence of pathogens and chemicals 

among processed catfish carcasses, particularly if significant levels are found. As FSIS 

inspectors work to improve the compliance of processors with its regulations, such 

violations, if found, would be expected to decline. The nature (i.e., severity) of potential 

violations would also be expected to trend downward. It is expected that subsequent 

quantitative risk assessments for catfish then could be developed and used to evaluate 

future policy options intended to control any chronic violations that might be key risk 
42

indicators for carcass contamination. Public health monitoring may provide indications of 

program effectiveness. Outbreaks and recall investigations associated with catfish may 

similarly serve as indicators of the program’s effectiveness. 

5.3.2 Poultry evidence

Although the risk assessment model includes uncertainty about the effectiveness 

of FSIS inspection as a range between 10% and 90%, evidence from before and after 

implementation of the FSIS HACCP regulation provides some indication of a midpoint 

between these extremes. For example, the 1994-95 nationwide broiler chicken 

microbiological baseline study found 20% of 1,297 poultry carcasses Salmonellapositive. That study was repeated in 1999-2000 and found 8.7% of 1,225 poultry 

carcasses Salmonella-positive. In 2007-2008, FSIS again completed a nationwide 

baseline study of poultry carcasses and found approximately 7.5% (volume-weighted) of 

3,275 samples Salmonella-positive31. 

The trend in Salmonella prevalence among poultry carcasses does not directly 

demonstrate the effectiveness of the HACCP regulation; too many factors might 

influence Salmonella occurrence among carcasses to attribute the trend solely to FSIS 

inspection activities. Nevertheless, this trend provides an empirical estimate of how FSIS 

inspection might influence Salmonella occurrence among FSIS-regulated products. This 

trend – if fully attributed to FSIS’ inspection program – implies that Salmonella 

prevalence might decrease (i.e., by 

8.7% 1 56.5%

20%

or

7.5% 1 62.5%

20%

) following 

implementation of FSIS’ catfish inspection program.

5.4 Program effectiveness estimates

Estimates of the potential effectiveness of FSIS catfish program are presented 

relative to the default (baseline) estimated number of salmonellosis cases associated with 

catfish consumption. As discussed previously, predicted program effectiveness depends 

upon the number of baseline Salmonella illnessses, the peak effectiveness rate of an FSIS

catfish inspection program, and the timeframe required to achieve peak effectiveness.

 

31 See http://www.fsis.usda.gov/Science/Baseline_Data/index.asp for all baseline studies
43

Depending on whether catfish is defined as Siluriformes or Ictaluridae for 

rulemaking purposes, estimates of the number of baseline Salmonella illnesses 

attributable to catfish differ. If a subset of product is not included in the definition; and 

therefore, does not fall under FSIS’s authority to regulate, then consumption of that 

subset of product is unaffected by any FSIS regulation. Thus the number of baseline 

Salmonella illnesses estimated is higher (2,308 illnesses per year) for Siluriformes, and 

lower (1,764 illnesses per year) for Ictaluridae. Because these baseline Salmonella

illnesses are assumed to be Poisson distributed, the risk assessment estimates (Table 10) 

also include 5

th and 95th percentiles.

Table 10. Estimated baseline Salmonella illnesses per year.

Catfish 

definition mean

confidence interval

5

th 95th

Siluriformes 2308 2229 2387

Ictaluridae 1764 1695 1833

Substantial uncertainty remains about the level of peak effectiveness that could be 

achieved by an FSIS inspection program for catfish. In theory, FSIS inspection of catfish 

could be completely effective (100% peak effectiveness) at reducing salmonellosis cases 

assocated with the consumption of catfish, or FSIS inspection could be totally ineffective 

(0% peak effectiveness). Model results are summarized for three plausible effectiveness 

levels (i.e., 10%, 50%, and 90%).

Additional uncertainty exists about the number of years required to achieve peak 

effectiveness. Based on Agency projections, any FSIS regulation of the catfish industry, 

whether catfish is defined as Siluriformes or Ictaluridae, would likely take approximately 

11⁄2 years to become fully implemented. So it is assumed in the risk assessment that the

soonest that FSIS’ catfish inspection program could achieve peak effectiveness could be 

2 years (therefore the 3rd year would be the first year of inspection under peak 

effectiveness). At the other extreme, it is assumed that an FSIS catfish inspection 

program could take 15 or more years to achieve peak effectiveness. Other plausible 

scenarios modeled include a 5-year timeframe and a 10-year timeframe.
44

5.4.1 Using Siluriformes definition

Figure 6 shows several aspects of the the uncertainty about the estimated peak 

effectiveness of an FSIS regulation in predicting the annual number of Salmonella

illnesses avoided, using the Siluriformes definition of catfish. This graph assumes a 5-

year timeframe for reaching an uncertain peak effectiveness. Note that the relative 

estimanted number of illnesses avoided across 10 years of an FSIS catfish inspection 

program is directly related to the assumption about the timing of peak effectiveness.

Figure 6. Uncertainty in the potential effectiveness of regulation on the annual number of Salmonella

illnesses avoided over 10-yrs following FSIS regulation of catfish. These values assume a 5-yr 

timeframe and the Siluriformes definition of catfish.

Table 11 through Table 13 show the number of Salmonella illnesses avoided each 

year over a 10-year planning horizon for 10%, 50%, and 90% peak effectiveness, 

assuming a 2, 10 or 15-year timeframe for achieving peak effectiveness. 

Estimated illnesses avoided are then projected for each of 10-years following 

policy implementation. If the peak effectiveness of an FSIS catfish inspection progam is 

assumed to be a 50% decline in salmonellosis cases related to catfish, then a comparison 

of Table 11 through Table 13 shows that predicted Salmonella illnesses from catfish 

avoided in the first year ranges from a low of 72 (assuming a 15-year timeframe to 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

250

500

750

1000

1250

1500

1750

2000

2250

2500

1 2 3 4 5 6 7 8 9 10

year Annual number of illnesses avoided
45

achieve peak effectiveness) to a high of 384 (assuming a 2-year timeframe to achieve 

peak effectiveness). 

If the peak effectiveness of an FSIS catfish inspection progam is assumed to be at 

90%, then comparing Table 11 through Table 13 shows predicted Salmonella illnesses 

from catfish avoided in the first year ranges from a low of 129 (assuming a 15-year 

timeframe to achieve peak effectiveness) to a high of 692 (assuming a 2-year timeframe 

to achieve peak effectiveness). 

In this analysis, the risk asssessment model is used to project the estimated 

number of Salmonella illnesses associated with catfish that might be avoided over a 10-

year period to allow for the calculation of the discounted value of human illnessess 

avoided for a 10-year benefit-cost analysis. It is worth noting that the longer timeframes 

require more time to achieve higher predicted illnesses avoided. For example, using the 

15-year timeframe and assuming peak effectiveness at 90%, the risk assessment model 

estimates 1,298 Salmonella illnesses avoided in the tenth year. In a valuation calculation 

of these human illnesses in the present, they must be discounted for 10 years. The shorter 

the timeframe required to achieve peak effectiveness, the smaller the gap between the 

estimated number of Salmonella illnesses associated with catfish avoided in the nearyears of the planning horizon and the estimated number of illnesses avoided in the outyears of the planning horizon. This has potentially important implications for benefit-cost 

analysis.
46

Table 11. Estimated Number of Salmonella illnesses avoided due to FSIS regulation assuming the 

Siluriformes definition of catfish and a 2-year to effectiveness timeframe. 

year

90% effectiveness 50% effectiveness 10% effectiveness

confidence interval confidence interval confidence interval

Mean

5th 

percentile

95th 

percentile mean

5th 

percentile

95th 

percentile mean

5th 

percentile

95th 

percentile

1 692 649 735 384 352 416 76 62 90

2 1,384 1,323 1,445 769 723 815 153 133 173

3 2,077 2,002 2,152 1,154 1,098 1,210 230 205 255

4 2,077 2,002 2152 1,154 1,098 1,210 230 205 255

5 2,077 2,002 2,152 1,154 1,098 1,210 230 205 255

6 2,077 2,002 2,152 1,154 1,098 1,210 230 205 255

7 2,077 2,002 2,152 1,154 1,098 1,210 230 205 255

8 2,077 2,002 2,152 1,154 1,098 1,210 230 205 255

9 2,077 2,002 2,152 1,154 1,098 1,210 230 205 255

10 2,077 2,002 2,152 1,154 1,098 1,210 230 205 255
47

Table 12. Estimated Number of Salmonella illnesses avoided due to FSIS regulation assuming the 

Siluriformes definition of catfish and a 10-year to effectiveness timeframe. 

year

90% effectiveness 50% effectiveness 10% effectiveness

confidence interval confidence interval confidence interval

Mean

5th 

percentile

95th 

percentile mean

5th 

percentile

95th 

percentile mean

5th 

percentile

95th 

percentile

1 188 165 211 104 87 121 20 13 28

2 377 345 409 209 185 233 41 31 52

3 566 527 605 314 285 343 62 49 75

4 755 710 800 419 385 453 83 68 98

5 944 893 995 524 486 562 104 87 121

6 1,133 1,078 1,188 629 588 670 125 107 143

7 1,321 1,261 1,381 734 689 779 146 126 166

8 1,510 1,446 1,574 839 791 887 167 146 188

9 1,699 1,631 1,767 944 893 995 188 165 211

10 1,888 1,817 1,959 1,049 996 1,102 209 185 233
48

Table 13. Estimated Number of Salmonella illnesses avoided due to FSIS regulation assuming the 

Siluriformes definition of catfish and a 15-year to effectiveness timeframe. 

Year

90% effectiveness 50% effectiveness 10% effectiveness

mean

confidence interval

mean

confidence interval

mean

confidence interval

5th 

percentile

95th 

percentile

5th 

percentile

95th 

percentile

5th 

percentile 95th percentile

1 129 110 148 72 58 86 14 8 20

2 259 233 285 144 124 164 28 20 37

3 389 357 421 216 192 240 43 33 54

4 519 482 556 288 260 316 57 45 70

5 649 607 691 360 329 391 72 58 86

6 778 732 824 432 398 466 86 71 101

7 908 858 958 504 467 541 100 84 116

8 1,038 985 1,091 577 537 617 115 97 133

9 1,168 1,112 1,224 649 607 691 129 110 148

10 1,298 1,239 1,357 721 677 765 144 124 164

5.4.2 Using Ictaluridae definition

Figure 7 shows the uncertainty about the peak effectiveness of FSIS regulation in 

predicting the estimated annual number of Salmonella illnesses avoided using the 

Ictaluridae definition of catfish. This graph assumes a 5-year timeframe for reaching an 

uncertain peak effectiveness.

Table 14 through Table 16 show the estmated number of Salmonella illnesses 

avoided each year over a 10-year planning horizon for 10%, 50%, and 90% peak 

effectiveness assuming a 2, 10, and 15-year timeframe for achieving the peak 

effectiveness. If the peak effectiveness of an FSIS catfish inspection progam is assumed 

to be a 50% decline in Salmonella illnesses related to catfish, then a comparison of Table 

14 through Table 16 shows that predicted Salmonella illnesses from catfish avoided in 

the first year ranges from a low of 55 (assuming a 15-year timeframe to achieve peak 
49

effectiveness) to a high of 294 (assuming a 2-year timeframe to achieve peak 

effectiveness). 

