Estimated Fish Consumption Rates for the U.S. 

Population and Selected Subpopulations 

(NHANES 2003-2010) 

Final Report

April 2014

EPA-820-R-14-002
Acknowledgments 

The U.S. Environmental Protection Agency (EPA) Project Manager for this project was Jeffrey D. 

Bigler, who provided management and technical direction for the project. EPA was supported in 

this effort by Westat under EPA Contract No. EP-C-10-023. The Westat project manager was 

Rebecca Jeffries Birch who was responsible for the overall technical accuracy and quality of the 

project. John Rogers served as the senior statistician. The methodology developed for the project 

was externally peer-reviewed by Versar under EPA Contract No. EP-C-13-010. 

ii
Table of Contents 

Chapter Page 

1 Background .......................................................................................................... 1 

2 National Health and Nutrition Examination Survey ..................................... 4 

2.1 Survey Description................................................................................ 4 

2.2 Survey Data............................................................................................ 5 

2.2.1 24-Hour Recall ...................................................................... 5 

2.2.2 30-Day Fish Consumption Frequency .............................. 6 

2.3 Regions ................................................................................................... 7 

3 Data Processing Methodology .......................................................................... 9 

3.1 Habitat Apportionment ....................................................................... 9 

3.1.1 NHANES Fish Groupings.................................................. 11

3.1.2 Use of NOAA Landings Data ............................................ 12

3.1.3 Imported Fish and Farmed Fish ........................................ 13

3.2 Trophic Level Assignments................................................................. 15

3.3 Extracting Reported Amounts of Fish Consumed .......................... 17

4 Statistical Methodology ...................................................................................... 21 

4.1 Overview of the NCI Method ............................................................ 21 

4.2 Calculation Steps for the NCI and EPA Models ............................. 24 

4.2.1 NCI Method.......................................................................... 24 

4.2.2 EPA Method ......................................................................... 27 

4.3 Simulation of the Usual Fish Consumption ...................................... 31 

4.4 Calculation of Standard Errors and Confidence Intervals .............. 33 

4.5 Application of EPA Method ............................................................... 35 

4 6 Comparison of Results from the EPA and NCI Methods ............. 38 

4.6.1 Analysis of Simulated Data ................................................. 38 

4.6.2 Confidence Intervals for Percentiles of Fish 

Consumption ......................................................................... 39

4.6.3 Analysis of NHANES Fish Data Using Various 

Models .................................................................................... 42 

iii
Contents (continued) 

Chapter Page 

5 Results.................................................................................................................. 46 

5.1 Sample Size ............................................................................................ 46 

5.2 Usual Fish Consumption Rates ........................................................... 48 

5.3 Uncertainty............................................................................................. 93

5.3.1 Seasonality.............................................................................. 94

5.3.2 Bias in the Reported Fish Consumption ........................... 94

5.3.3 Use of Standard Recipes...................................................... 94

5.3.4 Habitat Assignments............................................................ 95

5.3.5 Estimation of Usual Fish Consumption ........................... 95

5.3.6 Weights for Coastal Versus Non-Coastal 

Regions ................................................................................... 97

6 Discussion............................................................................................................ 98

7 References............................................................................................................ 99

Appendixes

A Final Habitat Apportionment ........................................................................... A-1 

B FNDDS Processing and Fish-Containing Food Codes ................................ B-1 

C Supplemental Statistical Methodology ............................................................. C-1 

D EPA Method SAS Code .................................................................................... D-1 

E UFCR Raw Weight, Edible Portion................................................................. E-1 

F UFCR as Prepared Weight................................................................................ F-1 

G Unweighted Sample Sizes.................................................................................. G-1 

H Response to Peer Review Comments.............................................................. H-1 

iv
Contents (continued) 

Tables Page 

1 Habitat assignments of NHANES fish groups .............................................. 10

2 NOAA landings data, clam apportionment .................................................... 14

3 Trophic level assignments................................................................................. 16

4 Estimated moisture loss due to cooking or processing ................................ 19

5 λ ∗values used for each combination of dependent variable and 

data set.................................................................................................................. 29

6 Comparison of NCI and EPA Methods using NHANES fish 

data........................................................................................................................ 43 

7 Sample size and number reporting fish consumption, by fish type ............ 47 

8a UFCR estimates (g/day raw weight, edible portion): Total fish, 

adults, 21 years and older, by demographic characteristics.......................... 49

8b UFCR estimates (g/day raw weight, edible portion): Total fish, 

adults, 21 years and older, by geographic area................................................ 50

9a UFCR estimates (g/day raw weight, edible portion): Freshwater 

+ estuarine fish, adults, 21 years and older, by demographic 

characteristics...................................................................................................... 51 

9b UFCR estimates (g/day raw weight, edible portion): Freshwater 

+ estuarine fish, adults, 21 years and older, by geographic area.................. 52 

10a UFCR estimates (g/day raw weight, edible portion): Marine fish, 

adults, 21 years and older, by demographic characteristics.......................... 53 

10b UFCR estimates (g/day raw weight, edible portion): Marine fish, 

adults, 21 years and older, by geographic area................................................ 54 

11a UFCR estimates (g/day raw weight, edible portion): Total finfish, 

adults, 21 years and older, by demographic characteristics.......................... 55 

11b UFCR estimates (g/day raw weight, edible portion): Total finfish, 

adults, 21 years and older, by geographic area................................................ 56 

v 
Contents (continued) 

Tables (continued) Page 

12a UFCR estimates (g/day raw weight, edible portion): Total 

shellfish, adults, 21 years and older, by demographic 

characteristics...................................................................................................... 57 

12b UFCR estimates (g/day raw weight, edible portion): Total 

shellfish, adults, 21 years and older, by geographic area ............................... 58 

13a UFCR estimates (g/day raw weight, edible portion): Total trophic 

level 2 fish, adults, 21 years and older, by demographic 

characteristics...................................................................................................... 59

13b UFCR estimates (g/day raw weight, edible portion): Total trophic 

level 2 fish, adults, 21 years and older, by geographic area........................... 60

14a UFCR estimates (g/day raw weight, edible portion): Total trophic 

level 3 fish, adults, 21 years and older, by demographic 

characteristics...................................................................................................... 61 

14b UFCR estimates (g/day raw weight, edible portion): Total trophic 

level 3 fish, adults, 21 years and older, by geographic area........................... 62 

15a UFCR estimates (g/day raw weight, edible portion): Total trophic 

level 4 fish, adults, 21 years and older, by demographic 

characteristics...................................................................................................... 63 

15b UFCR estimates (g/day raw weight, edible portion): Total trophic 

level 4 fish, adults, 21 years and older, by geographic area........................... 64 

16a UFCR estimates (g/day raw weight, edible portion): Total 

freshwater + estuarine trophic level 2 fish, adults, 21 years and 

older, by demographic characteristics.............................................................. 65 

16b UFCR estimates (g/day raw weight, edible portion): Total 

freshwater + estuarine trophic level 2 fish, adults, 21 years and 

older, by geographic area................................................................................... 66 

17a UFCR estimates (g/day raw weight, edible portion): Total 

freshwater + estuarine trophic level 3 fish, adults, 21 years and 

older, by demographic characteristics.............................................................. 67 

vi
Contents (continued) 

Tables (continued) Page 

17b UFCR estimates (g/day raw weight, edible portion): Total 

freshwater + estuarine trophic level 3 fish, adults, 21 years and 

older, by geographic area................................................................................... 68 

18a UFCR estimates (g/day raw weight, edible portion): Total 

freshwater + estuarine trophic level 4 fish, adults, 21 years and 

older, by demographic characteristics.............................................................. 69

18b UFCR estimates (g/day raw weight, edible portion): Total 

freshwater + estuarine trophic level 4 fish, adults, 21 years and 

older, by geographic area................................................................................... 70

19a UFCR estimates (g/day raw weight, edible portion): Total fish, 

youth, <21 years, by demographic characteristics......................................... 71

19b UFCR estimates (g/day raw weight, edible portion): Total fish, 

youth, <21 years, by geographic area............................................................... 72

20a UFCR estimates (g/day raw weight, edible portion): Freshwater 

+ estuarine fish, youth, <21 years, by demographic characteristics............ 73

20b UFCR estimates (g/day raw weight, edible portion): Freshwater 

+ estuarine fish, youth, <21 years, by geographic area................................. 74

21a UFCR estimates (g/day raw weight, edible portion): Marine fish, 

youth, <21 years, by demographic characteristics......................................... 75

21b UFCR estimates (g/day raw weight, edible portion): Marine fish, 

youth, <21 years, by geographic area............................................................... 76

22a UFCR estimates (g/day raw weight, edible portion): Total finfish, 

youth, <21 years, by demographic characteristics......................................... 77

22b UFCR estimates (g/day raw weight, edible portion): Total finfish, 

youth, <21 years, by geographic area............................................................... 78

23a UFCR estimates (g/day raw weight, edible portion): Total 

shellfish, youth, <21 years, by demographic characteristics......................... 79

vii
Contents (continued) 

Tables (continued) Page 

23b UFCR estimates (g/day raw weight, edible portion): Total 

shellfish, youth, <21 years, by geographic area .............................................. 80

24a UFCR estimates (g/day raw weight, edible portion): Total trophic 

level 2 fish, youth, <21 years, by demographic characteristics.................... 81

24b UFCR estimates (g/day raw weight, edible portion): Total trophic 

level 2 fish, youth, <21 years, by geographic area.......................................... 82

25a UFCR estimates (g/day raw weight, edible portion): Total trophic 

level 3 fish, youth, <21 years, by demographic characteristics.................... 83

25b UFCR estimates (g/day raw weight, edible portion): Total trophic 

level 3 fish, youth, <21 years, by geographic area.......................................... 84

26a UFCR estimates (g/day raw weight, edible portion): Total trophic 

level 4 fish, youth, <21 years, by demographic characteristics.................... 85

26b UFCR estimates (g/day raw weight, edible portion): Total trophic 

level 4 fish, youth, <21 years, by geographic area.......................................... 86

27a UFCR estimates (g/day raw weight, edible portion): Total 

freshwater + estuarine trophic level 2 fish, youth, <21 years, by 

demographic characteristics.............................................................................. 87

27b UFCR estimates (g/day raw weight, edible portion): Total 

freshwater + estuarine trophic level 2 fish, youth, <21 years, by 

geographic area.................................................................................................... 88

28a UFCR estimates (g/day raw weight, edible portion): Total 

freshwater + estuarine trophic level 3 fish, youth, <21 years, by 

demographic characteristics.............................................................................. 89

28b UFCR estimates (g/day raw weight, edible portion): Total 

freshwater + estuarine trophic level 3 fish youth, <21 years, by 

geographic area.................................................................................................... 90

viii
Contents (continued) 

Tables (continued) Page 

29a UFCR estimates (g/day raw weight, edible portion): Total 

freshwater + estuarine trophic level 4 fish, youth, <21 years, by 

demographic characteristics.............................................................................. 91

29b UFCR estimates (g/day raw weight, edible portion): Total 

freshwater + estuarine trophic level 4 fish, youth, <21 years, by 

geographic area.................................................................................................... 92

Figures

1 Comparison of NCI, EPA, and true percentiles using simulated 

data and different transformations ................................................................... 40

2 Comparison of confidence intervals using the NCI and EPA 

Methods ............................................................................................................... 41 

3 Comparison of NCI and EPA percentiles of fish consumption 

rates....................................................................................................................... 45 

ix
Background 1 

The U.S. population is exposed to environmental contaminants through the consumption of 

contaminated finfish and shellfish (Thompson and Boekelheide, 2013; National Research Council, 

2000; Ahmed, Hattis, Wolke, and Steinman, 1993). The analysis presented here provides EPA’s 

recommended methodology for developing a national-level fish consumption rate (FCR) for use in 

developing ambient water quality criteria as required under Section 304(a) of the Clean Water Act. 

As more current data are available and new analytical methodologies have been developed, the 

Office of Water has conducted a new analysis of FCR. These new FCRs were estimated using data 

from the National Health and Nutrition Examination Survey (NHANES) 2003-2010. NHANES is a 

continuous survey designed to collect data on the health and nutritional status of the U.S. 

population. Each 2-year cycle is designed to be representative of the general U.S. population. 

An individual’s FCR is the expected quantity of fish consumed per unit time. For a population, there 

is a distribution of FCR; some individuals consume more fish per unit time and some less. With 

adequate data, we can calculate the average FCR across the population or percentiles, such as the 

90th percentile (10 percent of the population has an individual FCR greater than the 90th 

percentile).

Different time units can be used to express the same rate, e.g., per day or per week. The FCR is a 

theoretical quantity and is often estimated using statistical analysis. It may change over time, for 

example, be higher in the summer than the winter. Thus, the FCR depends on the time frame (e.g., 

summer, winter, annual). 

Due to the infrequent consumption of fish, the estimated FCR may be variable or imprecise. If a 

person eats fish for dinner every Friday and not at other times, the FCR is one fish meal per week 

and the estimated FCR is likely to be relatively constant. If a fish meal is consumed on average once 

every 7 days but sometimes 3 days in a week and other times not for several weeks, the estimated 

FCR over a short time frame can be quite variable, even though the true FCR is constant and is the 

same as in the first example. As the time frame covered by the data gets longer, the estimated FCR 

becomes less variable. Assuming the true long-term FCR is constant over time, if the time frame 

1 
covered by the data is very long, the estimated FCR becomes a relatively precise estimate of the true 

long-term or usual FCR. 

Assuming the FCR is constant over time, methodologies can be designed to estimate the distribution 

of the true, long-term, FCR even though the data are collected over a limited time frame. We can 

add the term “usual” to “fish consumption rate” (UFCR) to imply that the resulting estimates are 

those that correspond to long-term averages, rather than short-term estimates and to avoid a 

distinction between the true rate and the estimated rate. 

In the mid-2000s, the National Cancer Institute (NCI) developed a statistical methodology to 

estimate usual intake of episodically consumed foods. This method, known as the NCI Method, has 

been published and statistical programs are available on NCI’s web site. There are other methods 

that have been developed to estimate the distribution of usual intake of episodically consumed 

foods. However, the NCI Method is preferred because it accounts for days without consumption; 

distinguishes within-person from between-person variation; allows for the correlation between the 

probability of consumption and the consumption-day amount; and can use covariate data to better 

predict usual intake. 

The NCI Method provides estimates of UFCR representing the long-term average grams of fish 

consumed per day. Due to the episodic nature of fish consumption, the NCI Method models both 

the probability of consumption on a given day and the amount consumed on days when some fish is 

consumed. These two predicted values are then multiplied together to get a usual intake value. The 

calculations using the NCI Method are very time consuming. To get estimates in a reasonable time, 

EPA created a program, hereinafter referred to as the EPA Method, which approximates the results 

from the NCI Method. Details of the NCI Method, the EPA Method, and how they compare are 

provided in Section 4, Statistical Methods.

UFCRs were estimated for the general U.S. population, the youth population under 21 years of age, 

and the adult population 21 years and older. UFCR estimates were calculated for various 

subpopulations, e.g., by age, gender, race/ethnicity, income, U.S. Census region, and coastal and 

noncoastal populations. We estimated UFCR for 18 different categories of fish, both raw weight of 

edible portion and as-prepared weight. These fish types were chosen as they represent various 

categories of interest to states and tribes. For example, a coastal state may be interested in knowing 

the UFCRs of total fish and of marine and freshwater + estuarine, separately. An inland state may 

only be interested in freshwater fish UFCRs. Additionally, as fish bioaccumulate toxins at different 

2 
rates depending on their trophic level, UFCR were also calculated for fish by trophic level. The fish 

types are the following: 

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Total fish; 

Total finfish; 

Total shellfish; 

Marine fish; 

Freshwater fish; 

Estuarine fish; 

Freshwater + estuarine fish; 

Freshwater + marine fish; 

Estuarine + marine fish; 

Trophic level 2 fish; 

Trophic level 3 fish; 

Trophic level 4 fish; 

Marine trophic level 2 fish; 

Marine trophic level 3 fish; 

Marine trophic level 4 fish; 

Freshwater + estuarine trophic level 2 fish; 

Freshwater + estuarine trophic level 3 fish; and

Freshwater + estuarine trophic level 4 fish. 

This report presents the methodologies used to extract fish consumption data from the NHANES 

data sets, the habitat apportionment methodology, the trophic level assignment methodology, the 

statistical methodology, and the UFCR estimates and 95 percent confidence intervals (95% CI) of 

the mean and the 25th, 50th, 75th, 90th, 95th, 97th, and 99th percentiles.

3 
National Health and Nutrition Examination 

Survey 2 

2.1 Survey Description 

NHANES is designed to assess the health and nutritional status of adults and children in the United 

States. It is conducted by the National Center for Health Statistics (NCHS, 2013), part of the 

Centers for Disease Control and Prevention (CDC) that is responsible for producing vital and health 

statistics for the United States. NHANES began in the 1960s. In 1999, the survey became a 

continuous program that examines a nationally representative sample of about 5,000 persons located 

in 15 counties across the country each year. 

The NHANES interview includes demographic, socioeconomic, dietary, and health-related 

questions. The examination component consists of medical, dental, and physiological measurements, 

as well as laboratory tests.

NHANES collects 2 days of dietary data from all participants. The first day, the data are collected in

person at the examination portion of the survey. The second day’s data are collected by telephone 

interview 3 to 10 days after the in-person interview. Both interviews include a 24-hour dietary recall 

section. The primary goal of the 24-hour recall is to collect a detailed list of all the foods and 

beverages consumed within a 24-hour period. Food models are used to help participants estimate 

the amount consumed. The in-person interview also includes a section on the frequency of 

consumption of fish and shellfish in the past 30 days (NCHS, 2009). Survey participants are not 

asked to provide detailed recipes for mixed dishes. For those, standard default recipes are used.

A complex, multistage probability sampling design is used to select participants representative of the 

civilian, noninstitutionalized U.S. population. 

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Stage 1: Primary sampling units (PSUs) are selected with probability proportional to a 

measure of size (PPS). These are mostly single counties or, in a few cases, groups of 

contiguous counties. 

Stage 2: The PSUs are divided up into segments (generally city blocks or their 

equivalent). As with each PSU, sample segments are selected with PPS.

4 
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Stage 3: Households within each segment are listed, and a household sample is 

randomly drawn. In geographic areas where the proportion of age, ethnic, or income 

groups selected for oversampling is high, the probability of selection for those groups is 

greater than in other areas.

Stage 4: Individuals are chosen to participate in NHANES from a list of all persons 

residing in selected households. Individuals are drawn at random within designated agesex-race/ethnicity screening subdomains. On average, 1.6 persons are selected per 

household. Oversampling of certain population subgroups is done to increase the 

reliability and precision of health status indicator estimates for these groups. 

The NHANES data files include analysis weights to account for the complex survey design 

(including oversampling), survey nonresponse, and poststratification. Weighted NHANES results

describe the U.S. Census civilian noninstitutionalized population. A person’s analysis weight is a 

measure of the number of people in the population represented by that sampled person. 

2.2 Survey Data 

2.2.1 24-Hour Recall 

The 24-hour dietary recall interview data provide (1) what food items the participants ate and (2) 

how much of each food item they ate. All NHANES participants are eligible for the dietary 

interview component that occurs during the examination portion of the survey. The first interview is 

conducted in person via a computer-assisted dietary interview software program that was developed 

for NHANES. The interviewer uses a standard set of measuring guides to help the participant report 

the volume and dimensions of the foods consumed. The second dietary interview is conducted via 

telephone. It occurs 3 to 10 days after the first dietary interview. The participants are given a set of 

measuring guides to take home and use during the telephone interview. 

The 24-hour recall data are collected using the USDA Automated Multiple-Pass Method (AMPM).

Detailed information on the method can be found on USDA’s web site at

http://www.ars.usda.gov/Services/docs.htm?docid=7710. The method is computerized and 

research based. It uses five steps designed to assist participants with complete and accurate food 

recall and reduce respondent burden. 

5 
The five steps follow: 

1. Collect a list of foods and beverages consumed the previous day. 

2. Probe for foods forgotten during step 1. 

3. Collect the time and the name of the eating occasion for each food.

4. For each food, collect detailed description, amount, and additions (i.e., anything that 

may have been added to the food). Review 24-hour day. 

5. Final probe for anything else consumed. 

We assume that the reports of 24-hour consumption are unbiased estimates of each respondent’s 

true consumption. 

2.2.2 30-Day Fish Consumption Frequency 

The 30-day fish consumption frequency data are derived from questionnaire data that ask 

participants how often in the past 30 days they consumed different fish species. These species are 

clams, crabs, crayfish, lobster, mussels, oysters, scallops, shrimp, other shellfish, unknown shellfish, 

breaded fish products, tuna, bass, catfish, cod, flatfish, haddock, mackerel, perch, pike, pollock, 

porgy, salmon, sardines, sea bass, shark, swordfish, trout, walleye, other fish, and unknown fish. 

Using these data, we can derive a variable for the number of times fish was consumed in the past 30 

days by summing up the values for all 31 variables. This information improves intake estimates for 

episodically consumed foods like fish, as even people who consumed fish frequently do not do so 

every day; therefore, it is not always reported in 24-hour recall data. This derived frequency of 

consumption can then be used as a predictor in statistical models of the probability of fish 

consumption and fish consumption amount. 

In 2003-2004, only children less than 6 years of age and women 16 to 49 years old were asked these 

questions. As frequency of fish consumption is an important predictor in the statistical models, we 

only included these age and gender groups from NHANES 2003-2004 in the analysis. The analysis 

weights of male participants in 2005-2010 and females not in these age groups were adjusted to 

account for this difference. Since they are only in three of the four cycles of NHANES their weights 

were multiplied by a factor of 4/3. 

6 
2.3 Regions 

Patterns of fish and shellfish consumption may vary by geography, such as between U.S. residents 

who live on or near the coast and those who live inland, or among regions of the United States as 

defined by the U.S. Census Bureau (Mahaffey, Clickner, and Jeffries, 2009). Fish consumption 

patterns may also vary by specific coast (e.g., residents near the Atlantic coast may have different 

fish consumption patterns than those on the Gulf of Mexico coast). To estimate FCRs by region 

and coast, we assigned NHANES respondents to U.S. Census Bureau regions and coastal or 

noncoastal status, which when combined created the following: Atlantic Coast, Northeast, Great 

Lakes, Midwest, South, Gulf of Mexico, West, and Pacific Coast. The geography data were obtained 

from the NCHS Research Data Center through its restricted-use data access procedures. 

The geographic unit used by NHANES is a county or county equivalent; therefore our definitions of 

coastal and noncoastal were limited to county boundaries. All counties that bordered the Pacific or 

Atlantic Oceans, the Gulf of Mexico or any of the Great Lakes were defined as coastal. Additionally, 

counties that bordered estuaries and bays were defined as coastal as were counties whose centroid 

was within approximately 25 miles of any coast even if not directly bordering a coast. The four 

coastal regions were then defined based on nearest body of water. The following provides 

definitions of each region: 

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U.S. Census Regions 

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Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, and KS 

Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, and ME

South = DE, MD, DC, VA, WV, KY, TN, NC, SC, GA, AL, MS, FL, LA, AR, 

OK, and TX

West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, and HI 

Coastal and Inland Regions 

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Pacific Coast = coastal counties in CA, OR, WA, AK, and HI 

Atlantic Coast = coastal counties in CT, DE, DC, FL (bordering Atlantic Ocean), 

GA, ME, MD, MA, NH, NJ, NY, NC, PA, RI, SC, and VA

Gulf of Mexico Coast = coastal counties in AL, FL (bordering Gulf of Mexico), 

LA, MS, and TX

Great Lakes Coast = counties bordering the Great Lakes in MI, WI, OH, NY, 

MN, IN, IL, and PA

7 
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Inland West = remaining counties in CA, OR, WA, AK, and HI and all of NM, 

CO, WY, MT, ID, UT, AZ, and NV

Inland South = remaining non-coastal counties in DE, MD, DC, VA, NC, SC, 

GA, AL, MS, FL, LA, and TX and all of WV, KY, TN, AR, and OK

Inland Northeast = remaining counties in PA, NY, NJ, CT, RI, MA, NH, and 

ME and all of VT. 

Inland Midwest = remaining counties in OH, MI, IN, WI, IL, and MN and all of 

MO, IA, SD, ND, NE, and KS. 

8 
Data Processing Methodology 3 

3.1 Habitat Apportionment 

To make estimates of FCRs for marine fish, estuarine fish, freshwater fish, and various 

combinations of these types, the fish species reported as consumed by NHANES participants were 

apportioned to habitats. The assignments of species were completed by a fisheries biologist. 

Appendix A contains the detailed documentation of the assignments for each species. 

The fish were apportioned to align with EPA’s long-standing interpretation of section 303(c) (2) (A) 

of the Clean Water Act that state and tribal waters should support safe consumption of fish and 

shellfish and that the standards need to be set to enable residents to safely consume from local 

waters the amount of fish they would normally consume from all fresh and estuarine (including near 

coastal) waters. Thus marine species that are harvested in near coastal waters were assigned to the 

estuarine habitat in order to be included in the freshwater + estuarine FCR. The following decisions 

concerning habitat assignments were made:

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Estuarine fish and shellfish include estuarine species harvested in near-coastal areas

(clams, mussels, crabs, lobster, shrimp) and single species that live in both marine and 

estuarine habitats (e.g., specific clam and octopus species or the single jellyfish species 

that constitutes the U.S. jellyfish fishery).

Tilapia was assigned 50 percent freshwater and 50 percent estuarine, even though it is 

rare in U.S. waters, to be consistent with EPA’s long-standing interpretation of section 

303(c) (2) (A) of the Clean Water Act, as mentioned above, that the standards need to 

be set to enable residents to safely consume from local waters the amount of fish they 

would normally consume from all fresh and estuarine (including near coastal) waters. 

Shrimp was assigned 17.6 percent marine and 82.4 percent estuarine. National Oceanic 

and Atmospheric Administration (NOAA) landings data show that 17.6 percent of 

shrimp harvested in 2009-2010 were “Ocean Shrimp (Oregon Pink Shrimp),” “Rock 

Shrimp,” “Royal Red Shrimp,” and “Marine Shrimp, Other.” 

Salmon was assigned 96 percent marine, 0.5 percent freshwater, and 3.5 percent 

estuarine. The freshwater percent is landlocked sockeye salmon (Kokanee) found 

natively in Alaska, Washington, and Oregon, but they have also been introduced to 

many other states for recreational fishing. The estuarine percent includes saltwater trout, 

9 
which are included in the NHANES salmon group, and the small proportion of salmon 

that are harvested in estuaries. Note that farmed Atlantic salmon were assigned to the 

marine habitat as they are produced outside of the United States in marine waters.

Table 1 presents the final proportion of each NHANES fish group that is assigned to marine, 

freshwater, and estuarine habitats. Note that unspecified fish consumed was assigned the overall 

average habitat apportionment of all species reported consumed. The remainder of Section 3.1 

describes the habitat apportionment methodology. 

Table 1. Habitat assignments of NHANES fish groups

Species/group 

Proportion 

Marine Freshwater Estuarine 

Abalone 1.000 0.000 0.000

Anchovy 0.000 0.000 1.000

Barracuda 1.000 0.000 0.000

Breaded Fish Products (e.g., fish sticks) 1.000 0.000 0.000

Carp 0.000 1.000 0.000

Catfish 0.000 0.900 0.100

Clam 0.840 0.000 0.160

Cod 1.000 0.000 0.000

Conch 1.000 0.000 0.000

Crab 0.273 0.000 0.727

Crayfish 0.000 1.000 0.000

Croaker 0.071 0.050 0.879

Eel 0.000 1.000 0.000

Fish, not specified 0.520 0.160 0.320

Flatfish 0.870 0.000 0.130

Haddock 0.945 0.050 0.006

Halibut 0.780 0.000 0.220

Herring 0.304 0.010 0.686

Jellyfish 0.000 0.000 1.000

Lobster 0.044 0.000 0.956

Mackerel 0.411 0.000 0.589

Mullet 0.000 0.000 1.000

Mussel 0.000 0.000 1.000

Octopus 0.620 0.000 0.380

Oyster 0.000 0.000 1.000

Perch 0.000 1.000 0.000

Pike 0.000 1.000 0.000

Pompano 0.661 0.002 0.338

Rockfish/Ocean Perch 0.925 0.000 0.075

Roe 0.085 0.235 0.680

Salmon 0.960 0.005 0.035

Sardine 0.900 0.000 0.100

Scallop 0.000 0.000 1.000

Scup/Porgy 0.981 0.000 0.019

Sea Bass 0.925 0.025 0.050

Shad 0.304 0.010 0.686

Shark 0.866 0.000 0.134

Shrimp 0.176 0.000 0.824

Snail 0.450 0.100 0.450

10
Table 1. Habitat assignments of NHANES fish groups (continued)

Species/group 

Proportion 

Marine Freshwater Estuarine 

Snapper 0.981 0.000 0.019

Squid 0.800 0.000 0.200

Sturgeon 0.000 0.420 0.580

Swordfish 1.000 0.000 0.000

Tilapia 0.000 0.500 0.500

Trout 0.106 0.869 0.025

Tuna 1.000 0.000 0.000

Whelk 0.000 0.000 1.000

Whitefish 0.877 0.000 0.123

Whiting 1.000 0.000 0.000

3.1.1 NHANES Fish Groupings 

When the raw 24-hour recall data are processed by NHANES, fish species reported consumed are 

grouped, and foods (e.g., Pompano, baked or broiled) are assigned food codes. The list below 

presents the species of fish that are specified in the USDA Food and Nutrient Database for Dietary 

Studies (FNDDS) and the additional species that are included in each group. 

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Abalone 

Anchovy 

Barracuda

Carp (bream; buffalofish; and 

sucker)

Catfish (bullhead) 

Clams

Cod 

Conch 

Crab

Crayfish

Croaker (angelfish; butterflyfish; 

drumfish; goatfish; kingfish; sea 

trout; freshwater sheepshead; 

spadefish; spot; surgeonfish; 

weakfish; weke; goo; and 

gaspergou) 

Eel

Fish stick, patty, or fillet, not 

specified as to type (commercial 

products such as Mrs. Paul’s, 

Gorton’s, Van de Kamp’s)

Fish, not specified as to type 

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Flounder (dab; fluke; halibut; 

sole; and turbot) 

Haddock (blowfish; burbot; cusk; 

hake; ling; monkfish; pollock; and 

scrod) 

Halibut 

Herring (alewife; milkfish; and 

shad)

Jellyfish

Lobster 

Mackerel (garfish; ono; 

needlefish; and wahoo) 

Mullet

Mussels

Ocean perch (bocaccio; 

menpachi; orange roughy; redfish; 

and rockfish) 

Octopus 

Oysters

Perch (freshwater bass; bluegill; 

crappie; sunfish; and walleye)

Pike (muskellunge; and pickerel)

11
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Pompano (akule; blackfish; 

bluefish; butterfish; dolphinfish; 

jack; mahimahi; paplo; parrot 

fish; sablefish; scad; tilefish; ulva; 

and yellowtail)

Porgy (scup; sea bream; marine 

sheepshead; and snapper) 

Ray (skate) [not reported ever 

consumed] 

Roe 

Roe, sturgeon (caviar)

Salmon (saltwater trout)

Sardines 

Scallops

Sea bass (grouper; striped bass; 

wreakfish; and bass)

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Shark (dogfish and grayfish) 

Shrimp

Smelt [not reported ever 

consumed in the 2003-2010 data] 

Snails

Snapper 

Squid (cuttlefish) 

Sturgeon 

Swordfish (marlin) 

Tilapia

Trout (cisco; lake herring; 

steelhead; and whitefish)

Tuna (ahi; aku; and bonito) 

Whelk 

Whitefish

Whiting 

This grouping of species complicates the assignment of habitat because in many cases, the grouped 

fish inhabit different habitats. For example, burbot, a freshwater fish, is part of the haddock group, 

which is defined by the Order Gadiformes (excluding cod). All of the other species in this group are 

marine and estuarine. For these groups, we used raw (uncoded) 24-hour recall files from NHANES 

from 2007-2008 (which are not publically available, and the only cycle made available to us) and 

counted the number of times a species was reported. Using the haddock group as an example, in 

2007-2008 blowfish, burbot, cusk, hake, ling, and monkfish were reported 0 times, pollock was 

reported 10 times, scrod was reported 2 times, and haddock was reported 4 times. These counts 

were then used to assign proportions of each species in the group to the total group. No species in a 

group was assigned 0 percent based on a 0 count in the files, because it may be reported in another 

NHANES cycle. These species were assigned between 1 and 5 percent depending on how many 

species are included in the group and how many times other species in their group were reported 

consumed. Appendix A provides the percentages assigned to each species. The assigned proportions 

were then multiplied by the habitats and summed to get the total habitat proportions for the fish 

group. 

3.1.2 Use of NOAA Landings Data 

Other assignments were complicated by the fact that a species lives in multiple habitat types, either 

at different life stages or because different species occupy different habitats. For these species, 

habitat apportionment was aided by using the NOAA landings data 

(http://www.st.nmfs.noaa.gov/commercial-fisheries/).

12
Table 2 is an example of the NOAA landings data for clams for 2010. To apportion the total 

consumption of clams to estuarine and marine, we first assigned a habitat to each clam species listed. 

According to these data, excluding the catch-all category, 84 percent of all clams landed in 2010 were

from the marine environment and 16 percent were from the estuarine environment (multiplying the 

proportion of total without catch-all by the habitat proportion for each species and then summing 

for each habitat). These proportions excluding the catch-all category were then applied to the catchall category, and the overall proportions were re-calculated. 

This methodology was used to assist the apportionment of the following species: catfish, clam, crab, 

flatfish, flounder, sole, halibut, lobster, mackerel, porgy, shrimp, and whiting and species in the 

following food code groups: croaker, pompano, sardine, and trout. 

3.1.3 Imported Fish and Farmed Fish 

It is known that the United States imports a large proportion of the fish consumed from overseas. 

According to NOAA Fish Watch, 86 percent of the fish consumed in the United States are 

imported (http://www.fishwatch.gov/wild_seafood/outside_the_us.htm). The top imported species 

are shrimp, freshwater fish (mainly tilapia and catfish), tuna, salmon, groundfish (e.g., cod, haddock, 

flounder), crab, and squid. As marine fish are not harvested from U.S. waters for which states would 

be developing water quality standards, the issue of importation for these species is not relevant. 

However, shrimp is the most commonly consumed fish by U.S. consumers. It is unknown whether 

the proportion consumed that was harvested in non-U.S. waters is distributed equally across the 

distribution of fish consumers. For example, it is possible that high fish consumers eat more locally 

caught fish as they may be more likely to be recreational or subsistence fishers. For the purposes of 

developing UFCR, we assumed that all estuarine, freshwater, and near coastal fish that were 

consumed were from U.S. waters. The reason for this is that standards need to be set to enable 

residents to safely consume from local waters the amount of fish they would normally consume 

from all fresh and estuarine (including near coastal) waters. 

13
Table 2. NOAA landings data, clam apportionment

Pounds 

landed, 2010 

Proportion of 

total 

Proportion of total 

(without catch-all 

category) Habitat 

Habitat 

percent 

Clam, Arc, Blood 23,738 0.0003 0.0003 Estuarine & marine harvested near coast 100E

Clam, Atlantic Jackknife 67,334 0.0008 0.0008 Estuarine 100E

Clam, Atlantic Surf 37,465,740 0.4188 0.4542 Marine 100M

Clam, Butter 15,133 0.0002 0.0002 Estuarine & marine harvested near coast 100E

Clam, Manila 937,915 0.0105 0.0114 Estuarine 100E

Clam, Northern Quahog 4,406,313 0.0493 0.0534 Estuarine 100E

Clam, Ocean Quahog 31,704,091 0.3544 0.3844 Marine 100M

Clam, Pacific Geoduck 2,777,529 0.0310 0.0337 Estuarine & marine harvested near coast 100E

Clam Pacific Littleneck 26,811 0.0003 0.0003 Estuarine & marine harvested near coast 100E

Clam, Pacific Razor 138,826 0.0016 0.0017 Marine 100M

Clam Pacific Gaper 6,061 0.0001 0.0001 Estuarine & marine harvested near coast 100E

Clam, Quahog 634,131 0.0071 0.0077 Estuarine 100E

Clam, Softshell 4,278,356 0.0478 0.0519 Estuarine & marine harvested near coast 100E

Clams or Bivalves 6,980,468 0.0780 estuarine & marine (catch-all category) 16E/84M

Total Pounds 89,462,446 

Total Pounds without catch-all 82,481,978 

Without catch-all

Proportion 

Estuarine 0.15971

Proportion 

Marine 0.84029

Total 

Proportion 

Estuarine 0.15973 

Proportion 

Marine 0.84027

14
There are similar issues with farmed freshwater fish. Freshwater fish can be farmed in man-made 

ponds or tanks for which the states will not be developing water quality standards. However, as 

noted above in the discussion concerning imported fish, the proportion of freshwater fish 

consumed that is farmed may not be evenly distributed across the distribution of consumption. 

Again, it is possible that high fish consumers are eating locally caught fish through recreational or 

subsistence fishing and thus eating a smaller proportion of farmed fish than those at the middle and 

low end of the consumption distribution. Therefore farmed species were assumed to be wild caught. 

This allows residents to safely consume from local waters the amount of fish they would normally 

consume from fish farms.

