CONTENTS FOR ATTACHMENTS 1 AND 2 

ATTACHMENT 1: Documentation for the Continuing Survey of Food Intakes by 

Individuals (CSFII) ........................................... 

1. CONTENTS OF THE FOOD COMMODITY INTAKE DATABASE CD-ROM .......

1.1. TREE DIAGRAM OF FILES ON CD-ROM .............................

1.2. FOOD COMMODITY INTAKE DATABASE FOR THE 1994-96

CONTINUING SURVEY OF FOOD INTAKES BY INDIVIDUALS ITS

SUPPLEMENTAL CHILDREN’S SURVEY (CSFII 1994-96, 1998) ..........

1.3. SUMMARY DESCRIPTIONS OF FOOD COMMODITY INTAKE 

DATABASE FILES ................................................

1.3.1. Documentation Files ..........................................

1.3.2. EPA Commodity Files .........................................

1.3.3. Food Commodity Intake Data Files ...............................

1.3.4. Supporting Data Files .........................................

1.4. SUGGESTED CITATIONS, ACQUISITION, CONTACTS AND

DISCLAIMERS ....................................................

2. ESSENTIAL INFORMATION ..............................................

3. METHODOLOGY FOR TRANSLATING THE 1994-96 CONTINUING 

SURVEY OF FOOD INTAKES BY INDIVIDUALS AND 

ITS SUPPLEMENTAL CHILDREN’S SURVEY (CSFII 1994-96, 1998) 

INTO INTAKES OF EPA-DEFINED FOOD COMMODITIES .................... 

3.1. FOOD COMMODITY INTAKE DATABASE ............................

3.2. CONTINUING SURVEY OF FOOD INTAKES BY INDIVIDUALS 

(CSFII) ...........................................................

3.3. EPA LIST OF FOOD COMMODITIES .................................

3.4. TRANSLATING CSFII TO EPA FOOD COMMODITIES ..................

3.5. DEFINING MIXTURES IN TERMS OF INGREDIENTS ...................

3.6. ASSIGNING FOODS AND INGREDIENTS TO EPA COMMODITIES .......

3.6.1. Matching USDA Food Codes to EPA Commodities ..................

3.6.2. Converting Weights of Foods and Ingredients .......................

3.6.3. CSFII Food-Code-to-Commodity Translation File ...................

3.7. SPECIAL REQUIREMENTS FOR ASSIGNING FOODS AND 

INGREDIENTS TO EPA FOOD COMMODITIES ........................

3.7.1. Alcoholic Beverages ..........................................

3.7.2. Baby Foods .................................................

3.7.3. Chips ......................................................

3.7.4. Coffee and Tea ...............................................

3.7.5. Egg, Dried ..................................................

i 
CONTENTS FOR ATTACHMENTS 1 AND 2

3.7.6. Fats and Oils ................................................

3.7.6.1. Vegetable Oils .........................................

3.7.6.2. Margarine and Butter ...................................

3.7.7. Fish ........................................................

3.7.8. Fruit .......................................................

3.7.9. Gelatin, Dry .................................................

3.7.10. Legumes ....................................................

3.7.11. Meat .......................................................

3.7.11.1. Beef ..............................................

3.7.11.2. Game .............................................

3.7.11.3. Goat ..............................................

3.7.11.4. Kidney ............................................

3.7.11.5. Meat, Not Further Specified............................

3.7.11.6. Pork ..............................................

3.7.11.7. Rabbit .............................................

3.7.11.8. Sheep .............................................

3.7.12. Meat and Poultry, Processed ....................................

3.7.13. Milk .......................................................

3.7.14. Poultry .....................................................

3.7.14.1. Chicken ...........................................

3.7.14.2. Turkey ............................................

3.7.14.3. Poultry Other Than Chicken and Turkey ..................

3.7.14.4. Mixtures Described as Containing Chicken or Turkey .......

3.7.14.5. Mixtures Described as Containing Meat and/or Poultry ......

3.7.15. Spices and Herbs .............................................

3.7.16. Sweeteners, Carbohydrate ......................................

3.7.17. Vegetables ..................................................

3.7.18. Water ......................................................

3.7.19. Commodities Not Consumed ....................................

3.7.20. Special Requirement To Include Commodities Consumed in Very

Small Amounts ..............................................

3.8. NOTES ABOUT REVIEWING THE FOOD-CODE-TO-COMMODITY

TRANSLATION FILE ..............................................

3.9. LIMITATIONS OF THE FOOD COMMODITY INTAKE DATABASE .......

3.10. REFERENCES ....................................................

Table 1. CSFII food codes representing commercial baby foods ..............

Table 2. EPA food commodities not appearing in the intake database ..........

4. CHARACTERISTICS AND FORMATS OF THE FOOD COMMODITY INTAKE 

DATABASE ............................................................ 

4.1. INTRODUCTION ..................................................

ii 
CONTENTS FOR ATTACHMENTS 1 AND 2

4.2. DATABASE STRUCTURE ..........................................

4.2.1. Documentation Files (Files in Directory “\document”) ................

4.2.2. EPA Commodity Files (Files in Directory “\epa_comm”) .............

4.2.3. Food Commodity Intake Data Files (Files in Directory “\intake”) .......

4.2.4. Supporting Data Files (Files in Directory “\support”) .................

4.3. GENERAL NATURE OF THE DATA ..................................

4.3.1. Data File Characteristics .......................................

4.3.2. List of Key Fields .............................................

4.3.3. Sampling Weights ............................................

4.4. DATA FILE FORMATS .............................................

4.4.1. Introduction to File Formats for ASCII Delimited Files ...............

4.4.2. Formats for the Food Commodity Intake Data Files ..................

4.4.2.1. Format of the Commodity Intakes File (comm9498.txt) ........

4.4.2.2. Format for the Files of Commodity Intakes by Cooked

 Status, Food Form and Cooking Method (ffcm9498.txt) .............

4.4.2.3. File Format for Sample Person Data (smpl9498.txt) ...........

4.4.3. Formats for the Supporting Files .................................

4.4.3.1. Food Code-to-Commodity Translation File (fc_comm.txt) ......

4.4.3.2. Food Code Descriptions (fcdesc.txt) ........................

4.4.3.3. Food Code Include Statements (fcincl.txt) ...................

4.4.3.4. Food Code Outline (fcscheme.txt) .........................

4.4.3.5. Descriptions of Recipe Modifications (moddesc.txt) ...........

4.5. MISCELLANEOUS NOTES ..........................................

4.5.1. Responding Sample Persons With No Foods Reported for a Day ........

4.5.2. Unreported Body Weights ......................................

4.5.3. Lower Limit for Reporting Intakes ...............................

4.5.4. Using This Database in Conjunction With the Continuing Survey

of Food Intakes by Individuals and the Supplemental Children’s 

Survey (CSFII 1994-96, 1998) ...................................

5. CONTROL STATISTICS FOR THE SMPL9498.TXT DATA FILE, ALL 

RECORDS,UNWEIGHTED ................................................ 

APPENDIX A: EPA’S USE OF FOOD CONSUMPTION DATA IN ASSESSING 

 DIETARY RISK FROM PESTICIDES ................................. 

APPENDIX B: CSFII 1994-96, 1998 METHODOLOGY (SECTION 3 FROM CSFII

 1994-96, 1998 CD-ROM DOCUMENTATION) ......................... 

APPENDIX C: CSFII 1994-96, 1998 SAMPLING WEIGHTS AND STATISTICAL

 NOTES (EXCERPTS FROM SECTIONS 5 AND 6, CSFII 1994-96, 1998

 CD-ROM DOCUMENTATION) .....................................

iii 
CONTENTS FOR ATTACHMENTS 1 AND 2

ATTACHMENT 2: The EPA Food Commodity Vocabulary (Source: CSFII 

Documentation) .............................................. 

APPENDIX A: THE FISH LIST ...................................................

APPENDIX B: THE HERB LIST ..................................................

APPENDIX C: THE SPICE LIST ..................................................

APPENDIX D: SELECTED REFERENCES FOR THE EPA FOOD COMMODITY

 VOCABULARY .................................................. 

iv 
ATTACHMENT 1 

Documentation for the Continuing Survey 

of Food Intakes by Individuals (CSFII) 

Attachment 1-1 
1. CONTENTS OF THE FOOD COMMODITY INTAKE DATABASE CD-ROM


1.1 Tree diagram of files on CD-ROM


These files comprise the Food Commodity Intake Database for the


1994-96 Continuing Survey of Food Intakes by Individuals and its


Supplemental Children’s Survey (CSFII 1994-96, 1998)


Directory and


 File Names Title or Description 

[CD-ROM drive]: Drive letter for CD-ROM 

\readme.txt Readme file 

\document Documentation files 

\section1.wpd Contents, tree diagram, data 

file list 

\section2.wpd Essential information 

\section3.wpd Methodology for translating food 

intakes into EPA food 

commodities 

\section4.wpd Data file characteristics and 

formats 

\section5.wpd Control counts 

\appenda.wpd Appendix A 

\appendb.wpd Appendix B 

\appendc.wpd Appendix C 

\epa_comm EPA Commodity Files 

\csffcm.wpd EPA procedures for assigning EPA 

cooked status, food form, and 

cooking method values to USDA 

food codes 

\foodvoc.wpd EPA Food commodity vocabulary 

\intake Food Commodity Intake Data Files 

\comm9498.txt Commodity intakes for the 

Continuing Survey of Food 

Intakes by Individuals (CSFII 

1994-96, 1998) 

\ffcm9498.txt Commodity intakes by cooked 

status, food form, and cooking 

method for CSFII 1994-96, 1998 

\smpl9498.txt Sample person data for CSFII 

1994-96, 1998 

\Support Supporting Data Files 

\fc_comm.txt CSFII 1994-96, 1998 foods linked 

to EPA commodities (grams of 

commodities per 100 grams food) 

\fcdesc.txt Food code descriptions 

\fcincl.txt Food code include statements 

(extensions of food code 

descriptions) 

\fcscheme.txt Food code outline 

\moddesc.txt Descriptions of recipe 

Attachment 1-2 
modifications


1.2	 Food Commodity Intake Database for the 1994-96 Continuing


Survey of Food Intakes by Individuals its Supplemental


Children’s Survey (CSFII 1994-96, 1998)


Table of Database Files 

Documentation Files 

section1.wpd Table of contents, File names 

section2.wpd Essential information 

section3.wpd Methodology for translating food 

intakes into EPA food commodity 

intakes 

section4.wpd Data file characteristics and 

formats 

section5.wpd Control counts 

appenda.wpd Appendix A 

appendb.wpd Appendix B 

appendc.wpd Appendix C 

EPA documents 

csffcm.wpd EPA procedures for assigning EPA 

cooked status, food form, and 

cooking method values to USDA 

food codes 

foodvoc.wpd EPA Food commodity list 

Intake Data Files 

comm9498.txt Commodity intakes for CSFII 

1994-96, 1998 

ffcm9498.txt Commodity intakes by cooked 

status, food form, and cooking 

method for CSFII 1994-96, 1998 

smpl9498.txt Sample person data for CSFII 

1994-96, 1998 

Supporting Data Files 

fc_comm.txt CSFII 1994-96, 1998 foods linked to 

EPA commodities (grams of 

commodities per 100 grams food) 

fcdesc.txt Food code descriptions 

fcincl.txt Food code include statements 

(extensions of food code 

descriptions) 

fcscheme.txt Food code outline 

moddesc.txt Descriptions of recipe 

modifications


1.3 Summary descriptions of Food Commodity Intake Database Files


Attachment 1-3 
This CD-ROM contains the Food Commodity Intake Database. Summary


descriptions of files included on this CD-ROM are presented


below. Complete information on data file characteristics and


formats are found in \document\section4.wpd. 


1.3.1 Documentation files (files in directory \document)


Eight documentation files (5 documentation sections and 3


appendices) are included as WordPerfect files. They explain the


methodology used to develop the Food Commodity Intake Database,


data file characteristics and formats, and other information


important for using this database. 


1.3.2 EPA Commodity files (files in directory \epa_comm)


The Environmental Protection Agency (EPA) provided two


WordPerfect files:


EPA Food Commodity Vocabulary - “foodvoc.wpd”


This file contains a list of EPA commodities used in this


project. The list includes the name of each EPA commodity, its


EPA code, and a description of the weight basis for the


commodity. 


PROCEDURES FOR CODING CSFFCM - “csffcm.wpd”


EPA assigned codes for cooked status, food form, and cooking


method to each CSFII food code. This file discusses the


procedures used by EPA to assign those codes, which were then


used to create the “Commodity Intakes by Cooked Status, Food


Form, and Cooking Method” file described below. 


1.3.3 Food Commodity Intake Data Files (files in directory


\intake)


These files include (a) two commodity intake data files that were


created by translating the CSFII 1994-96, 1998 food intakes into


EPA-defined food commodities, and (b) one file containing


characteristics of sample persons in the survey.


COMMODITY INTAKE DATA - “comm9498.txt” 


The Commodity Intake File contains one record per EPA-defined


commodity, per person, per day. Each record contains the daily


total intake of a commodity, aggregated from the foods reported


by the sample person. If the sample person provided 2 days of


intake data, records for daily averages of each commodity are


also present. Data are presented as grams of commodity consumed


per kilogram of body weight. Records are not present for


commodities not consumed by the sample person. 


COMMODITY INTAKES BY “COOKED STATUS, FOOD FORM, AND COOKING


Attachment 1-4 
METHOD” - “ffcm9498.txt” 


This file further defines the CSFII commodity intake data by the


EPA codes for “Cooked Status,” “Food Form,” and “Cooking Method”


(CSFFCM). They contain one record per commodity per unique


CSFFCM, per person, per day. Each record contains the daily


total intake of a “commodity-CSFFCM” combination, aggregated from


the foods reported by the sample person. Records of daily


averages for each Commodity-CSFFCM combination are present, if


the sample person provided 2 days of intake data. Data are


presented as grams consumed per kilogram of body weight. Records


are not present for Commodity-CSFFCM combinations not consumed by


the sample person. For an explanation of “cooked status, food


form, and cooking method” codes, see directory \epa_comm on this


CD-ROM. 


SAMPLE PERSON DATA - “smpl9498.txt” 


The Sample Person data file contains demographic, sampling


weight, and other respondent and household information. One


record per responding sample person is present. Data in these


files were extracted from the CSFII 1994-96, 1998 Household and


Sample Person data records. 


1.3.4 Supporting Data Files (files in directory \support)


Five data files provide supporting documentation for the


translation of the CSFII 1994-96, 1998 intake data into EPA food 


commodities. 


FOOD-CODE-TO-COMMODITY TRANSLATION FILE - “fc_comm.txt”


The Food-Code-to-Commodity Translation file documents the


assignment of USDA food codes to EPA commodities as used in this


translation of food intakes into commodity intakes. Data are


expressed as the amount of each EPA commodity contained within a


100 gram portion of each specific CSFII food code. A separate


record exists for each commodity assignment for a food code. For


a discussion about the assignment of food codes to commodities,


see section 3 on Methodology. 


FOOD CODE DESCRIPTIONS - “fcdesc.txt”


The Food Code Descriptions file provides descriptions for each


food code used in the CSFII 1994-96, 1998. Descriptions are


usually generic in nature except for certain breakfast cereals,


infant formulas, and candies. The file includes descriptions up


to 200 characters long, as well as abbreviated descriptions up to


60 characters long.


FOOD CODE INCLUDE STATEMENTS - “fcincl.txt”


Food Include Statements are names of specific foods associated


with a particular generic food description in the Food Code


Attachment 1-5 
Descriptions file.


FOOD CODE OUTLINE - “fcscheme.txt”


The food code scheme identifies the meanings associated with the


first, second, and third (and sometimes fourth) digits of the 8­


digit food codes.


DESCRIPTIONS OF RECIPE MODIFICATIONS - “moddesc.txt”


In the CSFII, some database recipes were modified when a


responding sample person supplied specific information about


certain food ingredients. This file contains descriptions of


those modifications.


1.4 Suggested citations, acquisition, contacts and disclaimers


Suggested citation for this database:


U.S. Environmental Protection Agency, Office of Pesticide


Programs and U. S. Department of Agriculture, Agricultural


Research Service. 2000. Food Commodity Intake Database,


Version 2.1. CD-ROM.


Suggested citations when identifying the original sources of data


for this database:


U.S. Environmental Protection Agency. 2000. The EPA Food


Commodity Vocabulary, Master List dated June 15, 2000.


U.S. Department of Agriculture, Agricultural Research


Service. 2000. Continuing Survey of Food Intakes by


Individuals 1994-96, 1998. National Technical Information


Service, 5285 Port Royal Road, Springfield VA


22161;(703)487-4650. CD-ROM. NTIS Accession no. PB2000­


500027. 


Acquisition


Copies of the Food Commodity Intake Database may be purchased


from the National Technical Information Service, 5285 Port Royal


Road, Springfield, VA 22161; telephone 1-800-553-6847.


Contacts


U.S. Environmental Protection Agency(7509C)


Ariel Rios Building


1200 Pennsylvania Avenue, N.W.


Washington, DC 20460


FAX: 703-305-0871


Disclaimers


Mention of trade names, commercial products, or companies in this


Attachment 1-6 
database is solely for the purpose of providing specific


information and does not imply recommendation or endorsement by


the U.S. Department of Agriculture over others not mentioned.


The United States Department of Agriculture (USDA) prohibits


discrimination in its programs on the basis of race, color,


national origin, sex, religion, age, disability, political


beliefs, and marital or familial status. (Not all prohibited


bases apply to all programs.) Persons with disabilities who


require alternative means for communication of program


information (Braille, large print, audiotape, etc.) should


contact the USDA Office of Communications at (202) 720-2791.


To file a complaint, write the Secretary of Agriculture, U.S.


Department of Agriculture, Washington, DC 20250, or call (202)


720-7327 (voice) or (202) 720-1127 (TDD). USDA is an equal


employment opportunity employer.


Attachment 1-7 
2 

Food Commodity Intake Database, CSFII 1994-96, 1998


ESSENTIAL INFORMATION


The notes in this section provide information to help use this


database effectively, although they do not substitute for reading


the remainder of the documentation.


The Food Commodity Intake Database is based on:


(a) Food consumption surveys conducted by the U.S. Department of


Agriculture (USDA)--the 1994-96 Continuing Survey of Food Intakes


by Individuals and its Supplemental Children’s Survey (CSFII


1994-96, 1998), and 


(b) A list of commodities prepared by the U.S. Environmental


Protection Agency (EPA)--the Food Commodity Vocabulary, dated 


June 15, 2000.


The Food Commodity Intake Database was developed for the purpose


of estimating human exposures to pesticide residues through


intakes of foods and beverages. For information about how to use


these data for dietary risk assessment, users should contact the


EPA Office of Pesticide Programs, Division of Health Effects,


Ariel Rios Building, 1200 Pennsylvania Avenue, NW(7509C),


Washington, DC 20460, FAX 703-305-0871.


Food commodity intakes are included for each sample person from


the surveys. Intakes are expressed as grams of commodity


consumed, per kilogram of body weight, per day. Two days of


intakes, plus daily averages, are present for most sample


persons. 


Data files are also included which indicate how commodities were


consumed in terms of their “cooked status, food form, and


cooking method.” Codes for these parameters were assigned to the


USDA food codes by EPA.


The smallest amount of commodity consumption represented in this


database is 0.000001 grams, per kilogram of body weight, per day. 


Amounts smaller than 0.000001 were set to this amount. This


expression is not intended to suggest that data can be estimated


this precisely. It was set to this small amount at the request


of EPA to reduce the likelihood that small amounts would round to


zero.


Water consumption is not included in this database at this time. 


Intakes of water, as defined in the EPA food commodity list, are 


being prepared by the EPA Office of Water.


A Food-Code-to-Commodity Translation File is included in this


database to document how each USDA food code was assigned to the


EPA commodities. Data are expressed as grams of commodity per


100 grams of food. 


Attachment 1-8 
The amounts of commodities assigned to one USDA food code do not


always equal 100. This occurs because (a) some food constituents


were not assigned to commodities, for example, salt was excluded,


and (b) conversions were sometimes necessary in order to express


the commodities as defined by EPA; for example, rice as consumed


was converted to the dry weight of the grain. 


The smallest amount of a commodity identified in the Food-Code­


to-Commodity Translation File is 0.001 grams per 100 grams of


food. Commodity amounts lower than this limit, but greater than


zero, were set to 0.001 grams. Three decimal places were


selected for expression of the commodity amounts to accommodate


an EPA requirement that the presence of commodities in very small


amounts be represented in this database. It is not intended to


suggest that data can be estimated this precisely.


Several other special requirements were requested by EPA to guide


the translation of USDA food codes to commodities. These


requirements are discussed in Section 3.


Users may observe some unexpected findings in the Food-Code-to-


Commodity Translation File. For example, the following five


commodities are present in small amounts in many foods:


“Cassava,” “corn, field, starch,” “Potato, flour,” “Rice, flour,”


and “Wheat, flour.” This occurs because modified food starch is


frequently listed as an ingredient in processed foods. When the


source of the starch was not identified, it was assigned to those


commodities. Other examples of unexpected findings can be found


in section 3.8, Notes about reviewing the Food-Code-to-Commodity


Translation File. Reasons for unexpected findings can be found in


discussions on matching USDA food codes to EPA commodities


(section 3.6.1), converting weights of foods and ingredients


(sections 3.6.2), and special requirements for assigning foods


and ingredients to EPA commodities (section 3.7).


Not all commodities were consumed.


Data users are encouraged to read section 3, Methodology for


Translating the CSFII 1994-96, 1998 into intakes of EPA-defined


commodities. Limitations of the data are discussed in section


3.9.


Attachment 1-9 
Food Commodity Intake Database, CSFII 1994-96, 1998


3 Methodology for translating the 1994-96 Continuing Survey of


Food Intakes by Individuals and its Supplemental Children’s


Survey (CSFII 1994-96, 1998) into intakes of EPA-defined


commodities


3.1 Food Commodity Intake Database


The Food Commodity Intake Database contains intake data and


related information for approximately 548 commodities listed in


the Environmental Protection Agency’s (EPA) Food Commodity


Vocabulary (16). The database is the result of cooperative


efforts by the U.S. Department of Agriculture (USDA),


Agricultural Research Service (ARS), and EPA’s, Health Effects


Division. It was developed for use in estimating human exposures


to pesticide residues through intakes of foods and beverages. A


discussion of EPA’s use of food consumption data in assessing


dietary risk from pesticides is provided in Appendix A. 


The database contains several different types of data files


(intake data, supporting data, documentation). A complete list


of the data files is located in section 1. 


The intake data are based on the 1994-96 Continuing Survey of


Food Intakes by Individuals (CSFII 1994-96) and its Supplemental


Children’s Survey (CSFII 1998) (8), conducted by ARS. 


Commodities listed in the Food Commodity Vocabulary were selected


and defined by EPA. Because the survey data are expressed in


terms of foods as eaten, conversions of the data were sometimes


necessary in order to express them in terms of commodities as


defined by EPA. For example, consumption of cooked rice needed


to be expressed in terms of the EPA commodity, “Rice, dry weight


of grain.” Data conversions and other steps used to develop the


commodity intake data are referred to, collectively, as a


translation. The data sets and processes used in the translation


are identified below. For information about how to use these


data for dietary risk assessments, users should contact the EPA


Office of Pesticide Programs, Division of Health Effects, Ariel


Rios Building, 1200 Pennsylvania Avenue, NW (7509C), Washington,


DC 20460.


3.2 Continuing Survey of Food Intakes by Individuals (CSFII) 


The CSFII is a series of U. S. Department of Agriculture surveys


designed to measure the kinds and amounts of foods eaten by


Americans. The CSFII 1994-96 was conducted between January 1994


and January 1997 with a target population of noninstitutionalized


individuals in all 50 states and Washington, D.C. In each of the


3 survey years, data were collected for a nationally


representative sample of individuals of all ages. The CSFII 1998


was a survey of children ages 0 through 9 which was a supplement


to the CSFII 1994-96. It used the same sample design as the


Attachment 1-10 
CSFII 1994-96 and was intended to be merged with CSFII 1994-96 to


increase the sample size for children. The merged surveys are


designated as CSFII 1994-96, 1998.


In the CSFII 1994-96, 1998, dietary intakes were collected


through in-person interviews using 24-hour recalls on 2


nonconsecutive days. A total of 21,662 individuals provided data


for the first day; of those individuals, 20,607 provided data for


a second day. Over 11,000 of the sample persons represent


children up to 18 years of age.


Several sets of sampling weights are available for use with the


intake data, and all sets have been included in this database.


Using appropriate weights, data can be analyzed in the following


ways: (1) one year alone, i.e., 1994, 1995, 1996, or 1998; (2) 3


years combined for the CSFII 1994-96; or (3) 4 years combined for


CSFII 1994-96, 1998. It is recommended that all 4 years be


combined in order to provide an adequate sample size for


children. For an explanation of sample weighting, see section 5


of the documentation on the survey CD-ROM (8). Selections from


this section are also included here for easy reference


(\document\appendc.wpd).


Complete results from the surveys in microdata form are available


on CD-ROM (7,8). Documentation on the CD-ROMs includes survey


instruments, detailed descriptions of survey and data processing


methods, sample design, and calculation of weights. The survey


instruments, as well as a Design and Operation Report for CSFII


1994-96 (5), can also be found on the Food Surveys Research Group


web site: http://www.barc.usda.gov/bhnrc/foodsurvey/home.htm.


Several sections of the documentation from the CSFII 1994-96,


1998 CD-ROM are included here for easy reference. Section 3, a


discussion of survey methodology, is located in Appendix B;


excerpts from sections 5 and 6 on sampling weights and


statistical notes are located in Appendix C in \document. 


Three different data files (record types) from CSFII 1994-96,


1998 were used during the translation of the survey data to EPA


commodities: 


1. Record Type 15 (Household Level Data)


2. Record Type 25 (Sample Person Data)


3. Record Type 30 (Food Intake Data)


In addition, information was used from the following three ARS


technical databases (7a,7b,7c):


1. Food Coding Database


2. Recipe Database


3. Survey Nutrient Database


Attachment 1-11 
These databases are maintained by ARS for processing and


analyzing food consumption data, and the versions used for a


survey are released along with the survey data. Primarily,


databases from the CSFII 1994-96 CD-ROM were used for this


translation (7c). However, if a food was reported for the first


time during 1998, it may not appear on the earlier databases. 


For those foods, entries can be found on the CSFII 1994-96, 1998


CD-ROM (8). 


A total of 5,831 different foods were reported as consumed in the


CSFII 1994-96, 1998. Each food is identified by a unique 8-digit


food code in the CSFII data (Record type 30). An additional


3,913 variations of these foods were also reported and identified


by a unique 6-digit modification code. Appendix B, section 3.3.3


(\document\appendb.wpd) contains a discussion about food codes


and descriptions. A complete list of food codes and modification


codes are included in the directory \support. 


3.3 EPA list of food commodities


EPA provided ARS with the EPA Food Commodity Vocabulary for this


project. It is a list of over 540 commodity items for use in


estimating human exposure to pesticide residues through food


consumption. The list, which includes foods, beverages, and


water, is included as a WordPerfect document on this CD-ROM


(\epa_comm\foodvoc.wpd). It lists the name of each EPA


commodity, its EPA code, and a description of the weight basis


for the commodity. For example, the code for the commodity


“Apple, juice” is 11000100 and the weight basis is “weight of


juice at single strength (or standard dilution).” Many foods


have separate entries and codes on the list to represent baby


foods. For example, “Apple, juice, baby food,” has a separate


entry with a unique code, 11000101.


A discussion by EPA of the basis for selecting the various


commodities is included in the EPA Food Commodity Vocabulary. In


general, items on the list (other than water) can be categorized


by the following different types: 

Commodity type Example 

Food (no further description) Blackberry 

Food with distinguishing characteristic Apple, peeled fruit 

Food part Turnip, tops 

Food, processing specific Peach, dried 

Food component Milk, fat 

Food consumed as baby food Carrot, baby food 

Some foods, for example, peach, may be included on the list as


several different commodity types:


Attachment 1-12 
EPA Code Commodity Description 

12002600 Peach 

12002601 Peach, baby food 

12002610 Peach, dried 

12002611 Peach, dried, baby food 

12002620 Peach, juice 

12002611 Peach, juice, baby food 

3.4 Translating CSFII to EPA food commodities


Four major steps were involved in translating the CSFII 1994-96,


1998 to EPA commodities. 1) Many of the foods reported in the


survey were mixtures, and the first step was to define those


foods in terms of their ingredients and amounts. 2) Next, each


food or ingredient was assigned to its appropriate EPA commodity,


or commodities, including converting amounts to meet EPA’s weight


basis requirements when necessary. The assignments were


documented in a Food-Code-to-Commodity Translation File. 


3) The Food-Code-to-Commodity Translation File was then used in


conjunction with the food intake data from CSFII 1994-96, 1998 to


produce commodity intake estimates. During this step, each


record type 30 (food intake data record) was translated to


represent consumption of specific EPA commodities. 4) Finally,


consumption of each EPA commodity was totaled into a daily amount


for each sample person and expressed in terms of grams consumed


per kilogram of body weight. Two-day averages of commodity


intakes were calculated for sample persons providing two days of


food intake.


The lower limit for representing intake of a commodity was


0.000001 grams per kilogram of body weight per day. Any daily


amount less than .000001 grams, but greater than zero, was set to


this lower limit of daily consumption on the commodity intake


data files. The purpose of expressing the commodity intake data


to six decimal places was not to imply precision to that level. 


Expression to six decimal places was requested by EPA in order to


ensure that intakes of very small amounts would not be


overlooked. See section 3.7.20 for discussion of EPA’s special


requirement to include commodities consumed in very small


amounts. See section 1.3 for a description of the Food Commodity


Intake Data file. 


Additional details regarding preparation of the Food-Code-to-


Commodity Translation file are provided below.


3.5 Defining mixtures in terms of ingredients


During processing of CSFII intake data, recipes had been


identified for many of the food mixtures for the purpose of


estimating nutrient content. These recipes appear on the CSFII


CD-ROMs in the Recipe Database (7c). A discussion of the Recipe


Attachment 1-13 
Database can be found in Appendix B, section 3.3.5


(\document\appendb.wpd). The recipes are considered


“representative,” meaning they were not exact for every sample


person, nor were they constructed for the purpose of estimating


consumption of ingredients. Nevertheless, the recipes had been


carefully developed and refined over many years for USDA food


consumption surveys; and prior to the CSFII 1994-96, 1998 they


had been reviewed and updated to reflect the food marketplace at


that time. 


These recipes were used as the starting point for the translation


project. EPA identified several situations where changes to the


recipes would provide a broader representation of commodity


sources. Such changes were incorporated when ingredients were


assigned to the commodities. These situations are discussed


below in Section 3.7, “Special Requirements for Assigning Foods


and Ingredients to EPA Commodities.” 


When a recipe ingredient was also a mixture (e.g., bread crumbs),


its ingredient amounts were estimated for this project using


procedures described in Appendix B, section 3.3.5


(\document\appendb.wpd). Ingredient amounts in brand-name


products were estimated based on label information using


procedures previously described by Marcoe and Haytowitz (1).


During CSFII 1994-96, 1998 food coding, a recipe could be


modified to match more closely the foods eaten by sample persons. 


There were three main purposes for recipe modifications: to


record the type of fat (for example, butter); the type of milk


(for example, skim milk); and the amount of water or milk used to


dilute foods (for example, condensed soup). Modified recipes are


included in the recipe database and were used in this


translation. 


3.6 Assigning foods and ingredients to EPA commodities


3.6.1 Matching USDA food codes to EPA commodities


Most CSFII food codes were matched to one or more commodity items


on EPA’s list. Food codes which represented mixtures were


assigned based on their recipe ingredients. Ingredients which


were not assigned to commodities included water, which was


assigned to commodities by EPA’s Office of Water, and several


ingredients which were not included on EPA’s list: salt,


leavening, artificial sweeteners, pectin, added nutrients, and


some forms of alcohol.


Commodity assignments for brand-name foods were based on


ingredients listed on labels from the marketplace at the time of


the CSFII 1994-96, 1998. CSFII food codes frequently represented


more than one brand of a product. Assignments were based on


ingredient listings of national brands, and frequently


Attachment 1-14 
represented a blending of ingredient lists for two or more


brands. 


Modified food starch is an ingredient in many processed foods. 


When the type of modified food starch was not specified on the


label, it was assigned as 95% to “Corn, field, starch,” and the


remaining 5% was assigned in equal amounts to the following


commodities: “Cassava,” “Potato, flour,” “Rice, flour,” and


“Wheat, flour.” Maltodextrin was assigned to commodities in the


same proportions as modified food starch. Soya lecithin was


assigned to “Soybean, oil.” Carrageenan, alginate, and agar were


assigned to “Seaweed.” 


3.6.2 Converting weights of foods and ingredients


As stated above, conversions were made as needed to conform to


the weight basis of the commodity as defined on the EPA list of


commodities. For example, the EPA commodity “Corn, field, syrup”


is based on the weight of the syrup; therefore, when corn syrup


solids was an ingredient within a mixture, the weight was


converted to the syrup form (100 grams of corn syrup solids = 131


grams of corn syrup). Factors used in this type of adjustment


are based on differences between total solids of the consumed


foods or ingredients and total solids for the commodities, and


were derived by dividing the percent total solids of the food or


ingredient by the percent total solids of the corresponding


commodity. Values for total solids were derived from the 1994-96


Survey Nutrient Database (7b) as follows: 100 - percent moisture


content = percent total solids.


Since grain commodities are defined as the dry weight of grain or


flour, they frequently required weight adjustments to account for 


the amount of moisture which is absorbed during cooking. For


example, rice absorbs moisture when it is steamed, so the weight


of cooked rice was adjusted downward to eliminate the weight of


the water.


Sometimes grain ingredients, such as flour, actually lose


moisture during food processing. For example, ready-to-eat


breakfast cereals may lose moisture during processing, so the


weight of flour ingredients were adjusted upward to approximate


their original weights before the moisture loss.


Examples of how weights were adjusted to account for differences


in total solids between foods as consumed and foods as defined in


EPA’s commodity list:


Attachment 1-15 
Example 1:


Food or ingredient = Rice, cooked


Total solids = 31.56 percent


EPA-defined Commodity = Rice, dry weight of grain


Total solids = 88.38 percent


Conversion factor = 31.56/88.38 = 0.357


Example 2:


Food or ingredient = Orange juice concentrate Total


solids = 42.15 percent


EPA-defined Commodity = Orange juice (weight of single 


strength juice)


Total solids = 11.90 percent


Conversion factor = 42.15/11.90 = 3.542


When the weight basis of a commodity was identified as “edible


portion as consumed,” the amount was assigned according to the


form in which the food was consumed. For example, if a carrot


was consumed raw, it was assigned according to its raw weight. 


If it was consumed cooked, assignment was made according to the


cooked weight. If the carrot was consumed cooked, but it appeared


in the recipe ingredient list as a raw carrot, its weight was


adjusted to the cooked form before assignment. Adjustment


factors embedded within the recipes were used for this type of


adjustment. These factors were based on data from Agriculture


Handbook No. 102, Food Yields Summarized by Different Stages of


Preparation (2); Agricultural Handbook No. 8, Composition of


Foods...Raw, Processed, Prepared (6), or unpublished data from


ARS food laboratories.


3.6.3 CSFII Food-Code-to-Commodity Translation File


A CSFII Food-Code-to-Commodity Translation File was prepared to


identify the commodity assignments (excluding water) for each


food code from the CSFII 1994-96, 1998. Some food codes have


more than one set of commodity values on this database. This


occurs when a modification was made to the food code’s


corresponding recipe during the survey. (See section 3.2 above.) 


To identify one set of commodity values in the database, use the


8-digit food code in conjunction with the 6 digit modification


code. The translation file is included on this CD-ROM in


\support. 


In this file, information is expressed as the amount in grams of


each commodity per 100 grams of a CSFII food. However, the


amounts of all commodities associated with one food code, plus


modification code, may not total exactly to 100 grams. This


occurs for several reasons: 1) Commodities are not always


expressed in the same forms as foods are consumed; for example,


the commodity form for white rice is “dry form of grain.” As a


Attachment 1-16 
result, 100 grams of food code 56205010, “Rice, white, cooked,”


translate to only 35.7 grams of the EPA commodity, “Rice, white,


dry form of grain.” See section 3.6.2 above for examples of how


weights of foods and ingredients were adjusted during the


translation. Additional examples of adjustments include: 100


grams of frozen orange juice concentrate translate to 354 grams


of single strength orange juice, 100 grams of corn syrup solids


translate to 131 grams of corn syrup; 100 grams of dry egg powder


translate to 393 grams of raw egg; and 100 grams of cooked


macaroni translate into 37.9 grams of wheat flour. Anytime


grains, concentrated juices, corn syrup solids, or dry egg powder


are identified as an ingredient within a CSFII food, the total of


commodities for the CSFII food code will likely add to more or


less than 100 on the Food-Code-to-Commodity translation file. 2)


Water as a commodity was excluded from this database. For


example, the weight of water used to reconstitute canned


condensed soups is not represented in this data file. When water


is an ingredient in a CSFII food, commodities will add to less


than 100 on the Food-Code-to-Commodity Translation file. 3) Some


food constituents were not assigned to commodities: salt,


leavening, artificial sweeteners, pectin, added nutrients, and


some forms of alcohol. Anytime these foods are ingredients in a


CSFII food, commodities will add to less than 100 on the Food


Code-to-Commodity Translation file.


The smallest amount of a commodity that was included in this


database was 0.001 grams per 100 grams of the CSFII food. 


Commodity amounts lower than this limit, but greater than zero,


were set to 0.001 grams. Three decimal places were selected for


expression of the commodity amounts to accommodate an EPA


requirement that the presence of commodities in very small


amounts be represented in this database. See section 3.7.19 for


discussion of EPA’s special requirement to include commodities


consumed in very small amounts. 


3.7 Special requirements for assigning foods and ingredients to


EPA food commodities


In addition to converting amounts of foods and ingredients to


account for weight bases specified in the EPA food commodity


list, several special requirements were established to guide the


assignment of foods and ingredients to commodities. These


requirements, which are listed below, were based either on


descriptions of foods within the EPA food commodity list, or on


communications between EPA and ARS. 


3.7.1 Alcoholic beverages


Rum was assigned to the commodity “Sugar, cane, sugar,” adjusted


for the difference in total solids. 


Beer was assigned 9.6 percent to the commodity “Barley, flour”


Attachment 1-17 
0.7 percent to “Hop,” and 4.1 percent to “Rice, flour.” The


remainder was considered non-commodities, or was water and was


excluded from this database.


Wine was assigned to the commodity “Grape, wine and sherry,”


except rice wine was assigned to the commodity “Rice, white, dry


form of grain,” adjusted for the difference in total solids. 


Other forms of alcohol were not required by EPA to be assigned to


commodities.


3.7.2 Baby foods


Commodity codes are specific for commercial baby foods. All


ingredients from food codes identified as baby foods were


assigned to commodities specified as “from baby food.” CSFII


food codes representing commercial baby foods are listed in Table


1.