If the peak effectiveness of an FSIS catfish inspection progam is assumed to be 

90%, then a comparison of Table 14 through Table 16 shows that estimated illnesses 

avoided in the first year range from a low of 99 (assuming a 15 timeframe to achieve 

peak effectiveness) to a high of 529 (assuming a 2-year timeframe to achieve peak 

effectiveness). 

Figure 7. Uncertainty in the potential effectiveness of regulation on the annual number of Salmonella

illnesses avoided over 10-yrs following FSIS regulation of catfish. These values assume a 5-yr 

timeframe and the Ictaluridae definition of catfish.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

250

500

750

1000

1250

1500

1750

2000

1 2 3 4 5 6 7 8 9 10

year Annual number of illnesses avoided
50

Table 14. Estimated Number of Salmonella illnesses avoided due to FSIS regulation assuming the 

Ictaluridae definition of catfish and a 2-year to effectiveness timeframe.

year

90% effectiveness 50% effectiveness 10% effectiveness

mean

confidence interval

mean

confidence interval

mean

confidence interval

5th 

percentile

95th 

percentile

5th 

percentile

95th 

percentile

5th 

percentile

95th 

percentile

1 529 491 567 294 266 322 58 46 71

2 1,058 1,004 1112 588 548 628 117 99 135

3 1,587 1,521 1653 882 833 931 176 154 198

4 1,587 1,521 1653 882 833 931 176 154 198

5 1,587 1,521 1653 882 833 931 176 154 198

6 1,587 1,521 1653 882 833 931 176 154 198

7 1,587 1,521 1653 882 833 931 176 154 198

8 1,587 1,521 1653 882 833 931 176 154 198

9 1,587 1,521 1653 882 833 931 176 154 198

10 1,587 1,521 1653 882 833 931 176 154 198
51

Table 15. Estimated Number of Salmonella illnesses avoided due to FSIS regulation assuming the 

Ictaluridae definition of catfish and a 10-year to effectiveness timeframe.

Year

90% effectiveness 50% effectiveness 10% effectiveness

mean

confidence interval

mean

confidence interval

mean

confidence interval

5th 

percentile

95th 

percentile

5th 

percentile

95th 

percentile

5th 

percentile

95th 

percentile

1 144 124 164 80 65 95 16 10 23

2 288 260 316 160 139 181 32 23 42

3 432 398 466 240 215 265 48 37 60

4 577 537 617 320 291 349 64 51 77

5 721 677 765 400 367 433 80 65 95

6 865 817 913 481 445 517 96 80 112

7 1,010 958 1,062 561 522 600 112 95 129

8 1,154 1,098 1,210 641 599 683 128 109 147

9 1,298 1,239 1,357 721 677 765 144 124 164

10 1,443 1,381 1,505 801 754 848 160 139 181
52

Table 16. Estimated Number of Salmonella illnesses avoided due to FSIS regulation assuming the 

Ictaluridae definition of catfish and a 15-year to effectiveness timeframe.

Year

90% effectiveness 50% effectiveness 10% effectiveness

mean

confidence interval

mean

confidence interval

mean

confidence interval

5th 

percentile

95th 

percentile

5th 

percentile

95th 

percentile

5th 

percentile

95th 

percentile

1 99 83 115 55 43 67 11 6 17

2 198 175 221 110 93 127 22 15 30

3 297 269 325 165 144 186 33 24 43

4 396 363 429 220 196 244 44 33 55

5 496 459 533 275 248 302 55 43 67

6 595 555 635 330 300 360 66 53 79

7 694 651 737 385 353 417 77 63 91

8 793 747 839 441 406 476 88 73 103

9 893 844 942 496 459 533 99 83 115

10 992 940 1044 551 512 590 110 93 127
53

5.5 Sensitivity of default illnesses estimates to changes in model inputs 

A limited sensitivity analysis was completed on inputs to the catfish risk 

assessment model to inform the uncertainty analysis and to test the sensitivity of certain 

modeling assumptions. This sensitivity analysis was conducted on the baseline number of 

Salmonella illnesses. 

Below is a description of the procedures used to evaluate the influence of various 

risk assessment model parameters on public health estimates (i.e., the sensitivity of model 

variables). 

Elasticities, Є, are calculated for every sensitivity test based on the following 

formula:

Є = %∆ in output / (%∆ in input)

where %∆ is read “percent change”. The greater the absolute value of the elasticity, the 

more effect a change in a model input can be expected to have on the outputs of this risk 

assessment model.

For the sensitivity procedure, the model was run for 3 million Salmonellacontaminated catfish servings and the output was collected (YBASE). A change to an 

input parameter was initiated in the model, then the model was re-run for 3 million 

Salmonella-contaminated servings and the output was again collected (YSHOCK). To 

assure consistency of comparisons, the same starting seed value was used in all baseline 

and sensitivity scenario runs of the model.

The %∆ in output is calculated as:

%∆ in output = (YSHOCK - YBASE ) / YBASE

Similarly, the %∆ in input values could be determined. In practice these were entered as 

exogenous pre-run changes to one input variable at a time in the model code.

This model uses several input parameters. The advantage of elasticity-based 

sensitivity analysis is that the analyst can compare and contrast sensitivities across all 

inputs using a common metric.
54

5.5.1 Prevalence

Both import and domestic prevalence assumptions were tested by increasing the 

default inputs by 10%.

5.5.2 Growth

One sensitivity scenario tested Salmonella growth assumptions in post- process 

catfish carcasses by increasing the most likely parameter of the log growth Pert

distribution by 100%. This large change was needed for this sensitivity scenario because 

very few servings experience growth. 

5.5.3 Cooking practices

Pert distributions were used to model cooking times and temperatures for baked 

or fried servings. Sensitivities to cooking time and cooking temperature were tested 

separately for baking and frying. The most likely cooking times were adjusted by 10%.

The most likely cooking temperatures were adjusted by 8.5% because a 10% increase 

would exceed the maximum parameter in the Pert distribution. 

Additional sensitivity scenarios tested model assumptions about fraction of catfish 

meals baked and the fraction of servings breaded. Both of these scenarios assumed 10% 

increases in the respective input parameter.

5.5.4 Serving Size

The sensitivity of the model’s output to serving size was modeled by multiplying 

random draws for the non-parametric serving size distribution (documented elsewhere in 

this report) by a factor of 1.1 – thus achieving a 10% increase in serving size.

Additionally, sensitivity analyses were developed on assumptions about the 

reduction in serving size due to breading. These scenarios involved increasing the 

minimum and maximum reductions in serving size from breading by 10%. 
55

5.5.5 Dose-Response

The beta-Poisson dose response function for Salmonella was tested for sensitivity 

by analyzing small changes to the individual parameters of the beta-Poisson. Both the

and parameters were adjusted by 10%.

5.5.6 Assumptions about extrapolating from poultry to catfish

Two assumptions regarding extrapolation of poultry to catfish carcasses were 

tested. A 10% change to the assumption about the average weight of a poultry carcass in 

a rinse bag (used to estimate Salmonella concentration) was tested. Also, this risk 

assessment includes an evaluation of model assumptions about the effect of skin removal 

on overall carcass contamination by adjusting that value by 10%.

5.5.7 Sensitivity analysis findings

Sensitivity scenario results can be loosely grouped into 3 categories (|Є|>1, |Є|~1, 

|Є|<<1). By far, the most sensitive model inputs are the assumptions about cooking

((|Є|>1). Frying parameters seem much more sensitive than baking parameters. Cooking 

temperature also seems more sensitive than cooking time. Elasticities for a second 

category of input parameters are close to one; therefore a proportional change in 

Salmonella illnesses results for a given change in input values. Those include the and

parameters of the dose response function, serving size, underlying assumptions about 

poultry contamination (effect of skin removal, and weight of chicken carcass in the rinse 

solution used to extrapolate contamination levels) and domestic prevalence. All other 

parameters fall into a third category of inputs having elasticities less than one. Results of 

the sensitivity analysis on all relevant Salmonella input variables are shown in Figure 8.
56

Figure 8. Tornado diagram describing the elasticity of the model’s annual illness estimates to various 

model inputs. The x-axis here is in elasticity units.

Sensitivty analysis of input parameters: ranked 

by absolute value of elasticity 

-11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1

Salmonella growth

fraction of servings that are breaded

fraction baked

minimum reduction in serving size due to

breading

import prevalence

Cooking time -baked

maximum reduction in serving size due to

breading

Cooking temperature -baked

domestic prevalence

average weight of chicken carcass

-beta parameter in Beta-Poisson dose

response

adjustment to Salmonella contamination for

skin-off consumption

serving size

-alpha parameter in Beta-Poisson dose

response

Cooking time -fried

Cooking temperature -fried
57

5.6 Uncertainty scenario analyses

The purpose of uncertainty analysis is to examine the effect of some of the default 

estimates on the annual number of human salmonellosis cases estimated from catfish

consumption. Although this risk assessment uses a simple model, the default input values 

were usually based on assumptions or very limited data. Uncertainty analysis can also 

highlight the need for specific data to improve a risk assessment’s estimates. Both these 

objectives are treated in a mostly qualitative manner in this section. 

To evaluate the uncertainty in some model estimates, the risk assessment 

estimates annual numbers of illnesses for various model inputs using the Siluriformes 

definition of catfish. For various model inputs, potential lower and upper bound values 

are used (Table 17). In some cases, the potential lower and upper bounds are determined 

from statistical confidence limits; while in other cases the settings are determined based 

on judgments of the data underlying the default assumptions. For example, there is some 

evidence suggesting an upward trend in imported catfish products (USDA-NASS, 2009).

In addition, there is some evidence to suggest Salmonella prevalence might be larger than 

2% for some share of imported catfish (Broughton and Walker 2009)

32

. Given this 

limited evidence, an upper bound scenario for prevalence adjusts the prevalence among 

imports and increases the share of imports. Nevertheless, a similar effect could have been 

modeled by simply increasing prevalence among domestic catfish. Alternatively, a lower 

bound scenario for prevalence assumes that Salmonella prevalence among all catfish is 

closer to 1% based on limited sampling at retail (Pao et al. 2008).

The default growth, breading effect, and post-processing concentration modeling 

assumptions were considered already to be near lower bound settings, so only upper 

bound scenarios were developed for these inputs. Potential upper and lower bound 

settings for cooking effectiveness were established by adjusting cooking times such that 

frying became equivalent to baking (lower bound) or baking became equivalent to frying 

(upper bound).

Each change (lower and upper) was simulated to estimate the annual number of 

illnesses and the change from the default model estimate was noted. For each potential 

 

32 Nevertheless, there is also historic evidence that Salmonella prevalence among domestic catfish may also 

be higher than 2% (Wyatt et al. 1979).
58

upper and lower bound value, the inputs were sorted from smallest change to largest 

change. Scenarios that progressively combined more changes were simulated next, so 

that the incremental effect on estimated illnesses could be examined.