3.2 Trophic Level Assignments 

The trophic level of an organism is the place it occupies in the food web. Organisms with higher 

trophic levels have higher exposures to environmental contaminants. 

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Trophic level 1 organisms are primary producers (plants and algae). 

Trophic level 2 organisms are herbivores, also called primary consumers. 

Trophic level 3 organisms are carnivores that consume herbivores. 

Trophic level 4 organisms are carnivores that consume other carnivores. 

Trophic level 5 organisms are the apex predators. 

Trophic level assignments were made using the data provided in the following documents: (1) 

Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (2000), Table 6-4

(U.S. Environmental Protection Agency, 2003) and (2) Trophic Level and Exposure Analyses for Selected 

Piscivorous Birds and Mammals: Volume III: Appendices (U.S Environmental Protection Agency, 2002b). 

For species that were not in those documents, we performed a search of literature available on the 

Internet and applied the same rules that were described in the December 2003 document: 

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For game fish, data were used for edible size ranges (about 20 cm [8 inches] or larger).

For species where multiple size ranges were available, preference was given to the larger 

specimens in determining the species trophic level.

15
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Trophic level 2 was assigned to a species if appropriate trophic level data ranged 

between 1.6 and 2.4; trophic level 3 if trophic level data ranged from 2.5 to 3.4; and 

trophic level 4 if trophic level data were 3.5 or higher. This is consistent with the 

approach taken in the Great Lakes Water Quality Initiative guidance (U.S. 

Environmental Protection Agency, 1995).

In determining NHANES fish grouping trophic level assignments, best professional 

judgment was used. If the vast majority of the species in a group are within one trophic 

level, then that trophic level is assigned. If species span two levels it is split 50-50. For 

example, the NHANES grouping for catfish includes four species that are assigned to 

trophic level 3 and three species assigned to trophic level 4. Thus, it is assumed that half 

(50 percent) of consumption in the catfish NHANES grouping is from TL3 and half 

from TL4. Other fish this rule applies to are croaker, flatfish, and shrimp. 

Table 3 presents the final trophic level assignments.

Table 3. Trophic level assignments

Fish species/group 

Proportion of Assigned to Trophic Level 

Trophic level 2 Trophic level 3 Trophic level 4 

ABALONE 1 0 0

ANCHOVY 0.5 0.5 0

BARRACUDA 0 0 1

BREADED FISH PRODUCTS (e.g., fish sticks) 0 0.5 0.5

CARP 0 1 0

CATFISH 0 0.5 0.5

CLAM 1 0 0

COD 0 0 1

CONCH 1 0 0

CRAB 0 1 0

CRAYFISH 0 1 0

CROAKER 0 0.5 0.5

EEL 0 0 1

FISH NOT SPECIFIED 0 0.5 0.5

FLATFISH 0 0.5 0.5

HADDOCK 0 0 1

HALIBUT 0 0 1

HERRING 0 1 0

JELLYFISH 1 0 0

LOBSTER 0 1 0

MACKEREL 0 0 1

MULLET 1 0 0

MUSSEL 1 0 0

ROCKFISH/OCEAN PERCH 0 0 1

OCTOPUS 0 0.5 0.5

OYSTER 1 0 0

PERCH 0 0 1

PIKE 0 0 1

POMPANO 0 0 1

PORGY/SCUP 0 0 1

ROE 0 0 0

SALMON 0 0 1

16
Table 3. Trophic level assignments (continued)

Fish species/group 

Proportion of Assigned to Trophic Level 

Trophic level 2 Trophic level 3 Trophic level 4 

SARDINE 0 1 0

SCALLOP 1 0 0

SEA BASS 0 0 1

SHAD 0 1 0

SHARK 0 0 1

SHRIMP 0.5 0.5 0

SNAIL 1 0 0

SNAPPER 0 0 1

SQUID 0 0.5 0.5

STURGEON 0 0 1

SWORDFISH 0 0 1

TILAPIA 1 0 0

TROUT 0 0 1

TUNA 0 0 1

WHELK 1 0 0

WHITEFISH 0 1 0

WHITING 0 1 0

3.3 Extracting Reported Amounts of Fish Consumed 

The FNDDS is the underlying database used to code dietary intakes for NHANES. It is a database 

of foods, their nutrient values, and gram weight equivalents for various ingredients in the foods. For 

each new version of FNDDS, foods, gram weights, and nutrient values are reviewed and updated to 

reflect the U.S. food supply by incorporating new foods based on what is reported in the survey and 

updating existing entries. 

In FNDDS, each food is given an 8-digit food code. The first digit identifies one of nine major food 

groups. The second, third, and fourth digits identify increasingly more specific subgroups. Most fishcontaining foods are found under “26 – Fish and Shellfish,” “27 – Meat, Poultry, Fish with nonmeat 

items,” and 28, which includes soups and frozen meals. Other fish-containing foods are found under 

“5 – Grains” such as seafood pizza and pasta dishes and “7 – Vegetables” for dishes that are mainly 

vegetables but also contain fish and/or shellfish. 

The NHANES 24-hour recall data include these same food codes for each reported food consumed; 

therefore the reported foods can be merged to the FNDDS files to obtain recipe information. The 

FNDDS files are available from the Agriculture Research Service of the USDA (USDA, 2006; 

USDA, 2008; USDA 2010; Ahuja et al., 2012). FNDDS includes several files (or tables), including a 

file that is linked to the USDA National Nutrient Database for Standard Reference (SR) that

17
provides recipes for reported foods. For example, the standard recipe for “Perch, baked or broiled,” 

consists of the ingredients (1) fish, perch, mixed species, raw; (2) margarine, stick, salted; (3) lemon 

juice, raw; and (4) salt, table. The FNDDS-SR link file provides weights in grams for each ingredient 

in each recipe. In the above example, these amounts are 907.2 grams of fish, 28.2 grams of 

margarine, 30.5 grams of lemon juice, and 6 grams of table salt. From these amounts, the fraction by 

weight of the recipe that is fish can be calculated. In the example, 907.2 / (907.2+28.2+30.5+6) = 

0.933 grams of prepared fish per gram of recipe.

The FNDDS files were searched to find all food codes that contain finfish and/or shellfish. These 

records were then processed to determine the weight of each fish ingredient as a fraction of the 

weight across all ingredients in the recipe. The recipe ingredients may be raw, canned (cooked), or 

otherwise processed before being put into the recipes. The FNDDS description of each ingredient 

generally includes the processing before the ingredient is added to the recipe. After the dish is 

prepared from the ingredients, the food dish may have additional cooking or processing, such as 

baking. This processing is often described in the FNDDS food description. 

As NHANES participants report the amount consumed “as prepared” (which is converted to a 

weight, in grams, in the NHANES file), it is relatively easy to estimate the grams of prepared fish 

that is consumed. However, because cooking can change the moisture content of the fish, 

calculating the grams of raw fish consumed requires to a weight conversion based on the likely 

moisture loss due to cooking. The calculation of the weight of as-prepared and raw fish consumed 

are based on the following: 

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Estimates of the moisture loss associated with various cooking methods. 

Assuming the weight of fish as a proportion of the weight of the food is the same for 

the recipe in the FNDDS files as in the final as-prepared dish. In effect, we assume the 

proportional weight loss due to cooking of the prepared recipe as the same for the fish 

and non-fish ingredients. 

If the recipe specifies two cooking steps, one for the fish used in the recipe (for 

example, using canned ingredients) and one for the prepared recipe (for example, 

baking before serving), assuming a moisture loss associated with the cooking method 

with the most moisture loss.

The uncooked amount of fish was determined using the recipe databases, which list the amount of 

each ingredient in the food code. The weight of each ingredient as a fraction of the weight of the 

recipe was calculated, as above. During this data processing, each fish ingredient in the recipe was 

apportioned to marine, estuarine, and freshwater habitat and to trophic levels 2, 3, and 4, as 

18
discussed in Sections 3.1 and 3.2. As many food codes comprise multiple fish species, each of these 

values was summed, along with total fish percent, across all fish-containing ingredients to get total 

values for each habitat, trophic level, and total fish for each fish-containing food code. 

The adjustment factors for cooking by dry heat, moist heat, and frying and the adjustment factors 

for canning and restructured fish are also used in the analysis of the CSFII data published in 2002 

(EPA, 2002a) and in the Mercury Study Report to Congress (EPA, 1997, Volume 4). These cooking and 

processing methods represent 90 percent of all reported fish consumed. The percent moisture loss 

for the remaining cooking and processing methods (dried, kippered, smoked, salted, and pickled) are 

estimated using the FNDDS “MoistNFatAdjust” file. This file provides the percent moisture and fat 

loss or gain due to cooking, by food code; there is a file specific to each NHANES release. These 

adjustments are used in the calculations of nutrient intake (e.g., calcium, protein) for NHANES 

participants. However, for many food codes they are set to zero because the FNDDS recipe uses a 

cooked or processed fish as the ingredient, and no further adjustments were needed for nutrient 

intake calculations. We calculated the mean value of moisture loss for the remaining cooking 

methods for those fish food codes that did not have a 0 value, using this file. Table 4 provides the 

adjustments applied by cooking and processing method. For unspecified cooking method, 

approximately 5 percent of all reported fish consumed, an average adjustment across all reported 

fish food codes was applied (22 percent moisture loss). 

Table 4. Estimated moisture loss due to cooking or processing

Cooking/Processing method Percent moisture loss 

Dried 57

Kippered 46

Smoked, (other than salmon) 36

Salted 33

Canned 25

Cooked, dry heat 25

Restructured 25

Cooked, moist heat 21

Smoked salmon 17

Pickled 16

Fried 12

Raw 0 

There is uncertainty associated with these values. They are average values of moisture loss given the 

various processing and cooking methods. If participants cooked their fish a bit longer, then the 

19
moisture loss would be a bit greater than average, and if they cooked it a bit less, the moisture loss 

would be a bit less than average.

Appendix B provides a detailed description of how the fish foods were abstracted and processed 

from FNDDS and it provides the final number of grams of raw weight, of the edible portion fish 

per 1 gram of the final prepared recipe in each fish-containing food code reported in the NHANES 

data 2003-2010. It contains the values for total, marine, estuarine, freshwater, and trophic levels 2 

through 4 fish. 

As an example calculation, the standard recipe for food code 27250400 “shrimp cake or patty” 

contains 0.475 grams of shrimp per gram of total recipe. The shrimp ingredient in the recipe is 

canned; therefore moisture loss is estimated to be 25 percent. We divide .475 by 0.75 to get the 

grams of raw fish in 1 gram of the final prepared recipe, which is .633 g. Shrimp was apportioned to 

the habitats as 17.6 percent marine and 82.4 percent estuarine. We then multiply these percentages 

by the grams of raw shrimp in 1 gram of the final prepared recipe, 0.176*.633 = .111 grams raw 

marine fish in 1 gram of the final prepared recipe, and 0.824*.633 = .522 grams raw estuarine fish in 

1 gram of the final prepared recipe. Similar calculations are made to determine grams of raw fish by 

trophic levels in 1 gram of the prepared recipe. These amounts are then multiplied by the reported 

grams of food code 27250400 consumed by the participants and summed across all fish-containing 

food codes reported by each respondent to get the reported 24-hour intake. 

Using this example, we can see the uncertainty added to the estimates by using standard recipes. 

Recipes used for shrimp cakes could vary from the assumed 47.5 percent fish by weight composition 

by using more or less eggs or bread crumbs. Or the shrimp cake could have been prepared using raw 

shrimp that was fried, instead of canned shrimp, which would change the weight loss estimate to 12 

percent. Thus a participant who reported consuming a shrimp cake probably consumed somewhat 

less or somewhat more than is estimated through the calculations. Nevertheless, these data are the 

best data available on a nationally representative sample.

An additional complication is that recipes may include two steps of processing; for example, a 

salmon loaf may list canned salmon as an ingredient, but it is then mixed with other ingredients and 

baked. Canning and baking have different moisture losses in Table 4. It was decided to use the 

adjustment that indicates the greatest moisture loss and apply that to the estimation of raw weight. 

In some recipes the second processing step is not categorized. We reviewed these and were able to 

impute the logical unreported process (e.g., pizza is baked, soup is wet cooked in moist heat) for 

many recipes; those that remained uncategorized were assumed to have the average moisture 

reduction described above. 

20
Statistical Methodology 4 

The NCI Method (Tooze et al., 2006; Tooze et al., 2010) is the preferred approach for estimating 

usual dietary intake, such as usual fish consumption. NHANES has data for many individuals, 

allowing fitting models with many parameters. With many individuals and many parameters, the 

computation time to implement the NCI Method was unacceptable. Therefore, EPA developed an 

alternate approach to estimate the usual fish consumption that requires relatively little computation 

time and provides a good approximation to the results from the NCI Method. The following 

sections describe both the NCI and the EPA Methods and compare the two approaches. Appendix 

C provides some further discussion and Appendix D provides the macro code for estimating 

parameters and simulating the UFCR. 

4.1 Overview of the NCI Method 

The NCI Method can be used to estimate the distribution of usual intake for a population or 

subpopulation. Two steps are required to estimate usual intake: 

1. Fit the NCI model to the reported consumption data. 

2. Calculate the usual intake from the model parameters.

The premise of the NCI model (step 1 above) is that usual fish intake is equal to the probability of 

consumption on a given day times the average amount consumed on a day when some fish is 

consumed, i.e., a “consumption day.” For episodically consumed foods, such as fish, the NCI model 

consists of two parts, or sub-models. The first sub-model estimates the probability of consumption 

using logistic regression with a person-specific random effect. The second sub-model uses linear 

regression on a transformed scale to estimate the consumption-day amount, also with a personspecific random effect. The two sub-models are linked by allowing the person-specific effects to be 

correlated and by including common predictors in both sub-models. Data from one or more nonconsecutive 24-hour recalls provide the values for the dependent variable. At least a subset of the 

population (generally 50 or more individuals) needs to have reported fish consumption from two or 

more 24-hour recalls. Predictors related to either the probability of consumption or consumption 

amount, such as gender, age, race, and income can be included in the modeling. In most cases, the 

21
most important predictor is a measure of frequency of consumption of the food of interest (in this 

case, fish) obtained from a food frequency questionnaire. 

In the second step, the parameters from the NCI model are used to estimate population and 

subpopulation distributions of usual fish intake. The NCI Method calculates the distribution using 

simulated values for the probability of fish consumption and the mean consumption amount. The 

usual fish consumption (or usual fish intake) is the product of the probability of fish consumption 

and the mean amount of fish consumed, when it is consumed. 

Evidence for the validity of the NCI Method has been published in a series of papers in the Journal of 

the American Dietetic Association, Statistics in Medicine, and Biometrics (Dodd et al., 2006; Tooze et al.,

2006; Tooze et al., 2010; Kipnis et al., 2009).

The NCI Method is an improvement over other methods designed to estimate usual intake of 

episodically consumed foods because it: 

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Accounts for reported days without consumption or for consumption-day amounts that 

are positively skewed; 

Distinguishes within-person from between-person variation; 

Allows for the correlation between the probability of consumption and the 

consumption-day amount; and 

Relates covariate information to usual intake. 

The sub-model predicting the probability of fish consumption in a 24-hour period is a logistic 

regression model. The logistic regression model is commonly used to model the probability of an 

event, such as consuming fish. The model assumes the logit-transformed probability is a linear 

function of various continuous and discrete predictor variables. The logit transformation is 

commonly used as the link between the continuous predictors and the probability of a discrete 

outcome, as in logistic regression. The sub-model has two variance components, person-specific 

random effects for an individual’s long-term probability of consuming fish and within-individual 

binomial variation between days when fish was or was not consumed. The logit-transformed personspecific random effects are assumed to be normally distributed. 

The amount sub-model involves a Box-Cox transformation such that the transformed amount of 

fish consumed in a 24-hour recall is reasonably normally distributed. The Box-Cox transformation is 

22
a power transformation, such as raising the amount to the 1⁄4 power (taking the fourth root), 

followed by rescaling to keep the variance relatively constant. In the transformed units, the amount 

sub-model has two variance components, person-specific random effects for an individual’s longterm mean fish consumption and within-individual differences in the amount of fish consumed on 

different days. In the transformed units, the person-specific mean fish consumption and the withinindividual daily fish consumption are assumed to have normal distributions. 

The person-specific random effects in the two sub-models may be correlated, for example, those 

with a higher probability of consuming fish in a 24-hour period may also tend to consume larger 

daily amounts of fish. The assumption that the random effects are normally distributed is a 

characteristic of the model which is not directly testable. However, the distribution of the Box-Coxtransformed reported consumption amounts is roughly normally distributed, suggesting that the 

assumption is at least reasonable. 

Both sub-models can have additional predictors, such as person-specific demographic characteristics 

and reported frequency of fish consumption. In addition, the model can incorporate the following 

within-person predictors: (1) differences between weekends (Friday to Sunday) and weekdays 

(Monday to Thursday),1 and (2) consistent differences between the first 24-hour recall and the 

second 24-hour recall in NHANES (the first was completed in person and the second was 

completed by phone). 

We consider the NCI Method as the preferred method for estimating fish consumption rates and 

believe the results to have minimal bias. However, with large sample sizes and many predictors, the 

computation time required to run the NCI Method and calculate confidence intervals was 

unacceptable given the schedule and budget. Additionally, our preferred model has more predictors 

than the NCI Method is set up to handle. The EPA Method was developed to provide acceptably 

unbiased estimates within a reasonable computation time. We are using non-publically available data 

from NHANES that can only be accessed on site at NCHS. This precludes our use of alternative 

computing scenarios that might reduce the computation time. 

The following illustrates the time savings. We ran a simplified model with 4 main effects (age, 

race/ethnicity, income, and frequency of fish consumption). The NCI Method took 9.5 hours for 

1 The NCI Method includes Friday as part of the weekend. A study of CSFII data showed that intake on Friday was 

more similar to Saturday and Sunday than to the rest of the weekdays, Monday through Thursday (Haines, Hama, 

Guilkey, and Popkin, 2003). 

23

 
one run of one fish type. To obtain an estimate of the precision of the estimates, we need to run the 

model 65 times, one for each replicate weight. This gives us an estimated time of over 25 days of 

continuous computer time for each fish type. There are 18 fish types. Therefore, to obtain estimates 

for all fish types would take 450 days. The EPA Method took 1.5 minutes to run the same model, 

approximately 1.5 hours for each fish type. 

The NCI Method can be implemented using two SAS macros (programs) available from the NCI 

web site (the MIXTRAN and DISTRIB macros). The equations fit using the NCI macros are 

presented below. More information concerning the NCI Method can be found in Tooze et al., 2006, 

Tooze et al., 2010, and on NCI’s web site at: 

http://appliedresearch.cancer.gov/diet/usualintakes/method.html. 

EPA created a SAS macro to approximate the results from the NCI Method while taking 

considerably less time for the calculations, referred to as the EPA Method in this report. The 

following sections describe both the NCI and EPA Methods. The macro code for estimating 

parameters and simulating the usual fish consumption is in Appendix D. 

4.2 Calculation Steps for the NCI and EPA Models 

4.2.1 NCI Method 

In the NHANES data, each individual (indicated by i) has results from one or two 24-hour dietary 

recalls (indicated by j, j = 1 or 2). In the data, most individuals have two 24-hour recalls; only a 

portion of individuals have 24-hour recalls reporting fish consumption, and a smaller portion have

two 24-hour recalls with reported fish consumption. Using the i and j subscripts, the following 

describes the statistical model fit using the NCI MIXTRAN macro. The parameters and variables 

are described below. In these equations, the parameters for the probability model are represented by 

π, the parameters for the amount model are represented by α, the standard deviations of the 

variance components are represented by σ. 

Data for each individual and 24-hour recall, extracted from the NHANES data files follow: 

 Aij is the reported grams of fish consumed (zero if no fish consumption was reported 

in the 24-hour recall). 

24






Wij is an indicator of whether the 24-hour recall was for a weekday (Wij = 0) or 

weekend (Wij = 1). 

Sij indicates if the 24-hour recall was the first (in-person, Si1 = 0) or the second (by 

phone, Si2 = 1) dietary recall.

Xik represent k individual level covariates (demographic variables; see Section 4.5 for 

additional details). 

Transformed data: 





Cij is an indicator for reported fish consumption in a 24-hour recall (1 indicates fish 

consumption, otherwise 0). This is the dependent variable in the logistic probability 

model. 

 { 

 

Tij is the amount of fish consumed after being transformed using a Box-Cox power 

transformation. This is the dependent variable for the linear regression model predicting 

the consumption-day amount of fish consumed. 

Parameters for the probability sub-model: 













π0 is the intercept parameter for the probability model. 

πXk is a vector of regression parameters for the k person-specific covariates in the 

probability model. 

πW is the regression parameter for the difference between weekend and weekday days. 

πS is the regression parameter for the difference between the second 24-hour recall and 

the first 24-hour recall. 

πi is the person-specific random effect for the probability model, assumed to be 

normally distributed on the logit scale. This value has theoretical meaning but is not 

observed. 

Pij is the probability of fish consumption for a 24-hour recall. This value has theoretical 

meaning but is not observed. 

25
Parameters for the amount sub-model: 















λ is the power used for the Box-Cox transformation. 

α0 is the intercept parameter for the amount model. 

αXk is a vector of regression parameters for the k person-specific covariates in the 

amount model. 

αW is the regression parameter for the difference between weekend and weekday days. 

αS is the regression parameter for the difference between the second 24-hour recall and 

the first 24-hour recall. 

αi is the person-specific random effect for the amount model, assumed to be normally 

distributed. This value has theoretical meaning but is not observed. 

αij is the within-person random effect for the amount model representing different 

amounts for fish consumed on different days, assumed to be normally distributed. This 

value has theoretical meaning but is not observed. 

Variance and correlation parameters: 









σ1

2 is the variance of the person-specific random effect in the probability model (πi). 

σ2

2 is the variance of the person-specific random effect in the amount model (αi). 

ρ is the correlation between πi and αi. 

σ3

2 is the variance of the within-person random effect in the amount model (αij). 

The NCI macro fits some preliminary models to obtain approximate parameter estimates to use as 

starting values for the NLMIXED procedure (a SAS procedure that fits non-linear mixed models), 

which fits the following set of equations simultaneously, using maximum likelihood. The following 

equations describe the model fit using the NCI Method. The tilde (~) symbol can be read as “is 

distributed as.”

 ( ) ( 

 ) 

 ( )

 

 

 

 

 

 ( 

 

)

[ ] ([ ] [ 

 

 

 

])

26
The NCI model can be fit assuming the probability of consuming fish and the amount of fish 

consumed, if consumed, are uncorrelated, i.e., ρ = 0. However, we assume these values may be 

correlated and thus specified a correlated model when using the NCI Method to compare the NCI 

and EPA Method results.

4.2.2 EPA Method 

The EPA and NCI Methods differ as follows:











As part of fitting the model, the NCI Method finds the power transformation (λ) that 

best fits the data and is consistent with the assumption that the variance components 

are normally distributed, as judged by maximum likelihood. The EPA Method finds the 

power transformation that makes the transformed fish consumption amounts (Tij) 

roughly normally distributed as judged by the correlation between the transformed 

amounts and the expected values for a normal distribution. The power with the highest 

correlation is then used when fitting the amount sub-model. 

The EPA Method assumes the person-specific random effects in the probability and 

amount sub-models are uncorrelated. The assumption of zero correlation is for 

computational convenience, not because these values should be uncorrelated. When 

using the NCI Method, we assumed these random effects may be correlated and let the 

NCI algorithm estimate the correlation. The correlation estimates were generally close 

to zero. 

When assuming the person-specific random effects are uncorrelated, the two submodels can be fit separately rather than simultaneously. The EPA Method fits the two 

models separately.

For the probability sub-model, the NCI model fits the parameter values and random 

effects in one non-linear mixed model. The EPA Method approximates that approach 

using two steps: (1) using logistic regression without random effects to estimate the 

parameter values and calculate predicted values (in the logit scale); and (2) using a nonlinear mixed model to fit the random effects using only the predicted values from the 

previous logistic regression as a predictor. 

For the amount sub-model, the NCI Method fits the parameter values, transformation, 

and random effects in one non-linear mixed model. The EPA Method approximates 

that approach by (1) selecting the transformation as described above; (2) using linear 

regression without random effects to estimate the parameter values and calculate 

predicted values; and (3) using a non-linear mixed model to fit the random effects using 

the predicted values from linear regression as the only predictor. 

27
The equations for the EPA Method are described below using the same notation as above for the 

NCI Method. The EPA Method has the following steps: 

1. Estimate λ. 

2. Use logistic regression to predict the probability of fish consumption as a function of 

various predictors; save the predicted values. 

3. Use a non-linear mixed model to estimate the person-specific variance component using 

the predicted values from logistic regression as the predictor. 

4. Use linear regression to predict the Box-Cox-transformed amount of fish consumed, 

when consumed, as a function of various predictors; save the predicted values. 

5. Use a non-linear mixed model to estimate the variance components for the amount 

model using the predicted values from linear regression as the predictor. 

In the NCI Method, the maximum likelihood procedure finds the best transformation, defined byλ, 

consistent with the data and the assumption that the random effects are normally distributed. In the 

EPA model, λ = λ∗ is set prior to fitting the amount sub-model, using the following steps: 

1. Calculate normal scores associated with each observation by first, ignoring the 

distinction between the first and second recall; second, for amounts greater than zero, 

summing the weights across tied values (values with the same reported amounts) to get 

one record for each unique amount (Ar) and the associated weight (Wr); third, sorting 

the R unique amounts from smallest to largest; fourth, calculating cumulative weight for 

the each unique value, Sr = ∑ Wm r

m=1 ; and fifth, calculating the normal scores as

 ( 

 

 

 

)

2. Using values of λ∗ which are multiples of 0.01 between -0.20 and 0.30, find the λ∗ value, 

which maximizes the Pearson correlation between Zr and 

 {

 ( 

) 

 

 

 

 

 

( 

 ) 

 

 

 

where G is the geometric mean of Ar. This form of the Box-Cox transformation allows 

λ∗ = 0, corresponding to using a log transformation. 

3. If λ∗ = 0 then set λ∗ = 0.005 (this case was encountered for marine tropic level 2 fish).

28
Table 5 shows the λ∗ values used for each of the dependent variables. 

Table 5. λ∗ values used for each combination of dependent variable and data set

Dependent variable λ∗ 

All fish 0.21

Marine fish 0.24

Estuarine fish 0.13

Freshwater fish -0.04

Finfish 0.25

Shellfish 0.11

Freshwater + estuarine fish 0.11

Marine + estuarine fish 0.21

Marine + freshwater fish 0.21

Trophic level 2 fish 0.11

Trophic level 3 fish 0.16

Trophic level 4 fish 0.20

Marine trophic level 2 fish 0.005

Marine trophic level 3 fish 0.09

Marine trophic level 4 fish 0.23

Freshwater + estuarine trophic level 2 fish 0.08

Freshwater + estuarine trophic level 3 fish 0.12

Freshwater + estuarine trophic level 4 fish -0.05

For two types of fish consumption (freshwater and freshwater plus estuarine trophic level 4), λ∗ was 

less than zero. The NCI Method constrains λ to be greater than 0.01. As a result, the results from 

the NCI Method and the EPA Method differ somewhat when λ∗ < 0.01. 

The transformed consumption amounts for 24-hour recalls with reported fish consumption are

shown in the following equation: 

Tij = Aij

λ∗

− 1

λ∗

To estimate the probability of consumption, the following logistic regression model was fit using the 

SAS SURVEYLOGISTIC procedure and the NHANES survey weights, strata, and PSU variables. 

This logistic regression model predicts the probability of consuming fish in a 24-hour recall without 

considering a person-specific random effect; Bij

′ is the linear predictor of the logit transformed 

probability. The apostrophes indicate values from the logistic model that has no random effects.

29
 ( 

 

) ( 

 

 

 

) 

 

 

 

 

 

The person-specific random effect is included by assuming the predicted logit (Bij) when including 

the random effect is proportional to the predicted logit when excluding the random effect (Bij

′ ). 

This approximation is justified in Appendix C. The following non-linear mixed model is fit to 

estimate the variance of the person-specific random effect and the inflation factor (β) for scaling the 

parameter estimates from the model above. 

 ( ) ( 

 ) 

 

 ( 

 

)

 ( )

The SAS SURVEYREG procedure is used to fit the amount sub-model assuming no person-specific 

random effect. The variance of the regression error (σ4

2) is the combination of the variance of the 

person-specific random effect and the within-person variation. Dij is the linear predictor of the 

transformed amount of fish consumed. 

If Aij > 0 then Tij = Aij

λ∗

− 1

λ∗ = α0 + XikαXk + WijαW + SijαS + aij

′ = Dij + aij′

aij

′ ~Normal(0, σ4

2)

The variance of the within-person and between-person variance components is estimated using a 

non-linear mixed model with the linear predictor from the regression above as the only predictor. 

If Aij > 0 then Tij = Aij

λ∗

− 1

λ∗ = Dij + αij + αi

αij~Normal 0, σ3 , αi~Normal 0, σ2 ( 2) ( 2)

Because of different estimation methods, the parameters calculated using the NCI Method are 

slightly different than those from the EPA Method. 

30
4.3 Simulation of the Usual Fish Consumption 

The distribution of usual fish consumption can be calculated from the model parameters. Due to the 

complexity of the model, the direct calculation of the distribution of usual fish consumption 

involves numerical integration and is relatively complex. The integration is simplified by (1) 

simulating values of usual fish consumption and (2) calculating mean and percentiles of fish 

consumption rates from the simulated values. When using simulations, the estimated fish 

consumption rates have a small random component that can be reduced by increasing the number 

of simulations. The default number of simulations in the NCI DISTRIB macro is 100. Analysis of 

preliminary results showed that the precision of the parameter estimates increased as the number of 

simulations increased; however, the precision was similar when using either 50 or 100 simulations. 

The final analysis used 100 simulated fish consumption values for each NHANES respondent. The 

NHANES data set provides the population distribution of the independent predictors in the 

probability and amount sub-models. For each NHANES respondent, the simulated values represent 

possible fish consumption rates for a respondent with the same independent predictors as the 

NHANES respondent. 

Because usual fish consumption is different from reported fish consumption, the equation used to 

simulate usual fish consumption is slightly different from the equation fit to the data from the 24-

hour recalls. The equation fit to the data was modified as follows to simulate usual fish 

consumption: 







The simulated values reflect a standard week (3 weekend days and 4 weekday days) 

rather than the distribution of weekday and weekend recalls in the data. Given Friday is 

part of the weekend, the average for the standard week would be 4/7 x (weekday 

average) + 3/7 x (weekend average). Since the weekend parameter models the 

difference between the three weekend days and the four weekday days, this average can 

be obtained by setting the parameter for the weekend variable to 3/7.

The simulated values assume the first (in-person) 24-hour recall is unbiased by ignoring 

the difference between the first and second recall, i.e., αS = πS = 0. 

The simulated values do not include the within-person variation, i.e., binomial variation 

within persons in the probability model and the within-person variation in the amount 

model. 

The equations for simulating usual fish consumption use the parameters estimated from the models 

predicting probability of reported fish consumption and the amount for fish consumed, when 

consumed. 

31
In the equations below, the V subscript represents the simulation number (V = 1 to 100). The 

following equations are used by the NCI DISTRIB macro to simulate an individual’s long-term 

probability of fish consumption (QVi) and transformed long-term mean fish consumption when fish 

is consumed (TVi); the logistic function is the inverse of the Logit function. 

 ( 

 

 

 )

TVi = α0 + XikαXk + αVi +

3

7 πW

[ ] ([ ] [ 

 

 

 

])

A slightly modified version of these equations is used in the EPA procedure because the EPA 

approach uses a two-step procedure with the inflation factor (β) to fit the probability model and 

assumes the random effects are uncorrelated. The EPA equations are as follows: 

 (( 

 

 

 

 

 

 

) )

TVi = α0 + XikαXk + αVi +

3

7 αW

[ ] ([ ] [ 

 

 

 

])

Finally, the simulated transformed consumption amounts are untransformed using the following 

equation (see Tooze et al., 2010):

BVi = (TViλ + 1)(1/λ) +

σ3

2(1 − λ)

2 (TViλ + 1)(1/λ−2)

This equation includes an adjustment involving the within person variance in the fish consumption 

amount (σ3

2). The NCI Method assumes the reported fish consumption amounts in the 24-hour 

recalls are unbiased. This adjustment makes the untransformed usual fish consumption essentially 

unbiased. 

32
Although when Aij = 0, the transformed fish consumption is defined (Tij = −1

λ ), it is possible to 

simulate a value of TVi such that TVi <

−1

λ , for which the untransformed value is not defined. In the 

NCI macro, these small simulated values in the transformed scale are set to half of the minimum 

reported fish consumption for any 24-hour recall. The same procedure is used in the EPA 

calculations. The probability that TVi <

−1

λ depends on several factors. The expected probability is 

less than or equal to 1/N, where N is the number of respondents with reported fish consumption. 

In preliminary analysis, no values were set to half the minimum reported value.

The usual fish consumption for a simulated person is then: 

UVi = QViBVi

The following summary statistics for the usual fish consumption were calculated using the simulated 

values (UVi) and the NHANES analysis weights: mean and 25th, 50th, 75th, 90th, 95th, 97th, and 

99th percentiles. 

4.4 Calculation of Standard Errors and Confidence Intervals 

Both the NCI Method and the EPA Method use the NHANES survey weights for all the 

calculations (i.e., weighted regressions and weighted estimates of the variance components). The 

NHANES survey weights are inversely proportional to the probability of selection for each 

respondent. The survey weights are adjusted for nonresponse and allow for calculation of national 

estimates. 

The SURVEYLOGISTIC and SURVEYREG procedures calculate standard errors for only the 

linear parameters in the models, using a Taylor series linearization approach. These standard errors 

were used to select the independent predictors for the probability and amount models. Standard 

errors for all EPA and NCI Method parameters, including the random effects and percentiles of the 

distribution of usual fish consumption, can be calculated by (1) preparing replicate weights 

consistent with the NHANES survey design and strata and PSU variables; (2) running the macros 

using the full-sample weight and each replicate weight; and (3) combining the results using each 

weight to estimate the standard errors. EPA constructed replicate weights for calculating the 

standard errors and confidence intervals for percentiles of usual fish consumption. See Wolter, 1985, 

for a discussion of variance estimation procedures for complex survey designs such as NHANES. 

33
In general, the variance of the weighted estimates from the NCI Method or the EPA Method can be 

calculated by repeatedly dividing the sampling PSUs into subgroups (or replicates) and comparing 

the estimates from each subgroup to the estimate for the entire sample. Several approaches have 

been developed to efficiently estimate the variance with a minimum number of carefully constructed 

subgroups. In general, dividing the respondents into subgroups can be achieved by creating an 

analysis weight for each subgroup, i.e., a replicate weight. One such approach is Balanced Repeated 

Replication (BRR) which divides the PSUs into two equal size groups on each division. A 

modification of the basic BRR method due to Fay compares a weighted estimate from one half of 

the PSUs to a different weighted estimate for the other half of PSUs. The Fay approach was selected 

because it has advantages when estimating percentiles. The replicate weights for the BRR method 

using the Fay factor adjustment (Fay factor K = .3) were created using standard procedures (see 

Judkins, 1990) and the strata and PSU variables in the NHANES files provided for variance 

estimation. The basic BRR procedure assumes two PSU values in each stratum. However, a few of 

the NHANES strata have three PSU values, which required slightly modified calculations for 

creating the weights (Wolter, 1985; Rust, 1986). We created 64 replicate weights. Parameter estimates 

were calculated using the NHANES (full sample) weight and each of the replicate weights. 

Given the replicate BRR weights, the variance of an estimate of θ can be calculated using the steps 

below; θ might be a regression parameter, an estimated percentile of usual fish consumption, or the 

log-transformed percentile of usual fish consumption. 

1. Calculate θ using the full sample NHANES weight and using each of the 64 replicate 

weights (θg, g = 1 to G, G = 64), and

2. Calculate the variance of θ as

 ( ) 

 

 ( )

 ∑( ) 

 

 

3. A 95 percent confidence interval for is √ ( ) √ ( ). 

Various summary statistics (means and percentiles) are calculated using the simulated usual fish 

consumption values. Since the usual fish consumption estimates are generally skewed with a roughly 

lognormal distribution, calculating the confidence intervals on the log scale appears reasonable and 

has the beneficial effect that confidence limits cannot be negative. As a result, the confidence 

intervals for the summary statistics are calculated by (1) fitting the EPA Method using the full 

sample weight and each replicate weight; (2) log-transforming the estimates; (3) calculating the 

34
confidence intervals for the estimates assuming a normal distribution using the equations above; and 

(4) un-transforming the confidence interval bounds. 

See Gilbert (1987) for additional comments on calculating confidence intervals for log-normally 

distributed values. 

4.5 Application of EPA Method 

The EPA Method was used to model and predict usual consumption for the following types of fish 

and shellfish: 





































Total finfish and shellfish; 

Finfish; 

Shellfish; 

Marine fish; 

Estuarine fish; 

Freshwater fish; 

Freshwater + estuarine fish; 

Marine + estuarine fish; 

Marine + freshwater fish; 

Trophic level 2 fish; 

Trophic level 3 fish; 

Trophic level 4 fish; 

Freshwater + estuarine trophic level 2 fish; 

Freshwater + estuarine trophic level 3 fish; 

Freshwater + estuarine trophic level 4 fish; 

Marine trophic level 2 fish; 

Marine trophic level 3 fish; and 

Marine trophic level 4 fish. 

