Assessment of the Potential Change in 

Human Health Risk associated with

Applying Inspection to Fish of the 

order Siluriformes

Prepared by the

Risk Assessment and Analytics Staff

Office of Public Health Science

Food Safety and Inspection Service

United States Department of Agriculture

January 2015

1
Contributors to the Risk Assessment of the Potential 

Human Health Effect of Applying Inspection to Fish of the 

order Siluriformes

Nathan Bauer2

Victor Cook1

Michelle Catlin2

Terry Disney2

Alexander Domesle2

Eric Ebel2

Chuanfa Guo2

John Johnston2

David LaBarre2

Joy Lee3

Erica McCoy3

Jamie Morrison4

Wayne Schlosser2

Michael Williams2

1 Science Staff, Office of Public Health Science, Food Safety and Inspection Service, U.S. Department of 

Agriculture, Washington, DC 20250

2 Risk Assessment and Analytics Staff, 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

2

 
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’s, Food Safety Inspection Service,

Office of Budget and Program Analysis, Office of Risk Assessment and Cost-Benefit 

Analysis, Agricultural Research Service, Agricultural Marketing Service and Office of 

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

Food and Drug Administration’s (FDA’s) Center for Food Safety and Applied 

Nutrition’s, and 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,6 Patty

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

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

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

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

10 James Schaub,5 Carl 

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

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 11 University of Texas at Austin, Austin, Texas 78712 12 Mississippi State University, Mississippi State, MS 39762 13Colorado State University, Fort Collins, Colorado 80523 14Centers for Disease Control and Prevention, Atlanta, Georgia 30333 15 Minnesota Department of Health, St. Paul, Minnesota 55155

3

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

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

Edelstein,7 Kirk Smith,15 Jay Vodela,7 Harry Walker,7 James 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,7 Sherri 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.

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

4

 
Table of Contents

List of Figures..................................................................................................................... 6

List of Tables ...................................................................................................................... 7

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

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

2. Hazard Identification ...................................................................................... 15

2.1 Prioritization of Potential Microbial Hazards................................................. 15

2.2 Identification of Potential Chemical Hazards................................................. 22

2.3 Selected Chemical Residues Detected in Siluriformes................................... 44

2.4 Summary of Hazard Identification ................................................................. 46

3. Model overview.............................................................................................. 48

4. Exposure Assessment ..................................................................................... 54

4.1 Siluriformes-associated Hazard Concentration .............................................. 54

4.2 Storage and Cooking Effect............................................................................ 59

4.3 Product Consumption ..................................................................................... 61

5. Hazard Characterization (Dose-Response)..................................................... 66

6. Risk characterization ...................................................................................... 67

6.1 Default estimation of numbers of Salmonella illnesses per year using the 

process model.................................................................................................. 68

6.2 Illnesses per year: Application of an Attribution-Based Modelling Approach .

........................................................................................................................ 70

6.3 Modeling program effectiveness.................................................................... 72

6.4 Program effectiveness estimates..................................................................... 75

6.5 Sensitivity of default illnesses estimates to changes in some model inputs... 84

6.6 Uncertainty scenario analyses ........................................................................ 88

7. Summary......................................................................................................... 95

8. References ...................................................................................................... 97

Addendum....................................................................................................................... 103

5
List of Figures

Figure 1. Inputs to number of Salmonella illnesses among U.S. consumers per year...... 49

Figure 2. Inputs to probability of illness per contaminated serving. ................................ 51

Figure 3. Log reductions of Salmonella due to baking..................................................... 60

Figure 4. Log reductions of Salmonella due to frying...................................................... 61

Figure 5. The cumulative empirical distribution for serving size..................................... 64

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

Salmonella illnesses avoided over 10-yrs following FSIS regulation of 

Siluriformes. ..................................................................................................... 77

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

Salmonella illnesses avoided over 10-yrs following FSIS regulation if it were 

specific to Ictaluridae. ...................................................................................... 81

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

estimates to various model inputs. ................................................................... 87

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

potential lower bound scenarios....................................................................... 91

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

potential upper bound scenarios....................................................................... 93

6
List of Tables

Table 1. FDA Violation Codes for Catfish Refusals (1998-2004) ................................... 18

Table 2. Summary of Recent Catfish Residue Data ......................................................... 45

Table 3. Summary of Salmonella concentrations in enumerated positive broiler carcass 

rinse samples......................................................................................................... 57

Table 4. The estimated distribution of Salmonella per gram of contaminated fish carcass.

............................................................................................................................... 58

Table 5. Parameters for cooking and growth inputs......................................................... 59

Table 6. The distribution of growth effect multiplier per serving (G) estimated by the 

model is shown. .................................................................................................... 60

Table 7. Kilograms of varieties of catfish available for consumption in the United States, 

2008....................................................................................................................... 65

Table 8. Mean Estimates of Catfish Servings................................................................... 66

Table 9. Model outputs for the estimated probability of illness per contaminated serving 

for the combinations of cooking and breading effects.......................................... 69

Table 10. Estimates for annual Salmonella illnesses for each definition of catfish. ........ 70

Table 11. Salmonella Foodborne Outbreaks from 1990 through 2007 ............................ 71

Table 12. Estimate of baseline Salmonella illnesses per year .......................................... 76

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

Siluriformes assuming a 2-year to effectiveness timeframe. ................................ 79

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

Siluriformes assuming a 10-year to effectiveness timeframe. .............................. 79

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

Siluriformes assuming a 15-year to effectiveness timeframe. .............................. 80

Table 16. Estimated Number of Salmonella illnesses avoided if FSIS were to specifically 

regulate Ictaluridae and assuming a 2-year to effectiveness timeframe. .............. 82

Table 17. Estimated Number of Salmonella illnesses avoided if FSIS were to specifically 

regulate Ictaluridae and assuming a 10-year to effectiveness timeframe. ............ 82

Table 18. Estimated Number of Salmonella illnesses avoided if FSIS were to specifically 

regulate Ictaluridae and assuming a 15-year to effectiveness timeframe. ............ 83

Table 19. 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. .................... 90

Table 20. Estimated Number of Salmonella illnesses avoided by FSIS regulation of 

Siluriformes Assuming a 5-year Timeframe and 50% Effectiveness of FSIS 

inspection .............................................................................................................. 94

Table A-1. Summary of the Agricultural Marketing Service’s Pesticide Data Program 

(PDP) analysis of pesticide residues in Domestic and Imported Catfish: 2008 to 

2010..................................................................................................................... 106

Table A-2. Chemotherapeutics in FDA’s Seafood Program (2009-2013): Catfish and 

other Pangasius sp Data. ..................................................................................... 106

7
Page Left Intentionally Blank

8
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. On February 7, 2014, the Agricultural Act of 2014 (Pub. L. 113-79, Sec. 12106), 

known as the 2014 Farm Bill, amended Section 1(w) of the FMIA to remove the phrase

“catfish, as defined by the Secretary,” and replace it with “all fish of the order 

Siluriformes,” thus including these fish among the amenable species (21 U.S.C. 

601(w)(2)). Hereafter in this risk assessment, the term “catfish,” defined in proposed 9 

CFR 531, and used throughout this text, is replaced by the term “fish of the order 

Siluriformes,” “Siluriformes fish,” or simply “fish,” understood to mean any fish of the 

order Siluriformes, with the following exceptions:

1. when discussing original publications and reference sources that 

specifically use the term catfish or other specific subsets of Siluriformes or 

other fish; and

2. when the risks from a specific subset of Siluriformes are being discussed. 

The USDA Food Safety and Inspection Service (FSIS) considers it useful in the 

context of this rulemaking to attempt to quantify the microbiological risk associated with 

consuming farm-raised Siluriformes in the United States. However, limited information 

on the extent of microbial contamination and chemical residues on Siluriformes limit our 

ability to make strong statements about the baseline risk. Furthermore, the lack of 

experience with implementing the inspection program associated with this rulemaking in 

the context of aquaculture makes estimating the impact of such a program on any 

baseline risk difficult. As such, the assessment FSIS presents here simply provides 

insight into the potential risk reductions that might accompany the implementation of the 

type of inspection program used for poultry (i.e., broiler) in the U.S. This report also 

identifies potential chemical and microbial hazards. Once implemented, the inspection 

program will generate data on whether there are concentrations of chemical and microbial 

9
hazards present in these fish, and thus whether the inspection program is actually 

changing risks to consumers.

This risk assessment focuses on exposure to Salmonella because a broad hazard 

identification study identified Salmonella as one of the few potential hazards that there 

was sufficient data to assess in Siluriformes. This risk assessment provides different 

scenarios for the benefits that might result from an inspection system in Siluriformes 

similar to FSIS’ inspection system for poultry. We are particularly interested in 

Salmonella because the general burden of illness from this pathogen in the U.S. remains a 

concern. We also note that there is evidence that at least one outbreak of human 

salmonellosis may have been related to Siluriformes consumption. FSIS acknowledges, 

however, that applying its empirical evidence describing the effectiveness of an FSIS 

inspection program for Salmonella control in another regulated species (i.e., poultry) 

carries with it significant limitations. 

The objectives of this risk assessment are:

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

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

implementation of an FSIS 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 focuses on catfish within the order Siluriformes, but also 

includes an analysis of catfish defined more narrowly as Ictaluridae. The risk assessment 

uses statistical models to estimate human illnesses that might be associated with catfish 

and the illness cases that might be avoided by implementing an FSIS inspection program. 

When modeling the potential effects of an FSIS inspection program, the assessment 

assumes that the Siluriformes inspection system would be similar to the system FSIS uses 

for poultry. The risk assessment model uses Monte Carlo techniques to combine the 

random variables that estimate exposures for four different exposure classes, which 

10
include the most common ways catfish (of the order Siluriformes) 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 Siluriformes

• Salmonella contamination data from poultry were used as surrogates for 

Siluriformes contamination

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

Siluriformes

• Catfish (of the order Siluriformes) handling during retail and home storage 

was considered independent of the initial Salmonella concentration on the 

fish carcass.

2) Estimated amount of catfish (of the order Siluriformes) consumption in the US

• Each fish serving was derived from a single 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 inspection program 

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

broilers) was used.

The assumptions listed above and the quality of available data introduce substantial 

uncertainty both the estimated baseline number of salmonellosis cases attributable to 

catfish (of the order Siluriformes) consumption. The modeled lower and upper bound 

scenarios suggest estimates between 100 and 6,200 salmonellosis cases might be 

associated with catfish (of the order Siluriformes) consumption annually. 

The 2014 Farm Bill defined catfish as all fish in the order Siluriformes, whereas 

another definition considered during the proposal included only fish within the 

Ictaluridae family, which are the subset most commonly sold in the United States. This

risk assessment estimates an annual average of 2,308 salmonellosis cases that may be

from Siluriformes as defined in the 2014 Farm Bill, with an annual average of 1,764 of 

those salmonellosis cases potentially attributable to Ictaluridae. These estimates seem 

consistent with a different estimate that we modeled 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 these fish. 