This approach progressively assumes that uncertainty about model inputs is 

perfectly correlated. In other words, if the true value for one input is its lower bound, then 

the true values for one, two, three, etc. other inputs is/are also their lower bounds. Such 

an approach predicts extreme boundaries because any assumption about uncertainties not 

being perfectly correlated will demonstrate less change in the model estimates than 

shown. Nevertheless, the progressive inclusion of multiple inputs into scenarios 

illustrates the range of uncertainty about the lower and upper bounds. As opposed to only 

providing the most extreme result (setting all inputs to their potential lower/upper 

bounds), we assume the range for these boundaries is qualitatively useful for decisionmakers.
59

Table 17. An outline of potential lower and upper bound values for various model inputs is shown. 

Symbols are used to identify changes in Figure x and y. 

Input name Symbol Lower bound scenario Upper bound scenario

Serving size S

Use lower 95% confidence 

limit for quantiles of 

empiric distribution

Use upper 95% confidence 

limits for quantiles of 

empiric distribution

Dose-response parameters DR

Use lower 95% confidence 

limit alpha and beta 

parameters from 

WHO/FAO, 2002; 

=0.094, =43.75

Use upper 95% confidence 

limit alpha and beta 

parameters from 

WHO/FAO, 2002; 

=0.1817, =56.39

Skinless effect adjustment K

Reduce poultry 

contamination data by 0.9 

logs 

Reduce poultry 

contamination data by 0.5 

logs 

Prevalence of contaminated catfish P

Reduce prevalence to 1% 

for both domestic and 

imported catfish

Increase prevalence for 

imported catfish to 4% and 

increase import share to 

40%

Cooking effectiveness C

Frying time distribution set 

equal to baking time 

distribution

Baking time distribution set 

equal to frying time 

distribution

Growth effect G No change

Set probability of growth to 

0.2%; most likely log 

growth=0.40; maximum log 

growth=1.5

Breading effect B No change

Minimum adjustment=0.85; 

maximum=0.95 (i.e., 

breading constitutes 5% to 

15% of serving size)

Post-processing concentration X No change

Truncate Salmonella

concentration distribution at 

0.03 Salmonella per gram, 

but re-distribute lower 

values randomly to values 

above threshold
60

The analysis completed for the potential lower bound scenario illustrates that the 

estimated annual Salmonella illnesses associated with catfish may range from 1,942 to 

100 (relative to a default of 2,308) depending on how many inputs assume the potential

lower bound values (Figure 9). Although other combinations are possible (e.g., 

S K

or

S P C

), this approach is one system for examining how the model’s estimate is 

reduced as more of its inputs are set at their lower bounds. 

As the graph progressively includes another uncertain input set to its potential 

lower bound, the estimated annual illnesses decreases. The pattern suggests a weak 

exponential-like decline as each input limits the amount of exposure (or response to 

exposure) among catfish servings. Nevertheless, the incremental effect of improved 

cooking (

C

) at the end of the progression is limited in Figure 9 relative to its effect when 

all other inputs are set at their default values. If the cooking inputs for the lower bound 

scenario are the only change to the default model, then estimated annual illnesses are 524 

(i.e., the incremental effect of lower bound cooking adjustment eliminates 1784 illnesses 

relative to the default estimate). This effect alone is nearly equivalent to the effect 

estimated by the

S DR K P

combined scenario. But, when the cooking change is 

modeled in combination with all other changes, its influence on illnesses is modulated 

because fewer contaminated servings with fewer organisms (and a lower probability of 

illness) are available in the model to be affected by the improved cooking effectiveness. 
61

The analysis completed for the potential upper bound scenario illustrates that the 

estimated annual illnesses may range from 2,397 to 16,000 (relative to a default of 2,308) 

depending on how many inputs assume their potential upper bound values (Figure 10). 

The trend in this graph suggests an exponential-like increase in estimated annual illnesses 

as more inputs are set to their upper bounds. 

The largest increase in estimated annual illnesses occurs when the Salmonella 

concentration data (derived from poultry) is truncated differently. The default assumption 

is that concentrations less than 0.003 Salmonella per gram are equal to 0.003. This 

assumption creates a high frequency of contaminated catfish carcasses with exactly 0.003 

Salmonella per gram. In the potential upper bound scenario, any value less than 0.003/g 

is randomly redistributed to values above the threshold. This approach is conceptually 

similar to zero-truncated discrete distributions in which values of zero are removed from 

the distribution and the probabilities for remaining feasible values are adjusted to sum to 

one (Klugman 2004). 

 For example, the default model estimates a mean (mode) of 0.13 (0.003) 

Salmonella/g on contaminated catfish carcasses. The potential upper bound scenario 

estimates a mean (mode) of 0.35 (0.016) Salmonella/g (i.e., nearly a 3-fold increase in 

 Figure 9. Cumulative reduction in the estimated number of illnesses for combined potential lower 

bound scenarios. 

0

500

1000

1500

2000

2500

Annual 

Salmonella

Illnesses

Lower bound combination scenarios
62

average concentration). This increase in concentration translates into a nearly 

proportional increase in annual illnesses (i.e., from 2308 to 6318 cases per year). 

Although the truncation method is a modeling assumption, the default model 

creates a contamination distribution that is consistent with conventional wisdom. It is 

generally reasonable to assume that the frequency of contamination levels decreases with 

increasing contamination levels (i.e., that contamination frequency is a monotonically 

decreasing function of contamination). Nevertheless, there are no catfish contamination 

data currently available for testing this assumption; once FSIS inspection begins, this 

uncertainty can be addressed by collecting catfish samples and enumerating Salmonella

levels on positive samples.

Figure 10. Cumulative increase in the estimated number of illnesses for combined potential upper 

bound scenarios.

A comparison of these potential upper and lower bound analyses with the 

inferences drawn from public health surveillance data suggests that the potential lower 

bound estimates are similar for both (i.e., 100 versus 280 estimated from public health 

data), but the potential upper bound estimates from this boundary analysis trend toward 

substantially larger annual illnesses (e.g., 16,000) than those estimated from public health 

data (6,700). Because it is assumed that a large number of annual illnesses is inconsistent 

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

Annual 

Salmonella

illnesses

Upper bound combination scenarios
63

with current public health surveillance data (i.e., if catfish were truly responsible for tens 

of thousands of Salmonella illnesses each year, it is expected that there would be more 

evidence of this food source based on epidemiological data), the risk assessment model is 

designed to examine the scenario from the upper bound analysis that most closely 

approximates the 6,700 annual illnesses estimated from the analysis of public health data. 

This scenario,

G B S C DR K , estimates 6,193 annual illnesses. The risk 

assessment model is used to examine the effect of FSIS inspection on this scenario and 

the 

S DR K P C

lower bound scenario.

Recalling that the effect of FSIS regulation on catfish is dependent on the 

assumption about the peak effectiveness of the regulation and on the timeframe required 

to achieve peak effectiveness, incorporating additional uncertainty with respect to 

baseline illness projections further complicates presentation of these results. For 

illustrative purposes, assume a 50% peak effectiveness of FSIS inspection and a 5-year 

timeframe for achieving that level of peak effectiveness (Table 18). Given these 

assumptions, Table 18 shows the number of Salmonella illnesses avoided for both the 

lower bound scenario (

S DR K P C

lower) and the plausible upper bound scenario 

(

G B S C DR K

upper). Annual illnesses avoided ranges from a low of 8 

(potential lower bound scenario) to a high of 516 (potential upper bound scenario) in the 

first year of the program. Since these average estimates are again Poisson distributed, 

confidence intervals are placed around these estimates of Salmonella illnesses avoided 

per year. Similar to the estimates using the default parameter values, these estimates will 

vary by year depending on the assumption about timeframe and peak effectiveness of 

FSIS regulation.
64

Table 18. Estimated Number of Salmonella illnesses avoided by FSIS regulation of catfish using the 

Siluriformes definition of catfish; assuming a 5-year timeframe and 50% effectiveness of FSIS 

inspection. 

year

lower bound scenario1upper bound scenario2

mean

confidence interval

Mean

confidence interval

95th 

percentile

5th 

percentile

95th 

percentile

5th 

percentile

1 8 13 4 516 553 479

2 16 23 10 1032 1085 979

3 25 33 17 1548 1613 1483

4 33 43 24 2064 2139 1989

5 42 53 32 2580 2664 2496

6 50 62 39 3096 3188 3004

7 50 62 39 3096 3188 3004

8 50 62 39 3096 3188 3004

9 50 62 39 3096 3188 3004

10 50 62 39 3096 3188 3004

1

combines lower bound assumptions for serving size, dose response parameter values, 

skinless effect on fillet contamination levels, Salmonella prevalence in catfish, and cooking 

affect.

2

combines upper bound assumptions for growth, breading affect, serving size, cooking 

affect, dose response parameter values, skinless affect on fillet contamination levels.
65

6. Summary

This risk assessment was completed to inform regulatory rule-making for 

establishing an FSIS catfish inspection program. This risk assessment considered two 

definitions for catfish – the definition of the order Siluriformes and the subset of the 

family Ictaluridae. As a baseline, i.e. prior to the establishment of an FSIS catfish 

inspection program, this risk assessment estimates an average of 2,309 Salmonella 

illnesses per year in the U.S. associated with the consumption of catfish, based on the 

definition of catfish as Siluriformes. The assessment estimates an average of 1,764 

illnesses based on the definition of catfish as Ictaluridae. These estimates are not 

inconsistent with those that might be projected by extrapolating current CDC 

epidmiological data (i.e., outbreak data). Based on the total number of servings of catfish 

consumed in the U.S., regardless of the definition of catfish, the default probability of 

Salmonella illness per catfish serving is estimated to be 

6

1.5 10 . This probability 

incorporates the prevalence of Salmonella-contaminated catfish servings and suggests 

salmonellosis from consuming a serving of catfish is an uncommon event.

There is substantial uncertainty regarding the actual effectiveness of a future FSIS 

catfish inpsection program. The actual effectiveness of FSIS’ catfish inspection program 

in reducing the prevalence of Salmonella-contaminated catfish will directly influence the 

size of the likely benefits of such a program. To illustrate, this risk assessment predicts 

that if FSIS has a catfish inspection program fully operational within a two year 

timeframe, then between 230 and 2,077 salmonellosis cases from catfish consumption (if 

defined as Siluriformes) might be prevented per year, depending on whether the program 

is 10% or 90% effective.