35
All models included the weekend indicator and the indicator of the first or second recall for both the 

amount and probability sub-models. 

Other candidate variables for inclusion into the models included the following: 

















Age group: 1 to < 3, 3 to <6, 6 to < 11, 11 to < 16, 16 to <18, 18 to < 21, 21 to <35, 

35 to <50, 50 to <65, and 65 years and older;

Income: $0 to <$20K, $20 to <$45K, $45 to <$75K, $75K+, >$20k, Refused/DK 

Income, Income Missing; 

Male: an indicator, 1 = male 0 = female;

Race/Ethnicity: Mexican American, other Hispanic, non-Hispanic white, non-Hispanic 

black, and other race;

Region: U.S. Census regions (Northeast, Midwest, South, and West); 

Coastal Status: Coastal counties and non-coastal counties;

Bodyweight, log-transformed; and 

Reported frequency of fish consumption in 30 days (Fish30), transformed. 

Based on preliminary analysis, the following transformations were used: for the probability submodel: Ln(Fish30 + 0.1) and for the amount sub-model: Fish300.45. 

The following process was used to select the final predictors for the models: 

1. For each dependent variable, start with all main effects, sequentially drop the least 

significant main effect until all remaining main effects are significant at the 5 percent

level. 

2. Use any main effects that were selected when predicting any dependent variable. 

3. For each dependent variable, include the selected main effects and all two-way 

interactions of the selected main effects, sequentially dropping the least significant 

interaction until all remaining interactions are significant at the 1 percent level. 

4. Use any two-way interaction selected for predicting at least three of the dependent 

variables.

The selected main effects and two-way interactions were then used as independent predictors in the 

final models predicting all the dependent variables. 

36
The procedure above was performed separately for the amount model and the probability model. 

The significance was based on the SURVEYLOGISTIC and SURVEYREG output. 

The final lists of independent variables for the probability and amount sub-models follow: 





Probability Sub-Model: reported frequency of fish consumption (transformed), 

bodyweight (log-transformed), race/ethnicity, income, age group, region, coastal status, 

race/ethnicity*income, race/ethnicity*age group, income*age group, income*region, 

income*coastal status, and age group*region

Amount Sub-model: reported frequency of fish consumption (transformed), 

bodyweight (log-transformed), race/ethnicity, male, age group, region, coastal status, 

race/ethnicity*age group, race/ethnicity*region, male*age group, age group*region, and 

age group*coastal status

The simulated usual fish consumption values were summarized by the following demographic 

categories:























Gender; 

Age group; 

Women of childbearing age (13 to 49 years); 

Race; 

Income; 

Region; 

Coastal status; 

Four coastal regions (Atlantic, Gulf of Mexico, Pacific, and Great Lakes); 

Four inland regions (Inland Northeast, Inland Midwest, Inland South, Inland West); 

Youth (<21 years of age) by gender, race, income, region, coastal status, coastal regions, 

and inland regions; and 

Adults (≥21 years of age) by gender, race, income, region, coastal status, coastal regions, 

and inland regions. 

When fitting the NCI and EPA Methods, the distribution can be sensitive to the magnitude of the 

variance components. With small sample sizes, the number of respondents with reported fish 

consumption on two 24-hour recalls can be small resulting in imprecise variance estimates and 

37
possible convergence problems. The authors of the NCI Method (Kipnis et al., 2009) generally 

recommend having at least 50 respondents with at least two 24-hour recalls with reported 

consumption of fish (or whatever dietary component is being assessed). 

The NCI and EPA Methods can be applied to all NHANES respondents or to subsets, such as 

subsets defined by demographic characteristics. Fitting one model using all NHANES respondents 

implicitly assumes that the magnitude of the variance components are the same for all individuals 

and do not vary by, say, demographic characteristics. Fitting all respondents has the advantage that 

there are plenty of individuals from which to estimate the variance components. Fitting the models 

separately by subset allows the variance components to be different for different subsets of 

individuals. However, small subsets may not have adequate numbers of individuals with two recalls 

with fish consumption. After considering the trade-offs, the EPA Method was applied to all 

NHANES respondents. For each fish type, the subgroup estimates were derived from simulated 

usual fish consumption values and the associated demographic covariates and sampling weights for 

the NHANES respondents. 

4.6 Comparison of Results from the EPA and NCI Methods 

In order to evaluate how estimates from the EPA Method compared to estimates from the NCI 

Method, we ran both methods using different dependent variables, different sets of independent 

predictors, and different numbers of simulated values. The procedures used to evaluate the EPA 

Method and to compare the two methods are described in this section. 

4.6.1 Analysis of Simulated Data 

Fish consumption data (both usual intake and reported intake) were simulated consistent with the 

model assumed by the NCI Method. Ideally, when analyzing the simulated data, parameter estimates 

from the NCI and EPA Methods will agree with the parameters used to simulate the data and the 

estimated percentiles of usual fish consumption will agree with the corresponding percentiles in the 

simulated data. Differences can indicate programming errors or possible bias associated with to the 

estimation method. Different scenarios were used to evaluate the EPA Method, with good 

agreement between the parameter estimates and percentiles compared to the values used to simulate 

the data. 

38
As an illustration, the following plots (Figure 1) show how the parameters and percentiles from the 

NCI and EPA Methods compare to the simulated values for data with six different Box-Cox 

transformations (Lambda = -0.12, -0.06, 0.06, 0.12, 0.18, and 0.30). For these simulations, the other 

parameters were set to values similar to those found when analyzing freshwater fish for adults (for 

which Lambda = -0.06), with the exception that the intercept for the probability was increased 

somewhat to raise the number of simulated individuals with two 24-hour recalls with fish 

consumption. The plots show the percentile estimates derived from the NCI and EPA models 

compared to the values from the simulated data. The plots and analysis suggest that the EPA 

Method provides a good approximation to the NCI Method and the true values when lambda is 

greater than zero; and, for negative lambda, the EPA Method appears to provide better estimates 

than the NCI Method when compared to the true values. It should be noted that (1) for positive 

lambda, whether the NCI or EPA estimates are closer to the true value is different for different 

simulated data sets using the same simulation parameters; (2) for negative lambda, the NCI and EPA 

Methods provide more similar results when the magnitudes of the variance components are smaller; 

and (3) the NCI Method could be modified to allow for negative lambda values. 

4.6.2 Confidence Intervals for Percentiles of Fish Consumption 

Figure 2 shows confidence intervals for parameters and percentiles of fish consumption, calculated 

using both the NCI and EPA Methods. Due to the long computation time required for the NCI 

Method, only simple models with few predictors were used. The confidence intervals were 

calculated for freshwater and estuarine fish consumption by all respondents, marine fish 

consumption by all respondents, and shellfish consumption by adults. The predictors are 

transformed frequency of fish consumption in the past 30 days, an indicator of weekend versus 

weekday, and the difference between the first and second recall. The first column in Figure 2 

compares parameter estimates and confidence intervals. The second column compares percentile 

estimates and confidence intervals for various percentages and demographic groups. In general, 

there is good agreement between both the NCI and EPA estimates and the width of the confidence 

intervals.

39
Figure 1. Comparison of NCI, EPA, and true percentiles using simulated data and different 

transformations

40
Figure 2. Comparison of confidence intervals using the NCI and EPA Methods 

41
4.6.3 Analysis of NHANES Fish Data Using Various Models 

NHANES fish consumption data were analyzed using the NCI and EPA Methods with different 

sets of independent predictors and different numbers of simulations. These comparisons between 

the EPA and NCI Methods were selected to represent a range of fish types. Each dependent 

variable was predicted by different independent variables to assess the effect of the choice of 

predictors and the number of parameters on the results, all using 50 simulations. Comparisons were 

also run using different numbers of simulations to assess how many simulations to use. Table 6 

summarizes the results followed by example plots comparing the percentile estimates from the two 

methods. 

For each of the comparisons, Table 6 shows the following values: 



















Pred Vars: the independent predictors, F = transformed frequency of fish consumption, 

A = Age groups, I = Income groups, R = Race groups, and M = Male indicator. 

Sim Num: the number of simulated values of usual fish consumption generated for each 

individual. 

Geo Mean Ratio (EPA/NCI): The geometric mean ratio of the EPA percentile to the 

NCI percentile across multiple percentiles and population subgroups. A ratio of 1.00 

corresponds to no difference between the geometric means, on average.

RMSE (percent): The RMSE difference between the log-transformed EPA and NCI 

percentiles estimated across multiple percentiles and population subgroups, converted 

to a percentage difference. This can be thought of as the average absolute percent 

difference between the NCI and EPA percentiles. Smaller values are better. Larger 

values are generally associated with fish types that are consumed less often. 

90th percentile (Adults≥21): the 90th percentile of fish consumption (a value of 

particular interest to EPA) calculated using the NCI and EPA Methods.

Rel. Time (NCI/EPA): the computation time for the NCI Method relative to the EPA 

Method. These values are not precise and depend on what other programs were running 

at the same time. 

Num Parms (NCI): the number of parameters in each model. 

NCI Lambda: the power for the Box-Cox transformation estimated using the NCI 

Method. 

EPA lambda: the power for the Box-Cox transformation used in the EPA Method. 

42
 NCI Rho: The correlation between the person-specific random effects in the probability 

and amount sub-models, as estimated by the NCI Method. 

Note that the geometric mean ratio, RMSE, and percentiles in the table are subject to random 

variation associated with the simulation process. As a result, somewhat different values would be 

obtained if the calculations were repeated.

Table 6. Comparison of NCI and EPA methods using NHANES fish data

Fish type 

Pred. 

vars 

Sim 

num 

Geo mean 

ratio 

(EPA/ NCI) 

RMSE 

(%) 

90th percentile 

(Adults≥21) 

Rel. time 

(NCI/ 

EPA) 

Num 

parms 

NCI 

lambda 

EPA 

lambda 

NCI 

EPA NCI Rho 

All Fish F 5 1.008 5.258 48.77 49.89 13.5 13 0.210 0.21 0.17

All Fish F 10 1.008 4.690 49.64 49.36 12.4 13 0.210 0.21 0.17

All Fish F 20 1.006 4.367 49.13 49.92 10.1 13 0.210 0.21 0.17

All Fish F 50 1.004 4.084 49.08 49.73 5.7 13 0.210 0.21 0.17

All Fish F 100 1.003 4.230 49.14 49.82 2.5 13 0.210 0.21 0.17

All Fish FA 50 1.002 4.005 51.62 52.51 44.7 31 0.208 0.21 0.18

All Fish FAIRM 50 1.001 4.361 51.59 52.47 170.2 53 0.211 0.21 0.25

Finfish F 50 1.006 2.803 36.98 36.13 8.7 13 0.255 0.26 0.03

Finfish FA 50 1.016 14.544 38.37 41.00 26.0 31 0.252 0.26 -0.01

Finfish FAIRM 50 1.010 12.936 38.51 40.85 118.2 53 0.256 0.26 -0.07

Fresh F 50 1.030 36.877 6.70 6.47 8.3 13 0.010 -0.04 -0.21

Fresh F 50 0.872 21.225 5.92 6.49 9.57 13 0.010 0.01 -0.21

Fresh FA 50 1.049 35.486 7.43 7.12 77.3 31 0.010 -0.04 -0.19

Fresh FAIRM 50 1.022 36.953 7.14 6.83 303.7 53 0.010 -0.04 -0.26

FWEst F 5 0.987 4.746 20.04 20.03 17.2 13 0.105 0.11 0.15

FWEst F 10 0.992 4.092 20.35 20.41 13.7 13 0.105 0.11 0.15

FWEst F 20 0.992 2.781 20.19 20.40 6.6 13 0.105 0.11 0.15

FWEst F 50 0.995 2.500 20.27 20.38 8.3 13 0.105 0.11 0.15

FWEst F 100 0.991 2.361 20.23 20.38 2.4 13 0.105 0.11 0.15

FWEst FA 50 0.991 2.998 21.81 22.12 64.6 31 0.104 0.11 0.16

FWEst FAIRM 50 0.995 2.465 21.91 22.07 290.1 53 0.106 0.11 0.15

Marine F 5 1.010 8.605 31.16 32.10 11.9 13 0.218 0.23 0.15

Marine F 10 1.003 8.017 30.96 31.76 8.5 13 0.218 0.23 0.15

Marine F 20 1.013 7.336 31.09 32.01 3.6 13 0.218 0.23 0.15

Marine F 50 1.006 7.596 30.96 31.84 5.5 13 0.218 0.23 0.15

Marine F 100 1.005 7.641 31.11 31.93 1.9 13 0.218 0.23 0.15

Marine FA 50 1.008 7.410 32.39 33.24 22.1 31 0.218 0.23 0.15

Marine FAIRM 50 1.004 7.747 32.17 33.10 126.9 53 0.220 0.23 0.18

Shellfish F 5 0.980 4.629 14.00 14.20 21.5 13 0.112 0.11 0.50

Shellfish F 10 0.988 4.262 14.17 13.98 19.6 13 0.112 0.11 0.50

Shellfish F 20 0.990 3.560 14.07 14.05 10.5 13 0.112 0.11 0.50

Shellfish F 50 0.988 3.059 14.06 14.03 10.1 13 0.112 0.11 0.50

Shellfish F 100 0.987 3.194 14.06 14.03 3.9 13 0.112 0.11 0.50

Shellfish FA 50 0.988 3.508 15.34 15.23 48.6 31 0.112 0.11 0.50

Shellfish FAIRM 50 0.991 3.542 15.40 15.28 228.1 53 0.111 0.11 0.50

43
For freshwater and freshwater and estuarine trophic level 2, the lambda estimated using the EPA 

Method was less than zero (only freshwater is shown in Table 6). The NCI Method constrains 

lambda to be greater than 0.01. For freshwater fish consumption, the model with frequency of fish 

consumption as the only predictor was run using the transformation selected for the EPA Method, 

λ∗ = −0.04, and with λ∗ = 0.01, the transformation used in the NCI Method (see the yellow 

shaded cell). When the preferred transformation corresponds to a negative lambda, the EPA and 

NCI Methods give different results (see the cells with the dark border with high RMSE), although 

the 90th percentiles are relatively close. Based on the simulations in Figure 1, the EPA percentiles 

appear to be preferred to the NCI percentiles when lambda is less than 0.01. 

Analysis of the data in Table 6 suggests the following: 















Relative to the NCI estimates, the percentile estimates from the EPA Method are 

essentially unbiased (the average of the geometric mean ratio is 0.9923, using only rows 

with 50 simulations and excluding the cases with negative lambda). 

The results from the EPA Method are acceptably close to those from the NCI Method: 

the average RMSE is 6.5 percent (using only rows with 50 simulations and excluding the 

cases with negative lambda). 

There is little additional reduction in RMSE when using more than 50 simulations. The 

final runs used 100 simulations. 

Uncertainty in the estimated percentiles is associated with (1) the NHANES sampling 

error (represented by the confidence intervals); (2) selection of independent predictors 

(which depends in part on what variables are available); (3) which analysis method is 

used (EPA versus NCI); and (4) the number of simulations. These sources of 

uncertainty are ordered roughly from most to least important. 

The transformation (λ) estimated for the EPA model is very close to the transformation 

estimated in the NCI model. Differences in lambda do not explain differences in the 

90th percentiles from the two procedures. 

The estimated correlation between the variance components (Rho) is not significantly 

correlated with the difference between the 90th percentiles from the NCI and EPA 

Methods. 

Computation time for the NCI Method relative to the EPA Method increases 

significantly with increasing numbers of parameters.

The shaded rows in Table 6 correspond to the example plots shown in Figure 3. These plots were 

selected to show the comparison with the best RMSE, the median RMSE, and the worst RMSE 

among models with positive lambda and 50 simulations, and the comparison for fresh water fish, for 

which the lambda estimated using the EPA Method is negative. Figure 3 shows EPA versus NCI 

means and percentiles for different subpopulations. 

44
Figure 3. Comparison of NCI and EPA percentiles of fish consumption rates

45
Results 5 

This section presents the sample sizes and the estimated UFCR (raw weight, edible portion) for all 

fish, freshwater + estuarine fish, marine fish, all finfish, all shellfish, trophic level 2 fish, trophic level 

3 fish, trophic level 4 fish, trophic level 2 freshwater + estuarine fish, trophic level 3 freshwater + 

estuarine fish, and trophic level 4 freshwater + estuarine fish, for adults and youth, by demographic 

characteristics and geographic area. Full tables including UFCR for the total population (youth and 

adults combined), adults only, and youth only are in Appendix E. Appendix F contains the UFCR 

for as prepared weights. The fish types selected to be presented in the body of the report represent 

those that are of most interest to EPA, states, and tribes. 

Note that the adult population is defined as people aged 21 years and over. The US EPA Exposure 

Factors Handbook classifies those aged 21 years and over as adults. Children are grouped as follows: 

1 to <3 years, 3 to <6 years, 6 to <11 years, 11 to <16 years, 16 to <18 years, and 18 to <21 years. 

Note that children 1 to <2 and 2 to <3 were combined due to small sample sizes for these age 

groups. 

5.1 Sample Size 

Table 7 presents the sample sizes for each subpopulation that reported fish consumption on at least 

one 24-hour recall. An expanded table that includes the other fish types for which rates were 

calculated can be found in Appendix G. 

46
Table 7. Sample size and number reporting fish consumption, by fish type

 N

Any 

fish

FW+ 

Est Marine

Fin 

fish

Shell 

fish

Trophic 

level 2

Trophic 

level 3

Trophic 

level 4

Total 29,463 6,891 4,868 6,286 5,095 2,612 2,706 4464 4,578

Gender

Female 15,694 3,807 2,667 3,495 2,792 1,448 1,495 2,435 2,521

Male 13,769 3,084 2,201 2,791 2,303 1,164 1,211 2,029 2,057

Age, years

1 to <3 2,325 345 198 305 269 101 111 209 243

3 to <6 2,185 350 196 322 272 106 118 225 246

6 to <11 2,705 454 264 416 351 137 143 286 315

11 to <16 2,806 445 310 402 301 179 180 296 273

16 to <18 1,417 252 177 237 164 104 98 173 155

18 to <21 1,662 311 208 294 209 132 131 209 199

21 to <35 4,381 1,070 801 992 723 509 531 745 651

35 to <50 4,522 1,332 997 1,221 962 546 566 883 848

50 to <65 3,730 1,216 901 1,101 938 454 468 775 842

65 and older 3,730 1,116 816 996 906 344 360 663 806

WCAa (13 to 49 years) 7,870 1,919 1,421 1,785 1,409 768 839 1,300 1,179 

Income

<$20k 6,679 1,374 897 1,256 1,043 470 491 911 936

$20k to <$45k 8,955 1,969 1,382 1,775 1,442 732 792 1,286 1,263

$45k to <$75k 5,561 1,334 959 1,211 1,002 498 511 856 904

$75k and over 6,288 1,768 1,308 1,634 1,285 739 740 1,108 1,176

>$20k 825 203 151 182 144 81 86 140 126

Ref/DK incomeb 808 164 118 153 122 61 57 111 117

Income missing 347 79 53 75 57 31 29 52 56

Race/Ethnicity

Mexican American 6,868 1,350 949 1,212 937 535 618 886 828

Other Hispanic 2,405 532 351 490 390 187 202 329 350

Non-Hispanic white 11,980 2,678 1,854 2,509 2,000 1,023 1,006 1,573 1,855

Non-Hispanic black 6,734 1,818 1,308 1,603 1,376 654 669 1,291 1,188

Other race 1,476 513 406 472 392 213 211 385 357

U.S. Region

Midwest 6,445 1,235 821 1,070 938 400 431 773 853

Northeast 4,475 1,202 805 1,154 867 484 445 733 812

South 11,036 2,688 1,925 2,416 2,003 1,036 1,087 1,828 1,739

West 7,507 1,766 1,317 1,646 1,287 692 743 1,130 1,174

Coastal Status

Noncoastal 17,251 3,719 2,532 3,377 2,813 1,287 1,345 2,363 2,566

Coastal 12,212 3,172 2,336 2,909 2,282 1,325 1,361 2,101 2,012

US Coastal/Inland 

Region

Pacific 3,802 976 739 900 720 385 425 621 646

Atlantic 4,646 1,320 938 1,247 939 553 524 865 840

Gulf of Mexico 1,370 361 292 316 255 196 203 269 202

Great Lakes 2,394 515 367 446 368 191 209 346 324

Inland Northeast 2,584 628 409 600 454 248 234 364 420

Inland Midwest 4,137 741 463 645 588 213 226 437 547

Inland South 6,825 1,560 1,082 1,386 1,204 519 567 1,053 1,071

Inland West 3,705 790 578 746 567 307 318 509 528

a Women of childbearing age. b Income refused or don’t know. 

47
5.2 Usual Fish Consumption Rates 

Tables 8 through 18 present the UFCR estimates of raw weight, edible portion for adults 21 years 

and older for total fish, freshwater + estuarine fish, marine fish, all finfish, all shellfish, trophic level 

2 fish, trophic level 3 fish, trophic level 4 fish, trophic level 2 freshwater + estuarine fish, trophic 

level 3 freshwater + estuarine fish, and trophic level 4 freshwater + estuarine fish. The tables 

provide the 50th, 75th, 90th, 95th, 97th, and 99th percentiles, along with their respective 95 percent

confidence intervals. Tables 19 through 29 present the UFCR estimates of raw weight, edible 

portion for youth less than 21 years old. 

The tables show percentiles for total fish consumed and for various fish types that make up the 

total. The mean consumption for all fish should be equal, not counting random errors, to the sum of 

the mean consumption across different types of fish, e.g., marine, estuarine, and freshwater or 

trophic levels 2, 3, and 4. The same cannot be said about percentiles. At the extreme, the sum of the 

maximum fish consumption across fish types will not equal the maximum fish consumption for all 

fish except in the very unusual case where one individual is the largest consumer in all fish type 

categories. For a selected percentile, the difference between the sum of the percentiles across fish 

types and the percentile for the sum across all fish types will increase as the percentile of interest 

increases from the 50th percentile to 90th percentile, 99th percentile, and the maximum. The 90th 

percentile for all fish will be greater than or equal to the 90th percentile for any one type of fish and 

will usually be less than the sum of the 90th percentiles across all types. 

There are two tables for each fish type, a and b. Tables 8a–29a present the UFCR by demographic 

characteristics (gender, age, income, and race/ethnicity) and Tables 8b–29b present the UFCR by 

geographic area. 

48
Table 8a. UFCR estimates (g/day raw weight, edible portion): Total fish, adults, 21 years and older, by demographic characteristics

All Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 17.6 (15.8,19.7) 32.8 (30.1,35.7) 52.8 (48.0,58.1) 68.1 (61.2,75.8) 79.7 (71.0,89.5) 105.1 (92.0,120.2)

Age 

21 to <35 yrs 13.1 (11.1,15.4) 26.8 (23.6,30.4) 46.6 (40.5,53.6) 63.5 (54.1,74.5) 77.2 (64.6,92.4) 109.7 (87.5,137.4)

35 to <50 yrs 18.3 (16.0,20.9) 33.1 (29.4,37.2) 52.7 (46.1,60.2) 67.5 (58.4,78.0) 78.5 (67.4,91.5) 102.4 (86.8,120.7)

50 to <65 yrs 22.4 (19.1,26.2) 38.8 (34.2,44.1) 59.3 (52.3,67.2) 74.4 (65.3,84.7) 85.5 (74.7,97.9) 109.1 (94.1,126.5)

65+ yrs 16.9 (14.4,20.0) 31.1 (27.1,35.6) 49.5 (43.2,56.9) 63.8 (55.1,73.9) 74.0 (63.6,86.1) 96.2 (81.6,113.5)

Women of childbearing 

age (13 to 49 yrs) 11.6 (10.2,13.2) 23.6 (21.5,25.9) 39.4 (35.4,43.8) 51.7 (45.9,58.2) 61.0 (53.7,69.2) 81.5 (70.6,94.1)

Gender

Female 15.3 (13.7,17.1) 28.4 (26.0,31.0) 45.2 (40.9,50.0) 57.8 (51.7,64.6) 67.1 (59.7,75.5) 87.2 (76.4,99.5)

Male 20.6 (18.2,23.3) 38.0 (34.5,41.8) 60.6 (54.6,67.2) 77.6 (69.2,87.2) 90.5 (79.8,102.5) 118.1 (102.3,136.3)

Race/Ethnicity1

Mexican American 16.7 (13.8,20.1) 31.3 (27.0,36.3) 50.8 (43.8,59.0) 66.1 (56.4,77.4) 77.5 (65.7,91.5) 103.8 (86.2,125.1)

Other Hispanic 16.6 (13.3,20.7) 31.4 (25.8,38.4) 50.7 (42.1,61.1) 65.0 (54.2,77.9) 75.7 (63.3,90.7) 99.5 (82.7,119.7)

Non-Hispanic White 16.7 (14.7,18.9) 31.0 (28.2,34.2) 49.8 (44.8,55.4) 64.3 (57.1,72.4) 75.1 (66.1,85.4) 98.1 (85.0,113.2)

Non-Hispanic Black 19.6 (16.9,22.7) 35.3 (31.5,39.6) 55.7 (49.9,62.2) 71.1 (63.4,79.8) 82.7 (73.2,93.3) 107.2 (93.5,122.8)

Other Race 32.3 (25.8,40.4) 54.0 (44.5,65.5) 81.1 (66.3,99.1) 102.7 (82.7,127.4) 117.6 (93.4,148.1) 153.0 (117.1,200.0)

Income

$0 to <$20K 13.6 (11.7,15.8) 27.0 (24.1,30.2) 45.2 (40.1,51.1) 59.9 (52.4,68.4) 71.7 (61.9,82.9) 99.6 (82.8,119.9)

$20 to <$45K 15.4 (13.4,17.7) 28.8 (26.0,31.9) 46.7 (42.1,51.8) 61.1 (54.5,68.4) 71.9 (63.9,81.0) 96.7 (84.6,110.5)

$40 to <$75K 16.5 (14.2,19.2) 30.7 (27.2,34.7) 49.6 (43.5,56.5) 64.0 (55.6,73.8) 75.0 (64.6,87.1) 99.8 (84.4,118.1)

$75+K 23.1 (20.5,26.1) 40.1 (36.1,44.6) 61.3 (54.8,68.7) 77.1 (68.2,87.2) 88.6 (77.6,101.1) 113.5 (97.7,131.7)

>$20K 17.1 (12.6,23.0) 31.3 (24.6,39.8) 48.4 (38.9,60.3) 61.7 (49.5,77.0) 72.0 (57.7,89.7) 93.2 (74.7,116.4)

Refused/Don’t Know 

Income 16.9 (10.9,26.2) 36.6 (25.2,53.3) 64.5 (46.9,88.8) 83.5 (62.8,111.2) 96.4 (73.3,126.7) 124.8 (95.5,163.3)

Income Missing 8.8 (4.1,18.8) 22.0 (12.0,40.4) 46.6 (27.0,80.3) 65.0 (40.2,105.0) 76.6 (49.6,118.2) 99.5 (68.3,145.0)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 49

 
Table 8b. UFCR estimates (g/day raw weight, edible portion): Total fish, adults, 21 years and older, by geographic area

All Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 17.6 (15.8,19.7) 32.8 (30.1,35.7) 52.8 (48.0,58.1) 68.1 (61.2,75.8) 79.7 (71.0,89.5) 105.1 (92.0,120.2)

Region1

Northeast 23.9 (20.0,28.7) 42.5 (36.3,49.8) 65.2 (55.9,76.1) 82.0 (70.0,96.1) 93.7 (79.9,110.0) 119.6 (100.9,141.6)

Midwest 12.9 (10.6,15.6) 24.1 (20.6,28.3) 39.2 (33.4,46.0) 50.9 (43.1,60.2) 60.0 (50.3,71.6) 79.7 (66.5,95.6)

South 17.6 (15.1,20.4) 32.4 (28.8,36.4) 52.1 (46.3,58.7) 67.4 (59.1,76.8) 79.0 (68.6,90.9) 105.1 (89.0,124.1)

West 20.0 (17.1,23.4) 35.6 (30.7,41.2) 55.7 (47.7,65.0) 71.1 (60.3,83.9) 82.6 (69.5,98.2) 108.4 (89.8,130.9)

Coastal Status2

Noncoastal 15.9 (13.7,18.5) 30.0 (26.4,34.1) 48.3 (42.3,55.3) 62.4 (54.1,72.1) 73.0 (62.8,85.0) 96.2 (81.4,113.7)

Coastal 20.9 (18.4,23.7) 37.9 (34.1,42.1) 59.9 (53.7,66.9) 76.7 (68.3,86.2) 89.3 (78.8,101.2) 115.9 (100.6,133.6)

Coastal/Inland Region1,2

Pacific 22.1 (18.2,26.7) 39.3 (33.2,46.4) 61.2 (51.3,72.9) 78.2 (64.5,94.7) 91.0 (74.3,111.4) 118.7 (94.8,148.5)

Atlantic 24.5 (20.7,28.9) 43.4 (37.6,50.2) 67.2 (58.8,76.9) 84.8 (74.0,97.2) 97.7 (85.0,112.4) 124.6 (106.7,145.4)

Gulf of Mexico 19.0 (15.2,23.8) 34.5 (29.4,40.5) 55.0 (47.1,64.3) 70.6 (59.5,83.7) 82.4 (68.6,98.9) 106.8 (87.9,129.8)

Great Lakes 14.6 (12.1,17.5) 26.5 (22.6,31.1) 41.8 (35.7,49.0) 53.5 (45.6,62.9) 62.2 (52.8,73.2) 80.5 (68.3,94.9)

Inland Northeast 22.1 (17.5,28.0) 39.6 (32.6,48.1) 60.7 (50.3,73.2) 76.1 (62.9,92.0) 87.1 (71.9,105.4) 111.7 (91.4,136.6)

Inland Midwest 12.4 (10.1,15.1) 23.3 (19.7,27.6) 38.3 (32.3,45.4) 49.9 (41.7,59.7) 59.1 (49.0,71.3) 79.5 (64.9,97.4)

Inland South 15.6 (13.1,18.4) 29.0 (25.6,32.9) 46.9 (41.4,53.1) 60.7 (53.0,69.5) 71.2 (61.4,82.6) 95.6 (79.2,115.5)

Inland West 18.4 (15.1,22.5) 32.6 (27.0,39.4) 50.6 (41.5,61.5) 64.2 (52.7,78.3) 74.5 (61.0,91.0) 96.3 (78.6,118.1)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 50

 
Table 9a. UFCR estimates (g/day raw weight, edible portion): Freshwater + estuarine fish, adults, 21 years and older, by 

demographic characteristics

Freshwater + Estuarine 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 5.0 (4.1,6.0) 11.4 (9.9,13.1) 22.0 (19.1,25.4) 31.8 (26.9,37.6) 40.2 (33.3,48.5) 61.1 (48.7,76.6)

Age

21 to <35 yrs 3.8 (3.1,4.8) 9.9 (8.3,11.7) 21.1 (17.6,25.1) 32.2 (26.2,39.7) 42.3 (33.3,53.7) 68.1 (50.1,92.5)

35 to <50 yrs 5.2 (4.1,6.6) 11.9 (9.6,14.8) 23.0 (18.4,28.7) 33.0 (26.0,41.8) 41.4 (32.2,53.1) 62.5 (46.8,83.4)

50 to <65 yrs 6.3 (5.0,7.9) 13.2 (10.9,15.9) 23.8 (19.7,28.9) 33.3 (26.9,41.3) 41.4 (32.7,52.4) 60.4 (45.9,79.4)

65+ yrs 4.5 (3.3,6.1) 9.9 (7.9,12.4) 18.7 (15.4,22.7) 26.5 (21.9,32.2) 33.1 (26.9,40.6) 48.8 (38.4,62.1)

Women of childbearing age (13 to 49 yrs) 2.9 (2.3,3.6) 7.6 (6.4,9.1) 15.8 (13.2,19.0) 23.5 (19.2,28.7) 29.9 (24.1,37.0) 46.6 (36.4,59.6)

Gender

Female 4.1 (3.4,5.0) 9.3 (8.1,10.8) 18.0 (15.4,21.0) 25.7 (21.5,30.7) 32.1 (26.4,39.1) 48.2 (38.0,61.2)

Male 6.2 (5.0,7.6) 13.8 (11.8,16.2) 26.3 (22.4,30.9) 38.0 (31.6,45.6) 47.7 (39.0,58.4) 71.9 (56.4,91.8)

Race/Ethnicity1

Mexican American 6.8 (5.3,8.6) 15.3 (12.4,18.9) 28.7 (23.1,35.8) 40.9 (32.2,51.9) 51.0 (39.6,65.6) 75.7 (56.8,100.8)

Other Hispanic 6.1 (4.4,8.6) 14.1 (10.3,19.3) 27.2 (19.5,37.9) 38.7 (27.5,54.5) 47.8 (33.7,67.6) 69.7 (48.3,100.6)

Non-Hispanic White 4.2 (3.4,5.2) 9.4 (8.0,11.1) 17.9 (15.1,21.1) 25.5 (21.2,30.8) 31.9 (26.0,39.0) 47.9 (37.2,61.6)

Non-Hispanic Black 7.2 (5.8,8.9) 15.4 (13.0,18.1) 28.2 (23.8,33.4) 39.6 (32.7,48.0) 48.8 (39.4,60.3) 70.8 (55.0,91.3)

Other Race 12.6 (9.4,16.9) 25.1 (19.2,32.9) 44.5 (33.3,59.6) 62.3 (45.2,86.1) 78.3 (55.0,111.5) 114.7 (76.4,172.1)

Income

$0 to <$20K 3.5 (2.8,4.4) 9.1 (7.7,10.7) 19.2 (16.3,22.6) 28.9 (24.2,34.6) 37.4 (30.9,45.4) 59.3 (47.5,74.0)

$20 to <$45K 4.3 (3.5,5.4) 9.9 (8.5,11.5) 19.4 (16.6,22.7) 28.4 (23.7,33.9) 35.9 (29.6,43.6) 55.4 (43.8,70.0)

$40 to <$75K 4.8 (3.8,6.2) 10.8 (9.1,12.9) 20.6 (17.6,24.3) 29.6 (24.6,35.5) 37.3 (30.4,45.9) 56.8 (43.4,74.4)

$75+K 6.6 (5.4,8.1) 13.9 (11.7,16.6) 25.6 (21.3,30.8) 36.2 (29.4,44.5) 45.0 (35.9,56.5) 66.2 (51.1,85.9)

>$20K 5.5 (3.6,8.3) 12.1 (8.5,17.1) 22.3 (16.5,30.2) 30.9 (23.3,40.9) 38.7 (29.2,51.1) 56.1 (41.8,75.4)

Refused/Don’t Know Income 5.4 (3.2,9.1) 13.8 (9.2,20.8) 29.0 (19.7,42.6) 43.1 (28.6,65.0) 56.6 (36.3,88.3) 88.6 (54.5,144.1)

Income Missing 1.9 (0.8,4.5) 7.1 (3.6,13.9) 18.9 (10.6,33.7) 31.7 (18.4,54.5) 41.6 (24.5,70.7) 65.9 (39.3,110.5)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 51

 
Table 9b. UFCR estimates (g/day raw weight, edible portion): Freshwater + estuarine fish, adults, 21 years and older, by geographic 

area

Freshwater + Estuarine 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 5.0 (4.1,6.0) 11.4 (9.9,13.1) 22.0 (19.1,25.4) 31.8 (26.9,37.6) 40.2 (33.3,48.5) 61.1 (48.7,76.6)

Region1

Northeast 5.8 (4.4,7.6) 12.6 (9.9,16.0) 23.1 (18.3,29.2) 32.3 (25.4,41.0) 39.9 (31.0,51.5) 58.5 (44.2,77.5)

Midwest 3.2 (2.5,4.2) 7.4 (6.0,9.0) 14.3 (11.8,17.4) 20.8 (16.9,25.7) 26.3 (21.0,33.0) 41.1 (31.3,54.0)

South 6.4 (4.7,8.5) 14.0 (11.3,17.4) 26.3 (21.6,32.0) 37.5 (30.5,46.1) 46.7 (37.6,58.1) 69.0 (54.3,87.7)

West 5.1 (3.9,6.6) 11.4 (8.8,14.8) 22.4 (16.8,29.8) 32.7 (23.9,44.9) 42.0 (30.0,58.8) 66.9 (45.4,98.5)

Coastal Status2

Noncoastal 4.2 (3.4,5.2) 9.8 (8.2,11.6) 19.0 (15.8,22.9) 27.4 (22.3,33.8) 34.6 (27.7,43.3) 52.8 (40.7,68.4)

Coastal 6.6 (5.1,8.4) 14.4 (11.8,17.5) 27.1 (22.4,32.8) 38.6 (31.4,47.6) 48.4 (38.6,60.6) 72.7 (55.6,95.0)

Coastal/Inland Region1,2

Pacific 6.3 (4.4,9.0) 14.0 (10.1,19.5) 27.3 (19.3,38.6) 39.7 (27.4,57.7) 51.2 (34.3,76.3) 81.2 (51.6,127.8)