Regardless of whether considering Siluriformes or Ictaluridae, the model estimates an 

11
average probability of illness of 1.5 × 10-6 salmonellosis cases per serving, though this 

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

contamination is limited. This probability incorporates the estimated prevalence of 

contaminated servings and suggests that Salmonella illness from these fish is an 

uncommon event.

There is substantial uncertainty regarding the effectiveness of a future FSIS 

inspection program aimed at reducing the estimated prevalence of Salmonellacontaminated Siluriformes, 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% inspection program effectiveness – to estimate public 

health benefit outcomes. For example, if an FSIS inspection program is 50% effective 

within a 5-year timeframe, model estimates of between approximately 50 and 3,100 

Salmonella illnesses prevented annually using the Siluriformes definition. 

As noted above, the risk reduction estimates are subject to substantial uncertainty

regarding both the estimated baseline number of salmonellosis cases attributable to 

catfish (of the order Siluriformes) consumption and the extent to which the experience 

associated with controlling Salmonella in poultry is applicable to controlling Salmonella

in Siluriformes. 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 Siluriformes.

12
1. Introduction

This risk assessment is designed to meet the following objectives:

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

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

implementation of an FSIS 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 focuses on catfish within the order Siluriformes, but also 

includes an analysis of catfish defined more narrowly as Ictaluridae. The risk assessment 

uses statistical models to estimate human illnesses that might be associated with catfish 

and the illness cases that might be avoided by implementing an FSIS inspection program. 

This risk assessment provides a range of estimates of the differential effect of introducing 

a Food Safety and Inspection Service (FSIS) Siluriformes inspection program on the

potential number of human Salmonella illnesses from consumption of farm-raised catfish

of the order Siluriformes each year. 

This risk assessment model estimates the potential change in risk associated with 

implementing an FSIS inspection program for farm-raised Siluriformes that is similar to 

that used for poultry. Incorporated into the model was the consideration of two different 

potential definitions of these fish. The definition of these fish that is consistent with the 

2002 Farm Bill25 is specific to the family Ictaluridae, native to North America. There are 

additional families of fish on the commercial market in the order Siluriformes, where the 

North American family Ictaluridae resides. On February 7, 2014, however, the 

Agricultural Act of 2014 (Pub. L. 113-79, Sec. 12106), known as the 2014 Farm Bill, 

amended Section 1(w) of the FMIA to remove the phrase “catfish, as defined by the 

Secretary,” and replace it with “all fish of the order Siluriformes,” thus including these 

fish among the amenable species (21 U.S.C. 601(w)(2)). Therefore, an evaluation of 

potential change in risk associated with using the Ictaluridae definition of “catfish” 

25 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).

13

 
represents a different range of exposure to that of the Siluriformes definition for 

“catfish”. This risk assessment generates an estimate of the underlying public health risks 

resulting from exposure to Salmonella for both Siluriformes and Ictaluridae, as well as 

how that risk might be reduced through implementation of an inspection program. 

Chapter 2 and the Addendum provide the hazard identification for Siluriformes by 

cataloging chemical and other microbiological hazards that have been found in some 

other types of seafood or aquaculture and, thus, have the potential to be present in 

Siluriformes. They also summarize available data on chemical residues in catfish and 

information from Centers for Disease Control and Prevention’s (CDC’s) outbreak 

database26. The remainder of this risk assessment report describes the quantitative 

modeling approach used for the analysis of potential Salmonella contamination. The 

choice of Salmonella as a microbial hazard of potential concern is outlined in Chapter 2.

After discussing the hazard identification in Chapter 2, the remainder of the report

focuses on Salmonella and is divided into four sections: 1) an overview section that 

explains the conceptual model and its mathematical structure; 2) an exposure assessment 

section that explains the modeling inputs used to estimate potential exposures of humans 

to Salmonella in catfish (of the order Siluriformes) servings; 3) a hazard characterization 

section introduces the dose-response relationship used to estimate the probability of 

illness per Salmonella-contaminated catfish (of the order Siluriformes) serving; and 4) a

risk characterization section combines the exposure assessment with the hazard 

characterization. 

26 Testing data are from the US Department of Agriculture’s (USDA’s) Agricultural Marketing Service 

(AMS) and Food Safety and Inspection Service (FSIS), and the FDA tested between 2001 and 2010.

14

 
2. Hazard Identification 

This section constitutes the hazard identification for this risk assessment. It begins 

with a discussion of potential microbial hazards in farmed fish and a summary of 

information available on contamination of Siluriformes. That is followed by a discussion 

of potential chemical hazards, which are identified through consideration of aquaculture 

and processing practices, as well as environmental factors. The last subsection 

summarizes the available data on residues detected in catfish samples tested by the 

Agricultural Marketing Service (AMS), US Food and Drug Administration (FDA) and 

the Food Safety and Inspection Service (FSIS). Although there is limited data on the 

chemicals present in Siluriformes, this hazard identification does put the potential 

microbial contamination—and the decision to focus this risk assessment on Salmonella—

in the overall context of range of potential hazards in Siluriformes and the limited 

information available on those hazards in Siluriformes. 

2.1 Prioritization of Potential Microbial Hazards

Several bacterial pathogens have been associated with farmed fish (Ramos and 

Lyon, 2000). Because fish is typically cooked prior to consumption, Siluriformesassociated 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 Siluriformes 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 potential concern were categorized based on a combination of 

findings from literature reviews and fish-associated outbreak information obtained from 

the Centers for Disease Control and Prevention (CDC) into two priority groups (higher

and lower – acknowledging that even the “higher” priority group may have an absolute 

risk which is low). Categorization was based on microbial association with the water in 

which fish are raised, the fish themselves and the final product, and also with the 

potential of the microorganisms to cause adverse public health effects if consumed. 

15
Hazards are further delineated in terms of their potential relevance to raw or ready-to-eat 

(RTE) Siluriformes (i.e., the relevant pathogen-product pairs).

For illustrative purposes, the subsequent steps (exposure assessment, hazard 

characterization (dose-response) and risk characterization) in this risk assessment were 

applied to just the higher priority hazard identified via the risk characterization process. 

Higher Priority Microbial Hazards

o Non-Typhi Salmonella spp. (raw and RTE product)

o Listeria monocytogenes (RTE product)

o Clostridium botulinum and toxins (raw and RTE product)

o Enterohemophagic, shigatoxigenic, enterotoxigenic and enteropathogenic E. coli

(raw product)

o Lower Priority Microbial Hazards (Raw and Ready-to-Eat product) 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

o Generic Escherichia coli (raw product)

o Gas-forming anaerobic bacteria (RTE product)

o Other indicator bacteria for assessing sanitation (raw and RTE product)

2.1.1 Higher priority microbial hazards

These include 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 fish products. Non-typhi Salmonella are regarded as

one of the higher priority hazards because the general burden of illness from this 

16
pathogen in the U.S. remains a concern. We also note that 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 catfish27 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. Heinitz et al. 

(2000) reported FDA Salmonella testing from imported (11,312 samples) and domestic 

(768) seafood samples tested from 1990 to 1998. They found that 10% of imported and 

2.8% of domestic raw seafood was positive for Salmonella. For Fin Fish/Skin Fish in that 

study, the percent positive was 12.2% and 1.3% for imported and domestic, respectively.

An examination of 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 1). The combination of data presented in the 

literature, along with outbreak data and FDA import refusal data shown in Table 1 below, 

suggest that the highest microbial hazard associated with catfish (of the order 

Siluriformes) may be Salmonella. 

27 As mentioned above, the term catfish is used here and elsewhere to be consistent with the original source.

17

 
Table 1. FDA Violation Codes for Catfish Refusals (1998-2004) 

FDA Violation 

Codea Frequency

Percent of 

Refusals

Cumulative 

Frequency

Cumulative 

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 a 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 fish products. Because it is 

a common environmental and aquatic contaminant, its presence in raw fish may pose an 

indirect risk in the form of 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 (of the order 

Siluriformes) 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 the fish. McCaskey et al. (1998) found a 

prevalence of 5.9% Lm on raw catfish fillets. Chou et al. (2006) found that Lm was more

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., 

18
1990). While toxins A and B are the primary botulinum toxin types associated with meat 

and poultry products, 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 fish. 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.

2.1.2 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 (of the order Siluriformes) as a vector for foodborne illness remains unclear. This 

list includes:

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 Siluriformes 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 (of 

19
the order Siluriformes) 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 (of the order Siluriformes) pathogen that can also 

cause gastrointestinal illness in immunocompromised people, though human illness has

not been definitively linked to Siluriformes 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 it might be possible that Siluriformes are vectors for 

typhoid fever.

Waterborne parasites. Organisms with potential to contaminate domestic and/or 

imported fish include Taenia solium, Giardia lamblia, Enterobius, Cryptosporidium, 

Gnathostoma spinigerum, Opisthorchis viverrini and others. 

20
Viruses (e.g., rotavirus). These can be associated with aquatic environments, but the 

potential for Siluriformes as a vector for foodborne illness remains unclear.

Other potential pathogens have been tied to Siluriformes and aquatic farm environments, 

but illness associations with Siluriformes 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.

2.1.3 Potential Indicator Bacteria

Potential indicator bacteria include organisms that could be used to 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 products. Generic E. coli is associated with fecal 

contamination and is a commonly used indicator organism for sanitation in the foodprocessing 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 log 

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).

Gas-forming anaerobic bacteria. Testing could be applied as indicators of potential C. 

botulinum and related proteolytic Clostridium spp.

Other indicator bacteria for consideration in assessing sanitation on raw and RTE 

catfish (of the order Siluriformes). These could include aerobic plate count (APC), 

psychrotrophs, coliforms, Enterobacteriaceae, and enterococci. Farid et al. (2000) found 

21
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 samples 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 

samples. 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.

2.1.4 Literature Summary: Chemical Hazards

McCaskey et al. (1998) suggest that, in general, catfish consumption is considered 

to pose a relatively low risk to consumers from a microbiological perspective. The author 

speculates that this may be “because their incidence on catfish is low and because catfish 

are well cooked prior to being consumed”. 

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). 

2.2 Identification of Potential Chemical Hazards

Consideration of the likelihood of contamination for catfish (of the order 

Siluriformes) must include the conditions under which the fish are raised, transported, 

and processed. As such, the potential impacts of environmental factors, aquaculture and 

processing practices on the exposure of hazards to consumers are considered here.

The information obtained about potential hazards is based on current Siluriformes

aquaculture practices. It was gathered through discussions with representatives from 

multiple federal agencies, academic institutions, industry representatives and nongovernment organizations. Information was also obtained through numerous literature 

22
searches in PubMed, Food Science and Technology Abstracts, Chemical Abstracts, 

USDA DigiTop and Web of Science databases.28 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 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 several hazards that might be associated 

with catfish (of the order Siluriformes). 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 fish farms and processing facilities. Some chemicals 

are used in multiple ways and may therefore appear in more than one of the following 

lists, including drugs, pesticides, and other chemicals associated with aquaculture.

2.2.1 Drugs

The following is an alphabetical list of drugs that were identified to be linked with 

aquaculture generally. The focus is on domestic drugs due to available information from 

the FDA website (www.FDA.gov). At the end of this section is a list of some of the drugs 

used in foreign aquaculture. 