Another factor that directly effects the benefits of an FSIS catfish inspection 

program is the definition of catfish, either as the order Siluriformes or as the family

Ictaluridae. Given the same scenario (i.e., 2-year timeframe and a progam that ranges 

from 10% to 90% effective), between 176 and 1,587 salmonellosis cases per year might 

be prevented if catfish is defined as Ictaluridae. This is about 24% less salmonellosis 

cases prevented per year compared to if catfish are defined as Siluriformes. 
66

Finally, the range of risk estimates depends on uncertainty about the model inputs 

(e.g., data quality and assumptions). Uncertainty about these inputs translates into 

substantial uncertainty about the baseline estimates of the annual number of human 

salmonellosis cases attributable to the consumption of catfish. Consideration of potential

lower and upper bound model scenarios suggest plausible model estimates between 100 

and 6,200 salmonellosis cases associated with catfish per year. Given these public health 

estimates, this risk assessment estimates that between 50 and about 3,100 salmonellosis 

cases might be prevented if an FSIS inspection program is 50% effective within a 5-year 

timeframe. These estimates are based on a definition of catfish as Siluriformes. If catfish 

were defined as Ictaluridae, then between 38 and about 2,353 cases might be prevented 

each year.

This food safety risk assessment for catfish provides estimates of potential public 

health benefits of an FSIS catfish inspection program given current uncertainties about 

the effectiveness of this future program and limited contamination data for catfish.
67

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73

8. Appendix: Hazard Identification 

8.1 Prioritization of Microbial Hazards

Several bacterial pathogens have been associated with farmed fish (Ramos and 

Lyon, 2000). Because catfish is typically cooked prior to consumption, catfish-associated 

microbes do not routinely present problems of public health concern (Engle et al., 2009).

Therefore, defining specific microbiological hazards based on historical trends is a 

challenge with catfish because the pathogen-product pair relationships are not well 

established through epidemiological data. For these reasons, the microbial hazard 

identification was general to foodborne and waterborne pathogens potentially associated 

with fresh-water fish products. 

Pathogens of concern were categorized based on a combination of findings from 

literature reviews and catfish-associated outbreak information obtained from the Centers 

for Disease Control and Prevention (CDC) into two priority groups (higher and lower). 

Categorization was based on microbial association with the water in which catfish are 

raised, the catfish themselves and the final product, and also with the potential of the 

microorganisms to cause adverse public health effects through catfish consumption. 

Hazards are further delineated in terms of their potential relevance to raw or ready-to-eat 

(RTE) catfish (i.e., the relevant pathogen-product pairs).

For illustrative purposes, the subsequent steps (exposure assessment, hazard 

characterization (dose-response) and risk characterization) in this catfish risk assessment 

were applied to just the higher priority hazards identified via the risk characterization 

process. 

Higher Priority Microbial Hazards (Section 8.1.1)

o Non-Typhi Salmonella spp. in raw and RTE catfish

o Listeria monocytogenes on RTE catfish

o Clostridium botulinum and toxins on raw and RTE catfish

o Enterohemophagic, shigatoxigenic, enterotoxigenic and enteropathogenic E. coli

on raw catfish
74

Lower Priority Microbial Hazards (Raw and Ready-to-Eat Catfish) (Section 8.1.2)

o Vibrio spp.

o Toxins associated with cyanobacteria

o Edwardsiella tarda

o Shigella dysenteriae

o Pleisiomonas shigelloides

o Salmonella Serotype typhi

o Waterborne parasites

o Viruses

Potential Indicator Bacteria (Section 8.1.3)

o Generic Escherichia coli on raw catfish

o Gas-forming anaerobic bacteria on RTE catfish

o Other indicator bacteria for assessing sanitation on raw and RTE catfish

8.1.1 Higher priority microbial hazards

These are recognized foodborne pathogens historically linked to consumption of 

various freshwater fish products.

Salmonella is a potential microbial hazard for aquatic environments and, thus,

may be a concern with respect to catfish products. Non-typhi Salmonella are regarded as 

one of the higher priority hazards because the general burden of illness from this 

pathogen in the U.S. remains a concern and there is evidence that at least one outbreak of 

human salmonellosis may have been related to catfish consumption. Specifically CDC 

surmised that an outbreak of 10 cases of salmonellosis (Salmonella hadar) at a restaurant 

in 1991 may have been caused by catfish consumption. (U.S. CDC, 1991). 

Salmonella was reported in 21% of 153 aquaculture catfish collected from 

aquaculture ponds and retail markets (Wyatt, 1979) and can be harbored within catfish 

for 30 days after exposure to high levels (Ward, 1989). McCaskey et al. (1998) found 

Salmonella on 2.3% of 220 fillets sampled from three processing plants. Most recently, 

an examination of U.S. Food and Drug Administration (FDA) seafood import refusal data 

from 1998-2004 identified Salmonella contamination to be the most frequent violation in 

catfish (41.91% of violation categories) (Buzby, 2009) (Table 19). The combination of 
75

data presented in the literature, along with outbreak data and FDA import refusal data 

show that the highest microbial hazard associated with catfish consumption is 

Salmonella. 

Table 19. FDA Violation Codes for Catfish Refusals 

_______________________________________________________________________

FDA

Violation Cumulative Cumulative

Code1 Frequency Percent Frequency Percent

_______________________________________________________________________

__

 

FALSE 1 0.74 1 0.74

FALSECAT 4 2.94 5 3.68

FILTHY 18 13.24 23 16.91

IMPTRHACCP 1 0.74 24 17.65

INCONSPICU 2 1.47 26 19.12

INSANITARY 5 3.68 31 22.79

LABELING 2 1.47 33 24.26

LACKS FIRM 7 5.15 40 29.41

LACKS N/C 3 2.21 43 31.62

LIST INGRE 1 0.74 44 32.35

LISTERIA 3 2.21 47 34.56

MFR INSAN 2 1.47 49 36.03

NO ENGLISH 1 0.74 50 36.76

NO PROCESS 2 1.47 52 38.24

NUTRIT LBL 3 2.21 55 40.44

SALMONELLA 57 41.91 112 82.35

USUAL NAME 19 13.97 131 96.32

WRONG IDEN 5 3.68 136 100.00

_______________________________________________________________________

_

1 All capitalized terms are FDA shorthand code for import violations. List can 

be found at www.fda.gov/ora/oasis/ora_oasis.

Listeria monocytogenes is a potential hazard for certain RTE catfish products. Because it 

is a common environmental and aquatic contaminant, its presence in raw catfish may 

pose an indirect risk for cross-contaminating RTE product (Fernandes et al., 1997). 

Because L. monocytogenes (Lm) often contaminates and grows in cold-smoked fish 

products, there are likely to be similar risks for cold-smoked, RTE catfish products. Chou 

et al. (2006) identified Lm in 25 to 47% of raw catfish fillets at three U.S. processing 

plants. Some isolates were persistently found in processed fillets, suggesting either that 

the sanitation was inadequate or that these isolates originated from the natural habitats of 
76

the catfish. McCaskey et al. (1998) found a prevalence of 5.9% Lm on catfish fillets. 

Chou et al. (2006) found that Lm was most commonly isolated from catfish in the winter 

with a prevalence rate of 51%, compared to 41% in the spring, 36.7% in the fall and 19% 

in the summer.

Clostridium botulinum is a toxin-forming bacterium capable of causing a rare but lifethreatening illness. C. botulinum has been isolated from catfish at retail (Baker et al., 

1990). While toxins A and B are the primary botulinum toxin types associated with meat 

and poultry products, catfish and other products from aquatic environments have the 

potential for contamination by Toxin E-type C. botulinum. Unlike the A- and B-type 

strains, type-E C. botulinum can grow and develop neurotoxin during refrigerated storage 

and, given the serious nature of the illness, warrants special consideration for effective 

control measures. 

Enterohemorrhagic, shigatoxigenic, enterotoxigenic and enteropathogenic E. coli are

fecal contaminants that cause waterborne and foodborne gastroenteritis. A 2003 outbreak 

linked catfish and coleslaw consumption to 41 cases of Enterotoxigenic E. coli (ETEC) 

O169:H41-related illness (Beatty, 2004). In this case the outbreak may have been due to 

cross-contamination, however it remains clear that enterotoxigenic (ETEC) and 

enteropathogenic E. coli (EPEC) are recognized waterborne hazards that could be 

associated with raw catfish. Shigatoxigenic (STEC) and Enterohemorrhagic (EHEC) E. 

coli, including E. coli Serotype O157:H7, have been associated with both waterborne and 

foodborne gastroenteritis outbreaks. Runoff from ruminant animal farms is a common 

source for waterborne E. coli O157:H7 contamination; proximity to animal farms or 

access of wild animals to aquaculture ponds could be significant contributing factors.

8.1.2 Lower Priority Microbial Hazards

These include a broad scope of recognized waterborne pathogens, both within and 

beyond the U.S., that could be harbored on raw fish products, however, the potential for 

catfish as a vector for foodborne illness remains unclear. This list includes:
77

Vibrio spp. These known aquatic pathogens include V. parahaemolyticus and V. 

vulnificus, associated with seafood products from saltwater and brackish water sources. 

Their potential for association with catfish from freshwater environments is unclear. 

Although most environmental V. cholerae isolates do not produce cholera toxin, 

Fernandes et al. (1997) found V. cholerae in 10-45% of catfish fillets tested. Catfish 

consumption has been associated, though not definitively implicated, in Vibrio illness in 

immunocompromised people.

Toxins associated with cyanobacteria (i.e., blue-green algae). These can be hepatotoxic 

and neurotoxic for humans. Contamination is typically associated with off flavor, so 

acute exposure has been rare. It is not clear whether there could be more subtle public 

health risks for exposure to lower levels of these toxins.

Edwardsiella tarda. This is a catfish pathogen that can also cause gastrointestinal illness 

in immunocompromised people, though human illness has not been definitively linked to 

catfish consumption. 

Shigella dysenteriae. This is a common cause of waterborne gastroenteritis in the 

developing world, and has the potential to contaminate fish and other food products.

Plesiomonas shigelloides. This has been isolated from freshwater fish in tropical 

climates. P. shigelloides strains associated with human gastrointestinal disease have been 

isolated from patients living in tropical and subtropical areas. Such infections are rarely 

reported in the U.S. or Europe.

Salmonella serotype typhi. This has not been a focus of FSIS testing because it is 

typically associated with human rather than food animal carriage; in fact, it has not been 

grouped with other Salmonella species in this assessment because it is not readily 

detected by the current FSIS Salmonella testing method. However, it is a known agent for 

waterborne gastroenteritis, so catfish are hypothetical vectors for typhoid fever.
78

Waterborne parasites. Organisms with potential to contaminate domestic and/or 

imported catfish include Taenia solium, Giardia lamblia, Enterobius, Cryptosporidium, 

Gnathostoma spinigerum, Opisthorchis viverrini and others. 

Viruses (e.g., rotavirus). These can be associated with aquatic environments, but the 

potential for catfish as a vector for foodborne illness remains unclear.

Other potential pathogens have been tied to catfish and aquatic farm environments, but 

illness associations with catfish handling or consumption remain unclear. These 

pathogens include Pseudomonas aeruginosa and Klebsiella pneumoniae, two 

opportunistic human pathogens that are typically not associated with either foodborne or 

waterborne illness, and Aeromonas hydrophila, a bacterium that has been considered a 

suspect but unsubstantiated pathogen.