3.7.3 Chips


The potato portion of regular potato chips was assigned to the


commodity, “Potato, chips.” The potato from restructured potato


chips was assigned to “Potato, dry.” Corn from corn chips was


assigned to the commodity, “Corn, field, meal.” Apple from apple


chips was assigned to “Apple, dried.” Banana from banana chips


was assigned to “Banana, dried.” The fat ingredients from all


these types of chips were assigned to commodity oils as described


in the section 3.7.6 on fats and oils. 


3.7.4 Coffee and tea


Commodities for coffee and tea are:


Coffee, roasted bean


Coffee, instant


Tea, dried (leaf)


Tea, instant


Prepared coffee and tea were assigned to their dry commodity


forms, based on amounts typically recommended for preparation. 


Coffee not specified as brewed or made from instant was assigned


90% “Coffee roasted bean,” and 10 percent “Coffee, instant” (4). 


Tea not specified as brewed from leaves or made from instant was


assigned 86 percent “Tea, dried (leaf)” and 14 percent “Tea,


instant” (13). Herbal teas were assigned to the commodity,


“Herbs, other.” 


3.7.5 Egg, dried


Dried egg ingredients (whole, yolk, or white) were assigned to


the commodities for their fresh forms, adjusted for differences


Attachment 1-18 
in total solids. 


3.7.6 Fats and oils


3.7.6.1 Vegetable oils


When CSFII food descriptions represented one specific brand name,


oil ingredients listed on the labels were assigned to their


corresponding commodities; for example, coconut oil was assigned


to the commodity “Coconut, oil.” 


When a label listed more than one type of oil, for example,


coconut and/or palm oil, and no information was available on the


proportion of each oil in the food product, assignment was made


according to their relative proportions in the food supply. When


labels from more than one brand were used to identify


ingredients, all identified oils were used in the assignments.


Information on relative proportions of certain oils in the food


supply was used in situations as described below. These relative


proportions are based on unpublished data from the USDA Center


for Nutrition Policy and Promotion’s Food Supply Database. Their


data were based upon information from the USDA’s Economic


Research Service and Foreign Agricultural Service (3,10,14).


Percent Oil 

74.6 Soybean

 8.8 Corn

 6.6 Cottonseed

 3.7 Rapeseed (canola)

 3.6 Olive

 2.4 Peanut

 0.3 Sunflower

 0.001 Safflower

 0.001 Sesame 

When specific oil ingredients could not be determined,


assignments were based on composites from the above list of oils,


based on relative proportions of each oil. Judgments were made


for each recipe regarding the most appropriate oils to include,


based on information from popular and ethnic cookbooks, and


consultation among food and nutrition specialists. 


Two composites were used in assigning shortening to oil


commodities. One was for commercial foods (soybean and


cottonseed oils) and the other for home-prepared foods (soybean


and rapeseed oils). Proportions of each oil within the


composites were based on the above food supply data. For foods


Attachment 1-19 
that could be either commercial or home prepared, assignments


were made 50 percent to each composite. Oil sources for


shortening were based on information from the Nutrient Data


Laboratory(9). 


3.7.6.2 Margarine and Butter 


In most instances, the oil ingredients in margarine and


margarine-like spreads were assigned to commodities according to


the following proportions (3):


Percent Oil


81.7 Soybean


10.2 Corn


 5.7 Rapeseed (canola)


 0.8 Cottonseed


 0.8 Safflower


 0.8 Sunflower


However, when labels of manufactured foods identified a specific


type of margarine as an ingredient, e.g., corn oil margarine, the


oil ingredient was assigned to the specific oil source. 


In the Survey Recipe Database, recipes frequently contained


either margarine or butter as a “default” ingredient (specific


source of fat not known by the survey respondent). In those


cases, the butter or margarine ingredient was divided into 70


percent regular margarine and 30 percent butter before assignment


to EPA commodities, based on the proportion of those foods in the


food supply (3). Recipe modifications, when the respondent


specifically named margarine or butter as the ingredient, were


not changed.


Butter was assigned to milk commodities as discussed in section


3.7.13. 


3.7.7 Fish


EPA commodities for fish include:


Fish, freshwater finfish


Fish, freshwater finfish, farm raised


Fish, saltwater finfish, tuna


Fish, saltwater finfish, other


Fish, shellfish, crustacean


Fish, shellfish, mollusc


Species of fish and seafood were assigned to their appropriate


EPA commodities. Catfish was assigned 100 percent to the


commodity, “Fish, freshwater finfish, farm raised.” Trout was


assigned 50 percent to the commodity “Fish, freshwater, finfish,”


Attachment 1-20 
and 50 percent to “Fish, freshwater finfish, farm raised” (9). 


No other species reported in the CSFII 1994-96, 1998 were


assigned to the “farm raised” category. 


3.7.8 Fruit


Fruit juices and nectars were assigned to commodities for their


respective juices, when such commodities existed, adjusted to the


weight of single-strength juice. When juice was not listed as a


separate commodity for a fruit, juice was assigned to the regular


form of the fruit. Sweeteners from sweetened juice and juice


drinks were assigned to the appropriate commodities for


carbohydrate sweeteners.


Fruits consumed dried were assigned to commodities for the dried


forms. Re-hydrated or stewed dried fruits were required by EPA


to be assigned to the succulent forms.


The recipe for food code 62101050, “Fruit mixture, dried (mixture


includes three or more of the following: apples, apricots, dates,


papaya, peaches, pears, pineapples, prunes, raisins),” was


assigned to all 9 commodities listed in the description, in equal


proportions. 


Sweeteners in canned fruit were assigned to the appropriate


commodities for carbohydrate sweeteners.


In the CSFII 1994-96, 1998, yogurt items containing fruit were


coded under umbrella codes described as “Yogurt, fruit variety.” 


The kind of fruit was not specified. For this project, the fruit


ingredients were assigned in equal proportions to the commodities


“Strawberry,” “Blueberry,” “Cherry,” “Peach,” “Banana,” and


“Raspberry.” The yogurt portion was assigned to milk as


described in section 3.7.13. 


In CSFII ice cream and ice milk were coded as either “chocolate,”


or “flavors other than chocolate.” For ice cream or ice milk


coded as “flavors other than chocolate,” an amount of .001 grams


per 100 grams of food was assigned to each of the following


commodities: “Strawberry,” “Blueberry,” “Cherry,” “Peach,”


“Banana,” and “Raspberry.” 


3.7.9 Gelatin, dry


Dry gelatin as an ingredient in nonmeat foods was assigned in


equal proportions to the commodities “Beef, meat byproducts” and


“Pork, meat byproducts.” As an ingredient in beef-containing


foods, e.g., beef broth, dry gelatin was assigned to “Beef, meat


byproducts” only. As an ingredient in pork-containing foods, it


was assigned to “Pork, meat byproducts” only.


3.7.10 Legumes 


Attachment 1-21 
Sprouted or cooked legumes (dry beans and peas) were assigned to


their dry forms. 


Beans described as white beans were assigned 50 percent to the


commodity “Bean, navy” and 50 percent to the commodity “Bean,


great northern.” 


3.7.11 Meat


3.7.11.1 Beef. EPA commodities for beef are:


Beef, fat


Beef, kidney


Beef, liver


Beef, meat


Beef, meat byproducts


Beef, meat, dried


Each beef item, other than kidney, liver, and byproducts, was


separated into two components. The total fat component, as


specified in the Survey Nutrient Database (7c), was assigned to


the commodity “Beef, fat.” After subtracting total fat, the


remainder of an item was assigned to the commodity “Beef, meat.” 


Beef kidney, liver, and byproducts were assigned to their


respective commodities, which included both their fat and nonfat


components. See section 3.7.11.4 regarding kidney. Beef


byproducts are defined in the EPA list of commodities, located in


the \epa_comm.


Dried chip beef and beef jerky, including both fat and nonfat


components, were assigned to the commodity “Beef, meat, dried.” 


Veal, bison, and buffalo items were included in beef commodities. 


3.7.11.2 Game. Bear, caribou, deer, frog, and squirrel were


assigned to the EPA commodity, “Meat, game.” Other game meats


(armadillo, beaver, elk, groundhog, moose, snake, opossum, and


raccoon) listed in the description of this commodity were not


reported in CSFII 1994-96, 1998.


3.7.11.3 Goat. EPA commodities for goat are:


Goat, fat


Goat, kidney


Goat, liver


Goat, meat


Goat, meat byproducts


No goat kidney, liver or meat byproducts were reported in the


CSFII 1994-96, 1998. Other goat items were separated into two


components. The total fat component, as specified in the Survey


Attachment 1-22 
Nutrient Database (7b), was assigned to the commodity “Goat,


fat.” After subtracting total fat, the remainder was assigned to


the commodity “Goat, meat.” 


3.7.11.4 Kidney. The CSFII 1994-96, 1998 did not distinguish


between different types of kidney. Kidney was assigned 56


percent to “Beef, kidney,” 43 percent to “Pork, kidney,” and 1


percent to “Sheep, kidney” (4). 


3.7.11.5 Meat, not further specified. Some foods were described


as containing meat without being specific for the type of meat,


for example, “Meat Pie, Not Further Specified.” The Survey


Recipe Database entries for those foods usually contained no more


than one meat ingredient. For most of those items, the meat


ingredient was assigned as 56 percent beef, 43 percent pork, and


1 percent lamb. Recipes for processed meats and pizzas were not


changed. These proportions were based on USDA food supply data


for 1991-1995 (4). 


For foods described as containing meat and/or poultry, see


section 3.7.14.5. 


3.7.11.6 Pork. EPA commodities for pork are:


Pork, fat


Pork, kidney


Pork, liver


Pork, meat


Pork, meat byproducts


Pork, skin


Pork products, other than pork kidney, liver, byproducts, and


skin, were separated into two components. The total fat


component, as specified in the Survey Nutrient Database (7b), was


assigned to the commodity “Pork, fat.” After subtracting total


fat, the remainder of the pork was assigned to the commodity


“Pork, meat.” Pork includes both fresh and cured forms. Pork


kidney, liver, skin, and byproducts were assigned to their


respective commodities. See section 3.7.11.4 regarding Kidney. 


Pork byproducts are defined in the EPA commodity list in


\epa_comm. 


3.7.11.7 Rabbit. The commodity “Rabbit, meat” includes both fat


and nonfat components. It also includes both wild and domestic


forms of rabbit.


3.7.11.8 Sheep. EPA commodities for sheep are:


Sheep, fat


Sheep, kidney


Sheep, liver


Sheep, meat


Attachment 1-23 
Sheep, meat byproducts


In CSFII 1994-96, 1998, food descriptions for sheep products were


identified as “lamb (including mutton).” These products, other


than kidney and byproducts, were separated into two components.


The total fat component, as specified in the Survey Nutrient


Database (7b), was assigned to the commodity “Sheep, fat.” After


subtracting total fat, the remainder was assigned to the


commodity “Sheep, meat.” See section 3.7.11.4 regarding Kidney. 


Sheep byproducts are defined in the EPA commodity list, located


in \epa_comm.


3.7.12 Meat and Poultry, Processed 


CSFII foods and ingredients for processed meats were assigned to


commodities in proportions that reflected the amount of meat,


fat, organ meats, and other ingredients allowed by the USDA meat


and poultry standards (11, 12, 12a, 12b).


3.7.13 Milk


EPA commodities for milk are:


Milk, fat


Milk, nonfat solids


Milk, water


Milk, lactose


Foods assigned to milk commodities included dry, fluid, and


concentrated forms of milk, cheese, yogurt, cream, and butter. 


Goat milk was included, but not soy milk. The milk items were


divided into fat, nonfat solids, and water based on the 1994-96


Survey Nutrient Database (7b) and were assigned to the


appropriate commodities. The nutrient database entries for total


fat and moisture were used to determine proportions of the


commodities “Milk, fat” and “Milk, water.” “Milk, nonfat solids”


was the remainder after subtracting total fat and moisture. For


example, commodity assignments for 100 grams of whole milk were


3.34 grams to “Milk, fat,” 87.99 grams to “Milk, water,” and 8.67


grams (100-(3.34 + 87.99)) to “Milk, nonfat solids.”


Lactose identified as a separate ingredient in a commercial baby


food was assigned to the commodity “Milk, lactose.” Lactose as


an ingredient most often appeared in infant formulas. The lactose


from non-baby-foods and lactose inherent in milk and milk


products was included in the commodity “Milk, nonfat solids.”


3.7.14 Poultry


3.7.14.1 Chicken. EPA commodities for chicken include:


Chicken, fat


Attachment 1-24 
Chicken, meat


Chicken, skin


Chicken, liver


Chicken, byproducts


The total fat components of both chicken flesh and chicken skin


were assigned to the commodity “Chicken, fat.” The nonfat


component of chicken flesh was assigned to “Chicken, meat.” The


nonfat component of chicken skin was assigned to the commodity


“Chicken, skin.” Chicken neck was assigned to “Chicken,


byproducts.”


The ratio of flesh to skin for different poultry pieces was


obtained from Agricultural Handbook No. 8 (6). The amount of


total fat present in different poultry pieces was based on the


1994-96 Survey Nutrient Database (7b). 


3.7.14.2 Turkey. EPA commodities for turkey are the same as for


chicken (fat, meat, skin, liver, and byproducts). Assignments of


fat and nonfat components of turkey flesh and skin were made the


same as for chicken. The commodity, “Turkey, byproducts,”


included turkey neck. For turkey items described as dark meat, a


portion was assigned to turkey byproducts to represent turkey


neck. 


3.7.14.3 Poultry other than chicken and turkey. EPA commodities


for “Poultry, other” are the same as for chicken (fat, meat,


skin, liver, and byproducts). Assignments of fat and nonfat


components of the poultry pieces were similar to assignments for


chicken. Dove, duck, partridge, pheasant, pigeon, quail, and


squab were assigned to the “Poultry, other” commodities. Emu,


goose, guinea hen, and ostrich were also listed by EPA as


“Poultry, other,” but those foods were not reported in the


surveys. 


3.7.14.4 Mixtures described as containing chicken or turkey. In


the CSFII 1994-96, 1998 several food mixtures were described as


containing either chicken or turkey, for example, “Chicken or


turkey teriyaki.” The Survey Recipe Database entries usually


contained chicken, but not turkey, as an ingredient for those


foods. Therefore, for most of these foods the chicken ingredient


was assigned to commodities for both chicken and turkey in the


proportions of 77 percent chicken and 23 percent turkey. This


distribution was based on chicken and turkey food supply data for


the years 1990-1994 (3). 


3.7.14.5 Mixtures described as containing meat and/or poultry. 


In the CSFII 1994-96, 1998 several food mixtures were described


as containing either meat or poultry, for example, “Tamale with


meat and/or poultry.” Assignment of meat and/or poultry


ingredients in most of these foods were handled in two steps. 


First, they were divided into 50 percent meat and 50 percent


Attachment 1-25 
poultry. Then, meat was divided into 56 percent beef, 43 percent


pork, and 1 percent lamb. (See section 3.7.11.8, Meat, not


further specified.) Poultry was divided into 77 percent chicken


and 23 percent turkey (3).


3.7.15 Spices and herbs


The EPA Food Commodity Vocabulary lists some spices and herbs as


separate commodities. Others are grouped together in “Spices,


other” and “Herbs, other.” Complete listings of spices and herbs


included in these commodities can be found in the Food Commodity


Vocabulary’s Appendices B and C in \epa_comm\foodvoc.wpd. Spices


and herbs usually were not included in CSFII recipes, but were


added before commodity assignments were made.


3.7.16 Sweeteners, carbohydrate 


EPA commodities for carbohydrate sweeteners are:


Beet, sugar, molasses


Beet, sugar


Corn, field, syrup


Honey


Maple sugar


Maple syrup


Sorghum syrup


Sugarcane, molasses


Sugarcane, sugar


Sugar and molasses were not identified by source (sugarcane vs.


sugar beet) in the CSFII 1994-96, 1998. Therefore, sugar and


molasses were assigned to the commodities “Sugarcane, sugar” and


“Beet, sugar,” according to the proportions of 56 percent


sugarcane and 44 percent sugar beet. These proportions reflect an


estimate of refined sugar use in the continental United States


from 1990-94 (15). Sugar included table sugar, confectioner’s


sugar, light brown sugar, dark brown sugar, raw sugar, and


caramelized sugar. 


Dry corn syrup solids, dextrose, and fructose were assigned to


the commodity of “Corn, field, syrup,” adjusting from the dry


forms to liquid based on differences in total solids. Sugarcane


syrup was assigned to the commodity “Sugarcane, sugar,” adjusting


from the liquid to dry form. 


Corn syrup, maple sugar, maple syrup, sorghum syrup, and honey


were assigned directly to their respective commodities. No


adjustments were necessary.


Attachment 1-26 
3.7.17 Vegetables


Dried vegetables and vegetable juices were assigned to


commodities for their respective dried or juice forms when such


commodities were listed. If dried vegetables or vegetable juices


were not present in the commodities list, they were assigned to


their respective fresh vegetable forms. No adjustments were made


to their weights. 


3.7.18 Water


The EPA Food Commodity Vocabulary includes the following entries


for water:


Water, dilution, source not specified


Water, tapwater- direct (drinking)


Water- indirect (cooking)


Water, bottled water


Water, commercial beverage


Water in foods and beverages is being assigned to the above


categories by the EPA Office of Water. The information is not a


part of this database at this time.


3.7.19 Commodities not consumed 


Several commodities on EPA’s list were not consumed at all during


the CSFII 1994-96, 1998 and do not appear in the intake database. 


Table 2 lists these commodities. Several commodities were


included with food codes for similar foods. For example, the


description for food code 53304000 is “Pie, blueberry, two crust


(include huckleberry).” In this latter situation, EPA requested


that a small portion of the food code be assigned to the


“included” commodity, if the commodity was not covered in any


other food-code-to-commodity translation. Since huckleberry had


received no other assignment, a small portion of food code


53304000 was assigned to the commodity “Huckleberry.” In


accordance with EPA’s request, assignment was 0.001 grams of


“Huckleberry” per 100 grams of pie (the smallest amount the Food


Code-to-Commodity Translation File could accept).


3.7.20 Special requirement to include commodities consumed in


very small amounts


EPA requested that data processing procedures be established to


ensure that commodities consumed in very small amounts be


represented in the resulting database files. While overstating


precision is a concern, the possibility of small amounts rounding


to zero was an even greater concern to EPA. Therefore, data in


the Food-Code-to-Commodity Translation File are presented to 3


decimal places, allowing an estimate as small as 0.001 to be


Attachment 1-27 
included. Data in the commodity intake file are presented to 6


decimal places, allowing intake estimates as small as 0.000001


gram per kilogram of body weight to be included. These


expressions are not intended to suggest that data can be


estimated to these small amounts with such precision. 


3.8 Notes about reviewing the Food-Code-to-Commodity Translation


File


When reviewing the Food-Code-to-Commodity Translation File, some


unexpected findings may be observed. Reasons for these findings


can be found in the above discussions on matching USDA food codes


to EPA commodities (section 3.6.1), converting weights of foods


and ingredients (section 3.6.2), and special requirements for


assigning foods and ingredients to EPA commodities (section 3.7). 


Examples of unexpected findings are listed below:


1. The following five commodities are present in small amounts


in many foods: “Cassava,” “Corn, field, starch,” “Potato, flour,”


“Rice, flour,” and “Wheat, flour.” This occurs because modified


food starch is frequently listed as an ingredient in processed


foods. When the source of the starch was not identified, it was


assigned to these commodities.


2. “Barley, flour,” is found in small amounts in many foods. 


This occurs when malt was listed as an ingredient.


3. “Beef, byproducts” and Pork, byproducts” are found in non-


meat foods. Presence of these commodities in non-meat foods


usually results from the presence of gelatin as an ingredient in


the food. For example, gelatin is an ingredient in marshmallows. 


Therefore, when marshmallows are used in other foods, meat


byproducts occur in the commodity assignments for those foods. 


4. “Beef, byproducts” are found in ground beef and in foods


containing ground beef as an ingredient. Because the Standards


of Identity for ground beef allow for inclusion of beef cheeks, a


small proportion of ground beef was assigned to beef byproducts.


5. Small amounts of “Soybean, oil” may be found in foods not


expected to have a fat ingredient. This results from the presence


of soya lecithin as an ingredient.


6. Many foods were assigned to nine different oil commodities


(per EPA requirement), including small amounts of “Olive, oil”


and “Sesame, oil.” This occurs because a composite of oils was


used when the specific type of oil was unknown, such as in home


prepared foods (see section 3.7.6).


7. Fruit juices are present in concentrates at greater than 100


percent. This occurs because, upon assignment to a commodity,


Attachment 1-28 
the juice concentrate was adjusted to the corresponding weight of


the reconstituted product. Thus, 100 grams of orange juice


concentrate translates to 354.2 grams of the commodity “Orange,


juice.”


3.9 Limitations of the Food Commodity Intake Database


Users of the Food Commodity Intake Database should understand the


nature of food survey data, as well as the assumptions required


for translating survey food codes into EPA-defined commodities. 


This information should be considered in the interpretation of


analyses based on the Food Commodity Intake Database.


--The food intake surveys were based on ability of sample


persons, with assistance of probing by trained interviewers, to


recall the foods they ate during the 24-hour period prior to the


day of their interview. 


--Amounts of foods consumed were estimated, involving two steps. 


First, the sample persons provided a description of the volumes,


using standard survey measurement aids or label information. 


Then, the volumes were converted to grams using a database of


weight/volume relationships. 


--Ingredient amounts for commercial products were estimated. 


Ingredients and amounts for other food mixtures were based on


representative recipes, usually from popular cookbooks but from


regional or specialty cookbooks when necessary. The recipes 


were not specific for each sample person. 


--Sometimes ingredients were proportioned among commodities based


on food supply information, for example, oils and sweeteners (see


section 3.7 above). 


--Body weights were self reported. Missing body weights were


assigned default values (see section 4.5.2).


Attachment 1-29 
3.10 References


(1) Marcoe, K.K., and D.B. Haytowitz. 1993. Estimating nutrient


values of mixed dishes from label information. Food Technology


47(4):69-75.


(2) Matthews, R.H., and Y.J. Garrison. 1995. Food Yields


Summarized by Different Stages of Preparation. U.S. Department of


Agriculture, Agricultural Research Service, Agriculture Handbook


No. 102. 


(3) Putnam, J.J. and J.E. Allshouse. 1996. Food Consumption,


Prices, and Expenditures, 1996: Annual Data 1970-94. U.S.


Department of Agriculture, Economic Research Service, Statistical


Bulletin No. 928.


(4) Putnam, J.J. and J.E. Allshouse. 1997. Food Consumption,


Prices, and Expenditures, 1970-95. U.S. Department of


Agriculture, Economic Research Service, Statistical Bulletin No.


939.


(5) Tippett, K.S., and Y.S. Cypel (eds.). 1998. Design and


Operation: The Continuing Survey of Food Intakes by Individuals


and the Diet and Health Knowledge Survey, 1994-96. U.S.


Department of Agriculture, Agricultural Research Service,


Nationwide Food Surveys Report No. 96-1.


(6) U.S. Department of Agriculture, Agricultural Research


Service. 1976-92. Composition of Foods...Raw, Processed,


Prepared. Agriculture Handbook No. 8, Revised Sections 1-22. 


(7) U.S. Department of Agriculture, Agricultural Research


Service. 1998. 1994-96 Continuing Survey of Food Intakes by


Individuals and 1994-96 Diet and Health Knowledge Survey.


National Technical Information Service, 5285 Port Royal Road,


Springfield VA 22161;(703)487-4650. CD-ROM: Accession no. PB98­


500457.


(7a) ----. Food Coding Database for the 1994-1996 Continuing


Survey of Food Intakes by Individuals. On CD-ROM: 1994-96


Continuing Survey of Food Intakes by Individuals and 1994-96 Diet


and Health Knowledge Survey. NTIS Accession no. PB98-500457.


(7b) ----. Survey Nutrient Database for the 1994-1996 Continuing


Survey of Food Intakes by Individuals. On CD-ROM: 1994-96


Continuing Survey of Food Intakes by Individuals and 1994-96 Diet


and Health Knowledge Survey. NTIS Accession no. PB98-500457.


(7c)----. Survey Recipe Database for the 1994-1996 Continuing


Survey of Food Intakes by Individuals. On CD-ROM: 1994-96


Continuing Survey of Food Intakes by Individuals and 1994-96 Diet


and Health Knowledge Survey. NTIS Accession no. PB98-500457.


Attachment 1-30 
(8) U.S. Department of Agriculture, Agricultural Research


Service. 2000. Continuing Survey of Food Intakes by Individuals


1994-96, 1998. National Technical Information Service, 5285 Port


Royal Road, Springfield VA 22161;(703)487-4650. CD-ROM. NTIS


Accession no. PB2000-500027. 


(9) U.S. Department of Agriculture, Agricultural Research


Service, Nutrient Data Laboratory. 1998. Unpublished Data.


Personal communication between Nutrient Data Laboratory staff and


Food Surveys Research Group staff.


(10) U.S. Department of Agriculture, Economic Research Service.


July 1995. Oil Crops Yearbook.


(11) U.S. Department of Agriculture, Food Safety and Inspection


Service, Inspection Operations, Regulatory Programs. 1996. Food


Standards and Labeling Policy Book.


(12) U.S. Department of Agriculture, Food Safety and Inspection


Service. 1998. Animal and animal products: Approval of


substances for use in the preparation of products. Code of


Federal Regulations, Title 9, Pt. 318.7.


(12a)----. Animals and animal products: Definitions and standards


of identity or composition. Code of Federal Regulations, Title 9,


Pts. 319.1-319.881.


(12b)----. Animals and animal products: Poultry products


inspection regulations, Subpart P, Definitions and standards of


identity or composition. Code of Federal Regulations, Title 9,


Pts. 381.155-381.174. 


(13) U.S. Department of Agriculture, Foreign Agricultural


Service. 1995. Tropical Products: World Markets and Trade,


Circular Series FTROP 3-95, page 21, Table: “Tea: Sales by U.S.


Retail Stores, 1995 with Comparison”.


(14) U.S. Department of Agriculture, Foreign Agricultural


Service, Oilseeds and Products Division, Import and Export Data.


1998. Personal communication between Foreign Agricultural Service


and Center for Nutrition Policy and Promotion staff.


(15) U.S. Department of Agriculture, National Agricultural


Statistics Service. 1997. Agricultural Statistics 1997, page II­


20, Table 2-33.


(16) U.S. Environmental Protection Agency. 2000. The EPA Food


Commodity Vocabulary, Master List dated June 15, 2000. 


Table 1. CSFII food codes representing commercial baby foods


Attachment 1-31 
Food code 

or food code 

prefix Foods 

117----- * Infant formulas 

133----- Milk desserts 

14220000 Cottage cheese, with fruit 

20000070 Meat, not specified as to type 

20000090 Meat sticks, not specified as to type of meat 

217----­ Beef 

228----­ Pork 

234----­ Lamb or veal 

247----­ Poultry 

2518---­ Variety meats 

276----­ Meal, poultry, or fish mixtures with nonmeat items 

34-----­ Eggs 

53203050 Cookie, fruit 

53203100 Cookie 

53242250 Cookie, teething 

54350000 Crackers 

54408100 Pretzel 

578----­ Cereals 

585----­ Grain mixtures 

67-----­ Fruits, fruit juices, and fruit mixtures 

76-----­ Vegetables and mixtures mostly vegetable 

* All food codes with “117" in the first three digits.


Attachment 1-32 
Table 2. EPA food commodities not appearing in the intake


database


Acerola


Bean, broad, succulent


Broccoli raab


Buckwheat, flour


Burdock


Canistel


Cardoon


Celeriac


Celtuce


Cherimoya


Chickpea, flour


Citrus, oil


Crabapple


Cress, garden


Cress, upland


Elderberry


Feijoa


Fennel, Florence


Filbert, Oil


Goat, meat byproducts


Goat, kidney


Goat, liver


Hickory nut


Horse, meat


Jaboticaba


Jackfruit


Kohlrabi


Kumquat


Lemongrass


Spanish Lime


Loganberry


Longan


Loquat


Lychee, dried


Mamey apple


Parsley, turnip rooted


Pawpaw


Peppermint, oil


Pummelo


Quince


Quinoa


Radish, Oriental, tops


Radish, tops


Rape greens


Rye, flour *


Salsify, roots


Salsify, tops


Sapote


Sorghum


Soursop


Spearmint


Spearmint, oil


Sugar apple


Tree Tomato


Water, dilution, source NS** 


Water, tapwater-direct


(drinking) **


Water-Indirect (cooking) **


Water, bottled water **


Water, commercial beverage** 


Almond, oil-babyfood


Almond-babyfood 


Arrowroot, flour-babyfood


Basil, fresh leaves-babyfood


Coriander, leaves-babyfood


Coriander, seed-babyfood


Corn, field, meal-babyfood


Egg, white (solids)-babyfood


Ginger-babyfood


Guar, seed-babyfood


Guava-babyfood


Honey-babyfood


Papaya-babyfood


Passionfruit, juice-babyfood


Passionfruit-babyfood


Peach, dried-babyfood


Pepper, bell-babyfood


Plum, prune, dried-babyfood


Potato, tuber, w/peel-babyfood


Sesame, oil-babyfood


Sesame, seed-babyfood


Soybean, soy milk-babyfood


Triticale, flour-babyfood


Turkey, liver-babyfood


* Rye flour translated to EPA


Commodity “Rye, Whole grain”


**Water translated by EPA


Office of Water


Attachment 1-33 
4	 CHARACTERISTICS AND FORMATS OF THE FOOD COMMODITY INTAKE


DATABASE 


4.1 Introduction


The Food Commodity Intake Database contains several different


data files related to intake of over 500 EPA-defined commodities.


It is based on food consumption surveys conducted by the U.S.


Department of Agriculture’s Agricultural Research Service. The


surveys are the 1994-96 Continuing Survey of Food Intakes by


Individuals (CSFII 1994-96) and its Supplemental Children’s


Survey (CSFII 1998). The database was developed for use in


estimating human exposure to pesticide residues through food and


beverage intake.


The general file structure and information concerning the use of


the data are discussed in Sections 4.2 and 4.3. Section 4.4


contains the data file formats, which provide detailed


descriptions of all of the fields included in each file. The


field number, name, width, and type are given, along with a full


description of the field and its applications, valid values for


the field, and, if applicable, a reference to the question number


on the original CSFII questionnaire. Section 4.5 contains


miscellaneous notes about the data files.


4.2 Database Structure


The Commodity Intake Database is made up of 18 data files. These


data files are classified into four categories as follows: (1) 8 


Documentation Files, (2) 2 EPA Commodity Files, (3) 3 Commodity


Intake Data Files, and (4) 5 Supporting Data Files. Records in


the different data files can be linked or connected through the


key fields that are described Section 4.3.2. (See section 1


“\document\section1.wpd” for a tree diagram and summary


description of data files on this database.)


4.2.1 Documentation Files (files in directory “\document”)


Each of the 5 documentation sections and the 3 appendices listed


in the Table of Contents is contained in a separate WordPerfect


file, as indicated below.


Section1.wpd Table of contents, File names 

Section2.wpd Essential information 

Section3.wpd Methodology for translating food intakes 

into EPA commodity intakes 

Section4.wpd Data file characteristics and formats 

Section5.wpd Control counts 

Appenda.wpd Appendix A. EPA’s use of food consumption 

Attachment 1-34 
data in assessing dietary risk from


pesticides


Appendb.wpd	 Appendix B. CSFII 1994-96, 1998


Methodology (Section 3 from CSFII 1994­


96, 1998 CD-ROM documentation)


Appendc.wpd	 Appendix C. CSFII 1994-96, 1998 Sampling


Weights and Statistical Notes (excerpts


from sections 5 and 6 of the CSFII 1994­


96, 1998 CD-ROM documentation)


4.2.2 EPA Commodity Files (files in directory “\epa_comm”)


The Environmental Protection Agency (EPA) provided 2 files. Both


are included as WordPerfect documents:


foodvoc.wpd	 EPA commodity list


csffcm.wpd	 Procedures for assigning EPA cooked


status, food form, and cooking method


values to USDA food codes


4.2.3 Commodity intake data files (files in directory “\intake”)


The commodity intake data files include 2 files that were created


by translating the CSFII 1994-96, 1998 food intakes into EPA


commodities, and one file of sample person data. These files,


which are in ASCII delimited format, include:


comm9498.txt	 Commodity intakes for CSFII 1994-96, 1998


ffcm9498.txt	 Commodity intakes by cooked status, food


form, and cooking method for CSFII 1994­


96, 1998


smpl9498.txt	 Sample person data for CSFII 1994-96,


1998


4.2.4 Supporting data files (files in directory “\support”)


Five data files provide supporting documentation for the


translation of the CSFII 1994-96, 1998 intake data into EPA


commodities. These files, which are in ASCII delimited format,


are:


fc_comm.txt	 CSFII 1994-96, 1998 food codes linked to


EPA commodities (grams of commodities per


100 grams of food)


fcdesc.txt Food code descriptions


fcincl.txt Food code include statements (extensions


of food code descriptions)


fcscheme.txt Food code outline


moddesc.txt Descriptions of recipe modifications


Attachment 1-35 
---- -----------------------

4.3 General nature of the data


The data files that make up the Commodities Intake Database are


in the following formats:


Directory Format 

--------- ------

\Document WordPerfect 

\EPA_Comm WordPerfect 

\Intake ASCII delimited 

\Support ASCII delimited 

ASCII delimited fields are separated by the caret (^) symbol and


alpha-numeric/character fields are enclosed in tilde (~) marks. 


The majority of the data fields are numeric, but there are some


alpha-numeric text fields. The numeric fields include explicit


decimal points, and do not include leading zeros.


Some fields have "missing" values due to a nonspecific or absent


response, or due to the lack of the necessary data for


calculations. These fields are not left blank, but are filled


with codes to indicate the separate categories of "refused,"


"don't know," and "not ascertained," or sometimes


"indeterminable." The usual convention for a one column field is


a '7' for "refused," '8' for "don't know," and '9' for "not


ascertained." Two-column fields have values of '97,' '98,' and


'99' to represent these categories of “missing” values. 


Continuous fields may also have codes for "missing" values.


When a blank or null value is found in a field, it means that a


response or calculation for that field does not apply to a


particular situation. For example, the two-day sampling weight


fields will be blank for sample persons who have not provided two


days of intake. In general, if a skip pattern dictates that a


question should not be asked of a respondent, the corresponding


field on the record will be blank. Blank or Null fields appear


in the data files as two delimiters side by side (e.g., ^^).


4.3.1 Data File Characteristics


The ASCII delimited files have variable record lengths. The five 


ASCII delimited data files have the following record counts. 


File Total number of records


Attachment 1-36 
comm9498.txt 3,173,197 

ffcm9498.txt 5,781,938 

fcincl.txt 5,652 

smpl9498.txt 21,662 

fc_comm.txt 99,011 

fcdesc.txt 5,845 

moddesc.txt 3,902 

4.3.2 List of Key Fields 

Specific fields in each data file have been designated as “key”


fields to (1) ensure that each record is unique, and (2) allow


data in different files to be linked or joined. Following is a


list of fields designated as key fields:


HHID Household identification number


SPNUM Sample person number


DAYCODE Day of intake identifier


COM_CODE EPA commodity code


CKD_STAT EPA cooked status code


FD_FORM EPA food form code


CKG_METH EPA cooking method code


FOODCODE USDA food code


MODCODE Recipe modification code


4.3.3 Sampling weights


Note: For an in-depth discussion of weighting for the CSFII 1994­


96, 1998, see Appendix C in \document\appendc.wpd.


The “smpl9498.txt” data file contains several different sampling


weights. By using appropriate weights: (a) data for each survey


year (1994, 1995, 1996, or 1998) can be used separately; (b) data


for the 3 years of CSFII 1994-96 can be combined; or (c) data for


all 4 years of the surveys can be combined. In addition, weights


are present for using only one day of intake for each sample


person; other weights provide for the use of only sample persons


who provided 2 days of intake.


Attachment 1-37 
Use the annual sampling weight fields, in conjunction with the


year field, when data are to be analyzed for one survey year


(1994, 1995, 1996, or 1998). Annual sampling weight fields are:


WTA_DAY1 	 The annual day 1 sampling weight for all responding 


CSFII 1994-96, 1998 sample persons. This annual


sampling weight is used whenever the sample of


interest includes sample persons who provided the


first day of intake data regardless of whether they


provided the second day.


WTA_2DAY 	 The annual 2-day sampling weight for all CSFII 1994­


96, 1998 sample persons with two days of intake. 


This annual sampling weight is used whenever the


sample of interest includes only sample persons who


provided 2 days of intake data.


The 3-year sampling weight fields for CSFII 1994-96 are:


WT3_DAY1 	 The 3-year day 1 sampling weight for all responding


CSFII 1994-96 sample persons. This 3-year sampling


weight is used whenever the sample of interest 


includes sample persons who provided the first day of


intake data regardless of whether they provided the


second day.


WT3_2DAY 	 The 3-year 2-day sampling weight for all CSFII 1994­


96 sample persons with two days of intake. This


3-year sampling weight is used whenever the sample of


interest includes only sample persons who provided 2


days of intake data.


The sample weight field for combining all 4 years of the CSFII


1994-96, 1998 are:


WT4_DAY1	 The 4-year day 1 sampling weight for all responding


CSFII 1994-96, 1998 sample persons. This 4-year


sampling weight is used whenever the sample of


interest includes sample persons from both surveys


who provided the first day of intake data regardless


of whether they provided the second day.


WT4_2DAY	 The 4-year 2-day sampling weight for all CSFII 1994­


96, 1998 sample persons with two days of intake. 


This 4-year sampling weight is used whenever the


sample of interest includes only sample persons from


both surveys who provided 2 days of intake data.


4.4 Data File Formats


Attachment 1-38 
4.4.1 Introduction to the File Formats for ASCII delimited files


Seven data files on the Food Commodity Intake Database are stored


in ASCII delimited formats (files in directories “\intake” and


“\support”). Fields are separated by the caret (^) symbol and


alpha-numeric/character fields are enclosed in tilde (~) marks. 


Blank fields appear as two delimiters side by side (i.e., ^^).


This section on file formats describes the contents of each of


the 7 data files. The fields in each file are listed and for


each field, the following information is provided: field number,


field name, field width, field type, and description. 


- The field number identifies the position or order in which the


field can be found on the data record. 


- The field name will be no longer than eight characters and


will always be referred to in the file formats in uppercase


letters.


- The width is the maximum number of columns allocated to the


field including, where appropriate, an explicit decimal point. 


All alpha-numeric fields are encased in tildes (~), which are


not counted in the field width. 


- The type of the field is either 'N' for numeric or 'A' for


alpha-numeric or character. If a numeric field has a


fractional part the number of decimal places follows the N. 


For example, 'N2' indicates a field with two decimal places. 


- The description includes allowed values and their meanings and


skip patterns dictated by specific field values. 