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, Methyltestosterone and oxytetracycline 

(U.S. FDA, 2008a). 

28PubMed: 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.

23

 
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.

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.

24
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 generally 

recognized as safe (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 

have been tested by the National Toxicology Program. The results for carcinogencity for 

isoeugenol and eugenol were equivocal carcinogen, methyleugenol was carcinogenic to 

rodents. 

Copper sulfate

FDA has deferred regulatory action on copper sulfate pending further study. It can be 

used under the Investigational New Animal Drugs (INAD). Such 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.

25
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)

FDA classifies garlic as an aquaculture drug and categorizes it as low regulatory priority. 

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

Hydrogen peroxide is classified as a drug for aquaculture by FDA and approved as an 

immersion for the control of columnaris disease caused by Flavobacterium columnare 

(Flexibacter columnaris) and for the control of 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

FDA classifies ice as an aquaculture drug and categorizes it as low regulatory priority. 

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.

26
Onion (whole form)

FDA classifies whole onions as an aquaculture drug and categorizes it as low regulatory 

priority. 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)

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)

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

Papain 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

Potassium chloride 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

27
FDA has deferred regulatory action on potassium permanganate pending further study. It 

can be used under the Investigational New Animal Drugs (INAD). Such products can be 

used in accordance with the EPA registered label. There is no withdrawal time or 

regulatory residue level.

Povidone iodine

Povidone iodine 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

FDA classifies sodium bicarbonate as an aquaculture drug and categorizes it as low 

regulatory priority. 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

FDA classifies sodium chloride (salt) as an aquaculture drug and categorizes it as low 

regulatory priority. 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

Sodium sulfite 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

28
Sufadimethoxine 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

Thiamine hydrochloride 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)

Tricaine methanesufonate 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

Urea and tannic acid are FDA low regulatory priority aquaculture drugs. 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.

Box 2.1 contains a non-inclusive list of drugs used in foreign aquaculture. These 

drugs are currently not approved for use in aquaculture by the FDA. 

29
Box 2.1 Non-inclusive List of Drug Used in Foreign Agriculture 29

Azamethiphos Josamycin Praziquantel

Chloramphenicol Kanamycin Rifampicin

Dichlorovos Levamisole Saponin

Diflubenzuron Malachite green Sarafloxacin

Enrofloxacin Methyltestosterone Spiramycin

Eugenol Nalidixic Acid Streptomycin

Fenthion Nifurpirinol Teflubenzuron

Flumequine Nitrofuran Testosterone

Furazolidone Nitrofurantoin Thiamphenicol

Glucans Nitrofurazone Tributyltin

Isoeugenol Norfloxacin Trichlorfon

Ivermectin Oxolinic Acid Trifluralin

Drugs prohibited under the Animal Medicinal Drug Use Clarification Act

The Animal Medicinal Drug Use Clarification Act (AMDUCA) of 1994 (21 CFR 

530) allows veterinarians to use approved FDA drugs outside of the 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)

29 This list was generated with help from Dr. Fran Pell at the Center for Veterinary Medicine, Food and 

Drug Administration (U.S. FDA, 2008b).

30

 
• 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)

• 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)

2.2.2 Pesticides

The section contains an alphabetical list of pesticides that were identified to be 

linked with aquaculture and a brief description of some toxic endpoints that have been 

associated with those chemical. It is important to note, however, that some of the data on 

toxicity comes from animal studies and often with very high doses. This list was 

generated from the 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 issued by the Federal Joint 

Subcommittee on Aquaculture working group on quality assurance in aquaculture 

production (US Federal Joint Committee on Aquaculture, 2007). Tolerances are included 

when available. Information on toxicity was found using the U.S. EPA Integrated Risk 

Information System (IRIS) (U.S. EPA, 2009) and other sources when noted. 

31
2, 4-Dichlorophenoxyacetic acid ( 2, 4-D)

2,4-D (Chemical Abstracts Service Registry Number (CASRN) 94-75-7) is one of a 

family of herbicides known as the chlorophenoxy herbicides. Hematologic, hepatic and 

renal toxicity has been seen in rats orally exposed to 2,4-D. EPA established an oral 

reference dose (RfD30) for 2,4-D of 0.01 mg/kg/day using that study; the RfD includes a 

100-fold uncertainty factor. The FDA/CFSAN Fish and Fisheries Products and Control 

Guidance set a tolerance level for 2, 4-D at 0.1 ppm. 

Acetic Acid

Acetic acid (CASRN 64-19-70) is an EPA registered aquatic herbicide. It is the main 

ingredient in vinegar apart from water. The EPA has not developed an RfD for acetic 

acid. Much of its toxic effects are related its caustic properties if at a high-enough 

concentration, 

Aldrin/Dieldrin

U.S. production of the organochlorine pesticicides aldrin and dieldrin was discontinued in 

1989, but they take decades to break down in the environment and they can 

bioaccumulate in fish. In 1988 EPA has set an RfD for aldrin (CASRN 309-00-2) of 

3x10−5 mg/kg/day that includes a 1,000-fold uncertainty factor, and for dieldrin (CASRN 

60-57-1) of 5x10−5 mg/kg/day; the RfD includes a 100-fold uncertainty factor. EPA 

classifies both as class B2 probable human carcinogens. The Agency of Toxic Substances 

& Disease Registry, however, indicates that “current mechanistic data suggest that the 

mouse carcinogenicity data may not be highly relevant to humans” (ATSDR, 2002). The 

FDA/CFSAN Fish and Fisheries Products and Control Guidance lists an action level for 

Aldrin/Dieldrin at 0.3 ppm. 

30 EPA’s reference dose (RfD) is “[a]n estimate (with uncertainty spanning perhaps an order of magnitude) 

of a daily oral exposure to the human population (including sensitive subgroups) that is likely to be without 

an appreciable risk of deleterious effects during a lifetime. It can be derived from a NOAEL, LOAEL, or 

benchmark dose, with uncertainty factors generally applied to reflect limitations of the data used. Generally 

used in EPA's noncancer health assessments.”

(Available at: http://www.epa.gov/risk_assessment/glossary.htm#r; accessed July 31, 2014)

32

 
Ammonia

EPA has not established an RfD for amonia (CASRN 7664-41-7), but ATSDR “cites 

irritative and corrosive properties” from excessive exposures as a main concern and does 

not consider oral exposure to be an exposure route of concern (ATSDR 2004).

Antimycin A

Antimycin A (CASRN 1397-94-0) is an EPA registered fish toxicant. In a 2007 

Reregistration Eligibility Decision document, EPA (2007) determined that there is 

insufficient data for conducting a risk assessment, and EPA has not developed an IRIS 

profile for this chemical. It is considered a Restricted Use Pesticide (RUP) and, therefore, 

each application must be approved by appropriate state and federal fish and wildlife 

agencies and there are requirements for not harvesting fish that survived a selective kill 

with antimycin A for 12 months. 

Benzenepropanoic acid (Phenylpropanoic acid)

Benzenepropanoic acid (CASRN 501-52-0) is an EPA registered aquatic herbicide. The 

EPA does not have information for benzenepropanoic acid available on IRIS.

Bleach (Calcium Hypochlorite, Sodium Hypochlorite)

Calcium hypchlorite (CASRN 7778-54-3) and sodium hypochlorite (CASRN 7681-52-9) 

are chlorinated inorganic disinfectants commonly referred to as bleach. In an RED in 

1991, EPA concluded that “the risks from chronic and subchronic exposure to low levels 

of these pesticides are minimal and without consequence to human health.”

Butoxyethyl 2,4-dichlorophenyoxyacetate

Butoxyethyl 2,4-dichlorophenyoxyacetate (1929-73-3) is the butoxyethyl ester form of 

2,4-D and is an EPA registered chlorophenoxy aquatic herbicide. The potential toxicity of 

2,4-D is discussed above.

33
Chlordane

Although the EPA cancelled the use of chlordane (CASRN 12789-03-6) as a pesticide in 

1988, residues could still persist in soil and the chemical is capable of bioaccumulating in 

both marine and freshwater species. EPA has established an RfD of 5x10−4 mg/kg/day for 

chlordane on the basis of an oral study in mice; the RfD includes a 300-fold uncertainty 

factor. The potential adverse effects of high dose of this chemical include hepatic 

necrosis. Chlordane is classified as a class B2 probable human carcinogen. The 

FDA/CFSAN Fish and Fisheries Products and Control Guidance lists an action level for 

Chlordane at 0.3 ppm.

Chlordecone

Chlordecone (CASRN 143-50-0) is no longer made or used in the United States. All U.S. 

product registrations were cancelled by EPA by 1978 and it revoked all residue 

tolerances in or on raw agricultural products within its purview. Chlordene has the ability 

to bioaccumulate in fish. EPA has established an RfD of 3x10−4 mg/kg/day for 

chlordecone on the basis of a rat feeding study showing renal lesions; the RfD includes 

an uncertainty factor of 300. The FDA/CFSAN Fish and Fisheries Products and Control 

Guidance lists an action level for Chlordecone at 0.3 ppm. Chlordecone is listed as likely 

to be carcinogenic to humans and has an oral slope factor of 10 mg/kg/day. 

Chlorine

Chlorine (CASRN 7782-50-5) is an EPA registered algaecide. EPA has established an 

RfD of 0.1 mg/kg/day on the basis of a chronic drinking water study in rats and a 100-

fold uncertainty factor from the no-observed-adverse-effect-level (NOAEL); no adverse 

effects were seen at the highest dose used in the study. 

Chlorophenoxy compounds

Chlophenoxy compounds refers to a family of herbicides. The toxicity of the relevant 

members of this family are discussed under the specific chemical.

34
Copper Compounds

A number of copper compounds are registered by EPA as algaecides and aquatic 

herbicides, including: copper carbonate (CASRN 12069-69-1), copper ethanolamine 

complex (CASRN 14215-52-2), copper hydroxide (20427-59-2), copper sulfate (1344-

73-6) and copper triethanolamine complex (82027-59-6). The EPA has not established an 

RfD for copper compounds, but states that a risk assessment conducted as part of a 2009 

Revised Eligibility Decision for copper compounds indicates “that there are no 

residential, dietary, occupational, or aggregate risks of concern resulting from the use of 

copper pesticides” (EPA, 2010) (EPA EPA-HQ-OPP-2010-0212; Coppers Summary 

Document Registration Review: Initial Docket September 2010; .

Crystal Violet

FDA has designated crystal violet (also known as Gentian violet; CASNR 548-62-9) as 

an unapproved antifungal agent. Crystal violet is absorbed into fish tissue and is reduced 

metabolically to leucocrystal violet. Crystal violet is mutagenic, and chronic studies in 

mice demonstrate carcinogenicity. The EPA does not have information for crystal violet 

available on IRIS.