Atlantic 8.3 (6.4,10.7) 17.0 (13.9,20.8) 30.8 (25.3,37.5) 42.8 (34.5,53.0) 52.3 (41.8,65.5) 75.8 (58.8,97.7)

Gulf of Mexico 7.3 (4.8,11.1) 15.7 (11.7,21.1) 28.6 (22.5,36.4) 40.1 (31.8,50.6) 50.3 (39.3,64.4) 73.8 (55.6,97.8)

Great Lakes 4.0 (3.1,5.1) 8.7 (7.1,10.7) 16.5 (13.5,20.2) 23.6 (19.1,29.1) 29.4 (23.5,36.8) 44.5 (34.1,57.9)

Inland Northeast 5.0 (3.5,7.3) 11.3 (8.0,16.0) 21.0 (14.8,29.7) 29.5 (20.6,42.2) 36.5 (25.3,52.8) 54.4 (36.7,80.6)

Inland Midwest 3.0 (2.3,4.0) 6.9 (5.5,8.7) 13.5 (10.8,17.0) 19.8 (15.5,25.2) 25.1 (19.4,32.6) 39.5 (29.1,53.5)

Inland South 5.3 (4.0,7.1) 12.0 (9.7,14.9) 22.8 (18.6,27.9) 32.7 (26.2,40.7) 40.9 (32.3,51.7) 61.0 (46.7,79.7)

Inland West 4.3 (3.3,5.4) 9.4 (7.4,12.1) 18.2 (13.7,24.3) 26.3 (19.1,36.1) 33.3 (23.8,46.7) 51.6 (35.5,74.9)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 52

 
Table 10a. UFCR estimates (g/day raw weight, edible portion): Marine fish, adults, 21 years and older, by demographic characteristics

Marine Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 9.9 (8.5,11.5) 19.4 (17.4,21.7) 32.8 (29.6,36.3) 43.3 (38.8,48.4) 51.5 (45.5,58.1) 69.4 (60.1,80.2)

Age 

21 to <35 yrs 7.3 (6.0,9.0) 15.4 (13.2,18.0) 27.4 (23.4,32.1) 37.4 (31.5,44.5) 45.5 (37.7,54.9) 64.7 (51.7,81.0)

35 to <50 yrs 10.2 (8.7,11.8) 19.2 (17.0,21.8) 31.8 (27.8,36.5) 41.7 (35.8,48.5) 49.3 (41.8,58.0) 66.4 (54.7,80.5)

50 to <65 yrs 13.0 (10.6,16.1) 24.0 (20.4,28.1) 38.3 (33.3,44.0) 49.4 (42.9,56.9) 57.8 (49.8,67.0) 75.6 (64.5,88.5)

65+ yrs 9.5 (7.5,12.1) 18.9 (15.8,22.5) 32.3 (27.6,37.8) 42.9 (36.3,50.7) 51.0 (42.8,60.6) 68.8 (56.8,83.5)

Women of childbearing 

age (13 to 49 yrs) 6.8 (5.8,8.0) 14.5 (13.0,16.1) 25.3 (22.8,28.1) 34.0 (30.2,38.2) 40.5 (35.7,45.9) 55.5 (47.8,64.4)

Gender

Female 8.9 (7.5,10.5) 17.5 (15.5,19.7) 29.3 (26.1,32.9) 38.6 (34.1,43.6) 45.7 (40.2,52.1) 61.8 (53.0,72.2)

Male 11.2 (9.6,13.1) 21.8 (19.5,24.3) 36.4 (32.7,40.4) 47.9 (42.6,53.8) 56.7 (49.8,64.5) 75.7 (65.1,88.1)

Race/Ethnicity1

Mexican American 7.9 (6.4,9.8) 15.7 (13.4,18.5) 26.6 (22.8,31.0) 35.5 (30.2,41.8) 42.2 (35.6,50.0) 57.9 (47.6,70.4)

Other Hispanic 8.2 (6.6,10.2) 16.4 (13.6,19.8) 28.4 (23.5,34.3) 38.1 (31.5,46.2) 45.5 (37.5,55.2) 62.2 (50.3,76.8)

Non-Hispanic White 9.9 (8.3,11.9) 19.4 (17.0,22.2) 32.5 (28.7,36.9) 42.9 (37.6,49.0) 50.9 (44.1,58.7) 68.6 (58.3,80.7)

Non-Hispanic Black 9.3 (7.8,11.1) 18.1 (15.8,20.8) 30.6 (26.6,35.2) 40.7 (34.9,47.5) 48.6 (41.1,57.4) 66.0 (54.4,80.0)

Other Race 17.3 (13.6,22.2) 30.8 (25.5,37.2) 47.7 (39.7,57.4) 60.4 (49.0,74.3) 70.2 (56.2,87.7) 92.4 (71.3,119.7)

Income

$0 to <$20K 7.4 (6.0,9.0) 15.5 (13.4,17.9) 27.3 (23.8,31.3) 36.9 (31.9,42.6) 44.6 (38.2,52.2) 62.7 (51.5,76.3)

$20 to <$45K 8.4 (7.0,9.9) 16.5 (14.5,18.7) 28.4 (25.2,31.9) 37.9 (33.4,43.0) 45.5 (39.8,52.1) 62.6 (53.4,73.4)

$40 to <$75K 9.5 (8.0,11.4) 18.6 (16.0,21.6) 31.3 (27.0,36.4) 41.5 (35.6,48.3) 49.1 (42.0,57.3) 66.4 (55.9,78.9)

$75+K 13.4 (11.4,15.8) 24.3 (21.7,27.3) 38.5 (34.4,43.1) 49.6 (43.8,56.3) 58.1 (50.7,66.6) 76.2 (65.2,89.2)

>$20K 9.4 (6.6,13.4) 18.0 (13.8,23.4) 29.8 (23.3,38.1) 39.2 (30.6,50.2) 46.1 (35.9,59.1) 62.7 (48.4,81.1)

Refused/Don’t Know Income 9.6 (6.2,14.9) 22.1 (15.0,32.6) 40.5 (28.9,56.8) 54.8 (40.1,74.9) 64.6 (47.6,87.6) 85.5 (63.6,114.9)

Income Missing 5.2 (2.5,10.8) 13.8 (7.5,25.3) 27.9 (16.2,48.2) 39.4 (24.1,64.5) 48.4 (30.4,77.2) 67.4 (43.6,104.1)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 53

 
Table 10b. UFCR estimates (g/day raw weight, edible portion): Marine fish, adults, 21 years and older, by geographic area

Marine Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 9.9 (8.5,11.5) 19.4 (17.4,21.7) 32.8 (29.6,36.3) 43.3 (38.8,48.4) 51.5 (45.5,58.1) 69.4 (60.1,80.2)

Region1

Northeast 15.0 (12.1,18.6) 28.1 (23.5,33.4) 44.4 (37.5,52.6) 56.7 (47.8,67.3) 65.8 (55.5,78.1) 85.4 (70.9,102.7)

Midwest 7.4 (5.7,9.6) 14.6 (11.5,18.3) 24.6 (19.7,30.8) 32.9 (26.2,41.4) 39.2 (31.0,49.5) 53.5 (42.1,68.1)

South 8.8 (7.6,10.1) 16.9 (15.3,18.8) 28.4 (25.2,31.9) 37.6 (32.7,43.2) 44.9 (38.4,52.4) 61.5 (50.6,74.6)

West 12.2 (9.8,15.2) 22.5 (18.8,26.9) 36.1 (30.6,42.7) 46.7 (39.5,55.2) 54.9 (46.0,65.5) 72.2 (59.6,87.6)

Coastal Status2

Noncoastal 9.1 (7.4,11.2) 18.0 (15.3,21.3) 30.7 (26.1,35.9) 40.5 (34.4,47.7) 48.2 (40.7,57.1) 65.5 (54.2,79.1)

Coastal 11.4 (9.6,13.6) 22.0 (19.1,25.3) 36.3 (31.8,41.4) 47.6 (41.5,54.8) 56.2 (48.6,65.1) 74.9 (63.7,88.1)

Coastal/Inland Region1,2

Pacific 13.0 (10.4,16.1) 24.1 (20.3,28.6) 38.6 (33.0,45.2) 49.9 (42.2,58.9) 58.5 (49.1,69.8) 77.0 (63.2,93.7)

Atlantic 13.1 (10.2,16.7) 24.9 (20.0,30.9) 40.6 (33.1,50.0) 52.8 (43.2,64.5) 61.8 (50.6,75.5) 81.1 (66.3,99.3)

Gulf of Mexico 9.5 (7.9,11.3) 18.1 (15.7,20.8) 29.7 (25.4,34.8) 39.7 (32.8,48.1) 47.1 (38.5,57.6) 64.3 (50.6,81.7)

Great Lakes 8.0 (6.3,10.2) 15.4 (12.6,18.9) 25.5 (20.9,31.1) 33.5 (27.2,41.2) 39.8 (32.2,49.1) 54.5 (43.0,69.0)

Inland Northeast 14.0 (11.1,17.6) 26.4 (21.9,31.8) 41.7 (34.8,50.0) 53.6 (44.4,64.8) 62.1 (51.2,75.2) 80.7 (65.8,98.8)

Inland Midwest 7.2 (5.4,9.6) 14.3 (11.1,18.4) 24.4 (19.2,31.0) 32.8 (25.6,41.9) 39.1 (30.4,50.2) 53.4 (41.4,68.8)

Inland South 7.9 (6.5,9.5) 15.2 (13.2,17.6) 25.7 (22.1,29.9) 34.1 (28.8,40.3) 40.7 (33.8,48.9) 56.6 (45.0,71.3)

Inland West 11.6 (8.8,15.4) 21.2 (16.6,27.2) 33.9 (26.8,43.0) 43.6 (34.6,55.0) 51.3 (40.6,64.9) 68.2 (53.0,87.7)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 54

 
Table 11a. UFCR estimates (g/day raw weight, edible portion): Total finfish, adults, 21 years and older, by demographic 

characteristics

All Finfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 12.8 (11.3,14.6) 24.1 (21.9,26.5) 39.3 (35.5,43.5) 51.2 (45.7,57.3) 60.1 (53.2,67.9) 80.1 (69.8,92.0)

Age

21 to <35 yrs 9.2 (7.6,11.1) 18.7 (16.2,21.6) 32.9 (28.3,38.4) 44.9 (37.9,53.2) 54.6 (45.5,65.5) 77.5 (62.5,95.9)

35 to <50 yrs 12.4 (10.6,14.5) 22.8 (20.2,25.7) 37.1 (32.6,42.1) 48.6 (42.0,56.2) 57.3 (49.1,66.7) 76.7 (64.6,91.0)

50 to <65 yrs 17.1 (14.2,20.6) 29.9 (25.7,34.8) 46.0 (39.7,53.2) 57.9 (49.7,67.5) 66.8 (57.1,78.2) 85.6 (72.4,101.4)

65+ yrs 13.8 (11.6,16.4) 24.9 (21.6,28.9) 39.5 (33.9,46.0) 50.6 (43.1,59.4) 59.0 (50.0,69.6) 77.9 (65.0,93.5)

Women of childbearing 

age (13 to 49 yrs) 8.2 (7.1,9.4) 16.7 (15.2,18.3) 28.6 (25.8,31.7) 38.1 (33.9,42.8) 45.6 (40.1,51.9) 62.2 (53.8,71.8)

Gender

Female 11.4 (10.1,12.9) 21.5 (19.5,23.6) 34.7 (31.2,38.6) 44.9 (39.9,50.6) 52.5 (46.2,59.6) 69.1 (59.7,80.1)

Male 14.6 (12.6,16.9) 27.2 (24.4,30.3) 44.1 (39.4,49.5) 57.2 (50.7,64.6) 67.1 (59.1,76.1) 89.0 (77.4,102.3)

Race/Ethnicity1

Mexican American 11.3 (9.1,13.9) 21.5 (18.3,25.4) 35.4 (30.0,41.7) 46.7 (39.3,55.7) 55.5 (46.3,66.6) 75.6 (61.9,92.3)

Other Hispanic 10.7 (8.4,13.5) 21.5 (17.1,27.1) 36.3 (28.8,45.8) 48.4 (38.5,60.8) 57.5 (45.7,72.2) 78.3 (61.8,99.3)

Non-Hispanic White 12.3 (10.6,14.2) 22.9 (20.5,25.5) 36.9 (32.8,41.5) 47.8 (42.0,54.4) 55.9 (48.6,64.2) 73.5 (62.9,85.8)

Non-Hispanic Black 14.6 (12.3,17.4) 27.1 (23.6,31.0) 43.8 (38.5,49.8) 56.7 (49.7,64.7) 66.5 (57.9,76.4) 88.1 (75.0,103.6)

Other Race 24.2 (18.8,31.3) 41.0 (32.7,51.4) 62.2 (50.3,76.8) 78.8 (63.6,97.5) 90.0 (72.4,112.0) 114.9 (91.6,144.0)

Income, finer detail

$0 to <$20K 10.4 (8.9,12.3) 20.8 (18.7,23.3) 35.4 (31.5,39.8) 47.3 (41.7,53.7) 56.4 (49.5,64.4) 78.0 (66.9,91.0)

$20 to <$45K 11.9 (10.1,13.9) 22.1 (19.6,24.8) 36.1 (32.2,40.4) 47.3 (41.9,53.4) 55.8 (49.2,63.3) 75.0 (65.3,86.2)

$40 to <$75K 12.2 (10.3,14.5) 23.1 (19.8,26.9) 37.7 (32.0,44.3) 49.2 (41.5,58.3) 57.9 (48.7,69.0) 78.3 (64.9,94.5)

$75+K 15.9 (13.6,18.5) 28.2 (24.9,32.0) 44.1 (38.5,50.5) 56.1 (48.6,64.8) 64.8 (55.8,75.3) 84.4 (71.2,100.0)

>$20K 12.1 (8.3,17.6) 23.3 (17.0,32.0) 38.9 (29.0,52.2) 51.5 (38.2,69.4) 61.3 (45.2,83.2) 81.4 (59.9,110.7)

Refused/Don’t Know Income 10.7 (6.5,17.4) 24.3 (17.0,34.8) 43.6 (32.9,57.8) 57.7 (44.0,75.6) 67.6 (51.5,88.6) 87.4 (63.6,120.1)

Income Missing 4.9 (2.2,11.2) 13.0 (6.7,25.2) 28.8 (14.8,55.9) 41.9 (22.9,76.8) 53.0 (30.8,91.1) 75.7 (48.9,117.2)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 55

 
Table 11b. UFCR estimates (g/day raw weight, edible portion): Total finfish, adults, 21 years and older, by geographic area

All Finfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 12.8 (11.3,14.6) 24.1 (21.9,26.5) 39.3 (35.5,43.5) 51.2 (45.7,57.3) 60.1 (53.2,67.9) 80.1 (69.8,92.0)

Region1

Northeast 15.4 (12.6,18.7) 28.5 (23.9,33.9) 45.0 (37.7,53.6) 57.2 (47.9,68.5) 66.1 (55.1,79.4) 85.6 (70.2,104.3)

Midwest 10.3 (8.1,13.2) 19.4 (15.9,23.7) 32.0 (26.4,38.7) 42.0 (34.5,51.0) 49.9 (40.7,61.2) 67.7 (54.5,84.1)

South 12.5 (10.8,14.4) 23.4 (21.2,26.0) 38.4 (34.4,42.9) 50.1 (44.2,56.8) 59.0 (51.6,67.6) 79.0 (67.8,92.2)

West 14.9 (12.6,17.7) 27.0 (22.7,32.1) 43.0 (35.8,51.5) 55.4 (46.1,66.6) 64.9 (53.8,78.3) 86.0 (70.8,104.3)

Coastal Status2

Noncoastal 12.1 (10.3,14.3) 22.9 (20.0,26.1) 37.2 (32.5,42.6) 48.4 (41.9,56.0) 56.8 (48.8,66.1) 76.0 (64.3,89.9)

Coastal 14.2 (12.4,16.2) 26.4 (23.7,29.4) 42.9 (38.3,48.0) 55.7 (49.4,62.9) 65.2 (57.4,74.0) 85.5 (74.2,98.5)

Coastal/Inland Region1,2

Pacific 15.5 (12.9,18.7) 28.5 (23.9,34.1) 45.5 (37.9,54.6) 59.0 (48.8,71.4) 69.2 (56.9,84.3) 90.6 (73.7,111.2)

Atlantic 15.8 (13.3,18.7) 29.1 (25.0,33.7) 46.5 (40.2,53.7) 59.6 (51.4,69.1) 68.9 (59.1,80.3) 88.9 (74.8,105.7)

Gulf of Mexico 12.5 (10.0,15.5) 23.2 (19.7,27.5) 38.2 (31.8,45.9) 49.6 (40.6,60.6) 58.7 (47.3,73.0) 77.4 (62.1,96.5)

Great Lakes 10.7 (8.4,13.7) 19.7 (16.0,24.2) 32.1 (26.4,39.0) 42.1 (34.6,51.2) 49.6 (40.7,60.5) 67.0 (54.8,81.8)

Inland Northeast 14.7 (11.8,18.3) 27.4 (22.7,32.9) 42.8 (35.9,51.1) 54.5 (45.7,64.9) 63.0 (52.8,75.3) 82.0 (68.4,98.3)

Inland Midwest 10.2 (7.9,13.2) 19.3 (15.6,23.8) 31.9 (26.0,39.1) 41.8 (33.9,51.5) 49.9 (40.0,62.2) 67.8 (53.4,86.1)

Inland South 11.5 (9.7,13.7) 21.8 (19.2,24.7) 35.7 (31.7,40.2) 46.9 (40.9,53.7) 55.2 (47.8,63.8) 75.1 (62.9,89.6)

Inland West 14.5 (11.5,18.3) 25.8 (20.4,32.7) 40.8 (32.1,51.9) 52.5 (41.3,66.7) 61.2 (48.2,77.7) 81.0 (64.2,102.4)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 56

 
Table 12a. UFCR estimates (g/day raw weight, edible portion): Total shellfish, adults, 21 years and older, by demographic 

characteristics

All Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 3.1 (2.4,3.9) 7.6 (6.4,9.0) 15.6 (13.2,18.5) 23.1 (19.2,27.8) 29.1 (23.9,35.4) 43.7 (35.2,54.2)

Age

21 to <35 yrs 2.7 (2.0,3.7) 7.2 (5.7,9.1) 15.6 (12.4,19.6) 24.1 (18.8,30.7) 31.3 (24.2,40.5) 49.4 (36.7,66.6)

35 to <50 yrs 3.7 (2.8,4.8) 8.8 (6.9,11.2) 17.6 (13.6,22.8) 25.6 (19.3,33.9) 31.8 (23.7,42.6) 46.9 (33.9,64.8)

50 to <65 yrs 3.7 (2.8,4.7) 8.3 (6.7,10.4) 16.0 (12.7,20.1) 22.7 (17.8,28.9) 28.0 (21.9,35.9) 39.9 (31.1,51.3)

65+ yrs 1.9 (1.4,2.7) 5.1 (3.8,6.8) 11.1 (8.6,14.5) 16.9 (13.2,21.6) 21.8 (17.0,27.9) 33.3 (25.8,43.1)

Women of childbearing age (13 to 49 yrs) 2.0 (1.5,2.7) 5.4 (4.4,6.6) 11.4 (9.5,13.8) 17.1 (13.9,20.9) 21.8 (17.5,27.0) 32.7 (25.5,41.8)

Gender

Female 2.5 (2.0,3.2) 6.2 (5.2,7.3) 12.5 (10.6,14.8) 18.2 (15.1,22.0) 22.9 (18.7,28.0) 33.7 (27.1,42.0)

Male 3.8 (3.0,4.9) 9.4 (7.8,11.4) 19.0 (15.7,23.0) 27.9 (22.7,34.3) 35.0 (28.2,43.5) 51.5 (40.5,65.6)

Race/Ethnicity1

Mexican American 3.6 (2.6,4.9) 8.9 (6.9,11.6) 18.2 (13.8,24.0) 26.7 (19.9,35.9) 33.6 (24.7,45.8) 49.5 (34.7,70.8)

Other Hispanic 3.6 (2.2,5.8) 9.1 (6.1,13.6) 18.2 (12.9,25.7) 26.4 (19.0,36.7) 32.9 (23.8,45.4) 48.1 (34.4,67.3)

Non-Hispanic White 2.9 (2.2,3.7) 7.1 (5.9,8.6) 14.5 (12.0,17.6) 21.4 (17.4,26.3) 27.0 (21.7,33.4) 40.1 (31.9,50.5)

Non-Hispanic Black 2.8 (2.1,3.6) 6.9 (5.6,8.4) 14.0 (11.4,17.3) 20.7 (16.3,26.1) 26.0 (20.3,33.2) 38.3 (29.5,49.9)

Other Race 5.9 (3.9,9.0) 13.5 (9.3,19.6) 27.0 (18.6,39.3) 39.7 (26.9,58.7) 50.2 (33.6,74.9) 73.4 (48.7,110.6)

Income, finer detail

$0 to <$20K 1.9 (1.3,2.6) 5.2 (4.1,6.7) 11.7 (9.4,14.5) 18.1 (14.5,22.6) 23.7 (18.7,30.1) 37.4 (28.4,49.2)

$20 to <$45K 2.4 (1.8,3.2) 5.9 (4.7,7.4) 12.2 (10.0,14.9) 18.2 (14.9,22.2) 23.5 (19.1,28.9) 36.2 (29.1,45.1)

$40 to <$75K 2.8 (2.1,3.8) 6.8 (5.3,8.6) 13.8 (10.9,17.5) 20.6 (16.0,26.4) 26.2 (20.1,34.1) 40.3 (29.8,54.4)

$75+K 4.9 (3.9,6.0) 10.8 (9.1,13.0) 20.3 (16.6,24.8) 28.3 (22.8,35.3) 34.7 (27.6,43.7) 49.3 (38.4,63.3)

>$20K 2.7 (1.4,5.2) 6.6 (4.2,10.3) 13.5 (9.6,19.1) 19.8 (14.3,27.5) 25.1 (18.0,34.8) 37.5 (26.4,53.4)

Refused/Don’t Know Income 3.6 (1.7,7.9) 10.9 (5.4,22.0) 24.6 (13.3,45.3) 36.7 (20.6,65.2) 45.7 (26.6,78.8) 63.7 (37.8,107.5)

Income Missing 0.9 (0.2,4.9) 4.7 (1.4,16.0) 17.4 (6.5,46.4) 30.1 (12.2,74.3) 40.4 (17.8,91.6) 60.8 (31.8,116.2)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 57

 
Table 12b. UFCR estimates (g/day raw weight, edible portion): Total shellfish, adults, 21 years and older, by geographic area

All Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 3.1 (2.4,3.9) 7.6 (6.4,9.0) 15.6 (13.2,18.5) 23.1 (19.2,27.8) 29.1 (23.9,35.4) 43.7 (35.2,54.2)

Region1

Northeast 5.9 (4.5,7.7) 13.3 (10.7,16.5) 24.6 (19.6,30.8) 34.2 (27.1,43.2) 41.7 (32.8,53.0) 58.3 (44.8,75.8)

Midwest 1.6 (1.1,2.2) 3.7 (2.7,5.2) 7.6 (5.4,10.6) 11.1 (7.8,15.9) 14.2 (9.8,20.5) 21.5 (14.6,31.5)

South 3.4 (2.4,4.8) 8.0 (6.0,10.7) 15.7 (11.9,20.8) 22.7 (17.2,30.1) 28.4 (21.5,37.5) 41.7 (31.8,54.7)

West 3.5 (2.4,5.0) 8.0 (5.6,11.3) 15.8 (11.1,22.6) 23.2 (16.0,33.5) 29.1 (19.9,42.5) 43.8 (29.1,65.8)

Coastal Status2

Noncoastal 2.4 (1.9,3.1) 6.0 (4.8,7.4) 12.3 (9.8,15.5) 18.2 (14.2,23.5) 23.3 (17.8,30.5) 35.4 (26.4,47.4)

Coastal 4.7 (3.5,6.2) 10.9 (8.6,13.8) 21.0 (16.6,26.4) 29.8 (23.5,37.8) 36.9 (29.0,46.9) 53.2 (41.6,68.0)

Coastal/Inland Region1,2

Pacific 4.6 (3.0,7.0) 10.5 (7.0,15.7) 20.3 (13.4,30.7) 29.0 (18.8,44.5) 36.1 (23.2,56.2) 53.4 (34.1,83.5)

Atlantic 6.4 (4.8,8.6) 13.9 (10.9,17.8) 25.7 (20.3,32.5) 35.2 (28.0,44.3) 42.8 (33.8,54.3) 59.5 (46.0,76.8)

Gulf of Mexico 4.8 (3.2,7.4) 10.8 (7.7,15.2) 20.1 (14.7,27.4) 28.4 (21.0,38.4) 35.0 (26.1,46.9) 48.6 (37.0,64.0)

Great Lakes 2.3 (1.6,3.3) 5.4 (3.9,7.5) 10.5 (7.5,14.6) 15.0 (10.6,21.2) 18.8 (13.3,26.7) 27.5 (19.2,39.2)

Inland Northeast 4.9 (3.4,6.9) 11.2 (8.2,15.2) 20.7 (14.8,28.9) 28.7 (20.1,41.0) 35.3 (24.4,51.0) 49.0 (32.9,73.1)

Inland Midwest 1.4 (1.0,2.0) 3.3 (2.3,4.6) 6.6 (4.7,9.4) 9.8 (6.8,14.2) 12.6 (8.6,18.5) 19.2 (12.9,28.7)

Inland South 2.6 (1.9,3.7) 6.1 (4.7,7.9) 12.1 (9.5,15.3) 17.6 (13.8,22.4) 22.2 (17.2,28.7) 33.6 (25.7,44.0)

Inland West 2.7 (1.9,3.9) 6.1 (4.4,8.5) 11.8 (8.5,16.5) 17.1 (12.1,24.0) 21.4 (15.0,30.6) 31.6 (21.9,45.7)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 58

 
Table 13a. UFCR estimates (g/day raw weight, edible portion): Total trophic level 2 fish, adults, 21 years and older, by demographic

characteristics

Trophic Level 2 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 1.9 (1.5,2.4) 4.7 (4.0,5.5) 9.6 (8.3,11.0) 14.2 (12.1,16.8) 18.1 (15.1,21.8) 27.7 (21.8,35.2)

Age

21 to <35 yrs 1.6 (1.2,2.2) 4.2 (3.5,5.1) 9.0 (7.7,10.6) 13.7 (11.4,16.5) 17.7 (14.4,21.6) 27.9 (21.5,36.1)

35 to <50 yrs 2.1 (1.5,2.8) 5.3 (4.1,6.8) 11.2 (8.5,14.7) 16.7 (12.2,22.9) 21.4 (15.2,30.3) 32.6 (21.9,48.6)

50 to <65 yrs 2.3 (1.7,3.2) 5.1 (3.9,6.6) 9.6 (7.6,12.2) 13.8 (10.9,17.4) 17.1 (13.5,21.7) 24.9 (19.3,32.2)

65+ yrs 1.4 (1.0,1.9) 3.7 (2.8,4.8) 7.7 (6.0,9.8) 11.4 (8.9,14.5) 14.5 (11.2,18.6) 21.5 (16.4,28.3)

Women of childbearing age (13 to 49 yrs) 1.2 (0.9,1.6) 3.1 (2.6,3.9) 6.7 (5.6,8.1) 10.1 (8.2,12.4) 12.9 (10.2,16.3) 19.7 (14.6,26.5)

Gender

Female 1.5 (1.2,2.0) 3.7 (3.2,4.4) 7.5 (6.5,8.7) 11.0 (9.3,13.0) 13.8 (11.5,16.7) 20.6 (16.3,26.1)

Male 2.4 (1.9,3.1) 5.8 (4.9,6.9) 11.9 (10.1,14.0) 17.5 (14.5,21.1) 22.2 (17.9,27.5) 33.3 (25.6,43.4)

Race/Ethnicity1

Mexican American 2.9 (2.2,3.9) 7.2 (5.7,9.0) 14.5 (11.5,18.4) 21.3 (16.4,27.7) 26.9 (20.3,35.5) 40.5 (29.0,56.7)

Other Hispanic 2.5 (1.7,3.6) 6.3 (4.5,8.6) 12.7 (9.3,17.5) 18.5 (13.1,26.1) 23.4 (16.3,33.7) 34.2 (22.5,52.1)

Non-Hispanic White 1.7 (1.3,2.2) 4.1 (3.4,4.9) 8.3 (7.0,9.8) 12.3 (10.2,14.9) 15.6 (12.7,19.2) 23.7 (18.3,30.8)

Non-Hispanic Black 2.1 (1.6,2.9) 5.1 (4.0,6.4) 10.0 (8.0,12.6) 14.5 (11.4,18.4) 18.3 (14.2,23.6) 26.9 (20.4,35.6)

Other Race 3.5 (2.2,5.5) 7.9 (5.3,11.6) 15.1 (10.4,21.9) 21.8 (14.8,32.1) 27.3 (18.0,41.3) 39.8 (24.8,63.9)

Income

$0 to <$20K 1.2 (0.9,1.7) 3.4 (2.6,4.4) 7.6 (6.1,9.5) 11.9 (9.5,14.8) 15.7 (12.4,19.8) 25.1 (19.1,32.9)

$20 to <$45K 1.6 (1.2,2.2) 4.0 (3.2,5.0) 8.3 (7.0,10.0) 12.6 (10.5,15.0) 16.2 (13.5,19.4) 25.2 (20.2,31.4)

$40 to <$75K 1.8 (1.3,2.4) 4.3 (3.5,5.3) 8.8 (7.4,10.5) 13.1 (10.7,16.0) 16.6 (13.4,20.6) 25.2 (19.6,32.5)

$75+K 2.7 (2.2,3.4) 6.0 (5.1,7.1) 11.6 (9.6,14.0) 16.5 (13.2,20.7) 20.7 (16.0,26.9) 30.4 (21.9,42.2)

>$20K 1.9 (1.1,3.2) 4.5 (3.1,6.6) 9.3 (6.8,12.6) 13.6 (9.9,18.5) 17.6 (12.8,24.3) 27.6 (19.3,39.5)

Refused/Don’t Know Income 2.1 (0.8,5.4) 6.1 (2.9,12.9) 13.5 (7.3,25.2) 20.5 (11.5,36.4) 26.2 (14.8,46.6) 39.0 (19.5,78.3)

Income Missing 0.6 (0.1,2.8) 2.6 (0.8,8.3) 9.1 (3.5,23.5) 15.6 (6.5,37.5) 21.5 (9.2,50.4) 36.0 (16.2,79.7)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 59

 
Table 13b. UFCR estimates (g/day raw weight, edible portion): Total trophic level 2 fish, adults, 21 years and older, by geographic 

area

Trophic Level 2 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 1.9 (1.5,2.4) 4.7 (4.0,5.5) 9.6 (8.3,11.0) 14.2 (12.1,16.8) 18.1 (15.1,21.8) 27.7 (21.8,35.2)

Region1

Northeast 3.1 (2.4,3.9) 7.1 (5.6,8.9) 13.6 (10.3,18.0) 19.6 (14.0,27.3) 24.3 (16.8,35.1) 35.9 (23.0,56.1)

Midwest 1.0 (0.7,1.4) 2.4 (1.7,3.4) 4.9 (3.4,7.2) 7.4 (4.9,11.1) 9.5 (6.2,14.6) 14.7 (9.2,23.4)

South 2.3 (1.6,3.3) 5.4 (4.2,7.0) 10.7 (8.7,13.1) 15.4 (12.6,18.9) 19.4 (15.7,24.0) 28.8 (22.7,36.6)

West 2.0 (1.4,2.8) 4.7 (3.5,6.2) 9.3 (7.3,11.9) 13.7 (10.7,17.7) 17.4 (13.4,22.6) 26.8 (20.1,35.6)

Coastal Status2

Noncoastal 1.5 (1.2,2.0) 3.8 (3.1,4.7) 7.9 (6.3,10.0) 11.9 (9.0,15.7) 15.3 (11.3,20.8) 24.4 (16.9,35.2)

Coastal 2.7 (2.0,3.7) 6.3 (5.1,7.9) 12.3 (10.2,14.8) 17.5 (14.5,21.0) 21.8 (17.9,26.5) 31.8 (25.3,40.0)

Coastal/Inland Region1,2

Pacific 2.5 (1.7,3.8) 5.8 (4.2,8.1) 11.3 (8.4,15.3) 16.2 (12.0,21.9) 20.4 (15.0,27.7) 30.2 (21.9,41.6)

Atlantic 3.8 (2.8,5.1) 8.1 (6.6,10.1) 15.1 (12.3,18.4) 20.9 (16.9,25.8) 25.3 (20.1,31.7) 35.8 (26.5,48.3)

Gulf of Mexico 2.9 (1.9,4.6) 6.6 (4.8,9.1) 12.5 (9.7,16.1) 17.4 (13.7,22.2) 21.4 (16.8,27.1) 31.2 (23.7,41.0)

Great Lakes 1.4 (0.9,2.1) 3.3 (2.3,4.8) 6.6 (4.6,9.6) 9.6 (6.5,14.2) 12.0 (8.0,17.9) 17.9 (11.4,28.0)

Inland Northeast 2.6 (1.8,3.7) 6.1 (4.1,8.9) 11.8 (7.5,18.8) 17.3 (10.3,29.0) 21.9 (12.6,38.1) 33.4 (18.2,61.3)

Inland Midwest 0.9 (0.6,1.3) 2.1 (1.5,3.1) 4.4 (2.9,6.7) 6.6 (4.3,10.2) 8.6 (5.4,13.6) 13.5 (8.2,22.2)

Inland South 1.9 (1.3,2.6) 4.4 (3.4,5.6) 8.7 (7.0,10.9) 12.9 (10.1,16.5) 16.4 (12.7,21.3) 25.5 (18.5,35.1)

Inland West 1.6 (1.2,2.3) 3.7 (2.9,4.9) 7.4 (5.7,9.7) 11.0 (8.4,14.6) 14.2 (10.6,19.0) 22.4 (15.9,31.5)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 60

 
Table 14a. UFCR estimates (g/day raw weight, edible portion): Total trophic level 3 fish, adults, 21 years and older, by demographic 

characteristics

Trophic Level 3 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 4.7 (3.9,5.7) 9.6 (8.5,10.8) 16.6 (14.7,18.8) 22.4 (19.4,25.9) 27.0 (23.0,31.6) 37.4 (30.7,45.7)

Age

21 to <35 yrs 3.7 (2.9,4.8) 8.2 (6.8,9.8) 15.4 (12.9,18.3) 22.0 (17.9,27.1) 27.6 (21.6,35.3) 41.3 (29.1,58.5)

35 to <50 yrs 5.0 (4.1,6.1) 9.8 (8.3,11.6) 16.8 (14.1,20.0) 22.5 (18.7,27.1) 26.9 (22.0,32.7) 36.5 (29.3,45.5)

50 to <65 yrs 6.0 (4.9,7.4) 11.3 (9.5,13.3) 18.4 (15.4,21.9) 23.9 (19.7,28.9) 28.1 (22.9,34.4) 37.4 (29.3,47.7)

65+ yrs 4.0 (3.0,5.3) 8.2 (6.8,10.0) 14.6 (12.7,16.9) 20.1 (17.4,23.2) 24.3 (20.8,28.4) 33.8 (27.9,40.8)

Women of childbearing age (13 to 49 yrs) 3.1 (2.5,3.8) 6.7 (5.9,7.8) 12.4 (10.8,14.2) 17.1 (14.6,19.9) 20.8 (17.6,24.6) 29.6 (24.4,35.9)

Gender

Female 4.0 (3.3,4.9) 8.2 (7.1,9.3) 14.1 (12.4,16.1) 19.0 (16.4,22.0) 22.7 (19.4,26.6) 31.1 (25.8,37.4)

Male 5.6 (4.6,6.8) 11.2 (9.8,12.8) 19.2 (16.6,22.2) 25.7 (21.8,30.4) 30.8 (25.7,36.9) 42.4 (33.9,53.1)

Race/Ethnicity1

Mexican American 5.2 (4.0,6.6) 10.4 (8.6,12.5) 17.7 (14.6,21.3) 23.5 (19.2,28.9) 28.1 (22.5,35.2) 38.2 (29.5,49.5)

Other Hispanic 4.4 (3.2,6.1) 9.1 (6.8,12.1) 15.6 (11.9,20.4) 21.0 (16.0,27.5) 25.2 (19.0,33.3) 34.2 (25.0,46.9)

Non-Hispanic White 4.2 (3.4,5.2) 8.4 (7.3,9.7) 14.5 (12.5,16.7) 19.3 (16.5,22.7) 23.1 (19.3,27.5) 31.4 (25.4,38.9)

Non-Hispanic Black 6.1 (5.1,7.4) 11.8 (10.2,13.5) 19.5 (16.8,22.5) 25.6 (21.7,30.2) 30.1 (25.2,35.9) 40.0 (32.6,49.3)

Other Race 11.9 (9.2,15.5) 21.2 (16.8,26.7) 33.2 (26.0,42.3) 42.4 (31.9,56.2) 49.1 (35.9,67.1) 62.0 (43.1,89.3)

Income

$0 to <$20K 3.8 (3.0,4.9) 8.4 (7.1,9.9) 15.3 (13.2,17.8) 21.1 (18.0,24.8) 25.8 (21.7,30.6) 37.0 (29.8,46.0)