4.4.2 Formats for the food commodity intake data files


4.4.2.1 Format of the Commodity Intakes file (comm9498.txt)


Field Field Width


Number Name /Type Description/Application/Values


====== ====== ====== ==================================


1 HHID 5 N Household identification number.


10001 - 52999 = HHID


2 SPNUM 2 N Sample person (SP) number.


1 - 23 = SP number


Attachment 1-39 
3 DAYCODE 1 N Day 1 / Day 2 / Average indicator. 

Note: There is one record per EPA 

commodity per SP per day of 

intake. Where two days were 

reported there is also a record 

containing daily averages. 

1 = Day 1 

2 = Day 2 

4 = Average of day 1 and day 2 

4 COM_CODE 8 N EPA commodity code. 

Note: See “foodvoc.wpd” for codes and 

complete descriptions of each 

COM_CODE. 

5 COM_AMT 10 N6 Total (or average) amount of the 

EPA commodity consumed during that 

day. 

Amount in grams per kilogram body 

weight 

4.4.2.2 Format for the files of commodity intakes by cooked 

status, food form and cooking method (ffcm9498.txt) 

Field 

Number 

====== 

Field 

Name 

====== 

Width 

/Type 

====== 

Description/Application/Values 

====================================== 

1 HHID 5 N Household identification number. 

10001 - 52999 = HHID 

2 SPNUM 2 N Sample person (SP) number. 

1 - 23 = SP number 

3 DAYCODE 1 N Day 1 / Day 2 / Average indicator. 

Note: There is one record for each EPA 

commodity and Cooked Status-Food 

Form-Cooking Method 

combination(COM-FFCM), per SP, 

per day of intake. Where two 

days were reported there is also 

a record containing daily 

Attachment 1-40 
averages. 

1 = Day 1 

2 = Day 2 

4 = Average of day 1 and day 2 

4 COM_CODE 8 N EPA commodity code. 

Note: See “foodvoc.wpd” for possible 

codes and complete descriptions 

of each COM_CODE. 

5 CKD_STAT 1 N EPA cooked status identifier. 

0 = Not applicable 

1 = Uncooked 

2 = Cooked 

3 = Processed oil 

6 = Frozen meal 

7 = Salad 

8 = Sandwich 

9 = Not specified as to cooked 

or uncooked 

6 FD_FORM 1 N EPA food form identifier. 

0 = not applicable 

1 = Fresh 

2 = Frozen 

3 = Dried 

4 = Canned 

5 = Cured, pickled, smoked,

 salted 

8 = Other process forms not

 listed above 

9 = NS as to form or multiple

 forms 

7 CKG_METH 1 N EPA cooking method identifier. 

0 = None or Not applicable 

1 = Baked 

2 = Boiled 

3 = Fried 

4 = Fried or baked 

5 = Boiled or baked 

8 = Not specified as to cooking

 method or multiple cooking

 methods 

9 = Not specified as to whether 

Attachment 1-41 
 further cooking occurred 


8 CFFCMAMT 10 N6	 Total amount consumed of the commodity


and cooked status-food form-cooking


method combination.


Amount in grams per kilogram body


weight


4.4.2.3 File Format for Sample Person Data (smpl9498.txt)


Where appropriate, the description includes references to the


original question number from the CSFII questionnaires. For


example “H52" indicates Household question number 52. 


Questionnaire symbols are:


S = Screener questionnaire


H = Household questionnaire


DA = Individual Intake Questionnaire (day 1)


DB = Individual Intake Questionnaire (day 2)


Field Field Width


Number Name /Type Description/Application/Values
 ====== ====== ====== ======================================


1 HHID 5 N	 Household identification number.


10001 - 52999 = HHID


2 SPNUM 2 N	 Sample person (SP) number.


1 - 23 = SP number


3 REGION 1 N	 Region of the United States.


 1 = Northeast


 2 = Midwest


 3 = South


 4 = West


4 URB 1 N	 Urbanization; Metropolitan Statistical


Area(MSA) status.


 1 = MSA, central city


 2 = MSA, outside central city


 3 = Non-MSA


5 HHSIZE 2 N H Household size; count of household


members.


1 - 23 = Count


Attachment 1-42
6 INCOME 


7 INCREP 


8 INCCODE 


6 N 	 H52. During the previous calendar


year, approximately how much income


from all sources did you and other


household members have before taxes?


(Please give me your best estimate.) 


Note: annual incomes have been imputed for


households that could not or would not provide


a response to this question. See documentation


for the CSFII 1994-96, 1998 for an explanation


of the methods employed. See INCREP for the


original response to H52. See IMPFLAG for the


method of imputation employed.


 0 - 99999 = Dollars


 100000 = $100,000 or more


1 N	 H52. Type of original response to H52.


 * 1 = Value of INCOME is the actual


amount reported.


 * 5 = No household interview


 * 6 = Not a household in the


previous calendar year


 7 = Refused


 8 = Don't know


 9 = Not ascertained


 * Skip INCCODE.


1 A	 H53. Please tell me which letter on this


card best represents your combined


household income before taxes for the


previous calendar year.


Note: H53 is only asked of households


that could not or would not answer H52.


 Applies if: INCREP >= 7


 A = Under $5000


 B = $5,000 - $9,999


 C = $10,000 - $14,999


 D = $15,000 - $19,999


 E = $20,000 - $24,999


 F = $25,000 - $29,999


 G = $30,000 - $34,999


 H = $35,000 - $39,999


Attachment 1-43 
 I = $40,000 - $44,999

 J = $45,000 - $49,999

 K = $50,000 - $59,999

 L = $60,000 - $74,999

 M = $75,000 - $99,999

 N = $100,000 and over

 7 = Refused

 8 = Don't know

 9 = Not ascertained

 Blank = Not applicable 

9 IMPFLAG 1 N Annual income imputation flag.

 1 = 

2 = 

3 = 

4 = 

5 = 

Not imputed, value of INCOME 

is the actual amount 

reported.

Imputed, value based on H53 

(INCCODE)

Imputed, value based on 

monthly income

Imputed, value based on 

regression equation

Imputed, based on segment 

level mean income 

10 PCTPOV 3 N Annual income expressed as a percentage 

of the poverty threshold. Based on 

INCOME (using imputed values) and HHSIZE.

 0 - 299 = Percentage of the poverty

 threshold

 300 = 300% or more 

11 AGE 2 N Age of household member in years. 

Note: Age at time of day 1 intake.

 0 = Under 1 year old

 * 1 - 89 = Age in years

 * 90 = 90 or older

 * Skip AGE_M. 

12 AGE_M 2 N Age of household member in months. 

only for children 11 months old or 

younger. 

Valid 

Note: Age at time of day 1 intake.

 Applies if: AGE = 0

 0 = Less than one month old 

Attachment 1-44
 1 - 11 = Months of age


 Blank = Not applicable


13 SEX 1 N	 Sex of household member.


 1 = Male


 2 = Female


14 WGT_SP 3 N	 DA30. How much do you weigh without


shoes?


 1 - 995 = Pounds


 997 = Refused


 998 = Don't know


 999 = Not ascertained


15 WGT_KG 3 N1 Weight of sample person in kilograms.


 1 - 452 = Kilograms


The amount of each commodity consumed is


reported in grams per kilogram body weight. 


The CSFII 1994-96, 1998 reports each sample


persons body weight (WGT_SP) in pounds, as


reported by the sample person. These body


weights were converted from pounds to kilograms


using the following formula:


Weight in Kilograms = Weight in pounds


 2.2046 


Note: default body weights were used for sample


persons whose WGT_SP = ‘997', ‘998', or ‘999'. See


section 4.5.2 about Unreported Body Weights. 


16 REL_REF 2 N	 S8. What is your relationship to the


reference person?


 0 = Reference person


 1 = Spouse


 2 = Natural or adopted child;


step child


 3 = Grandchild


 4 = Parent


 5 = Brother or sister


 6 = Other relative


 7 = Foster child


 8 = Partner; roommate;


girlfriend; boyfriend


 9 = Roomer or boarder


 10 = Employee


 11 = Guest


Attachment 1-45
 12 = Other unrelated


17 RACE 1 N S9. Which of the groups on this card 

best describes your race?

 1 = 

2 = 

3 = 

4 = 

5 = 

White

Black

Asian, Pacific Islander

American Indian, Alaskan 

native

Other 

18 ORIGIN 1 N S10. Do any of these groups (from a 

card) represent your national origin?

 1 = 

2 = 

3 = 

4 = 

5 = 

Mexican, Mexican American, 

Chicano

Puerto Rican

Cuban

Other Spanish / Hispanic

None of the above 

19 PL_STAT 1 N Pregnant / lactating status. 

Note: From questions H26, H27, H29 and 

H31. Also, these questions were only 

asked of households with certain 

characteristics as identified at 

screening.

 1 = Pregnant

 2 = Lactating

 3 = Pregnant and lactating

 4 = Not pregnant or lactating

 5 = Not female 10-55 

20 BF_STAT 1 N Breast-feeding status. 

Note: From questions H29 and H30. Also, 

these questions were only asked of 

households with children 3 years old or 

less identified at screening.

 1 = Breast-feeding

 2 = Not breast-feeding

 3 = Over 3 years old 

21 H2O_COOK 2 N H18. What is the main source of the 

water used for cooking in your home?

 1 = Community water supply

 2 = Well or rain cistern 

Attachment 1-46
(household's)

 3 = Spring (household's or public)

 4 = Bottled water (purchased)

 96 = Other

 98 = Don't know

 99 = Not ascertained 

22 H2O_BEVR 2 N H19. What is the main source of the 

water used in your home for preparing 

beverages such as coffee, tea, juices, 

and baby formula?

 1 = Community water supply

 2 = Well or rain cistern 

(household's)

 3 = Spring (household's or public)

 4 = Bottled water (purchased)

 96 = Other

 98 = Don't know

 99 = Not ascertained 

23 H2O_DRNK 2 N H20. What is the main source of plain 

drinking water in your home?

 1 = Community water supply

 2 = Well or rain cistern 

(household's)

 3 = Spring (household's or public)

 4 = Bottled water (purchased)

 96 = Other

 98 = Don't know

 99 = Not ascertained 

24 D1_H2O_O 3 N DA15. How many fluid ounces of plain 

drinking water, that is, tap water or any 

bottled water that is not carbonated, 

with nothing added to it, did you drink 

yesterday - day 1?

 * 0 = None

 1 - 995 = Fluid ounces

 998 = Don't know

 999 = Not ascertained

 * Skip D1_H2O_H - D1_H2O_A 

25 D1_H2O_H 1 N DA16. How much of this plain drinking 

water came from your home? Would you say 

all, most some, or none - day 1?

 Applies if: D1_H2O_O > 0 

Attachment 1-47 
 * 1 = All

 2 = Most

 3 = Some

 4 = None

 8 = Don't know

 9 = Not ascertained

 * Skip D1_H2O_A 

26 D1_H2O_A 1 N DA17. What was the main source of plain 

drinking water that did not come from 

your home? Was it tap water, water from 

a drinking fountain, bottled water, or 

something else - day 1?

 Applies if: D1_H2O_H > 1

 1 = Tap water / drinking 

fountain

 2 = Bottled water

 6 = Other

 8 = Don't know

 9 = Not ascertained

 Blank = Not applicable 

27 D2_H2O_O 3 N DB13. How many fluid ounces of plain 

drinking water, that is, tap water or any 

bottled water that is not carbonated, 

with nothing added to it, did you drink 

yesterday - day 2?

 Applies if: COMP_D2 = 1

 * 0 = None

 1 - 995 = Fluid ounces

 998 = Don't know

 999 = Not ascertained

 Blank = Not applicable

 * Skip D2_H2O_H - D2_H2O_A 

28 D2_H2O_H 1 N DB14. How much of this plain drinking 

water came from your home? Would you say 

all, most some, or none - day 2?

 Applies if: D2_H2O_O > 0

 * 1 = All

 2 = Most

 3 = Some

 4 = None 

Attachment 1-48
 8 = Don't know

 9 = Not ascertained

 Blank = Not applicable

 * Skip D2_H2O_A 

29 D2_H2O_A 1 N DB15. What was the main source of plain 

drinking water that did not come from 

your home? Was it tap water, water from 

a drinking fountain, bottled water, or 

something else - day 2?

 Applies if: D2_H2O_H > 1

 1 = Tap water/drinking fountain

 2 = Bottled water

 6 = Other

 8 = Don't know

 9 = Not ascertained

 Blank = Not applicable 

30 COMP_D1 1 N Is there complete Day 1 intake data for 

this individual?

 1 = Yes 

31 COMP_D2 1 N Is there complete Day 2 intake data for 

this individual?

 1 = Yes

 * 2 = No

 * Skip WT3_2DAY, WTA_2DAY. 

32 WT3_DAY1 8 N 3-year day 1 sample weight for CSFII 

1994-96. Read section 4.3.3 on sampling 

weights. 

Applies to day 1 records with 1994, 

1995, or 1996 in YEAR field.

 1 - 99999999 = Weight

 Blank = Not applicable 

33 WT3_2DAY 8 N 3-year 2-day sample weight for CSFII 

1994-96. Read section 4.3.3 on sampling 

weights. 

Applies to day 1 and day 2 records 

with 1994, 1995, or 1996 in YEAR 

Attachment 1-49
field and 1 COMP_D2 field. 

1 - 99999999 = Weight

 Blank = Not applicable 

34 WTA_DAY1 8 N Annual day 1 sample weight. 

4.3.3 on sampling weights. 

Read section 

Use in conjunction with YEAR field

 Applies if: COMP_D1 = 1

 1 - 99999999 = Weight

 Blank = Not applicable 

35 WTA_2DAY 8 N Annual 2-day sample weight. 

4.3.3 on sampling weights. 

Read section 

Use in conjunction with YEAR field

 Applies if: COMP_D2 = 1

 1 - 99999999 = Weight

 Blank = Not applicable 

36 WT4_DAY1 8 N 4-year day 1 sample weight. 

4.3.3 on sampling weights. 

Read section 

Applies if: COMP_D1 = 1

 1 - 99999999 = Weight

 Blank = Not applicable 

37 WT4_2DAY 8 N 4-year 2-day sample weight. 

4.3.3 on sampling weights. 

Read section 

Applies if: COMP_D2 = 1

 1 - 99999999 = Weight

 Blank = Not applicable 

38 YEAR 4 N Year of the survey.

 1994 = 1994 sample

 1995 = 1995 sample

 1996 = 1996 sample 

1998 = 1998 sample 

39 D1_NREC 2 N Day 1: number of food records.

 0 - 99 = Number 

Attachment 1-50 
40 D2_NREC 2 N Day 2: number of food records.


 Applies if: COMP_D2 = 1


 0 - 99 = Number


 Blank = Not applicable


4.4.3 Formats for the supporting files


4.4.3.1 Food Code-to-commodity Translation File - (fc_comm.txt)


Field Field Width 

Number Name /Type Description/Application/Values 

====== ====== ====== ====================================== 

1 FOODCODE 8 N Food code. 

2 MODCODE 6 N Modification code. 

3 COM_CODE 8 N EPA commodity code. 

4 COM_AMT 10 N3 Total amount in grams of the EPA 

commodity in 100 grams of the food. 

4.4.3.2 Food code descriptions (fcdesc.txt)


Field Field Width


Number Name /Type Description/Application/Values
 ====== ====== ====== ======================================


1 FOODCODE 8 N Food code.


2 DESCR 200 A Description of food.


3 ABBRDESC 60 A Abbreviated description of food.


4.4.3.3 Food code include statements (fcincl.txt)


Field Field Width


Number Name /Type Description/Application/Values


====== ====== ====== ======================================


1 FOODCODE 8 N Food code.


2 SEQUENCE 2 N Sequence number.


3 INCLUDE 80 A Include statement


4.4.3.4 Food Code outline (fcscheme.txt)


ASCII text file


Attachment 1-51 
----------------------------------------- -----------

4.4.3.5 Descriptions of recipe modifications (moddesc.txt)


Field Field 

Number Name 

====== ====== 

Width 

/Type 

====== 

Description/Application/Values 

====================================== 

1 MODCODE 6 N Recipe modification code number. 

2 MODDESC 240 A Recipe modification description 

4.5 Miscellaneous Notes 

4.5.1 	Responding sample persons with no foods reported


 for a day


There are sample persons who completed an individual intake


interview but reported consuming no foods or beverages for that


day. The “smpl9498.txt” fields providing the number of foods


reported for a day, D1_NREC and D2_NREC, will have a value of 0


in such cases. Such sample persons do not have records in the


commodity intake (“comm9498.txt”) nor the cooked status-food


form-cooking method commodity intake (“ffcm9498.txt”) data files


for that day. 


4.5.2 Unreported body weights


The amount of each commodity consumed is reported in grams per


kilogram body weight. The CSFII, 1994-96 reports each sample


persons body weight (WGT_SP) in pounds, which was converted into


kilograms. There are some sample persons who completed an


individual intake interview but failed to provide a body weight. 


Where a body weight was not reported, the following default


weights were used:


 Gender and Age 	 Body weight (Kg)


Children < 6 months of age 6.0 Kg


 Males 6 - 11 months of age 9.4 Kg


 1 year 11.8 


2 years 13.6 


3 years 15.7 


4 years 17.8 


5 years 19.8 


6 years 23.0 


7 years 25.1 


8 years 28.2 


9 years 31.1 


10 years 36.4 


11 years 40.3 


12 years 44.2 


Attachment 1-52 
 13 years 49.9 

14 years 57.1 

15 years 61.0 

16 years 67.1 

17 years 66.7 

> 17 years 70.0 

Females 6-11 months of age 8.8 Kg

 1 year 10.8 

2 years 13.0 

3 years 14.9 

4 years 17.0 

5 years 19.6 

6 years 22.1 

7 years 24.7 

8 years 27.9 

9 years 31.9 

10 years 36.1 

11 years 41.8 

12 years 46.4 

13 years 50.9 

14 years 54.8 

15 years 55.1 

16 years 58.1 

17 years 59.6 

> 17 years 60.0 

Body weights used as default weights, in instances in which body


weights were not reported, were provided by EPA. The reference


weights for children less than 6 months of age are derived from


Table 7-1 of the Exposure Factors Handbook, U.S. Environmental


Protection Agency, National Center for Environmental Assessment,


Office of Research and Development, Washington, DC, (EPA/600/C­


99/001), February 1999. Table 7-1 is derived from: Hamill PTV,


TA Dried, CL Johnson, R. Reed, AF Riche, WE Moore. Physical


Growth: National Center for Health Statistics Percentiles. Am J


Cain Nut 32:607-629, 1979. The weights of children 6 months


through the 17th year are from Table 7-3, Body Weights of


Children, adapted from the National Center for Health Statistics


(NCHS), 1987: Anthropometric reference data and prevalence of


overweight, United States, 1976-1980. Data from the National


Health and Nutrition Examination Survey (NHANES II), Series 11,


No. 238, Hyattsville, MD: U.S. Department of Health and Human


Services, Public Health Service, NCHS. DHHS Publication No.


(PHS) 87-1688. The weights provided for those 18 year of age and


older are those traditionally used in risk assessments by EPA’s


Office of Pesticide Programs, Health Effects Division for adult


males and females. 


4.5.3 Lower limit for reporting intakes


The commodity intake data files (“comm9498.txt” and


Attachment 1-53
“ffcm9498.txt”) contain the amount of each EPA commodity consumed


by each sample person providing at least one day of dietary


intake in the CSFII 1994-96, 1998. Commodities not consumed by a


sample person are not included in the database. The smallest


amount that could be included in these intake files is 0.000001


grams per kilogram body weight. When an amount of a commodity


consumed from a food was less than this amount, this lower limit


amount of 0.000001 grams per kilogram body weight was assigned,


as requested by EPA.


4.5.4 Using this database in conjunction with the Continuing


Survey of Food Intakes by Individuals and the Supplemental


Children’s Survey (CSFII 1994-96, 1998)


The Food Commodity Intake Database was created by translating the


CSFII 1994-96, 1998 food and beverage intakes into EPA


commodities. The household identification number (HHID) and


sample person number (SPNUM)uniquely identify each sample person


and provide linkage with the survey data. These identifiers,


along with the Day code (DAYCODE), provide linkage to the


survey’s daily intake records.


Attachment 1-54 
 5. 	 Control statistics for the SMPL9498.txt data file,


 all records, unweighted


This file contains descriptive statistics for variables in the


SMPL9498.txt file. These statistics are not population


estimates. They are provided for checking purposes. The SAS


MEANS procedure was used to generate this listing which includes


a count of records with non-missing values (N) for each variable,


the unweighted mean of all values, and the minimum, maximum, and


sum of each variable. They are unweighted and were computed


using all values of each variable including values such as ‘998'


indicating a “don’t know” response. 


Variable Label N 


Mean


 -----------------------------------------------------------­


--------

HHID Household ID 21662 

26262.0

 SPNUM Sample person number 21662 

1.8

 REGION Region 21662 

2.6

 URB Urbanization 21662 

1.9

 HHSIZE Household size 21662 

3.8

 INCOME Annual income: total 21662 

38624.9

 INCREP Annual income: actual report 21662 

2.5

 IMPFLAG Annual income: imputation flag 21662 

1.4

 PCTPOV Annual income: percent of poverty 21662 

205.0

 AGE Age in years 21662 

25.4

 AGE_M Age in months 1551 

5.5

 SEX Sex 21662 

1.5

 WGT_SP Weight of SP 21662 

138.9

 WGT_KG Weight of SP in Kilograms 21662 

47.2

 REL_REF Relationship to reference person 21662 

1.6

 RACE Race 21662 

1.5 

Attachment 1-55
--------

 ORIGIN Hispanic origin 
 21662 


4.6


 PL_STAT Pregnant/lactating status 
 21662 


4.8


 BF_STAT Breastfeeding status 
 21662 


2.7


 H2O_COOK Source of water: cooking 
 21662 


2.4


 H2O_BEVR Source of water: beverages 
 21662 


3.0


 H2O_DRNK Source of water: drinking 21662 


3.3


 D1_H2O_O Day 1: amount of water 21662 


29.3


 D1_H2O_H Day 1: water from home 17359 


1.8


 D1_H2O_A Day 1: away from home water 6513 


1.6


 D2_H2O_O Day 2: amount of water 20607 


29.5


 D2_H2O_H Day 2: water from home 16260 


1.9


 D2_H2O_A Day 2: away from home water 5906 


1.8


 COMP_D1 Day 1 flag 21662 


1.0


 COMP_D2 Day 2 flag 21662 


1.0


 WT3_DAY1 Final 3-year day 1 weight 16103 


16263.9


 WT3_2DAY Final 3-year two day weight 15303 


17114.1


 WTA_DAY1 Final annual day 1 weight 21662 


38123.3


 WTA_2DAY Final annual two day weight 20607 


40075.0


 WT4_DAY1 Final 4-year day 1 weight 21662 


12090.2


 WT4_2DAY Final 4-year two day weight 20607 


12709.1


 YEAR Year of survey 21662 


1995.8


 D1_NREC Day 1: number of food records 21662 


14.4


 D2_NREC Day 2: number of food records 20607 


14.0


 -----------------------------------------------------------­


Variable Label Minimum 


Maximum


Attachment 1-56
--------------

 -----------------------------------------------------------­


HHID Household ID 10001.0 

52852.0

 SPNUM Sample person number 1.0 

11.0

 REGION Region 1.0 

4.0

 URB Urbanization 1.0 

3.0

 HHSIZE Household size 1.0 

16.0

 INCOME Annual income: total 0.0 

100000.0

 INCREP Annual income: actual report 1.0 

9.0

 IMPFLAG Annual income: imputation flag 1.0 

5.0

 PCTPOV Annual income: percent of poverty 0.0 

300.0

 AGE Age in years 0.0 

90.0

 AGE_M Age in months 0.0 

11.0

 SEX Sex 1.0 

2.0

 WGT_SP Weight of SP 4.0 

999.0

 WGT_KG Weight of SP in Kilograms 1.8 

208.7 

REL_REF Relationship to reference person 0.0 

12.0

 RACE Race 1.0 

5.0

 ORIGIN Hispanic origin 1.0 

5.0

 PL_STAT Pregnant/lactating status 1.0 

5.0

 BF_STAT Breastfeeding status 1.0 

3.0

 H2O_COOK Source of water: cooking 1.0 

99.0

 H2O_BEVR Source of water: beverages 1.0 

99.0

 H2O_DRNK Source of water: drinking 1.0 

99.0

 D1_H2O_O Day 1: amount of water 0.0 

999.0

 D1_H2O_H Day 1: water from home 1.0 

9.0

 D1_H2O_A Day 1: away from home water 1.0 

9.0 

Attachment 1-57
--------------

 D2_H2O_O Day 2: amount of water 0.0 

999.0

 D2_H2O_H Day 2: water from home 1.0 

9.0

 D2_H2O_A Day 2: away from home water 1.0 

9.0

 COMP_D1 Day 1 flag 1.0 

1.0

 COMP_D2 Day 2 flag 1.0 

2.0

 WT3_DAY1 Final 3-year day 1 weight 1404.0 

226692.0

 WT3_2DAY Final 3-year two day weight 1016.0 

434881.0

 WTA_DAY1 Final annual day 1 weight 580.0 

669591.0

 WTA_2DAY Final annual two day weight 507.0 

1058203.0

 WT4_DAY1 Final 4-year day 1 weight 340.0 

226692.0

 WT4_2DAY Final 4-year two day weight 286.0 

434881.0

 YEAR Year of survey 1994.0 

1998.0

 D1_NREC Day 1: number of food records 0.0 

56.0

 D2_NREC Day 2: number of food records 0.0 

52.0


 -----------------------------------------------------------­


Attachment 1-58
 Variable Label 


Sum
 -----------------------------------------------------------­


-


HHID Household ID 


568887640.0


 SPNUM Sample person number 


38281.0


 REGION Region 


57357.0


 URB Urbanization 


41798.0


 HHSIZE Household size 


81549.0


 INCOME Annual income: total 


836693653.0


 INCREP Annual income: actual report 


55063.0


 IMPFLAG Annual income: imputation flag 


30647.0


 PCTPOV Annual income: percent of poverty 


4441342.0


 AGE Age in years 


550082.0


 AGE_M Age in months 


8598.0


 SEX Sex 


32337.0


 WGT_SP Weight of SP 


3008008.0


 WGT_KG Weight of SP in Kilograms 


1021680.0


 REL_REF Relationship to reference person 


35644.0


 RACE Race 


33251.0


 ORIGIN Hispanic origin 


100481.0


 PL_STAT Pregnant/lactating status 


104004.0


 BF_STAT Breastfeeding status 


58905.0


 H2O_COOK Source of water: cooking 


51221.0


 H2O_BEVR Source of water: beverages 


65725.0


 H2O_DRNK Source of water: drinking 


71879.0


 D1_H2O_O Day 1: amount of water 


634554.0


Attachment 1-59
 D1_H2O_H Day 1: water from home 


31896.0


 D1_H2O_A Day 1: away from home water 


10513.0


 D2_H2O_O Day 2: amount of water 


607609.0


 D2_H2O_H Day 2: water from home 


30141.0


 D2_H2O_A Day 2: away from home water 


10739.0


 COMP_D1 Day 1 flag 


21662.0


 COMP_D2 Day 2 flag 


22717.0


 WT3_DAY1 Final 3-year day 1 weight 


261897277.0


 WT3_2DAY Final 3-year two day weight 


261897260.0


 WTA_DAY1 Final annual day 1 weight 


825826029.0


 WTA_2DAY Final annual two day weight 


825825998.0


 WT4_DAY1 Final 4-year day 1 weight 


261897244.0


 WT4_2DAY Final 4-year two day weight 


261897236.0


 YEAR Year of survey 


43231966.0


 D1_NREC Day 1: number of food records 


311153.0


 D2_NREC Day 2: number of food records 


287676.0


 -----------------------------------------------------------­


-


Attachment 1-60
Commodity Intake Database


Appendix A


EPA’s Use of Food Consumption Data in Assessing Dietary Risk from


Pesticides (Provided by EPA)


EPA is responsible for regulating the nature and amount of


pesticide residues in food under the Federal Food, Drug and


Cosmetic Act which authorizes EPA to set a legal and enforceable


tolerance, or an exemption from the requirement of a tolerance. 


EPA’s Office of Pesticide Programs regulates pesticides to ensure


that their use does not pose unreasonable risks to human health


and that pesticide residues found in food are safe. Assessing


the amount of pesticide in or on the foods that we eat - both for


fresh, raw foods such as lettuce or processed foods such as


frozen french fries - is a complex process that requires data


from numerous sources along with an understanding of risk


analysis and risk management methods. 


The dietary risk posed by a pesticide in food can be expressed


as a function of exposure and toxicity. The dietary exposure is


derived from the amount of pesticide residue that is present in


and on food (the residue) and the types and amounts of food in a


person’s diet (i.e., food consumption). Toxicity is expressed as


a reference dose to which a person can be safely exposed over


time. 


Dietary exposure to pesticides in foods is estimated by


considering pesticide residues in foods and the amount of food


consumed. In an attempt to conserve limited resources, EPA


assesses dietary exposure in a tiered approach proceeding from


conservative to more refined assumptions as the risk management


situation dictates. Dietary exposure estimates based on


tolerance level residues (farm-gate residues) reflect a


Theoretical Maximum Residue Contribution (TMRC) which


overestimate actual dietary exposure. To better estimate dietary


exposure, EPA developed a process by which pesticide tolerance


data (40 CFR 158.240) and compliance monitoring data are refined


to reflect pesticide residues in food as consumed (dinner-plate


residues). The best estimate of pesticide residues in food, as


consumed, is termed the Anticipated Residue (AR) estimate. When


estimating ARs, EPA uses all available data. It should be noted


that since data sets vary in quality, considerable scientific


judgement is required to derive anticipated residue estimates.


For many years, EPA has used food consumption data collected by


USDA through various large, nationally representative surveys for


its dietary risk assessments. These surveys have sampled


thousands of households to learn about what, and how much, people


eat. The Commodity Intake Database is a result of cooperative


work by EPA and USDA in taking information on the foods people


Attachment 1-61 
reported eating in the 1994-96 Continuing Survey of Food Intakes


by Individuals (CSFII) and the Supplemental Children’s Survey


(SCS) and converting “foods” to the “agricultural commodity”


terms and quantities used in EPA regulation of pesticide residues


in foods. “Agricultural commodity” is a term used by EPA to mean


plant or animal parts consumed by humans as food; when such items


are raw or unprocessed, they are referred to as “raw agricultural


commodities.” Many food items contain more than one commodity,


as, for example, an apple pie may contain the commodities apples,


flour, fat, sugar and spices. (See also Section 3 of the


Documentation of the Commodity Intake Database for more


information on the methods used to translate foods to


agricultural commodities). 


Finally, toxicity of a pesticide is brought into the risk


assessment equation. EPA toxicologists review study data on


possible toxicity associated with exposure to a pesticide (often


animal studies) and establish reference doses that are estimates


of the level of either one-day exposure (acute) or daily exposure


over a life-span (chronic) that are believed to have no


significant harmful effects on humans. Safety factors are


incorporated into these reference doses to account for the


differences in response that may occur when animal data are used


to estimate possible human response, and also to account for the


variation of response to the pesticide that may occur within the


human population. 


Having information on pesticide residues, the number of various


commodities treated with a given pesticide, the quantity of the


commodities consumed by the population in the United States, in


conjunction with toxicity data expressed in a reference dose,


allows EPA to complete dietary risk assessments used to


determine that pesticide residues found in food are within safe


limits.


Attachment 1-62 
Commodity Intake Database


Appendix B


3 	METHODS IN THE CONTINUING SURVEY OF FOOD INTAKES BY INDIVIDUALS 


1994-96, 1998


The methods used in the Supplemental Children's Survey to the 1994-96


Continuing Survey of Food Intakes by Individuals (CSFII 1998) were


identical to those used in the CSFII 1994-96. The CSFII 1998 sample


design shared most basic features with the CSFII 1994-96, but differed


in a few respects outlined in documentation section 3.1.2, "CSFII 1998


sample design."


3.1 Sample Design


3.1.1 CSFII/DHKS 1994-96 sample design


The primary goal of the sample design for the CSFII/DHKS 1994-96 was to


obtain nationally representative samples of noninstitutionalized persons


residing in households in the United States for each of 40 analytic


domains defined by sex, age (10 age groups), and income level (a


"low-income" group and an "all-income" group) that were aimed to meet


specified precision levels for estimates of mean day-1 saturated fat and


iron intakes. Excluded were persons who lived in group quarters or


institutions, who resided on military installations, or who were


homeless. The specific precision goals were that the coefficients of


variation (CVs) for mean saturated fat and iron intakes should be 3


percent or less for each of the 20 all-income sex-age domains and 5


percent or less for each of the 20 low-income sex-age domains. These


precision goals were translated into 3-year target sample sizes. In


addition, the sample design specified that one day-1 intake respondent


20 years of age or older be selected for the DHKS from each household


with at least one day-1 intake respondent age 20 or over. For the


CSFII/DHKS 1994-96, a single sample was selected that met precision


requirements by income level, in contrast to past CSFII/DHKS surveys


where a separate sample of low-income persons was also chosen in


addition to the basic general sample. 


The sample selection process was designed by Westat, Inc., a private


research firm in Rockville, MD, under contract to ARS. The sample for


the CSFII/DHKS 1994-96 was derived from a Westat, Inc., master sample.


This master sample, which was in existence prior to the award of the


contract for the CSFII/DHKS 1994-96, is a stratified, multistage area 


Attachment 1-63 
probability sample. The sampling frame was organized using estimates of


the U.S. population in 1990 (USDC/BOC 1993). The stratification plan


took into account geographic location, degree of urbanization, and


socioeconomic characteristics. 


At the first stage of sampling, the entire United States was divided


into primary sampling units (PSU's) consisting of Metropolitan


Statistical Areas (MSA's) (see section 3.6, "Glossary," below),


counties, or groups of counties. Because of its size, the New York MSA


was divided into three PSU's. For the same reason, the Los Angeles and


Chicago MSA's were each divided into two PSU's. Apart from these, each


of the other MSA's constituted a single PSU. Some counties outside MSA's


were grouped to form PSU's containing at least 15,000 people. A total of


1,404 PSU's was created, and 62 PSU's were selected for use in the


CSFII/DHKS 1994-96, as described below. 


The 24 PSU's with the largest populations were included with certainty.


The remaining (noncertainty) PSU's were then assigned to 1 of 38 strata


of approximately equal size (in terms of 1990 population), and one PSU


was selected from each stratum with probability proportional to the 1990


population. Stratification factors included region of the country (four


census regions) (see section 3.6, "Glossary," below); whether or not the


PSU was an MSA and the population size of the MSA; percentage of the


population that was black or Hispanic; and per capita income. Among the


noncertainty strata, 26 were MSA strata and 12 were non-MSA strata. 


The second stage was the selection from each PSU of 36 area segments


consisting of blocks or groups of blocks. Area segments were chosen with


probability proportional to size. The CSFII/DHKS 1994-96 was designed so


that data collection would be spread evenly over the 3 years of the


survey and over the quarters of the year. From each sampled PSU, twelve


segments were subsampled for each of the 3 years of the survey, three


segments for each quarter of the year. Addresses of all dwelling units


in the subsampled area segments were then listed in accordance with 1990


Census listing rules and consistent with the 1990 Census definition of a


housing unit (see section 3.6, "Glossary," entry for "Dwelling unit"). 


In the third stage, listed dwelling units in the selected area segments


were drawn into the sample from the listings. For the three years of the


CSFII/DHKS 1994-96, a sample of 34,016 dwelling units in all was


designated for screening. Calculation of the number of dwelling units to


be screened took into account the sample sizes needed to achieve the


desired levels of precision specified by ARS prior to contract award,


the percentages of individuals in each sex-age group living in


households at or below 130 percent of the Federal poverty guidelines


(DHHS 1996), a projected figure for vacant dwelling units, and a safety


factor allowing for random sampling variation. Sample households were


screened to identify appropriate numbers of sample persons in specified


sex-age groups. 


The last sampling stage involved selection of individuals from the


sampled households. As described in the first paragraph of this section


(section 3.1.1), the CSFII 1994-96 was designed to obtain sample sizes


for the sex-age groups that would produce estimates with equivalent


coefficients of variation over the sex-age groups, both for the total


Attachment 1-64 
population and for the low-income population. To obtain the desired


numbers of individuals, sex-age subgroups were sampled at different


rates. This procedure was implemented at the screening stage of the


survey. The age groups used were 1 to 2 years, 3 to 5 years, 6 to 11


years, 12 to 19 years, 20 to 29 years, 30 to 39 years, 40 to 49 years,


50 to 59 years, 60 to 69 years, and 70 years and over. The approach used


to select persons for the intake interviews was to designate subsets of


households within which persons meeting specified sex-age/income


criteria would be included in the study. For example, for a


predesignated subset of households in the dwelling unit sample, only


children between the ages of 1 and 2 years and low-income males between


the ages of 50 and 59 years were to be included in the sample. Sampled


households were assigned to the various subsets in a random fashion to


ensure the unbiased selection of sample persons for the study. In


addition, all infants under 1 year of age in households that contained


at least one sample person 1 year or older were included in the sample. 


To facilitate the selection of sample persons in the field, each


screening questionnaire carried a sampling message specifying the


characteristics of the persons to be included in the sample. The


proportion of households receiving a particular message was determined


to satisfy the target sampling rates for the various sex-age/income


domains. After completing the listing of household members, the


interviewer identified which, if any, of the household members fell into


the sex and age groups that had been predetermined for that household.


The interviewer had no discretion as to whom to include. In the CSFII


1994-96, a total of 20,126 individuals was initially selected into the


sample. 


Respondents for the DHKS 1994-96 were selected from among sample persons


20 years of age and over who had completed the day-1 intake interview in


the CSFII 1994-96. Only one DHKS respondent per household was selected


in households with eligible participants. In households with more than


one CSFII participant 20 years of age or over, one of the participants


was selected randomly with probability assigned to maintain


distributions of all-income and low-income individuals in the six


sex-age groups age 20 years and over in the DHKS that conformed


approximately to the corresponding distributions of individuals in the


CSFII. In the DHKS 1994-96, a total of 7,842 individuals was selected


into the sample. 


For more detailed information on the CSFII/DHKS 1994-96 sample design,


see Tippett and Cypel (eds.) 1997, which is included on Disk 1 in


\pdffiles\dor.pdf.


3.1.2 CSFII 1998 sample design


The CSFII 1998 had its roots in the Food Quality Protection Act of 1996,


which required the Secretary of Agriculture to provide the Environmental


Protection Agency (EPA) with information on food consumption patterns of


a statistically valid sample of infants and children. This requirement


followed a report entitled Pesticides in the Diets of Infants and


Children (NAS/NRC 1993) that concluded that current food consumption


data for children did not provide sufficient sample sizes for adequate


estimation of dietary exposure to pesticide residues. In response to the


Attachment 1-65 
1996 mandate, the Agricultural Research Service (ARS) of the U.S.