DDT (p,p’-Dichlorodiphenyltrichloroethane), TDE, DDE (p,p’-

Dichlorodiphenyldichloroethylene)

DDT (CASRN 50-29-3) has not been permitted in the U.S. since 1972 except in cases of 

public emergency, but it is still used elsewhere in 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 lists an action level for DDT, TDE, DDE at 5 

ppm. EPA has established an RfD of 5x10−4 mg/kg/day which is based on liver lesions; 

the RfD includes a 100-fold uncertainty factor. DDT and DDE (72-55-9) are classified as 

a class B2 probable human carcinogen under. The EPA does not have information for 

TDE available on IRIS.

35
Diflubenzuron

Diflubenzuron (CASRN 35367-38-5) is an EPA registered invertebrate toxicant. EPA has 

established an RfD of 0.02 mg/kg/day on the basis of methemoglobin and 

sulfhemoglobin formation; the RfD includes a 100-fold uncertainty factor. 

Dimethylamine salt of 2,4-D

The dimethylamine salt of 2,4-D (CASRN 2008-39-1) is an EPA registered 

chlorophenoxy aquatic herbicide. The EPA does not have information for dimethylamine 

available on IRIS.

Diquat (Diquat Dibromide)

Diquat (CASRN 85-00-7) is an EPA registered algaecide and aquatic herbicide. The 

FDA/CFSAN Fish and Fisheries Products and Control Guidance lists a tolerance level for 

Diquat at 0.1 ppm. The EPA has established an RfD of 2.2x10−3 mg/kg/day on the basis 

of a study in rats showing minimal lens opacity and cataracts; the RfD includes a 100-

fold uncertainty factor. EPA does not have a carcinogenicity assessment for diquat.

Endothall 

Endothall (CASRN 145-73-3) is an EPA registered algaecide and aquatic herbicide. The 

EPA has set a tolerance in fish at 0.1ppm for Endothall residues. The EPA has has 

established an RfD of 0.02 mg/kg/day on the basis of a study in dogs showing increased 

absolute and relative weights of stomach small intestine; the RfD includes a 100-fold 

uncertainty factor. EPA does not include a carcinogenicity assessment of endothall.

Endrin

Endrin (CASRN 72-20-8) is an organochlorine pesticide banned by EPA because of its 

persistence in the environment. The EPA has established an RfD of 3x10−4 mg/kg/day on 

the basis of a study in dogs showing mild histological lesions in liver and occasional 

convulsions; the RfD includes a 100-fold uncertainty factor. Endrin is classified as class 

D, not classifiable as to carcinogenicity for humans. 

36
Fluridone (Ansi)

Fluridone (CASRN 59756-60-4) is an EPA registered aquatic herbicide. The 

FDA/CFSAN Fish and Fisheries Products and Controls Guidance lists a tolerance level 

for Fluridone at 0.5 ppm. The EPA has an RfD of 0.08 mg/kg/day on the basis of a rat 

study glomerulonephritis, atrophic testes, eye keratitis along with a decrease in body and 

organ weights; the RfD includes a 100-fold uncertainty factor. 

Glyphosate Isopropylamine Salt (Glyphosate)

Glyphosate (1071-83-6) is an EPA registered aquatic herbicide. The FDA/CFSAN Fish 

and Fisheries Products and Controls Guidance lists a tolerance level for Glyphosate at 

0.25 ppm. The EPA has an RfD of 0.1 mg/kg/day for glyphosate on the basis of a 3-

generational rat study showing kidney defects in subsequent generations; the RfD

includes a 100-fold uncertainty factor. Glyphosate is classified as class D, not classifiable 

as a human carcinogen. 

Heptachlor/Heptachlor Epoxide

Heptachlor (CASRN 76-44-8) and heptachlor epoxide (1024-57-3) have not been used 

since 1988, but are still registered by the EPA for killing fire ants in buried power 

transformers. They have the ability to bioaccumulate in fish. The FDA/CFSAN Fish and 

Fisheries Products and Controls Guidance lists an action level for both chemicals at 0.3 

ppm. The EPA has an RfD for heptachlor of 5x10−4 mg/kg/day on the basis of a study in 

rats showing increased liver weight; the RfD for heptachlor includes a 300-fold 

uncertainty factor. The RfD for heptachlor epoxide is 1.3x10−5 mg/kg/day on the basis of 

a study in dogs showing an increased liver-to-body ration in both males and females; the 

RfD for heptachlor epoxide includes a 300-fold uncertainty factor. EPA classifies both 

heptachlor and heptachlor epoxide as class B2, probable human carcinogen. 

Hexachlorobenzene

The pesticide hexachlorobenzene (CASRN 118-74-1) is banned by the EPA. The EPA 

has established an RfD of 8x10−4 mg/kg/day on the basis of findings of liver effects in a 

37
chronic rat study; the RfD includes a 100-fold uncertainty factor. EPA classifies 

hexachlorobenzene as a class B2, probable human carcinogen. 

Imazapyr (isopropylamine salt)

Imazapyr (CASRN 81334-34-1) is an EPA registered aquatic herbicide. The EPA does 

not have information for Imazapyr available on IRIS.

Lime (calcium/magnesium hydroxide)

Lime (calcium hydroxide, CASRN 1305-62-0; magnesium hydroxide, 1309-42-8) is used 

to improve pond water quality. It is considered low toxicity.

Lindane (gamma-hexachlorocyclohexane)

Lindane (CASRN 58-89-9) is a pesticide for which EPA has established an RfD of 

3x10−4 mg/kg/day on the basis of liver and kidney toxicity in a rat oral bioassay; the RfD 

has includes a 1000-fold uncertainty factor. EPA indicates that there is no data for 

conducting a carcinogenicity assessment for lindane.

Malachite Green and Leucomalachite Green

Malachite green (CASRN 569-64-2) and leucomalachite green (CASRN 129-73-7) are 

fungicides that are prohibited by FDA and are not registered for use with EPA. Malachite 

green is excreted rapidly but >80% is metabolized into leucomalachite green which can 

remain in the muscle for months. A National Toxicology Program (NTP) feed studies of 

these compounds in rats and mice found equivocal evidence of carcinogenic activity in 

some species. Nonneoplastic lesions were also seen in the thyroid gland and liver. 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. 

Mirex

The use of the pesticide mirex (CASRN 2385-85-5) 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 lists an action level for Mirex at 0.1 ppm. The 

38
EPA has established an RfD of 2x10−4 mg/kg/day on the basis of liver cytomegaly, fatty 

metamorphosis, angiectasis and thyroid cystic follicles in a chronic rat feeding study; the 

RfD includes a 300-fold uncertainty factor. EPA indicates that there is no data for 

conducting a carcinogenicity assessment for mirex.

Rotenone (Cube Resins Other than Rotenone) (Piperonyl Butoxide Technical)

Rotenone (CASRN 83-79-4) is an EPA registered fish toxicant that is a Restricted Use 

Pesticide (RUP). Each application, therefore, must be approved by appropriate state and 

federal fish and wildlife agencies. The EPA has established an oral RfD of 4x10−3

mg/kg/day for rotenone on the basis of a 2-generation rat study showing reduced pup 

weight; the RfD includes a 100-fold uncertainty factor. EPA indicates that there is no 

data for conducting a carcinogenicity assessment for rotenone.

Simazine

The FDA/CFSAN Fish and Fisheries Products and Controls Guidance lists a tolerance 

level for Simazine (CASRN 122-34-9) at 12 ppm. EPA has established an oral RfD of 

5x10−3 mg/kg/day on the basis of a reduction in weight gain and hematological changes 

in females in a 2-year rat study; the RfD includes a 100-fold uncertainty factor. EPA 

indicates that there is no data for conducting a carcinogenicity assessment for simazine.

Sodium 2,4-dichlorophenoxyacetate (2,4-D, sodium salt)

Sodium 2,4-D is the sodium salt of 2,4-D and is an EPA registered chlorophenoxy 

aquatic herbicide. The potential toxicity of 2,4-D is discussed above.

Sodium Bromide

EPA re-registered the algaecide sodium bromide (CASRN 7647-15-6) in 1993. At that 

time it concluded that the use of “products containing sodium bromide and sodium 

chloride as labeled and specified in the [Reregistration Eligibility Decision] will not pose 

unreasonable risks or adverse effects to humans.” 

39
Sodium Percarbonate

Sodium percarbonate (sodium carbonate peroxyhydrate; CASRN 15630-89-4) dissolves 

into hydrogen peroxide, carbonate and sodium. It acts as an oxidizing agent commonly 

used in cleaning products, and is registered with EPA as an algaecide and fungicide. This 

is an EPA registered algaecide. EPA determined the risks to humans from sodium 

percarbonate use are very low.

Tartrazine/Erioglaucine

Tartrazine (1934-21-0) and erioglaucine (3844-45-9) are EPA registered algaecides and 

aquatic herbicides. In a 2005 RED, EPA concludes that “erioglaucine and tartrazine both 

have very low toxicity potentials.” 

Tea seed oil and mahua oil cake (sapogenin glycosides)

Tea seed oil is an edible oil made by pressing the seeds of the camellia trees. Mahua oil 

cake is also an edible oil.

Triazine Herbicides

The family of triazine herbicides includes atrazine (CASRN 1912-24-9) and propazine 

(139-40-2). EPA has established an RfD for atrazine of 0.035 mg/kg/day on the basis of a 

2-year rat study showing a reduction in weight gain; the RfD includes a 100-fold 

uncertainty factor. EPA has established an RfD for propazine RfD of 0.02 mg/kg/day in a 

2-year rat feeding study; the RfD includes a 300-fold uncertainty factor. EPA indicates 

that there is no data for conducting a carcinogenicity assessment for atrazine or 

propazine.

2.2.3 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).

40
Agricultural limestone

Water treatment used to raise pH and to sterilize pond soils between production cycles. 

FDA lists ground limestone as a GRAS food additive.

Aluminum Sulfate

Aluminum sulfate (CASRN 10043-01-3) is a flocculant used to cause suspended clay 

particles to precipitate to clear water turbidity. EPA has set a non-enforceable secondary 

drinking water quality standard for aluminum for aesthetics (odor, taste or color) not 

health (0.05–0.2 mg/L) and FDA has established an allowable level for bottled water (0.2 

mg/L). FDA lists aluminum sulfate as a food and animal feed additive.

Ammonium phosphate (mono- and dibasic)/Ammonium Sulfate/Ammonium Nitrate

Ammonium phosphate (CASRN 10361-65-6), ammonium sulfate (CASRN 7783-20-2), 

and ammonium nitrate (6484-52-2) are fertilizers for phytoplankton. The main toxic 

concerns those chemicals are acute effects from spills or accidents. FDA lists ammonium 

phosphate and sulfate as food additives. 

Benzalkonium chloride (alkyldimethylbenzylammonium chloride)

Benzalkonium chloride (CASRN 63449-41-2) a qarternary ammonium compound used in 

aquaculture to disinfect equipment and holding pens. 

Calcium Peroxide

Calium peroxide (CASRN 1305-79-9) is an oxidizing agent that is used to control 

phytoplankton, kill disease organisms and oxidize bottom soils. The main toxic concerns 

are acute effects from irritation at high concentrations. FDA lists calcium phosphate as an 

additive for some foods.