$20 to <$45K 4.1 (3.3,5.2) 8.5 (7.2,10.0) 14.8 (12.8,17.2) 20.2 (17.4,23.5) 24.5 (20.8,28.7) 34.7 (28.6,42.2)

$40 to <$75K 4.4 (3.6,5.5) 8.8 (7.5,10.3) 15.3 (13.2,17.9) 20.9 (17.5,24.8) 25.3 (20.8,30.7) 35.4 (27.7,45.3)

$75+K 6.0 (5.0,7.3) 11.5 (9.9,13.3) 19.1 (16.2,22.5) 25.0 (20.8,30.2) 29.5 (24.3,35.9) 39.6 (31.6,49.8)

>$20K 4.6 (3.0,7.2) 9.1 (6.8,12.2) 15.2 (11.8,19.5) 20.2 (15.9,25.7) 23.9 (18.6,30.6) 32.2 (24.7,42.1)

Refused/Don’t Know Income 5.1 (3.0,8.4) 11.7 (7.7,17.9) 21.6 (14.8,31.7) 29.1 (19.1,44.2) 35.0 (22.4,54.7) 47.7 (29.3,77.5)

Income Missing 1.7 (0.6,4.7) 5.4 (2.4,12.4) 14.7 (6.6,32.6) 22.8 (10.9,47.5) 29.4 (15.1,57.1) 42.1 (24.6,72.1)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 61

 
Table 14b. UFCR estimates (g/day raw weight, edible portion): Total trophic level 3 fish, adults, 21 years and older, by geographic 

area

Trophic Level 3 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 4.7 (3.9,5.7) 9.6 (8.5,10.8) 16.6 (14.7,18.8) 22.4 (19.4,25.9) 27.0 (23.0,31.6) 37.4 (30.7,45.7)

Region1

Northeast 5.9 (4.5,7.7) 11.7 (9.3,14.6) 19.4 (15.6,24.1) 25.3 (20.3,31.5) 29.7 (23.7,37.3) 39.7 (30.9,51.0)

Midwest 3.0 (2.2,3.9) 5.8 (4.7,7.2) 10.1 (8.2,12.5) 13.6 (11.0,17.0) 16.5 (13.1,20.8) 23.0 (17.9,29.6)

South 5.4 (4.3,6.9) 10.6 (8.9,12.6) 17.9 (15.1,21.1) 23.8 (19.9,28.5) 28.4 (23.6,34.1) 38.8 (31.5,47.9)

West 5.6 (4.3,7.1) 10.8 (8.7,13.4) 18.6 (14.7,23.4) 25.1 (19.5,32.3) 30.3 (23.2,39.6) 42.1 (30.6,57.9)

Coastal Status2

Noncoastal 4.1 (3.3,5.1) 8.3 (7.1,9.8) 14.6 (12.5,17.1) 19.7 (16.6,23.5) 23.7 (19.7,28.6) 32.8 (26.4,40.7)

Coastal 6.0 (4.9,7.4) 11.8 (10.1,13.7) 19.9 (17.0,23.2) 26.5 (22.3,31.4) 31.7 (26.2,38.3) 43.3 (34.0,54.9)

Coastal/Inland Region1,2

Pacific 6.4 (4.9,8.4) 12.7 (9.8,16.5) 21.7 (16.5,28.6) 29.4 (21.9,39.6) 35.3 (25.7,48.5) 48.2 (32.8,70.8)

Atlantic 7.2 (5.7,9.1) 13.2 (11.1,15.8) 21.6 (18.3,25.4) 27.9 (23.6,33.1) 32.8 (27.3,39.4) 43.7 (34.9,54.5)

Gulf of Mexico 6.3 (4.5,8.7) 12.2 (9.7,15.2) 20.2 (16.7,24.5) 26.8 (21.9,32.8) 31.9 (25.9,39.4) 43.4 (34.0,55.5)

Great Lakes 3.7 (2.9,4.7) 7.0 (5.7,8.6) 11.7 (9.4,14.5) 15.4 (12.2,19.4) 18.3 (14.4,23.4) 24.9 (19.2,32.4)

Inland Northeast 5.3 (3.9,7.1) 10.6 (8.3,13.6) 17.7 (13.9,22.7) 23.4 (18.1,30.2) 27.5 (21.1,35.8) 36.9 (27.8,48.9)

Inland Midwest 2.7 (2.0,3.7) 5.4 (4.3,6.9) 9.5 (7.6,11.9) 13.0 (10.2,16.4) 15.7 (12.3,20.0) 22.2 (17.0,29.0)

Inland South 4.7 (3.7,6.0) 9.3 (7.8,11.1) 15.9 (13.5,18.8) 21.3 (17.8,25.4) 25.5 (21.0,30.9) 35.0 (28.1,43.6)

Inland West 4.9 (3.7,6.6) 9.4 (7.3,12.0) 15.8 (12.3,20.2) 21.0 (16.3,27.1) 25.2 (19.2,33.0) 34.2 (25.6,45.7)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 62

 
Table 15a. UFCR estimates (g/day raw weight, edible portion): Total trophic level 4 fish, adults, 21 years and older, by demographic 

characteristics

Trophic Level 4 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 8.6 (7.5,9.9) 17.1 (15.5,18.8) 28.8 (26.1,31.9) 38.3 (34.1,42.9) 45.5 (40.1,51.7) 61.6 (53.2,71.2)

Age

21 to <35 yrs 6.0 (4.8,7.4) 12.9 (10.9,15.3) 23.8 (19.9,28.6) 33.2 (27.2,40.5) 40.9 (33.0,50.5) 58.4 (45.9,74.3)

35 to <50 yrs 8.3 (7.1,9.8) 16.0 (14.1,18.2) 26.7 (23.2,30.8) 35.4 (30.3,41.5) 42.2 (35.6,50.1) 57.0 (47.1,69.0)

50 to <65 yrs 11.8 (9.7,14.3) 21.5 (18.3,25.3) 34.4 (29.3,40.3) 44.4 (37.6,52.4) 51.8 (43.5,61.7) 68.1 (56.5,82.0)

65+ yrs 9.5 (7.7,11.6) 17.8 (15.1,21.0) 29.3 (24.7,34.7) 38.5 (32.1,46.2) 45.3 (37.5,54.9) 60.8 (49.2,75.0)

Women of childbearing age (13 to 49 yrs) 5.4 (4.6,6.3) 11.7 (10.5,13.0) 20.8 (18.7,23.1) 28.2 (25.1,31.6) 33.9 (29.9,38.4) 46.7 (40.4,54.0)

Gender

Female 7.8 (6.8,8.9) 15.4 (14.1,16.9) 25.9 (23.3,28.7) 34.1 (30.3,38.3) 40.3 (35.4,45.9) 54.2 (46.3,63.3)

Male 9.7 (8.3,11.3) 19.0 (17.0,21.2) 32.0 (28.6,35.9) 42.6 (37.5,48.5) 50.5 (44.1,57.8) 68.4 (58.4,80.1)

Race/Ethnicity1

Mexican American 6.4 (5.2,8.0) 13.1 (11.1,15.4) 22.8 (19.3,26.8) 30.7 (25.7,36.7) 37.3 (30.7,45.3) 52.9 (42.0,66.6)

Other Hispanic 6.8 (5.3,8.8) 14.2 (11.1,18.1) 25.2 (19.9,31.9) 34.1 (27.1,43.0) 41.1 (32.9,51.5) 57.2 (45.7,71.6)

Non-Hispanic White 8.8 (7.5,10.2) 17.1 (15.4,19.1) 28.7 (25.7,32.1) 38.0 (33.5,43.1) 45.1 (39.3,51.8) 60.8 (52.0,71.1)

Non-Hispanic Black 8.4 (6.9,10.1) 16.4 (14.1,19.2) 28.0 (24.0,32.8) 37.3 (31.8,43.9) 44.5 (37.6,52.7) 60.7 (50.4,73.1)

Other Race 14.2 (10.6,19.0) 25.6 (20.0,32.7) 39.6 (31.3,50.3) 51.1 (40.3,64.9) 59.5 (46.4,76.4) 80.0 (59.9,107.0)

Income

$0 to <$20K 6.9 (5.7,8.2) 14.5 (12.9,16.3) 25.6 (22.6,29.0) 34.9 (30.3,40.2) 42.4 (36.1,49.7) 59.3 (49.0,71.8)

$20 to <$45K 7.7 (6.5,9.2) 15.1 (13.4,17.1) 25.7 (22.7,29.1) 34.4 (30.1,39.3) 41.1 (35.7,47.4) 56.8 (47.9,67.3)

$40 to <$75K 8.3 (6.9,9.9) 16.4 (14.0,19.1) 27.6 (23.4,32.5) 36.7 (30.8,43.8) 43.8 (36.4,52.7) 59.2 (48.5,72.1)

$75+K 11.1 (9.5,13.0) 20.5 (18.1,23.1) 33.0 (29.1,37.4) 42.7 (37.3,48.9) 50.0 (43.2,57.9) 66.1 (56.2,77.8)

>$20K 7.9 (5.3,11.8) 16.0 (11.3,22.6) 27.7 (19.8,38.7) 36.8 (26.2,51.7) 44.2 (31.3,62.4) 60.5 (43.2,84.7)

Refused/Don’t Know Income 7.3 (4.4,12.1) 18.0 (12.2,26.6) 34.6 (25.4,47.1) 47.4 (35.8,62.8) 56.4 (43.1,73.9) 74.9 (57.1,98.2)

Income Missing 3.7 (1.7,8.4) 10.9 (5.3,22.2) 23.5 (11.8,46.5) 33.8 (17.8,64.3) 41.6 (23.2,74.6) 60.7 (37.9,97.3)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 63

 
Table 15b. UFCR estimates (g/day raw weight, edible portion): Total trophic level 4 fish, adults, 21 years and older, by geographic 

area

Trophic Level 4 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 8.6 (7.5,9.9) 17.1 (15.5,18.8) 28.8 (26.1,31.9) 38.3 (34.1,42.9) 45.5 (40.1,51.7) 61.6 (53.2,71.2)

Region1

Northeast 11.5 (9.4,14.1) 22.0 (18.4,26.2) 36.0 (30.1,43.1) 46.7 (38.8,56.2) 55.0 (45.5,66.5) 73.1 (60.1,89.0)

Midwest 7.2 (5.4,9.6) 14.5 (11.5,18.3) 24.8 (19.9,30.8) 33.1 (26.6,41.3) 39.8 (31.6,50.0) 54.5 (42.7,69.6)

South 7.6 (6.6,8.9) 15.1 (13.4,17.0) 25.8 (22.6,29.4) 34.2 (29.6,39.6) 40.9 (34.9,48.0) 56.0 (46.5,67.3)

West 10.2 (8.5,12.3) 19.1 (16.1,22.8) 31.2 (25.9,37.6) 40.8 (33.5,49.7) 48.0 (39.2,58.8) 63.5 (51.4,78.4)

Coastal Status2

Noncoastal 8.3 (7.0,9.8) 16.3 (14.3,18.7) 27.5 (23.9,31.6) 36.4 (31.3,42.3) 43.4 (36.9,50.9) 58.7 (49.1,70.2)

Coastal 9.3 (7.9,11.0) 18.4 (16.3,20.9) 31.2 (27.5,35.4) 41.4 (36.2,47.3) 49.1 (42.7,56.5) 65.9 (56.3,77.1)

Coastal/Inland Region1,2

Pacific 10.5 (8.5,12.9) 19.9 (16.5,24.1) 32.8 (27.0,39.8) 42.7 (34.8,52.3) 50.4 (40.7,62.5) 66.8 (53.0,84.1)

Atlantic 10.4 (8.4,12.9) 20.4 (17.0,24.5) 34.5 (28.9,41.2) 45.7 (38.1,54.8) 53.9 (44.9,64.7) 72.1 (59.0,88.0)

Gulf of Mexico 7.6 (6.0,9.5) 14.9 (12.3,18.1) 25.7 (21.0,31.6) 33.8 (27.5,41.6) 40.3 (32.6,49.8) 53.9 (43.0,67.5)

Great Lakes 7.0 (5.2,9.5) 14.1 (11.0,18.2) 24.4 (19.2,30.9) 32.6 (25.8,41.1) 39.0 (30.9,49.2) 53.7 (42.0,68.8)

Inland Northeast 11.1 (8.8,13.8) 21.1 (17.6,25.3) 34.2 (28.7,40.7) 44.0 (37.0,52.3) 51.6 (43.1,62.0) 68.7 (56.6,83.4)

Inland Midwest 7.3 (5.4,9.9) 14.6 (11.4,18.7) 24.8 (19.7,31.2) 33.2 (26.3,42.0) 40.0 (31.3,51.1) 54.7 (42.2,71.0)

Inland South 7.1 (5.9,8.5) 13.9 (12.0,16.2) 23.7 (20.3,27.7) 31.6 (26.6,37.5) 37.5 (31.3,44.8) 52.2 (41.8,65.1)

Inland West 10.0 (7.8,12.9) 18.5 (14.5,23.5) 30.0 (23.4,38.4) 39.0 (30.4,50.1) 45.9 (35.8,58.9) 60.8 (47.6,77.7)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 64

 
Table 16a. UFCR estimates (g/day raw weight, edible portion): Total freshwater + estuarine trophic level 2 fish, adults, 21 years and 

older, by demographic characteristics

Freshwater + Estuarine Trophic Level 2 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 1.5 (1.1,1.9) 3.6 (3.0,4.4) 7.6 (6.4,9.1) 11.5 (9.4,14.0) 14.7 (11.8,18.3) 23.0 (17.6,30.2)

Age

21 to <35 yrs 1.4 (1.0,1.9) 3.6 (2.9,4.3) 7.8 (6.6,9.3) 12.0 (9.8,14.6) 15.5 (12.5,19.2) 24.7 (18.7,32.5)

35 to <50 yrs 1.6 (1.2,2.3) 4.2 (3.1,5.6) 8.9 (6.5,12.1) 13.4 (9.5,18.7) 17.3 (12.1,24.7) 27.1 (18.1,40.6)

50 to <65 yrs 1.7 (1.2,2.4) 3.8 (2.9,5.0) 7.4 (5.7,9.5) 10.6 (8.2,13.8) 13.3 (10.1,17.4) 20.0 (14.6,27.2)

65+ yrs 1.0 (0.7,1.4) 2.7 (2.0,3.6) 5.7 (4.4,7.5) 8.7 (6.6,11.4) 11.1 (8.3,14.8) 16.8 (12.3,23.0)

Women of childbearing age (13 to 49 yrs) 0.9 (0.7,1.3) 2.6 (2.0,3.2) 5.6 (4.5,7.0) 8.5 (6.6,10.9) 11.0 (8.4,14.4) 17.1 (12.4,23.7)

Gender

Female 1.2 (0.9,1.6) 3.0 (2.5,3.7) 6.2 (5.1,7.5) 9.2 (7.4,11.3) 11.6 (9.2,14.7) 17.7 (13.3,23.5)

Male 1.8 (1.4,2.3) 4.4 (3.6,5.4) 9.3 (7.6,11.2) 13.8 (11.1,17.2) 17.8 (14.0,22.5) 27.5 (20.6,36.6)

Race/Ethnicity1

Mexican American 2.5 (1.8,3.3) 6.2 (4.9,7.9) 12.9 (9.9,16.6) 19.1 (14.3,25.5) 24.5 (18.0,33.5) 37.6 (26.3,53.9)

Other Hispanic 2.0 (1.3,3.0) 5.1 (3.5,7.3) 10.5 (7.3,15.1) 15.3 (10.3,22.7) 19.6 (12.9,29.6) 29.7 (18.7,47.3)

Non-Hispanic White 1.2 (0.9,1.6) 3.1 (2.5,3.8) 6.3 (5.1,7.7) 9.3 (7.5,11.6) 11.9 (9.3,15.1) 18.2 (13.6,24.3)

Non-Hispanic Black 1.8 (1.3,2.4) 4.3 (3.4,5.5) 8.6 (6.7,11.1) 12.6 (9.6,16.5) 15.8 (11.9,21.1) 23.6 (17.2,32.6)

Other Race 3.0 (1.9,4.8) 6.9 (4.6,10.3) 13.3 (9.0,19.4) 19.0 (12.6,28.4) 23.6 (15.4,36.3) 35.0 (21.2,57.8)

Income

$0 to <$20K 1.0 (0.7,1.3) 2.7 (2.1,3.5) 6.4 (5.1,7.9) 10.0 (8.0,12.7) 13.3 (10.4,17.0) 21.9 (16.1,29.8)

$20 to <$45K 1.2 (0.9,1.7) 3.1 (2.5,4.0) 6.7 (5.5,8.3) 10.4 (8.5,12.7) 13.6 (10.9,16.8) 21.6 (16.8,27.7)

$40 to <$75K 1.4 (1.0,1.9) 3.4 (2.8,4.2) 7.0 (5.8,8.5) 10.5 (8.5,12.9) 13.4 (10.7,16.9) 21.3 (16.0,28.3)

$75+K 2.1 (1.6,2.7) 4.6 (3.7,5.7) 9.0 (7.1,11.4) 13.0 (9.9,17.0) 16.3 (12.1,21.9) 24.5 (17.1,35.1)

>$20K 1.4 (0.8,2.5) 3.5 (2.3,5.2) 7.4 (5.4,10.2) 11.1 (8.0,15.3) 14.5 (10.4,20.2) 23.1 (16.0,33.5)

Refused/Don’t Know Income 1.6 (0.6,4.3) 4.7 (2.1,10.1) 10.5 (5.6,19.8) 16.3 (9.0,29.5) 21.6 (12.0,38.6) 34.3 (17.1,68.7)

Income Missing 0.5 (0.1,2.3) 2.1 (0.7,6.4) 7.4 (2.9,19.2) 13.1 (5.5,31.3) 17.8 (7.8,40.4) 30.4 (14.0,65.8)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 65

 
Table 16b. UFCR estimates (g/day raw weight, edible portion): Total freshwater + estuarine trophic level 2 fish, adults, 21 years and 

older, by geographic area

Freshwater + Estuarine Trophic Level 2 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 1.5 (1.1,1.9) 3.6 (3.0,4.4) 7.6 (6.4,9.1) 11.5 (9.4,14.0) 14.7 (11.8,18.3) 23.0 (17.6,30.2)

Region1

Northeast 2.1 (1.5,2.8) 4.8 (3.5,6.6) 9.5 (6.6,13.6) 13.9 (9.3,20.7) 17.6 (11.5,27.1) 27.0 (16.4,44.4)

Midwest 0.8 (0.5,1.1) 1.9 (1.3,2.8) 4.1 (2.7,6.1) 6.2 (4.0,9.5) 8.0 (5.1,12.7) 12.8 (7.7,21.2)

South 1.9 (1.3,2.8) 4.5 (3.4,5.9) 9.0 (7.2,11.3) 13.2 (10.6,16.4) 16.7 (13.3,21.0) 25.4 (19.4,33.2)

West 1.5 (1.0,2.3) 3.7 (2.7,5.0) 7.7 (5.7,10.2) 11.5 (8.6,15.4) 14.7 (10.9,19.9) 23.2 (16.7,32.1)

Coastal Status2

Noncoastal 1.2 (0.9,1.5) 3.0 (2.4,3.8) 6.3 (4.8,8.3) 9.6 (7.1,13.0) 12.5 (9.0,17.4) 20.2 (13.9,29.3)

Coastal 2.1 (1.5,2.9) 4.9 (3.9,6.3) 9.8 (7.9,12.1) 14.1 (11.4,17.5) 17.7 (14.1,22.2) 26.5 (20.3,34.8)

Coastal/Inland Region1,2

Pacific 1.9 (1.2,3.1) 4.6 (3.1,6.8) 9.2 (6.5,13.2) 13.5 (9.5,19.0) 16.9 (12.0,23.9) 25.9 (18.1,37.2)

Atlantic 2.8 (2.0,3.9) 6.2 (4.8,8.0) 11.6 (9.1,14.8) 16.4 (12.7,21.2) 20.4 (15.4,27.1) 29.6 (20.9,42.1)

Gulf of Mexico 2.3 (1.4,3.7) 5.3 (3.8,7.3) 10.4 (8.0,13.5) 14.6 (11.5,18.6) 18.4 (14.2,23.8) 27.1 (20.3,36.2)

Great Lakes 1.1 (0.7,1.7) 2.7 (1.8,3.9) 5.4 (3.7,8.0) 7.9 (5.3,12.0) 10.1 (6.6,15.5) 15.6 (9.7,25.1)

Inland Northeast 1.7 (1.1,2.7) 4.1 (2.6,6.6) 8.2 (4.8,14.1) 12.1 (6.7,21.9) 15.5 (8.3,28.8) 24.3 (12.4,47.5)

Inland Midwest 0.7 (0.5,1.0) 1.7 (1.1,2.5) 3.6 (2.3,5.6) 5.5 (3.5,8.9) 7.2 (4.4,11.9) 11.6 (6.7,20.0)

Inland South 1.6 (1.1,2.2) 3.7 (2.8,4.9) 7.6 (5.8,9.8) 11.3 (8.6,15.0) 14.6 (10.8,19.6) 23.1 (16.3,32.9)

Inland West 1.3 (0.9,1.8) 2.9 (2.2,4.0) 6.2 (4.6,8.3) 9.3 (6.8,12.9) 12.3 (8.7,17.3) 20.0 (13.6,29.4)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 66

 
Table 17a. UFCR estimates (g/day raw weight, edible portion): Total freshwater + estuarine trophic level 3 fish, adults, 21 years and 

older, by demographic characteristics

Freshwater + Estuarine Trophic Level 3 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 2.0 (1.6,2.5) 4.5 (3.9,5.3) 8.6 (7.2,10.2) 12.2 (9.9,15.0) 15.2 (12.0,19.3) 22.5 (16.6,30.6)

Age

21 to <35 yrs 1.6 (1.2,2.1) 3.9 (3.2,4.9) 8.1 (6.4,10.3) 12.4 (9.3,16.5) 16.1 (11.6,22.4) 25.8 (16.5,40.5)

35 to <50 yrs 2.1 (1.6,2.8) 4.8 (3.8,6.0) 9.0 (7.2,11.2) 12.7 (9.9,16.2) 15.6 (12.0,20.4) 22.6 (16.5,31.1)

50 to <65 yrs 2.6 (2.0,3.4) 5.3 (4.3,6.5) 9.4 (7.5,11.8) 12.9 (9.9,16.7) 15.7 (11.7,20.9) 22.2 (15.6,31.7)

65+ yrs 1.5 (1.2,2.1) 3.6 (2.8,4.7) 7.0 (5.6,8.9) 10.0 (7.9,12.7) 12.4 (9.6,15.9) 17.7 (13.0,24.0)

Women of childbearing age (13 to 49 yrs) 1.2 (0.9,1.5) 3.0 (2.5,3.5) 6.0 (4.9,7.2) 8.7 (7.0,10.9) 11.1 (8.7,14.1) 16.7 (12.4,22.7)

Gender

Female 1.6 (1.3,2.1) 3.7 (3.1,4.3) 6.9 (5.8,8.3) 9.7 (7.9,12.0) 12.0 (9.4,15.3) 17.5 (12.9,23.7)

Male 2.5 (2.0,3.2) 5.5 (4.7,6.6) 10.3 (8.5,12.5) 14.6 (11.6,18.4) 18.1 (14.0,23.5) 26.5 (19.0,37.1)

Race/Ethnicity1

Mexican American 2.6 (2.0,3.4) 5.7 (4.6,7.1) 10.5 (8.2,13.4) 14.6 (11.1,19.2) 18.0 (13.3,24.2) 25.6 (18.1,36.2)

Other Hispanic 2.3 (1.6,3.3) 5.2 (3.7,7.4) 9.7 (6.8,13.7) 13.7 (9.5,19.7) 16.9 (11.5,24.8) 24.4 (16.0,37.3)

Non-Hispanic White 1.7 (1.3,2.2) 3.8 (3.2,4.6) 7.2 (5.9,8.7) 10.1 (8.1,12.7) 12.5 (9.7,16.0) 18.2 (13.3,24.7)

Non-Hispanic Black 2.7 (2.2,3.3) 5.7 (4.8,6.7) 10.3 (8.5,12.5) 14.3 (11.4,17.9) 17.5 (13.6,22.6) 25.3 (18.4,34.9)

Other Race 5.3 (4.0,7.0) 10.3 (7.8,13.6) 17.7 (12.4,25.4) 24.2 (15.5,37.6) 29.5 (17.9,48.5) 41.9 (23.5,74.7)

Income

$0 to <$20K 1.5 (1.1,2.1) 3.9 (3.1,4.8) 7.9 (6.6,9.5) 11.7 (9.7,14.2) 15.0 (12.2,18.4) 23.4 (18.1,30.1)

$20 to <$45K 1.7 (1.3,2.2) 3.9 (3.2,4.7) 7.5 (6.2,9.0) 10.7 (8.6,13.3) 13.4 (10.5,17.1) 20.1 (14.8,27.2)

$40 to <$75K 1.9 (1.5,2.4) 4.1 (3.4,5.1) 7.7 (6.2,9.6) 11.0 (8.5,14.2) 13.7 (10.2,18.3) 20.3 (13.7,30.2)

$75+K 2.7 (2.1,3.4) 5.6 (4.7,6.8) 10.1 (8.2,12.4) 13.9 (10.9,17.7) 17.0 (12.9,22.3) 24.2 (17.3,34.0)

>$20K 1.8 (1.1,2.9) 4.0 (2.9,5.5) 7.4 (5.5,10.1) 10.5 (7.6,14.5) 12.9 (9.1,18.4) 18.4 (12.1,28.0)

Refused/Don’t Know Income 2.4 (1.3,4.2) 6.0 (3.8,9.5) 12.2 (8.0,18.6) 17.6 (10.7,29.1) 22.2 (12.8,38.5) 32.7 (17.3,61.7)

Income Missing 0.9 (0.3,2.8) 3.2 (1.3,7.7) 8.9 (4.1,19.6) 14.8 (7.2,30.6) 19.8 (9.9,39.8) 30.2 (16.1,56.4)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 67

 
Table 17b. UFCR estimates (g/day raw weight, edible portion): Total freshwater + estuarine trophic level 3 fish, adults, 21 years and 

older, by geographic area

Freshwater + Estuarine Trophic Level 3 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 2.0 (1.6,2.5) 4.5 (3.9,5.3) 8.6 (7.2,10.2) 12.2 (9.9,15.0) 15.2 (12.0,19.3) 22.5 (16.6,30.6)

Region1

Northeast 2.6 (2.0,3.4) 5.5 (4.4,6.8) 9.7 (7.7,12.1) 13.1 (10.2,16.8) 15.7 (12.0,20.6) 22.2 (15.8,31.3)

Midwest 1.0 (0.8,1.4) 2.3 (1.8,2.9) 4.3 (3.4,5.5) 6.2 (4.8,8.1) 7.8 (5.9,10.3) 12.0 (8.7,16.5)

South 2.7 (1.9,3.7) 5.6 (4.3,7.3) 10.2 (7.9,13.0) 14.2 (10.9,18.4) 17.5 (13.2,23.1) 25.2 (18.5,34.4)

West 2.3 (1.7,3.0) 4.9 (3.7,6.5) 9.2 (6.6,12.8) 13.3 (9.1,19.4) 16.7 (11.0,25.3) 25.2 (15.1,42.3)

Coastal Status2

Noncoastal 1.7 (1.3,2.1) 3.8 (3.2,4.4) 7.2 (5.9,8.7) 10.2 (8.1,12.7) 12.6 (9.8,16.3) 18.6 (13.6,25.4)

Coastal 2.8 (2.1,3.7) 5.9 (4.7,7.5) 10.9 (8.7,13.7) 15.3 (11.8,19.7) 18.9 (14.2,25.1) 27.7 (19.1,40.1)

Coastal/Inland Region1,2

Pacific 2.8 (1.9,4.1) 6.0 (4.2,8.7) 11.4 (7.7,16.9) 16.3 (10.5,25.5) 20.5 (12.5,33.7) 31.0 (16.6,57.7)

Atlantic 3.6 (2.7,4.7) 7.1 (5.8,8.8) 12.3 (10.0,15.2) 16.6 (13.1,21.0) 20.1 (15.4,26.2) 28.5 (20.4,39.7)

Gulf of Mexico 3.2 (2.0,5.0) 6.6 (4.8,9.2) 11.9 (8.9,15.7) 16.4 (12.5,21.7) 20.2 (15.1,27.0) 28.9 (20.9,40.0)

Great Lakes 1.4 (1.0,2.0) 3.0 (2.3,3.8) 5.4 (4.3,6.9) 7.6 (5.9,9.7) 9.3 (7.1,12.1) 13.7 (10.1,18.7)

Inland Northeast 2.3 (1.6,3.3) 4.9 (3.7,6.6) 8.8 (6.5,11.8) 11.9 (8.7,16.4) 14.3 (10.2,20.1) 20.3 (13.7,30.2)

Inland Midwest 0.9 (0.7,1.3) 2.1 (1.6,2.7) 3.9 (3.0,5.2) 5.7 (4.2,7.7) 7.2 (5.2,10.0) 11.2 (7.7,16.3)

Inland South 2.2 (1.7,3.0) 4.7 (3.8,5.9) 8.6 (6.7,10.9) 11.9 (9.1,15.5) 14.7 (11.0,19.6) 21.4 (15.2,30.0)

Inland West 1.9 (1.4,2.5) 3.9 (3.1,5.0) 7.3 (5.4,9.9) 10.3 (7.2,14.6) 12.9 (8.8,18.9) 19.0 (12.2,29.4)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 68

 
Table 18a. UFCR estimates (g/day raw weight, edible portion): Total freshwater + estuarine trophic level 4 fish, adults, 21 years and 

older, by demographic characteristics

Freshwater + Estuarine Trophic Level 4 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 0.6 (0.4,0.9) 1.9 (1.5,2.5) 5.1 (4.0,6.4) 9.1 (7.0,11.7) 13.2 (10.0,17.4) 27.1 (19.4,38.0)

Age

21 to <35 yrs 0.3 (0.2,0.5) 1.0 (0.7,1.4) 3.0 (2.2,4.1) 5.8 (4.2,8.1) 9.1 (6.2,13.3) 20.9 (12.1,36.0)

35 to <50 yrs 0.6 (0.4,0.9) 1.7 (1.2,2.3) 4.2 (3.1,5.8) 7.3 (5.2,10.2) 10.4 (7.2,15.0) 20.4 (13.2,31.7)

50 to <65 yrs 1.0 (0.7,1.5) 2.9 (2.1,4.0) 7.4 (5.4,10.2) 12.8 (9.0,18.2) 18.2 (12.6,26.4) 37.0 (23.4,58.4)

65+ yrs 0.8 (0.5,1.3) 2.4 (1.5,3.7) 6.0 (3.8,9.5) 10.6 (6.7,16.8) 15.1 (9.5,24.0) 29.7 (18.1,48.7)

Women of childbearing age (13 to 49 yrs) 0.3 (0.2,0.5) 1.0 (0.7,1.5) 2.9 (2.1,3.9) 5.3 (3.8,7.4) 7.7 (5.4,11.0) 16.1 (10.4,24.8)

Gender

Female 0.5 (0.4,0.7) 1.6 (1.2,2.1) 4.2 (3.3,5.4) 7.5 (5.7,9.7) 10.8 (8.1,14.3) 21.4 (15.2,30.0)

Male 0.7 (0.5,1.1) 2.3 (1.7,3.0) 6.0 (4.6,7.8) 10.8 (8.2,14.3) 15.8 (11.7,21.3) 32.8 (22.7,47.6)

Race/Ethnicity1

Mexican American 0.6 (0.4,0.9) 1.9 (1.3,2.7) 5.1 (3.6,7.3) 9.2 (6.3,13.6) 13.5 (8.8,20.6) 28.8 (16.9,49.0)

Other Hispanic 0.5 (0.3,0.9) 1.7 (0.9,3.0) 4.6 (2.5,8.7) 8.2 (4.1,16.5) 11.7 (5.5,25.1) 23.4 (9.8,55.9)

Non-Hispanic White 0.5 (0.4,0.8) 1.5 (1.2,2.0) 3.9 (3.0,5.1) 6.8 (5.1,9.0) 9.6 (7.2,13.0) 18.7 (13.1,26.7)

Non-Hispanic Black 1.1 (0.7,1.6) 3.2 (2.3,4.5) 8.4 (6.2,11.4) 14.7 (10.7,20.2) 21.1 (15.0,29.6) 41.2 (28.0,60.8)

Other Race 2.4 (1.3,4.3) 6.6 (3.8,11.3) 16.1 (9.3,27.7) 28.2 (16.0,49.7) 40.2 (22.5,72.0) 77.9 (41.4,146.5)

Income

$0 to <$20K 0.5 (0.3,0.7) 1.6 (1.2,2.2) 4.7 (3.6,6.0) 8.6 (6.5,11.2) 12.6 (9.5,16.7) 26.4 (18.8,37.2)

$20 to <$45K 0.5 (0.4,0.8) 1.7 (1.2,2.3) 4.5 (3.4,6.1) 8.3 (6.1,11.2) 12.1 (8.8,16.7) 25.1 (17.4,36.1)

$40 to <$75K 0.6 (0.4,0.9) 1.9 (1.4,2.5) 5.2 (4.0,6.7) 9.4 (7.0,12.6) 13.7 (10.0,18.8) 28.5 (19.6,41.4)

$75+K 0.8 (0.5,1.1) 2.2 (1.7,2.9) 5.5 (4.2,7.2) 9.4 (7.0,12.7) 13.5 (9.8,18.6) 26.9 (17.9,40.4)

>$20K 0.6 (0.3,1.2) 2.0 (1.2,3.6) 5.5 (3.2,9.3) 9.5 (5.8,15.5) 13.7 (8.5,22.2) 27.2 (16.8,44.2)

Refused/Don’t Know Income 0.6 (0.3,1.2) 2.2 (1.3,3.5) 6.7 (4.3,10.5) 12.8 (7.9,20.7) 19.6 (11.5,33.4) 43.7 (23.6,80.6)

Income Missing 0.1 (0.0,0.5) 0.9 (0.3,2.4) 3.7 (1.5,9.0) 8.2 (3.7,18.3) 13.0 (6.1,27.8) 28.3 (13.5,59.3)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 69

 
Table 18b. UFCR estimates (g/day raw weight, edible portion): Total freshwater + estuarine trophic level 4 fish, adults, 21 years and 

older, by geographic area

Freshwater + Estuarine Trophic Level 4 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Adults (≥21 yrs) 0.6 (0.4,0.9) 1.9 (1.5,2.5) 5.1 (4.0,6.4) 9.1 (7.0,11.7) 13.2 (10.0,17.4) 27.1 (19.4,38.0)

Region1

Northeast 0.4 (0.3,0.6) 1.3 (0.9,1.8) 3.2 (2.3,4.4) 5.5 (4.0,7.6) 7.8 (5.5,10.9) 15.1 (10.3,22.1)

Midwest 0.5 (0.3,1.1) 1.8 (1.0,3.1) 4.9 (2.9,8.1) 8.9 (5.4,14.5) 13.0 (8.0,21.2) 26.6 (16.1,44.0)

South 0.8 (0.5,1.1) 2.4 (1.8,3.2) 6.3 (4.9,8.2) 11.2 (8.5,14.9) 16.2 (12.1,21.8) 32.7 (22.8,46.8)

West 0.7 (0.5,0.9) 1.9 (1.5,2.5) 5.0 (3.6,6.9) 8.9 (5.9,13.3) 12.8 (8.0,20.5) 27.1 (14.6,50.0)

Coastal Status2

Noncoastal 0.6 (0.4,0.9) 1.8 (1.3,2.4) 4.9 (3.6,6.5) 8.7 (6.4,11.8) 12.6 (9.2,17.5) 25.8 (17.8,37.5)

Coastal 0.7 (0.5,1.0) 2.1 (1.6,2.7) 5.4 (4.3,7.0) 9.7 (7.4,12.7) 14.2 (10.5,19.1) 29.0 (20.2,41.8)

Coastal/Inland Region1,2

Pacific 0.7 (0.5,1.1) 2.2 (1.5,3.1) 5.9 (3.9,8.7) 10.5 (6.6,16.7) 15.6 (9.1,26.5) 33.8 (17.1,66.9)

Atlantic 0.8 (0.5,1.1) 2.2 (1.7,3.0) 5.8 (4.3,7.8) 10.2 (7.4,13.9) 14.7 (10.5,20.7) 28.8 (19.6,42.5)

Gulf of Mexico 0.7 (0.5,1.2) 2.1 (1.5,3.0) 5.4 (3.8,7.7) 9.7 (6.7,14.2) 13.8 (9.1,20.9) 28.1 (17.1,46.1)

Great Lakes 0.5 (0.2,0.9) 1.5 (0.9,2.5) 4.0 (2.5,6.5) 7.3 (4.5,11.9) 10.9 (6.6,18.3) 22.9 (12.8,41.0)

Inland Northeast 0.4 (0.3,0.6) 1.2 (0.8,1.7) 3.1 (2.2,4.4) 5.3 (3.7,7.6) 7.5 (5.1,10.9) 14.6 (9.7,22.1)

Inland Midwest 0.6 (0.3,1.2) 1.8 (1.0,3.4) 5.1 (3.0,8.8) 9.3 (5.5,15.7) 13.5 (8.1,22.5) 27.8 (16.4,46.9)