Department of Agriculture (USDA) conducted the CSFII 1998 as a


supplement to the CSFII/DHKS 1994-96. CSFII 1998 data used in


conjunction with CSFII/DHKS 1994-96 data, with appropriate weights (see


documentation section 5, "Sampling Weights"), meet the requirement for a


larger sample of children.


The goal of the sample design for the CSFII 1998 was to obtain


nationally representative samples of noninstitutionalized persons 9


years of age or younger residing in households in the United States for


each of 28 analytic domains defined by sex, age (7 age groups), and


income level (a "low-income" group and an "all-income" group). The age


groups used were under 1 year, 1 year, 2 years, 3 years, 4 years, 5 to 6


years, and 7 to 9 years. 


A complex multistage area probability sample design that incorporated


the same primary and second stage sampling units developed for the


CSFII/DHKS 1994-96 was used to select children for the CSFII 1998. The


same 62 PSU's that were selected for the CSFII/DHKS 1994-96 were used


for the CSFII 1998. The PSU's were selected with probabilities


proportional to the 1990 population. From each PSU, the 24 area segments


used in the last 2 years of the CSFII/DHKS 1994-96 were used for the


CSFII 1998. Those 24 segments were selected because they were the


segments with the most up-to-date listing information. 


Dwelling units (DU's) were selected from the area segments using listing


information from the CSFII 1994-96 along with quality control procedures


referred to as the "missed structure" and "missed dwelling unit"


procedures. In preparation for the CSFII/DHKS 1994-96, interviewers had


listed over 210,000 DU’s within the 1,488 area segments included in the


CSFII 1998. DU’s that had been selected for the CSFII/DHKS 1994-96 were


excluded from the CSFII 1998 sample. A sample of 65,519 DU’s (i.e., an


average of 44 DU’s per sample segment) was drawn for the CSFII 1998 from


the existing area segment listings. An additional 2,905 DU’s were added


to the sample through quality control procedures referred to as the


"missed structure" and "missed DU" procedures. Thus, 68,424 DU’s were


selected for the CSFII 1998.


Each sampled dwelling unit was screened to determine whether it


contained children who were eligible for the survey. From the DU's with


children 9 years of age or younger, a sample of eligible children was


selected by a probability sampling process designed to achieve the


target sample sizes. Finally, to increase the number of 3-year-old girls


in the sample, a special "supplemental" sample was selected and fielded


in the fourth quarter of the study. The sampling procedures described


above resulted in the initial selection into the sample of 6,413


children (including 2,100 low-income children). 


For more detailed information on the CSFII 1998 sample design, see


"Sample Design -- Supplemental Children's Survey to the 1994-96


Continuing Survey of Food Intakes by Individuals (CSFII 1998)", which is


included on Disk 1 in \pdffiles\98_samp.pdf.


3.2 Data collection


Attachment 1-66 
3.2.1 CSFII/DHKS 1994-96 and CSFII 1998


The CSFII 1998 methods were nearly identical to those used in the CSFII


1994-96. A few questions were omitted because they were considered


inappropriate for interviews with or about children, for example,


questions about alcoholic beverages and smoking. See indented sections


below regarding differences between CSFII 1998 and CSFII 1994-96.


Data were collected by Westat, Inc. Prior to data collection, listers


visited every sample address in person to determine by visual inspection


whether that location represented a dwelling unit (see section 3.6,


"Glossary," below). An introductory letter and a brochure describing the


survey were mailed to each dwelling unit 1 week before the initial


in-person contact by the interviewer. In all materials for respondents,


the survey was referred to as the "What We Eat in America" survey rather


than by the official survey name. To contact individuals in the dwelling


units, interviewers made at least four visits before referring the case


to a supervisor. In a number of difficult cases, contact attempts


exceeded the level of effort required by the contract in order to


complete the interview. In cases where a dwelling unit was determined to


contain a household but the household could not be contacted after four


visits, interviewers were instructed to ask two neighbors for


information on the number of household members and their sexes and ages


as well as on the time household members were most likely to be home. 


At each dwelling unit, the interviewer attempted a screening interview


to determine whether any members of the household were eligible to


participate in the survey. Any household member 18 years of age or older


was an acceptable respondent for the screening questionnaire (screener).


However, it was recommended that interviewers attempt to conduct this


portion of the survey with either the main meal planner/preparer (see


section 3.6, "Glossary," below) or a person knowledgeable about


household characteristics such as income because those persons were the


preferred respondents for the household questionnaire, which typically


followed the screener. It was not necessary for the respondent(s)


completing the screener and/or household questionnaire to be sample


persons (see section 3.6, "Glossary," below). If a household member (see


section 3.6, "Glossary," entry for "Household") refused to complete the


screener, the interviewer was instructed to ask the household member for


information on the number of household members and their sexes and ages


so that the number of eligible respondents could be determined. (The


number of eligible respondents was important for calculating the


response rates provided in documentation section 4, "Response Results.")


At the beginning of the screening interview, the interviewer reminded


the respondent about the letter and brochure that had been sent and


provided new ones if the respondent did not remember. During the


interview, information was collected on the number of persons living in


the household; the first name of the person or one of the persons who


owned or rented the home (reference person); the first name of the


reference person's spouse, if any; and the first name, race, ethnicity


(Hispanic or non-Hispanic), date of birth, age, sex, and relationship to


the reference person of any other people living in the household,


including friends, relatives, roomers, boarders, employees, and


Attachment 1-67 
household members who were away from home at the time of the interview


but who usually lived there. 


One screener question asked whether the total income of all household


members from all sources during the previous year was more or less than


an amount specific to the household's size. That question was part of


the strategy for meeting the low-income sample size goals discussed in


documentation section 3.1, "Sample Design." 


In the CSFII 1994-96, the screener income question was asked only when


the household included individuals in sex and age groups specified in


the sampling message for that dwelling unit. In the CSFII 1998, the


question was asked in all households. 


The maximum income level used, where necessary, during the screening


process to determine the household's eligibility for inclusion in the


low-income group corresponded to 130 percent of the Federal poverty


guidelines (DHHS 1998), which are based on household size and income.


This income level was selected because it is the same as one of the


income criteria used to determine whether nonelderly households are


eligible to participate in the Food Stamp Program. Not all households


meeting the income criteria are eligible for food stamps; other


criteria, such as asset limitations, must also be met. The CSFII 1994-96


and CSFII 1998 screened households for income level only, not for food


stamp eligibility. 


At households where one or more sample persons were selected, the


interviewer administered the household questionnaire--a series of


questions about the educational level and employment status of household


members 15 years of age and older, household income, food assistance


program participation, food expenditures, and some other food-related


practices. During the household interview, the interviewer asked the


respondent to identify the "female head of household" and the "male head


of household"; this question was included for the benefit of researchers


who wish to make historical comparisons involving those variables.


Interviewers made up to three visits after screening to complete the


household questionnaire before referring the case to a supervisor. 


Interviewers' visits were scheduled in a manner designed to ensure that


at least 10 percent of day-1 food intake interviews took place on each


day of the week. A label specified 3 days of the week that would be


acceptable for collecting day-1 food intake information from that


attached to the survey materials for each household. Repeated in-person


visits were made as necessary to attempt to complete day-1 intakes with


sample persons on the scheduled days of the week. In some cases, when


repeated visits had been made on different scheduled days and at


different times, interviewers were permitted to change the day of the


week in order to obtain an interview. In households with more than one


sample person, if one of the sample persons was not at home when the


interviewer visited, the protocol required the interviewer to make up to


three additional visits in an attempt to obtain a day-1 intake for that


sample person. Often the number of visits required by the contract was


exceeded in order to obtain the interview. An extensive range of


strategies was employed in order to convert refusals, sometimes


involving efforts by two or more interviewers. 


Attachment 1-68 
Day-1 intakes were to be collected in person. Before conducting the


day-1 interview, the interviewer told the sample person that her or his


participation would involve two in-person interviews (and possibly, for


one sample person in the household, the DHKS interview by telephone). At


the conclusion of the day-1 interview, the interviewer notified the


sample person that she or he would be returning in a few days to conduct


another interview. 


According to the survey protocol, the day-2 interview was to be


conducted 3 to 10 days after the day-1 interview but not on the same day


of the week. In the CSFII 1994-96, less than 1 percent of day-2


interviews were conducted sooner than 3 days after the day-1 interview,


20 percent were conducted more than 10 days after the day-1 interview,


and 1 percent were conducted on the same day of the week as the day 1


intake exactly 1 week later. In the CSFII 1998, less than 1 percent of


day-2 interviews were conducted sooner than 3 days after the day-1


interview, 17 percent were conducted more than 10 days after the day-1


interview, and 2 percent were conducted on the same day of the week as


the day 1 intake exactly 1 week later. Five percent of day-2 interviews


in the CSFII 1994-96 and 16 percent in the CSFII 1998 were conducted by


telephone, with supervisory permission. Sample persons interviewed by


telephone were asked to report food quantities using the measuring


guides that had been used in the day-1 interview (described below) and


given to the household. 


The day-1 and day-2 questionnaires were very similar. Both included a


1-day dietary recall using a multiple-pass method in order to maximize


the sample person's ability to remember what she or he ate and drank


[Tippett and Cypel (eds.) 1997, DeMaio et al. 1993, Guenther et al.


1995]. 


The 1-day recall began with the sample person being asked to report


everything eaten or drunk the previous day between midnight and


midnight. The interviewer did not interrupt the sample person during


this initial listing of the day's intake. The sample person was invited


to add any other items remembered as the interview progressed. Then, for


each food and drink listed, the interviewer asked the name of the eating


occasion and the time it began. 


For the CSFII 1998, the introduction was revised to delete references to


coffee and alcoholic beverages, and the category "alcoholic beverage


break" was deleted from the card the interviewer handed the respondent


as an aid in naming the eating occasion. These changes were made to both


day-1 and day-2 questionnaires. 


The interviewer used a Food Instruction Booklet (FIB) to probe for a


complete description of every food item and the amount eaten. Under each


appropriate category of food/drink listed in the FIB, there was a list


of the questions (probes) the interviewer was required to ask in order


to collect enough detail for the food to be coded. Probes varied with


the type of food or beverage being recalled. Some examples of FIB probes


are "What was the brand name?" and "Were they regular, reduced calorie,


high fiber, or something else?" When appropriate, questions were asked


about the use of salt ("Was salt used in cooking or preparing the


[food]?") and fat ("Was any kind of fat or oil used in cooking or


Attachment 1-69 
preparing the [food]?") in food preparation and about additions ("Did


you add anything to the [food]?"). The interviewer was directed to ask


for ingredients in some categories (for example, soups; tacos, burritos,


enchiladas, and fajitas; sandwiches; salads; and mixed dishes,


casseroles, and stews). Interviewers were required to use the FIB to


obtain a detailed description of every food item recalled by the sample


person, including additions remembered as the result of questions asked


in describing another food. The FIB also suggested the types of measures


(weight, volume, or size) appropriate for the food.


For the CSFII 1998, the FIB was refined to reflect some changes in food


products since 1996, as well as changes in food terminology. For


example, food label regulation changes for milk that went into effect in


January 1998 narrowed the use of the term "lowfat" from 1-percent or 2­


percent milk to only 1-percent. The regulations also introduced the term


"reduced-fat" for 2-percent milk. As a consequence, the term "low-fat"


for milk was deleted from FIB probes, and respondents were asked to


specify the percent fat in the milk they used. 


Measuring guides used to aid the sample person in estimating amounts


were household measuring cups (1/4 cup, 1/3 cup, 1⁄2 cup, and 1 cup) and


spoons (1/4 teaspoon, 1⁄2 teaspoon, 1 teaspoon, and 1 tablespoon); a


12-inch ruler with 1/8-inch increments marked; "thickness sticks," a set


of 8 small rectangular pieces of hard plastic, each 1/8 inch in


thickness; a laminated card printed with concentric circles 1 inch to 6


inches in diameter, two perpendicular 6-inch rulers, pictures of a fish


filet and chicken parts, and diagrams specifying the dimensions to be


measured or estimated when describing and quantifying various shapes.


The cups and spoons could also be used to measure the capacity of


tableware. One additional measuring guide, a 2-cup measuring cup, could


be used only when the sample person referred to a bowl or cup in her or


his home. The sample person could then fill the bowl or cup with water


to represent the amount eaten or drunk, and the interviewer could


measure the volume of water by pouring it into the 2-cup measure. 


After each item on the initial list of the day's intake had been


described and quantified, the interviewer reviewed for the sample person


all the foods listed for each eating occasion and probed for additional


foods eaten before the first eating occasion listed, in between listed


occasions, and after the last occasion listed. Then, for each food or


drink reported, the interviewer asked where it was obtained and whether


it was eaten at home or not. For foods eaten away from home, the sample


person was also asked whether the food or drink had ever been in the


home before it was eaten; this question was included for the benefit of


researchers choosing to make historical comparisons involving the


variable "food from the home supply." 


Additional questions asked on both day 1 and day 2 pertained to whether


the sample person's intake on the previous day had been usual or unusual


and why, how much plain drinking water the sample person drank on the


previous day and whether it came from home or another source, and how


many hours of television or videos the sample person watched on the


previous day. Further questions in the day-1 questionnaire included the


type of salt usually used by the sample person and frequency of use at


the table; whether the sample person was on a diet and, if so, the type


Attachment 1-70 
and source of the diet; whether the sample person considered herself or


himself to be vegetarian; frequency of vitamin or mineral supplement use


and type of supplement; use of fish oil and fiber supplements; whether


the sample person ever had a blood cholesterol check; self-reported


height and weight (without shoes); self-assessed health status; food


allergies; physician-diagnosed medical conditions; frequency of vigorous


exercise; cigarette smoking status and number of cigarettes smoked per


day; and consumption (ever or never) of alcoholic beverages during the


past 12 months. The day-2 interview contained an additional question on


the consumption (ever or never) of 28 foods during the past 12 months.


For the CSFII 1998, questions on exercise, smoking, and consumption of


alcoholic beverages were removed from the questionnaires. 


Proxy interviews were conducted routinely for child sample persons under


6 years of age and any other sample persons (including adults) who could


not report for themselves due to physical or mental limitations; proxy


interviews were not permitted for any other reason. Proxy interviews


were not considered to be an acceptable substitute for an in-person


interview with adult sample persons who were difficult for the


interviewer to reach or who were nonrespondents. Child sample persons 6


to 11 years of age (6 to 9 years of age in CSFII 1998) were asked to


provide their own food intake data assisted by an adult household member


(referred to as the assistant). The preferred proxy or assistant was the


person responsible for preparing the sample person's meals. If the


sample person, proxy, or assistant could not provide enough descriptive


or quantitative information about the foods eaten, it was sometimes


necessary to seek that information from another caregiver such as a


babysitter or school cafeteria personnel. It was permissible for any


number of caregivers to contribute intake data for a sample person. 


The first use of Spanish-language questionnaires in the CSFII and DHKS


was in 1994-96. Interviewers who were bilingual in English and Spanish


were provided with questionnaires and survey materials translated into


standard Spanish and received an extra day of training in their use. The


Spanish questionnaires reduced the number of language barrier cases and


provided a standardized translation of the questionnaire content. They


also minimized the need for interpreters, a practice that raises


concerns about consistency of interpretation and interview length. If a


sample person spoke neither English nor Spanish, a family member or


neighbor 16 years of age or older was permitted to serve as an


interpreter. 


Spanish questionnaires were used in 2.8 percent of CSFII 1994-96


interviews (excluding screeners) and 4.4 percent of CSFII 1998


interviews. 


The CSFII 1994-96 and CSFII 1998 used in-kind incentives. The


interviewer told the screener respondent that each participating


household would receive a gift. A set of measuring cups and spoons was


given to the screener respondent after the screener was completed and


the household was found to contain any sample person(s). An insulated


nylon sack was given to each sample person prior to the collection of


the intake, and at the conclusion of the day-2 interview each responding


Attachment 1-71 
sample person received a travel-type beverage mug as a thank-you gift


for participating.


Average questionnaire administration time in the CSFII 1994-96 was about


7 minutes for the screener, 19 minutes for the household questionnaire,


32 minutes for the day-1 intake, and 29 minutes for the day-2 intake. 


Average questionnaire administration time in the CSFII 1998 was about 7


minutes for the screener, 20 minutes for the household questionnaire, 32


minutes for the day-1 intake, and 30 minutes for the day-2 intake.


3.2.2 Diet and Health Knowledge Survey 1994-96


The DHKS was conducted only with respondents 20 years of age and older


and so was not part of the CSFII 1998. This section is included because


DHKS 1994-96 data are included in this release. 


The Diet and Health Knowledge Survey was conducted as a telephone


follow-up to the CSFII 1994-96. According to survey design, telephone


contact was to be initiated 2 to 3 weeks after the day-2 intake. For


households without telephones or with unlisted numbers not provided to


interviewers, in-person interviews were the designated mode of contact.


When all sample persons in a household either had completed a day-1


intake or had been judged to be day-1 nonrespondents, the DHKS


respondent was randomly selected by a computerized process from among


eligible CSFII sample persons 20 years of age and over who had provided


a day-1 intake. Sample persons were not eligible if their intake(s) had


been completed by proxy, nor were any proxies allowed to complete the


DHKS. Due to these criteria, not all households had a DHKS respondent.


The interviewer scheduled an appointment for the telephone interview


when the selected DHKS respondent had completed a day-2 intake. The same


interviewer who administered the CSFII typically administered the DHKS.


This continuity of interviewers maintained any rapport established


between interviewer and respondent and was expected to have a beneficial


effect on the response rate. Interviewers operating out of their own


homes administered the questionnaire from a hard copy without computer


assistance.


The interviewer mailed a DHKS reminder card 3 to 5 days prior to the


scheduled interview. In addition to the appointment date and time, this


card contained a list of response categories for selected questions in


the DHKS questionnaire. During the interview, the respondent was


directed to look at the set of response categories applicable to a


particular question, thus reducing the need for the interviewer to


repeat the response options. The card served both as an appointment


reminder and as a means of improving the flow of the interview. 


The first telephone contact was attempted on the scheduled day and time;


if this attempt was unsuccessful, additional calls were made as needed


at different times of the day and on different days of the week to reach


respondents. The survey protocol required at least six telephone


attempts at each number (as needed to obtain the interview), followed by


four in-person visits. In a number of difficult cases, contact attempts


exceeded the required level of effort in order to complete the


Attachment 1-72 
interview. Overall, the DHKS interview in 1994-96 took an average of 30


minutes to complete; it took longer to complete the DHKS in person (34


minutes, on average).


The telephone interview began with a request to speak to the person with


whom the appointment had been made. The interviewer identified herself


or himself and reminded the respondent that during the CSFII she or he


had been told she or he would be recontacted later by telephone to


answer a few more questions about food and nutrition issues. The DHKS


respondent's name and age were verified at this time.


The gift that was provided at the end of CSFII day 2 also served as an


incentive to complete the DHKS. Pretests and interviewer debriefings


suggested that interest in the questionnaire content was also a


motivating factor in completing the interview for some respondents. 


Of all DHKS 1994-96 interviews, 84 percent were completed by telephone


and 16 percent in person. The primary reasons for conducting interviews


in person were that the household did not have a telephone or that


limitations were posed by respondents' physical conditions (e.g., hard


of hearing, feeble). Another reason was language barrier cases where an


interpreter was needed. 


In 1994-96, 74 percent of DHKS interviews were completed between 2 and 3


weeks after the last CSFII interview, as contractually specified.


Interviews completed earlier than 2 weeks or later than 3 weeks were


considered mistimed. Four percent of cases were completed earlier than 2


weeks due to reasons such as prior knowledge of extended periods of


absence from the household (e.g., hospitalization, travel) and


interviewer error. In 22 percent of cases, the length of time between


the CSFII and the DHKS interviews was extended beyond 3 weeks because


numerous contacts were required to complete the interview. These


mistimings often centered on broken appointments where respondents were,


for example, too busy or not at home at the scheduled time. Refusal


conversion efforts also contributed to mistimings; some cases required


intensive, prolonged efforts on the part of two or more interviewers to


complete the interview. 


In the DHKS 1994-96, a Spanish version of the questionnaire was


available for use by bilingual interviewers. It served to reduce the


number of language barrier cases and provided a standardized translation


of the questionnaire content. The Spanish questionnaire also minimized


the need for interpreters, a practice that raises concerns about


consistency of interpretation and interview length. In 1994-96, 147 DHKS


interviews (2.6 percent) were conducted using the Spanish questionnaire.


In 1994-96, there were 61 cases (1.1 percent of DHKS interviews) where


bilingual interviewers and telephones were not available or the


respondent spoke a foreign language other than Spanish, interpreters


were used. In these in-person interviews, the interpreters were required


to be 16 years of age or older.


The content of the DHKS 1994-96 questionnaire was governed by a need for


data on knowledge and attitudes about the Dietary Guidelines for


Americans (USDA/DHHS 1990), food labeling issues, and dietary behaviors


related to fat intake. Information from the DHKS can contribute to the


Attachment 1-73 
research base needed to develop food guidance materials and identify


strategies for targeting nutrition education efforts. Thus, the data


collected include self-perceptions of the adequacy of intake levels of


nutrients and other dietary components, awareness of diet-health


relationships, perceived importance of following dietary guidance for


specific nutrients and other dietary components, behaviors related to


fat intake and food safety, knowledge about food sources of fats and


cholesterol, and self-perceptions about weight status. Also asked in the


DHKS 1994-96 was a new series of questions regarding food labels. It


covered use of various sections of the food label, use of specific


information on the nutrient panel, frequency of using food labels when


buying specified categories of food, ease of understanding food label


information, and level of confidence in food label information. 


3.3 Data Processing


3.3.1 Food coding and editing


The food intake data for the CSFII 1998 were coded and edited using


Survey Net, the same computer-assisted food coding and data management


system used with the CSFII 1994-96. Survey Net was developed


cooperatively by ARS and the University of Texas-Houston Health Science


Center's School of Public Health, and was tailored specifically to the


questions, quality control needs, and data processing needs of the CSFII


1994-96. A general-use version of the software, the Food Intake Analysis


System (FIAS), is available to researchers interested in using ARS


survey food coding and nutrient databases. [For FIAS program and price


information contact the University of Texas-Houston Health Science


Center, School of Public Health, P.O. Box 20186, Houston, Texas 77225.


Phone: (713) 500-9775. Fax: (713) 500-9329.]


Survey Net is a multilevel software system used by both the survey


contractor and ARS. It operates on a computer network with multiple


users accessing a set of central databases. These include (1) a food


coding database containing food descriptions and food measures with


their corresponding gram weights, (2) a predefined recipe database,


and(3) the Survey Nutrient Database. All three databases are available


with their documentation in the \TSF98 directory on Disk 2. 


Westat's food coders used Survey Net to match descriptions of foods


eaten by sample persons to foods listed in the food coding database.


Coders entered partial or complete words or phrases from the sample


person's descriptions of foods to retrieve food codes containing the


same terms. Once a matching food description was found and selected,


Survey Net provided a list of common household measures (such as 1 cup


or 1 small piece) appropriate for that food. Coders selected the measure


corresponding to the sample person's description of the amount eaten.


When descriptions of foods or quantities not present in the food coding


database were encountered, they were entered as "unknowns" for ARS to


resolve later.


A recipe modification feature of Survey Net allowed coders to view the


predefined recipes which list ingredients and amounts for every food


code in the Food coding database, and to modify the recipes to match


Attachment 1-74 
more closely the foods eaten by sample persons. Recipes were modified


primarily by deleting or substituting ingredients. Modified recipes were


numbered for reference purposes and are included with the recipe


database on the CD-ROM. Recipe modification numbers appear in the field


MODCODE in record type 30 (rt30.dat).


There were three main purposes for recipe modifications: to record the


specific type of fat, the type of milk, and the dilutions of foods.


Recipes for foods such as vegetables, eggs, pasta, rice, and hot cereals


were modified to reflect the type of fat (such as oil, margarine,


margarine spreads, or butter) used in cooking. Recipes for foods such as


puddings, soups, and beverages were modified to reflect the type of milk


(such as whole, lowfat, 2-percent, 1-percent, or skim) used in their


preparation. Some foods commonly modified for both type of fat and type


of milk were scrambled eggs and omelets, and macaroni and cheese.


Recipes for foods such as soups, infant formulas, and beverages were


modified to reflect dilutions with amounts of milk or water that


differed from label directions. For example, the survey recipe for


orange juice was modified if one can of frozen concentrate was mixed


with four cans of water, instead of three cans of water.


Another aspect of the flexibility of food coding in the CSFII 1994-96


and CSFII 1998 is the use of combination codes, whose development and


auxiliary use in analyses are discussed in detail in documentation


section 3.3.8, "Combination codes." Combinations were often instances of


one food being added to another, such as margarine to toast or gravy to


potatoes. For some types of food made up of several components that are


relatively easy to describe and quantify separately (such as sandwiches


and salads) as well as for some mixed dishes, two or more food codes


linked together in a food combination present a more precise picture of


what was actually eaten by respondents than if a single food code is


used. 


Each food in the combination was coded separately and assigned the same


combination type number (COMBTYPE) and sequence number (COMBNUM) in


record type 30 fields (rt30.dat) separate from the food code. There were


11 combination types: beverage, cereal, bread/baked product, salad,


sandwich, soup, frozen meal, ice cream/frozen yogurt, vegetable, fruit,


and other mixture. Two-digit sequence numbers (01 and so on) linked the


foods in a particular combination with each other and distinguished them


from foods in other combinations. For example, a sample person might


have cereal with milk in the morning and again in the afternoon. All the


components of these two combinations would be assigned the combination


type number for a cereal combination. The morning cereal with milk would


be assigned one sequence number, and the afternoon cereal with milk


would be assigned a different sequence number. 


Survey Net's capabilities include a "copy foods" feature that allowed


entries from a particular eating occasion, day, or sample person to be


copied to a different eating occasion or day for the same person or to


the food intake of another sample person in the same household. Survey


Net also automatically performed gram weight checks of food quantities


entered against maximum and minimum values established by ARS for each


food. This weight check allowed coders to correct entry errors


immediately. Coders recorded any questions regarding their food and


Attachment 1-75 
quantity selections in a notepad within Survey Net, which coding


supervisors then reviewed and answered. 


3.3.2 Processing of intakes by ARS


Westat electronically transmitted all coded intakes to ARS. All entries


in each intake requiring review or resolution by ARS were highlighted in


Survey Net's food summary screens. These included all "unknowns" (those


foods or quantities that could not be coded by Westat coders); newly


created recipe modifications; and notepad entries of questions and


explanations of coding decisions. Feedback was provided to Westat on


reviewed intakes.


As the final step in Survey Net processing, the nutritive value of each


food eaten was calculated using the weight of the food and data from the


Survey Nutrient Database. Where recipes had been modified, nutritive


values reflected those modifications.


3.3.3 Food coding database


As mentioned previously, three databases are used in Survey Net. These


include a food coding database (food descriptions, food measures, and


gram weights of those measures); a recipe database; and a nutrient


database.


The food coding database for CSFII 1998 contained 7,321 food codes, each


bearing a complete description of the food and, if relevant, the


preparation method. Each food code consists of 8 digits used to classify


foods into groups for study. The first digit in the food code identifies


one of nine major food groups: (1) milk and milk products; (2) meat,


poultry, fish, and mixtures; (3) eggs; (4) legumes, nuts, and seeds; (5)


grain products; (6) fruits; (7) vegetables; (8) fats, oils, and salad


dressings; and (9) sugars, sweets, and beverages. The second, third, and


(sometimes) fourth digits of a food code identify increasingly more


specific subgroups within the nine major food groups. The remaining


digits are used for identification of particular foods within a


numerical sequence. 


Documentation section 12.1, "Food Coding Scheme," provides an outline of


the major food groups and subgroups identified by the first 1 to 3


digits of the food code. Documentation section 12.2, "Food Codes and


Abbreviated Descriptions," provides a list of the complete 8-digit food


codes with abbreviated descriptive information about each code. Below


are examples of the information found in documentation section 12.2. 


CODE NUMBER ABBREVIATED FOOD DESCRIPTION 

28141010 Chicken, fried, pot, veg, dessert (froz meal, lg meat) 

53105260 Cake, choc, devil's food/fudge, w/icing, homemade 

More detailed food descriptive information is available on Disk 2 in the


following files -- Food Description File (\tsf98\fcdb\cbdes.txt), Food


Includes File (\tsf98\fcdb\cbincl.txt), Subcode Descriptions File


(\tsf98\fcdb\cbsubdes.txt), and Subcode Includes File


(\tsf98\fcdb\cbsubinc.txt). For example, information from the Food


Attachment 1-76 
Descriptions and Food Includes files is provided below for both food


items listed above.


CODE NUMBER COMPLETE FOOD DESCRIPTION 

28141010 Chicken, fried, with potatoes, vegetable, dessert 

(frozen meal, large meat portion) 

(Include Banquet Extra Helping Fried Chicken 

Dinner; Swanson Hungry Man Fried Chicken Dinner) 

53105260 Cake, chocolate, devil's food, or fudge, with icing, 

coating, or filling, made from home recipe or purchased 

ready-to-eat 

(Include chocolate, devil's food, or fudge, NS from 

home recipe, from mix or bought RTE; 

Jack-in-the-Box Double Fudge Cake) 

Sample persons varied in their knowledge of foods as well as in their


ability to recall or describe foods eaten. Thus, the descriptions of


foods provided by sample persons varied from very specific to very


general. Also, sample persons could not always provide details regarding


food preparation (such as the method of cooking or whether the food was


cooked with or without fat); the original form of the food (such as


fresh, frozen, dry, or canned); or the ingredients in a mixture. 


Generally, foods reported with complete descriptions were assigned codes


that preserved the identity or name of the food and the amount of detail


specified. However, if the description of a food was general, such as


"bread," "juice," or "beef," a "not further specified" (NFS) code was


assigned. (See documentation section 3.3.5, "Recipe database.") In other


cases, foods were reported with descriptions lacking only one detail.


These foods were placed in codes providing as much detail as given and


noting the one lacking detail as "not specified" in the code


description, e.g., "chicken breast, fried, no coating, not specified as


to skin eaten." 


Identification by brand names is widespread in the food coding database.


Several types of survey codes are specific to brands in the description


of the code or in the weights provided. Codes may be unique to a


particular brand if warranted, such as for breakfast cereals that differ


in fortification levels, or they may encompass several brands of similar


foods, such as cheese crackers. When appropriate, measures and their


gram weight equivalents are specified by brand. 


The guidelines used to decide if a new code is needed for a brand name


food are the same as for other foods. A new code may be created for one


or more of the following reasons: (1) no code presently exists for a


food similar to the food reported, (2) the reported food contains either


sizable amounts or intentionally reduced amounts of one or more


nutrients, (3) the food is likely to be reported again, or (4) the form


or type of food is of special interest to data users. Special effort is


made to incorporate ethnic foods and foods modified to be lower in fat,


sodium, or sugar. 


Attachment 1-77 
3.3.4 Food measures and weights


Prior to the CSFII 1994-96, the food coding database's list of food


measures and their corresponding weights in grams were examined for


consistency by a Weights and Measures Team that included members from


both ARS and the National Center for Health Statistics, U.S. Department


of Health and Human Services. Cubic inch weights of many meats and fluid


ounce weights of beverages were reviewed and revised if necessary. Cup


weights for breakfast cereals and fluid ounce weights for infant


formulas were updated based on new information from the manufacturers.


Dimensions were added to the measure description for many fresh fruits


and vegetables. New foods and ethnic foods were prepared and weighed in


a USDA food laboratory and added to the database. Brand-specific and


household measures as needed were also added to the list. There are


presently over 30,000 weights for measures of foods in the food coding


database.


3.3.5 Recipe database


The purpose of the recipe database is to provide information for use


during generation of the Survey Nutrient Database. It contains a recipe


entry for each unique food code in the food coding database. These


entries include ingredients and their amounts, as well as information


for determining changes that may occur in nutrients during cooking.


Foods that are not mixtures, e.g., whole milk, are represented as


single-ingredient recipes. Ingredients are identified with codes linking


them to the Primary Data Set of nutrient values (see documentation


section 3.3.6.2, "Primary Data Set"). The recipe database also serves as


public documentation for how nutrient values were calculated for each


survey food code. Recipes are considered "representative," meaning they


are not exact for every sample person nor were they developed to


determine the intake of specific food ingredients. A variety of popular,


regional, and specialty cookbooks were consulted to aid in constructing


representative recipes. Recipes for many of the commercially available


mixtures were estimated from label information (Marcoe and Haytowitz


1993).


In preparation for the CSFII 1994-96, recipes for "Not Further


Specified" (NFS) food codes were reviewed. These NFS codes are used when


sample persons are unable to provide further detail about a food. For


example, the "Milk, NFS" code is used when sample persons do not give


the fat content of the milk they drank. The present recipe for "Milk,


NFS" is a composite of whole milk, 2-percent milk, 1-percent milk, and


skim milk in proportions that reflect milk production statistics. The


"Milk, NFS" recipe is revised each year to reflect the most current


production data. Recipes for other NFS codes may be based on composites,


as for milk, or they may be based on the form of food most frequently


consumed in the food group in question. For example, the recipe for


"Bread, NFS" is white bread. 


Attachment 1-78 
-----------------------------------

-----------------------------------

----------------

---------------- 

3.3.6 Survey Nutrient Database 


About the Survey Nutrient Database


The Survey Nutrient Database is maintained specifically for use with


nationwide food surveys (Perloff et al. 1990). It is updated once a year


when a nationwide food survey is under way. Its source of nutrient


values is the Primary Data Set of nutrient values maintained in the ARS


Nutrient Data Laboratory (see "Primary Data Set" below).


The Survey Nutrient Database includes values for food energy and the


following nutrients and food components: protein, total fat, saturated


fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids,


19 individual fatty acids, cholesterol, total carbohydrate, dietary


fiber, vitamin A (as international units and as retinol equivalents),


carotenes, vitamin E, vitamin C, thiamin, riboflavin, niacin, vitamin


B-6, folate, vitamin B-12, calcium, phosphorus, magnesium, iron, zinc,


copper, sodium, potassium, alcohol, moisture (water), selenium*,


caffeine*, and theobromine*. Values for the three items with asterisks


(*) were added to the database for the first time with this release. 


The Survey Nutrient Database contains two files of nutrient values: (1)


The Survey Nutrient Values, Set 1, which includes data for each unique


survey food code from the food coding database (see documentation


section 3.3.3, "Food coding database" above); and (2) the Survey


Nutrient Values, Set 2, which is identical to Set 1 of the Survey


Nutrient Values with the following exception: In recipes where salt is


considered an optional ingredient, it was removed from the recipe before


the nutrients were calculated. 


Both Set 1 and Set 2 of the Survey Nutrient Values were used during the


last step of Survey Net processing when the nutritive value for each


consumed food was calculated. If the sample person indicated salt was


used in cooking the food, or if she or he did not know, data were


selected from Set 1. If salt was not used, data were selected from Set


2. 


Primary Data Set 


The Primary Data Set of nutrient values is maintained by the ARS


Nutrient Data Laboratory in support of the National Nutrition Monitoring


and Related Research Program. These nutrient values are used to create


the Survey Nutrient Database. The Primary Data Set is updated each year


when a nationwide survey is being conducted. The main source of data for


this version of the Primary Data Set (1998) was Release No. 11 of the


USDA Nutrient Database for Standard Reference (USDA/ARS 1996), the same


as used for the CSFII 1994-96. Unpublished data collected by the


Nutrient Data Laboratory were also used as needed, especially for new


products and for foods that recently changed. The most notable changes


were to folate values as discussed below. As the survey was conducted,


data for new foods were added as they were reported by sample persons,


Attachment 1-79 
and the final number of foods in the data set was 3,067. New values in


the Primary Data Set can be identified by the "date added/modified"


field [see 1998 formats document accompanying the Technical Support


Files (on Disk 2 in \tsf98\formats\formats.txt)]. 


Selenium, caffeine, and theobromine values were added to the Primary


Data Set for this release. The selenium content of plants, in particular


cereal grains, is strongly influenced by the quantity of biologically


available selenium in the soil in which they grow and, hence, their


geographical origin (Holden et al. 1991). Values for major dietary


contributors of selenium are based on laboratory analyses of food


samples drawn from retail outlets according to nationwide sampling


plans, in order to provide average values appropriate for national food


surveys (Holden et al. 1991, Gebhardt et al. 1990). 


Most of the values for major contributors of nutrients are supported by


laboratory analyses (Matthews 1991). Nutrient values not available from


laboratory analyses were imputed by Nutrient Data Laboratory


nutritionists from data for other forms of the food or from data for


similar foods (Gebhardt 1992). For each value in the Primary Data Set, a


source code is present that indicates whether the value is analytical or


imputed.


Folate values in this version of the Primary Data Set were updated to


reflect regulations that became effective on January 1, 1998, requiring


the addition of folic acid to enriched cereal grain products subject to


standards of identity (DHHS/FDA 1996).These products include flour,


cornmeal and grits, farina, rice, macaroni, noodles, bread, rolls, and


buns. Folic acid may continue to be added (with some restrictions on


amounts) to breakfast cereals, infant formulas, medical foods, food for


special dietary use, and meal replacement products. For the most part,


values in this data set were calculated based on enrichment levels


specified in the regulations, since analytical values were not yet


available. For those foods where the enrichment level is given as a


range, the midpoint was used to set the value. Food items containing any


of these products as ingredients, such as baked products made with


enriched flour, were also updated. 


The state of analytical methodology for measuring nutrients in foods has


been evaluated by Beecher and Matthews (1990), and they reported that


adequate methodology for folate is lacking. The current microbiological


method approved by the Association of Official Analytical Chemists


International applies only to foods that contain the free forms of the


vitamin. Data generated by ARS for use in food composition databases


were obtained by a modified method using enzymes to release bound forms.


Recent research on determining the folate content of high-protein and


high-carbohydrate foods indicates that additional improvements in


methodology are needed (Martin et al. 1990). 


Data users should note that values for carotenes are those used by ARS


in arriving at the values for total vitamin A and are not solely


beta-carotene. Also, the values for vitamin E (quantified as


alpha-tocopherol equivalents) are based on somewhat limited data.


Attachment 1-80 
-------------------

------------------- 

Recipe calculations


Entries in the recipe database identify the Primary Data Set (PDS)


item(s) used to derive the Survey Nutrient Values, Set 1 and Set 2. As


mentioned in the recipe database discussion, some survey food codes have


a one-to-one correspondence with items in the Primary Data Set and are


represented by single ingredient recipes, such as the following:


 Survey food code: 


111-12110, Milk, cow's fluid, 2% fat


 Recipe ingredient:


 PDS Number PDS item Amount


 01079 Milk, 2% Fat, with Vit A 100 grams


However, many survey food codes require multiple ingredients, for


example: 


Survey food code: 


423-01010, Peanut butter sandwich


 Recipe ingredients:


 PDS Number PDS item Amount

 16098 Peanut butter 24.0 grams

 18069 Bread, white 52.0 grams 

The retention factor method (Powers and Hoover 1989) was used for


calculating the nutrient content of recipes. Perloff has described how


this method is used for generating values in the Survey Nutrient


Database, including how factors estimating changes in nutrients due to


cooking or processing are used in the calculations (Perloff 1985).