Calcium Phosphate

Calcium phosphate (CASRN 7758-87-4) is a fertilizer for phytoplankton. FDA lists 

calcium phosphate as an additive for some foods and for animal feed.

41
Calcium Sulfate (gypsum)

Calcium sulfate (CASRN 7778-18-9) is a flocculant used to cause suspended clay 

particles to precipitate to clear water turbidity. It is also an osmoregulator that is applied 

to the water to improve conditions. FDA lists calcium phosphate as an additive for some 

foods.

Ferric Chloride

Ferric chloride (CASRN 7705-08-0) is a flocculant used to cause suspended clay particles 

to precipitate to clear water turbidity. FDA lists ferric chloride as a food additive.

Methyl Mercury

Methyl mercury (CASRN 22967-92-6) is an environmental pollutant and historically was 

used as pesticide. Methyl mercury bioaccumulates in fish. The FDA/CFSAN Fish and 

Fisheries Products and Controls Guidance lists a guidance level for Methyl Mercury at 

1.0 ppm for finfish. EPA has established an RfD on the basis of neurodevelopmental 

impairment in human epidemiological studies; the RfD includes a 10-fold uncertainty 

factor. EPA classifies methyl mercury as class C, possible human carcinogen. 

Phosphoric Acid

Phosphoric acid (CASRN 7664-38-2) is used as a fertilizer in aquaculture. Although 

there is an inhalation reference concentration, EPA indicates that there is insufficient data 

to establish an oral RfD for phosphoric acid or to assess its carcinogenicity. FDA lists 

phosphoric acid as an additive for some foods and for animal feed practices.

Polychlorinated Biphenyls (PCB’s)

Manufacture of PCB’s (CASRN 1336-36-3) stopped in the U.S. in August of 1977, but 

residues persist and they have the ability to bioaccumulate in fish. EPA has established 

RfDs for individual PCBs. The FDA/CFSAN Fish and Fisheries Products and Controls 

Guidance lists a tolerance level for PCB’s at 2.0 ppm. 

42
Polyvidone iodine (polyvinylpyrrolidone-iodine complex)

Polyvidone iodine (CASRN 25655-41-8) is an iodine-based disinfectant used on 

equipment and holding pens. It is also a widely used topical antiseptic.

Potassium nitrate/Sodium Nitrate

Potassium nitrate (CASRN 7757-79-1) and sodium nitrates (7631-99-4) are oxidizing 

agent used in aquaculture to control phytoplankton, kill disease organisms and oxidize

bottom soils. FDA lists potassium and sodium nitrates as additives for preserving meat 

and poultry products.

Sodium Silicate

Sodium silicate (CASRN 1344-09-8) is used as a fertilizer for phytoplankton. It is 

designated by FDA as a corrosion-inhibiting compound for canned potable water. In a 

2007 biopesticides registration action document EPA concluded that the “overall 

toxicological risk from human exposure to potassium silicate is negligible”

Trace element mixes including iron, zinc, copper, boron and molybdenum

Trace element mixes (including iron, zinc, copper, boron and molybdenum) are used as 

fertilizers for phytoplankton. Many of those elements are considered essential elements. 

EPA has established an RfD for zinc, boron and molybdenum of 0.3 mg/kg/day, 0.2 

mg/kg/day and 5x10−3 mg/kg/day, respectively. 

Zeolite

Zeolite (CASRN 68989-22-0) is a flocculant used to cause suspended clay particles to 

precipitate to clear water turbidity. The EPA does not have information on zeolite

available on IRIS.

43
2.3 Selected Chemical Residues Detected in Siluriformes

A variety of potential chemical hazards have been detected in a limited number of catfish 

samples. Prevalence and concentrations of residues for some of these hazards in samples 

collected through 2008 are presented in Table 2. Information on additional pesticides detected by 

the USDA Agricultural Marketing Service’s Pesticide Data Program (PDP) through 2010 has 

been included in the Addendum. For each analyte, the residue data for domestic and imported 

samples 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 product. 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.

44
Table 2. Summary of Recent Catfish Residue Data

Concentration (ppb) Regulatory Levels (ppb)

% 

Positive

% 

Violative N

Data 

Source

Sampling 

Year

LOD 

(ppb)

Mean of 

All 

Mean of 

Positives Max U.S. Codex

Domestic

DDT 93% 0% 281 1 2008 1 34.4 37 651 5000

PCB* 98% 0% 120 2 2001-

2003 

1x10−7 to 

1x10−3

6.0x10−4 6.2x10−4 4.6x10−3 2000

Chlorpyrifos 1% 1% 303 1 2008 1 0.0 1.8 2.6 0

Arsenic 0% NA 20 3 2008 200 0.0 0 0

Cadmium 0% NA 20 3 2008 10 0.0 0 0

Lead 0% NA 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 - ND - 1 ND ND ND 0

Imported

DDT 46% 0% 70 1 2008 1 3.1 6.7 53 5000

PCB* 63% 0% 8 2 2001-

2003 

1x10−7 to 

1x10−3

3.8x10−6 6.0x10−6 1.4x10−5 2000

Chlorpyrifos 32% 32% 75 1 2008 1 0.9 2.9 16.4 0

Arsenic 2% NA 110 3 2008 200 23.6 1300 1600

Cadmium 0% NA 112 3 2008 10 0.0 0 0

Lead 10% NA 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% 150 2 2007 1 1.2 12.5 122 0

Gentian Violet 2% 53 2 2008 0.1 0.0 2.5 3 0

Nitrofurans ND - ND - 1 ND ND ND 0

*PCB data reported as toxicity equivalents; LOD varies for each congener analyzed; 

Abbreviations: NA, Not Applicable; ND, No Data.

Data sources: 1)USDA-AMS 2008; 2) US FDA 2008b; and 3)USDA-FSIS, 2009.

45
2.4 Summary of Hazard Identification

Although not as much data are available for seafood and catfish as for other products

like poultry, the studies and data reviewed in the Prioritization of Potential Microbial 

Hazards demonstrate that foodborne pathogens have been detected in seafood and, 

specifically, in catfish. Listeria monocytogenes and Salmonella have been detected in raw 

catfish samples. Listeria was detected in 5.9% to 47% of raw catfish samples in the 

studies. Salmonella was detected in either fish or specifically catfish in both research 

studies published in the peer review literature and in FDA’s sampling program, with the 

percent positives in the studies ranging from 2.3% to 21%, depending on the study and 

the source of the catfish (domestic or imported). Given that Salmonella is a leading cause 

of foodborne illness in the United States, and the presence of Salmonella in catfish and 

fish in general, this hazard identification supports Salmonella as a focus of this risk 

assessment.

A number of chemicals that might be present on catfish are briefly reviewed in the

Identification of Potential Chemical Hazards section. Those chemicals were identified 

either through testing results or by identifying chemicals that are commonly used in 

aquaculture. A number of the chemicals have very low toxicity and, therefore, pose little 

to no risk from consumption of catfish. Other chemicals, however, are associated with 

toxic endpoints and, if present at a high enough concentration in catfish, would have the 

potential to lead to health effects. Data in Table 2 indicate that some hazardous chemicals

have been detected in raw catfish samples, including some at violative levels and 

chemicals that are not approved by the FDA for use in aquaculture. One domestic sample 

and 32% of imported samples had chlorpyrifos above the violative level. In addition, 

malachite green and gentian violet—both of which are not approved by FDA for 

aquaculture and for which there is evidence of mutagenicity, carcinogenicity or 

teratogenicity, depending on the compound—have both been detected in imported catfish 

samples. Arsenic and lead have also been detected in samples of imported catfish. Given 

the small number of test results and the low percentage of samples tested positive—and 

even lower number of samples above violative levels—compared with Salmonella, the 

46
remainder of this risk assessment focuses on estimating the potential range of risks from 

Salmonella associated with catfish consumption. 

47
3. Model overview

Modeling annual human salmonellosis cases potentially resulting from consuming 

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

catfish (of the order Siluriformes) servings consumed each year, based on domestic 

production and import data, and estimates of the prevalence of Salmonella contamination 

of Siluriformes (Figure 1). Note that both inputs to this first step—the number of servings 

consumed and the number of fish that have Salmonella contamination—are estimated on

the basis of very limited data. Second, the average probability of illness across 

contaminated Siluriformes servings is estimated by modeling contaminated servings of 

catfish (of the order Siluriformes) 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.

48
Figure 1. Inputs to number of Salmonella illnesses among U.S. consumers per year.

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

number of Salmonella-contaminated Siluriformes servings.

Equation 1 ( ) ( )

 = 

(1 ) N f prev servings imports imports imports domestic   × +− × f prev  

# contaminated servings / yr

where Nservings is the total number of servings of Siluriformes consumed in the U.S. per 

year, import f is the fraction (share) of all servings generated by imported product, 

domestic prev and import prev are the proportions of domestic and imported product

contaminated with some level of Salmonella. This modeling approach assumes that each 

fish carcass produces roughly the same average number of servings regardless of its 

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

49
contamination status. The values for these inputs are described in the Exposure 

Assessment section.

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

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

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

randomly varies among contaminated fish. It is assumed that the average concentration of 

Salmonella per gram of a contaminated fish is independent of the size of serving 

generated from a fish (i.e., the grams 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 the fish during retail and home storage (and cooking 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 carcass, these assumptions seem reasonable.) 

50
Figure 2. Inputs to probability of illness per contaminated serving.

Mathematically, the average exposure dose of Salmonella31 consumed in a 

random contaminated serving is modeled as;

Equation 2 D X SGC = ×× ×

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 carcass, S is 

31 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. 

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

51

 
one instance of a serving size (in grams consumed),G is one instance of the growth of 

Salmonella on a carcass (to account for handling and storage between processing and 

consumption), andC is one instance of the expected reduction of Salmonella in a serving 

caused by the effects of cooking. The inputs to this calculation ( X SGC ,,, ) are random 

variables. The inputs for serving size and the effect of cooking, however, are somewhat 

complicated.

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

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

servings. Therefore, there are actually two variables for the servings – 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 – 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 contaminated with Salmonella. There are four categories of product 

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 product 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

α

b

−

=

    = −−          

∑

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 α and β .

52
Given the preceding discussion, the annual number of human salmonellosis cases 

from catfish (of the order Siluriformes) 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 servings that are breaded and Baked f is the fraction of servings 

that are baked.

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

variables ( X SGC ,,, ) 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 across all four exposure pathways.

53
4. Exposure Assessment

The exposure assessment estimates the annual exposures to Salmonella from

catfish (of the order Siluriformes) consumed in the U.S. Model inputs for the exposure 

assessment are explained in the following sections. The Siluriformes-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 product consumption describes the development of the inputs Bread S , 

Nonbread S , Breaded f , Baked f , imports f , and Nservings .

4.1 Siluriformes-associated Hazard Concentration ( , , and X prev prev domestic imports )

No empiric evidence regarding concentrations of Salmonella on processed Siluriformes 

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

Salmonella contaminated catfish of the order Siluriformes. 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 ( domestic prev ) in the model.

Although the FDA’s Office of Regulatory Affairs/Office of Regulatory Science

(formerly the 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 data would likely over-estimate the prevalence of 

Salmonella contamination of catfish (of the order Siluriformes) consumed in the U.S. 