Inland South 0.7 (0.5,1.1) 2.3 (1.7,3.1) 6.1 (4.6,8.1) 10.9 (8.1,14.8) 15.8 (11.5,21.9) 32.5 (22.0,48.0)

Inland West 0.6 (0.5,0.9) 1.7 (1.3,2.4) 4.3 (3.0,6.3) 7.4 (4.7,11.7) 10.7 (6.4,17.8) 20.9 (10.9,40.3)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 70

 
Table 19a. UFCR estimates (g/day raw weight, edible portion): Total fish, youth, <21 years, by demographic characteristics

All Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 4.9 (4.0,6.1) 12.5 (10.6,14.7) 24.0 (20.5,28.3) 34.2 (28.6,40.8) 42.4 (35.1,51.3) 61.7 (49.1,77.5)

Age 

 1 to <3 yrs 2.7 (1.9,3.8) 6.7 (5.0,9.0) 12.7 (9.8,16.4) 17.9 (13.8,23.2) 22.3 (17.0,29.2) 32.8 (24.1,44.4)

 3 to <6 yrs 3.6 (2.6,5.1) 8.8 (6.9,11.2) 16.3 (13.3,19.8) 22.5 (18.4,27.7) 27.5 (22.2,34.1) 39.6 (31.4,50.1)

 6 to <11 yrs 5.1 (3.6,7.3) 12.8 (9.2,17.7) 24.2 (16.7,35.1) 34.1 (22.6,51.4) 41.9 (26.9,65.0) 58.6 (36.6,93.8)

 11 to <16 yrs 5.0 (3.6,7.0) 12.3 (9.5,15.9) 22.6 (17.9,28.5) 30.9 (24.5,39.0) 37.4 (29.8,47.1) 53.2 (42.7,66.2)

 16 to <18 yrs 6.1 (4.3,8.8) 14.5 (11.5,18.3) 26.9 (22.0,32.7) 36.8 (30.4,44.7) 44.3 (36.4,54.0) 61.0 (49.7,74.8)

 18 to <21 yrs 9.1 (6.3,13.0) 20.9 (15.5,28.2) 38.5 (28.2,52.5) 53.4 (37.9,75.4) 65.0 (45.3,93.3) 88.1 (60.6,128.0)

Gender

Female 4.5 (3.6,5.6) 11.4 (9.7,13.5) 21.9 (19.0,25.4) 30.8 (26.7,35.5) 37.7 (32.6,43.5) 52.9 (44.4,63.1)

Male 5.5 (4.4,6.8) 13.5 (11.2,16.3) 26.1 (21.2,32.1) 37.6 (29.7,47.6) 47.1 (36.4,60.9) 69.2 (51.8,92.4)

Race/Ethnicity1

Mexican American 4.4 (3.4,5.8) 10.9 (9.0,13.2) 20.3 (17.1,24.1) 28.9 (24.2,34.6) 36.2 (30.0,43.8) 54.3 (43.4,67.8)

Other Hispanic 4.2 (2.7,6.5) 10.9 (7.5,15.9) 20.8 (14.8,29.1) 28.7 (20.5,40.1) 35.0 (24.9,49.2) 49.4 (34.2,71.3)

Non-Hispanic White 4.2 (3.3,5.5) 10.9 (8.7,13.7) 21.4 (16.7,27.5) 30.8 (23.3,40.6) 38.9 (28.9,52.4) 58.9 (41.0,84.6)

Non-Hispanic Black 7.8 (6.0,10.1) 16.8 (13.7,20.7) 28.9 (23.9,35.0) 38.6 (32.1,46.3) 45.8 (38.2,55.0) 62.3 (51.7,74.9)

Other Race 10.3 (7.4,14.2) 23.8 (18.6,30.3) 40.9 (31.9,52.4) 53.4 (40.3,70.8) 62.7 (46.6,84.2) 83.4 (62.2,111.8)

Income

$0 to <$20K 5.0 (3.8,6.7) 13.0 (10.4,16.3) 24.5 (20.2,29.8) 33.7 (27.8,41.0) 41.0 (33.9,49.8) 57.3 (46.8,70.2)

$20 to <$45K 4.9 (3.9,6.2) 12.1 (10.2,14.4) 23.2 (19.8,27.2) 33.3 (27.9,39.8) 41.7 (33.8,51.4) 61.9 (44.3,86.5)

$40 to <$75K 5.0 (3.7,6.6) 13.0 (9.9,16.9) 26.0 (19.3,35.1) 38.0 (27.6,52.5) 46.9 (33.4,65.9) 69.1 (47.6,100.5)

$75+K 5.1 (3.9,6.6) 12.5 (10.0,15.7) 23.5 (18.7,29.4) 32.4 (25.4,41.4) 39.7 (30.4,51.7) 56.5 (41.5,76.9)

>$20K 3.8 (1.8,7.9) 8.2 (4.5,15.0) 15.4 (8.8,27.1) 21.2 (12.0,37.3) 25.9 (14.4,46.7) 38.0 (20.8,69.6)

Refused/Don’t Know 

Income 4.2 (0.9,18.7) 10.7 (2.4,48.1) 27.5 (6.8,111.8) 43.8 (15.4,124.3) 54.8 (25.4,118.3) 76.2 (44.7,130.1)

Income Missing 6.4 (2.6,15.7) 17.0 (7.4,39.0) 39.2 (15.6,98.4) 55.9 (23.4,133.5) 67.9 (30.1,152.8) 87.2 (44.2,172.2)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 71

 
Table 19b. UFCR estimates (g/day raw weight, edible portion): Total fish, youth, <21 years, by geographic area

All Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 4.9 (4.0,6.1) 12.5 (10.6,14.7) 24.0 (20.5,28.3) 34.2 (28.6,40.8) 42.4 (35.1,51.3) 61.7 (49.1,77.5)

Region1

Northeast 5.7 (4.1,7.8) 14.1 (11.2,17.7) 27.6 (21.1,36.1) 40.4 (27.4,59.6) 50.6 (31.3,82.0) 75.1 (43.6,129.5)

Midwest 3.3 (2.5,4.3) 8.8 (7.4,10.5) 17.5 (15.1,20.3) 25.5 (21.9,29.6) 31.7 (27.1,37.1) 47.7 (40.2,56.6)

South 5.7 (4.2,7.7) 13.5 (10.7,17.0) 24.6 (19.9,30.3) 33.9 (27.4,41.9) 41.1 (33.0,51.3) 58.5 (45.6,75.1)

West 5.9 (4.1,8.7) 14.6 (10.3,20.6) 27.8 (19.5,39.7) 39.2 (27.3,56.3) 47.6 (33.1,68.4) 67.0 (46.4,96.8)

Coastal Status2

Noncoastal 4.5 (3.5,5.7) 11.5 (9.3,14.2) 22.2 (17.6,28.1) 31.7 (24.3,41.4) 39.8 (29.8,53.2) 58.8 (41.5,83.2)

Coastal 5.9 (4.7,7.4) 14.5 (12.2,17.1) 27.2 (23.4,31.8) 38.0 (32.4,44.6) 46.4 (39.1,55.1) 65.7 (54.1,79.7)

Coastal/Inland Region1,2

Pacific 5.9 (4.3,8.1) 15.1 (11.3,20.0) 28.8 (22.2,37.5) 40.2 (31.1,52.0) 48.9 (37.7,63.4) 68.6 (52.1,90.1)

Atlantic 7.2 (5.4,9.6) 16.4 (13.3,20.2) 29.3 (24.4,35.3) 40.3 (33.6,48.3) 48.6 (40.4,58.5) 68.2 (55.8,83.4)

Gulf of Mexico 7.0 (4.3,11.5) 16.0 (11.0,23.3) 29.8 (20.6,43.3) 41.5 (27.4,62.9) 51.6 (33.1,80.5) 72.6 (45.2,116.6)

Great Lakes 3.9 (2.9,5.2) 10.2 (8.2,12.8) 20.1 (15.9,25.5) 28.4 (21.7,37.1) 35.5 (26.9,46.7) 50.4 (36.9,68.9)

Inland Northeast 5.1 (3.6,7.2) 12.9 (9.9,16.8) 25.9 (17.8,37.6) 39.1 (21.7,70.4) 50.5 (24.6,103.6) 76.6 (35.3,166.1)

Inland Midwest 3.1 (2.3,4.1) 8.3 (6.8,10.1) 16.4 (14.0,19.3) 24.0 (20.4,28.2) 29.9 (25.4,35.3) 45.6 (38.1,54.6)

Inland South 4.9 (3.7,6.4) 12.1 (9.6,15.2) 22.0 (17.7,27.3) 30.3 (24.3,37.6) 36.6 (29.4,45.5) 51.3 (41.2,63.8)

Inland West 6.0 (3.5,10.1) 14.2 (8.9,22.8) 27.1 (16.1,45.5) 38.4 (22.2,66.5) 46.6 (26.9,80.8) 65.8 (38.5,112.5)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 72

 
Table 20a. UFCR estimates (g/day raw weight, edible portion): Freshwater + estuarine fish, youth, <21 years, by demographic 

characteristics

Freshwater + Estuarine 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 1.1 (0.8,1.4) 3.3 (2.7,4.2) 8.0 (6.6,9.8) 12.9 (10.5,15.9) 17.3 (13.9,21.6) 28.9 (22.2,37.7)

Age

 1 to <3 yrs 0.6 (0.3,1.2) 1.9 (1.2,3.2) 4.7 (3.1,7.0) 7.5 (5.1,10.9) 10.1 (6.9,14.7) 17.1 (11.3,25.9)

 3 to <6 yrs 0.7 (0.4,1.3) 2.4 (1.6,3.6) 5.8 (4.1,8.3) 9.5 (6.7,13.5) 12.9 (8.9,18.7) 22.3 (13.8,36.1)

 6 to <11 yrs 1.1 (0.8,1.5) 3.3 (2.5,4.4) 7.7 (5.7,10.5) 12.3 (8.8,17.3) 16.3 (11.3,23.5) 27.3 (18.3,40.7)

 11 to <16 yrs 1.1 (0.7,1.7) 3.4 (2.4,4.9) 8.3 (6.4,10.7) 13.2 (10.4,16.7) 17.7 (14.0,22.4) 29.6 (22.9,38.2)

 16 to <18 yrs 1.4 (0.7,2.8) 4.2 (2.5,6.9) 9.5 (6.5,13.8) 14.9 (10.6,21.0) 19.3 (13.7,27.2) 32.2 (22.6,45.9)

 18 to <21 yrs 1.7 (1.1,2.7) 5.0 (3.5,7.2) 11.6 (8.3,16.2) 18.2 (12.8,25.8) 23.8 (16.4,34.4) 37.5 (24.3,57.8)

Gender

Female 0.9 (0.7,1.3) 3.0 (2.3,4.0) 7.4 (5.8,9.5) 12.1 (9.5,15.4) 16.2 (12.5,20.9) 27.2 (20.4,36.3)

Male 1.2 (0.9,1.6) 3.6 (2.9,4.5) 8.6 (7.0,10.5) 13.7 (11.1,16.9) 18.4 (14.7,23.0) 30.5 (23.1,40.2)

Race/Ethnicity1

Mexican American 1.3 (1.0,1.9) 3.9 (3.0,5.1) 8.7 (6.9,11.0) 13.6 (10.8,17.2) 18.0 (14.1,23.0) 29.9 (22.2,40.2)

Other Hispanic 1.1 (0.6,2.2) 3.6 (1.9,6.9) 8.7 (4.5,16.9) 13.9 (6.8,28.4) 18.6 (8.8,39.6) 30.3 (13.4,68.6)

Non-Hispanic White 0.7 (0.5,1.0) 2.2 (1.7,3.0) 5.1 (3.9,6.7) 8.1 (6.1,10.8) 10.7 (7.9,14.5) 18.0 (12.6,25.7)

Non-Hispanic Black 2.6 (1.8,3.9) 7.2 (5.3,9.9) 15.1 (11.2,20.1) 22.4 (16.7,30.0) 28.5 (21.2,38.2) 44.5 (32.8,60.4)

Other Race 2.7 (1.7,4.2) 7.4 (4.9,11.1) 15.3 (9.9,23.6) 22.5 (14.1,35.6) 28.2 (17.3,45.9) 43.0 (25.7,71.8)

Income

$0 to <$20K 1.1 (0.7,1.7) 3.6 (2.6,5.0) 8.7 (6.6,11.6) 14.0 (10.7,18.4) 18.7 (14.2,24.6) 30.2 (22.4,40.9)

$20 to <$45K 1.2 (0.8,1.7) 3.4 (2.5,4.7) 8.2 (6.2,10.7) 13.0 (9.9,17.1) 17.2 (13.0,22.9) 28.4 (21.0,38.5)

$40 to <$75K 0.9 (0.6,1.4) 2.9 (2.0,4.1) 7.0 (5.0,9.7) 11.4 (8.3,15.6) 15.4 (11.3,21.1) 26.4 (19.1,36.4)

$75+K 1.2 (0.9,1.6) 3.6 (3.0,4.4) 8.3 (6.8,10.1) 13.1 (10.5,16.3) 17.3 (13.6,22.0) 29.0 (21.5,39.1)

>$20K 0.6 (0.2,2.4) 2.2 (0.9,5.5) 6.1 (3.1,12.0) 10.2 (5.3,19.6) 14.4 (7.6,27.4) 24.7 (12.5,48.8)

Refused/Don’t Know Income 0.3 (0.1,0.9) 1.3 (0.6,3.0) 4.4 (1.8,11.0) 8.8 (3.2,24.6) 14.6 (4.8,45.0) 32.6 (8.9,119.7)

Income Missing 1.0 (0.3,3.3) 4.3 (1.0,18.6) 15.0 (2.7,84.4) 25.6 (5.2,126.3) 35.6 (8.2,155.0) 55.4 (15.5,198.1)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 73

 
Table 20b. UFCR estimates (g/day raw weight, edible portion): Freshwater + estuarine fish, youth, <21 years, by geographic area

Freshwater + Estuarine 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 1.1 (0.8,1.4) 3.3 (2.7,4.2) 8.0 (6.6,9.8) 12.9 (10.5,15.9) 17.3 (13.9,21.6) 28.9 (22.2,37.7)

Region1

Northeast 0.9 (0.5,1.4) 2.6 (1.7,4.0) 6.0 (3.8,9.5) 9.5 (5.9,15.4) 12.6 (7.7,20.5) 20.7 (12.4,34.6)

Midwest 0.7 (0.5,1.0) 2.3 (1.6,3.2) 5.8 (4.2,8.1) 10.0 (7.0,14.4) 14.0 (9.6,20.5) 25.2 (16.6,38.4)

South 1.6 (1.0,2.6) 4.6 (3.2,6.8) 10.4 (7.5,14.4) 16.2 (11.8,22.2) 21.3 (15.4,29.5) 34.7 (24.7,48.7)

West 1.2 (0.8,1.7) 3.5 (2.5,5.0) 8.3 (5.9,11.8) 13.3 (9.3,19.1) 17.7 (12.1,25.8) 28.6 (18.5,44.1)

Coastal Status2

Noncoastal 0.9 (0.6,1.2) 2.7 (2.1,3.4) 6.3 (5.1,7.8) 10.1 (8.1,12.6) 13.7 (10.9,17.1) 22.9 (17.7,29.8)

Coastal 1.6 (1.1,2.3) 4.8 (3.5,6.5) 11.1 (8.4,14.8) 17.5 (13.1,23.4) 22.9 (16.9,31.0) 37.0 (26.3,52.0)

Coastal/Inland Region1,2

Pacific 1.5 (0.9,2.3) 4.5 (3.0,6.8) 10.6 (7.2,15.6) 16.7 (11.2,25.0) 22.0 (14.5,33.3) 34.6 (21.8,54.8)

Atlantic 1.9 (1.3,2.9) 5.4 (3.8,7.7) 12.0 (8.6,16.7) 18.4 (13.2,25.4) 23.8 (17.1,33.1) 37.2 (25.9,53.3)

Gulf of Mexico 2.3 (1.1,4.9) 6.3 (3.3,11.8) 13.8 (7.8,24.4) 21.3 (11.9,38.0) 27.9 (15.3,50.9) 46.4 (25.4,84.7)

Great Lakes 1.0 (0.6,1.7) 3.3 (2.1,5.3) 8.6 (5.3,14.0) 14.3 (8.5,24.0) 19.4 (11.3,33.3) 32.9 (18.7,58.1)

Inland Northeast 0.7 (0.4,1.2) 2.2 (1.3,3.6) 5.1 (3.0,8.5) 7.8 (4.5,13.6) 10.1 (5.7,17.9) 16.2 (8.8,29.8)

Inland Midwest 0.6 (0.4,0.9) 1.9 (1.3,2.7) 4.7 (3.4,6.5) 8.0 (5.7,11.1) 11.2 (7.8,15.9) 19.9 (13.1,30.3)

Inland South 1.2 (0.8,1.9) 3.6 (2.6,5.2) 8.2 (6.2,11.0) 12.8 (9.7,16.9) 16.8 (12.7,22.2) 27.5 (20.6,36.8)

Inland West 1.0 (0.7,1.5) 2.9 (2.0,4.2) 6.7 (4.8,9.6) 10.6 (7.4,15.2) 14.1 (9.7,20.6) 23.0 (15.1,35.2)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 74

 
Table 21a. UFCR estimates (g/day raw weight, edible portion): Marine fish, youth, <21 years, by demographic characteristics

Marine Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 3.1 (2.4,4.1) 8.1 (6.5,10.0) 16.4 (13.3,20.3) 24.3 (19.3,30.5) 31.0 (24.2,39.6) 48.2 (35.6,65.2)

Age 

 1 to <3 yrs 1.7 (1.2,2.5) 4.4 (3.2,6.0) 8.8 (6.6,11.9) 12.9 (9.6,17.4) 16.5 (12.1,22.6) 25.2 (17.7,35.7)

 3 to <6 yrs 2.3 (1.6,3.4) 5.7 (4.3,7.6) 11.0 (8.6,14.2) 15.8 (12.4,20.3) 19.7 (15.3,25.4) 29.3 (22.4,38.4)

 6 to <11 yrs 3.2 (2.1,5.0) 8.3 (5.5,12.5) 16.7 (10.3,27.0) 24.2 (14.0,41.9) 30.2 (16.9,54.1) 44.5 (24.1,82.4)

 11 to <16 yrs 3.1 (2.1,4.5) 7.7 (5.6,10.6) 14.7 (10.9,19.8) 20.6 (15.2,27.9) 25.3 (18.6,34.5) 36.8 (26.8,50.5)

 16 to <18 yrs 3.9 (2.8,5.5) 9.6 (7.5,12.3) 18.3 (14.6,23.0) 25.7 (20.4,32.4) 31.7 (25.0,40.2) 45.6 (35.5,58.4)

 18 to <21 yrs 5.6 (3.7,8.5) 14.3 (9.8,20.9) 29.4 (19.3,44.8) 43.5 (26.7,70.8) 54.1 (31.7,92.4) 80.3 (45.3,142.2)

Gender

Female 2.7 (2.1,3.6) 7.1 (5.8,8.8) 14.2 (11.8,17.1) 20.5 (17.0,24.7) 25.6 (21.1,31.1) 37.9 (30.9,46.5)

Male 3.5 (2.6,4.7) 9.1 (7.1,11.7) 18.7 (14.4,24.4) 28.1 (21.0,37.7) 36.2 (26.3,49.7) 56.7 (37.7,85.4)

Race/Ethnicity1

Mexican American 2.4 (1.7,3.3) 6.1 (4.7,8.0) 12.2 (9.8,15.2) 18.0 (14.6,22.3) 22.9 (18.4,28.5) 36.1 (28.3,45.9)

Other Hispanic 2.3 (1.4,3.6) 6.2 (4.2,9.2) 12.6 (9.1,17.5) 18.1 (13.2,24.7) 22.2 (16.0,30.8) 31.7 (21.3,47.3)

Non-Hispanic White 3.0 (2.2,4.2) 8.0 (5.9,10.8) 16.4 (12.1,22.2) 24.5 (17.7,33.8) 31.4 (22.3,44.3) 49.9 (32.1,77.6)

Non-Hispanic Black 3.6 (2.7,4.8) 8.4 (6.8,10.4) 15.4 (12.8,18.6) 21.3 (17.7,25.5) 25.9 (21.6,31.1) 37.0 (30.6,44.8)

Other Race 6.3 (4.4,9.0) 15.8 (11.6,21.4) 29.8 (22.8,38.9) 40.7 (30.6,54.3) 48.6 (35.1,67.4) 67.8 (45.2,101.9)

Income

$0 to <$20K 2.9 (2.2,4.0) 7.8 (6.2,9.9) 15.7 (12.5,19.8) 22.7 (17.8,29.0) 28.5 (22.2,36.6) 43.1 (33.1,56.1)

$20 to <$45K 2.9 (2.3,3.7) 7.5 (6.1,9.1) 15.2 (12.5,18.4) 22.6 (18.1,28.3) 29.3 (22.2,38.6) 47.2 (28.0,79.6)

$40 to <$75K 3.2 (2.3,4.5) 8.5 (6.1,11.8) 18.1 (12.4,26.4) 27.4 (18.1,41.7) 35.5 (22.7,55.6) 56.5 (33.1,96.4)

$75+K 3.4 (2.4,4.8) 8.8 (6.6,11.6) 17.0 (13.0,22.2) 24.2 (18.3,32.2) 30.1 (22.4,40.3) 44.3 (32.3,60.8)

>$20K 2.5 (1.1,6.0) 5.5 (2.8,10.7) 10.5 (5.4,20.3) 15.1 (7.7,29.3) 18.5 (9.3,36.8) 26.5 (12.9,54.5)

Refused/Don’t Know Income 2.5 (0.6,11.4) 7.1 (1.5,34.2) 20.0 (4.3,93.7) 35.3 (9.6,129.5) 47.1 (16.5,134.6) 70.9 (36.2,138.6)

Income Missing 3.9 (1.5,9.9) 11.0 (4.6,26.5) 25.9 (11.1,60.7) 39.4 (17.4,89.3) 49.2 (22.8,106.3) 69.5 (35.8,134.6)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 75

 
Table 21b. UFCR estimates (g/day raw weight, edible portion): Marine fish, youth, <21 years, by geographic area

Marine Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 3.1 (2.4,4.1) 8.1 (6.5,10.0) 16.4 (13.3,20.3) 24.3 (19.3,30.5) 31.0 (24.2,39.6) 48.2 (35.6,65.2)

Region1

Northeast 4.0 (2.6,6.0) 10.3 (7.3,14.6) 21.4 (14.2,32.0) 32.7 (18.8,56.9) 43.0 (21.9,84.5) 68.5 (31.1,150.9)

Midwest 2.2 (1.6,3.0) 5.8 (4.5,7.5) 12.0 (9.8,14.6) 17.6 (14.7,21.1) 22.1 (18.5,26.4) 33.8 (28.2,40.6)

South 3.3 (2.4,4.4) 8.0 (6.2,10.3) 15.6 (12.2,19.9) 22.3 (17.2,28.9) 27.9 (21.2,36.6) 42.4 (31.1,57.8)

West 3.7 (2.3,6.1) 9.6 (6.0,15.3) 19.5 (12.0,31.8) 28.6 (17.3,47.0) 35.5 (21.9,57.8) 51.0 (32.2,80.8)

Coastal Status2

Noncoastal 2.9 (2.1,4.1) 7.7 (5.7,10.3) 15.8 (11.5,21.6) 23.2 (16.3,33.0) 29.8 (20.4,43.7) 47.2 (29.4,75.8)

Coastal 3.5 (2.7,4.4) 8.8 (7.4,10.5) 17.8 (15.1,20.9) 26.0 (21.9,30.7) 32.9 (27.6,39.2) 49.5 (40.0,61.4)

Coastal/Inland Region1,2

Pacific 3.5 (2.4,5.0) 9.3 (6.9,12.6) 19.2 (14.5,25.4) 28.6 (21.5,38.0) 35.7 (27.1,47.2) 50.8 (37.4,69.0)

Atlantic 4.3 (3.2,5.9) 10.2 (7.9,13.1) 19.5 (15.3,24.9) 27.9 (21.7,35.8) 35.0 (27.0,45.4) 53.5 (39.2,73.2)

Gulf of Mexico 3.8 (2.6,5.7) 9.3 (6.6,13.2) 18.7 (12.8,27.3) 28.0 (18.3,42.9) 36.2 (22.8,57.5) 56.6 (33.7,95.2)

Great Lakes 2.2 (1.6,3.1) 6.1 (4.9,7.7) 12.6 (10.1,15.6) 18.5 (14.4,23.7) 23.3 (17.7,30.7) 34.4 (24.8,47.7)

Inland Northeast 3.7 (2.4,5.8) 9.8 (6.7,14.4) 20.7 (12.0,35.8) 32.8 (14.4,74.5) 44.5 (16.9,117.4) 73.6 (25.7,211.4)

Inland Midwest 2.1 (1.5,3.1) 5.8 (4.3,7.8) 11.8 (9.2,15.1) 17.3 (13.9,21.5) 21.8 (17.8,26.8) 33.6 (27.7,40.8)

Inland South 2.9 (2.1,3.9) 7.3 (5.5,9.7) 14.3 (10.7,19.1) 20.3 (15.0,27.4) 25.2 (18.5,34.3) 37.2 (27.2,50.9)

Inland West 3.9 (2.0,7.6) 9.8 (5.2,18.6) 19.7 (9.9,39.4) 28.5 (13.9,58.3) 35.4 (17.4,71.8) 51.1 (26.1,100.0)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 76

 
Table 22a. UFCR estimates (g/day raw weight, edible portion): Total finfish, youth, <21 years, by demographic characteristics

All Finfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 3.8 (3.0,4.8) 9.4 (7.9,11.3) 18.5 (15.4,22.2) 26.8 (21.8,32.9) 33.8 (26.9,42.5) 51.7 (39.1,68.4)

Age 

 1 to <3 yrs 2.1 (1.5,2.9) 5.2 (4.1,6.8) 10.1 (8.1,12.7) 14.6 (11.6,18.4) 18.4 (14.5,23.5) 27.2 (20.4,36.2)

 3 to <6 yrs 2.7 (1.9,4.0) 6.6 (5.1,8.7) 12.8 (10.2,16.0) 18.2 (14.5,22.8) 22.6 (17.8,28.6) 33.1 (25.4,43.2)

 6 to <11 yrs 4.2 (2.9,6.1) 10.4 (7.3,14.7) 20.1 (13.3,30.4) 28.1 (17.6,45.0) 34.6 (20.8,57.6) 49.9 (28.4,87.7)

 11 to <16 yrs 3.8 (2.5,5.6) 9.1 (6.6,12.4) 16.6 (12.7,21.8) 23.1 (17.7,30.2) 28.7 (22.0,37.5) 42.7 (32.2,56.5)

 16 to <18 yrs 4.5 (3.1,6.5) 10.5 (8.0,14.0) 19.7 (15.1,25.6) 27.5 (20.9,36.1) 34.2 (26.0,44.9) 50.5 (38.7,65.9)

 18 to <21 yrs 6.3 (4.3,9.1) 15.2 (10.9,21.2) 30.1 (20.5,44.4) 43.8 (27.7,69.2) 54.9 (33.2,90.7) 78.5 (46.7,131.9)

Gender

Female 3.4 (2.6,4.3) 8.6 (7.1,10.3) 16.8 (14.1,19.9) 23.8 (19.9,28.6) 29.6 (24.6,35.6) 43.3 (35.9,52.2)

Male 4.2 (3.2,5.4) 10.3 (8.5,12.6) 20.3 (16.4,25.2) 29.9 (23.1,38.7) 38.1 (28.3,51.4) 59.2 (40.6,86.5)

Race/Ethnicity1

Mexican American 3.3 (2.5,4.5) 8.2 (6.6,10.2) 15.6 (12.9,18.9) 22.3 (18.3,27.1) 28.2 (23.0,34.6) 44.0 (34.8,55.7)

Other Hispanic 3.2 (1.9,5.4) 8.4 (5.7,12.4) 16.8 (12.2,23.2) 24.3 (17.7,33.5) 30.1 (21.5,42.1) 43.9 (30.4,63.3)

Non-Hispanic White 3.4 (2.5,4.5) 8.5 (6.6,10.9) 17.0 (12.8,22.4) 24.8 (18.1,34.0) 31.8 (22.5,45.0) 50.4 (32.5,78.1)

Non-Hispanic Black 5.6 (4.2,7.3) 12.0 (9.5,15.1) 21.0 (16.7,26.4) 28.6 (22.7,36.0) 34.4 (27.3,43.5) 48.4 (38.2,61.2)

Other Race 6.8 (4.5,10.1) 17.1 (12.5,23.3) 31.4 (23.3,42.3) 42.7 (30.4,60.0) 51.6 (35.9,74.1) 69.4 (45.7,105.4)

Income, finer detail

$0 to <$20K 4.1 (3.0,5.6) 10.5 (8.2,13.4) 20.0 (15.8,25.3) 28.0 (22.0,35.8) 34.4 (26.7,44.4) 50.9 (39.0,66.4)

$20 to <$45K 3.6 (2.7,4.7) 8.8 (7.0,11.1) 17.1 (13.9,21.1) 24.7 (19.7,30.9) 31.4 (24.3,40.6) 48.5 (30.2,77.9)

$40 to <$75K 4.1 (3.0,5.7) 10.7 (7.8,14.8) 21.9 (15.2,31.5) 32.6 (21.8,48.7) 41.8 (27.1,64.5) 64.9 (40.2,104.7)

$75+K 3.7 (2.7,5.0) 8.9 (7.1,11.3) 16.8 (13.3,21.3) 23.7 (18.2,31.0) 29.5 (22.1,39.4) 43.7 (30.7,62.3)

>$20K 2.6 (1.3,5.3) 6.2 (3.2,12.1) 12.7 (5.8,27.8) 18.2 (7.7,43.1) 22.5 (9.3,54.5) 32.7 (13.1,81.4)

Refused/Don’t Know Income 2.9 (0.6,14.2) 8.0 (1.4,46.8) 23.1 (3.8,141.6) 38.0 (8.8,163.8) 50.5 (15.4,165.8) 73.3 (32.4,165.7)

Income Missing 4.9 (1.7,13.5) 13.2 (6.4,27.2) 26.4 (14.9,46.8) 37.3 (22.9,60.7) 45.2 (28.3,72.2) 65.3 (40.5,105.4)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 77

 
Table 22b. UFCR estimates (g/day raw weight, edible portion): Total finfish, youth, <21 years, by geographic area

All Finfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 3.8 (3.0,4.8) 9.4 (7.9,11.3) 18.5 (15.4,22.2) 26.8 (21.8,32.9) 33.8 (26.9,42.5) 51.7 (39.1,68.4)

Region1

Northeast 4.1 (2.7,6.3) 10.0 (7.1,13.9) 20.2 (13.2,31.1) 31.4 (16.9,58.4) 42.0 (19.8,88.7) 70.1 (31.2,157.3)

Midwest 2.6 (1.9,3.8) 7.0 (5.4,8.9) 13.8 (11.3,16.8) 20.1 (16.6,24.3) 25.1 (20.6,30.6) 38.2 (30.9,47.3)

South 4.2 (3.2,5.5) 10.0 (7.9,12.7) 18.5 (14.7,23.4) 25.7 (20.2,32.7) 31.4 (24.6,40.2) 45.1 (34.8,58.4)

West 4.5 (2.9,7.1) 11.5 (7.6,17.5) 22.8 (14.7,35.5) 32.6 (20.7,51.6) 40.8 (25.6,65.1) 58.6 (37.2,92.5)

Coastal Status2

Noncoastal 3.5 (2.7,4.7) 9.0 (7.1,11.4) 17.7 (13.6,23.0) 25.7 (18.8,35.0) 32.5 (22.9,46.2) 50.9 (33.0,78.5)

Coastal 4.2 (3.3,5.4) 10.3 (8.5,12.4) 20.0 (16.7,23.9) 28.7 (23.9,34.5) 35.9 (29.8,43.2) 52.8 (43.1,64.6)

Coastal/Inland Region1,2

Pacific 4.2 (2.9,6.0) 11.1 (7.9,15.6) 22.5 (16.1,31.4) 32.6 (23.4,45.5) 40.8 (29.2,57.1) 57.9 (41.0,81.6)

Atlantic 4.9 (3.6,6.9) 11.1 (8.5,14.4) 20.8 (16.4,26.4) 29.4 (23.0,37.6) 36.6 (28.2,47.4) 55.5 (41.2,74.7)

Gulf of Mexico 4.8 (3.2,7.0) 11.3 (8.0,15.8) 21.2 (15.2,29.8) 29.8 (21.4,41.4) 36.7 (26.4,50.9) 50.3 (35.7,70.9)

Great Lakes 3.0 (2.0,4.3) 7.7 (5.6,10.5) 15.1 (11.0,20.8) 21.8 (15.3,31.0) 27.4 (18.9,39.8) 41.5 (28.0,61.6)

Inland Northeast 3.9 (2.4,6.1) 9.5 (6.6,13.7) 19.6 (11.0,34.8) 31.3 (12.9,76.0) 43.1 (15.3,121.4) 73.0 (24.9,213.8)

Inland Midwest 2.5 (1.8,3.7) 6.7 (5.2,8.7) 13.3 (10.9,16.1) 19.2 (16.1,22.8) 24.2 (20.5,28.5) 36.3 (30.9,42.8)

Inland South 3.8 (2.9,4.9) 9.3 (7.3,11.9) 17.3 (13.5,22.3) 23.9 (18.3,31.2) 29.3 (22.2,38.6) 41.9 (31.8,55.3)

Inland West 4.8 (2.6,8.7) 11.8 (6.8,20.6) 23.0 (12.5,42.6) 32.6 (17.0,62.7) 40.9 (20.8,80.4) 59.1 (30.5,114.6)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 78

 
Table 23a. UFCR estimates (g/day raw weight, edible portion): Total shellfish, youth, <21 years, by demographic characteristics

All Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 0.6 (0.4,0.9) 1.9 (1.5,2.5) 4.8 (3.9,6.1) 8.1 (6.4,10.2) 11.0 (8.4,14.4) 19.2 (13.9,26.7)

Age 

 1 to <3 yrs 0.3 (0.2,0.6) 0.9 (0.5,1.8) 2.1 (1.1,4.0) 3.2 (1.6,6.5) 4.4 (2.1,9.0) 7.3 (3.2,16.5)

 3 to <6 yrs 0.5 (0.3,1.0) 1.6 (0.9,2.7) 3.5 (2.2,5.6) 5.5 (3.5,8.6) 7.2 (4.6,11.4) 12.0 (7.2,20.0)

 6 to <11 yrs 0.5 (0.3,0.9) 1.5 (0.9,2.2) 3.4 (2.4,4.8) 5.5 (3.9,7.9) 7.5 (5.0,11.1) 13.0 (7.4,22.8)

 11 to <16 yrs 0.7 (0.4,1.2) 2.2 (1.5,3.4) 5.5 (3.7,8.2) 9.0 (6.1,13.3) 12.0 (8.1,17.8) 19.8 (13.3,29.7)

 16 to <18 yrs 0.9 (0.5,1.7) 2.8 (1.7,4.6) 6.8 (4.5,10.2) 10.9 (7.5,15.9) 14.6 (10.1,21.2) 23.2 (15.5,34.6)

 18 to <21 yrs 1.1 (0.5,2.4) 3.5 (2.0,6.3) 8.5 (5.3,13.6) 13.6 (8.4,22.0) 18.1 (10.9,30.0) 28.7 (16.3,50.8)

Gender

Female 0.6 (0.4,0.9) 1.9 (1.4,2.5) 4.8 (3.7,6.1) 8.0 (6.2,10.3) 10.9 (8.3,14.5) 19.2 (13.7,26.9)

Male 0.6 (0.4,0.9) 2.0 (1.5,2.6) 4.9 (3.8,6.4) 8.1 (6.1,10.7) 11.1 (8.1,15.1) 19.3 (13.2,28.4)

Race/Ethnicity1

Mexican American 0.7 (0.4,1.0) 2.0 (1.4,2.7) 4.8 (3.5,6.4) 7.9 (5.7,10.9) 10.9 (7.6,15.6) 19.8 (12.5,31.3)

Other Hispanic 0.5 (0.2,1.5) 1.8 (0.7,4.2) 4.8 (2.2,10.5) 8.3 (3.8,17.8) 11.5 (5.4,24.5) 19.4 (9.3,40.6)

Non-Hispanic White 0.5 (0.3,0.7) 1.4 (1.0,2.1) 3.4 (2.4,5.0) 5.7 (3.9,8.4) 7.8 (5.2,11.7) 14.0 (8.9,22.1)

Non-Hispanic Black 1.2 (0.8,1.8) 3.4 (2.4,4.8) 7.7 (5.6,10.7) 12.0 (8.7,16.7) 15.8 (11.3,22.1) 25.0 (17.5,35.8)

Other Race 1.8 (1.0,3.3) 5.0 (3.0,8.3) 10.5 (6.3,17.6) 15.7 (9.1,27.2) 19.8 (10.8,36.4) 29.9 (15.1,59.5)

Income, finer detail

$0 to <$20K 0.5 (0.3,0.8) 1.6 (1.1,2.4) 4.0 (2.8,5.6) 6.6 (4.7,9.2) 8.9 (6.3,12.6) 15.1 (10.4,21.9)