8.1.3 Potential Indicator Bacteria

Potential indicator bacteria include organisms that indicate the presence of fecal 

contamination or insanitary conditions, and include:

Generic Escherichia coli. These may be useful for understanding the relative risk of 

aquatic farm environments and raw catfish products. Generic E. coli is exclusively 

associated with fecal contamination and is a commonly used indicator organism for 

sanitation in the food-processing environment. The International Commission on 

Microbiological Specifications for Foods recommends that good-quality fresh or frozen 

fish contain less than 11 CFU E. coli per gram (ICMSF, 1986). McCaskey et al. (1998) 

isolated 2.2 log10 CFU E. coli per gram from catfish fillets. Ramos and Lyon (2000) 

reported levels of -0.8/0 log CFU/g E. coli for whole catfish and catfish fillets, 

respectively. The Sea Grant Extension Program and the International Commission on 

Microbiological Specifications for Foods recommends E. coli limits of 1 and 2.7 log 

CFU/g for good quality and marginally acceptable fresh and frozen fish products, 

respectively (ICMSF, 1986).
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Gas-forming anaerobic bacteria. Testing could be applied as indicators of potential C. 

botulinum and related proteolytic Clostridium spp.

Other indicator bacteria for assessing sanitation on raw and RTE catfish. These 

could include aerobic plate count (APC), psychrotrophs, coliforms, Enterobacteriaceae, 

and enterococci. Farid et al. (2000) found levels of 4.3, 2.9 and 2.9 log CFU/g for APC, 

psychrotrophs and coliforms, respectively. Ramos and Lyon (2000) reported levels of 

6.9/7.4 log CFU/g for APC, 6.11/7.11 log CFU/g for anaerobic plate count, and 2.41/2.73 

log CFU/g for coliforms for whole catfish and catfish fillets respectively. Andrews et al.

(1977) observed APC ranging from 3.8 to 8.3 log CFU/g, total coliforms from <0.48 to 

3.97 log CFU/g in fresh catfish. The Sea Grant Extension Program and the International 

Commission on Microbiological Specifications for Foods recommends that fresh or 

frozen fish contain less than 5.7 log CFU/g APC for good quality products and less than 

7.0 log CFU/g APC for marginally acceptable products.

8.1.4 Literature Summary

In general, catfish consumption is considered to pose a relatively low risk to 

consumers from a microbiological perspective (McCaskey et al., 1998). This can be 

attributed largely to the competitive advantage of the indigenous spoilage bacterial 

population over the pathogen flora during the storage resulting in spoilage prior to the 

multiplication of pathogens to dangerous levels and due to the destruction of pathogens 

during cooking. 

The CDC reports that fish and shellfish account for 5% of the individual cases and 

10% of all foodborne illness outbreaks, with most of these resulting from consumption of 

raw molluscan shellfish. Food poisoning microorganisms associated with fish include 

bacteria indigenous to water, those associated with pollution of aquatic environments, 

and those introduced to animals and their products during post harvest handling and 

processing (Flick, 2008). 
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8.2 Identification of Chemical Hazards

The Food Safety and Inspection Service has been tasked with the development of 

a food safety program for catfish and catfish products. The development of an FSIS 

regulation to ensure the safety of catfish for human consumption considers the conditions 

under which catfish are raised, transported, and processed. As such, the potential impacts 

of environmental factors, aquaculture and processing practices on the exposure of hazards 

to catfish consumers were considered. This hazard identification represents the initial 

FSIS characterization of hazards based on current catfish aquaculture. 

The information obtained for the hazard identification was gathered through 

discussions with experts from multiple federal agencies, academic institutions, industry 

representatives and non-government organizations. Information was also obtained 

through numerous literature searches in PubMed, Food Science and Technology 

Abstracts, Chemical Abstracts, USDA DigiTop and Web of Science databases.33 The key 

words used during chemical oriented database searches included, but were not limited to 

“catfish” in combination with one or more of the following: “hazard”, “food safety”, 

“food borne”, “retail”, “process*”, “human”, “chemical”, “pesticide”, “organo*”,

“polychlorinated”, “dioxin”, “herbicide”, “veterinar*” and “drug”. The United States 

Department of Agriculture, United States Environmental Protection Agency (EPA), the 

Food and Drug Administration (FDA), and the Centers for Disease Control and 

Prevention (CDC) websites were also used to identify the statistics and regulations to 

analyze in the hazard identification process. 

This approach led to the identification of an extensive list of catfish-associated 

hazards of potential public health concern. Potential chemical hazards included veterinary 

drugs used in aquaculture as well as pesticides and heavy metals likely to be present in 

the environment in and/or around catfish farms and catfish processing facilities. Some 

chemicals are used in multiple ways and may therefore appear in more than one of the 

 

33PubMed: http://www.ncbi.nlm.nih.gov/pubmed/;

Food Science and Technology Abstracts: http://www.foodsciencecentral.com/;

Chemical abstracts: http://pubs.acs.org/;

USDA DigiTop: http://riley.nal.usda.gov/digitop_interim/proxy_stop403.html;

Web of Science: http://www.isiwebofknowledge.com. These websites were accessed between 

July 2008 and November 2009.
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following lists, including drugs (section 8.2.1), pesticides (section 8.2.2), and other 

chemicals associated with aquaculture (section 8.2.3).

8.2.1 Drugs

The following is an alphabetical list of drugs that were identified to be linked with 

aquaculture. The focus is on domestic drugs due to available information from the Food 

and Drug Administration website (www.FDA.gov). At the end of this section is a list of 

drugs used in foreign aquaculture that was generated with help from Dr. Fran Pell at the 

Center for Veterinary Medicine, Food and Drug Administration. 

According to an email correspondence between Dr. Barbara Montwill at the 

Office of Food Safety, Center for Food Safety and Applied Nutrition within the Food and 

Drug Administration and Dr. Jay Vodela (Office of Public Health Science, Food Safety 

and Inspection Service, U.S. Department of Agriculture) on April 30, 2008, FDA has a 

drug residue monitoring program that includes Chloramphenicol, Nitrofurans, and 

Fluoroquinolones, Malachite green (and its metabolite Leucomalachite green), Crystal 

(Gentian) violet (and its metabolite Leucogentian violet), Quinolones (Oxolinic acid and 

Flumequine), Ivermectin and Methyltestosterone (U.S. FDA, 2008).

Acetic acid

This is an FDA low regulatory priority aquaculture drug. The allowed use is as a 

parasiticide for fish at a dose of 1,000 to 2,000 ppm dip for 1 to 10 minutes. There is no 

withdrawal time or regulatory residue level.

Calcium chloride

This is an FDA low regulatory priority aquaculture drug. The allowed use is to increase 

water calcium concentration to ensure proper egg hardening. Dosages used would be 

those necessary to raise calcium concentrations to 10-20 ppm. This drug can also be used 

up to 150 ppm indefinitely to increase the hardness of water during the holding and 

transport of fish to enable the maintenance of osmotic balance. There is no withdrawal 

time or regulatory residue level.
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Calcium oxide

This is an FDA low regulatory priority aquaculture drug. The allowed use is as an 

external protozoacide for fingerlings to adult fish at a concentration of 2000 mg/L for 5 

seconds. There is no withdrawal time or regulatory residue level.

Carbon dioxide gas

This is an FDA low regulatory priority aquaculture drug. The allowed use is as an 

anesthetic in cold, cool and warm water fish. There is no withdrawal time or regulatory 

residue level.

Chorionic Gonadotropin

This hormone drug (Chorulon) is FDA approved as an intramuscular injection for the use 

in brood fish to aid in spawning. The approved dosage is 50-510 IU/lb for male fish and 

67-1816 IU/lb for female fish. It has been approved for up to 3 doses, not to exceed 

25,000IU in fish intended for human consumption and is restricted to use by a licensed 

veterinarian. There is no withdrawal time or regulatory residue level.

Clove Oil

This substance is not an approved drug by the FDA for the use in aquaculture. Clove Oil 

is an anesthetic when used as an immersion for fish. There is some concern about 

anesthetic use in the transport of fish from the farm to the processing plant. According to 

the FDA Guidance for Industry #150 ‘Concerns Related to the use of Clove Oil as an 

Anesthetic for Fish’ (April 2007), even though clove oil and its components are GRAS 

for use in dental cement and as a food additive, it is not GRAS for use as an anesthetic for 

fish (U.S. FDA, 2007). Clove oil is made up of 85-95% eugenol and the rest consists of 

isoeugenol and methyleugenol. These ingredients are under investigation by the National 

Toxicology Program. Isoeugenol has been found to be an equivocal carcinogen, 

methyleugenol is carcinogenic to rodents and eugenol data is still under review. 

Copper sulfate
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This drug is currently under review by the FDA for use in aquaculture. It can be used 

under the Investigational New Animal Drugs (INAD). These products can be used in 

accordance with the EPA registered label. There is no withdrawal time or regulatory 

residue level. 

Florfenicol

This drug is FDA approved as an antibiotic feed additive for the control of catfish enteric 

septicemia caused by Edwardsiella ictaluri and columnaris associated with 

Flavobacterium columnare. The approved dosage is 10mg/kg/day for 10 consecutive 

days. It has a withdrawal time of 12 days, a tolerance level of 1 ppm.

Formalin

This drug is FDA approved as in immersion for the control of external protozoa and 

monogenetic trematodes on fish and fungi on eggs. The approved dosage for parasite 

control on adults in tanks and raceways is 250 IU/L indefinitely. For fungi control on 

eggs the approved dosage is 1000-2000ppm for 15 minutes. There is no withdrawal time 

or regulatory residue level.

Fuller’s earth

This is an FDA low regulatory priority aquaculture drug. The allowed use is to reduce the 

adhesiveness of fish eggs to improve hatchability. There is no withdrawal time or 

regulatory residue level.

Garlic (whole form)

This is an FDA low regulatory priority aquaculture drug. The allowed use is for the 

control of helminthes and sea lice infestations of marine salmonids at all life stages. 

There is no withdrawal time or regulatory residue level.

Hydrogen Peroxide

This drug is FDA approved as an immersion for the control of columnaris disease caused 

by Flavobacterium columnare (Flexibacter columnaris) and for the control of
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saprolegniasis fungi on eggs. The approved dosage for fungi control on eggs in warm 

water it is 750-1000mg/L for 15 minutes. For the treatment of columnaris disease the 

approved dosage is 100mg/L for 30 minutes or 50-100 mg/L for 60 minutes once per day, 

every other day for 3 treatments. There is no withdrawal time or regulatory residue level.

Ice

This is an FDA low regulatory priority aquaculture drug. The allowed use is to reduce 

metabolic rate of fish during transport. There is no withdrawal time or regulatory residue 

level.

Magnesium sulfate

This is an FDA low regulatory priority aquaculture drug. The allowed use is to treat 

external monogenic trematode infestations and external crustacean infestations in fish at 

all life stages. It is used in all freshwater species. The allowed dose is an immersion at 

30,000 mg MgSO4/L and 7000 mg NaCl/L solution for 5 to 10 minutes. There is no 

withdrawal time or regulatory residue level.