Furthermore, these inherent sampling and Salmonella testing biases make reasonable 

inferences about Salmonella prevalence among imported product from FDA data nearly 

impossible. Lacking any other evidence regarding Salmonella prevalence on imported 

product, this risk assessment assumed that the prevalence of Salmonella on imported 

54
product is the same as the prevalence of Salmonella found on domestic product (i.e., a 

default assumption that imports domestic prev prev = ).

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

the order Siluriformes, there is no data on the amount (concentration) of Salmonella per 

gram of these fish. Therefore, the concentration of Salmonella on contaminated product 

model input X , is assumed to be reflected by available Salmonella enumeration results 

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

poultry Salmonella testing data as a surrogate that impact what we might see after 

implementing an inspection program for Siluriformes. However, such data are 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 Siluriformes.

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

used for Siluriformes (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 these fish.

Although use of poultry concentration data as a surrogate for Siluriformes 

concentration data is arguable, the concept that Salmonella contamination levels on 

Siluriformes are variable is crucial to assessing risk. Ignoring this variability in the risk 

assessment would potentially undervalue the risk posed to consumers because catfish (of 

the order Siluriformes) 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 (of the order Siluriformes) 

55
(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 Siluriformes industry is similar to the 

decision made in the mid-1990s for broiler poultry and other FSIS-regulated species in 

that a new FSIS program 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 is designed to predict 

human illnesses avoided following implementation of a new FSIS-style regulatory system 

within the Siluriformes 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 3). 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 332. To adjust 

these data to units of Salmonella per gram of Siluriformes, 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 these fish are 

32 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 3 to model variability in 

Salmonella concentration.

56

 
generally sold skinless, broiler poultry concentrations are translated to fish concentrations 

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

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 − = ).

Equation 6

1 400 0.22

NoSkin 1500

MPN Salmonella ml

gram ml g

=×× ×

One last adjustment truncates the distribution of Salmonella concentration on 

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

average weight of a carcass (Morris, 1993)33. 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 3. 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

33It 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).

57

 
The resulting random variable X (average Salmonella per gram of contaminated 

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

0.003 per gram (the theoretic minimum level) (Table 4). Because a serving generally 

represents something less than the entire carcass weight, the amount of Salmonella

actually in a serving could be less than one Salmonella bacterium. The maximum 

concentration of Salmonella on a carcass is estimated to be 16.7 bacteria per gram. For 

comparison, the maximum concentration for broiler poultry was ~75 Salmonella per 

gram34. 

Table 4. The estimated distribution of Salmonella per gram of contaminated fish 

carcass.

Cumulative 

frequency

Salmonella per gram 

of fish 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

34 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

× = .

58

 
4.2 Storage and Cooking Effect ( , , and GC C Baked Fried )

Salmonella concentrations on raw processed Siluriformes at the point of consumption are 

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

potential cooking scenarios. 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 

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

these products, 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 5). 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 were based on expert opinion and review of several on-line cooking 

recommendations.

Table 5. Parameters for cooking and growth inputs

Factor Equation Cooking Type

Parameters

Min

Most 

likely Max

Cooking ( )

( )

1

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

min most likely max 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

59
The random variable for growth effect (G ) estimates 99.98% of servings are 

unchanged between processing and consumption (Table 6). 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. 

Table 6. 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.

0

0.02

0.04

0.06

0.08

0.1

0.12

-40 -30 -20 -10 0

Relative 

frequency

Log10 reduction from baking

60
The reduction in Salmonella per serving caused by frying (CFried ) extends from less than 

1 log to nearly 30 log (Figure 4). The log of the median and mean reductions are about 

4.5 and 2, respectively. 

Figure 4. Log reductions of Salmonella due to frying.

4.3 Product Consumption ( , ,,, Bread Nonbread Bread Baked imports SS f f f and Nservings )

Data on the consumption of catfish35 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 SAS-Callable SUDAAN 

35 National Health and Nutrition Examination Survey (NHANES).did not specify the fish beyond catfish so 

this term is used in this section to be consistent with the original reference. 

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

-30 -25 -20 -15 -10 -5 0

Relative 

frequency

Log10 reduction from frying

61

 
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 

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 

62
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. 

63
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 7). Imported 

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

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 

64
constitute a smaller fraction of total Ictalurus 10, 470,953 9%

116,150,192 imports f   = ≈    

than total 

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 7. Kilograms of varieties of catfish available for consumption in the United 

States, 2008.

Origin Ictalurus Pangasius

Other 

Siluriformes Total

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 8). 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  

65
Table 8. 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 

5. 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 Siluriformes. The 

dose-response function is:

Equation 7 ( | 11 ) D P ill exp

α

β

−

 

=− +    

with parameters (α = 0.1324 , β = 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.

66
6. Risk characterization

The model that is used to estimate the risk of Salmonella illness potentially 

associated with the estimated catfish (of the order Siluriformes) consumption distribution 

combines the exposure assessment with the hazard characterization. Equations 1 – 4 

outline the mathematics of this process.

This section will present the default model (baseline) estimation for the annual 

number of human cases of salmonellosis potentially associated with catfish (of the order 

Siluriformes) consumption. This estimation, which is characterized by a substaintial 

amount of uncertainty, stems from the number of exposures generated from estimates of 

contaminated product and the average probability of illness among those exposures.

The analysis also compared the modelled estimation of annual Salmonella illnesses 

associated with these fish with estimates based on the available public health surveillance 

(i.e., epidemiological) information. Estimates of total annual cases of human 

salmonellosis from all sources are adjusted by the fraction of cases attributable to 

Siluriformes.

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 description to illustrate how FSIS seeks to improve 

food safety for Siluriformes, as well as describing the mathematics, input data and 

uncertainty associated with modeling the effectiveness of an FSIS Siluriformes inspection 

program.

Estimates of the potential effectiveness of an FSIS Siluriformes inspection program 

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

be currently associated with these fish. Even allowing for significant uncertainty about 

the baseline 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 these fish to some changes in the risk 

67
assessment model inputs and the effects of this 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.

6.1 Default estimation of numbers of Salmonella illnesses per year using the 

process model

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 using the chosen 

input parameters 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 product; 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 9) 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 

68
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.

The modeled estimates 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 product 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 these fish consumed in the U.S. 

(Table 10).

Table 9. Model outputs for the estimated probability of illness per contaminated 

serving for the combinations of cooking and breading effects.

Probability of 

Salmonella 

illness per 

Contaminated 

Serving Minimum

25th

percentile Median Mean

75th 

percentile Maximum

Standard 

Deviation

Baked and 

non-Breaded

0.00 8.2x10−15 1.2x10−10 2.1x10−5 5.3x10−8 2.2x10−1 7.9x10−4

Fried and nonBreaded0.0 4.3x10−11 2.8x10−8 1.1x10−4 1.6x10−6 3.5x10−1 2.5x10−3

Baked and 

Breaded

0.0 6.2x10−15 9.3x10−11 1.6x10−5 4.0x10−8 2.0x10−1 6.4x10−4

Fried and 

Breaded

0.0 3.2x10−11 2.1x10−8 8.8x10−5 1.2x10−6 3.2x10−1 2.1x10−3

69
Table 10. Estimates for annual Salmonella illnesses for each definition of catfish.a

Definition of 

Catfish

Number of 

Contaminated Servings 

(Exposures)

Estimated Average 

Probability of Illness 

per Contaminated 

Serving

Estimate of 

Salmonella

Illnesses per 

Year

Siluriformes 30,970,392 7.452x10−5 2,308

Ictaluridae 23,673,768 7.452x10−5 1,764

a

Estimates derived using the process model.

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

2,308 illnesses/yr 6 1.5 10

 1,548,519,606 servings/yr

− = × . This probability incorporates the prevalence of 

estimated contaminated servings and suggests that Salmonella illness resulting from 

consuming a serving of such fish is an uncommon event.

6.2 Illnesses per year: Application of an Attribution-Based Modelling Approach

Salmonella illnesses attributable to Siluriformes 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, Siluriformes 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 (of the order Siluriformes) 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 11). These data 

are used to determine an alternative estimate for the number of Siluriformes-related 

Salmonella illnesses each year. This estimation multiplies the fraction of foodborne 

outbreaks with identified vehicles that were attributed to Siluriformes by the estimated 

total Salmonella illnesses per year.

70
Table 11. 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 

Siluriformes, 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 

71
Equation 9 1

1

1

2

m

t i

i

t m

ti ti

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 vehicle and 1158 outbreaks in which other vehicles were 

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

associated with this proportion 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. 

6.3 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 estimated prevalence of Salmonella-contaminated carcasses. 

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

on any contaminated carcasses, that effect is not modeled. 

For the purposes of this model, it is assumed that the effect of FSIS inspection 

will be equivalent for domestic and imported product, consistent with import regulations 

that establish equivalency in food safety risk between imports and products of domestic 

origin.

Given these assumptions, the number of human salmonellosis illnesses associated 

with Siluriformes avoided each year is predicted with Equation 1 and Equation 4. The 

72
effectiveness of FSIS inspection may be some fraction, FSIS g , of the default estimated 

prevalence of contaminated carcasses. For example, if gFSIS = 0.1, then a 10% reduction 

in prevalence of contaminated carcasses following implementation of FSIS inspection is 

expected. Similarly, the new Salmonella prevalence of contaminated carcasses can be 

modeled by multiplying the default prevalence (i.e., 2%) by (1 0.9 − = gFSIS ) . Because the 

estimated 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 Siluriformes 

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 avoided FSIS ×

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

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

are a number of Salmonella-contaminated 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 servings run through the model 

(trials) and a small probability of human illness (defined as a “success” in terms of 

73
mathematical probabilities), this binomial process can be approximated as a Poisson 

process with rate parameter λ = ×n p (Vose, 2000). This development allows an 

appreciation of the nature 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 at reducing human health risk of any newly established 

FSIS inspection of Siluriformes is unknown. This is because the baseline prevalance of 

both contamination and illness are uncertain, and the rate at which FSIS inspection would 

achieve its ultimate reductions is unknown as well. 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 what the public health effect would be if 

the FSIS Siluriformes inspection program achieves peak effectiveness in 2, 5, 10 or 15 

years following its implementation. Linear interpolation is used to predict the estimated 

illnesses avoided in the years prior to the designated timeframe to achieve peak 

effectiveness in a given scenario.

6.3.1 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 

74
baseline study of poultry carcasses and found approximately 7.5% (volume-weighted) of 

3,275 samples Salmonella-positive36. 

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’ Siluriformes inspection program.

We note, however, that under FSIS HACCP inspection, Salmonella prevalence 

has varied over time within meat and poultry product classes and among classes and 

establishment sizes. In a minority of cases, Salmonella prevalence has proved resistant to 

improvement. Therefore, the difference in Salmonella prevalence witnessed between the 

1994-95 and 2007-08 microbiological baselines for broilers may not be indicative of the 

future trends in the microbiological quality of catfish, and substantial time and 

adaptations may be required before improvements are realized.