$20 to <$45K 0.8 (0.5,1.1) 2.2 (1.6,3.1) 5.3 (4.0,7.0) 8.5 (6.4,11.4) 11.5 (8.6,15.5) 19.4 (13.9,27.2)

$40 to <$75K 0.5 (0.3,0.8) 1.5 (1.0,2.3) 3.9 (2.7,5.7) 6.7 (4.5,9.8) 9.2 (6.1,13.8) 15.9 (10.0,25.4)

$75+K 0.8 (0.5,1.2) 2.3 (1.5,3.3) 5.6 (4.0,7.9) 9.2 (6.5,13.2) 12.4 (8.5,18.0) 21.1 (13.6,32.6)

>$20K 0.3 (0.1,1.6) 0.9 (0.3,3.1) 2.2 (0.8,6.1) 3.7 (1.4,10.2) 5.2 (1.9,14.1) 9.2 (3.2,26.8)

Refused/Don’t Know Income 0.2 (0.0,1.5) 1.3 (0.3,5.8) 4.5 (1.2,16.7) 8.3 (2.4,28.7) 11.6 (3.3,40.4) 22.9 (5.9,89.1)

Income Missing 0.6 (0.1,2.2) 3.4 (0.6,18.7) 15.0 (1.8,126.0) 27.9 (4.8,163.5) 37.4 (9.6,146.3) 42.2 (23.6,75.5)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 79

 
Table 23b. UFCR estimates (g/day raw weight, edible portion): Total shellfish, youth, <21 years, by geographic area

All Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 0.6 (0.4,0.9) 1.9 (1.5,2.5) 4.8 (3.9,6.1) 8.1 (6.4,10.2) 11.0 (8.4,14.4) 19.2 (13.9,26.7)

Region1

Northeast 0.7 (0.4,1.5) 2.3 (1.3,4.2) 5.6 (3.1,10.1) 9.1 (5.1,16.3) 12.3 (7.0,21.7) 20.6 (11.9,35.6)

Midwest 0.4 (0.2,0.7) 1.2 (0.7,2.0) 3.1 (1.9,5.0) 5.3 (3.3,8.5) 7.4 (4.5,12.0) 13.5 (8.0,22.9)

South 0.8 (0.5,1.2) 2.2 (1.5,3.3) 5.4 (3.7,7.7) 8.7 (6.1,12.6) 11.8 (8.1,17.3) 20.6 (13.3,32.0)

West 0.7 (0.4,1.1) 2.2 (1.5,3.2) 5.6 (3.9,8.0) 9.3 (6.2,13.9) 12.6 (7.9,20.0) 21.2 (11.4,39.3)

Coastal Status2

Noncoastal 0.5 (0.3,0.7) 1.5 (1.1,2.1) 3.7 (2.8,4.9) 6.1 (4.6,8.1) 8.1 (6.0,11.0) 13.9 (9.8,19.8)

Coastal 0.9 (0.6,1.3) 2.8 (2.1,3.8) 7.1 (5.4,9.4) 11.7 (8.6,15.9) 15.8 (11.3,22.0) 26.4 (17.4,39.9)

Coastal/Inland Region1,2

Pacific 0.9 (0.6,1.4) 2.9 (2.0,4.3) 7.5 (5.0,11.3) 12.4 (7.4,20.7) 16.9 (9.2,30.9) 27.8 (13.6,56.7)

Atlantic 1.2 (0.7,1.8) 3.5 (2.4,5.0) 8.2 (5.8,11.7) 12.8 (9.0,18.3) 16.7 (11.8,23.8) 26.1 (18.0,37.9)

Gulf of Mexico 1.1 (0.5,2.3) 3.3 (1.6,6.6) 8.1 (4.1,16.1) 13.9 (6.7,29.1) 20.2 (8.9,45.9) 37.4 (19.2,73.1)

Great Lakes 0.6 (0.3,1.0) 1.8 (1.1,2.8) 4.5 (2.8,7.2) 7.5 (4.4,12.8) 10.2 (5.7,18.4) 17.7 (8.9,35.0)

Inland Northeast 0.6 (0.3,1.3) 2.0 (1.0,3.8) 4.6 (2.5,8.3) 7.2 (4.1,12.6) 9.4 (5.4,16.4) 15.6 (9.1,26.7)

Inland Midwest 0.3 (0.2,0.6) 1.0 (0.6,1.8) 2.5 (1.5,4.4) 4.3 (2.4,7.5) 6.0 (3.4,10.7) 10.9 (5.8,20.3)

Inland South 0.6 (0.4,0.9) 1.7 (1.2,2.4) 3.9 (2.9,5.2) 6.2 (4.7,8.2) 8.3 (6.2,11.0) 13.7 (9.9,19.0)

Inland West 0.6 (0.3,1.0) 1.8 (1.1,2.9) 4.3 (2.6,7.0) 7.0 (4.2,11.5) 9.4 (5.5,15.8) 15.5 (8.6,28.0)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 80

 
Table 24a. UFCR estimates (g/day raw weight, edible portion): Total trophic level 2 fish, youth, <21 years, by demographic 

characteristics

Trophic Level 2 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 0.4 (0.3,0.6) 1.2 (0.9,1.6) 3.1 (2.4,4.0) 5.2 (4.0,6.7) 7.1 (5.3,9.3) 12.0 (8.6,16.6)

Age

 1 to <3 yrs 0.2 (0.1,0.4) 0.6 (0.4,1.0) 1.5 (1.0,2.2) 2.4 (1.7,3.5) 3.3 (2.2,4.9) 5.8 (3.7,9.2)

 3 to <6 yrs 0.3 (0.2,0.6) 0.9 (0.6,1.5) 2.3 (1.5,3.5) 3.8 (2.6,5.6) 5.2 (3.6,7.6) 8.8 (6.0,12.9)

 6 to <11 yrs 0.3 (0.1,0.5) 0.8 (0.5,1.3) 2.2 (1.5,3.3) 3.9 (2.6,6.1) 5.6 (3.5,9.0) 10.5 (6.1,18.1)

 11 to <16 yrs 0.5 (0.3,1.0) 1.7 (1.0,2.7) 4.0 (2.5,6.3) 6.4 (4.1,10.1) 8.6 (5.4,13.5) 14.0 (8.8,22.3)

 16 to <18 yrs 0.5 (0.2,1.0) 1.5 (0.9,2.5) 3.6 (2.3,5.5) 5.6 (3.7,8.4) 7.3 (4.8,11.0) 11.6 (7.4,18.4)

 18 to <21 yrs 0.6 (0.3,1.1) 1.8 (1.1,3.1) 4.4 (2.7,7.1) 7.1 (4.3,11.6) 9.3 (5.5,15.7) 14.9 (8.7,25.7)

Gender

Female 0.4 (0.2,0.6) 1.2 (0.9,1.6) 3.0 (2.3,4.0) 5.1 (3.9,6.7) 7.0 (5.3,9.2) 11.8 (8.5,16.4)

Male 0.4 (0.3,0.6) 1.2 (0.9,1.7) 3.2 (2.3,4.3) 5.2 (3.8,7.2) 7.1 (5.1,9.9) 12.1 (8.3,17.7)

Race/Ethnicity1

Mexican American 0.6 (0.4,0.9) 1.7 (1.2,2.4) 4.1 (3.0,5.4) 6.4 (4.8,8.6) 8.4 (6.2,11.5) 13.8 (9.5,20.0)

Other Hispanic 0.4 (0.1,0.9) 1.2 (0.5,2.6) 2.8 (1.4,5.8) 4.6 (2.2,9.3) 6.0 (2.9,12.3) 10.1 (4.9,20.6)

Non-Hispanic White 0.3 (0.2,0.4) 0.8 (0.5,1.3) 2.2 (1.4,3.3) 3.7 (2.4,5.7) 5.1 (3.3,8.1) 9.2 (5.7,14.8)

Non-Hispanic Black 0.6 (0.4,0.9) 1.8 (1.3,2.3) 4.1 (3.0,5.4) 6.5 (4.8,8.8) 8.6 (6.2,11.9) 14.0 (9.8,20.0)

Other Race 1.1 (0.5,2.4) 3.2 (1.7,6.3) 6.9 (3.6,12.9) 10.1 (5.3,19.2) 12.6 (6.7,23.9) 18.5 (9.7,35.0)

Income

$0 to <$20K 0.3 (0.2,0.6) 1.1 (0.7,1.6) 2.7 (1.9,3.8) 4.4 (3.2,6.2) 6.0 (4.3,8.3) 9.8 (6.9,14.0)

$20 to <$45K 0.5 (0.3,0.7) 1.5 (1.1,2.0) 3.5 (2.7,4.7) 5.7 (4.3,7.6) 7.7 (5.7,10.4) 13.0 (9.4,17.9)

$40 to <$75K 0.3 (0.2,0.5) 0.9 (0.6,1.5) 2.5 (1.6,3.8) 4.2 (2.7,6.5) 5.8 (3.7,9.2) 10.0 (6.0,16.6)

$75+K 0.4 (0.3,0.7) 1.3 (0.9,2.1) 3.4 (2.2,5.2) 5.7 (3.7,8.7) 7.7 (5.0,11.9) 12.9 (8.2,20.3)

>$20K 0.2 (0.1,0.8) 0.6 (0.2,1.9) 1.5 (0.6,4.1) 2.6 (1.0,6.7) 3.6 (1.4,9.2) 6.3 (2.4,16.6)

Refused/Don’t Know Income 0.1 (0.0,0.8) 0.6 (0.1,3.3) 2.6 (0.7,10.2) 4.5 (1.3,15.5) 6.3 (1.8,21.8) 11.7 (3.2,42.1)

Income Missing 0.3 (0.1,1.4) 1.6 (0.3,7.8) 7.2 (1.1,45.2) 12.3 (3.1,47.7) 15.4 (5.3,44.6) 20.0 (10.2,39.3)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 81

 
Table 24b. UFCR estimates (g/day raw weight, edible portion): Total trophic level 2 fish, youth, <21 years, by geographic area

Trophic Level 2 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 0.4 (0.3,0.6) 1.2 (0.9,1.6) 3.1 (2.4,4.0) 5.2 (4.0,6.7) 7.1 (5.3,9.3) 12.0 (8.6,16.6)

Region1

Northeast 0.4 (0.2,0.8) 1.3 (0.8,2.3) 3.3 (1.8,6.3) 5.5 (2.8,10.8) 7.5 (3.7,15.0) 12.9 (6.5,25.7)

Midwest 0.2 (0.1,0.4) 0.8 (0.5,1.4) 2.1 (1.2,3.6) 3.7 (2.2,6.1) 5.1 (3.1,8.3) 8.9 (5.5,14.5)

South 0.4 (0.3,0.7) 1.3 (0.9,2.0) 3.2 (2.3,4.6) 5.3 (3.7,7.6) 7.2 (5.1,10.3) 12.2 (8.6,17.3)

West 0.5 (0.3,0.8) 1.5 (1.0,2.4) 3.9 (2.5,6.1) 6.3 (3.9,10.3) 8.4 (5.0,14.0) 13.4 (7.6,23.7)

Coastal Status2

Noncoastal 0.3 (0.2,0.5) 1.0 (0.7,1.4) 2.4 (1.8,3.2) 3.9 (2.9,5.3) 5.3 (3.9,7.2) 9.1 (6.4,12.9)

Coastal 0.6 (0.4,0.8) 1.8 (1.3,2.5) 4.5 (3.4,6.1) 7.3 (5.4,9.9) 9.7 (7.0,13.4) 15.5 (10.9,22.0)

Coastal/Inland Region1,2

Pacific 0.6 (0.4,1.1) 2.1 (1.3,3.3) 5.0 (3.0,8.5) 7.9 (4.5,13.9) 10.2 (5.7,18.5) 16.1 (8.9,29.2)

Atlantic 0.6 (0.4,0.9) 1.9 (1.4,2.8) 4.8 (3.2,7.1) 7.6 (4.9,11.7) 10.1 (6.4,15.8) 16.2 (10.0,26.3)

Gulf of Mexico 0.7 (0.4,1.2) 2.1 (1.2,3.8) 5.3 (3.0,9.3) 8.7 (5.2,14.6) 11.5 (7.2,18.4) 17.3 (12.0,25.1)

Great Lakes 0.3 (0.2,0.6) 1.1 (0.7,2.0) 3.1 (1.9,5.1) 5.1 (3.2,8.2) 6.9 (4.3,11.0) 11.6 (7.2,18.6)

Inland Northeast 0.4 (0.2,0.7) 1.1 (0.6,2.0) 2.7 (1.5,4.8) 4.1 (2.3,7.6) 5.6 (3.1,10.1) 9.3 (5.1,17.1)

Inland Midwest 0.2 (0.1,0.4) 0.7 (0.4,1.1) 1.7 (1.0,2.9) 3.0 (1.7,5.0) 4.2 (2.4,7.1) 7.4 (4.1,13.3)

Inland South 0.4 (0.2,0.6) 1.0 (0.7,1.6) 2.4 (1.7,3.4) 3.8 (2.7,5.4) 5.1 (3.6,7.2) 8.5 (6.0,12.1)

Inland West 0.4 (0.2,0.7) 1.2 (0.7,2.0) 3.0 (1.8,5.0) 5.0 (3.0,8.3) 6.7 (4.0,11.3) 11.1 (6.3,19.5)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 82

 
Table 25a. UFCR estimates (g/day raw weight, edible portion): Total trophic level 3 fish, adults, youth, <21 years, by demographic 

characteristics

Trophic Level 3 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 1.4 (1.0,1.8) 3.5 (2.8,4.5) 7.4 (6.0,9.2) 11.1 (8.9,13.8) 14.1 (11.2,17.7) 21.5 (16.5,27.9)

Age

 1 to <3 yrs 0.8 (0.5,1.3) 2.1 (1.4,3.2) 4.2 (2.7,6.3) 6.0 (4.0,9.0) 7.5 (5.0,11.4) 11.4 (7.4,17.8)

 3 to <6 yrs 1.1 (0.8,1.7) 2.9 (2.1,4.0) 5.9 (4.5,7.6) 8.5 (6.6,10.9) 10.7 (8.3,13.8) 15.7 (11.8,20.8)

 6 to <11 yrs 1.6 (1.1,2.4) 4.2 (2.7,6.3) 8.8 (5.7,13.7) 13.1 (8.2,20.9) 16.6 (10.2,27.1) 25.4 (15.0,42.9)

 11 to <16 yrs 1.3 (0.9,2.0) 3.3 (2.3,4.7) 6.8 (5.1,9.1) 10.0 (7.5,13.2) 12.7 (9.6,16.8) 19.4 (14.8,25.5)

 16 to <18 yrs 1.2 (0.7,2.1) 3.1 (2.1,4.8) 7.2 (5.3,9.8) 11.6 (8.5,15.6) 15.1 (11.0,20.7) 23.6 (16.6,33.5)

 18 to <21 yrs 2.2 (1.4,3.3) 5.3 (3.8,7.5) 10.0 (7.4,13.5) 14.0 (10.2,19.1) 17.0 (12.1,23.8) 23.9 (16.1,35.7)

Gender

Female 1.2 (0.9,1.6) 3.2 (2.5,4.0) 6.8 (5.5,8.5) 10.2 (8.2,12.8) 13.1 (10.4,16.5) 20.3 (15.6,26.5)

Male 1.5 (1.1,2.1) 3.9 (3.0,5.1) 8.0 (6.2,10.3) 11.8 (9.1,15.3) 14.9 (11.4,19.5) 22.4 (16.6,30.1)

Race/Ethnicity1

Mexican American 1.1 (0.8,1.5) 2.7 (2.1,3.5) 5.5 (4.4,6.9) 8.2 (6.4,10.4) 10.4 (8.0,13.5) 16.0 (11.5,22.2)

Other Hispanic 0.9 (0.6,1.5) 2.5 (1.6,4.0) 5.2 (3.2,8.5) 7.6 (4.4,13.2) 9.7 (5.5,17.0) 14.7 (8.0,26.9)

Non-Hispanic White 1.1 (0.8,1.6) 2.8 (2.1,3.8) 5.6 (4.1,7.6) 8.1 (5.9,11.2) 10.3 (7.3,14.4) 15.8 (10.7,23.1)

Non-Hispanic Black 3.1 (2.3,4.2) 7.0 (5.4,9.2) 12.6 (9.9,16.2) 17.1 (13.4,21.9) 20.7 (16.2,26.4) 28.6 (22.2,36.9)

Other Race 3.6 (2.4,5.6) 8.3 (5.7,12.0) 15.2 (10.1,22.9) 20.3 (12.9,32.0) 24.1 (14.9,38.9) 32.1 (19.5,52.9)

Income

$0 to <$20K 1.6 (1.2,2.1) 4.3 (3.2,5.7) 8.9 (6.9,11.5) 12.8 (9.9,16.7) 16.1 (12.3,21.0) 23.6 (17.8,31.3)

$20 to <$45K 1.4 (1.0,1.8) 3.5 (2.8,4.3) 7.1 (5.7,8.8) 10.5 (8.4,13.1) 13.4 (10.7,16.8) 20.3 (16.1,25.5)

$40 to <$75K 1.3 (0.9,2.0) 3.4 (2.3,5.1) 7.3 (4.7,11.3) 10.7 (6.8,16.9) 13.5 (8.5,21.4) 19.8 (12.4,31.6)

$75+K 1.3 (0.9,1.9) 3.3 (2.4,4.6) 6.9 (5.2,9.2) 10.3 (7.7,14.0) 13.3 (9.6,18.4) 20.5 (13.7,30.5)

>$20K 1.1 (0.4,2.6) 2.4 (1.2,4.9) 4.7 (2.7,8.2) 6.7 (3.9,11.4) 8.4 (4.9,14.4) 12.4 (6.9,22.3)

Refused/Don’t Know Income 1.8 (0.4,9.3) 4.3 (1.0,17.9) 8.4 (2.9,24.8) 12.4 (5.1,29.9) 15.4 (6.9,34.4) 24.4 (10.7,56.0)

Income Missing 1.3 (0.4,4.2) 5.3 (1.3,21.8) 16.1 (3.0,86.1) 29.6 (6.3,139.9) 38.2 (10.2,143.0) 51.8 (19.7,136.7)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 83

 
Table 25b. UFCR estimates (g/day raw weight, edible portion): Total trophic level 3 fish, youth, <21 years, by geographic area

Trophic Level 3 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 1.4 (1.0,1.8) 3.5 (2.8,4.5) 7.4 (6.0,9.2) 11.1 (8.9,13.8) 14.1 (11.2,17.7) 21.5 (16.5,27.9)

Region1

Northeast 1.4 (0.8,2.3) 3.4 (2.2,5.2) 6.5 (4.2,10.1) 9.3 (5.8,14.7) 11.5 (7.2,18.3) 17.0 (10.6,27.1)

Midwest 0.9 (0.6,1.3) 2.4 (1.7,3.3) 5.2 (3.9,7.0) 8.0 (5.9,10.9) 10.4 (7.4,14.5) 16.6 (11.4,24.2)

South 1.9 (1.3,2.7) 4.7 (3.4,6.4) 9.3 (7.0,12.4) 13.5 (10.2,18.0) 16.9 (12.7,22.7) 25.4 (18.4,35.1)

West 1.4 (0.9,2.0) 3.6 (2.5,5.2) 7.8 (5.2,11.7) 11.7 (7.5,18.3) 14.9 (9.2,24.1) 22.3 (12.9,38.6)

Coastal Status2

Noncoastal 1.2 (0.9,1.6) 3.1 (2.4,4.0) 6.4 (4.9,8.3) 9.6 (7.3,12.6) 12.2 (9.1,16.2) 18.6 (13.4,25.8)

Coastal 1.8 (1.3,2.4) 4.5 (3.4,5.9) 9.2 (7.1,12.0) 13.5 (10.3,17.7) 17.0 (12.8,22.6) 25.6 (18.5,35.3)

Coastal/Inland Region1,2

Pacific 1.5 (1.0,2.2) 4.0 (2.8,5.6) 8.6 (6.0,12.4) 13.0 (8.7,19.5) 16.4 (10.5,25.5) 23.6 (14.5,38.6)

Atlantic 2.3 (1.5,3.4) 5.4 (3.8,7.7) 10.4 (7.7,14.1) 14.7 (10.9,19.7) 18.0 (13.4,24.1) 25.8 (19.3,34.4)

Gulf of Mexico 2.3 (1.2,4.3) 5.7 (3.3,9.7) 11.2 (6.8,18.4) 16.8 (9.5,30.0) 22.3 (11.4,43.4) 37.6 (17.5,80.9)

Great Lakes 1.2 (0.9,1.7) 3.2 (2.4,4.2) 6.7 (4.7,9.5) 9.9 (6.4,15.4) 12.8 (7.9,20.6) 19.4 (11.2,33.6)

Inland Northeast 1.2 (0.8,1.9) 2.9 (2.0,4.2) 5.5 (3.9,7.8) 7.7 (5.4,11.0) 9.6 (6.6,13.9) 14.0 (9.5,20.8)

Inland Midwest 0.8 (0.6,1.2) 2.1 (1.5,3.0) 4.5 (3.3,6.2) 6.9 (4.9,9.8) 9.1 (6.3,13.0) 14.8 (10.2,21.6)

Inland South 1.6 (1.2,2.2) 4.0 (3.1,5.2) 8.1 (6.3,10.3) 11.7 (9.1,15.0) 14.6 (11.3,18.8) 21.4 (16.6,27.7)

Inland West 1.3 (0.8,2.2) 3.4 (2.0,5.6) 7.2 (4.0,12.9) 10.7 (5.5,20.5) 13.6 (6.7,27.9) 21.1 (9.3,47.8)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 84

 
Table 26a. UFCR estimates (g/day raw weight, edible portion): Total trophic level 4 fish, youth, <21 years, by demographic 

characteristics

Trophic Level 4 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 2.5 (1.9,3.1) 6.4 (5.3,7.7) 13.1 (10.8,15.9) 19.6 (15.8,24.4) 25.3 (19.9,32.3) 40.5 (28.8,56.9)

Age

 1 to <3 yrs 1.3 (0.9,1.8) 3.3 (2.5,4.2) 6.6 (5.2,8.4) 9.7 (7.5,12.4) 12.5 (9.7,16.3) 19.3 (14.3,26.1)

 3 to <6 yrs 1.8 (1.2,2.7) 4.4 (3.3,6.1) 8.6 (6.6,11.3) 12.4 (9.5,16.1) 15.5 (11.8,20.4) 23.2 (16.9,31.9)

 6 to <11 yrs 2.7 (1.9,4.0) 6.9 (4.8,10.0) 13.4 (8.6,21.0) 19.2 (11.6,32.0) 23.8 (13.8,41.0) 34.1 (19.1,60.9)

 11 to <16 yrs 2.4 (1.6,3.5) 5.9 (4.3,8.1) 11.3 (8.4,15.3) 16.2 (11.9,22.2) 20.2 (14.5,28.3) 30.4 (20.2,45.7)

 16 to <18 yrs 3.2 (2.1,4.9) 7.8 (5.6,10.9) 15.2 (11.0,21.0) 21.4 (15.3,30.1) 26.6 (18.6,38.2) 39.3 (26.7,57.7)

 18 to <21 yrs 4.4 (2.9,6.5) 11.6 (7.9,17.0) 24.8 (15.6,39.5) 37.3 (21.3,65.4) 48.4 (26.3,89.1) 72.4 (38.6,135.9)

Gender

Female 2.2 (1.7,2.8) 5.8 (4.8,7.0) 11.8 (9.7,14.2) 17.2 (14.2,20.9) 21.6 (17.7,26.5) 31.9 (25.6,39.8)

Male 2.7 (2.1,3.5) 7.0 (5.7,8.6) 14.5 (11.6,18.2) 22.1 (16.8,29.2) 29.3 (21.1,40.7) 49.5 (30.7,80.0)

Race/Ethnicity1

Mexican American 2.0 (1.4,2.9) 5.2 (4.0,6.8) 10.7 (8.4,13.7) 16.0 (12.5,20.6) 20.8 (16.0,27.2) 34.0 (25.0,46.4)

Other Hispanic 2.2 (1.2,3.7) 6.1 (3.8,9.7) 12.6 (8.4,19.0) 18.4 (12.2,27.7) 23.1 (15.2,35.1) 33.4 (20.5,54.4)

Non-Hispanic White 2.3 (1.7,3.2) 6.2 (4.7,8.0) 12.8 (9.6,17.1) 19.3 (13.8,26.9) 25.1 (17.2,36.5) 41.3 (24.2,70.5)

Non-Hispanic Black 3.0 (2.3,4.1) 6.9 (5.4,8.8) 12.8 (10.2,16.0) 17.8 (14.2,22.3) 21.9 (17.4,27.4) 31.2 (24.7,39.5)

Other Race 4.2 (2.7,6.5) 11.1 (7.8,15.7) 22.5 (16.7,30.4) 31.7 (22.2,45.3) 39.4 (25.6,60.7) 57.3 (32.3,101.7)

Income

$0 to <$20K 2.5 (1.8,3.4) 6.5 (5.2,8.2) 13.2 (10.6,16.3) 19.3 (15.1,24.5) 24.3 (18.6,31.8) 38.1 (27.9,52.1)

$20 to <$45K 2.3 (1.7,3.0) 5.8 (4.6,7.3) 12.0 (9.7,14.9) 18.4 (14.5,23.3) 24.0 (17.8,32.3) 39.5 (22.0,70.9)

$40 to <$75K 2.8 (2.0,3.8) 7.4 (5.3,10.4) 15.7 (10.4,23.5) 24.2 (15.5,37.9) 31.9 (19.6,51.9) 52.8 (29.4,94.8)

$75+K 2.5 (1.8,3.5) 6.4 (5.0,8.2) 12.4 (9.7,15.9) 17.9 (13.7,23.3) 22.2 (16.7,29.5) 32.9 (23.9,45.3)

>$20K 1.8 (0.8,3.9) 4.4 (2.2,8.9) 9.6 (4.1,22.4) 14.8 (5.7,38.0) 18.6 (6.9,50.2) 26.1 (9.2,74.2)

Refused/Don’t Know Income 2.0 (0.4,9.8) 5.9 (1.0,35.5) 17.4 (2.7,114.0) 31.0 (6.4,150.2) 41.0 (11.2,150.2) 63.9 (25.5,159.9)

Income Missing 2.9 (0.9,9.7) 8.3 (3.3,21.0) 18.0 (9.2,34.9) 26.3 (15.2,45.4) 32.7 (19.5,54.8) 50.0 (29.5,84.7)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 85

 
Table 26b. UFCR estimates (g/day raw weight, edible portion): Total trophic level 4 fish, youth, <21 years, by geographic area

Trophic Level 4 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 2.5 (1.9,3.1) 6.4 (5.3,7.7) 13.1 (10.8,15.9) 19.6 (15.8,24.4) 25.3 (19.9,32.3) 40.5 (28.8,56.9)

Region1

Northeast 3.0 (2.0,4.4) 7.6 (5.4,10.5) 16.4 (10.3,26.2) 26.7 (13.0,54.9) 37.0 (15.6,88.0) 63.5 (25.0,161.7)

Midwest 1.8 (1.2,2.7) 4.8 (3.5,6.4) 9.7 (7.7,12.1) 14.2 (11.6,17.4) 18.1 (14.8,22.1) 27.8 (22.3,34.6)

South 2.5 (1.9,3.4) 6.2 (4.9,8.0) 12.1 (9.5,15.5) 17.5 (13.5,22.7) 21.8 (16.6,28.6) 32.9 (24.4,44.4)

West 3.1 (1.9,5.0) 8.2 (5.0,13.2) 16.6 (10.2,27.1) 24.1 (14.9,39.2) 30.3 (19.0,48.3) 44.1 (28.5,68.1)

Coastal Status2

Noncoastal 2.4 (1.8,3.2) 6.4 (5.0,8.2) 13.2 (10.0,17.5) 19.7 (14.2,27.3) 25.3 (17.6,36.4) 40.6 (24.9,66.2)

Coastal 2.5 (1.9,3.3) 6.4 (5.2,7.7) 13.0 (10.9,15.5) 19.5 (16.3,23.4) 25.4 (20.9,30.8) 40.3 (31.3,52.0)

Coastal/Inland Region1,2

Pacific 2.6 (1.7,3.9) 7.2 (5.0,10.2) 15.4 (11.0,21.6) 23.0 (16.4,32.4) 29.5 (20.8,41.9) 44.5 (29.1,68.1)

Atlantic 3.0 (2.2,4.2) 6.9 (5.3,9.0) 13.6 (10.6,17.4) 20.2 (15.2,27.0) 26.6 (19.0,37.2) 45.4 (28.2,73.2)

Gulf of Mexico 2.6 (1.7,4.0) 6.4 (4.5,9.3) 12.9 (8.8,18.9) 19.1 (13.0,28.2) 24.0 (15.9,36.1) 36.6 (22.3,60.1)

Great Lakes 1.8 (1.1,2.8) 4.7 (3.4,6.6) 9.6 (7.0,13.2) 14.2 (10.1,19.9) 18.1 (12.6,26.1) 28.1 (18.2,43.2)

Inland Northeast 2.9 (1.9,4.4) 7.7 (5.3,11.2) 17.1 (9.3,31.5) 28.1 (10.8,73.4) 39.8 (13.2,119.5) 67.2 (21.6,209.3)

Inland Midwest 1.8 (1.2,2.7) 4.8 (3.5,6.5) 9.7 (7.8,12.2) 14.3 (11.8,17.3) 18.1 (15.1,21.7) 27.5 (22.9,33.1)

Inland South 2.4 (1.8,3.1) 6.1 (4.7,7.9) 11.8 (9.0,15.6) 17.0 (12.6,22.9) 21.2 (15.5,29.0) 31.2 (22.5,43.2)

Inland West 3.4 (1.8,6.6) 8.9 (4.7,16.6) 17.4 (9.1,33.5) 24.8 (12.8,48.2) 30.7 (15.9,59.1) 43.8 (23.9,80.4)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 86

 
Table 27a. UFCR estimates (g/day raw weight, edible portion): Total freshwater + estuarine trophic level 2 fish, youth, <21 years, by 

demographic characteristics

Freshwater + Estuarine Trophic Level 2 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 0.3 (0.2,0.4) 0.9 (0.7,1.2) 2.3 (1.8,3.0) 4.0 (3.1,5.2) 5.5 (4.2,7.3) 9.8 (7.1,13.4)

Age

 1 to <3 yrs 0.1 (0.1,0.3) 0.5 (0.3,0.8) 1.2 (0.8,1.8) 2.1 (1.4,3.0) 2.9 (2.0,4.3) 5.2 (3.3,8.1)

 3 to <6 yrs 0.2 (0.1,0.4) 0.6 (0.4,1.0) 1.7 (1.1,2.7) 3.0 (2.0,4.6) 4.2 (2.8,6.4) 7.5 (5.0,11.2)

 6 to <11 yrs 0.2 (0.1,0.4) 0.7 (0.4,1.1) 1.9 (1.3,2.8) 3.5 (2.2,5.5) 5.0 (3.1,8.3) 9.3 (5.3,16.5)

 11 to <16 yrs 0.3 (0.2,0.6) 1.1 (0.7,1.7) 2.9 (2.0,4.1) 4.7 (3.4,6.6) 6.4 (4.6,8.9) 11.0 (7.8,15.5)

 16 to <18 yrs 0.3 (0.2,0.7) 1.0 (0.6,1.8) 2.5 (1.5,3.9) 3.9 (2.6,6.1) 5.2 (3.4,8.1) 8.7 (5.5,13.8)

 18 to <21 yrs 0.4 (0.2,0.8) 1.4 (0.8,2.4) 3.4 (2.2,5.5) 5.6 (3.5,8.9) 7.4 (4.5,12.1) 12.3 (7.4,20.6)

Gender

Female 0.3 (0.2,0.4) 0.9 (0.6,1.2) 2.4 (1.8,3.1) 4.1 (3.2,5.3) 5.6 (4.3,7.4) 9.9 (7.2,13.5)

Male 0.3 (0.2,0.4) 0.9 (0.6,1.2) 2.3 (1.7,3.1) 3.9 (2.9,5.4) 5.4 (3.9,7.6) 9.6 (6.6,14.0)

Race/Ethnicity1

Mexican American 0.5 (0.3,0.7) 1.5 (1.0,2.1) 3.5 (2.6,4.8) 5.6 (4.1,7.7) 7.5 (5.4,10.4) 12.4 (8.4,18.2)

Other Hispanic 0.3 (0.1,0.8) 1.0 (0.4,2.2) 2.5 (1.2,5.2) 4.1 (2.0,8.4) 5.5 (2.7,11.3) 9.0 (4.3,19.0)

Non-Hispanic White 0.2 (0.1,0.3) 0.5 (0.4,0.8) 1.4 (0.9,2.0) 2.4 (1.6,3.5) 3.3 (2.2,5.0) 6.2 (4.0,9.4)

Non-Hispanic Black 0.5 (0.4,0.7) 1.5 (1.2,2.0) 3.5 (2.7,4.5) 5.6 (4.2,7.4) 7.4 (5.5,10.0) 12.3 (8.8,17.3)

Other Race 0.8 (0.4,1.8) 2.5 (1.2,5.1) 5.5 (2.8,11.0) 8.3 (4.2,16.4) 10.7 (5.4,21.2) 15.5 (7.6,31.8)

Income

$0 to <$20K 0.3 (0.2,0.4) 0.9 (0.6,1.3) 2.3 (1.6,3.3) 3.8 (2.7,5.4) 5.1 (3.6,7.3) 8.5 (5.7,12.7)

$20 to <$45K 0.4 (0.2,0.5) 1.1 (0.8,1.5) 2.9 (2.1,3.8) 4.8 (3.5,6.5) 6.5 (4.7,8.9) 11.1 (7.8,15.8)

$40 to <$75K 0.2 (0.1,0.3) 0.7 (0.4,1.0) 1.8 (1.2,2.7) 3.2 (2.1,4.8) 4.4 (2.9,6.8) 7.9 (4.9,12.6)

$75+K 0.3 (0.2,0.4) 0.9 (0.6,1.3) 2.3 (1.6,3.4) 4.0 (2.8,5.9) 5.5 (3.7,8.2) 9.8 (6.4,15.1)

>$20K 0.1 (0.0,0.5) 0.4 (0.2,1.2) 1.2 (0.5,3.0) 2.1 (0.8,5.4) 2.9 (1.1,7.5) 5.4 (2.0,14.3)

Refused/Don’t Know Income 0.1 (0.0,0.5) 0.4 (0.1,2.2) 1.8 (0.5,7.0) 3.2 (0.9,11.6) 4.6 (1.2,16.9) 9.0 (2.2,37.0)

Income Missing 0.2 (0.1,0.8) 1.2 (0.2,6.0) 5.7 (0.8,41.5) 10.1 (2.4,43.2) 13.0 (4.3,39.9) 18.2 (8.6,38.2)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 87

 
Table 27b. UFCR estimates (g/day raw weight, edible portion): Total freshwater + estuarine trophic level 2 fish, youth, <21 years, by 

geographic area

Freshwater + Estuarine Trophic Level 2 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 0.3 (0.2,0.4) 0.9 (0.7,1.2) 2.3 (1.8,3.0) 4.0 (3.1,5.2) 5.5 (4.2,7.3) 9.8 (7.1,13.4)

Region1

Northeast 0.3 (0.1,0.5) 0.8 (0.5,1.5) 2.1 (1.1,3.7) 3.4 (1.9,6.2) 4.7 (2.6,8.4) 8.0 (4.5,14.3)

Midwest 0.1 (0.1,0.2) 0.5 (0.3,0.8) 1.4 (0.9,2.3) 2.7 (1.7,4.2) 3.8 (2.4,6.1) 7.1 (4.2,11.7)

South 0.4 (0.2,0.6) 1.1 (0.7,1.7) 2.8 (2.0,4.1) 4.7 (3.3,6.7) 6.4 (4.5,9.2) 11.0 (7.6,16.0)

West 0.3 (0.2,0.6) 1.1 (0.7,1.7) 2.9 (1.8,4.6) 4.9 (3.0,8.1) 6.6 (3.9,11.3) 11.2 (6.3,20.0)

Coastal Status2

Noncoastal 0.2 (0.1,0.3) 0.7 (0.5,1.0) 1.8 (1.3,2.5) 3.1 (2.3,4.2) 4.3 (3.2,5.9) 7.7 (5.5,10.9)

Coastal 0.4 (0.3,0.6) 1.3 (0.9,1.8) 3.4 (2.5,4.5) 5.5 (4.1,7.5) 7.4 (5.3,10.3) 12.4 (8.6,17.7)

Coastal/Inland Region1,2

Pacific 0.4 (0.3,0.7) 1.5 (0.9,2.4) 3.8 (2.3,6.4) 6.1 (3.4,10.9) 8.1 (4.4,15.0) 13.5 (7.3,25.0)

Atlantic 0.4 (0.3,0.7) 1.4 (1.0,2.0) 3.5 (2.5,4.8) 5.5 (4.0,7.7) 7.3 (5.1,10.3) 11.7 (7.7,17.8)

Gulf of Mexico 0.5 (0.3,1.0) 1.7 (0.9,3.0) 4.3 (2.4,7.6) 7.1 (4.1,12.3) 9.6 (5.7,16.2) 15.2 (9.9,23.3)