Onion (whole form)

This is an FDA low regulatory priority aquaculture drug. The allowed use is to treat 

external crustacean parasites and to deter sea lice from infesting the external surfaces of 

salmonids at all life stages. There is no withdrawal time or regulatory residue level.

Oxytetracycline dihydrate (Terramycin)

This drug (Terramycin 200) is FDA approved as a medicated feed for the control of 

Pseudomonas disease caused by Pseudomonas and bacterial hemorrhagic septicemia 

caused by Aeromonas liquefaciens. The approved dosage is 2.5-3.75g/100lb/day for 10 

days. It has a withdrawal time of 21 days and a tolerance level of 2ppm. 

Oxytetracycline HCl (Terramycin)
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This drug is FDA approved as an immersion for the use with mark skeletal tissues. The 

approved dosage is 200-700mg/L for 2-6 hours. It has no withdrawal times and a 

tolerance level of 2ppm. 

Papain

This is an FDA low regulatory priority aquaculture drug. The allowed use is as a 0.2% 

solution in removing the gelatinous matrix of fish egg masses in order to improve 

hatchability and decrease the incidence of disease. There is no withdrawal time or 

regulatory residue level.

Potassium chloride

This is an FDA low regulatory priority aquaculture drug. The allowed use is as an aid in 

osmoregulation which helps to relieve stress and prevent shock. Allowed dosages are 

those that would be necessary to increase chloride ion concentration to 10-2000 mg/L. 

There is no withdrawal time or regulatory residue level.

Potassium permanganate

This drug is currently under review by the FDA for use in aquaculture. It can be used in 

accordance with the legal parameters of investigational use consistent with 

Investigational New Animal Drugs (INAD). There is no withdrawal time or regulatory 

residue level.

Povidone iodine

This is an FDA low regulatory priority aquaculture drug. The allowed use is as an egg 

surface disinfectant during and after water hardening at a dose of 100 ppm solution for 10 

minutes. There is no withdrawal time or regulatory residue level.

Sodium bicarbonate
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This is an FDA low regulatory priority aquaculture drug. The allowed use is as a means 

of introducing carbon dioxide into the water to anesthetize fish at a dose of 142-642 ppm 

for 5 minutes. There is no withdrawal time or regulatory residue level.

Sodium chloride

This is an FDA low regulatory priority aquaculture drug. The allowed use is as an 

osmoregulatory aid for the relief of stress and prevention of shock at a dose of 0.5% to 

1.0% solution for an indefinite period. Another allowed use is as a parasiticide at a dose 

of 3% solution for 10 to 30 minutes. There is no withdrawal time or regulatory residue 

level.

Sodium sulfite

This is an FDA low regulatory priority aquaculture drug. The allowed use is as a 15% 

solution for 5 to 8 minutes to treat eggs in order to improve their hatchability. There is no 

withdrawal time or regulatory residue level.

Sulfadimethoxine, ormetoprim

This drug is FDA approved as an antibiotic feed additive for the control of enteric 

septicemia caused by Edwardsiella ictaluri. The approved dosage is 50mg/kg/day for 5 

days. It has a withdrawal time of 3 days and a tolerance level of 0.1ppm. 

Thiamine hydrochloride

This is an FDA low regulatory priority aquaculture drug. The allowed use is to prevent or 

treat thiamine deficiency in salmonids. The allowed dose is to immerse the eggs in a 

solution of up to 100 ppm for up to 4 hours during water hardening. Sac fry are allowed 

to be immersed in a solution of up to 1,000 ppm for up to 1 hour. There is no withdrawal 

time or regulatory residue level.

Tricaine methanesulfonate (MS-222)
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This drug is FDA approved as an immersion for the temporary immobilization of fish. 

The approved dosage is 15-330mg/L and its use in fish intended for food is restricted to 

Ictaluidae, Salmonidae, Esocidae and Percidae. It has a withdrawal time of 21 days with 

no regulatory residue level.

Urea and Tannic acid

This is an FDA low regulatory priority aquaculture drug. The allowed use is to denature 

the adhesive component of fish eggs at concentrations of 15 g urea and 20 g NaCl per 5 

liters of water for about 6 minutes, followed by a separate solution of 0.75 g tannic acid 

per 5 liters of water for an additional 6 minutes. This dose should treat about 400,000 

eggs. There is no withdrawal time or regulatory residue level.

The following is a non-inclusive list of drugs used in foreign aquaculture. These 

drugs are currently not approved for use in aquaculture by the FDA. 

Azamethiphos

Chloramphenicol

Dichlorovos

Diflubenzuron

Enrofloxacin

Eugenol

Fenthion

Flumequine

Furazolidone

Glucans

Isoeugenol

Ivermectin

Josamycin

Kanamycin

Levamisole

Malachite green

Methyltestosterone

Nalidixic Acid

Nifurpirinol

Nitrofuran

Nitrofurantoin

Nitrofurazone

Norfloxacin

Oxolinic Acid

Praziquantel

Rifampicin

Saponin
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Sarafloxacin

Spiramycin

Streptomycin

Teflubenzuron

Testosterone

Thiamphenicol

Tributyltin

Trichlorfon

Trifluralin

AMDUCA prohibited drugs 

The Animal Medicinal Drug Use Clarification Act (AMDUCA) of 1994 (21 CFR 

530) allows veterinarians to use approved FDA drugs outside of there labeled species, 

indication, dose, frequency or route of administration so long as a valid veterinarianclient-patient relationship exists. This is called extra-label use. The following drugs are 

prohibited from extra-label use in food animals (21 CFR Part 530.41).

Chloramphenicol- broad spectrum antibiotic known to cause aplastic anemia in humans 

(U.S. FDA, 1992; Young, 2002)

Clenbuterol- β2 adrenergic agonist used as a growth enhancer and linked with acute 

poisoning of humans who consumed meat from animals given clenbuterol (U.S. FDA, 

1991; Chan, 1999)

Diethylstilbestrol – synthetic nonsteroidal estrogen and a teratogen when given to 

pregnant women (U.S. FDA, 1999)

Dimetridazole – a nitroimidazole 

Ipronidazole - a nitroimidazole

Other nitroimidazoles – antibiotic with mutagenic concerns (U.S. FDA, 2009)

Furazolidone – antibiotic and anti-protozoal whose residues in edible tissues are known 

carcinogens (U.S. FDA, 2002a)
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Nitrofurazone – a nitrofuran antibiotic whose residues in edible tissues are known 

carcinogens (U.S. FDA, 2002a)

Fluoroquinolones – broad spectrum antibiotic with toxicological concerns (U.S. FDA, 

2002b)

Glycopeptides – antibiotics banned from extra-label use due to toxicological concern

(U.S. FDA, 1997)

Sulfonamides – antibiotic banned from off label use in lactating dairy cattle; sulfonamide 

use in humans can cause severe allergic reactions to those allergic 

Phenylbutazone – non-steroidal anti-inflammatory (NSAID) banned from off label use in 

female dairy cattle over 20 months of age; can cause blood dyscrasias, hypersensitivity 

reactions and is carcinogen in humans (U.S. FDA, 2003)

8.2.2 Pesticides

The following is an alphabetical list of pesticides that were identified to be linked 

with aquaculture. This list was generated from the U.S. FDA/CFSAN Fish and Fisheries 

Products and Controls Guidance, third edition June 2001 (U.S. FDA/CFSAN, 2001); the 

Guide to Drug, Vaccine, and Pesticide Use in Aquaculture, April 2007 revision put out 

by the Federal Joint Subcommittee on Aquaculture working group on quality assurance in 

aquaculture production (US Federal Joint Committee on Aquaculture, 2007). Information 

on toxicity was found using the U.S. EPA Integrated Risk Information System (IRIS)

(U.S. EPA, 2009)..

2, 4-D (2, 4-Dichlorophenoxyacetic acid)

The FDA/CFSAN Fish and Fisheries Products and Control Guidance has set a tolerance 

level for 2, 4-D at 0.1 ppm. Under CASRN 94-75-7, the EPA has established an oral 

reference dose (RfD) of 1E-2 mg/kg/day. The adverse effect of exceeding this dose is 

hematologic, hepatic and renal toxicity. Under CASRN 94-75-7 there is not an 

established inhalation reference dose or carcinogenicity assessment.
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Acetic Acid

This is an EPA registered aquatic herbicide. The EPA does not have a CASRN number or 

information for acetic acid available on IRIS. 

Aldrin/Dieldrin

Production of this pesticide was discontinued in 1989. It has a characteristic of 

bioaccumulation in fish. The FDA/CFSAN Fish and Fisheries Products and Control 

Guidance has set an action level for Aldrin/Dieldrin at 0.3 ppm. Under Aldrin, CASRN 

309-00-2, the EPA has established an oral reference dose (RfD) of 3E-5 mg/kg/day. 

Under Dieldrin, CASRN 60-57-1, the EPA has established an oral reference dose (RfD) 

of 5E-5 mg/kg/day. The adverse effect of exceeding these doses for both Aldrin and 

Dieldrin is increased risk of liver toxicity. Aldrin and Dieldrin are classified as class B2 

probable human carcinogens. Neither CASRN 309-00-2 or CASRN 60-57-1, list an

established inhalation reference dose.

Ammonia

Under CASRN 7664-41-7, the EPA has established an inhalation reference concentration 

(RfC) of 1E-1 mg/cu.m. The adverse effect of exceeding this concentration includes 

increased severity of rhinitis and pneumonia with respiratory lesions. Under CASRN 

7664-41-7 there is not an established oral reference dose or carcinogenicity assessment.

Antimycin A

This is an EPA registered fish toxicant. It is considered a Restricted Use Pesticide (RUP). 

This means that each application must be approved by appropriate state and federal fish 

and wildlife agencies. The EPA does not have a CASRN number or information for 

antimycin A available on IRIS.

Benzenepropanoic acid

This is an EPA registered aquatic herbicide. The EPA does not have a CASRN number or 

information for benzenepropanoic acid available on IRIS.
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Butoxyethyl 2,4-dichlorophenyoxyacetate

This is an EPA registered aquatic herbicide. The EPA does not have a CASRN number or 

information for butoxyethyl 2,4-dichlorophenyoxyacetate available on IRIS.

Calcium Hypochlorite

This is an EPA registered algicide and fish toxicant. The EPA does not have a CASRN 

number or information for calcium hypochlorite available on IRIS.

Chlordane

The EPA cancelled its use as a pesticide in 1988. It has the ability to bioaccumulate in 

both marine and freshwater species as well as bacteria. The FDA/CFSAN Fish and 

Fisheries Products and Control Guidance has set an action level for Chlordane at 0.3 

ppm. Under CASRN 12789-03-6, the EPA has established an oral reference dose (RfD) 

of 5E-4 mg/kg/day. The adverse effect of exceeding this dose is hepatic necrosis. 

Chlordane is classified as a class B2 probable human carcinogen. Under CASRN 12789-

03-6, the EPA has established an inhalation reference concentration (RfC) of 5E-4 

mg/kg/day. The adverse effect of exceeding this concentration is hepatic effects. 