6.4 Program effectiveness estimates

Estimates of the potential effectiveness of FSIS Siluriformes program are 

presented relative to the estimated baseline number of salmonellosis cases potentially 

associated with catfish (of the order Siluriformes) consumption. As discussed previously, 

predicted program effectiveness depends upon the number of baseline Salmonella

illnessses, the peak effectiveness rate of an FSIS Siluriformes inspection program, and 

the timeframe required to achieve peak effectiveness.

It is important to note that estimates of the number of baseline Salmonella

illnesses attributable to these fish differ depending on whether considering all

Siluriformes or specifially Ictaluridae. The number of baseline Salmonella illnesses 

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

75

 
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 12) also include 5th and 95th

percentiles.

Table 12. Estimate of baseline Salmonella illnesses per year.a

Catfish Definition Mean

Confidence Interval

5th 95th

Siluriformes 2,308 2229 2387

Ictaluridae 1,764 1695 1833

a Estimates of the mean are derived using the process model and the confidence intervals are 

generated from an assumed Poisson distribution.

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

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

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

salmonellosis cases assocated with the consumption of catfish (of the order Siluriformes), 

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. The risk assessment assumes that the soonest that FSIS’ Siluriformes 

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 Siluriformes 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.

6.4.1 Analysis of Siluriformes

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-

76
year timeframe for reaching an uncertain peak effectiveness. Note that the relative 

estimanted number of illnesses avoided across 10 years of an FSIS Siluriformes 

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 Siluriformes. These 

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

Tables 13 through 15 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 Siluriformes inspection 

progam is assumed to be a 50% decline in salmonellosis cases related to these fish, then a 

comparison of Tables 13 through 15 shows that predicted Salmonella illnesses from 

estimated contmination of these fish avoided in the first year ranges from a low of 72 

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

77
(assuming a 15-year timeframe to achieve peak effectiveness) to a high of 384 (assuming 

a 2-year timeframe to achieve peak effectiveness). 

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

be at 90%, then comparing Tables 13 through 15 shows predicted Salmonella illnesses 

from these fish 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 these fish 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 these fish 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.

78
Table 13. Estimated Number of Salmonella illnesses avoided due to FSIS regulation 

of Siluriformes assuming a 2-year to effectiveness timeframe.

Year

90% Effectiveness 50% Effectiveness 10% Effectiveness

Mean

C.I. (Percentile)

Mean

C.I. (Percentile)

Mean

C.I. (Percentile)

5th 95th 5th 95th 5th 95th

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

Abbreviation: C.I., Confidence Interval.

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

of Siluriformes assuming a 10-year to effectiveness timeframe.

Year

90% Effectiveness 50% Effectiveness 10% Effectiveness

C.I. (Percentile) C.I. (Percentile) C.I. (Percentile)

Mean 5th 95th Mean 5th 95th Mean 5th 95th

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

Abbreviation: C.I., Confidence Interval.

79
Table 15. Estimated Number of Salmonella illnesses avoided due to FSIS regulation 

of Siluriformes assuming a 15-year to effectiveness timeframe.

Year

90% Effectiveness 50% Effectiveness 10% Effectiveness

Mean

C.I. (Percentile)

Mean

C.I. (Percentile)

Mean

C.I. (Percentile)

5th 95th 5th 95th 5th 95th

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

Abbreviation: C.I., Confidence Interval.

6.4.2 Analysis of Ictaluridae37

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

predicting the estimated annual number of Salmonella illnesses avoided if FSIS were to 

specfically inspect Ictaluridae instead of Siluriformes. This graph assumes a 5-year 

timeframe for reaching an uncertain peak effectiveness.

Tables 16 through 18 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 Ictaluridae inspection progam were assumed to be a 50% decline 

in Salmonella illnesses related to Ictaluridae, then a comparison of Tables 16 through 18 

shows that predicted Salmonella illnesses from these fish avoided in the first year ranges 

37 This section was developed and posted to FSIS’ website before the February 7, 2014 Agricultural Act of 

2014 (Pub. L. 113-79, Sec. 12106), known as the 2014 Farm Bill, amended Section 1(w) of the FMIA to 

remove the phrase “catfish, as defined by the Secretary,” and replace it with “all fish of the order 

Siluriformes,” thus including these fish among the amenable species (21 U.S.C. 601(w)(2)). This section 

has been included in this risk assessment for completeness and consistency with the previous version of the 

risk assessment, but represent a definition of catfish that is narrower than the Siluriformes that FSIS will 

inspect.

80

 
from a low of 55 (assuming a 15-year timeframe to achieve peak effectiveness) to a high 

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

If the peak effectiveness of an FSIS inspection progam that were specific to 

Ictaluridae is assumed to be 90%, then a comparison of Tables 16 through 18 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 if it were specific to 

Ictaluridae. These values assume a 5-yr timeframe and that there were an FSIS inspection 

system specifically for Ictaluridae.

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

81
Table 16. Estimated Number of Salmonella illnesses avoided if FSIS were to 

specifically regulate Ictaluridae and assuming a 2-year to effectiveness timeframe.

Year

90% Effectiveness 50% Effectiveness 10% Effectiveness

Mean

C.I. (Percentile)

Mean

C.I. (Percentile)

Mean

C.I. (Percentile)

5th 95th 5th 95th 5th 95th

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

Abbreviation: C.I., Confidence Interval.

Table 17. Estimated Number of Salmonella illnesses avoided if FSIS were to 

specifically regulate Ictaluridae and assuming a 10-year to effectiveness timeframe.

Year

90% Effectiveness 50% Effectiveness 10% Effectiveness

Mean

C.I. (Percentile)

Mean

C.I. (Percentile)

Mean

C.I. (Percentile)

5th 95th 5th 95th 5th 95th

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

Abbreviation: C.I., Confidence Interval.

82
Table 18. Estimated Number of Salmonella illnesses avoided if FSIS were to 

specifically regulate Ictaluridae and assuming a 15-year to effectiveness timeframe.

Year

90% Effectiveness 50% Effectiveness 10% Effectiveness

Mean

C.I. (Percentile)

Mean

C.I. (Percentile)

Mean

C.I. (Percentile)

5th 95th 5th 95th 5th 95th

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

Abbreviation: C.I., Confidence Interval.

83
6.5 Sensitivity of default illnesses estimates to changes in some model inputs 

A limited sensitivity analysis was completed on inputs to the 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 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 Salmonellacontaminated 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.

84
6.5.1 Prevalence

Both import and domestic prevalence assumptions were tested by increasing the 

default inputs by 10%.

6.5.2 Growth

One sensitivity scenario tested Salmonella growth assumptions in post- process 

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. 

6.5.3 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

(of the order Siluriformes) meals baked and the fraction of servings breaded. Both of 

these scenarios assumed 10% increases in the respective input parameter.

6.5.4 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%. 

85
6.5.5 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 α

and β parameters were adjusted by 10%.

6.5.6 Assumptions about extrapolating from poultry to Siluriformes

Two assumptions regarding extrapolation of poultry to fish 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%.

6.5.7 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 α and

β 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.

86
Figure 8. Tornado diagram describing the elasticity of the model’s annual illness estimates 

to various model inputs. The x-axis 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

87
6.6 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 (of 

the order Siluriformes) 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 19). 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 products (USDA-NASS, 2009). In 

addition, there is some evidence to suggest Salmonella prevalence might be larger than 

2% for some share of imported product (Broughton and Walker 2009)

38. 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 product. 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 

38 Nevertheless, there is also historic evidence that Salmonella prevalence among domestic catfish may also 

be higher than 2% (Wyatt et al. 1979).

88

 
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 

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.

89
Table 19. 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; α

=0.094, β =43.75

Use upper 95% confidence 

limit alpha and beta 

parameters from 

WHO/FAO, 2002; α

=0.1817, β =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 product

P Reduce prevalence to 1% for 

both domestic and imported 

product

Increase prevalence for 

imported product 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

The analysis completed for the potential lower bound scenario illustrates that the 

estimated annual Salmonella illnesses associated with Siluriformes 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

90
S PC + + ), 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 fish 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. 

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

91
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 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 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 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 contamination data 

currently available for testing this assumption; once FSIS inspection begins, this 

uncertainty can be addressed by collecting Siluriformes samples and enumerating 

Salmonella levels on positive samples.

92
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 

with current public health surveillance data (i.e., if Siluriformes 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.

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

93
Recalling that the effect of FSIS regulation on Siluriformes 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 20). Given these 

assumptions, Table 20 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.

Table 20. Estimated Number of Salmonella illnesses avoided by FSIS regulation of 

Siluriformes Assuming a 5-year Timeframe and 50% Effectiveness of FSIS 

inspection

Year

Lower Bound Scenario1 Upper Bound Scenario2

Mean

C.I. (Percentile)

Mean

C.I. (Percentile)

95th 5th 95th 5th 

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

Abbreviation: C.I., Confidence Interval.

1 Combines lower bound assumptions for serving size, dose response parameter values, skinless 

effect on fillet contamination levels, Salmonella prevalence, and cooking affect.

2 Combines upper bound assumptions for growth, breading affect, serving size, cooking effect, 

dose response parameter values, skinless effect on fillet contamination levels.

94
7. Summary

This risk assessment was completed to inform regulatory rule-making for 

establishing an FSIS Siluriformes inspection program. This risk assessment also 

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 

Siluriformes inspection program, this risk assessment estimates an average of 2,308 

(Confidence Intervals: 5th, 2,229; 95th, 2,387) Salmonella illnesses per year in the U.S. 

associated with the consumption of Siluriformes. The assessment estimates an average of 

1,764 illnesses associated with the consumption of 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 these 

fish consumed in the U.S., regardless of the definition, the default probability of 

Salmonella illness per serving of such fish is estimated to be 6 1.5 10− × . This probability 

incorporates the prevalence of Salmonella-contaminated servings and suggests 

salmonellosis from consuming a serving of such fish is an uncommon event.

There is substantial uncertainty regarding the actual effectiveness of a future FSIS 

Siluriformes inpsection program. The actual effectiveness of FSIS’ Siluriformes 

inspection program in reducing the prevalence of Salmonella-contaminated Siluriformes 

will directly influence the size of the likely benefits of such a program. To illustrate, this 

risk assessment predicts that if FSIS has a Siluriformes inspection program fully 

operational within a two year timeframe, then between 230 and 2,077 salmonellosis cases 

might be prevented per year, depending on whether the program is 10% or 90% effective.

Finally, the range of risk estimates depends on uncertainty about the model and 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 (of the order 

Siluriformes). Consideration of potential lower and upper bound model scenarios suggest 

plausible model estimates between 100 and 6,200 salmonellosis cases associated with 

Siluriformes 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 

95
inspection program is 50% effective within a 5-year timeframe. If only Ictaluridae are 

considered, then between 38 and about 2,353 cases might be prevented each year.

Given current uncertainties about the effectiveness of this future program and 

limited contamination data for Siluriformes, this food safety risk assessment provides

estimates of potential public health benefits of an FSIS Siluriformes inspection program.

96
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Addendum

Following the passage of the Agricultural Act of 2014 (Pub. L. 113-79, Sec. 