Great Lakes 0.2 (0.1,0.4) 0.7 (0.5,1.2) 2.1 (1.3,3.4) 3.6 (2.2,6.2) 5.1 (3.0,8.8) 9.0 (5.1,15.8)

Inland Northeast 0.2 (0.1,0.4) 0.7 (0.4,1.3) 1.7 (0.9,3.2) 2.7 (1.4,5.3) 3.7 (1.9,7.2) 6.5 (3.3,12.9)

Inland Midwest 0.1 (0.1,0.2) 0.4 (0.3,0.7) 1.2 (0.7,1.9) 2.1 (1.3,3.7) 3.1 (1.8,5.4) 5.9 (3.2,10.7)

Inland South 0.3 (0.2,0.5) 0.9 (0.6,1.4) 2.2 (1.5,3.1) 3.6 (2.5,5.1) 4.9 (3.5,6.9) 8.6 (6.0,12.1)

Inland West 0.3 (0.2,0.5) 0.9 (0.5,1.5) 2.3 (1.4,3.8) 3.9 (2.3,6.6) 5.4 (3.2,9.1) 9.2 (5.2,16.3)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 88

 
Table 28a. UFCR estimates (g/day raw weight, edible portion): Total freshwater + estuarine trophic level 3 fish, youth, <21 years, by 

demographic characteristics

Freshwater + Estuarine Trophic Level 3 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 0.4 (0.3,0.6) 1.3 (1.0,1.7) 3.1 (2.4,3.9) 5.0 (3.9,6.4) 6.7 (5.1,8.9) 11.5 (8.1,16.2)

Age

 1 to <3 yrs 0.2 (0.1,0.4) 0.6 (0.3,1.2) 1.4 (0.8,2.6) 2.2 (1.2,4.1) 3.0 (1.6,5.5) 5.2 (2.7,10.0)

 3 to <6 yrs 0.4 (0.2,0.8) 1.2 (0.7,1.9) 2.5 (1.6,3.9) 3.8 (2.5,5.9) 4.9 (3.2,7.6) 7.8 (4.8,12.7)

 6 to <11 yrs 0.5 (0.3,0.7) 1.3 (1.0,1.9) 3.1 (2.2,4.5) 5.0 (3.3,7.7) 6.8 (4.3,10.7) 11.3 (6.5,19.5)

 11 to <16 yrs 0.3 (0.2,0.5) 1.1 (0.8,1.6) 2.8 (2.1,3.7) 4.8 (3.6,6.4) 6.5 (4.8,8.9) 11.2 (7.9,15.8)

 16 to <18 yrs 0.6 (0.3,1.2) 1.6 (1.0,2.8) 3.8 (2.6,5.6) 6.2 (4.2,9.2) 8.4 (5.5,12.9) 14.2 (8.6,23.4)

 18 to <21 yrs 0.7 (0.4,1.4) 2.0 (1.2,3.4) 4.6 (3.1,6.8) 7.1 (4.6,11.1) 9.3 (5.7,15.1) 15.0 (8.7,26.0)

Gender

Female 0.4 (0.3,0.6) 1.2 (0.9,1.6) 3.0 (2.3,3.9) 4.9 (3.7,6.5) 6.7 (4.9,9.1) 11.5 (8.0,16.5)

Male 0.5 (0.3,0.7) 1.3 (1.0,1.8) 3.1 (2.4,4.0) 5.1 (3.9,6.6) 6.8 (5.1,9.0) 11.5 (8.0,16.5)

Race/Ethnicity1

Mexican American 0.5 (0.3,0.7) 1.3 (1.0,1.8) 3.0 (2.2,3.9) 4.7 (3.4,6.4) 6.3 (4.5,9.0) 11.2 (7.3,17.1)

Other Hispanic 0.4 (0.2,0.9) 1.3 (0.7,2.4) 2.8 (1.5,5.4) 4.3 (2.2,8.4) 5.6 (2.8,11.0) 8.9 (4.5,17.6)

Non-Hispanic White 0.3 (0.2,0.5) 0.8 (0.6,1.2) 1.8 (1.3,2.5) 2.7 (1.9,3.9) 3.5 (2.4,5.1) 5.8 (3.6,9.3)

Non-Hispanic Black 1.3 (0.9,1.9) 3.3 (2.4,4.7) 6.7 (4.7,9.3) 9.6 (6.7,13.5) 11.9 (8.3,17.0) 17.7 (12.2,25.9)

Other Race 1.3 (0.8,2.1) 3.4 (2.3,4.9) 6.8 (4.5,10.3) 9.8 (5.9,16.3) 12.4 (7.2,21.4) 17.3 (9.0,33.4)

Income

$0 to <$20K 0.5 (0.3,0.7) 1.5 (1.1,2.1) 3.7 (2.8,5.0) 6.0 (4.4,8.0) 7.8 (5.7,10.9) 12.7 (8.6,18.7)

$20 to <$45K 0.5 (0.3,0.7) 1.4 (1.0,1.9) 3.2 (2.3,4.3) 5.0 (3.7,6.9) 6.6 (4.7,9.3) 10.8 (7.4,15.7)

$40 to <$75K 0.4 (0.2,0.6) 1.1 (0.7,1.7) 2.7 (1.9,4.0) 4.5 (3.1,6.6) 6.1 (4.2,9.0) 10.2 (6.8,15.4)

$75+K 0.4 (0.3,0.7) 1.3 (0.9,1.8) 2.9 (2.1,3.9) 4.6 (3.4,6.4) 6.2 (4.4,8.9) 10.9 (7.0,17.1)

>$20K 0.2 (0.1,0.9) 0.7 (0.2,2.3) 1.8 (0.7,4.8) 2.9 (1.1,7.7) 3.8 (1.4,10.5) 6.6 (2.3,18.7)

Refused/Don’t Know Income 0.1 (0.0,0.5) 0.7 (0.3,2.0) 2.3 (0.9,5.5) 4.5 (1.7,12.1) 7.0 (2.3,21.0) 18.8 (5.1,68.7)

Income Missing 0.4 (0.1,1.2) 1.9 (0.4,9.3) 7.7 (0.8,71.2) 14.8 (2.1,104.0) 19.8 (3.7,105.9) 30.9 (7.7,123.0)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 89

 
Table 28b. UFCR estimates (g/day raw weight, edible portion): Total freshwater + estuarine trophic level 3 fish, youth, <21 years, by 

geographic area

Freshwater + Estuarine Trophic Level 3 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 0.4 (0.3,0.6) 1.3 (1.0,1.7) 3.1 (2.4,3.9) 5.0 (3.9,6.4) 6.7 (5.1,8.9) 11.5 (8.1,16.2)

Region1

Northeast 0.4 (0.2,0.7) 1.1 (0.6,1.8) 2.3 (1.3,4.0) 3.5 (2.0,6.3) 4.6 (2.6,8.4) 7.5 (4.0,14.2)

Midwest 0.3 (0.1,0.5) 0.8 (0.4,1.4) 2.0 (1.2,3.4) 3.4 (1.9,5.9) 4.8 (2.7,8.4) 8.9 (5.0,15.7)

South 0.7 (0.4,1.1) 1.8 (1.2,2.8) 4.2 (2.9,6.0) 6.5 (4.5,9.5) 8.6 (5.9,12.7) 14.0 (9.0,22.0)

West 0.5 (0.3,0.7) 1.4 (1.0,1.9) 3.2 (2.4,4.4) 5.3 (3.7,7.6) 7.1 (4.7,10.8) 12.0 (7.0,20.4)

Coastal Status2

Noncoastal 0.4 (0.2,0.5) 1.1 (0.8,1.5) 2.5 (1.9,3.2) 4.0 (3.1,5.2) 5.4 (4.1,7.1) 9.0 (6.6,12.4)

Coastal 0.6 (0.4,0.9) 1.7 (1.2,2.5) 4.2 (2.9,5.9) 6.7 (4.6,9.7) 9.0 (6.0,13.4) 15.0 (9.5,23.5)

Coastal/Inland Region1,2

Pacific 0.5 (0.3,0.9) 1.6 (1.1,2.5) 4.1 (2.6,6.6) 6.8 (4.0,11.7) 9.2 (5.0,16.9) 15.0 (7.6,29.6)

Atlantic 0.8 (0.5,1.2) 2.1 (1.3,3.2) 4.5 (3.1,6.7) 6.9 (4.7,10.1) 8.9 (6.0,13.1) 13.9 (9.2,20.9)

Gulf of Mexico 0.8 (0.3,1.9) 2.1 (1.0,4.5) 5.1 (2.6,10.2) 8.6 (3.9,18.9) 12.1 (5.0,29.4) 21.2 (9.0,50.2)

Great Lakes 0.4 (0.2,0.6) 1.1 (0.6,2.0) 3.0 (1.6,5.5) 5.1 (2.5,10.3) 7.2 (3.4,15.0) 12.6 (5.9,26.7)

Inland Northeast 0.3 (0.2,0.6) 0.9 (0.5,1.6) 2.0 (1.1,3.4) 2.9 (1.6,5.2) 3.8 (2.1,6.8) 5.9 (3.1,11.4)

Inland Midwest 0.2 (0.1,0.4) 0.7 (0.4,1.2) 1.6 (0.9,3.0) 2.6 (1.4,4.8) 3.7 (2.0,6.7) 6.7 (3.8,11.8)

Inland South 0.6 (0.4,0.8) 1.6 (1.1,2.2) 3.5 (2.6,4.8) 5.5 (4.1,7.5) 7.2 (5.2,10.0) 11.5 (7.9,16.6)

Inland West 0.4 (0.3,0.7) 1.2 (0.8,1.8) 2.7 (1.8,3.8) 4.2 (2.9,6.0) 5.5 (3.8,8.0) 9.0 (5.9,13.8)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 90

 
Table 29a. UFCR estimates (g/day raw weight, edible portion): Total freshwater + estuarine trophic level 4 fish, youth, <21 years, by 

demographic characteristics

Freshwater + Estuarine Trophic Level 4 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 0.2 (0.1,0.3) 0.6 (0.4,1.0) 2.0 (1.2,3.2) 3.9 (2.4,6.3) 6.0 (3.6,9.9) 13.2 (7.4,23.5)

Age

 1 to <3 yrs 0.1 (0.0,0.3) 0.4 (0.2,0.9) 1.2 (0.6,2.3) 2.2 (1.2,4.0) 3.3 (1.9,5.8) 7.1 (4.1,12.3)

 3 to <6 yrs 0.1 (0.0,0.2) 0.3 (0.2,0.6) 1.1 (0.6,2.1) 2.3 (1.0,5.0) 3.6 (1.4,9.5) 8.5 (1.8,39.4)

 6 to <11 yrs 0.2 (0.1,0.3) 0.7 (0.4,1.2) 2.2 (1.3,3.7) 4.1 (2.3,7.5) 6.2 (3.3,11.7) 13.7 (6.7,28.0)

 11 to <16 yrs 0.1 (0.1,0.3) 0.6 (0.3,1.1) 1.9 (0.9,3.7) 3.7 (1.8,7.6) 5.6 (2.7,11.8) 12.1 (5.4,27.3)

 16 to <18 yrs 0.3 (0.1,1.1) 1.1 (0.4,3.3) 3.2 (1.2,8.7) 5.8 (2.1,15.9) 8.7 (3.2,23.5) 18.4 (6.7,50.0)

 18 to <21 yrs 0.2 (0.1,0.5) 0.8 (0.4,1.7) 2.7 (1.3,5.4) 5.3 (2.5,11.1) 8.3 (3.8,17.9) 18.9 (7.3,48.8)

Gender

Female 0.1 (0.1,0.2) 0.5 (0.3,0.8) 1.5 (0.9,2.3) 2.9 (1.8,4.5) 4.4 (2.8,7.1) 9.8 (5.8,16.7)

Male 0.2 (0.1,0.4) 0.8 (0.5,1.4) 2.5 (1.5,4.3) 4.9 (2.9,8.4) 7.5 (4.2,13.1) 16.5 (8.8,30.8)

Race/Ethnicity1

Mexican American 0.1 (0.1,0.2) 0.4 (0.3,0.7) 1.4 (0.9,2.2) 2.7 (1.7,4.5) 4.3 (2.5,7.2) 9.8 (5.1,18.9)

Other Hispanic 0.1 (0.0,0.4) 0.6 (0.2,1.9) 2.0 (0.4,8.6) 4.1 (0.7,22.7) 6.2 (0.9,43.6) 13.9 (1.3,145.9)

Non-Hispanic White 0.1 (0.1,0.3) 0.5 (0.2,0.9) 1.5 (0.8,3.0) 2.9 (1.4,5.7) 4.3 (2.1,8.8) 9.1 (4.2,19.9)

Non-Hispanic Black 0.4 (0.2,0.8) 1.4 (0.7,2.6) 3.9 (2.1,7.5) 7.3 (3.8,14.1) 10.8 (5.5,20.9) 22.5 (11.1,45.3)

Other Race 0.4 (0.2,0.7) 1.4 (0.7,2.6) 4.4 (2.2,8.7) 8.8 (4.2,18.3) 13.3 (6.0,29.7) 30.7 (10.6,88.8)

Income

$0 to <$20K 0.2 (0.1,0.3) 0.7 (0.4,1.1) 2.1 (1.3,3.5) 4.1 (2.4,6.9) 6.2 (3.6,10.9) 13.3 (6.9,25.3)

$20 to <$45K 0.1 (0.1,0.3) 0.5 (0.3,0.9) 1.6 (0.9,2.9) 3.2 (1.8,5.8) 5.0 (2.7,9.2) 11.3 (5.6,22.9)

$40 to <$75K 0.2 (0.1,0.3) 0.6 (0.4,1.0) 2.0 (1.2,3.4) 4.0 (2.3,7.0) 6.2 (3.4,11.3) 14.6 (7.2,29.7)

$75+K 0.2 (0.1,0.4) 0.8 (0.4,1.4) 2.3 (1.3,4.1) 4.5 (2.6,7.7) 6.6 (3.7,11.6) 13.9 (7.5,25.8)

>$20K 0.0 (0.0,0.3) 0.3 (0.1,1.6) 1.7 (0.5,5.7) 3.7 (1.2,10.9) 5.8 (2.0,16.2) 14.1 (5.3,37.2)

Refused/Don’t Know Income 0.0 (0.0,0.1) 0.1 (0.0,0.5) 0.5 (0.1,2.1) 1.3 (0.3,5.5) 2.4 (0.5,11.5) 9.5 (1.4,64.2)

Income Missing 0.1 (0.0,0.4) 0.4 (0.1,1.9) 1.6 (0.3,7.8) 3.6 (0.8,15.5) 5.5 (1.4,21.4) 12.1 (3.5,41.6)

1 Race/ethnicity is as defined by NHANES. Respondents who self-identified as “Mexican American” were coded as such regardless of their other race-ethnicity identities. Otherwise, selfidentified “Hispanic” ethnicity was coded as “Other Hispanic.” All other non-Hispanic participants were then categorized based on their self-reported races: non-Hispanic white, non-Hispanic 

black, and other non-Hispanic race including non-Hispanic multiracial (other race).

 91

 
Table 29b. UFCR estimates (g/day raw weight, edible portion): Total freshwater + estuarine trophic level 4 fish, youth, <21 years, by 

geographic area

Freshwater + Estuarine Trophic Level 4 

Finfish and Shellfish 

Percentiles (95% CI) 

50th 75th 90th 95th 97th 99th 

Youth (<21 yrs) 0.2 (0.1,0.3) 0.6 (0.4,1.0) 2.0 (1.2,3.2) 3.9 (2.4,6.3) 6.0 (3.6,9.9) 13.2 (7.4,23.5)

Region1

Northeast 0.1 (0.0,0.2) 0.3 (0.1,0.7) 1.0 (0.4,2.3) 2.0 (0.8,5.0) 3.1 (1.1,8.5) 6.9 (1.9,24.9)

Midwest 0.2 (0.0,0.6) 0.7 (0.2,2.3) 2.4 (0.8,7.0) 4.9 (1.8,13.3) 7.6 (2.9,20.0) 17.9 (7.0,45.8)

South 0.2 (0.1,0.4) 0.8 (0.5,1.3) 2.4 (1.6,3.7) 4.6 (2.9,7.3) 6.9 (4.2,11.3) 14.7 (8.3,25.8)

West 0.2 (0.1,0.3) 0.6 (0.4,0.9) 1.7 (1.1,2.7) 3.3 (2.0,5.4) 4.9 (2.9,8.2) 10.2 (5.4,19.3)

Coastal Status2

Noncoastal 0.1 (0.1,0.3) 0.6 (0.3,1.0) 1.8 (1.0,3.1) 3.5 (2.0,6.0) 5.3 (3.0,9.3) 11.7 (6.4,21.6)

Coastal 0.2 (0.1,0.4) 0.7 (0.4,1.3) 2.4 (1.5,4.0) 4.7 (2.8,7.8) 7.2 (4.2,12.3) 15.9 (8.3,30.5)

Coastal/Inland Region1,2

Pacific 0.2 (0.1,0.3) 0.6 (0.3,1.1) 1.9 (1.1,3.2) 3.7 (2.1,6.2) 5.5 (3.1,9.7) 11.8 (6.0,23.2)

Atlantic 0.2 (0.1,0.4) 0.8 (0.4,1.4) 2.4 (1.3,4.3) 4.6 (2.5,8.5) 7.0 (3.7,13.5) 15.3 (6.5,35.8)

Gulf of Mexico 0.3 (0.1,0.5) 0.9 (0.5,1.8) 2.8 (1.5,5.2) 5.4 (3.0,9.8) 8.1 (4.5,14.5) 15.7 (7.7,32.0)

Great Lakes 0.2 (0.1,0.5) 0.8 (0.3,2.2) 2.8 (1.0,7.8) 5.7 (2.0,16.7) 9.1 (3.0,27.9) 22.6 (6.5,78.6)

Inland Northeast 0.1 (0.0,0.2) 0.2 (0.1,0.6) 0.8 (0.3,2.1) 1.7 (0.6,4.9) 2.7 (0.8,8.8) 6.0 (1.3,28.0)

Inland Midwest 0.2 (0.0,0.6) 0.7 (0.2,2.4) 2.3 (0.7,6.9) 4.5 (1.6,12.5) 6.8 (2.6,17.9) 15.7 (6.5,37.9)

Inland South 0.2 (0.1,0.3) 0.7 (0.4,1.1) 2.1 (1.3,3.3) 3.9 (2.4,6.4) 5.9 (3.5,9.9) 12.7 (7.2,22.4)

Inland West 0.2 (0.1,0.3) 0.5 (0.3,0.9) 1.6 (0.9,2.7) 3.0 (1.7,5.2) 4.4 (2.5,7.8) 9.1 (4.6,18.0)

1 U.S. regions are the U.S. Census Bureau regions. Midwest = OH, MI, IN, WI, IL, MO, IA, MN, SD, ND, NE, KS. Northeast = PA, NY, NJ, CT, RI, MA, NH, VT, ME. South = DE, MD, DC, VA, WV, KY, 

TN, NC, SC, GA, AL, MS, FL, LA, AR, OK, TX. West = NM, CO, WY, MT, ID, UT, AZ, NV, CA, OR, WA, AK, HI.

2 Coastal regions include counties bordering the 3 coasts (Pacific, Atlantic, and Gulf of Mexico) and the Great Lakes and estuaries and bays. Additionally, any county that did not directly 

border a coast, but the central point was within 25 miles of a coast was defined as coastal. The inland regions are the remaining counties in each of the 4 Census Regions.

 92

 
5.3 Uncertainty 

The estimated fish consumption rates may be uncertain due to either bias or random variation. Bias 

results in a consistently high or consistently low fish consumption rate relative to the true or desired 

value. Variation results in an uncertain fish consumption rate that might be either higher or lower 

than the true value. 

The primary sources of random variation are the following: 





Sampling error associated with the random selection of NHANES respondents. For 

example, if different counties and individuals had been selected for the NHANES data 

collection, the data and FCRs would be different. 

Random differences due to the simulation of usual fish consumption for each 

NHANES respondent. This source of variation can be reduced by increasing the 

number of simulations. 

The confidence intervals for the fish consumption rates account for both of these sources of 

variation. Estimates for coastal regions will be less precise than national estimates because the 

number of respondents in the coastal regions is a fraction of the number of NHANES respondents 

nationally. As a result, the confidence intervals for coastal regions are wider than for national 

estimates. Similarly, if there are fewer respondents with reported fish consumption in two 24-hour 

recalls, there is less data to estimate the parameters and particularly the variance components, 

resulting in more uncertainty in the fish consumption estimates and wider confidence intervals. 

There are multiple sources of bias that can affect the fish consumption rates including: 













Seasonality; 

Respondent bias; 

Use of standard recipes to calculate fish consumption amounts from the NHANES 24-

hour recalls; 

Classification of the fish consumed into types of fish habitats; 

Bias associated with the estimation method (either the NCI or EPA Method) and its 

assumptions; and 

Use of approximate analysis weights for coastal versus non-coastal comparisons. 

Each of these sources of bias is discussed in more detail below. 

93
5.3.1 Seasonality 

Fish consumption, especially of recreationally or sport-caught fish, is likely to vary by season. 

NHANES collects data throughout the year. However, NHANES generally collects data in northern 

counties in the summer and southern counties in the winter. Thus the estimates may overestimate 

usual intake in the northern regions of the United States and underestimate usual intake in the 

southern regions of the United States if summer fish consumption is higher than winter fish 

consumption. There is no way to estimate this season effect as there are little or no NHANES data 

from northern counties in the winter and southern counties in the summer. 

5.3.2 Bias in the Reported Fish Consumption 

The reported fish consumption is a combination of the frequency of fish consumption and the 

amount consumed if fish was consumed. The reported fish consumption may be biased if 

NHANES respondents tend to report consistently more or less fish consumption in the 24-hour 

recall than actually occurred. Assessing if the reported values are biased requires comparing reported 

values to estimates obtained using other data collection approaches, such as analysis of duplicate 

meals. Over the years, much research has gone into assessing dietary intake, resulting in the 

procedures used by NHANES. As a result, the estimates from NHANES are generally considered to 

have minimal bias. Nonetheless, the estimates may be biased and the bias may be different for 

different communities or subpopulations. 

5.3.3 Use of Standard Recipes 

The FNDDS utilizes standard recipes for foods reported consumed. NHANES participants do not 

supply specific recipes of the foods they consumed. They provide details such as whether the fish 

was breaded, cooked in margarine, baked or broiled, etc., but they do not provide exact recipes 

(which they are likely not to know anyway). For example, the standard recipe for the food “Scallops 

and noodles with cheese sauce” is approximately 35 percent fish. However, the true recipe for the 

food consumed by an NHANES respondent may have less fish or more fish than the standard 

recipe. Additionally, there is uncertainty associated with the moisture loss values for processing and 

cooking methods. They are average values of moisture loss given the various processing and cooking 

methods. If participants cooked their fish a bit longer than the moisture loss would be a bit greater 

than average, and if they cooked it a bit less, the moisture loss would be a bit less than average.

94
5.3.4 Habitat Assignments 

There is some uncertainty associated with the assignment of habitats to reported fish consumption. 

When the raw data are processed by NHANES, fish species reported consumed are combined into 

groups. Generally, these groupings are based on taxonomic groups. This grouping of species 

complicates the assignment of habitat because in some cases, the grouped fish can inhabit different 

habitats and there is no way to determine the exact species the participant consumed. For some 

species, apportioning relied on NOAA landings data to assign species of fish groups with many 

species (e.g., clams) to habitats. Bias in the proportion of each species assigned to each habitat will 

directly affect the corresponding fish consumption rate. For example, if more fish are assigned to 

the estuarine habitat, then the total amount of estuarine fish consumed and the percentiles of fish 

consumption will be higher than if fewer fish are assigned to the habitat. Even if the allocation to 

fish habitats is unbiased overall, there may be bias for local estimates. For example, if residents in 

coastal counties each more locally caught estuarine fish and non-coastal residents eat more 

commercial non-estuarine fish of the same species, the estimated proportion of estuarine fish will be 

biased low for the coastal counties and biased high for the non-coastal counties. 

5.3.5 Estimation of Usual Fish Consumption 

Measurements of usual fish consumption are very difficult to obtain. Since usual fish consumption is 

a long-term average, we would need many 24-hour recalls over a long time to approximate what 

“usual intake” is trying to assess; therefore we rely on a statistical model and associated assumptions 

to estimate usual intake. As a result, the estimates of usual fish consumption depend in part on the 

statistical assumptions.

The model makes certain assumptions, such as, 24-hour recalls provide unbiased estimates of fish 

consumption, all respondents are fish consumers (at least occasionally), and the distribution of fish 

consumption among those reporting consumption in a 24-hour recall is normally distributed for 

some power transformation. The validity of these assumptions can be discussed and, to some extent 

evaluated using data. 

The estimates of the frequency of fish consumption depend in part on how non-consumers (those 

who never eat fish or don’t eat fish for a long time) are treated. From two 24-hour recalls it is not 

possible to separate true non-consumers from those who did not happen to report fish consumption 

in either recall. A similar problem relates to consumption of small amounts for fish. Should a person 

95
who never eats an identifiable piece of fish but uses a salad dressing with a small amount of fish in it 

be considered a regular consumer of a very small amount or a non-consumer of fish? Whether a 

meal is classified as having fish may depend on the procedures used to ask the questions and the 

recipes used to estimate fish consumption. Unfortunately we do not have the data needed to identify 

non-consumers. Having non-consumers in the data will lower the overall probability of fish 

consumption (P) but increase the variance of the probability of fish consumption among individuals. 

The resulting effect on the upper percentiles of the distribution is not clear. 

The reported amount of fish consumption will vary from one 24-hour recall to another, in part 

because the respondents may be poor at estimating the amount consumed and in part because the 

consumption amounts are reported in rounded units, such as a cup or a pint, but not 1.267 cups. 

The rounding adds some uncertainty to the estimates. The within-person variance component 

accounts for uncertainty due to poor estimation by the respondent and rounding that is part of the 

process. Because the definition of usual fish consumption does not include the within-person 

variation, this source of error should contribute minimal bias to the estimates of usual fish 

consumption. 

The statistical models for the NCI and EPA Methods make some assumptions to simplify the 

computations, such as an assumption that variance components are normally distributed, additive in 

the transformed scale, and linearly correlated. The assumption that the person-specific random 

effect in the probability model is normally distributed is difficult to test without many more 24-hour 

recalls for each person. The assumption that the two variance components in the amount model are 

normally distributed is generally consistent with the observation that the Box-Cox transformed 

consumption amounts are roughly normally distributed. Nevertheless, other assumptions may imply 

a similar distribution for the reported amounts while using a somewhat different assumption for the 

person-specific variance component and thus somewhat different estimates of fish consumption. 

Because the estimated parameters must be consistent with the reported data, the general center and 

spread of the predicted distribution will be similar regardless of the distributional assumptions. 

Specific percentiles may be either higher or lower using different assumptions or may be relatively 

insensitive to the distributional assumptions. Although these assumptions are common in other 

statistical applications, it is difficult to assess how the estimates might change using other 

assumptions. 

If the model assumptions are accepted as reasonable, then the question is whether the estimates 

from the model are biased. The estimates are based on maximum likelihood, which can produce 

biased estimates, particularly variance estimates, with small sample sizes. However, convergence 

96
theory says maximum likelihood is best with large sample sizes. Due to the relatively large sample 

sizes, we expect the estimates to have relatively little bias compared to the size of the confidence 

intervals. 

The fish consumption estimates depend in part on the independent predictors used in the model. 

When different predictors are used, the estimates change. It is impossible to know what the best set 

of predictors is. A systematic approach was used to selecting the independent predictors from the 

available predictors in an effort to minimize any bias. The estimates have unknown bias due to the 

decisions that were made. 

Relative to the NCI Method, the EPA Method uses approximate methods to estimate the 

parameters and as a result, percentile estimates from the EPA Method may be more biased than 

from the NCI Method. The analysis of simulated data and the comparison of the FCR percentiles 

between the NCI and EPA Methods suggest that the results from the EPA Method have little bias 

overall, although estimates for some percentiles may be more biased than for others. The bias 

observed in the comparisons between the NCI Method and the EPA Method are very small 

compared to the spread of the overall distribution, generally small compared to the width of the 

confidence interval, and on the same order as bias due to other sources such as the selection of the 

independent predictors. 

5.3.6 Weights for Coastal Versus Non-Coastal Regions 

The U.S. Census regions are used in the calculation of the NHANES weights. However, the analysis 

using coastal versus non-coastal regions is looking at smaller areas than intended when the weights 

were constructed. Some of the coastal/noncoastal regions cross these Census regions. As a result, 

comparisons among coastal and non-coastal regions may be slightly biased or less precise then 

indicated by the confidence intervals. At the same time, the weights also adjust for oversampling of 

some populations and survey nonresponse, so we believe it is important to use the weights. While 

the estimates may be more imprecise and there may be some uncertainty due to the weighting, they 

are still a better representation for each coastal/noncoastal area then using unweighted or national 

estimates. 

97
Discussion 6 

Fish consumption is higher among males compared to females and increases with increasing age, 

although persons aged 65 and over show decreased consumption. People of races other than Black 

and White have the highest fish consumption rates of all race/ethnicity groups, with significant 

differences observed across all percentiles and many fish types, excluding freshwater and estuarine 

fish, trophic level 2 fish, trophic level 2 freshwater and estuarine fish, and trophic level 2 marine fish. 

The other race category consists of Asian, Native American, Pacific and Caribbean Islander, Alaska 

Native, multiracial, and unknown race. There is a general increase in consumption as income 

increases. 

People in the Northeast have higher total fish consumption rates than those living in the other 

Census regions, while people in the Midwest have the lowest rates. Significant differences are 

observed between the regions. The inland regions generally have lower fish consumption rates than 

the coastal regions except for the Great Lakes region, which is more similar to an inland region, and 

the inland Northeast, which appears more similar to a coastal region. This pattern is different for 

freshwater fish for which the people in the inland south, Great Lakes, and the Gulf of Mexico have 

the highest consumption rates.

98
References 7 Ahmed, F. E., Hattis, D., Wolke, R.E., and Steinman, D. (1993). Human health risks due to 

consumption of chemically contaminated fishery products. Environmental Health Perspectives, 101

(Supplement. 3), 297-302.

Ahuja, J. K. A., Montville, J.B., Omolewa-Tomobi, G., Heendeniya, K. Y., Martin, C. L., Steinfeldt, 

L. C., Anand, J., Adler, M. E., La Comb, R. P., and Moshfegh, A. J. (2012). USDA Food and 

Nutrient Database for Dietary Studies, 5.0. Beltsville, MD: U.S. Department of Agriculture, 

Agricultural Research Service, Food Surveys Research Group. 

Dodd, K. W., Guenther, P. M., Freedman, L. S., Subar, A. F, Kipnis, V., Midthune, D., Tooze, J., 

and Krebs-Smith, S. M. 2006. Statistical methods for estimating usual intake of nutrients and 

foods: A review of the theory. Journal of the American Dietetic Association, 106, 1640-1650. 

Gilbert, R. O. (1987). Characterizing lognormal populations. In Statistical methods for environmental 

pollution monitoring (pp. 164-176). New York: Van Norstrand Reinhold. 

Haines, P. S., Hama, M. Y., Guilkey, D. K., and Popkin, B. M. (2003). Weekend eating in the United 

States is linked with greater energy, fat, and alcohol intake. Obesity Research, 11: 945–949. 

Judkins, D. (1990). Fay’s method for variance estimation. Journal of Official Statistics, 6, 223-240.

Kipnis, V., Midthune, D., Buckman, D. W., Dodd, K. W., Guenther, P. M., Krebs-Smith, S. M., 

Subar, A. F., Tooze, J. A., Carroll, R. J., and Freedman, L. S. (2009). Modeling data with excess 

zeros and measurement error: Application to evaluating relationships between episodically 

consumed foods and health outcomes. Biometrics; 65, 1003-1010.

Mahaffey, K. R., Clickner, R. P., and Jeffries, R. A. (2009). Adult women’s blood mercury 

concentrations vary regionally in the United States: Association with patterns of fish 

consumption (NHANES 1999-2004). Environmental Health Perspectives, 117, 47-53. 

National Center for Health Statistics. (2005). Analytic and reporting guidelines: The National Health and 

Nutrition Examination Survey Examination Survey. December 2005. Retrieved March 27, 2014 from 

http://www.cdc.gov/nchs/data/nhanes/nhanes_03_04/nhanes_analytic_guidelines_dec_200

5.pdf. 

National Center for Health Statistics. (2009). National Health and Nutrition Examination Survey MEC inperson dietary interviewers Procedures Manual. Retrieved June 26, 2013 from http://www.cdc.gov/ 

nchs/data/nhanes/nhanes_09_10/DietaryInterviewers_Inperson.pdf. 

99
National Center for Health Statistics. Continuous NHANES Web Tutorial. Variance Estimation. Key 

concepts about variance estimation within NHANES. Retrieved March 27, 2014 from

http://www.cdc.gov/nchs/tutorials/NHANES/SurveyDesign/VarianceEstimation/Info1.htm. 

National Center for Health Statistics. (2013). About the National Health and Nutrition Examination 

Survey. Retrieved March 27, 2014 from 

http://www.cdc.gov/nchs/nhanes/about_nhanes.htm.

National Oceanic and Atmospheric Administration (NOAA). 2014. Commercial Fisheries Statistics. 

Retrieved March 27, 2014 from .http://www.st.nmfs.noaa.gov/commercial-fisheries/. 

National Oceanic and Atmospheric Administration (NOAA). 2014. Fishwatch. U.S. Seafood Facts.

Retrieved March 27, 2014 from 

http://www.fishwatch.gov/wild_seafood/outside_the_us.htm. 

National Research Council. (2000). Toxicological effects of methylmercury. Washington, DC: National 

Academies Press.

Rust, K. (1986). Efficient replicated variance estimation. Proceedings of the Section on Survey Research 

Methods (pp. 81-87). Alexandria, VA: American Statistical Association.

Thompson, M. R., and Boekelheide, K. (2013). Multiple environmental chemical exposures to lead, 

mercury and polychlorinated biphenyls among childbearing-aged women (NHANES 1999-

2004): Body burden and risk factors. Environmental Research, 12, 23-30. doi: 

10.1016/j.envres.2012.10.005. Epub 2012 Nov 16. 

Tooze, J. A., Kipnis, V, Buckman, D. W., Carroll, R. J., Freedman, L. S., Guenther, P. M., KrebsSmith, S. M., Subar, A. F., and Dodd, K. W. (2010). A mixed-effects model approach for 

estimating the distribution of usual intake of nutrients: The NCI Method. Statistics in Medicine, 

29, 2857-2868.

Tooze, J. A., Midthune, D., Dodd, K. W., Freedman, L. S., Krebs-Smith, S. M., Subar, A. F., 

Guenther, P. M., Carroll, R. J., and Kipnis, V. (2006). A new statistical method for estimating 

the usual intake of episodically consumed foods with application to their distribution. Journal of 

the American Dietetic Association, 106, 1575-1587.

U.S. Department of Agriculture. (2006). Food and Nutrient Database for Dietary Studies, 2.0. 

Beltsville, MD: Agricultural Research Service, Food Surveys Research Group.

U.S. Department of Agriculture. (2008). Food and Nutrient Database for Dietary Studies, 3.0. 

Beltsville, MD: Agricultural Research Service, Food Surveys Research Group.

U.S. Department of Agriculture. (2010). Food and Nutrient Database for Dietary Studies, 4.1. 

Beltsville, MD: Agricultural Research Service, Food Surveys Research Group.

U.S. Department of Agriculture, Agricultural Research Service. (2013). USDA Automated MultiplePass Method. Retrieved March 27, 2014 from 

http://www.ars.usda.gov/Services/docs.htm?docid=7710.

100
U.S. Environmental Protection Agency. (1995). Great Lakes Water Quality Initiative Technical Support 

Document for the Procedures to Determine Bioaccumulation Factors. EPA/820/B-95/005. Washington, 

DC: U.S. Environmental Protection Agency, Office of Water.

U.S. Environmental Protection Agency. (1997). Mercury study report to Congress: An assessment of exposure 

to mercury in the United States. (Volume 4). EPA-452/R-97-006. Washington, DC: U.S. 

Environmental Protection Agency, Office of Air Quality Planning and Standards and Office 

of Research and Development.

U.S. Environmental Protection Agency. (2002a). Estimated per capita fish consumption in the United States.

EPA-821- C- 02-003. Washington, DC: U.S. Environmental Protection Agency, Office of 

Science and Technology, Engineering and Analysis Division. 

U.S. Environmental Protection Agency. (2002b). Trophic level and exposure analyses for selected piscivorous 

birds and mammals. Volume III: Appendices. Washington, DC: U.S. Environmental Protection 

Agency, Office of Science and Technology, Office of Water.

U.S. Environmental Protection Agency. (2003). Methodology for deriving ambient water quality criteria for the 

protection of human health (2000). Technical Support Document Volume 2: Development of 

national bioaccumulation factors. EPA-822-R-03-030. Washington, DC: U.S. Environmental 

Protection Agency, Office of Science and Technology, Office of Water.

Wolter, K. M. (1985). Introduction to variance estimation. New York: Springer-Verlag.

101