Chlordecone

All products containing chlordecone were cancelled in the U.S. between 1977-1978. It 

has the ability to bioaccumulate in fish. The FDA/CFSAN Fish and Fisheries Products 

and Control Guidance has set an action level for Chlordecone at 0.3 ppm. Under CASRN 

143-50-0, the EPA has established an oral reference dose (RfD) of 3E-4 mg/kg/day. The 

adverse effect of exceeding this dose is renal lesions. Chlordecone is listed as likely to be 

carcinogenic to humans and has an oral slope factor of 10 mg/kg/day. Under CASRN 

143-50-0, there is not an established inhalation reference concentration.

Chlorine

This is an EPA registered algicide. Under CASRN 7782-50-5, the EPA has established an 

oral reference dose (RfD) of 1E-1 mg/kg/day. The adverse effect of exceeding this dose 
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is no observed adverse effects. Under CASRN 7782-50-5, there is not an established 

inhalation reference concentration or carcinogenicity assessment.

Chlorophenoxy compounds

This is an EPA registered aquatic herbicide. The EPA does not have a CASRN number or 

information for ‘chlorophenoxy compounds’ available on IRIS.

Copper Carbonate

This is an EPA registered algicide and aquatic herbicide. The EPA does not have a 

CASRN number or information for copper carbonate available on IRIS.

Copper Ethanolamine Complex

This is an EPA registered algicide and aquatic herbicide. The EPA does not have a 

CASRN number or information for ‘copper ethanolamine complex’ available on IRIS.

Copper Hydroxide

This is an EPA registered algicide. The EPA does not have a CASRN number or 

information for copper hydroxide available on IRIS.

Copper Sulfate 

This is an EPA registered algicide, aquatic herbicide, and invertebrate toxicant. The EPA 

does not have a CASRN number or information for copper sulfate available on IRIS.

Copper Triethanolamine Complex

This is an EPA registered algicide. The EPA does not have a CASRN number or 

information for ‘copper triethanolamine complex’ available on IRIS.

Crystal Violet (also known as Gentian Violet)
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This hazard is an FDA unapproved fungicide. Crystal violet is absorbed into fish tissue 

and is reduced metabolically to leucocrystal violet. Crystal violet is mutagenic. The EPA 

does not have a CASRN number or information for crystal violet available on IRIS.

DDT (p,p’-Dichlorodiphenyltrichloroethane), TDE, DDE (p,p’-

Dichlorodiphenyldichloroethylene)

After 1972 DDT pesticide was no longer permitted in the U.S. except in cases of public 

emergency. It is still used throughout the world for the control of malaria. This pesticide 

has the ability to bioaccumulate in fish. The FDA/CFSAN Fish and Fisheries Products 

and Control Guidance has set an action level for DDT, TDE, DDE at 5 ppm. Under DDT 

CASRN 50-29-3, the EPA has established an oral reference dose (RfD) of 5E-4 

mg/kg/day. The adverse effect of this dose is liver lesions. DDT and DDE are classified 

as a class B2 probable human carcinogen under DDE CASRN 72-55-9 and DDT CASRN 

50-29-3. Under CASRN 72-55-9 and 50-29-3, DDE and DDT do not have an established 

inhalation reference concentration. The EPA does not have a CASRN number or 

information for TDE available on IRIS.

Diflubenzuron

This is an EPA registered invertebrate toxicant. Under CASRN 35367-38-5, the EPA has 

established an oral reference dose (RfD) of 2E-2 mg/kg/day. The adverse effect of this 

dose is methemoglobin and sulfhemoglobin formation. Under CASRN, 35367-38-5, there 

is not an established inhalation reference concentration or carcinogenicity assessment.

Dimethylamine salt of 2,4-D

This is an EPA registered aquatic herbicide. Under CASRN 124-40-3 for dimethylamine, 

the EPA does not have information for dimethylamine available on IRIS.

Diquat (Diquat Dibromide)

This is an EPA registered algicide and aquatic herbicide. The FDA/CFSAN Fish and 

Fisheries Products and Control Guidance has set a tolerance level for Diquat at 0.1 ppm. 

Under CASRN 85-00-7, the EPA has an oral reference dose (RfD) of 2.2E-3 mg/kg/day. 
94

The adverse effect of this dose is minimal lens opacity and cataracts. Under CASRN 85-

00-7, there is not an established inhalation reference concentration or carcinogenicity 

assessment.

Endothall 

This is an EPA registered algicide and aquatic herbicide. The EPA has set a tolerance in 

fish at 0.1ppm for Endothall residues. Under CASRN 145-73-3, the EPA has an oral 

reference dose (RfD) of 2E-2 mg/kg/day. The adverse effect of this dose is increased 

absolute and relative weights of stomach small intestine. Under CASRN 145-73-3, there 

is not an established inhalation reference concentration or carcinogenicity assessment.

Endrin

EPA banned pesticide. Under CASRN 72-20-8, the EPA has an oral reference dose (RfD) 

of 3E-4 mg/kg/day. The adverse effect of this dose is mild histological lesions in liver 

and occasional convulsions. Endrin is classified as class D, not classifiable as to 

carcinogenicity for humans. Under CASRN 72-20-8, there is not an established 

inhalation reference concentration.

Fluridone (Ansi)

This is an EPA registered aquatic herbicide. The FDA/CFSAN Fish and Fisheries 

Products and Controls Guidance has set a tolerance level for Fluridone at 0.5 ppm. 

Under CASRN 59756-60-4, the EPA has an oral reference dose (RfD) of 8E-2 

mg/kg/day. The adverse effect of exceeding this dose is glomerulonephritis, atrophic 

testes, eye keratitis along with a decrease in body and organ weights. Under CASRN 

59756-60-4, there is not an established inhalation reference concentration or 

carcinogenicity assessment.

Glyphosate Isopropylamine Salt

This is an EPA registered aquatic herbicide. The FDA/CFSAN Fish and Fisheries 

Products and Controls Guidance has set a tolerance level for Glyphosate at 0.25 ppm. 

Under CASRN 1071-83-6, the EPA has an oral reference dose (RfD) of 0.1 mg/kg/day 
95

for glyphosate. The adverse effect of exceeding this dose is kidney defects in subsequent 

generations. Glyphosate is classified as class D, not classifiable as a human carcinogen.

Heptachlor

This pesticide hasn’t been used since 1988 but it is still registered by the EPA for killing 

fire ants in buried power transformers. It has the ability to bioaccumulate in fish. The 

FDA/CFSAN Fish and Fisheries Products and Controls Guidance has set an action level 

for Heptachlor at 0.3 ppm. Under CASRN 76-44-8, the EPA has an oral reference dose 

(RfD) of 5E-4 mg/kg/day. The adverse effect of exceeding this dose is an increase of 

liver weight in males. Heptachlor is classified as a class B2, probable human carcinogen. 

Under CASRN 76-44-8, there is not an inhalation reference concentration.

Heptachlor Epoxide

This pesticide hasn’t been used since 1988 but it is still registered by the EPA for killing 

fire ants in buried power transformers. It has the ability to bioaccumulate in fish. The 

FDA/CFSAN Fish and Fisheries Products and Controls Guidance has set an action level 

for Heptachlor Epoxide at 0.3 ppm. Under CASRN 1024-57-3, the EPA has an oral 

reference dose (RfD) of 1.3E-5 mg/kg/day. The adverse effect of exceeding this dose is 

an increased liver-to-body ration in both males and females. Heptachlor epoxide is 

classified as a class B2, probable human carcinogen. Under CASRN 1024-57-3, there is 

not an inhalation reference concentration.

Hexachlorobenzene

This pesticide is banned by the EPA. Under CASRN 118-74-1, the EPA has an oral 

reference dose (RfD) of 8E-4 mg/kg/day. The adverse effect of exceeding this dose is 

liver effects. Hexachlorobenzene is classified as a class B2, probable human carcinogen. 

Under CASRN 118-74-1, there is not an inhalation reference concentration.

Imazapyr (isopropylamine salt)
96

This is an EPA registered aquatic herbicide. The EPA does not have a CASRN number or 

information for Imazapyr available on IRIS.

Lime (calcium/magnesium hydroxide)

The EPA does not have a CASRN number or information for lime available on IRIS.

Lindane (gamma-hexachlorocyclohexane)

This is a pesticide. Under CASRN 58-89-9, the EPA has an oral reference dose (RfD) of 

3E-4 mg/kg/day. The adverse effect of exceeding this dose is liver and kidney toxicity. 

Under CASRN 58-89-9, there is not an inhalation reference concentration or 

carcinogenicity assessment.

Malachite Green and Leucomalachite Green

This is an FDA prohibited fungicide. Malachite green is excreted rapidly but >80% is 

metabolized into Leucomalachite green which can remain in the muscle for months. This 

hazard is considered a mutagen and teratogen. Brilliant Green is another compound 

similar in structure to Malachite Green and should also be considered a hazard. The EPA 

does not have a CASRN number or information for lime available on IRIS.

Methyl Mercury

The FDA/CFSAN Fish and Fisheries Products and Controls Guidance has set a guidance

level for Methyl Mercury at 1.0 ppm for finfish. Under CASRN 22967-92-6, the EPA has 

an oral reference dose of 1E-4. The adverse effect of exceeding this dose is 

developmental neuropsychological impairment. Methyl mercury is classified as a class C, 

possible human carcinogen. Under CASRN 22967-92-6, there is not an inhalation 

reference concentration.

Mirex

This pesticide’s use was cancelled in the U.S. between 1977-1978. It has the ability to 

bioaccumulate in fish. The FDA/CFSAN Fish and Fisheries Products and Controls 

Guidance has set an action level for Mirex at 0.1 ppm. Under CASRN 2385-85-5, the 
97

EPA has an oral reference dose (RfD) of 2E-4 mg/kg/day. The adverse effect of 

exceeding this dose is increased risk of liver cytomegaly, fatty metamorphosis, 

angiectasis and thyroid cystic follicles. Under CASRN 2385-85-5, there is not an 

inhalation reference concentration or carcinogenicity assessment.

Polychlorinated Biphenyls (PCB’s)

Manufacture of PCB’s stopped in the U.S. in August of 1977. It has the ability to 

bioaccumulate in fish. The FDA/CFSAN Fish and Fisheries Products and Controls 

Guidance has set a tolerance level for PCB’s at 2.0 ppm. Under CASRN 1336-36-3, the 

EPA does not have information for PCB available on IRIS, but does reference readers to 

see Aroclor 1016, 1248 and 1254.

Potassium Permanganate

The EPA does not have a CASRN number or information for potassium permanganate 

available on IRIS.

Rotenone (Cube Resins Other than Rotenone) (Piperonyl Butoxide Technical)

This is an EPA registered fish toxicant. It is considered a Restricted Use Pesticide (RUP). 

This means that each application must be approved by appropriate state and federal fish 

and wildlife agencies. Under CASRN 83-79-4, the EPA has an oral reference dose (RfD) 

of 4E-3 mg/kg/day. The adverse effect of exceeding this dose is reduced pup weight. 