Factors for calculating moisture and fat changes are stored in each


recipe. Factors for estimating losses in 18 vitamins and minerals are


stored in a separate data file, the Nutrient Retention Factors File,


which is accessed during the recipe calculation procedure. The presence


of special codes in the recipe entries indicate when the retention


factors are used. Retention factors for selenium and vitamin E are not


available. 


3.3.7 Multi-year databases


The nutrient intake data for the CSFII 1998 were calculated using the


1998 values from the multi-year food coding, nutrient, and recipe


databases that are included only on Disk 2. Some foods changed between


the CSFII 1994-96 and the CSFII 1998. For example, folic acid is now


added to enriched grain products. In such cases, both the Primary Data


Set and the Survey Nutrient Database contain multiple records for the


different nutrient levels in the food. Multiple records also exist for


some food weights and recipes. Multiple records do not exist for


modified recipes. 


All records in the multi-year food coding, nutrient, and recipe


databases have start- and end-date fields indicating the time period


Attachment 1-81 
--------------------------------------------------

--------------------------------------------------

when each record was available for coding. These date fields can be used


to extract a single-year version from the multi-year database. 


3.3.8 Combination codes


Rationale for and development of combination codes


A notable feature available on the CSFII 1994-96 and 1998 combined data


set is combination codes. Data users can find combination code data in


record type 30 (rt30.dat) fields COMBNUM (positions 104-5) and COMBTYPE


(positions 106-7). 


Combination codes were developed for two distinct purposes. First, a


greater level of specificity in coding is possible when sufficient


detail about the foods that make up a combination is collected. For some


foods, two or more food codes linked together in a food combination


present a more precise picture of what was actually eaten by respondents


than if a single food mixture code is used. Second, the use of


combination codes provides insight into patterns of food


consumption--what types and amounts of foods are eaten together and what


types and amounts of foods are eaten as separate items. This information


is helpful in answering questions about not only what people are eating,


but how they are eating it and how much. For example, do adults and


children consume milk differently? Do adults get more of their milk from


drinking it as a beverage, or from adding it to another food, such as


coffee or cereal? 


Recognition of the need for a way to express food combinations through


multiple food codes began with the NFCS 1977-78. For the NFCS 1977-78,


three "partition codes" were developed to indicate foods that were part


of a sandwich, part of a salad, or part of a frozen meal, as shown in


table 3-1 on the next page. Approximately 12 percent of all foods were


assigned one of these partition codes. 


Attachment 1-82 
 Table 3-1. Use of partition codes and combination codes, NFCS


 1977-78 through CSFII 1996


 -----------------------------------------------------------­
 1977-78 1985-86 1987-88 1989-91


 NFCS# CSFII* NFCS# CSFII##


 -----------------------------------------------------------­
 ---------------------Percent----------------­


Partition code 


type:


 Sandwich 10.7 13.0 12.6 13.1


 Salad 1.0 4.2 4.0 3.9


 Frozen meal **   NA


 Mixture NA 1.7 1.7 2.2


 Soup NA .1 .1 .2


 Beverage NA NA 8.3 9.3


 Missing .1 **  ** NA


 Single item 88.2 81.0 73.3 71.3


 -----------------------------------------------------------­


1994 1995 1996


 CSFII CSFII CSFII
 ------------------------------------------------------------


-----------Percent---------­


Combination type:


 Sandwich 13.8 13.7 14.8


 Salad 5.1 5.1 5.2


 Frozen meal ** ** .0


 Other mixture 5.0 5.3 5.3


 Soup .6 .6 .5


 Beverage 7.4 8.3 8.1


 Cereal 6.1 6.3 5.9


 Baked product 7.2 7.3 7.2


 Ice cream .4 .4 .5


 Vegetable 3.5 3.8 3.8


 Fruit .4 .5 .4


 Single item 50.5 48.8 48.5


 -----------------------------------------------------------­


#Basic sample. 


*Women and children, basic and low-income samples. 


##Combined basic and low-income samples.


 ** Calculated value is <0.1%.


The number of partition codes and the utilization of these codes


increased gradually through the years. In the CSFII 1985-86, partition


codes were added for mixtures and for soups, and 19 percent of all foods


were assigned a partition code. In the NFCS 1987-88, a partition code


was added for beverages with additions (for example, coffee with cream


and sugar) or with multiple ingredients (for example, "health shakes,"


that is, milk- or juice-based drinks with fruit, cereals, and other


ingredients pureed together), and the percent of foods assigned a


partition code increased to 27 percent. 


Attachment 1-83
---------------------------------------------------------------

---------------------------------------------------------------

The increased use of partition codes was also due to the concurrent


increase in the number of nutrients in the Survey Nutrient Database,


such as fiber. Greater specificity in reporting and coding of foods was


necessary in order for appropriate nutrient values to be assigned. The


use of partition codes allowed this information to be coded and at the


same time avoided having to add unmanageable numbers of new food codes.


In the CSFII 1994-96, nearly one-half of all foods items were reported


in combination. This near-doubling in the number of foods that were part


of a combination is attributable in part to two major changes in the way


mixture information was collected and coded. First, there was an


expansion of the concept of "partition codes" with the addition of five


more combination codes for cereal, baked product, ice cream, vegetable,


and fruit combinations. These codes were used to code the ingredients in


selected mixtures, as well as to link accompanying food items with the


foods they were combined with "at the table," such as cream cheese on a


bagel, margarine on a baked potato, or banana or berries on cereal.


Second, the Food Instruction Booklet (FIB) was revised to standardize


the collection of details about additions to foods and about mixtures,


thus enabling greater specificity in food coding. 


There were no changes in combination codes between CSFII 1994-96 


and CSFII 1998. 


Data collection and coding of combinations in the CSFII 1994-96


The FIB is described in documentation section 3.2.1, "CSFII/DHKS 1994-96


and CSFII 1998." Under each category of food/drink in the FIB, there was


a set of questions (probes) the interviewer was required to ask in order


to collect enough detail for the food to be coded. For the CSFII


1994-96, major changes made to the FIB include not only more food 


Attachment 1-84 
categories, but also more standardized probes, including probes about


ingredients of foods and any additions to foods. 


For the CSFII 1994-96, interviewers and coders were trained on how to


record and code combinations. Following instructions in the FIB, the


interviewers recorded ingredients of mixtures such as sandwiches and


salads and placed brackets around them to identify them as one food item


eaten. They also used brackets to link foods added together "at the


table," such as the cream added to coffee and the jam spread on toast.


Coders used this information to code the foods as eaten in combination.


If insufficient information was available to code separately all the


food items included in a salad or sandwich (for example, when detailed


descriptions or amounts of ingredients were not given), the coder would


attempt to find a close single-code match for the combination in the


food coding database. 


If enough information was available to code a combination as two or more


separate food items, all food codes for that combination were assigned


both a combination type number and a sequence number. The coder chose


the combination type from a list of categories provided by ARS (see


table 3-2 below). Each combination was assigned a sequence number which


served to distinguish that particular combination from other


combinations consumed by that sample person on that intake day. The


combination type and sequence number are labeled as COMBTYPE and


COMBNUM, respectively, on record type 30 (rt30.dat). 


Attachment 1-85 
-----------------------------------------------------------------------

Table 3-2. 	Combination types (and type numbers)--foods with additions 


or foods in combination


Beverage (01)-­


* 	Coffee/tea with: milk, cream/cream substitute; sugar/sugar 


substitute


 * 	Water with: lemon; lime; fruit juice


 * 	Infant formula with: instant baby cereal added to formula


 * 	All milkshake/float ingredients coded separately


 * 	All beverage/mixed drink ingredients coded separately


Cereal (02)-­


* 	Ready-to-eat breakfast cereals with: milk; sugar/sugar 


substitute; fruit


 * 	Cooked cereals such as oatmeal, cream of wheat, grits with: 


milk; sugar/sugar substitute; fruit; margarine/butter; gravy


 * 	Several breakfast cereals in a mixture coded separately


 * 	Instant baby cereal with: formula, milk, water, beverage added 


Bread/baked product (03)-­


* 	Toast, rolls, buns, bagels, biscuits, muffins, sweet breads, 


pancakes (including potato), waffles with: margarine/butter; 


jam/jelly; cheese/cream cheese; whipped cream; syrup; fruit; gravy


 * 	Cakes, pies, brownies, cookies with: ice cream; whipped cream; 


fruit


 * 	Crackers with: meat; cheese; dip; peanut butter; jam/jelly; 


margarine/butter


 * 	Nacho chips/corn chips with: cheese; dip; refried beans, etc. 


(nacho supremes)


 * 	Rice cakes with: peanut butter; jelly; cheese, etc.


 * 	Tortilla with salsa


Salad (04)-­


* 	All salad ingredients coded separately and/or additions


 * 	Green leafy salads, pasta salads, fruit salads, potato salad, 


taco salad, egg salad


 * 	Salad dressing added to salad 


Sandwich (05)-­


* 	All sandwich ingredients coded separately and/or additions


 * 	"Filled" tacos, enchiladas and burritos


 * 	Hamburger, hot dogs with ingredients coded separately and/or 


additions


 * 	Quesadilla


 --continued


Attachment 1-86 
-----------------------

Table 3-2 -- continued


Soup (06)-­


* 	Soup with: crackers; cheese; croutons; green spring onions


 * 	All soup ingredients coded separately and/or additions 


Frozen meal (07)-­


* 	Frozen meal with: catsup, tartar sauce, margarine/butter


 * 	All frozen meal ingredients coded separately


Ice cream/frozen yogurt (08)-­


* 	Ice cream or frozen yogurt with: syrup; toppings; fruit; nuts; 


whipped cream; candy; cookies


 * 	All ingredients of a sundae coded separately


Vegetables (09)-­


* 	French fries with: catsup; gravy; steak sauce; vinegar; dressing


 * 	Potato chips with: dip


 * 	Potatoes with: gravy; sour cream; toppings; butter/margarine added


 * 	Beans, legumes with: sauce; margarine/butter


 * 	Vegetables (not specified as salad) with: margarine/butter; sauce; 


dip; dressing 


* 	Vegetables in a mixture coded separately


Fruit (10)-­


* 	Fruit with: whipped topping; sugar; milk/cream; syrup; honey


 * 	Fruits in a mixture (not specified as salad) coded separately


Other mixtures (99)-­


* 	Rice with: butter; gravy; sauce


 * 	Pasta/spaghetti with: butter; gravy; sauce


 * 	Meat, poultry, fish with: gravy; sauce; onions 


* 	Eggs with: catsup, salsa 


* 	Pizza with: grated cheese


 * 	Yogurt (not frozen) with: nuts, fruit, cereal, etc.


 * 	Foods/mixtures of foods that do not fit in other combination 


categories 


----------------------------------------------------------------------­


Attachment 1-87 
--------------------------------------------

--------------------------------------------

Examples of analyses using combination codes


The presence of combination codes in the CSFII 1994-96 and 1998 combined


data set may be useful in planning analyses, especially concerning


salads, sandwiches, or foods combined "at the table," such as cereal and


milk or corn chips and salsa. Examples 1 and 2 below illustrate how


using combination codes can affect frequencies and mean food and


nutrient intakes. Example 3 illustrates how combination codes can also


provide insight into food consumption patterns. 


Two-day intake data from the CSFII 1994 were used for all examples. The


estimates are unweighted. 


Example 1 (using combination types to measure frequency and mean intake


of specific food mixtures)--To fully account for all reports of a food


mixture such as a sandwich or salad, consideration must be made of the


different ways that foods may have been recorded and coded. Depending on


how a food was reported, it may have been coded as a single item or as


multiple items linked via a combination type and sequence number.


Including both ways of reporting in an analysis requires familiarity


with the food coding database, but it can give a more complete picture


of the consumption of that food. 


For instance, suppose the research objective was to determine


consumption of hamburgers and cheeseburgers. All of the hamburgers and


cheeseburgers that were coded as a single item received codes in the


range 275-10210 through 275-10690 in the CSFII 1994 food coding


database. The number of reports, mean intakes by sex-age group, and


sources of hamburgers and cheeseburgers coded as a single item are


presented in tables 3-3 and 3-4. 


Table 3-3. Number of reports and mean intake of hamburgers and 


cheeseburgers, single-code items only*, CSFII 1994 


(unweighted) 


-----------------------------------------------------------­


Number of Number of Mean intake


 Sex and age individuals reports per report


 (years) (gm) 
 -----------------------------------------------------------­
 Children < 6 1,140 150 96


 Children 6-11 506 67 134


 Teens 12-19 529 108 207


 Women 20+ 1,541 120 188


 Men 20+ 1,547 215 220


 Total 5,263 660 --­


-----------------------------------------------------------­


*Includes hamburgers or cheeseburger codes in the range 275-10210 


through 275-10690 regardless of whether that food was eaten in 


combination with another food or not. 


Attachment 1-88 
 Table 3-4. 	Places where hamburgers and cheeseburgers were 


obtained, single-code items only*, CSFII 1994 


(unweighted)
 -----------------------------------------------------------­


Restau- Fast School 


Sex and age Store** rant food cafeteria Other


 (years) 


-----------------------------------------------------------­
 -----------------Number----------------­


Children <6 6 6 135 1 2


 Children 6-11 4 3 52 8 0


 Teens 12-19 0 2 96 5 5


 Women 20+ 3 3 111 0 3


 Men 20+ 1 3 208 0 3


 Total 14 17 602 14 13


 ----------------------------------------------------------­


*Includes hamburgers or cheeseburger codes in the range 275-10210 


through 275-10690 regardless of whether that food was eaten in 


combination with another food or not. 


**Includes prepared sandwiches or sandwich ingredients purchased 


from stores. 


Attachment 1-89
It is not surprising that most of the hamburgers and cheeseburgers coded


as a single item were from fast food places, because the preferred


method given in the FIB for reporting standardized items such as fast


food sandwiches from national chains was as a single item. Nonfast-food


(or nonstandardized) hamburgers and cheeseburgers were more commonly


coded as multiple food items linked with a combination code, because the


FIB specified probes for the ingredients of nonstandardized sandwiches. 


Using only the single-item food codes does not consider those hamburgers


and cheeseburgers that were coded as multiple food items linked with a


combination code. One way to expand the definition of hamburgers and


cheeseburgers would be to include all sandwich combinations (COMBTYPE =


05) containing at least one code from the range 215-00100 through


215-40100 (ground beef) and one code from the range 510 ----- through


518 ----- (yeast breads and rolls). Other ingredients might also be part


of these combinations. For example, this group would include a report of


a sandwich with ground beef, lettuce, tomato, and ketchup on a kaiser


roll.


The numbers of reports and amounts resulting from adding combinations of


food items eaten as hamburgers and cheeseburgers to hamburgers and


cheeseburgers coded as a single item appear in tables 3-5 and 3-6. The


number of reports of hamburger and cheeseburger consumption is nearly


double that shown in table 3-3, and the distribution is less dominated


by the fast food sandwiches, as expected. 


Table 3-5. 	Number of reports and mean intake of hamburgers and 


cheeseburgers, single-code items and combinations, 


CSFII 1994 (unweighted)


 ----------------------------------------------------------­


Number of Number of Mean intake


 Sex and age individuals reports per report 


(years) (gm) 


-----------------------------------------------------------­


Children < 6 1,140 233 107


 Children 6-11 506 160 148


 Teens 12-19 529 216 207


 Women 20+ 1,541 272 183


 Men 20+ 1,547 430 220


 Total 	 5,263 1,311 --­


----------------------------------------------------------­


Attachment 1-90 
 Table 3-6. 	Places where hamburgers and cheeseburgers were 


obtained, single-code items and combinations, CSFII 


1994 (unweighted)
 -----------------------------------------------------------­


Restau- Fast School 


Sex and age Store* rant food cafeteria Other


 (years) 


-----------------------------------------------------------­
 -------------------Number------------------­


Children <6 52 12 144 12 13 


Children 6-11 50 9 60 33 8 


Teens 12-19 43 6 125 29 13


 Women 20+ 91 27 135 5 14


 Men 20+ 107 30 251 5 37


 Total 343 84 715 84 85


 ----------------------------------------------------------­


*Includes prepared sandwiches or sandwich ingredients purchased 


at stores. 


Example 2 (using combination codes to aggregate food groups for nutrient


analyses)--Similarly, assessments of the nutrient contributions of


specific foods can be affected if the food was often eaten as part of a


mixture that was coded in combination with other foods as well as


separately. Lettuce can serve as an illustration of this type of


situation. There is a series of codes in the 1994 food coding database


for lettuce-based salads coded as a single item (751-43000 through


751-46000 and 751-48000). An example of these lettuce-based salads is


751-43000 (lettuce, salad with assorted vegetables including tomatoes


and/or carrots, no dressing). The nutrient contribution of this food


group is shown in table 3-7. 


Attachment 1-91
 Table 3-7. 	Number of reports, mean intake, and nutrient 


contribution of lettuce-based salads, single-code 


salads only*, CSFII 1994 (unweighted)
 -----------------------------------------------------------­


Mean


 Number salad 


of intake 


re- per Ener- Pro- Carbo-


Sex and age ports report gy tein hydrate Fat


 (years) (gm) (kcal) (gm) (gm) (gm)
 -----------------------------------------------------------­


Children <6 24 38 7 .4 1.4 .1


 Children 6-11 21 57 12 .7 2.1 .3


 Teens 12-19 15 85 34 1.9 3.2 1.7


 Women 20+ 46 115 36 1.8 4.3 1.6


 Men 20+ 38 140 57 3.0 5.3 2.9


 -----------------------------------------------------------­


*Includes the nutrients from all lettuce-based salad codes 


(751-43000 through 751-46000 and 751-48000) regardless of whether 


that food was eaten in combination with another food or not. 


Using only the single-code salads has two deficiencies that can be


corrected by the use of combination codes. First, it can be noted from


examination of the food coding database that the lettuce-based salads


coded as a single item do not include salad dressing. This is because


the FIB specified probes for salad dressing in order to obtain as much


information as possible about the type and amount of salad dressing


eaten. Salad dressing is always linked to salad via a combination type


(04, salad) and sequence number. Consequently, if only single-code


salads are considered, the contribution of lettuce-based salads to total


fat intake is underestimated. Second, restricting the analysis to


single-code lettuce-based salads misses any salad-type combinations with


lettuce coded simply as lettuce (751-13000). 


When all lettuce-containing salad combinations (COMBTYPE = 04) are added


to all single-code lettuce-based salads (this time incorporating any


other ingredients linked to them via combination type 04 and sequence


number), the nutrient contributions are considerably different, as shown


in table 3-8. Not surprisingly, the contribution of lettuce-based salads


to nutrient intake, most notably energy and fat, is dramatically


increased when mixtures linked by combination codes are included. Mean


salad intakes also increased markedly, and the number of reports of


salads increased five- to fourteen-fold across the sex-age groups. 


Attachment 1-92
--------------------------------

--------------------------------

 Table 3-8. 	Number of reports, mean intake, and nutrient 


contribution of lettuce-based salads, single-code and 


combination salads, CSFII 1994 (unweighted)
 -----------------------------------------------------------­


Mean


 Number salad 


of intake 


re- per Ener- Pro- Carbo-


Sex and age ports report gy tein hydrate Fat


 (years) (gm) (kcal) (gm) (gm) (gm)
 -----------------------------------------------------------­


Children <6 144 77 77 1.5 4.1 6.3


 Children 6-11 109 101 101 1.6 5.5 8.5


 Teens 12-19 106 170 197 4.9 9.2 16.3


 Women 20+ 594 179 179 4.2 9.9 14.3


 Men 20+ 541 205 210 4.4 11.4 17.2


 -----------------------------------------------------------­


Example 3 (using combination codes to examine food consumption


patterns)--The manner in which individuals consume foods, that is,


separately or together with other foods, may be determined by using


combination codes. It can be expected that population subgroups vary in


their consumption patterns. For instance, children consume milk


primarily as a single-code item whereas adults more often consume milk


in combination with another food, such as coffee or cereal, as shown in


table 3-9 on the next page. Although nearly one-third (32.6 percent) of


all reports of milk by women were milk consumed as part of a beverage


combination (such as in coffee), the largest percentage of the total


quantity (in grams) that was consumed by women was provided by milk


consumed as a single item (57.1 percent). Milk added to cereal made a


substantial contribution to total milk consumption for all sex-age


groups. 


Limitations of combination codes


While combination codes may be used to identify foods eaten together,


disaggregation of combinations is not sufficient to enable researchers


to look at the total intake of a specific food. For example, a


researcher who wished to look at the total intake of tomatoes from all


sources could not arrive at that number by combining tomatoes reported


separately with those that were reported as part of a combination. That


method of analysis would miss tomatoes that are included as ingredients


in many single-code mixtures such as 283-10220 (chili beef soup) and


581-30010 (lasagna with meat and/or poultry).


Attachment 1-93
 Table 3-9. Milk consumption by combination type, CSFII 1994 


(unweighted)


 ----------------------------------------------------------­
 Combination type 


Sex and age ------------------------------------------


(years) Beverage Cereal Other 


Single combina- combina- combina­


item tion tion tions
 ----------------------------------------------------------­


--------------Number of reports-----------­


Children <12 3,383 200 1,665 15


 Teens 12-19 471 27 283 3


 Women 20+ 788 739 719 18


 Men 20+ 959 641 764 24


 All ages 5,601 1,607 3,431 60


 --------Percent of all milk reports-------­


Children <12 64.3 3.8 31.6 0.3


 Teens 12-19 60.1 3.4 36.1 0.4


 Women 20+ 34.8 32.6 31.8 0.8


 Men 20+ 40.2 26.8 32.0 1.0


 All ages 52.4 15.0 32.1 0.6


 ----Percent of total quantity consumed----­


Children <12 70.5 4.0 25.3 0.2


 Teens 12-19 67.1 2.5 30.0 0.4


 Women 20+ 57.1 11.1 31.2 0.6


 Men 20+ 61.1 8.4 29.6 0.9


 All ages 65.6 6.1 27.9 0.5


 -----------------------------------------------------------­


3.4 Quality Control


At every step during the development and execution of the CSFII/DHKS


1994-96 and CSFII 1998, quality control has been one of ARS' primary


concerns. During the process of CSFII/DHKS 1994-96 questionnaire


development, ARS solicited input from the Continuing Survey Users Group,


which is made up of representatives from 13 Federal agencies, as well as


other Federal users. The CSFII intake questionnaire underwent cognitive


testing by the Census Bureau's Center for Survey Methods Research, and


the "multiple-pass" approach used for the first time in 1994 was


developed to optimize the completeness of intake data collected [Tippett


and Cypel(eds.) 1997, DeMaio et el. 1993, Guenther et al. 1995]. The


DHKS questionnaire was revised and expanded with input from members of


the Continuing Survey Users Group and an in-house DHKS working group. It


Attachment 1-94
was then pretested for comprehensibility and flow by ARS in


collaboration with the Census Bureau's Demographic Surveys Division. ARS


staff and the contractor revised the Food Instruction Booklet used in


conjunction with the intake section of the CSFII questionnaire,


expanding the booklet to standardize probing by interviewers and ensure


the collection of adequate detail for food coding. 


All field supervisors, interviewers, and coders attended extensive


training sessions. All sessions were scripted for consistency and were


monitored by ARS staff. Bilingual interviewers attended an additional


day of training in the use of Spanish language questionnaires. Practice


interviews were reviewed by supervisors, and telephone retraining was


conducted when necessary. Detailed instruction manuals were provided to


supervisors, interviewers, and coders. 


Electronic communications permitted close tracking by the contractor and


ARS of assigned cases in the field, their completion status, and


documents in various stages of processing. Electronic delivery of survey


data facilitated the timely resolution of such issues as errors in


sample person selection or clarification by the interviewer of data


received by the home office. 


Survey Net, a computer-assisted food coding system (see documentation


section 3.3.1, "Food coding and editing," for additional information on


Survey Net) developed under a cooperative agreement between ARS and the


University of Texas-Houston Health Science Center, School of Public


Health, provided efficiency and accuracy in on-line coding of foods and


editing. Edit checks were built into the system to reduce data entry of


erroneously high or low food amounts and to catch some of the most


common reporting, recording, and coding errors. 


A pilot study duplicating the planned survey design on a small scale was


conducted from April to June of 1993. The pilot study tested the


questionnaires, data collection methods, field management procedures,


data entry and processing procedures, and survey management software


slated for implementation in the CSFII 1994-96. This experience provided


an excellent opportunity to further refine the quality of survey


instruments and improve the efficiency of survey operations. 


As a result of the pilot study, interviewer training was lengthened to 7


full days to allow more thorough coverage of survey procedures.


Modifications that had been made to the questionnaires and data


collection procedures were judged to be effective in reducing respondent


burden and facilitating the collection of high-quality data. Interviewer


field notebooks and debriefing after the pilot study provided feedback


resulting in further revision of the questionnaires. Survey management


software programs used by the contractor and ARS were found to be


effective tools for monitoring survey activities and improving the


efficiency of survey operations. 


ARS data processing activities were reviewed by a panel of outside


experts in November 1994. The panel's primary recommendation was that


ARS scale back its exhaustive review of the data by prioritizing tasks


and streamlining the mechanics of data processing. Quality control


Attachment 1-95 
procedures described in this section reflect ARS' implementation of the


panel's recommendations. 


Achieving acceptable response rates in the CSFII/DHKS 1994-96 was a


priority for ARS. By contract, Westat, Inc., was required to meet


specified response rate requirements for each questionnaire (screener,


household, individual intake, and DHKS). 


Many steps were taken to monitor interviewer performance. These included


partial reinterviews of 10 percent of each interviewer's cases to


validate contact of households, audiotaping of at least one intake


interview and two DHKS interviews per interviewer per year, and


in-person observations. 


Interviewers were instructed to edit their own work as soon as possible


after the interview to identify and correct errors in recording and to


permit (with supervisory permission) retrieval of any missing


information from the respondents. Completed questionnaires were reviewed


within 2 days of receipt at the Westat central office to determine


whether they met ARS minimum criteria. If not, callbacks were made to


obtain missing information. Reviews sometimes led to telephone


mini-retrainings of interviewers. Field staff memos and a quarterly


newsletter provided all interviewers with answers to questions raised


during training and in the field, as well as feedback on problem areas


detected in data review by the contractor and ARS. 


Food coders were required to pass a certification test developed by ARS


before they were allowed to code survey data. Initially, 100 percent of


each food coder's work was verified by blind double-coding with


resolution of any differences. At the supervisor's discretion, this


adjudication process was applied to less of the coder's work; 10 percent


of the food coder's work continued to be verified routinely. Problems in


the food coding process were discussed at biweekly food coding meetings.


ARS monitored coder performance by occasionally observing food coders at


work, by periodically attending coder meetings and refresher trainings,


and by comparing information recorded on the questionnaire to coded


entries. 


Accuracy of nonfood data entry was verified by routine 100 percent


independent double entry with resolution of differences by coding


supervisors. Nonfood data were edited for reasonableness, logic, and


consistency; supervisors resolved discrepancies. 


ARS verified the accuracy of weekly data delivery by checking each


hard-copy document received against an electronic list of documents. At


least 10 percent of all food intake questionnaires were reviewed for


accuracy in coding and data entry. In addition, all foods and food


amounts that could not be coded by the contractor (i.e., "unknowns")


were reviewed and coded by ARS food coding staff. Other food codes and


amounts flagged by the contractor as questionable were reviewed for


accuracy. All recipe modifications (see documentation section 3.4.1,


"Food coding and editing") done by Westat were reviewed by ARS coding


staff.


Attachment 1-96 
A series of reviews was conducted on food intake data. Values of food


variables falling outside reasonable parameters were flagged, checked


against information recorded on the questionnaire, and corrected if in


error. ARS reviewed audiotaped intake interviews for proper interviewing


techniques. Any problems in interviewer or coder performance detected by


ARS were brought to the attention of the contractor. 


ARS review of nonfood data was also extensive, encompassing over 30


specific edit checks for reasonableness, consistency, and logic. Values


falling outside of reasonable or expected parameters were checked


against information recorded on the questionnaire and corrected if in


error.


All screeners from eligible households were reviewed to confirm that


proper sampling procedures had been followed. Sampling errors were


immediately brought to the contractor's attention. 


All household questionnaires and DHKS questionnaires were reviewed to


ensure that proper interviewing and coding procedures had been followed.


Any interviewer or coder problems were summarized in periodic reports to


Westat. Also, audiotaped DHKS interviews were reviewed by ARS, and


general feedback was provided to the contractor. 


The accuracy of the Survey Nutrient Database was also a priority for


ARS. Numerous quality control checks were performed on various


components of the Survey Nutrient Database, such as nutrient values for


new or updated codes in the Primary Data Set, the recipe file, and the


file of weights for household measures. Final nutrient values in the


1994-96 Survey Nutrient Database were confirmed by a series of


comparisons to earlier Survey Nutrient Databases, with subsequent review


of values falling outside of reasonable parameters. After food codes


were aggregated by type of food, averages of nutrients from those foods


were subjected to many of the same rigorous outlier checks conducted for


Primary Data Set codes. 


Every nutrient intake value (daily total) from each responding sample


person's intake was tested for reasonableness against parameters for


individuals of that age and sex. In addition to detecting errors in


coding of foods or amounts, this provided an additional quality check of


the nutrient database. 


Attachment 1-97 
3.5 Glossary


Age--Calculated from date of birth, if given. Otherwise, age as given by


respondent. For responding sample persons (see "Responding sample


person"), this is the age as of the day-1 intake; for others, this is


the age on the day of screening. 


Alpha-tocopherol equivalent--See "Vitamin E."


Assistant--Person who assisted in the dietary recall for a sample person


age 6 to 9 years in CSFII 1998 and age 6 to 11 years in CSFII 1994-96. 


Black--See "Race."


Breast-fed child--A child 3 years of age or younger at the time of the


household interview who was identified by the household respondent as


being breast fed currently. Breast- fed sample persons were included in


the weighting process, and the survey data set includes information on


breast-fed children as discussed in section 7.6.2, "Breast-fed


Children." 


Calcium conversion factor--A factor that expresses the amount of calcium


in 100 grams of a given milk product (that is, any food code beginning


with "1") as a proportion of the amount of calcium in 100 grams of fluid


whole cow's milk. For example, the calcium conversion factor for cheddar


cheese was calculated by dividing the amount of calcium in 100 grams of


cheddar cheese (721 milligrams) by the amount of calcium in 100 grams of


fluid whole cow's milk (119.4 milligrams), resulting in a calcium


conversion factor of 6.04. Used in calculation of calcium equivalent as


described below. 


Calcium equivalent--The amount, expressed in grams, of whole fluid cow's


milk that has the same quantity of calcium as the reported milk product.


Derived by multiplying the amount of the milk product eaten, expressed


in grams, by the calcium conversion factor (see "Calcium conversion


factor" above.) For example, the calcium equivalent of 2 ounces (57


grams) of cheddar cheese is calculated by multiplying 57 grams x 6.04


(the calcium conversion factor for cheddar cheese) = 344 grams. Thus,


the amount of calcium in 57 grams of cheddar cheese is equal to the


amount of calcium in 344 grams of whole fluid milk. Intakes of total


milk and milk products may be compared among population groups using


calcium equivalents to take into account the different calcium densities


of milk products subgroups (for example, fluid milk and cheese) that may


be used in varying proportions by the population groups. The calcium


equivalent is present on record type 30 (rt30.dat) in the field CALEQ.


Carotenes--Beta-carotene and other provitamin-A carotenoids. See


"Vitamin A." 


Central city--See "Urbanization." 


Combination--Foods combined together and consumed as a unit that were


coded using two or more food codes; identified by the record type 30


(rt30.dat) fields COMBNUM and COMBTYPE. For more discussion of


combinations, see sections 3.3.1, "Food coding and editing," and 3.3.8,


"Combination codes." 


Attachment 1-98 
Dietary fiber--Total dietary fiber including both the insoluble fraction


(cellulose, hemicellulose, and lignin) and the soluble fraction (for


example, gums in cereal grains and pectin in fruits and vegetables). 


Dietary intake--See "Food intake." 


Dwelling unit--House, apartment, room, or group of rooms occupied as


separate living quarters, when the occupants do not live and eat with


any other person in the structure and when there is direct access from


the outside or through a common area or hall. Synonymous with "housing


unit" as described in the definition of "households" for the 1990 Census


(Baugher and Lamison-White 1996). 


Eating occasion--Any report of eating or drinking by a sample person.


Each change in time of eating reported on the questionnaire was


considered to be a separate eating occasion. 


Educational level--For each household member 15 years of age or older,


the household respondent was asked to name the highest grade of formal


schooling completed, starting with "kindergarten or less" and continuing


in 1-grade or 1-year increments to "5 or more years of college." Formal


schooling does not include trade or vocational schooling, company


training, or tutoring, unless credit is given which would be accepted at


a regular school or college. High school equivalency (GED) was


considered equal to completing grade 12. 


Employment status--For each household member 15 years of age or older,


the household respondent was asked whether the person worked during the


week preceding the interview and, if so, how many hours. "Work" includes


any full-time or part-time activity for which money, goods, or services


were received. Employment includes active duty in the armed forces. An


individual was also "employed" if he or she had a job but was not


actually at work that week. Full-time status equals 35 hours or more


worked during the week; part-time status equals 1 to 34 hours. See 


Attachment 1-99 
discussion of the field EMP_STAT in section 9.3, "Additional


Documentation on Calculated Variables."


Ethnic origin--The screener respondent reported whether or not each


household member was of Mexican/Mexican-American/Chicano, Puerto Rican,


Cuban, or other Spanish or Hispanic origin. 


Exercise--Sample persons 12 years of age or older were asked "How often


do you exercise vigorously enough to work up a sweat?"


Female head of household--Person indicated as such by the household


respondent. (Included for purposes of historical comparison.) 


Folate--Total folate content; includes naturally occurring folate and


added folic acid. Folate values have been updated to reflect the


regulation requiring enriched grain products to include added folic acid


beginning January 1998.


Food intake--All beverages (except plain water with nothing in it) and


foods ingested. Does not include inedible parts of foods (such as bones,


rinds, and seeds); uneaten portions of food; or vitamin, mineral, or


other supplements. 


Health status--Self-appraised. 


Height--Self-reported. 


Home food supply--Foods and beverages ingested at home (including food


obtained away from home and carried home to be eaten) and food items


carried from home and eaten elsewhere, such as those in picnics and


packed lunches. (Included for purposes of historical comparison.) See


the file formats for record type 30 (rt30.dat) fields EATHOME and


EVERHOME.


Household--All persons who regularly share a house, an apartment, a


room, or a group of rooms used as separate living quarters. Household


membership is based on the place where a person usually lives or sleeps


for 6 or more months per year and where the person is free to return at


any time. Includes persons temporarily absent, such as those who were in


the hospital or traveling; students who live away from the sampled


dwelling unit in dormitories or sorority or fraternity housing while


attending school, who are scheduled to return to the household at the


end of the term, and who use the sampled dwelling unit as their


permanent address; domestic or other employees who usually live and


sleep at the sampled dwelling unit; boarders or roomers who usually live


and sleep at the sampled dwelling unit; and persons temporarily visiting


the dwelling unit who have no usual place of residence elsewhere, such


as a visitor who is house hunting. Excludes former household members who


live in institutions, nursing homes, convents, etc.; persons working


abroad; and members of the armed forces stationed elsewhere. Excludes


students who live in an off-campus dwelling unit while attending school,


persons who take their meals in the household but usually lodge or sleep


elsewhere, domestic or other employees who live in an adjacent but


separate dwelling unit, and persons temporarily visiting the household


who have a usual place of residence elsewhere to which they are free to


Attachment 1-100 
return at any time. Excludes noninstitutional group quarters of nine or


more unrelated persons living and eating together. 


Household income--Household respondent's estimate of the total income


from all sources, before taxes, of all household members for the


calendar year prior to the interview. Includes income of


roomers/boarders. Excludes income of live-in employees. See discussion


of the field INCOME in section 9.3, "Additional Documentation on


Calculated Variables."


Household member--See "Household." 


Household respondent--Person who answered the household questionnaire,


usually either the main meal planner/preparer or a person knowledgeable


about household characteristics such as income; not necessarily a sample


person. 


Household size--Number of individuals in a household. 


Income--Both monthly and annual household income were collected. See


section 9.3, "Additional Documentation on Calculated Variables," for a


discussion of income and for information on imputed incomes. 


Key field--A frequently-used field (variable) included in all record


types (data files). See section 7.4.1 for a list of the key fields. 


Lactating female--A female household member 10 to 55 years of age


identified by the household respondent as currently breast-feeding a


child 3 years of age or less. 


Main meal planner/preparer--Person who usually plans and/or prepares the


household's meals or does the major food shopping. This person was the


preferred household respondent, proxy, and assistant. 


Male head of household--Person indicated as such by the household


respondent. (Included for purposes of historical comparison.) 


Metropolitan Statistical Area--A geographic area consisting of a large


population nucleus together with adjacent communities that have a high


degree of economic and social integration with that nucleus; defined by


the Federal Office of Management and Budget for use in the presentation


of statistics by agencies of the Federal government (USDC/BOC and APDU


1993). 


Midwest--See "Region." 


Niacin--Nicotinic acid and nicotinamide present in foods. Does not


include potential niacin that could be converted from dietary


tryptophan, a niacin precursor, in the body. 


Nonmetropolitan areas--See "Urbanization." 


Nonrespondent--Sample person who did not complete an interview. 


Northeast-- See "Region." 


Attachment 1-101 
Nutrient intake--Nutrient content of all foods and beverages (except


plain water with nothing in it) ingested. Excludes vitamin, mineral, and


other supplements. 


One-day dietary recall--A recall of beverages and foods ingested during


the day preceding the interview--the 24 hours from 12:00 a.m. (midnight)


to 11:59 p.m. 


Percentage of poverty level--Household income for the previous calendar


year expressed as a percentage of the Federal poverty thresholds


(Baugher and Lamison-White 1996) adjusted for inflation. See discussion


of the field PCTPOV in section 9.3, "Additional Documentation on


Calculated Variables."


Poverty level--See "Percentage of poverty level." 


Pregnant female--Female household member 10 to 55 years of age


identified by the household respondent as currently pregnant. 


Proxy--Knowledgeable adult who completed the dietary recall for children


under 6 years of age and other sample persons unable to report for


themselves due to physical or mental limitations or because of illness.


Proxy interviews were not substituted for in-person interviews with


adult sample persons who were difficult for the interviewer to reach or


who were nonrespondents. 


Race--The screener respondent reported the race of each household member


as white, black, Asian/Pacific islander, American Indian/Alaskan native,


or some other race. 


Recommended Dietary (or Energy) Allowances (RDA or REA)-- Levels of


nutrient (or energy) intake considered by the Food and Nutrition Board


of the National Academy of Sciences to be adequate to meet the known


nutritional needs of practically all healthy individuals (NRC/FNB 1989).