12106), FSIS conducted a literature search to identify any research published since FSIS 

developed this risk assessment and searched the Centers for Disease Control and Prevent 

(CDC) outbreak database to determine whether any new research or outbreak information 

would affect the previously conducted risk assessment. 

The National Library of Medicine’s PubMed Database was searched using the 

key word Siluriformes for articles published since 2008. That search yielded about 1,200 

articles related to Siluriformes. The abstracts from those articles were reviewed to 

identify any articles which could affect the hazard identification or the risk assessment. A 

number of publications discussed the concentrations of different environment 

contaminants in wild catfish, and in some cases farm-raised catfish, from locations 

around the globe. Most of those contaminants, with the exception of polycyclic aromatic 

hydrocarbons (PAHs) and polybrominated diphenyl ethers (PBDEs), are already 

discussed in Chapter 2 of this report and did not contain information that would 

substantively change the hazard identification or risk assessment. Potentially relevant 

articles are discussed below.

Two articles that evaluated chemicals not previously discussed in the hazard 

identification are summarized here. Squadrone et al. (2014) analyzed European catfish

(Silurus glanis) samples from the upper Po River basin for the levels of nine PAHs 

(naphthalene, acenaphthene, fluorene, phenanthrene, anthracene, pyrene, 

benz[a]anthracene, chrysene, and benz[a]pyrene) and detected PAHs in a number of the 

muscle samples. Staskal et al. (2008) analyzed wild-caught and farm-raised catfish 

samples from southern Mississippi for 43 PBDEs. PBDE concentrations were 

significantly higher in wild-caught (median concentration of 2.7 ng/g wet weight) than 

farm-raised (median concentration of 0.5 ng/g wet weight) catfish samples. The authors 

estimated exposures from daily consumption, and concluded that the health risks from 

PBDEs in wild-caught or farm-raised catfish are substantially lower than risk levels 

“generally considered to be at the U.S. EPA minimum concern level.” 

103
There were a number of studies that analyzed US catfish, including farm-raised 

and commercially available catfish samples. Scott et al. (2009) analyzed the 

concentrations of “17 laterally substituted PCDD/Fs, 12 dioxin-like PCBs, and 97 nondioxin-like PCBs” in wild-caught and farm-raised catfish samples from southern 

Mississippi, and estimated the potential health effects from exposure through 

consumption of those fish. The authors found that the concentrations of the various 

chemicals were lower than seen in earlier studies, indicating that concentrations might be 

decreasing, and that the cancer risk from exposure to PCCDD/Fs and dioxin-like and 

non-dioxin-like PCBs in catfish is low (less than 27.0x10−6). Huwe and Archer (2013) 

summarized data from 202 catfish samples collected in 2009 under the USDA Pesticide 

Data Program and analyzed for PCDD, PCDF, PCB, and PBDE. PCDD/F TEQs and dl 

PCB TEQs ranged from 0.02–3.46 and 0.001–0.10 pg/g wet weight, respectively. The 

total TEQs had decreased from what had been seen in earlier studies. The patterns of 

contamination and levels indicated that the source of contamination might be from 

mineral clays used in catfish feed (Huwe and Archer, 2013).

Weintraub and Birmbahm (2008) identified catfish consumption as a potential 

source of elevated PCB levels in non-Hispanic Black anglers. The focus of that study on 

anglers who eat wild-caught catfish, however, makes it less relevant to this risk 

assessment. 

Two studies focused on microbial contamination in catfish. Chen et al. (2010) 

examined catfish skins, intestines, fresh fillets and environmental samples from a catfish 

processing facility for the presence of Listeria monocytogenes. No contamination was 

isolated from the skin and intestine, but 76.7% of the chilled fresh catfish fillets and 

43.3% of unchilled fillets were contaminated with Listeria monocytogenes. Listeria 

monocytogenes was also isolated from fish contact surfaces in the processing 

environment. The authors concluded that the processing environment might be the source 

of the contamination. Pao et al. (2008) tested fish samples purchased at retail markets in 

central Virginia or on the Internet for a number of microbes, including Salmonella, E. 

coli O157:H7, and Listeria. The researchers did not detect E. coli O157 or Salmonella in 

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any of the samples. 22.2% and 25.0% of retail and Internet purchased samples, 

respectively, tested positive for Listeria monocytogenes.

A number of studies have isolated antimicrobial resistant bacteria in fish from 

aquaculture. Zhao et al. (2003) evaluated 187 Salmonella isolates from imported foods 

entering the United States collected by FDA, including fish and, specifically, catfish. 

Fifteen (8%) of the isolates exhibited some antimicrobial resistance, including three 

isolates from frozen catfish samples. Elsewhere, researchers investigating aquacultureraised fish, including some catfish, have identified antibiotic resistant bacteria in a 

number of studies, including Salmonella species (Elhadi, 2014), E. coli (Ryu, 2011), and 

other bacteria (Akinbowale, 2006; Deng, 2014; Nagar, 2011; Petersen, 2002; and 

Resende, 2012). The relationship between antimicrobial chemicals in aquaculture and 

that antimicrobial resistance, however, has not been widely studied. Furthermore, Chen et 

al. (2010) did not identify antimicrobial resistant L. monocytogenes in catfish fillets or 

the catfish processing environment, but did note that “the presence of tet(M) gene in L. 

innocua raises the possibility of future acquisition of resistance by L. monocytogenes.” 

Korsak et al. (2012) found a low prevalence of antimicrobial resistance in L. 

monocytogenes strains in Poland.

In addition, following the publication of the first version of this risk assessment, 

McCoy et al. (2011) published a review article summarizing the foodborne agents 

associated with the consumption of aquaculture catfish.

A brief summary of the full Agricultural Marketing Service (AMS) Pesticide Data 

Program (PDP) analysis of pesticide residues in domestic and imported catfish from 2008 

to 2010 is included in Table A-1. Pesticides that were detected in more than five percent 

of samples were included in the table and are described in the following paragraphs. 

Table A-2 presents data on catfish and Pangasius sp from FDA’s 2009–2013 seafood 

program. 

Bifenthrin (or biphenthrin) is an EPA registered pyrethroid insecticide with no 

tolerance established in fish. Under CASRN 82657-04-3, the EPA has established an oral 

reference dose (RfD) of 0.015 mg/kg/day with tremors as the critical effect (dog). There 

is no carcinogenicity assessment.

105
Table A-1. Summary of the Agricultural Marketing Service’s Pesticide Data 

Program (PDP) analysis of pesticide residues in Domestic and Imported Catfish: 

2008 to 2010. a

Positive

(%)

Violative

(%)

Number 

of 

Samples

LOD

(ppb)

Maximum 

Concentration 

Detected (ppb)

Regulatory Level (ppb)

United States Codex

Bifenthrin 15% 15% 1479 1.0 60 0 0

Chlorpyrifos 7% 7% 1479 1.0 40 0 0

DDD o,p’ 13% 0% 1095 1.0-2.0 36 5,000 0

DDD p,p’ 36% 0% 1095 1.0-2.0 138 5,000 0

DDE p,p’ 75% 0% 1095 1.0-2.0 2310 5000 0

Diphenylamine 

(DPA)

11% 11% 1479 1.0-2.0 47 0 0

Diuron 7% 7% 1479 16 210 0 0

Endosulfan 

sulfate

7% 7% 1479 1.0 28 0 0

Toxaphene 8% 8% 1095 50 461 0 0

a

Data are shown for pesticides detected in more than 5% of the catfish samples tested in the USDA AMS 

Pesticide Data Program from 2008–2010.

Abbreviations: LOD, Level of Detection; ppb, parts per billion.

Table A-2. Chemotherapeutics in FDA’s Seafood Program (2009-2013): Catfish and 

other Pangasius sp Data.

Fiscal 

Year

Number of 

Samples Collected Number of Analyses

Number of 

positive Samples 

(Drug Residue 

Detected; 

Domestic Import CAM NF FQ FFC Country)

MG/

GV QL

Sulfa 

Drugs Stillbenes MT

2009 12 62 NT NT 64 NT 61 15 NT NT NT 1 (GV; Vietnam)

2010 2 72 NT 1 70 NT 68 10 NT NT NT 2 (FQ; China, 

Vietnam)

2011 7 72 NT 1 67 NT 41 3 NT NT NT 0

2012 16 134 NT 54 102 NT 91 17 NT 6 4 1 (NF; Vietnam)

5 (FQ; China, 

Vietnam)

1 (MG; China)

2013 32 100 15 71 107 1 46 32 9 NT 15 0

Abbreviations: CAM, Chloramphenicol; FDA, US Food and Drug Administration; FFC, Florfenicol; FQ, 

Fluorquinolones; GV, Gentian Violet; MG, Malachite Green; MT, Methyltestosterone; NT, Nitrofurans; 

NT, Not Tested; QL, Quinolones.

Source: FDA. 

106
Chlorpyrifos is an EPA registered insecticide. The chronic oral reference dose 

(RfD) is not given on the IRIS web site. The 2006 reregistration eligibility decision 

document for chlorpyrifos lists an oral RfD of 0.0003 mg/kg-bw/day, taking into account 

a NOEAL of 0.03 mg/kg-bw/day with an uncertainty factor of 100 and a Food Quality 

Protection Act factor of 10.

DDD and DDE are metabolites of DDT, which has been discussed earlier in the 

document.

Diphenylamine (DPA) is an EPA registered fungicide with no tolerance 

established in fish. Under CASRN 122-39-4, the EPA has established an oral reference

dose (RfD) of 0.025 mg/kg/day with decreased body weight and increased liver and 

kidney weights as critical effects (dog). There is no carcinogenicity assessment.

Diuron, also known as DCMU, is an EPA registered herbicide with no tolerance 

established in fish. Under CASRN 330-54-1, the EPA has established an oral reference

dose (RfD) of 2x10−3 mg/kg/day with abnormal pigments in blood as the critical effect 

(dog). There is no carcinogenicity assessment.

Endosulfan sulfate is a toxic oxidation product of endosulfan, an EPA registered 

organochlorine insecticide and acaricide that is currently being phased out by EPA. The 

last permitted use of endosulfan will expire July 31, 2016. There is no specific 

information in the EPA IRIS database on endosulfan sulfate, but endosulfan is listed with 

under CASRN 115-29-7. EPA has established an oral reference dose (RfD) of 6x10−3

mg/kg/day with reduced body weight gain in both males and females and increased 

incidence of marked progressive glomerulonephrosis and blood vessel aneurysms in 

males as the critical effects (rat).

Toxaphene is an insecticide that is banned globally by the Stockholm Convention 

on Persistent Organic Pollutants. Under CASRN 8001-35-2, the EPA has categorized 

toxaphene as a probable human carcinogen (B2) with a cancer slope factor of 1.1 per 

mg/kg/day, based on long-term mouse and rat studies. There is no oral reference dose.

In addition, a review of CDC’s outbreak database did not identify any outbreaks 

related to Siluriformes or catfish since 2007. A single catfish-related outbreak, with two 

107
cases, occurred in 2007, however, the agent or microorganism responsible for the 

illnesses was not identified. 

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