Under CASRN 83-79-4, there is not an inhalation reference concentration or 

carcinogenicity assessment.

Simazine

The FDA/CFSAN Fish and Fisheries Products and Controls Guidance has set a tolerance 

level for Simazine at 12 ppm. Under CASRN 122-34-9, the EPA has an oral reference 

dose (RfD) of 5E-3 mg/kg/day. The adverse effects of exceeding this dose are a reduction 

in weight gain and hematological changes in females. Under CASRN 122-34-9, there is 

not an inhalation reference concentration or carcinogenicity assessment.

Sodium 2,4-dichlorophenoxyacetate
98

This is an EPA registered aquatic herbicide. The EPA does not have a CASRN number or 

information available on IRIS.

Sodium Bromide

This is an EPA registered algicide. The EPA does not have a CASRN number or 

information available on IRIS.

Sodium Hypochlorite

This is an EPA registered algicide and fish toxicant. The EPA does not have a CASRN 

number or information available on IRIS.

Sodium Percarbonate

This is an EPA registered algicide. The EPA does not have a CASRN number or 

information available on IRIS.

Tartrazine/Erioglaucine

This is an EPA registered algicide and aquatic herbicide. The EPA does not have a 

CASRN number or information available on IRIS.

Teaseed and mahua oil cake (sapogenin glycosides)

The EPA does not have a CASRN number or information available on IRIS.

Triazine Herbicides

Under atrazine CASRN 1912-24-9, the EPA has an oral reference dose (RfD) of 3.5E-2

mg/kg/day. The adverse effects of exceeding this dose are a reduction in weight gain. 

Under propazine CASRN 139-40-2, the EPA has an oral reference dose (RfD) of 2E-2 

mg/kg/day. The adverse effects of exceeding this dose are a reduction in weight gain. 

There are no inhalation reference concentrations or carcinogenicity assessments for 

CASRN 1912-24-9 and 139-40-2.
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8.2.3 Other Chemicals Associated with Aquaculture

The following is an alphabetical list of other chemicals that were identified to be 

linked with aquaculture. This list was generated from WHO technical report series 883,

Food Safety Issues Associated with Products from Aquaculture from FAO/NACA/WHO, 

1999 (WHO/FAO/NACA, 1999).

Agricultural limestone

Water treatment used to raise pH and to sterilize pond soils between production cycles. 

The EPA does not have a CASRN number or information available on IRIS.

Ammonium phosphate (mono- and dibasic)

Fertilizer for phytoplankton. The EPA does not have a CASRN number or information 

available on IRIS.

Aluminum Sulfate

Flocculant used to cause suspended clay particles to precipitate to clear water turbidity. 

The EPA does not have a CASRN number or information available on IRIS.

Ammonium Sulfate

Fertilizer for phytoplankton. The EPA does not have a CASRN number or information 

available on IRIS.

Ammonium Nitrate

Fertilizer for phytoplankton. The EPA does not have a CASRN number or information 

available on IRIS.

Benzalkonium chloride (alkyldimethylbenzylammonium chloride)

Disinfectant used on equipment and holding pens. The EPA does not have a CASRN 

number or information available on IRIS.
100

Calcium Hypochlorite

Oxidizing agent for controling phytoplankton, killing disease organisms or oxidizing 

bottom soils. The EPA does not have a CASRN number or information available on IRIS.

Calcium Peroxide

Oxidizing agent for controling phytoplankton, killing disease organisms or oxidizing 

bottom soils. The EPA does not have a CASRN number or information available on IRIS.

Calcium Phosphate

Fertilizer for phytoplankton. The EPA does not have a CASRN number or information 

available on IRIS.

Calcium Sulfate (gypsum)

Flocculant used to cause suspended clay particles to precipitate to clear water turbidity, 

also an osmoregulator applied to the water to improve conditions for normal 

osmoregulation. The EPA does not have a CASRN number or information available on 

IRIS.

Ferric Chloride

Flocculant used to cause suspended clay particles to precipitate to clear water turbidity. 

The EPA does not have a CASRN number or information available on IRIS.

Hydrogen Peroxide 

Oxidizing agent for controling phytoplankton, killing disease organisms or oxidizing 

bottom soils. The EPA does not have a CASRN number or information available on IRIS.

Hypochlorite

Disinfectant used on equipment and holding pens. The EPA does not have a CASRN 

number or information available on IRIS.
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Lime (calcium/magnesium hydroxide)

Water treatment used to raise pH and to sterilize pond soils between production cycles. 

The EPA does not have a CASRN number or information available on IRIS.

Phosphoric Acid

Fertilizer for phytoplankton. Under CASRN 7664-38-2, the EPA has an inhalation 

reference concentration (RfC) of 1E-2 mg/cu.m. The adverse effects of exceeding this 

dose are bronchiolar fibrosis. Under CASRN 122-34-9, there is not an oral reference dose 

or carcinogenicity assessment available on IRIS.

Polyvidone iodine (polyvinylpyrrolidone-iodine complex)

Disinfectant used on equipment and holding pens. The EPA does not have a CASRN 

number or information available on IRIS.

Potassium Chloride

Fertilizer for phytoplankton. The EPA does not have a CASRN number or information 

available on IRIS.

Potassium nitrate

Fertilizer for phytoplankton. The EPA does not have a CASRN number or information 

available on IRIS.

Potassium Permanganate

oxidizing agent for controling phytoplankton, killing disease organisms or oxidizing 

bottom soils. The EPA does not have a CASRN number or information available on IRIS.

Sodium Chloride (salt)

Osmoregulator applied to the water to improve conditions for normal osmoregulation. 

The EPA does not have a CASRN number or information available on IRIS.
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Sodium Nitrate

Fertilizer for phytoplankton, oxidizing agent for controling phytoplankton, killing disease 

organisms or oxidizing bottom soils. The EPA does not have a CASRN number or 

information available on IRIS.

Sodium Silicate

Fertilizer for phytoplankton. The EPA does not have a CASRN number or information 

available on IRIS.

Trace element mixes including iron, zinc, copper, boron and molybdenum

Fertilizer for phytoplankton. The EPA does not have a CASRN number or information 

available for iron on IRIS. Copper has a CASRN of 7440-50-8, but he EPA does not have 

information on oral reference dose (RfD), inhalation concentration (RfC) or 

carcinogenicity available on IRIS. Zinc has a CASRN of 7440-66-6 and oral reference 

dose (RfD) of 0.3 mg/kg/day with an adverse effect of decreasing erythrocyte Cu, Znsuperoxide dismutase activity in healthy adult male and female volunteers. Under 

CASRN 7440-66-6, zinc does not have an inhalation reference concentration or 

carcinogenicity assessment. Boron has a CASRN of 7440-42-8 and oral reference dose 

(RfD) of 2E-1 mg/kg/day with an adverse effect of decreasing fetal weight. Under 

CASRN 7440-42-8, boron does not have an inhalation reference concentration or 

carcinogenicity assessment. Molybdenum has a CASRN of 7439-98-7 and oral reference 

dose (RfD) of 5E-3 mg/kg/day with an adverse effect of increasing uric acid levels. 

Under CASRN 7439-98-7, molybdenum does not have an inhalation reference 

concentration or carcinogenicity assessment.

Urea

Fertilizer for phytoplankton. The EPA does not have a CASRN number or information 

available on IRIS.
103

Zeolite

Flocculant used to cause suspended clay particles to precipitate to clear water turbidity. 

The EPA does not have a CASRN number or information available on IRIS.
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8.3 Selected Chemical Residues Detected in Catfish

A variety of potential chemical hazards have been detected in a limited sample of catfish

samples. Prevalence and concentrations of residues for some of these hazards are presented in

Table 20. For each analyte, the residue data for domestic and imported catfish were obtained 

using the same analytical procedures, the same laboratories, and fish that were harvested at 

approximately the same time; this facilitates the comparison of residues in imported and 

domestic catfish. Combining these data in a single table is illustrative of the concentrations 

found, but caution should be used in drawing conclusions from this table without taking into 

account variations in the sample design, thus the ability to generalize the findings. 

Additional catfish-specific data are being generated in order to determine both prevalence 

and concentration of the chemical hazards in catfish. USDA Agricultural Marketing Service 

(USDA-AMS) catfish pesticide study continues to provide useful data and the USDA 

Agricultural Research Service (ARS) is working with FSIS to conduct surveillance sampling to 

determine prevalence and concentration of pathogens and chemical residues in domestic and 

imported catfish at retail markets and processing plants. 

When the FSIS catfish inspection program is in place, the data generated will allow the 

Agency to further characterize catfish chemical and microbial hazards and to perform risk 

evaluations to address catfish consumption associated hazards that can cause adverse human 

health outcomes. 
105

Table 20. Summary of Recent Catfish Residue Data

Domestic Concentration Regulatory Levels

% 

Positive

% Violative N Data Sampling LOD Mean 

(all)

Mean 

(positives)

Max FDA Codex

Source Year ppb ppb ppb ppb ppb ppb

DDT 93% 0% 281 1 2008 1 34.4 37 651 5000

PCB* 98% 0% 120 2 2001-2003 1E-7 to 1E-3 6.0E-04 6.2E-04 4.6E-03 2000

Chlorpyrifos 1% 0% 303 1 2008 1 0.0 1.8 2.6 0

Arsenic 0% 0% 20 3 2008 200 0.0 0 0

Cadmium 0% 0% 20 3 2008 10 0.0 0 0

Lead 0% 0% 20 3 2008 25 0.0 0 0 300

Mercury 0% 0% 20 3 2008 100 0.0 0 0 1000 500

Malachite Green 0% 0% 16 2 2007 1 0 0 0 0

Gentian Violet 0% 0% 2 2 2008 0.1 0 0 0 0

Nitrofurans ND 0% ND - 1 ND ND ND 0

Import Concentration Regulatory Levels

% 

Positive

% Violative N Data Sampling LOD Mean 

(all)

Mean 

(positives)

Max FDA Codex

Source Year ppb ppb ppb ppb ppb ppb

DDT 46% 0% 70 1 2008 1 3.1 6.7 53 5000

PCB* 63% 0% 8 2 2001-2003 1E-7 to 1E-3 3.8E-06 6.0E-06 1.4E-05 2000

Chlorpyrifos 32% 0% 75 1 2008 1 0.9 2.9 16.4 0

Arsenic 2% 0% 110 3 2008 200 23.6 1300 1600

Cadmium 0% 0% 112 3 2008 10 0.0 0 0

Lead 10% 0% 112 3 2008 25 4.1 42 77 300

Mercury 0% 0% 112 3 2008 100 0.0 0 0 1000 500

Malachite Green 9% 0% 150 2 2007 1 1.2 12.5 122 0

Gentian Violet 2% 0% 53 2 2008 0.1 0.0 2.5 3 0

Nitrofurans ND 0% ND - 1 ND ND ND 0

*PCB data reported as toxicity equivalents; LOD varies for each conjener analyzed; ND = no data

Data sources: 1)USDA-AMS 2008; 2) US FDA 2008b; 3)USDA-FSIS, 2009
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