In a population group whose usual intake approximates or exceeds the


RDA, the likelihood of deficiency is small (NRC/FNB 1989). 


Region--An area of the United States as defined by the U.S. Department


of Commerce for the 1990 Census of Population. The four census regions


and their States are as follows:


 (1) Northeast: Connecticut, Maine, Massachusetts, New 

Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, 

Vermont;

 (2) Midwest: Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, 

Missouri, Nebraska, North Dakota, Ohio, South Dakota, 

Wisconsin; 

(3) South: Alabama, Arkansas, Delaware, District of Columbia, 

Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, 

North Carolina, Oklahoma, South Carolina, Tennessee, Texas, 

Virginia, West Virginia; 

(4) West: Alaska, Arizona, California, Colorado, Hawaii, Idaho, 

Attachment 1-102 
 Montana, Nevada, New Mexico, Oregon, Utah, Washington, 


Wyoming. 


Responding sample person--Household member who was selected to


participate in the individual intake component of the survey and who


provided at least 1 day of dietary intake data. 


Retinol equivalents--See "Vitamin A."


Sample person--Household member selected to participate in the


individual intake component of the survey. 


Sampling weights--Weights required in analysis to compensate for


variable probabilities of selection, differential nonresponse rates, and


possible deficiencies in the sampling frame. See section 5, "SAMPLING


WEIGHTS."


Screening respondent--Household member 18 years of age or older who


answered the screening questionnaire (screener). 


Source of food--The place where each food or beverage (or most of the


ingredients of a mixed item) was obtained, for example, from a store,


restaurant, vending machine, or Meals on Wheels; as a mail order


purchase; or as a gift from someone else. This information was provided


by the sample person, proxy, or assistant. 


South--See "Region."


Suburban areas--See "Urbanization." 


Supplements--Vitamins and minerals ingested in a form other than in food


or beverage. Not included in food and nutrient intake data.


Urbanization--Based on Metropolitan Statistical Areas (MSA's) defined by


the Federal Office of Management and Budget (OMB) using information and


recommendations provided by the U.S. Bureau of the Census. The three


levels of urbanization are as follows: 


(1) 	MSA, central city: All OMB-designated central cities, as 


defined by their corporate city limits, located in 1990 MSA's. 


These are primarily the urban cores of the MSA's. Although 


some MSA's contain no central city, most MSA's contain one or 


more. 


(2) 	MSA, outside central city: The remaining counties or county 


equivalents located in MSA's. 


(3) 	Non-MSA: All counties or county equivalents that were located 


outside of 1990 MSA's. 


Vitamin A--Vitamin A activity derived from both preformed vitamin A


(retinol) and provitamin A carotenoids. Values are expressed as


international units (IU) and as micrograms of retinol equivalents (RE).


One IU equals 0.3 micrograms of retinol, 0.6 micrograms of


beta-carotene, or 1.2 micrograms of other carotenoids having vitamin A


Attachment 1-103
activity. One RE equals 1 microgram of retinol, 6 micrograms of


beta-carotene, or 12 micrograms of other provitamin A carotenoids.


Vitamin E--Vitamin E activity derived from alpha-, beta-, and


gamma-tocopherol and alpha-tocotrienol. Values are expressed as


milligrams of alpha-tocopherol equivalents. One alpha-tocopherol


equivalent equals 1 milligram of alpha-tocopherol, 2 milligrams of


beta-tocopherol, 10 milligram of gamma-tocopherol, or 3.3 milligrams of


alpha-tocotrienol.


Weight--Self-reported. 


Weighting factors--See "Sampling weights."


West--See "Region."


White--See "Race."


Attachment 1-104 
3.6 References


Baugher, E. and L. Lamison-White. 1996. Poverty in the United States:


1995. U.S. Bureau of the Census, Current Population Reports, Series


P60-194 (see appendix A). 


Beecher, G.R., and R.H. Matthews. 1990. Nutrient composition of foods.


In: M.L. Brown, ed., Present Knowledge in Nutrition, 6th ed., pp.


430-443. International Life Sciences Institute Press, Washington, DC.


DeMaio, T.M., S. Ciochetto, and W.L. Davis. 1993. Research on the


Continuing Survey of Food Intakes by Individuals. In: 1993 Proceedings


of the Section on Survey Research Methods, papers presented at American


Statistical Association, San Francisco, August 8-12, 1993, and American


Association for Public Opinion Research, St. Charles, IL, May 20-23,


1993, vol. II, pp. 1021-1026. American Statistical Association,


Alexandria, VA. 


DHHS (U.S. Department of Health and Human Services). 1998. 1998 Poverty


Guidelines. Federal Register 63(36):9235-9238. 


DHHS/FDA (U.S. Department of Health and Human Services, Food and Drug


Administration). 1996. Food standards: Amendment of standards of


identity for enriched grain products to require addition of folic acid.


Final rule. Code of Federal Regulations, Title 21, Parts 136, 137, and


139. Federal Register 61(44--March 5, 1996):8781-8797. 


Federal Register. October 22, 1990. Public Law 101-445. National


Nutrition Monitoring and Related Research Act of 1990.


Gebhardt, S.E. 1992. Imputing nutrient values. In: 17th National


Nutrient Data Bank Conference Proceedings, Baltimore, MD, June 7-10,


1992, pp. 143-153. International Life Sciences Institute Press,


Washington, DC.


Gebhardt, S.E., J.M. Holden, and C.S. Davis. 1990. Preliminary report: A


nationwide study of the selenium content of U.S. foods. In: M.R.


Stewart, ed., Proceedings of the 15th National Nutrient Databank


Conference, Blacksburg, VA, June 3-6, 1990, pp. 43-52. The CBORD Group,


Inc., Ithaca, NY.


Guenther, P.M., T.J. DeMaio, L.A. Ingwersen, et al. 1995. The


multiple-pass approach for the 24-hour recall in the Continuing Survey


of Food Intakes by Individuals (CSFII) 1994-96. International Conference


on Dietary Assessment Methods, Boston, January 23, 1995. 


Holden, J.M., S. Gebhardt, C.S. Davis, and D.G. Lurie. 1991. A


nationwide study of the selenium contents and variability in white


bread. Journal of Food Composition and Analyses 4:183-195.


Marcoe, K.K., and D.B. Haytowitz. 1993. Estimating nutrient values of


mixed dishes from label information. Food Technology 47(4):69-75. 


Attachment 1-105 
Martin, J.I., W.O. Landen, Jr., A.M. Soliman, et al. 1990. Application


of a tri-enzyme extraction for total folate determination in foods.


Journal of the Association of Official Analytical Chemists 73:805-808.


Matthews, R.H. 1991. Current HNIS nutrient data research. In: 16th


National Nutrient Databank Conference Proceedings, San Francisco, June


17-19, 1991, pp. 129-132. The CBORD Group, Ithaca, NY. 


NAS/NRC (National Academy of Sciences, National Research Council). 1993.


Pesticides in the diets of infants and children. National Academy Press,


Washington, DC. 


NRC/FNB (National Research Council, Food and Nutrition Board). 1989.


Recommended Dietary Allowances, 10th ed. National Academy Press,


Washington, DC.


Perloff, B.P. 1985. Recipe calculations for NFCS database. In: S. Murphy


and D. Rauchwarter, eds., Proceedings of Tenth National Nutrient Data


Bank Conference, San Francisco, July 22-24, 1985, pp. 11-21. National


Technical Information Service, Springfield, VA.


Perloff, B.P., R.L. Rizek, D.B. Haytowitz, et al. 1990. Dietary intake


methodology II: USDA's nutrient data base for nationwide dietary intake


surveys. Journal of Nutrition 120:1530-1534.


Powers, P.M., and L.W. Hoover. 1989. Calculating the nutrient


composition of recipes with computers. Journal of the American Dietetic


Association 89:224-232.


Tippett, K.S., and Y.S. Cypel (eds.). 1997. Design and operation: The


Continuing Survey of Food Intakes by Individuals and the Diet and Health


Knowledge Survey 1994-96. U.S. Department of Agriculture, Agricultural


Research Service, Continuing Survey of Food Intakes by Individuals


1994-96, Nationwide Food Surveys Rep. No. 96-1. (Included on Disk 1 in


\pdffiles\dor.pdf.)


USDA/ARS (U.S. Department of Agriculture, Agricultural Research


Service). 1996. USDA Nutrient Database for Standard Reference, Release


11. ARS Nutrient Data Laboratory Home Page


<http://www.nal.usda.gov/fnic/foodcomp>.


USDA/DHHS (U.S. Departments of Agriculture and Health and Human


Services). 1990. Nutrition and your health, Dietary Guidelines for


Americans. Home and Garden Bulletin No. 232, 3rd ed. 


USDC/BOC (U.S. Department of Commerce, Bureau of the Census). 1993.


Current Population Survey, March 1992. Conducted for the Bureau of Labor


Statistics. Machine readable data file. 


USDC/BOC and APDU (U.S. Department of Commerce, Bureau of the Census,


and Association of Public Data Users). 1993. A guide to State and local


census geography. U.S. Department of Commerce, Bureau of the Census,


1990 CPH-I-18.


Attachment 1-106 
FOOD COMMODITY INTAKE DATABASE


Appendix C


[Note: This Appendix includes information from the documentation


section, Chapters 5 and 6, of the following CD-ROM: U.S. Department of


Agriculture, Agricultural Research Service. 2000. Continuing Survey of


Food Intakes by Individuals 1994-96, 1998. NTIS Accession No. PB2000­


500027.] 


5. SAMPLING WEIGHTS


5.1 Introduction to Sampling Weights Discussion


In general, the analysis of data from surveys having complex


designs requires the use of sampling weights to compensate for


variable probabilities of selection, differential nonresponse


rates, and possible deficiencies in the sampling frame. The


CSFII/DHKS 1994-96 data set release contained sets of sampling


weights appropriate for use in the analysis of the annual data


sets as well as sampling weights appropriate for the analysis


of the 3 years combined. In addition to the sampling weights


provided with the 1994-96 (3-year) release, this combined


CSFII 1994-96, 1998 (4-year) release provides sampling weights


appropriate for use in the analysis of the 4-year data set


and sampling weights for use in the analysis of the CSFII 1998


data separately. Table 5-1 provides counts of children in the


combined 1994-96, 1998 data set. Tables 5-2 and 5-3 summarize


the sampling weight sets.


Guidance in the choice of appropriate sampling weights and


in the application of the reporting guidelines followed by the


USDA in the preparation of general statistical reports is


provided in Section 5.2 below. Sections 5.3 through 5.5


document the construction of the weights. Section 5.6


discusses variance estimation procedures appropriate with the


analysis of data from this data set.


Sampling weights appropriate for the analysis of the combined


CSFII 1994-96 data at the household level were made available


in the spring of 1999. Those 3-year household weights have been


included with this release. Section 5.7 provides the


documentation that accompanied the original release of the


household weights.


Although the Diet and Health Knowledge Survey (DHKS) was not


administered for CSFII 1998, the DHKS data records and


sampling weights from 1994-96 have been included with this


release on record type 50. Furthermore, sampling weights


designed for the analysis of household-level data from both


the 3-year and 4-year data sets have been included with


this release.


5-1


Attachment 1-107 
Table 5-1. Number of children providing intakes in


 CSFII 1994-96 and CSFII 1998


 1994-96 1998 Total


Under 1 year old 376 1,175 1,151


1 year old 711 373 1,084


2 years old 705 402 1,107


3 years old 492 1,344 1,836


4 years old 511 1,348 1,859


5 years old 475 409 884


6 years old 256 343 599


7 years old 233 71 304


8 years old 236 53 289


9 years old 258 41 299


0 - 9 years old 4,253 5,559 9,812


Table 5-2. 	Final sampling weights* provided with the combined


 CSFII 1994-96, 1998 data release


 Annual# 	 1994-96 1994-96, 1998 Available on


 (3-year) (4-year) record types


One day WTA_DAY1 WT3_DAY1 WT4_DAY1 RT20, RT25,


of intake RT30, RT35,


 RT40


Two days WTA_2DAY WT3_2DAY WT4_2DAY RT20, RT25,


of intake RT30, RT35,


 RT40


Household Not WT3_HH WT4_HH RT15


level provided


DHKS@ WTA_DHK WT3_DHK Not RT50


 provided


DHKS with WTA_DHK2 WT3_DHK2 Not RT50


two days provided


of intake@


* 	 Columns 3, 4, and 5 give the names of the sampling weights


 where weights are available. Jackknife replicate weights for


 variance estimation are also provided for each of these sets


 of sampling weights (see section 5.6.2, "Estimation of


 Sampling Errors").


# 	 These weights are appropriate for separate analysis of


 years 1994, 1995, 1996, or 1998.


@ 	 DHKS sampling weights are only applicable for 1994, 1995,


 and 1996. The DHKS was not administered for the CSFII 1998.


 5-2


Attachment 1-108 
Table 5-3. 	Summary of sampling weights included in the


 combined CSFII 1994-96, 1998 release


 Ages 0-19


 Sample Sum of CV* VIF#

 size weights 

Day 1: 1994 2,298 76,641,610 64.83 1.42

 1995 1,981 77,498,715 69.98 1.49

 1996 1,952 78,316,471 59.52 1.35

 1998 5,559 40,134,208 209.16 5.37

 4-year 11,790 77,485,571 111.23 2.24

 3-year 6,231 77,485,604 62.06 1.39 

2-day: 1994 2,223 76,641,600 76.07 1.58

 1995 1,904 77,498,713 80.44 1.65

 1996 1,853 78,316,485 73.44 1.54

 1998 5,304 40,134,206 213.58 5.56

 4-year 11,284 77,485,611 122.57 2.50

 3-year 5,980 77,485,635 74.29 1.55

 Ages 20+


 Sample Sum of CV* VIF#


 size weights


Day 1: 1994 3,291 182,865,657 58.47 1.34


 1995 3,345 184,451,592 69.42 1.48


 1996 3,236 185,917,776 51.72 1.27


 3-year 	 9,872 184,411,673 59.68 1.36


2-day: 1994 3,088 182,865,609 70.04 1.49


 1995 3,168 184,451,679 86.34 1.75


 1996 3,067 185,917,706 63.91 1.41


 3-year 	 9,323 184,411,625 73.78 1.54


* 	 CV is the population coefficient of variation for the sampling


 weights (standard deviation / mean) expressed as a percentage


# 	 The variance inflation factor, VIF = 1 + (CV / 100)**2


 -- continued


 5-3


Table 5-3. 	Summary of sampling weights included in the


 combined CSFII 1994-96, 1998 release -- continued


 All ages


 Sample Sum of CV* VIF#


 size weights


Day 1: 1994 	 5,589 259,507,267 65.80 1.43


Attachment 1-109 
 1995 5,326 261,950,307 72.14 1.52

 1996 5,188 264,234,247 56.76 1.32

 1998 5,559 40,134,208 209.16 5.37

 4-year 21,662 261,897,244 91.40 1.84

 3-year 16,103 261,897,277 64.05 1.41 

2-day: 1994 5,311 259,507,209 77.59 1.60

 1995 5,072 261,950,392 87.73 1.77

 1996 4,920 264,234,191 69.11 1.48

 1998 5,304 40,134,206 213.58 5.56

 4-year 20,607 261,897,236 104.52 2.09

 3-year 15,303 261,897,260 77.74 1.60

 Household


 Sample Sum of CV* VIF#


 size weights


 4-year 12,364 98,574,787 85.67 1.73


 3-year 8,067 98,574,761 45.88 1.21


* 	 CV is the population coefficient of variation for the sampling


 weights (standard deviation / mean) expressed as a percentage


# 	 The variance inflation factor, VIF = 1 + (CV / 100)**2


 5-4


Attachment 1-110
5.2 Guidance for Sampling Weights and Reporting


5.2.1 Sampling weight guidance


As noted above, it is generally necessary to use sampling weights


in the analysis of data from surveys having complex designs.


This data release contains a variety of sets of sampling weights


designed to be used in various situations. The choice of which


sampling weight to use was straightforward with the


CSFII/DHKS 1994-96 release. Day 1 weights are used whenever day 1


intakes are analyzed and generally whenever analyzing CSFII data


at the person level. The 2-day weights need to be used when a


subset of the sample is used that is restricted to 2-day


respondents. The 3-year weights are generally used if all 3 years


of data are being analyzed. The annual weights are generally used


if the individual years are analyzed separately. However, results


do not tend to change very much if the annual and 3-year sampling


weights are used interchangeably because sampling procedures and


the target population were the same in each of 1994, 1995, and 1996.


With the CSFII 1998 the situation changes somewhat. Because


only children 9 years old or younger were targeted in 1998 and


relatively few of those children were in the age group 7-9 years,


the weights constructed for use with the CSFII 1998 and the


combined CSFII 1994-96, 1998 sample have several features that


should be noted. Among these features are:


1) The CSFII 1998 weights are more variable than the other

 annual weights. This is mainly due to the unequal

 distribution of ages in the 1998 sample as seen in Table 5-1.

 It should be noted that the weights for a subset of the

 CSFII 1998 sample that is more equally distributed across

 ages, such as children 1-5 years or children 7-9 years,

 are considerably less variable. 

2) The combined CSFII 1994-96, 1998 weights are more variable

 than the CSFII 1994-96 weights for children 0-9 years old.

 This is due to the more variable CSFII 1998 weights and to the

 difference in distribution of ages between the two samples. 

3) For convenience, there are sampling weights for adults 20

 years and older in the set of 4-year weights. These are

 exactly the same weights found in the 3-year weight set.

 Adults were not sampled in 1998. 

4) Although no data was collected for persons 10-19 years in

 the CSFII 1998, the 4-year weights for these persons are

 slightly different than the 3-year weights. This is

 because the final calibration process for the 4-year

 weights was done for persons 6-19 years as a group. The

 calibration adjustments necessary for the 4-year weights

 for 6-19 year olds differed from the adjustments

 necessary for the 3-year weights due to the inclusion of

 children 6-9 years from CSFII 1998.

 5-5 

It will be the USDA's convention to use the 4-year combined


CSFII 1994-96, 1998 weights whenever a statistical presentation


uses data from the CSFII 1994-96, 1998 data set and displays


statistics for children 9 years and under. For statistical


presentation of data for persons 10-19, years USDA also


recommends the usage of the 4-year combined weights for the


reasons explained in item (4) above.


Furthermore, the USDA recommends caution in analyzing the


CSFII 1998 by itself. Unlike the annual samples of


CSFII 1994-96, the CSFII 1998 is a supplemental sample,


Attachment 1-111 
designed to be merged with the CSFII 1994-96 in order to


increase the overall sample size of children of certain ages.


The CSFII 1998 sampling weights provide some calibration of


the CSFII 1998 sample to the population of 0 to 9 year olds


but the fact that there are proportionately fewer children


7 to 9 years in the sample than children of other ages might


affect analyses of groups that include both children 7 years


or older and younger children.


5.2.2 Reporting guidance


It is the USDA's convention to follow guidelines derived from a


report of the Life Sciences Research Office (FASEB/LSRO 1995)


in identifying or flagging estimates of means, percentages, and


percentiles presented in general reports that might be less


statistically reliable than other estimates due to small cell


size or high relative variability. The guidelines for


determining such estimates take into account the complex sample


design of a survey and the procedures used to weight the data by


specifying the use of a broadly calculated design effect.


The design effect is a measure of the variability introduced


into an estimate by these factors.


Each estimate has a unique design effect. A "broadly


calculated" design effect might be an average of design


effects among related statistics or population groups. For


the convenience of having a single measure of this type of


variability, it is the USDA's convention to use a variance


inflation factor (VIF) in this role in the presentation of


general statistical tables. A VIF is solely a function of the


sampling weights. Variance inflation factors for the


CSFII 1994-96 and CSFII 1998 sampling weight sets are


presented in table 5-3 above.


 5-6


Attachment 1-112 
Prior to the release of data from the CSFII 1998, the USDA


has used by convention a single VIF, derived from the weights


of individuals of all ages, in the presentation of statistics


from USDA survey data. This convention will be changed for


the analysis of data from the combined CSFII 1994-96, 1998


sample. Whenever a statistical presentation is based on data


for persons under 20 years of age from both CSFII 1994-96 and


CSFII 1998, a VIF based on the 4-year weights on persons 0-19


years will be used in applying the reporting guidelines. If


statistics for adults are also provided, a VIF based on the


weights of persons 20 years and older will be used. If


statistics for persons from both groups are presented, for


example, a table showing statistics for various age groups


including an all-ages group, the VIF for persons 0-19 years


will be used. The VIFs that would be used in such reports are:


 Day 1, 0-19: 2.24


 Day 1, 20+: 1.36


 Day 1, all: 2.24


 2-day, 0-19: 2.50


 2-day, 20+: 1.54


 2-day, all: 2.50


The reporting guidelines generally followed are:


1) 	 An estimated mean is flagged when it is based on a cell


 size of less than 30 times the average design effect


 (VIF) or when its coefficient of variation (cv) is


 equal to or greater than 30 percent. The cv is the ratio


 of the estimated standard error of the mean to the


 estimated mean, expressed as a percentage. Note that


 the cv statistic refereed to here is relative to the


 estimate of the mean, hence the use in the numerator of


 the standard error rather than the standard deviation as


 used in the calculation of the (population) coefficient of


 variation shown in Table 5-3.


2) 	 An estimated proportion (percent) that falls above


 25 percent and below 75 percent is flagged when it is


 based on a cell size of less than 30 times the average


 design effect (VIF) or when the cv is equal to or greater


 than 30 percent.


 An estimated proportion of 25 percent or lower or


 75 percent or higher is flagged when the smaller of np


 and n(1-p) is less than 8 times the average design


 effect, where "n" is the cell size on which the estimate


 is based and "p" is the proportion expressed as a fraction.


 5-7


Attachment 1-113 
3) Estimated percentiles are flagged according to rules that


 parallel the cell size rules applied to proportions


 (guideline 2). Estimated percentiles inside the 25 to 75


 range are flagged when the cell size is less than 30 times


 the average design effect. Estimates of the 25 and lower


 percentiles are flagged when the cell size is less than


 8 times the average design effect divided by p, where p


 is the level of the percentile expressed as a fraction.


 Estimates of the 75 and higher percentiles are flagged when


 the cell size is less than 8 times the average design


 effect divided by 1 - p.


5.3 CSFII 1998 (Annual) Sampling Weights


5.3.1 CSFII 1998 weighting design


The approach used in weighting the CSFII 1998 data followed


the approach used in weighting the 1994, 1995, and 1996


person-level data. These annual data sets were weighted


separately in the following steps. First, a base weight


equal to the reciprocal of the probability of selection was


assigned to each sample person. The base weights were then


adjusted for nonresponse within weighting classes defined by


variables that were determined to be correlated with response


rates. Finally, the nonresponse-adjusted weights were ratio


adjusted to population estimates from the March Current


Population Survey (CPS) of the appropriate year


(USDC/BOC 1994, 1995, 1996, 1998) to compensate for random


variation in the observed sample counts and possible


undercoverage of certain groups in the area sample frame.


Two sets of weights were constructed for the CSFII 1998:


a set for sample persons who completed the day-1 interview


and a set for sample persons who provided 2 days of intake.


 5-8


Attachment 1-114 
5.3.2 Base weights


The base weight associated with a sample person is the


reciprocal of the overall probability of including that


person in the survey. For the CSFII 1998, sample persons


were selected through a complex multistage sample design


involving the selection of primary sampling units (PSUs),


area segments within PSUs, households within segments,


and finally persons (sample persons) within households.


Consequently, the following components were required to


calculate the overall probabilities of selection:


1. 	 The probability of selecting the PSU.


2. 	 The probability of selecting the segment within the PSU.


3. 	 The probability of selecting the household within the


 segment.


4. 	 The probability of selecting an eligible sample person


 from within the household.


For any sample person, the product of these four factors is


the probability of being selected for the CSFII.


5.3.3 CSFII 1998 nonresponse adjustments


Not all sample persons completed an intake interview. To


compensate for this, the following procedures were used to


adjust the sample person base weights. First the weights


were adjusted for screening nonresponse. These adjustments


were made within classes created by grouping segments by


census region, MSA status, minority status (percent of the


population that was black or Hispanic), and quarter of the year


of field operations. Within each class, the base weight of


each eligible sample person was increased by a factor


corresponding to the screener nonresponse rate within the


class.


These screener nonresponse-adjusted weights were then


adjusted again to account for person nonresponse. A different


set of weighting classes was used for this adjustment.


These classes were defined by income level, age, sex, census


region, MSA status, quarter of the year of field operations,


and minority status of the segment. The result of this step


was a set of nonresponse-adjusted base weights for responding


sample persons.


 5-9


Attachment 1-115 
5.3.4 CSFII 1998 population adjustments


Lastly, the nonresponse-adjusted weights were calibrated using


an iterative process called "raking ratio weighting" so that


the sum of the final weights equaled the corresponding


1998 March CPS population totals (USDC/BOC 1998) within cells


defined by the following variables:


 1. Sex


 2. Age group (0-2, 3-5, 6-9)


 3. Home ownership


 4. Season of intake (winter, spring, summer, fall)


 5. Day of week of intake


 6. Census region


 7. MSA status (metropolitan/nonmetropolitan)


 8. Household income as percentage of poverty level (using


 the appropriate poverty thresholds)


 9. Household received food stamps in past 12 months


10. Number of persons in the household 18 and older


11. Presence in household of children under 6 years


12. Presence in household of children 6 to 17 years


13. Presence of female head of household 40 years or younger


 and no one in the household under 18 years


14. Employment status (for children this was the status of


 the female head, or if there was no female head, the male


 head of household)


15. Race (black or nonblack)


16. Ethnic origin (Hispanic or non-Hispanic)


Table 5-4 shows the adjustments necessary for calibration for


the weighting class age 0-5 years and Table 5-6 shows the same for


the weighting class age 6-9 years. Column 1 provides the number


of children with the various characteristics. Column 2 provides


the weighted percentages of the persons within the weighting class


in each of the categories using the nonresponse-adjusted sampling


weights. Column 3 shows the target percentage from the CPS, which


is also the weighted percentage for the sample using the final,


calibrated weights.


 5-10


Attachment 1-116 
------------------------------------------------------------

------------------------------------------------------------

Table 5-4. Children 5 years and younger: Unweighted sample


 sizes, weighted percentage distributions


 following nonresponse adjustments, and


 population targets, day 1, CSFII 1998


Sample Nonresponse Population


Variable size adjustment targets*


Number ----------Percent--------­


Total 5,051 100.0 100.0


Age/sex


 Male


 0-2 950 39.5 25.1


 3-5 1,573 21.9 26.1


 Female


 0-2 1,000 18.3 23.8


 3-5 1,528 20.3 25.0


Home ownership


 Home owned 2,828 60.1 58.3


 Home not owned 2,223 39.9 41.7


Season of intake


 Winter 1,166 25.7 25.0


 Spring 1,240 27.1 25.0


 Summer 1,667 29.6 25.0


 Fall 978 17.6 25.0


Day of week of intake


 Sunday 961 18.9 14.3


 Monday 786 15.8 14.3


 Tuesday 771 15.3 14.3


 Wednesday 638 12.7 14.3


 Thursday 580 11.2 14.3


 Friday 787 15.6 14.3


 Saturday 528 10.5 14.3


Census region


 Northeast 906 17.9 17.3


 Midwest 1,115 22.4 23.9


 South 1,661 32.8 34.3


 West 1,369 27.0 24.5


MSA status


 MSA (metropolitan) 4,134 80.6 81.7


 Non-MSA 917 19.4 18.3


 --continued


 5-11


Attachment 1-117 
------------------------------------------------------------

------------------------------------------------------------

Table 5-4. Continued.


Sample Nonresponse Population


Variable size adjustment targets*


Number ----------Percent--------­


Household income as


percentage of poverty


level


 0-75% 895 14.9 14.6


 76-130% 924 15.3 13.4


 131-300% 1,656 34.9 34.8


 Over 300% 1,576 35.0 37.3


Household received food


stamps in past 12 months


 Yes 1,073 17.4 17.8


 No 3,978 82.6 82.2


Presence in household


of persons 18 and older


 Exactly 1 655 11.7 13.5


 Exactly 2 3,656 74.5 74.0


 Other than 1 or 2 740 13.8 12.5


Presence in household


of children 6-17


 Children 6-17 2,326 44.8 44.5


 No children 6-17 2,725 55.2 55.5


Employment status of


female head of household


(or male head if there


is no female head)


 Have job 2,644 53.4 58.4


 Do not have job 2,407 46.6 41.6


 --continued


 5-12


Attachment 1-118 
------------------------------------------------------------

------------------------------------------------------------

-----------------------------------------------------------

Table 5-4. Continued.


Sample Nonresponse Population


Variable size adjustment targets*


Number ----------Percent--------­


Race


 Black 749 13.6 15.8


 Non-black 4,302 86.4 84.2


Ethnic origin


 Hispanic 901 16.1 17.7


 Non-Hispanic 4,150 83.9 82.3


* Calculated using 1998 Current Population Survey data


 (USDC/ BOC 1998) except for the variables "season of


 intake" and "day of week of intake." Since the goal


 of the CSFII was to estimate behavior on an average


 day, each day of the week received an equal value of


 14.3 percent, and each season received a value of


 25 percent.


 5-13


Attachment 1-119 
------------------------------------------------------------

------------------------------------------------------------

Table 5-5. 	Persons 6 to 9: Unweighted sample sizes,


 weighted percentage distributions following


 nonresponse adjustments, and population targets,


 day 1, CSFII 1998


Sample Nonresponse Population


Variable size adjustment targets*


Number ----------Percent--------­


Total 	 508 100.0 100.0


Sex


 Male 279 56.8 50.9


 Female 229 43.2 49.1


Home ownership


 Home owned 304 66.2 65.2


 Home not owned 204 33.8 34.8


Season of intake


 Winter 134 23.5 25.0


 Spring 126 25.9 25.0


 Summer 156 31.1 25.0


 Fall 92 19.5 25.0


Day of week of intake


 Weekend (Fri - Sun) 228 45.4 42.9


 Weekday (Mon - Thr) 280 54.6 57.1


Census region


 Northeast 77 12.6 18.6


 Midwest 110 23.6 23.0


 South 187 33.6 34.4


 West 134 30.2 24.0


MSA status


 MSA (metropolitan) 411 82.1 80.7


 Non-MSA 97 17.9 19.3


 --continued


 5-14


Attachment 1-120 
------------------------------------------------------------

------------------------------------------------------------

Table 5-5. Continued.


Sample Nonresponse Population


Variable size adjustment targets*


Number ----------Percent--------­


Household income as


percentage of poverty


level


 0-75% 101 16.1 13.8


 76-130% 85 11.5 12.1


 131-300% 149 33.0 35.5


 Over 300% 173 39.4 38.5


Household received food


stamps in past 12 months


 Yes 99 17.8 16.2


 No 409 82.2 83.8


Presence in household


of persons 18 and older


 Exactly 1 75 12.6 18.0


 Exactly 2 365 74.6 69.7


 Other than 1 or 2 68 12.8 12.3


Presence in household


of children under 6


 Children under 6 257 45.5 41.4


 No children under 6 251 54.5 58.6


Employment status of


female head of household


(or male head if there


is no female


 Have job 278 54.3 63.7


 Do not have job 230 45.7 36.3


 --continued


 5-15


Attachment 1-121 
------------------------------------------------------------

------------------------------------------------------------

-----------------------------------------------------------

Table 5-5. Continued.


Sample Nonresponse Population


Variable size adjustment targets*


Number ----------Percent--------­


Race


 Black 77 14.7 16.4


 Non-black 431 85.3 83.6


Ethnic origin


 Hispanic 81 12.7 15.3


 Non-Hispanic 427 87.3 84.7


* Calculated using 1998 Current Population Survey data


 (USDC/ BOC 1998) except for the variables "season of


 intake" and "day of week of intake." Since the goal of


 the CSFII was to estimate behavior on an average day,


 each day of the week received an equal value of 14.3


 percent, and each season received a value of 25 percent.


 5-16


Attachment 1-122 
5.4 CSFII 1994-96, 1998 (4-Year) Combined Person-Level


 Sampling Weights


5.4.1 Introduction to person-level sampling weights discussion


Although the CSFII 1998 was a nationally representative sample


of children 9 years of age and younger, it was primarily


intended to serve as a supplement to the sample of children in


the CSFII 1994-96. A composite estimation approach was used


to combine the CSFII 1994-96 and CSFII 1998 samples. Under


this approach, the combined estimator xcomp, is considered to


be a linear combination of the corresponding CSFII 1994-96 and


CSFII 1998 estimates, i.e.,


 xcomp = a * x[94-96] + (1 - a) * x[1998],


where a is a constant between 0 and 1.


Assuming that x[94-96] and x[1998] are both unbiased estimates,


the composite estimate, x[4-year], will also be unbiased for any


value of a. The approximately optimal value of a, i.e., the value


that minimizes the variance of x[4-year], is a function of the


effective sample sizes of the CSFII 1994-96 and the CSFII 1998:


 a = eff[94-96] / (eff[94-96] + eff[1998])


where eff[94-96] = n[94-96] / (1 + cv[94-96:w]**2),


n[94-96] = the actual CSFII 1994-96 sample size,


cv[94-96:w]**2 = the square of the coefficient of variation


 (expressed as a percentage) of the CSFII weights, and


eff[1998] is similarly defined with the CSFII 1998 sample


 size and weights.


The factors a and (1 - a) are known as compositing factors and


were computed by sex and age group for the person-level weights.


5.4.2 Day 1 person-level weights


The nonresponse-adjusted day 1 CSFII weights described in


section 5.3.3 were recalibrated to the corresponding 1994-96 CPS


population totals. This was done so that the CSFII weights


would be consistent with the previously computed CSFII 1994-96


(3-year) weights. The recalibration of the CSFII weights was


done separately for (1) children age 5 years or younger and


(2) children 6-9 years of age. The procedures used for


calibration were exactly the same as those described in


section 5.3.4 except that the 1994-96 CPS totals were used as


control totals.


Next, the compositing factors a and (1 - a) were computed using


the CSFII 1994-96 weights and the recalibrated CSFII weights by


sex / age groups. Table 5-6 shows the day 1 compositing factors.


 5-17


Attachment 1-123 
Next, the CSFII 1998 sample was combined with the CSFII 1994-96


sample by applying the appropriate CSFII compositing factor


(1 - a) to each recalibrated CSFII 1998 day 1 weight and by


applying the appropriate CSFII compositing factor a to each


CSFII 1994-96 day 1 weight. This was done for all children


age 9 years or younger in the combined sample.


Finally, the penultimate combined weights described in the above


paragraph were calibrated one final time to the March 1994-96


CPS totals along the dimensions used in the original calibration


of the CSFII 1994-96 day 1 weights. This final calibration


process was done separately for children 5 years of age and


younger and for persons 6 - 19 years of age.


Table 5-6. 	 Compositing factors for children age 9 and under


 completing the CSFII day 1 Intake


 1994-96 1998


 1994-96 1998 Total compositing compositing


 sample sample sample factor factor


 Sex Age size size size (a) (1-a)


Male 	 < 1 184 576 760 0.22 0.78


 1 362 174 536 0.70 0.30


 2 353 200 553 0.67 0.33


 3 251 687 938 0.28 0.72


 4 244 670 914 0.27 0.73


 5 246 216 462 0.54 0.46


 6 125 184 309 0.45 0.55


 7-9 383 95 478 0.83 0.17


Female < 1 192 599 791 0.22 0.78


 1 349 199 548 0.67 0.33


 2 352 202 554 0.67 0.33


 3 241 657 898 0.32 0.68


 4 267 679 946 0.30 0.70


 5 229 192 421 0.59 0.41


 6 131 159 290 0.50 0.50


 7-9 344 70 414 0.86 0.14


Total 4,253 5,559 9,812


5.4.3 	Two-day person-level weights


The procedure followed in constructing the day 1 combined


weights was followed in constructing the combined two-day


weights. The two-day CSFII 1998 weights were recalibrated


to the 1994-96 CPS population totals, compositing factors


were computed based on both the recalibrated CSFII 1998 two-day


weights and the CSFII 1994-96 two-day weights by sex and age


groups, penultimate combined weights were created by applying


the appropriate compositing factors to the appropriate weights,


and a final raking procedure was used to calibrate the


penultimate weights.


 5-18


Attachment 1-124 
5.5 CSFII 1994-96, 1998 (4-Year) Combined Household-Level


 Sampling Weights


5.5.1 Introduction to household-level sampling weights discussion


To permit calculation of household-level estimates for items


collected in the household interview (e.g., amount and sources of


income, general information about food shopping practices, the


amounts spent on food, source of cooking and drinking water,


food stamp eligibility), a set of household weights for analysis


of the combined CSFII 1998 and CSFII/DHKS 1994-96 data sets were


computed. The procedures followed in constructing these


household-level weights were similar to those used in


constructing the CSFII 1998 and combined CSFII 1994-96, 1998


person-level weights. First, household-level weights were


constructed for the CSFII 1998 by adjusting a base weight for


nonresponse and then calibrating the nonresponse adjusted weights


to population totals. Secondly, a compositing approach was used


to combine the CSFII 1998 and the CSFII 1994-96. The


construction of the CSFII 1994-96 household weights as documented


for the release of that sampling weight set is included in this


section as section 5.7.1.


5.5.2 CSFII 1998 Household Base Weights


The first step was to assign a base weight to each responding


CSFII 1998 household that is equal to the reciprocal of the


probability of retaining the household for the household


interview. For the CSFII 1998 (and also for the CSFII 1994-96),


only those households with eligible SPs were eligible for the


household interview. Thus, the probability of including a


household in the study was equal to the probability that any of


its members was selected for the intake interviews. Under the


procedures used to select persons for the CSFII 1998, the


probability of selecting a household for the household


interview is equal to maximum probability of selection of the


SPs in the household. Hence, the base weight for the I-th


sampled household was computed from the formula:


 w = min {w[1], w[2], ..., w[n]},


where w[1], w[2], ..., w[n] are the corresponding base weights


of the SPs in the household. In general, the household base


weights varied by quarter, as well as within quarter depending


on the composition of the household. In particular, households


with children under 1 or 3-4 years of age had considerably


smaller weights (larger probabilities of selection) than


households where the only children were between 7 and 9 years


of age.


 5-19


Attachment 1-125 
5.5.3 Household-level nonresponse Adjustments


The procedures used for nonresponse adjustments followed those


used in constructing the CSFII 1994-96 household weights and


were essentially as follows. First, the base weights were


adjusted for screening nonresponse within classes defined by


Census region, MSA status, minority status (percent of the


population that was black or Hispanic), and quarter of field


operations. Within each class, the base weight of each


eligible sample person was increased by a factor corresponding


to the screener nonresponse rate within the class.


Next, the screener-adjusted weights were adjusted to account


for household nonresponse. The weighting classes used for


this adjustment were defined by income level, Census region,


MSA status, and minority status of the segment. Note that for


the purpose of weighting, those households that contained at


least one sample person who completed at least one intake


interview were considered to be "respondents" regardless of


whether a household interview was completed.


5.5.4 Household-level population adjustments


Lastly, the nonresponse-adjusted weights were calibrated using


the same iterative process called "raking ratio weighting" used


in calibrating the person-level weights so that the sum of the


final weights equaled the corresponding 1994-96 March CPS


population totals (USDC/BOC 1994, 1995, 1996). Since the


CSFII 1998 was restricted to households with children 9 years


of age or younger (i.e., households without children 9 years


or younger had no chance of selection for the CSFII 1998), the


totals were only for households with children 9 years of age


or younger. Cells defined by the following variables were used:


 1. Home ownership and age of the head of household


 2. Season of household interview (winter, spring, summer, fall)


 3. Day of week of household interview


 4. Census region


 5. MSA status (metropolitan/nonmetropolitan)


 6. Household income as percentage of poverty level (using the


 appropriate poverty thresholds)


 7. Household received food stamps in past 12 months


 8. Presence in household of persons 18 and older


 9. Presence in household of children under 6 years


10. Presence in household of children 6 to 17 years


11. Presence of female head of household 40 years or younger


 and no one in the household under 18 years


12. Employment status of the head of household


13. Race (black or nonblack) of the head of household


14. Ethnic origin (Hispanic or non-Hispanic) of the head of


 household


15. Household size


 5-20


Attachment 1-126 
5.5.5 Combined CSFII 1994-96, 1998 household samples


The same compositing approach used to combine the person-level


samples was used in combining the household samples.


Compositing factors were computed using the CSFII 1994-96


household weights and the CSFII 1998 household weights by


income / household composition groups. Table 5-7 shows the


household compositing factors. Note that the choice of the


household composition grouping "households with children 7-9


years of age only" followed from the design of the CSFII 1998,


which selected a proportionately small group of 7-9 year olds,


resulting in some large CSFII 1998 household weights for


such households. Using this group for compositing purposes


reduced the impact of these large weights when the samples


were combined.


Next, the CSFII 1998 sample was combined with the


CSFII 1994-96 sample by applying the appropriate CSFII 1998


compositing factor (1 - a) to each CSFII 1998 household weight


and by applying the appropriate CSFII 1994-96 compositing


factor a to each CSFII 1994-96 household weight.


Finally, these penultimate combined weights were calibrated one


final time to the March 1994-96 CPS totals along the dimensions


specified above. Unlike the calibration of the


CSFII 1998-only household sample, this time the population


totals represented all U.S. households. The same cells listed


in section 5.5.4 were used.


Table 5-7. 	 Compositing factors for CSFII households with


 children 9 or younger


 1994-96 	 1998


 1994-96 1998 Total compositing compositing


Income HH sample sample sample factor factor


group comp. size# size size (a) (1-a)


>= 130% Children 299 50 349 0.88 0.12


poverty 7-9 only


 Others 1,796 2,813 4,609 0.51 0.49


< 130% Children 99 12 111 0.90 0.10


poverty 7-9 only


 Others 	 787 1,422 2,209 0.48 0.52


Total 	 2,981 4,297 7,278


 # Households with at least one SP who completed the day 1 Intake.


 5-21


Attachment 1-127 
--------------------

--------------------

5.6 Variance Estimation


5.6.1 Variance estimation fields


As described in Section 3, "Sample Design," Westat's 62


primary sampling unit (PSU) master sample was employed for both


the CSFII/DHKS 1994-96 and the CSFII 1998. This sample of PSUs


contains 24 PSUs selected with certainty. The remaining 38 PSUs


were selected with probability proportional to size from 38 strata,


1 PSU per stratum. Area segments were then selected from each of


the 62 PSUs also with probability proportional to size. The area


segments were randomly allocated to the annual samples, across


quarters of the year, so that the 62 PSUs were fielded at all times


throughout each year. The following approach was used to create a


framework of 2 sampling units per stratum to facilitate variance


estimation procedures. First, 19 variance estimation strata were


formed from the 38 noncertainty PSUs by pairing adjacent PSUs in


the sampling frame. Each PSU within a variance estimation stratum


defines what is referred to as a variance estimation unit. Next,


within each of the 24 certainty PSUs, one-half of the segments were


assigned to one variance estimation unit and the remaining one-half


to another. Because each certainty PSU is considered to be a


separate variance estimation stratum, a total of 43 variance


estimation strata (each containing 2 variance estimation units)


was formed by this process. See section 7.4.2, "Sampling weights


and variance estimation fields," for details of identifying these


variance estimation fields in the data set. This framework


applies to all weighted samples, annual or combined, of the


CSFII 1994-96 and CSFII 1998.


5.6.2 Estimation of Sampling Errors


Linearization method


Estimation of sampling errors may be conducted with a Taylor


series linearization method using the final sampling weights


described in the above sections along with the variance


estimation strata and variance estimation units described in


section 5.6.1. Software packages such as SUDAAN and Stata can


be used to obtain estimates using the linearization method.


 5-22


Attachment 1-128 
--------------------------

--------------------------

Jackknife replicate method


Alternatively, sampling errors may be estimated using the


jackknife technique described here. The construction of


jackknife replicate weights makes use of the variance


estimation stratum/variance estimation unit structure described


above in section 5.5.1. As an illustration of how a jackknife


variance estimator can be calculated, let y denote a weighted


survey estimate (for example, total fat intake) calculated using


the full-sample weights. Let y(j) be the corresponding weighted


estimate calculated using the j-th set of replicate weights


(j = 1, 2, ..., 43). The estimated variance of y is then given


by the formula


 Var(y) = SUM (y(j) - y)**2,


where the summation extends over the 43 sets of jackknife


replicate weights. Forty-three replicates were created by


applying this process to each of the 43 variance estimation


strata.


A jackknife replicate is created by dropping out one of the two


variance estimation units from a variance estimation stratum and


doubling the initial probability weights of the individuals in


the other variance estimation unit in that stratum. The entire


weighting process as described in the previous sections was


repeated for each replicate. Individuals who were not in the


current replicate subsample were assigned a corresponding


replicate weight of zero. In this way, series of replicate


weights were generated for each sample person or household.


Together with the final, full-sample weights, these replicate


weights were designed for the calculation of sampling errors.


Using a replication method to calculate sampling errors of


survey-based estimates makes complicated variance estimation


formulas unnecessary. The jackknife replication method used


here is also designed to reflect the stratification and


clustering used in the CSFII/DHKS sample design and to capture


the effects of the raking ratio adjustments mentioned in the


sections above.


 5-23


Attachment 1-129 
Replicate weights are provided for use with each of the sets of


sampling weights listed in Table 5-2. There are seven files


altogether, found in the \jacknife directory on disk 2:


jkw4yrcs.dat 	 Day 1 and two-day weights for the combined


 CSFII 1994-96, 1998 (4-year) sample


jkwanncs.dat 	 Day 1 and two-day weights for annual samples


 (1994, 1995, 1996, 1998)


jkw3yrcs.dat 	 Day 1 and two-day weights for the CSFII 1994-96


 combined (3-year) sample


jkw4yrhh.dat 	 Household weights for the combined


 CSFII 1994-96, 1998 (4-year) sample


jkw3yrhh.dat 	 Household weights for the CSFII 1994-96


 combined (3-year) sample


jkwanndh.dat 	 DHKS and two-day DHKS weights for the annual


 samples (1994, 1995, 1996)


jkw3yrdh.dat 	 DHKS and two-day DHKS weights for the


 DHKS 1994-96 combined (3-year) sample


Corresponding file formats are provided in section 9.4 and SAS


programs for reading the data files are provided in section 10.4.


The annual and 4-year person-level files each contain one record


per CSFII respondent (21,662 records total, 5,559 from CSFII 1998).


The 3-year person-level file contains one record per CSFII


respondent from 1994-96 (16,103 records). The 2-day weight fields


are blank for respondents providing only one day of intake.


The DHKS files each contain one record per DHKS respondent in


1994-96 (5,765 records). The 2-day DHKS weight fields are blank


for DHKS respondents who did not provide a second day of intake.


The 4-year household-level file contains one record per CSFII


household (12,364 records total, 4,297 from the CSFII 1998). The


3-year household-level file contains one record per CSFII household


from 1994-96 (8,067 records). A field indicating the survey


year, the full-sample sampling weights, and the variance-estimation


stratum and unit are included in each file.


The replicate weighting process described above was designed and


implemented by Westat, Inc., who have also created a variance


estimation program, WesVarPC, which runs on computers using the


Windows operating system and is available to the public at no


charge. A commercial version, WesVar Complex Samples, is also


available from SPSS. Information about both programs may be


found at Westat's home page at <http://www.westat.com>. Note that


in WesVarPC terminology, the JK2 method was used in constructing


these replicate weights.


 5-24


Attachment 1-130 
----------------

----------------

5.7 	CSFII 1994-96 (3-Year) Household Sampling Weights:


 Original Documentation


These weights permit household level estimates using the


fields that are present on household record type 15. The


data contained in the record type 15 fields include household


participation in programs such as WIC and Food Stamps, income


and food-related expenditures, and food sufficiency. The


3-year weights, calibrated to 3-year averages of population


characteristics, are intended to be used with the 3-year


CSFII data set. They may be used with the annual subsets,


however, as long as it is understood that the annual subsets


were not calibrated to annual population characteristics.


If annual totals are being estimated, the weights should each


be multiplied by 3 to scale the weights appropriately. Such


scaling is not necessary for the estimation of means or


percentages.


5.7.1 How the 3-year household sampling weights were constructed


In general, the analysis of data from surveys having complex


designs requires the use of sample weights to compensate for


variable probabilities of selection, differential nonresponse


rates, and possible deficiencies in the sampling frame. For


the 1994-96 CSFII/DHKS, the overall probabilities of


selecting sample persons were designed to vary by sex, age,


and income level to meet precision goals specified by ARS.


For this reason, the probability of selecting a household


into the sample is directly related to the composition of the


household at the time of screening. The construction of


household sample weights was performed by ARS using the


design developed by Westat, Inc.


Weighting design


The 3-year CSFII households were weighted in the following


steps. First, a base weight equal to the reciprocal of the


probability of selection was assigned to each household. The


base weights were then adjusted for nonresponse at two levels


within weighting classes defined by variables that were


determined to be correlated with response rates. The first


was a screener-level adjustment using 57 classes defined by


combinations of region, quarter, MSA status, and minority


status of the segment. The second was a household level


adjustment using 8 classes defined by combinations of region,


MSA status, minority status of segment and household income


as a percentage of poverty. Finally, to compensate for


random variation in the observed sample counts and possible


undercoverage of some groups, the nonresponse-adjusted


weights were ratio adjusted to the average population


estimates from the March Current Population Surveys for


1994, 1995, and 1996.


 5-25


Attachment 1-131 
-----------

------------

-----------------------------

-----------------------------

Base weights


The base weight associated with a household is the


reciprocal of the overall probability of including that


household in the survey. For each year of the CSFII/DHKS,


sample households were selected through a complex multistage


sample design involving the selection of primary sampling


units (PSUs), area segments within PSUs, and households


within segments. The eligibility of households for the CSFII


was determined by household income level and the sex and age


of its members at the time of screening. The product of


steps 1, 2, and 3 below determines the probability of


selection for eligible households. Since segments were


allocated for selection over the 3 years of the survey, a


factor of 3 is included in probability of selecting area


segments. As with the individual weights, the reciprocal of


this probability is the household base weight.


1. 	 The probability of selecting the PSU.


2. 	 The probability of selecting the segment within


 the PSU.


3. 	 The probability of selecting the household within


 the segment.


CSFII nonresponse adjustments


Not all households completed the household interview but


all households had a member to provide a Day-1 intake.


Those households that did not provide a household


interview are included in the nonresponse adjustment as


participating households. This was done because household


eligibility and participation were determined by the


presence and participation of a specific household member.


There were 41 households where a Day-1 intake was


completed but the household questionnaire was not. In


these cases most of the household information is missing


or was imputed on record type 15. Otherwise, to


compensate for nonresponse, the following procedures were


used to adjust the household base weights.


 5-26


Attachment 1-132 
----------------------------------------------

----------------------------------------------

First the weight was adjusted for screening nonresponse.


This adjustment was made within classes created by


grouping households by census region (see 1994-96 CD-ROM


documentation "Region" in section 14, "Glossary"), MSA


status (see 1994-96 CD-ROM documentation "Metropolitan


Statistical Area" in section 14, "Glossary"), minority


status of area segment (high or low minority) and


quarter of field operations. Within each class, the


base weight of each eligible household was increased by


a factor equal to the inverse of the screening rate


within the class. This adjustment is the same screener


adjustment made in constructing the individual


sample weights.


The screener nonresponse-adjusted weight was then


adjusted again to account for household nonresponse.


A different set of weighting classes was used for this


adjustment. A CHAID analysis was performed by ARS to


determine the groupings for the household level


nonresponse adjustments. The new classes were


defined by income level, census region, MSA status,


and minority status of the segment. Only those


households which had eligible sample persons but did


not complete any day 1 intakes were considered


nonresponding. As in the screener nonresponse


adjusted weight, this adjustment is equal to the


inverse of the household response rate within the


classes. The result of this step was a set of


nonresponse-adjusted base weights for responding


households. The nonresponse-adjusted base weight


(WT_H_ADJ) is included in the weight file.


Post-stratification and population adjustments


Finally, the nonresponse-adjusted weights were


calibrated using an iterative process called "raking


ratio weighting" to produce final weights that sum


to the average of population totals over the 3-year


period of the CSFII/DHKS. The totals are from the


March (1994, 1995, and 1996) Current Population


Surveys (CPS). The cells used to define the totals


were generally the same as those used for the


individual weight reflecting household totals. Day


and quarter of intake were replaced by day and


quarter of the household interview. Household size


was added. Race and ethnic origin variables are


based on characteristics of the female head of


household when present; otherwise, the male head of


household.


 5-27


Attachment 1-133 
1. 	Home ownership and age of the head of household


2. 	Season of household interview (winter, spring,


 summer, fall)


3. 	Day of week of household interview


4. 	Census region


5. 	MSA status (metropolitan/nonmetropolitan)


6. 	Household income as percentage of poverty level


 (using the appropriate poverty thresholds)


7. 	Household received food stamps in past 12 months


8. 	Presence in household of persons 18 and older


9. 	Presence in household of children under 6 years


10. Presence in household of children 6 to 17 years


11. Presence of female head of household 40 years or


 younger and no one in the household under 18 years


12. Employment status of the head of household


13. Race (black or nonblack) of the head of household


14. Ethnic origin (Hispanic or non-Hispanic) of the


 head of household


15. Household size


To illustrate the adjustments, table 5-8 shows, by


weighting variable, the 1994-96 CSFII unweighted sample


sizes, the weighted percentage distributions following


nonresponse adjustments (but before calibration to


population targets), and the population targets for all


responding households.


 5-28


Attachment 1-134 
---------------------------------------------------- --------

---------------------------------------------------- --------

Table 5-8. Unweighted household sample sizes, weighted


 percentage distributions following nonresponse


 adjustments, and population targets, CSFII 1994-96


Sample Nonresponse Population


Variable size adjustment targets*


Number ----------Percent- -------­


Total 8,067 100.0 100.0


Home ownership/age


Home owned


 20-39 1,813 22.7 19.3


 40-59 1,909 24.0 25.9


 60 and older 1,598 19.8 19.6


Home not owned


 20-39 1,623 19.9 20.7


 40-59 660 8.0 8.9


 60 and older 464 5.6 5.6


Season of interview


 Winter 1,943 24.3 25.0


 Spring 2,122 26.2 25.0


 Summer 1,988 24.7 25.0


 Fall 2,014 24.8 25.0


Day of week of interview


 Sunday 952 11.8 14.3


 Monday 1,348 16.6 14.3


 Tuesday 1,246 15.5 14.3


 Wednesday 1,226 15.3 14.3


 Thursday 968 12.1 14.3


 Friday 919 11.2 14.3


 Saturday 1,408 17.5 14.3


Census region


 Northeast 1,499 19.3 19.9


 Midwest 1,958 24.1 23.9


 South 2,866 34.7 35.1


 West 1,744 21.9 21.1


MSA status


 MSA (metropolitan) 6,092 76.2 78.8


 Non-MSA 1,975 23.8 21.2


 -- continued


 5-29


Attachment 1-135 
---------------------------------------------------- --------

---------------------------------------------------- --------

Table 5-8. Continued.


Sample Nonresponse Population


Variable size adjustment targets*


Number ----------Percent- -------­


Household income as


percentage of poverty


level


 0-75% 888 10.3 8.4


 76-130% 1,156 13.2 10.9


 131-300% 2,665 33.8 31.8


 Over 300% 3,358 42.8 48.9


Household received food


stamps in past 12 months


 Yes 1,011 11.7 9.0


 No 7,056 88.3 91.0


Presence in household


of persons 18 and older


 Exactly 1 2,019 24.5 31.3


 Exactly 2 4,832 60.2 54.2


 Other than 1 or 2 1,216 15.2 14.5


Presence in household


of children under 6


and 6-17


 Children under 6


 Children 6-17 1,128 13.9 8.5


 No children 6-17 1,329 16.6 9.5


 No children under 6


 Children 6-17 1,380 17.0 19.9


 No children 6-17 4,230 52.5 62.1


Presence of female head


of household 40 or


younger and no one in


household under 18


 Yes 449 5.7 9.7


 No 7,618 94.3 90.3


Employment status


 Have job 4,355 54.6 57.7


 Do not have job 3,712 45.4 42.3


 -- continued


 5-30


Attachment 1-136 
---------------------------------------------------- --------

---------------------------------------------------- --------

---------------------------------------------------- -------

Table 5-8. Continued.


Sample Nonresponse Population


Variable size adjustment targets*


Number ----------Percent- -------­


Race


 Black 993 12.1 11.6


 Non-black 7,074 87.1 88.4


Ethnic origin


 Hispanic 755 9.0 8.0


 Non-Hispanic 7,312 91.0 92.0


Household size


 1 Member 1,464 17.9 24.8


 2 Members 2,429 30.3 32.3


 3 or more members 4,174 51.8 42.9


* Calculated using 1994-96 Current Population Survey data


except for the variables "season of interview" and "day of


week of household interview." Since the goal of the CSFII


was to estimate behavior on an average day, each day of the


week received an equal value of 14.3 percent, and each


season received a value of 25 percent.


 5-31


Attachment 1-137 
----------------------------------

----------------------------------

---------------------------------------------------------

---------------------------------------------------------

---------------------------------------------------------

5.7.2 Use of the household sampling weights


The household sample in the 1994-96 CSFII consists of all


households where at least one sample person was selected


and provided a Day-1 intake. This is true regardless of


whether a household questionnaire was completed. The use


of the weights should be restricted to household


information only (record type 15). No connections to


sample persons or their intakes should be assumed in


using the household weights.


Summary of final household weights


Table 5-9 summarizes the set of final household weights.


The table shows the sample size; the sum of the weights;


the coefficient of variation of the weights (CV), defined


as the ratio of the standard deviation of the weights to


the mean of the weights expressed as a percentage; and


the variance inflation factor (VIF), defined as


1 + (CV/100)**2. This last statistic, which is


equivalent to the ratio of the mean of the squared weights


to the square of the mean of the weights, represents the


anticipated proportional increase in the variance of


survey estimates resulting from the variation in the


weights. For example, it is anticipated that the variance


of a household estimate will be 1.2 times what it would


have been had all the weights been equal. The VIF can be


used in the role of the "broadly calculated average design


effect" specified in reporting guidelines adopted by ARS


(FASEB/LSRO 1995).


Table 5-9. Summary of final household sample weights


Sample Sum of CV VIF =


 size weights 1+(CV/100)**2


8,067 98,574,761 45.88% 1.21


5-32


Attachment 1-138 
--------------------------

--------------------------

----------------------------------------------------

----------------------------------------------------

Variance Estimation Fields


As described in CSFII/DHKS 1994-96 documentation


(USDA 1998) section 3.2.1, "Sample design,"


Westat's 62 primary sampling unit (PSU) master sample


was employed for CSFII/DHKS 1994-96. This sample of


PSUs contains 24 PSUs selected with certainty. The


remaining 38 PSUs were selected with probability


proportional to size from 38 strata, 1 PSU per stratum.


Thirty-six area segments were then selected from each


of the 62 PSUs also with probability proportional to


size. The thirty-six area segments were randomly


allocated to the annual samples, twelve per year and


three per quarter, so that the 62 PSUs were fielded at


all times throughout the three years.


The following approach was used to create a framework


of 2 sampling units per stratum to facilitate variance


estimation procedures. First, 19 variance estimation


strata were formed from the 38 noncertainty PSUs by


pairing adjacent PSUs in the sampling frame. Each PSU


within a variance estimation stratum defines what is


referred to as a variance estimation unit. Next,


within each of the 24 certainty PSUs, one-half of the


segments were assigned to one variance estimation unit


and the remaining one-half to another. Because each


certainty PSU is considered to be a separate variance


estimation stratum, a total of 43 variance estimation


strata (each containing 2 variance estimation units)


was formed by this process. See CSFII/DHKS 1994-96


documentation (USDA 1998) section 7.4.2, "Sampling


weights and variance estimation fields," for details


on identifying these variance estimation fields in the


data set.


Estimation of Sampling Errors - Linearization method


Estimation of sampling errors may be conducted with a


Taylor series linearization method using the final


sample weights described in CSFII/DHKS 1994-96


documentation (USDA 1998) sections 5.1.2 and 5.1.3 along


with the variance estimation strata and variance


estimation units described in section 5.1.4. Software


packages such as SUDAAN and Stata can be used to


obtain estimates using the linearization method.


 5-33


Attachment 1-139 
----------------------------------------------------------

----------------------------------------------------------

Estimation of Sampling Errors - Jackknife replicate method


Alternatively, sampling errors may be estimated using the


jackknife technique described here. The construction of


jackknife replicate weights makes use of the variance


estimation stratum/variance estimation unit structure


described in CSFII/DHKS 1994-96 documentation (USDA 1998)


section 5.1.4. To illustrate how a jackknife variance


estimator can be calculated, let y denote a weighted


survey estimate (for example, total fat intake) calculated


using the full-sample weights. Let y(j) be the corresponding


weighted estimate calculated using the j-th set of replicate


weights (j = 1, 2, .,43). The estimated variance of y is


then given by the formula


 Var(y) = SUM (y(j) - y)**2,


where the summation extends over the 43 sets of jackknife


replicate weights. Forty-three replicates were created by


applying this process to each of the 43 variance estimation


strata.


A jackknife replicate is created by dropping out one of


the two variance estimation units from a variance


estimation stratum and doubling the initial probability


weights of the households in the other variance estimation


unit in that stratum. The entire weighting process as


described in the previous sections of this document was


repeated for each replicate. Households not in the current


replicate subsample were assigned a corresponding replicate


weight of zero. In this way, a series of replicate weights


was generated for each household. Together with the final,


full-sample weights, these replicate weights were designed


for the calculation of sampling errors.


Using a replication method to calculate sampling errors


of survey-based estimates makes complicated variance


estimation formulas unnecessary. The jackknife


replication method used here is also designed to reflect


the stratification and clustering used in the CSFII/DHKS


sample design and to capture the effects of the raking


ratio adjustments mentioned in CSFII/DHKS 1994-96


documentation section 5.1.2.5.


The replicate weighting process described above was


designed and implemented by Westat, Inc., who has also


created a variance estimation program, WesVarPC, which


runs on computers using the Windows operating system


and is available to the public at no charge. The


software can be downloaded from Westat's home page at


<http://www.westat.com>. In WesVarPC terminology, the


JK2 method was used in constructing these replicate weights.


 5-34


Attachment 1-140 
-------------------------------

-------------------------------

Programs and examples of output


Note: The following programs were written to accompany the


release of the CSFII 1994-96 household sampling weights,


not this 1994-96, 1998 release. The main purpose of two of


the programs was to merge the final household sampling weights


into an existing file derived from household record type 15.


That merging process is not necessary with this release


because both the 3-year and 4-year final household sampling


weights have been included on record type 15. Also note that


the 3-year household jackknife replicate weight file,


jkw3yrhh.dat, has a different format than the file read in


by the following program. Appropriate input programs for both


the survey data files and the replicate weight files from this


release may be found in section 10. Nevertheless, these input


programs and programming examples from the original


documentation may be useful to users of household level data.


The following are three SAS programs used to prepare data


files and three examples of using the household data and


sampling weights. Program 1 is a SAS program that reads


the ASCII household weight file (hhwgt.dat) and creates a


SAS system file. Program 2 adds the household sampling


weight to an existing CSFII household-level SAS system


file. Program 3 is a modified version of Program 2 that


prepares a data file used as input by two of the examples.


Example 1 is a simple SAS program that produces weighted


percentages of selected household variables. Examples 2


and 3 demonstrate the use of SUDAAN and WesVarPC for the


estimation of standard errors of percentages. Example 2


is a SUDAAN program; Example 3 describes the preparation


procedure necessary for using WesVarPC with CSFII data.


The latter two examples examine household income as a


percentage of poverty level (POVCAT) and the adequacy of


the food supply of the household (FOODDESC). Both


variables are modified by program 3 to create


two-category variables. Levels 1 and 2 of FOODDESC have


been combined to identify those households where "enough


food eaten" was reported. Levels 3 and 4 have been


combined to identify households where "not enough food


eaten" was reported. Households with other values of


FOODDESC are not represented in the analysis. Levels


2 and 3 of POVCAT have been combined for those households


that have income over 130% of poverty. Level 1


represents those households that are below 131% of


poverty (see documentation section 3.5, "Glossary").


 5-35


Attachment 1-141 
*******************************************************


* *


* program1.sas *


* *


* This SAS program reads the entire household weight *


* file and creates the SAS system file, HHWGT, *


* containing the same data. *


* *


* These programs assume that the directory \data9496 *


* holds all CSFII SAS files as well as the downloaded *


* ascii file containing the household sampling *


* weights. The LIBNAME and FILENAME statements *


* should be modified as appropriate. *


* *


*******************************************************;


options ls = 78 ps = 55;


libname dir9496 'c:\data9496';


filename hhwgt 'c:\data9496\hhwgt.dat';


data dir9496.hhwgt (compress = 'yes');


 infile hhwgt lrecl = 386;


 input hhid 1-5 wt3_hh 8-15 wt_h_adj 16-23


 (r3_hh_01-r3_hh_43) (43 * 8.)


 hh_bwt 368-375 wt_h_scr 376-383 varstrat 384-385


 varunit 386;


label hhid = "Household ID"


 wt3_hh = "Full-sample household weight"


 wt_h_adj = "Non-response adjusted base weight"


 r3_hh_01 = "Replicate household weight - 1"


 r3_hh_02 = "Replicate household weight - 2"


 r3_hh_03 = "Replicate household weight - 3"


 r3_hh_04 = "Replicate household weight - 4"


 r3_hh_05 = "Replicate household weight - 5"


 r3_hh_06 = "Replicate household weight - 6"


 r3_hh_07 = "Replicate household weight - 7"


 r3_hh_08 = "Replicate household weight - 8"


 r3_hh_09 = "Replicate household weight - 9"


 r3_hh_10 = "Replicate household weight - 10"


 r3_hh_11 = "Replicate household weight - 11"


 r3_hh_12 = "Replicate household weight - 12"


 r3_hh_13 = "Replicate household weight - 13"


 r3_hh_14 = "Replicate household weight - 14"


 r3_hh_15 = "Replicate household weight - 15"


 r3_hh_16 = "Replicate household weight - 16"


 r3_hh_17 = "Replicate household weight - 17"


 r3_hh_18 = "Replicate household weight - 18"


 r3_hh_19 = "Replicate household weight - 19"


 r3_hh_20 = "Replicate household weight - 20"


 r3_hh_21 = "Replicate household weight - 21"


 r3_hh_22 = "Replicate household weight - 22"


 5-36


Attachment 1-142 
 r3_hh_23 = "Replicate household weight - 23"


 r3_hh_24 = "Replicate household weight - 24"


 r3_hh_25 = "Replicate household weight - 25"


 r3_hh_26 = "Replicate household weight - 26"


 r3_hh_27 = "Replicate household weight - 27"


 r3_hh_28 = "Replicate household weight - 28"


 r3_hh_29 = "Replicate household weight - 29"


 r3_hh_30 = "Replicate household weight - 30"


 r3_hh_31 = "Replicate household weight - 31"


 r3_hh_32 = "Replicate household weight - 32"


 r3_hh_33 = "Replicate household weight - 33"


 r3_hh_34 = "Replicate household weight - 34"


 r3_hh_35 = "Replicate household weight - 35"


 r3_hh_36 = "Replicate household weight - 36"


 r3_hh_37 = "Replicate household weight - 37"


 r3_hh_38 = "Replicate household weight - 38"


 r3_hh_39 = "Replicate household weight - 39"


 r3_hh_40 = "Replicate household weight - 40"


 r3_hh_41 = "Replicate household weight - 41"


 r3_hh_42 = "Replicate household weight - 42"


 r3_hh_43 = "Replicate household weight - 43"


 hh_bwt = "Household base weight"


 wt_h_scr = "Screener adjusted household base weight"


 varstrat = "Variance strata"


 varunit = "Variance estimation unit"


 ;


proc means;


run;


************* End of Program 1 ************************;


 5-37


Attachment 1-143
*******************************************************


* *


* program2.sas *


* *


* This SAS program adds the household sampling *


* weight, WT3_HH, to an existing household-level SAS *


* file such as the file created from record type 15 *


* by the READ15.SAS program on the 1994-96 CD-ROM. *


* The file created by PROGRAM1.SAS supplies the *


* sampling weight. *


* *


* These programs assume that the directory \data9496 *


* holds all CSFII SAS files. The LIBNAME statement *


* should be modified as appropriate. *


* *


*******************************************************;


options ls = 78 ps = 55;


libname dir9496 'c:\data9496';


***********************************************


* *


* Delete or modify the KEEP option in the *


* following statement to add the replicate *


* sampling weights to the RT15 file. The *


* replicate weights are required if software *


* such as WESVAR, utilizing a replication *


* method, is used for variance estimation *


* (see example 3). *


* *


***********************************************;


data dir9496.rt15 (compress = 'yes');


 merge dir9496.rt15


 dir9496.hhwgt (keep = hhid wt3_hh);


 by hhid;


proc means;


run;


************* End of Program 2 ************************;


 5-38


Attachment 1-144 
************************************************************


* *


* program3.sas *


* *


* This SAS program prepares an input file for the two *


* variance estimation programs, examples 2 and 3. It is *


* used for three purposes. The first purpose is to create *


* a PC SAS file in an older than current version, version *


* 6.04, that both PC-based SUDAAN and Wesvar can read *


* directly. Secondly, the replicate weights are collected *


* from the file created by PROGRAM1.SAS. Thirdly, the *


* variables used for analysis in example programs are *


* created. Only the variables needed for the examples are *


* retained. *


* *


************************************************************;


options ls = 78 ps = 55;


libname dir9496 'c:\data9496';


libname dir2 v604 'c:\data9496';


data dir2.pgm3 (keep = hhid wt3_hh varstrat varunit


 r3_hh_01-r3_hh_43 under131 enough);


 merge dir9496.rt15 (keep = hhid povcat fooddesc wt3_hh


 varstrat varunit)


 dir9496.hhwgt (keep = hhid r3_hh_01- r3_hh_43);


 by hhid;


if fooddesc in(1, 2) then


 enough = 1;


else if fooddesc in(3, 4) then


 enough = 2;


if (povcat eq 1) then


 under131 = 1;


else


 under131 = 2;


label under131 = 'Income status'


 enough = 'Enough to eat'


 ;


proc means;


run;


************* End of Program 3 ************************;


 5-39


Attachment 1-145 
*******************************************************


* *


* example1.sas *


* *


* This SAS program produces weighted frequencies of *


* several household level variables. The input file *


* is the file created by PROGRAM2.SAS *


* *


* These programs assume that the directory \data9496 *


* holds all CSFII SAS files. The LIBNAME statement *


* should be modified as appropriate. *


* *


*******************************************************;


options ls = 78 ps = 60;


options nodate nonumber nocenter;


libname dir9496 'c:\data9496';


proc freq data = dir9496.rt15;


 tables povcat fooddesc fs_rcv12 urb region;


 weight wt3_hh;


 format povcat povcat. fooddesc fooddesc.


 fs_rcv12 yn789f. urb urb. region region.;


 title 'Example 1: Weighted frequencies of household '


 'level data, 1994-96 CSFII';


run;


************* End of Example 1 program ****************;


 5-40


Attachment 1-146 
Example 1: Weighted frequencies of household level data,


 1994-96 CSFII


 Annual income: % of poverty category


 Cumulative Cumulative


 POVCAT Frequency Percent Frequency Percent


 0 - 130% 19520256 19.8 19520256 19.8


131 - 350% 39468942 40.0 58989198 59.8


 Over 350% 39585563 40.2 98574761 100.0


 Description of food eaten in HH


 Cumulative Cumulative


 FOODDESC Frequency Percent Frequency Percent


Enough - 1 75210037 76.3 75210037 76.3


Enough - 2 20344690 20.6 95554727 96.9


Sometimes not enough 1803550 1.8 97358277 98.8


Often not enough 336251 0.3 97694528 99.1


Not ascertained 880233 0.9 98574761 100.0


 Food stamps: in last 12 months


 Cumulative Cumulative


 FS_RCV12 Frequency Percent Frequency Percent


Yes 8693044 8.8 8693044 8.8


No 88663791 89.9 97356835 98.8


Refused 237994 0.2 97594829 99.0


Don't know 102999 0.1 97697828 99.1


Not ascertained 876933 0.9 98574761 100.0


 Urbanization


 Cumulative Cumulative


 URB Frequency Percent Frequency Percent


MSA, central city 31977978 32.4 31977978 32.4


MSA, not central city 45717307 46.4 77695285 78.8


Non-MSA 20879476 21.2 98574761 100.0


 Region


 Cumulative Cumulative


 REGION Frequency Percent Frequency Percent


Northeast 19586188 19.9 19586188 19.9


Midwest 23591612 23.9 43177800 43.8


South 34604507 35.1 77782307 78.9


West 20792454 21.1 98574761 100.0


************* End of Example 1 output listing *********;


 5-41


/****************************************************************


* *


* Example2.prc *


* *


* This SUDAAN program provides an example of computing the *


* standard error of estimates from the CSFII 1994- 96. SUDAAN *


* is a program containing procedures designed to be used to *


* analyze data from complex sample surveys such as the CSFII. *


* *


* This program was written to be used by the stand- alone *


* version of SUDAAN. The input file is the SAS system file *


Attachment 1-147 
* created by PROGRAM2.SAS which created a version 6.04 PC SAS *


* system file. This program provides the basic statements *


* needed to inform SUDAAN of the CSFII sample design *


* information needed for the estimates. *


* *


* The SUDAAN procedure used here is PROC CROSSTAB. The *


* procedure call specifies a "with replacement" design *


* (design = wr). A NEST statement is used to define the *


* required design parameters, VARSTRAT, the variance-estimation *


* stratum, and VARUNIT, the variance-estimation unit which is *


* used as a primary sampling unit or PSU. *


* *


* Notes: The data directory must be set to the directory *


* containing the input file. Also, a LEVEL.DBS may be *


* placed in that directory to supply variable formats. *


* *


*****************************************************************/


proc crosstab data = pgm3 filetype = sas design = wr;


 nest varstrat varunit;


 weight wt3_hh;


 subgroup under131 enough;


 levels 2 2;


 tables under131 * enough;


 print nsum rowper colper serow secol /style = nchs ;


************* End of Example 2 program ****************;


 5-42


Attachment 1-148 
-----------------------------------------------------------------

-----------------------------------------------------------------

-----------------------------------------------------------------

Example 2 output listing


 Research Triangle Institute


 The CROSSTAB Procedure


by: Income status, Enough to eat.


Income status


 Enough to eat Sample Row Col SE Row SE Col


 Size Percent Percent Percent Percent


Total


 Total 8007 100.00 100.00 0.00 0.00


 Enough 7791 97.81 100.00 0.19 0.00


 Not enough 216 2.19 100.00 0.19 0.00


Below 131% of


 poverty


 Total 2083 100.00 19.90 0.00 0.96


 Enough 1908 91.80 18.68 0.71 0.93


 Not enough 175 8.20 74.54 0.71 3.19


131%+


 Total 5924 100.00 80.10 0.00 0.96


 Enough 5883 99.30 81.32 0.11 0.93


 Not enough 41 0.70 25.46 0.11 3.19


************* End of Example 2 output listing *********;


 5-43


Attachment 1-149 
*****************************************************************


* *


* Example3 *


* *


* Example 3 used the WesVarPC software to estimate percentages *


* and their standard errors. The file created by PROGRAM2.SAS *


* provided the input. During the preparation step, the SAS *


* file PGM3.SSD was imported, the analysis variables, full *


* sample weight and replicate weights identified, and the *


* replication method JK2 selected. *


* *


* The output shown below was produced by a table request of *


* under131 * enough, asking for percentages of the sum of the *


* weights, and with other specifications as shown below. *


* *


*****************************************************************/


Example 3 output listing


 PC WESVAR VERSION NUMBER: 2.12


 TIME THE JOB EXECUTED: 08:19:23 02/19/99


 INPUT DATASET NAME: C:\data9496\Pgm3.var


 OUTPUT LISTING: C:\data9496\pgm3.LST


 OPTION NOSUMMARY IS: OFF


 OPTION FUNCTION LOG IS: OFF


 OPTION ALIGNMENT IS: OFF


 OPTION EXPORT IS: OFF


 VARIANCE ESTIMATION METHOD: JK2


 FINITE POPULATION CORRECTION FACTOR: 1.00000


 VALUE OF ALPHA (CONFIDENCE INTERVAL %): 0.05000 (95.00000 %)


 DEGREES OF FREEDOM: INFINITE


 t VALUE: 1.960


 OPTION COMPLETE IS: ON


 FULL SAMPLE WEIGHT: WT3_HH


 REPLICATE WEIGHTS: R3_HH_01...R3_HH_43


 ANALYSIS VARIABLES: None Specified.


 COMPUTE STATISTIC: None Specified.


 TABLE REQUESTS: UNDER131*ENOUGH


 FACTOR(S): 1.00


 NUMBER OF REPLICATES: 43


 NUMBER OF OBSERVATIONS READ: 8067


 WEIGHTED NUMBER OF OBSERVATIONS READ: 98574761.000


 5-44


Attachment 1-150 
 TABLE REQUEST : UNDER131 * ENOUGH


UNDER131 ENOUGH EST_TYPE ESTIMATE STDERROR N


Below 131% Enough PERCENT 18.27 0.16 1908


Below 131% Not enough PERCENT 1.63 0.12 175


Below 131% MARGINAL PERCENT 19.90 0.11 2083


131%+ Enough PERCENT 79.54 0.13 5883


131%+ Not enough PERCENT 0.56 0.10 41


131%+ MARGINAL PERCENT 80.10 0.11 5924


MARGINAL Enough PERCENT 97.81 0.16 7791


MARGINAL Not enough PERCENT 2.19 0.16 216


MARGINAL MARGINAL PERCENT 100.00 0.00 8007


Below 131% Enough COLPCT 18.68 0.14 1908


Below 131% Not enough COLPCT 74.54 3.39 175


Below 131% MARGINAL COLPCT 19.90 0.11 2083


131%+ Enough COLPCT 81.32 0.14 5883


131%+ Not enough COLPCT 25.46 3.39 41


131%+ MARGINAL COLPCT 80.10 0.11 5924


MARGINAL Enough COLPCT 100.00 0.00 7791


MARGINAL Not enough COLPCT 100.00 0.00 216


MARGINAL MARGINAL COLPCT 100.00 0.00 8007


Below 131% Enough ROWPCT 91.80 0.58 1908


Below 131% Not enough ROWPCT 8.20 0.58 175


Below 131% MARGINAL ROWPCT 100.00 0.00 2083


131%+ Enough ROWPCT 99.30 0.12 5883


131%+ Not enough ROWPCT 0.70 0.12 41


131%+ MARGINAL ROWPCT 100.00 0.00 5924


MARGINAL Enough ROWPCT 97.81 0.16 7791


MARGINAL Not enough ROWPCT 2.19 0.16 216


MARGINAL MARGINAL ROWPCT 100.00 0.00 8007


************* End of Example 3 output listing *********;


 5-45


Attachment 1-151
5.8 References


FASEB/LSRO (Federation of American Societies for Experimental


Biology, Life Sciences Research Office). 1995. Joint policy


on variance estimation and statistical standards on NHANES III


and CSFII reports ... (Appendix III). In: Third Report on


Nutrition Monitoring in the United States. Prepared for the


Interagency Board for Nutrition Monitoring and Related Research.


USDA Publication.


SAS Institute, Inc. 1990. SAS language: Reference,


version 6 first edition. SAS Institute, Inc., Cary, NC.


Shah, BV, Barnwell, BG, and Bieler, GS. 1997.


SUDAAN User's Manual, Release 7.5. Research Triangle


Park, NC: Research Triangle Institute.


USDA (U.S. Department of Agriculture, Agricultural


Research Service). 1998. 1994-96 Continuing Survey


of Food Intakes by Individuals and 1994-96 Diet and


Health Knowledge Survey. CD-ROM. Available from


National Technical Information Service,


Springfield, VA. (NTIS Accession No. PB98-500457)


USDC/BOC (U.S. Department of Commerce, Bureau of the Census).


1994. Current Population Survey, March 1994. Machine-readable


data file.


USDC/BOC (U.S. Department of Commerce, Bureau of the Census).


1995. Current Population Survey, March 1995. Machine-readable


data file.


USDC/BOC (U.S. Department of Commerce, Bureau of the Census).


1996. Current Population Survey, March 1996. Machine-readable


data file.


USDC/BOC (U.S. Department of Commerce, Bureau of the Census).


1998. Current Population Survey, March 1998. Machine-readable


data file.


 5-46


Attachment 1-152 
6. USING THE CSFII 1994-96, 1998 DATA


6.3 Statistical Notes


6.3.1 Statistical software


Because of the complex sample design of the CSFII, ARS 


recommends that data users calculate standard errors and 


coefficients of variation for descriptive and related 


statistics using software that takes the sample design and 


weighting into account. The PSUs used in the design can be


paired as in a stratified sample where the Taylor Series 


expansion method can be used. This will allow software such 


as SUDAAN or Stata to be used when studying population 


subgroups. The fields VARSTRAT and VARUNIT are located in 


positions 11-12 and 13 in the data file, respectively. These 


fields represent the nesting fields STRATUM and PSU used for 


Taylor Series expansion estimation of standard errors. 


Also, see section 7.4.2, "Sampling weights and variance 


estimation fields." Replicate weights, as provided by the 


jackknife replication method, can also be used as described 


in section 5.6.2, "Estimation of Sampling Errors." See


section 5, "SAMPLING WEIGHTS," for more information on 


weighting procedures. 


6.3.2 Guidelines for the use of sampling weights


Weights should always be used when calculating descriptive 


statistics. This is because descriptive statistics are meant 


to provide summary information about the entire population 


under study, not just the sample. Included under the heading 


of descriptive statistics are measures of central tendency, 


such as means and medians, as well as measures of variability, 


such as variances.


Most statistical software packages allow the user to compute 


weighted descriptive statistics although they may not estimate 


variances properly. If in doubt, the analyst is advised


to consult a survey statistician.


Attachment 1-153 
ATTACHMENT 2 

The EPA Food Commodity Vocabulary 

(Source: CSFII Documentation) 

Attachment 2-1 
U.S. Environmental Protection Agency 

THE EPA FOOD COMMODITY VOCABULARY 

EPA’s list of food commodities, that is, agricultural food items from either plant or animal sources in raw or processed forms, includes items based on A food commodity is included by EPA if it is cited in the Residue Chemistry Guidelines, Table 1, entitled: Raw Agricultural and Processed CommodDerived from Crops (Guideline prepared by the Office of Prevention, Pesticides, and Toxic Substances; available electronically from the EPA Public 

access the guideline, go to: http://www.epa.gov/OPPTS_Harmonized/860_Residue_Chemistry_Test_Guidelines/Series. 860.1000).
Additionally, a food commodity is included in the following list if it currently has a tolerance. Ocommodities are included if foods known to contain them have been reported to be consumed in the 199prior food consumption surveys, or if they are known to be ingredients in commercially available babother food commodities may exist in the list below as they were revealed to be consumed by humans suresearch undertaken by EPA staff in the preparation of numerous internal reports on use of agricultucrops. 


THE EPA FOOD COMMODITY VOCABULARY


Master List June 15, 2000


EPA Food Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

95000010 Acerola weight of fruit; 

excluding seed 

18000020 Alfalfa, seed weight of dry seed Alfalfa 

human fo

14000030 Almond weight of nutmeat 14000031 Almond- babyfood same as almond 

Attachment 2-2 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

14000040 Almond, oil weight of oil 14000041 Almond, oil- babyfood weight of oil 04010050 Amaranth, leafy weight of leaf Includes 

spinach, and pokew

95000060 Amaranth, grain dry weight of grain; 

include Amaranth flour 

11000070 Apple, fruit with peel weight of apple; 

including peel, excluding core and stem 

11000080 Apple, peeled fruit weight of apple; 

excluding peel, core and stem 

11000081 Apple, peeled fruitbabyfood weight of apple; excluding peel, core 

and stem 

11000090 Apple, dried dry weight; excluding 

peel, core, stem 

11000091 Apple, dried - babyfood dry weight; excluding 

peel, core, stem 

Attachment 2-3 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

11000100 Apple, juice weight of juice at 

single strength (or standard dilution) 

11000101 Apple, juice - babyfood weight of juice at 

single strength (or standard dilution) 

11000110 Apple, sauce weight of applesauce 11000111 Apple, sauce - babyfood weight of applesauce 12000120 Apricot weight of pulp, with 

or without peel; excluding pit 

12000121 Apricot- babyfood weight of pulp, with 

or without peel; excluding pit 

12000130 Apricot, dried dry weight of pulp, 

with or without peel; excluding pit 

12000140 Apricot, juice weight of juice at 

single strength (or standard dilution) 

Attachment 2-4 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

12000141 Apricot, juice- babyfood weight of juice at 

single strength (or standard dilution) 

01030150 Arrowroot, flour dry weight of flour 01030151 Arrowroot, flour- babyfood dry weight of flour 95000160 Artichoke, globe edible portion of 

flowerhead 

01030170 Artichoke, Jerusalem edible portion of 

tuber 

04010180 Arugula weight of leaves 95000190 Asparagus weight of edible 

portion of spears/stems 

95000200 Avocado weight of pulp; 

excluding skin and pit 

09020210 Balsam pear weight of whole fruit Includes 

Chinese Bitterme

95000220 Bamboo, shoots weight of shoots Bamboo sh

human fo

Attachment 2-5 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

95000230 Banana weight of pulp; 

excluding peel; juice 

95000231 Banana- babyfood weight of pulp; 

excluding peel; juice 

95000240 Banana, dried dry weight of dried 

pulp; excluding peel (include weight of fruit from chips) 

95000241 Banana, dried- babyfood dry weight of dried 

pulp; excluding peel (include weight of fruit from chips) 

15000250 Barley, pearled barley dry weight of barley 15000251 Barley, pearled barley ­

babyfood 

dry weight of barley 

15000260 Barley, flour dry weight of flour Includes 

barley. 

15000261 Barley, flour- babyfood dry weight of flour Includes 

barley. 

15000270 Barley, bran dry weight of bran 

Attachment 2-6 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

19010280 Basil, fresh leaves weight of leaves and 

stems 

19010281 Basil, fresh leaves ­

babyfood 

weight of leaves and stems 

19010290 Basil, dried leaves dry weight of leaves 

and stems 

19010291 Basil, dried leavesbabyfood dry weight of leaves and stems 06030300 Bean, black, seed dry weight of bean Includes 

bean, Baybean. 

06020310 Bean, broad, succulent weight of bean and pod Also cal06030320 Bean, broad, seed dry weight of bean Also cal06020330 Bean, cowpea, succulent weight of bean; 

excluding pod 

Includes pea, blasouthern 

06030340 Bean, cowpea, seed dry weight of bean Includes 

pea, blasouthern 

06030350 Bean, great northern, 

seed 

dry weight of bean 

Attachment 2-7 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

06030360 Bean, kidney, seed dry weight of bean 06020370 Bean, lima, succulent weight of bean; 

excluding pod 

06030380 Bean, lima, seed dry weight of bean 06030390 Bean, mung, seed dry weight of bean Bean spr

human fo

06030400 Bean, navy, seed dry weight of bean Includes 06030410 Bean, pink, seed dry weight of bean 06030420 Bean, pinto, seed dry weight of bean Include 

Mexican b

06010430 Bean, snap, succulent weight of bean and pod Includes 

runner bbean. 

06010431 Bean, snap, succulentbabyfood weight of bean and pod Includes 

runner bbean. 

21000440 Beef, meat weight of meat; 

excluding the weight of bone, and all nutrient fat 

Consider equivalenInclude bbuffalo. 

Attachment 2-8 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

21000441 Beef, meat- babyfood weight of meat; 

excluding the weight of bone, and all nutrient fat 

21000450 Beef, meat, dried weight of dried meat; 

excluding bones, and trimmable fat 

21000460 Beef, meat byproducts weight of meat; 

excluding bone; may contain some fat 

Includes lung, swtripe, t

21000461 Beef, meat byproductsbabyfood weight of meat; excluding bone; may 

contain some fat 

Includes lung, swtripe an

21000470 Beef, fat weight of nutrient fat 

only; includes nutrient fat from beef meat 

21000471 Beef,fat- babyfood weight of nutrient fat 

only; includes nutrient fat from beef meat 

21000480 Beef, kidney weight of organ 

including nutrient fat 

Attachment 2-9 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

21000490 Beef, liver weight of organ 

including nutrient fat 

21000491 Beef, liver- babyfood weight of organ 

including nutrient fat 

01010500 Beet, garden, roots weight of roots; juice 01010501 Beet, garden, rootsbabyfood weight of roots 02000510 Beet, garden, tops weight of leaves Include p01010520 Beet, sugar dry weight of sugar 

(sucrose) 

Sucrose obtained and suga

01010521 Beet, sugar- babyfood dry weight of sugar 

(sucrose) 

Sucrose obtained and suga

01010530 Beet, sugar, molasses weight of molasses 01010531 Beet, sugar, molasses ­

babyfood 

weight of molasses 

95000540 Belgium endive weight of leaves Also cal

chicory. 

Attachment 2-10 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

13010550 Blackberry weight of berry Includes 

OlalliebYoungber

13010560 Blackberry, juice weight of juice at 

single strength (or standard dilution) 

Includes OlalliebYoungber

13010561 Blackberry, juice ­

babyfood 

weight of juice at single strength (or standard dilution) 

Includes OlalliebYoungber

13020570 Blueberry weight of berry 13020571 Blueberry- babyfood weight of berry 13010580 Boysenberry weight of berry 14000590 Brazil nut weight of nutmeat 95000600 Breadfruit weight of pulp; 

excluding peel 

05010610 Broccoli weight of flower heads 

and adjoining stems 

05010611 Broccoli- babyfood weight of flower heads 

and adjoining stems 

Attachment 2-11 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

05010620 Broccoli, Chinese weight of flower buds, 

adjoining stems and leaves 

05020630 Broccoli raab weight of flower buds, 

adjoining stems and leaves 

05010640 Brussels sprouts weight of leaf sprouts 15000650 Buckwheat dry weight of groats; 

whole groat flour 

15000660 Buckwheat, flour dry weight of flour 01010670 Burdock weight of roots 14000680 Butternut weight of nutmeat 05010690 Cabbage weight of leaves 05020700 Cabbage, Chinese, bok choy weight of leaves and 

stems 

05010710 Cabbage, Chinese, napa weight of leaves 05010720 Cabbage, Chinese, mustard weight of leaves 95000730 Cactus weight of pulp; 

excluding peel; juice 

Includes Cactus pNopales, 

Attachment 2-12 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

95000740 Canistel weight of pulp; 

excluding peel 

Also cal

09010750 Cantaloupe weight of pulp; 

excluding seeds and outer rind 

Includes 

04020760 Cardoon weight of leaf stalks 95000770 Carob dry weight of bean; 

flour 

01010780 Carrot weight of roots, with 

or without peel, excluding tops 

01010781 Carrot- babyfood weight of roots, with 

or without peel, excluding tops 

01010790 Carrot, juice weight of juice at 

single strength (or standard dilution) 

09010800 Casaba weight of pulp, 

excluding seeds and rind 

14000810 Cashew weight of nutmeat 

Attachment 2-13 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

01030820 Cassava weight of root; dry 

tapioca 

Includes 

01030821 Cassava- babyfood weight of root; dry 

tapioca 

Includes 

05010830 Cauliflower weight of flower heads 

and adjoining stems 

Includes green cau

01010840 Celeriac weight of tuberous 

root 

04020850 Celery weight of leaf stalk 04020851 Celery- babyfood weight of leaf stalk 04020860 Celery, juice weight of juice as 

single strength (or standard dilution) 

04020870 Celtuce weight of stalks and 

leaves 

09020880 Chayote, fruit weight of fruit Also cal

and Mirl

95000890 Cherimoya weight of fruit; 

excluding peel 

Also calapple. 

Attachment 2-14 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

12000900 Cherry weight of fruit; 

including skin; excluding pit and stem 

Includes sour or 

12000901 Cherry- babyfood weight of fruit; 

including skin; excluding pit and stem 

Includes sour or 

12000910 Cherry, juice weight of juice at 

single strength (or standard dilution) 

Includes sour or 

12000911 Cherry, juice- babyfood weight of juice at 

single strength (or standard dilution) 

Includes Sour or 

14000920 Chestnut weight of nutmeat 40000930 Chicken, meat weight of flesh; 

excluding the weight of bone, total nutrient fat, and skin 

40000931 Chicken, meat- babyfood weight of flesh; 

excluding the weight of bone, total nutrient fat, and skin 

40000940 Chicken, liver weight of organ; 

including nutrient fat 

Attachment 2-15 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

40000950 Chicken, meat byproducts weight of meat; 

excluding bone; may include some trimmable fat and/or skin 

Includes necks, gtail. 

40000951 Chicken, meat byproductsbabyfood weight of meat; excluding bone; may 

include some trimmable fat and/or skin 

Includes necks, gtail. 

40000960 Chicken, fat weight of nutrient fat 

only; includes weight of nutrient fat from chicken meat and skin 

40000961 Chicken, fat- babyfood weight of nutrient fat 

only; includes weight of nutrient fat from chicken meat and skin 

40000970 Chicken, skin weight of skin only 

(0 grams nutrient fat) 

40000971 Chicken, skin- babyfood weight of skin only 

(0 grams nutrient fat) 

06030980 Chickpea, seed dry weight of bean Also cal

bean. 

Attachment 2-16 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

06030981 Chickpea, seed - babyfood dry weight of bean Also cal

bean. 

06030990 Chickpea, flour dry weight of flour Also cal

bean. 

01011000 Chicory, roots weight of roots 02001010 Chicory, tops weight of leaves 09021020 Chinese waxgourd weight of flesh; 

including or excluding peel 

Includes winterme

19011030 Chive weight of leaves 04011040 Chrysanthemum, garland edible leaves and 

stems 

Includes edible l

19021050 Cinnamon dry weight of spice: 

stick or ground powder 

19021051 Cinnamon- babyfood dry weight of spice: 

stick or ground powder 

10001060 Citrus citron weight of pulp; 

excluding peel 

10001070 Citrus hybrids weight of pulp; 

excluding seeds and peel 

Includes Chironja

Attachment 2-17 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

10001080 Citrus, oil weight of oil 95001090 Cocoa bean, chocolate weight of chocolate; 

cocoa butter 

95001100 Cocoa bean, powder weight of powder 95001110 Coconut, meat weight of meat; 

excluding milk and shell 

95001111 Coconut- meat, babyfood weight of meat; 

excluding milk and shell 

95001120 Coconut, dried dry weight of meat; 

excluding milk and shell 

95001130 Coconut, milk weight of milk only; 

excluding meat and shell 

95001140 Coconut, oil weight of oil 95001141 Coconut, oil- babyfood weight of oil 95001150 Coffee, roasted bean dry weight of bean 95001160 Coffee, instant dry weight of powder 

or granules 

Attachment 2-18 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

05021170 Collards weight of leaves 19021180 Coriander, leaves weight of leaves Includes 

and cilan

19021181 Coriander, leaves ­

babyfood 

weight of leaves Includes 

and cilantro 

19021190 Coriander, seed weight of seed Includes 

and cilan

19021191 Coriander, seed - babyfood weight of seed Includes 

and cilantro 

15001200 Corn, field, flour dry weight of whole 

grain flour; masa harina 

15001201 Corn, field, flourbabyfood dry weight of whole grain flour; masa 

harina 

Attachment 2-19 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

15001210 Corn, field, meal dry weight of whole 

grain or degermed meal; dry weight of corn ingredient from corn or cornmeal based chips or snacks; dry weight of hominy 

Includes 

15001211 Corn, field, mealbabyfood dry weight of whole grain or degermed 

meal; dry weight of corn ingredient from corn or cornmeal based chips or snacks; dry weight of hominy. 

Includes 

15001220 Corn, field, bran dry weight of bran 15001230 Corn, field, starch dry weight of corn 

starch 

15001231 Corn, field, starchbabyfood dry weight of corn starch 15001240 Corn, field, syrup weight of syrup 15001241 Corn, field, syrupbabyfood weight of syrup 15001250 Corn, field, oil weight of oil 

Attachment 2-20 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

15001251 Corn, field, oil- babyfood weight of oil 15001260 Corn, pop weight of kernels; 

excluding cob and husk 

15001270 Corn, sweet weight of kernels; 

excluding cob and husk 

15001271 Corn, sweet- babyfood weight of kernels; 

excluding cob and husk 

95001280 Cottonseed, oil weight of oil 95001281 Cottonseed, oil - babyfood weight of oil 11001290 Crabapple weight of pulp; 

excluding core and stem; including or excluding peel 

95001300 Cranberry weight of berry 95001301 Cranberry- babyfood weight of berry 95001310 Cranberry, dried dry weight of berry 95001320 Cranberry, juice weight of juice at 

single strength (or standard dilution) 

Attachment 2-21 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

95001321 Cranberry, juice- babyfood weight of juice at 

single strength (or standard dilution) 

04011330 Cress, garden weight of leaves 04011340 Cress, upland weight of leaves Includes 

and Wint

09021350 Cucumber weight of flesh and 

seeds; including or excluding peel 

13021360 Currant weight of berry 13021370 Currant, dried dry weight of berry 04011380 Dandelion, leaves weight of leaves 01031390 Dasheen, corm weight of the corm Includes 02001400 Dasheen, leaves weight of the leaves Includes 95001410 Date weight of fruit, 

excluding pit 

13011420 Dewberry weight of berry 19021430 Dill, seed dry weight of seed 19011440 Dill weight of leaves Also cal

Attachment 2-22 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

70001450 Egg, whole weight of white and 

yolk; excluding shell 

70001451 Egg, whole - babyfood weight of white and 

yolk; excluding shell 

70001460 Egg, white weight of egg white 70001461 Egg, white (solids)­

babyfood 

rehydrated weight of egg white solids 

70001470 Egg, yolk weight of egg yolk 70001471 Egg, yolk- babyfood weight of egg yolk 08001480 Eggplant weight of whole 

vegetable; including seeds, with or without peel 

13021490 Elderberry weight of berry 04011500 Endive weight of leaves Includes 95001510 Feijoa weight of pulp, 

excluding peel 

Also calguava. 

04021520 Fennel, Florence weight of leaves Includes 

sweet fen

Attachment 2-23 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

95001530 Fig weight of fruit 95001540 Fig, dried dry weight of fruit 14001550 Filbert weight of nutmeat Also cal14001560 Filbert, oil weight of oil Also cal80001570 Fish- freshwater finfish weight of edible 

portion; excluding head, tail, scales, fins, viscera, inedible bones and skin 

See AppenList”) fof variouinto appfor specspecificfish, sacrustacemollusc-

80001580 Fish- freshwater finfish, 

farm raised 

weight of edible portion; excluding head, tail, scales, fins, viscera, inedible bones and skin 

See Appen

Attachment 2-24 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

80001590 Fish- saltwater finfish, 

tuna 

weight of edible portion; excluding head, tail, scales, fins, viscera, inedible bones and skin 

See Appen

80001600 Fish- saltwater finfish, 

other 

weight of edible portion; excluding head, tail, scales, fins, viscera, inedible bones and skin 

See Appen

80001610 Fish- shellfish, 

crustacean 

weight of edible portion; excluding shell, gills, and viscera 

See Appen

80001620 Fish- shellfish, mollusc weight of edible 

portion; excluding shells 

See Appen

95001630 Flaxseed, oil weight of oil; 

nutrient fat from flax seeds 

Also cal

03001640 Garlic weight of bulb; 

excluding skin (outer scales) 

Attachment 2-25 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

03001650 Garlic, dried dry weight of bulb; 

excluding skin (outer scales) 

03001651 Garlic, dried- babyfood dry weight of bulb; 

excluding skin (outer scales) 

01031660 Ginger weight of roots; 

excluding peel 

01031661 Ginger - babyfood weight of roots, 

excluding peel 

01031670 Ginger, dried dry weight of roots; 

excluding peel 

01031680 Ginseng, dried dry weight of roots 23001690 Goat, meat weight of meat; 

excluding weight of bone, and all nutrient fat and skin 

23001700 Goat, meat byproducts weight of meat; 

excluding bone; may include some trimmable fat and/or skin 

Includes and head

Attachment 2-26 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

23001710 Goat, fat weight of nutrient fat 

only; include nutrient fat from goat meat 

23001720 Goat, kidney weight of organ 

including nutrient fat 

23001730 Goat, liver weight of organ 

including nutrient fat 

13021740 Gooseberry weight of berry 95001750 Grape weight of grape, with 

skin, and with or without seeds 

Includes 

95001760 Grape, juice weight of juice at 

single strength (or standard dilution) 

95001761 Grape, juice- babyfood weight of juice at 

single strength (or standard dilution) 

95001770 Grape, leaves weight of leaves 95001780 Grape, raisin dry weight of raisin Includes 95001790 Grape, wine and sherry weight of wine or 

sherry 

Attachment 2-27 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

10001800 Grapefruit weight of pulp; 

excluding seeds and rind 

10001810 Grapefruit, juice weight of juice at 

single strength (or standard dilution) 

06031820 Guar, seed weight of bean 06031821 Guar, seed - babyfood weight of bean 95001830 Guava weight of pulp; 

excluding peel; juice 

95001831 Guava- babyfood weight of pulp; 

excluding peel; juice 

Attachment 2-28 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

19011840 Herbs, other weight of leaves and 

stems 

See AppenList”). herbs arthis lisunique Fin this vseparateinclude: coriandeleaf, difennel, and sweemarjoram parsley (winter 

Attachment 2-29 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

19011841 Herbs, other- babyfood weight of leaves and 

stems 

See AppenList”). herbs arthis lisunique Fin this vseparateinclude: coriandeleaf, difennel, and sweemarjoram parsley, (summer 

14001850 Hickory nut weight of nutmeat 95001860 Honey weight of honey 95001861 Honey- babyfood weight of honey 09011870 Honeydew melon weight of pulp; 

excluding seeds and rind 

95001880 Hop weight of dried hops 

Attachment 2-30 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

24001890 Horse, meat weight of meat; 

excluding bone, trimmable fat or skin 

01011900 Horseradish weight of root, ground 13021910 Huckleberry weight of berry 95001920 Jaboticaba weight of whole fruit 95001930 Jackfruit weight of pulp; 

excluding peel 

05021940 Kale weight of leaves Includes 95001950 Kiwifruit weight of pulp; 

excluding peel 

05011960 Kohlrabi weight of leaves and 

stems 

10001970 Kumquat weight of fruit; 

including peel; excluding seeds 

03001980 Leek weight of whole plant; 

including leaves and bulb 

Attachment 2-31 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

10001990 Lemon weight of pulp; 

excluding seeds and peel 

10002000 Lemon, juice weight of juice at 

single strength (or standard dilution) 

10002001 Lemon, juice- babyfood weight of juice at 

single strength (or standard dilution) 

10002010 Lemon, peel weight of peel only 19012020 Lemongrass weight of leaves 06032030 Lentil dry weight of edible 

seed 

04012040 Lettuce, head weight of leaves; 

juice 

04012050 Lettuce, leaf weight of leaves Includes 10002060 Lime weight of pulp; 

excluding seeds and peel 

10002070 Lime, juice weight of juice at 

single strength (or standard dilution) 

Attachment 2-32 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

10002071 Lime, juice- babyfood weight of juice at 

single strength (or standard dilution) 

13012080 Loganberry weight of berry 95002090 Longan weight of pulp; 

excluding seeds and rind 

11002100 Loquat weight of pulp; 

excluding seeds and skin 

95002110 Lychee weight of pulp; 

excluding peel 

95002120 Lychee, dried weight of dried pulp 14002130 Macadamia nut weight of nutmeat 95002140 Mamey apple weight of pulp; 

excluding peel, core and stem 

Also cal

95002150 Mango weight of pulp; 

excluding peel 

95002151 Mango- babyfood weight of pulp; 

excluding peel 

Attachment 2-33 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

95002160 Mango, dried weight of dried pulp 95002170 Mango, juice weight of juice at 

single strength (or standard dilution) 

95002171 Mango, juice - babyfood weight of juice at 

single strength (or standard dilution) 

95002180 Maple, sugar dry weight of sugar 95002190 Maple syrup weight of syrup 19012200 Marjoram weight of leaves and 

stems 

Includes 

19012201 Marjoram - babyfood weight of leaves and 

stems 

Includes 

28002210 Meat, game weight of meat; 

excluding bone, trimmable fat and skin 

Includes beaver, elk, frogmoose, snraccoon, turtle. 

27002220 Milk, fat weight of nutrient fat 

only 

Attachment 2-34 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

27002221 Milk, fat - baby 

food/infant formula 

weight of nutrient fat only 

27012230 Milk, nonfat solids Remaining weight of 

milk after subtracting milk fat and moisture content of milk 

Includes includes items thcommerciinfant f

27012231 Milk, nonfat solids- baby 

food/infant formula 

Remaining weight of milk after subtracting milk fat and moisture content of milk 

Includes note thato commeor infanincluded represenFood Comm#2703225[lactoseformula)

27022240 Milk, water Moisture content of 

milk 

27022241 Milk, waterbabyfood/infant formula Moisture content of milk 27032251 Milk, sugar (lactose)­

baby food/infant formula 

dry weight of lactosereport only when consumed from baby food/infant formula 

Includes items thcommerciinfant f

Attachment 2-35 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

15002260 Millet, grain dry weight of grain 95002270 Mulberry weight of fruit 95002280 Mushroom weight of caps or caps 

and stems 

05022290 Mustard greens weight of leaves and 

stems 

12002300 Nectarine weight of pulp; 

including peel; excluding pit and stem 

15002310 Oat, bran dry weight of bran 15002320 Oat, flour dry weight of flour 15002321 Oat, flour- babyfood dry weight of flour 15002330 Oat, groats/rolled oats dry weight 15002331 Oat, groats/rolled oatsbabyfood dry weight 95002340 Okra weight of pods; 

including seeds 

95002350 Olive weight of fruit; 

excluding pit 

95002360 Olive, oil weight of oil 

Attachment 2-36 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

03002370 Onion, dry bulb weight of bulb; 

excluding outer skin 

03002371 Onion, dry bulb- babyfood weight of bulb; 

excluding outer skin 

03002380 Onion, dry bulb, dried dry weight of bulb; 

excluding outer skin 

03002381 Onion, dry bulb, driedbabyfood dry weight of bulb; excluding outer skin 03002390 Onion, green weight of bulb or bulb 

and leaves 

10002400 Orange weight of pulp; 

excluding seeds and peel 

10002410 Orange, juice weight of juice at 

single strength (or standard dilution) 

10002411 Orange, juice- babyfood weight of juice at 

single strength (or standard dilution) 

10002420 Orange, peel weight of peel only 95002430 Palm heart, leaves weight of stem and 

leaves 

Attachment 2-37 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

95002440 Palm, oil weight of oil 95002441 Palm, oil - babyfood weight of oil 95002450 Papaya weight of pulp; 

excluding peel and seeds 

95002451 Papaya- babyfood as above 95002460 Papaya, dried weight of dried pulp 95002470 Papaya, juice weight of juice at 

single strength (or standard dilution) 

04012480 Parsley, leaves weight of leaves and 

stems 

19012490 Parsley, dried leaves dried weight of leaves 

and stems 

19012491 Parsley, dried leaves ­

babyfood 

dried weight of leaves and stems 

01012500 Parsley, turnip rooted weight of roots and 

leaves 

01012510 Parsnip weight of roots with 

or without peel 

Attachment 2-38 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

01012511 Parsnip - babyfood weight of roots with 

or without peel 

95002520 Passionfruit weight of pulp; 

excluding seeds and peel 

95002521 Passionfruit- babyfood weight of pulp; 

excluding seeds and peel 

95002530 Passionfruit, juice weight of juice at 

single strength (or standard dilution) 

95002531 Passionfruit, juicebabyfood weight of juice at single strength (or 

standard dilution) 

95002540 Pawpaw weight of pulp; 

excluding peel 

06022550 Pea, succulent weight of peas 06022551 Pea, succulent- babyfood weight of peas 06032560 Pea, dry dry weight of pea 06032561 Pea, dry- babyfood dry weight of pea 

Attachment 2-39 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

06012570 Pea, edible podded weight of pea and pod 06032580 Pea, pigeon, seed dry weight of pea 06022590 Pea, pigeon, succulent weight of pea In U.S. p

usually pigeon p

12002600 Peach weight of pulp, with 

or without peel; excluding pit 

12002601 Peach- babyfood weight of pulp, with 

or without peel; excluding pit 

12002610 Peach, dried weight of dried pulp, 

with or without peel; excluding pit 

12002611 Peach, dried- babyfood weight of dried pulp, 

with or without peel; excluding pit 

12002620 Peach, juice weight of juice at 

single strength (or standard dilution) 

12002621 Peach, juice- babyfood weight of juice at 

single strength (or standard dilution) 

Attachment 2-40 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

95002630 Peanut weight of nutmeat; 

excluding shell 

95002640 Peanut, butter weight of ground 

peanuts; excluding shell 

95002650 Peanut, oil weight of oil 12002660 Pear weight of pulp, with 

or without peel; excluding core and stem 

Include O

12002661 Pear- babyfood weight of pulp, with 

or without peel; excluding core and stem 

12002670 Pear, dried weight of dried pulp, 

with or without peel 

12002680 Pear, juice weight of juice at 

single strength (or standard dilution) 

12002681 Pear, juice- babyfood weight of juice at 

single strength (or standard dilution) 

14002690 Pecan weight of nutmeat 

Attachment 2-41 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

08002700 Pepper, bell weight of flesh; 

excluding seeds and stem 

Includes cooking pand banan

08002701 Pepper, bell- babyfood weight of flesh; 

excluding seeds and stem 

Includes cooking pand banan

08002710 Pepper, bell, dried dry weight of flesh 

only 

Includes cooking pand banan

08002711 Pepper, bell, driedbabyfood dry weight of flesh only Includes 

cooking pand banan

08002720 Pepper, non-bell weight of flesh, with 

or without seeds; excluding stem 

08002721 Pepper, non-bell, ­

babyfood 

weight of flesh, with or without seeds; excluding stem 

08002730 Pepper, non-bell, dried dry weight of flesh or 

flesh and seeds 

19022740 Pepper, black and white dry weight of pepper 19022741 Pepper, black and whitebabyfood dry weight of pepper 

Attachment 2-42 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

95002750 Peppermint weight of leaves and 

stems 

95002760 Peppermint, oil weight of oil 95002770 Persimmon weight of entire 

fruit, pulp and peel 

95002780 Pine nut weight of nutmeat Also cal95002790 Pineapple weight of pulp; 

excluding leaves and outer peel 

95002791 Pineapple- babyfood weight of pulp; 

excluding leaves and outer peel 

95002800 Pineapple, dried weight of dried pulp 

only 

95002810 Pineapple, juice weight of juice at 

single strength (or standard dilution) 

95002811 Pineapple, juice- babyfood weight of juice at 

single strength (or standard dilution) 

14002820 Pistachio weight of nutmeat 

Attachment 2-43 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

95002830 Plantain weight of pulp; 

excluding skin 

95002840 Plantain, dried weight of dried pulp 

only 

12002850 Plum weight of pulp with 

peel; excluding pit 

12002851 Plum- babyfood weight of pulp with 

peel; excluding pit 

12002860 Plum, prune, fresh weight of plum, with 

peel; excluding pit 

12002861 Plum, prune, freshbabyfood weight of plum, with peel; excluding pit 12002870 Plum, prune, dried weight of dried flesh, 

with or without peel; excluding pit 

12002871 Plum, prune, driedbabyfood weight of dried flesh, with or without peel; 

excluding pit 

12002880 Plum, prune, juice weight of juice at 

single strength (or standard dilution) 

Attachment 2-44 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

12002881 Plum, prune, juicebabyfood weight of juice at single strength (or 

standard dilution) 

95002890 Pomegranate weight of pulp; 

excluding peel and seeds 

Seeds arconsumed

25002900 Pork, meat weight of meat; 

excluding weight of bone, and all nutrient fat and skin 

25002901 Pork, meat- babyfood weight of meat; 

excluding weight of bone, and all nutrient fat and skin 

25002910 Pork, skin dry weight of skin 

including nutrient fat 

e.g., po

25002920 Pork, meat byproducts weight of meat; 

excluding bone; may include some trimmable fat and skin 

Includes chitterl(maw), f

25002921 Pork, meat byproductsbabyfood weight of meat; excluding bone; may 

include some trimmable fat and skin 

Includes chitterl(maw), f

Attachment 2-45 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

25002930 Pork, fat weight of nutrient 

fat only; includes weight of nutrient fat from meat 

25002931 Pork, fat- babyfood weight of nutrient 

fat only; includes weight of nutrient fat from meat 

25002940 Pork, kidney weight of organ 

including nutrient fat 

25002950 Pork, liver weight of organ 

including nutrient fat 

01012960 Potato, chips weight of potato from 

chip or stick, with or without peel 

01012970 Potato, dry (granules/ 

flakes) 

dry weight of granules or flakes 

01012971 Potato, dry (granules/ 

flakes)- babyfood 

dry weight of granules or flakes 

01012980 Potato, flour dry weight of flour or 

potato starch 

01012981 Potato, flour - babyfood dry weight of flour or 

potato starch 

Attachment 2-46 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

01012990 Potato, tuber, w/peel weight of tuber; 

including peel 

01012991 Potato, tuber, w/peelbabyfood weight of tuber; including peel 01013000 Potato, tuber, w/o peel weight of tuber; 

excluding peel 

01013001 Potato, tuber, w/o peelbabyfood weight of tuber; excluding peel 60003010 Poultry, other, meat weight of meat; 

excluding weight of bone, and all nutrient fat, and skin 

Includes goose, guostrich, pheasantsquab, w

60003020 Poultry, other, liver weight of organ, 

including nutrient fat 

Includes goose, guostrich, pheasantsquab, w

Attachment 2-47 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

60003030 Poultry, other, meat 

byproducts 

weight of meat; excluding bone; may contain some trimmable fat and/or skin 

Includes goose, guostrich, pheasantsquab, wByproducfor chicgizzard, tail, as species 

60003040 Poultry, other, fat weight of nutrient 

fat only; includes weight of nutrient fat from meat and skin 

Includes goose, guostrich, pheasantsquab, w

60003050 Poultry, other, skin weight of nutrient 

fat only; includes weight of nutrient fat from meat and skin 

Includes goose, guostrich, pheasantsquab, w

95003060 Psyllium, seed dry weight of psyllium 

husks 

10003070 Pummelo weight of pulp; 

excluding seeds and peel 

Attachment 2-48 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

09023080 Pumpkin weight of pulp; 

excluding seeds and rind 

09023090 Pumpkin, seed weight of dried seed 

only 

11003100 Quince weight of pulp; 

excluding seeds and peel 

95003110 Quinoa, grain weight of grain or 

Quinoa flour 

29003120 Rabbit, meat weight of meat; 

excluding bone, trimmable fat and skin 

04013130 Radicchio weight of leaves 01013140 Radish, roots weight of roots 02003150 Radish, tops weight of leaves 01013160 Radish, Oriental, roots weight of roots 02003170 Radish, Oriental, tops weight of leaves 05023180 Rape greens weight of leaves and 

stems 

95003190 Rapeseed, oil weight of oil Includes 

Attachment 2-49 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

95003191 Rapeseed, oil - babyfood weight of oil Includes 14013200 Raspberry weight of berry 14013201 Raspberry- babyfood weight of berry 14013210 Raspberry, juice weight of juice at 

single strength (or standard dilution) 

14013211 Raspberry, juice -

babyfood 

weight of juice at single strength (or standard dilution) 

04023220 Rhubarb weight of stalks; 

excluding leaves 

15003230 Rice, white dry weight of grain 15003231 Rice, white- babyfood dry weight of grain 15003240 Rice, brown dry weight of grain or 

brown rice flour 

15003241 Rice, brown- babyfood dry weight of grain or 

brown rice flour 

15003250 Rice, flour dry weight of flour 15003251 Rice, flour- babyfood dry weight of flour 15003260 Rice, bran dry weight of bran 

Attachment 2-50 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

15003261 Rice, bran- babyfood dry weight of bran 01013270 Rutabaga weight of roots; 

excluding tops 

15003280 Rye, grain dry weight of grain; 

weight of whole grain flour 

15003290 Rye, flour dry weight of flour 95003300 Safflower, oil weight of oil 95003301 Safflower, oil - babyfood weight of oil 01013310 Salsify, roots weight of roots Also cal02003320 Salsify, tops weight of leaves Also cal95003330 Sapote, Mamey weight of pulp; 

excluding peel 

Includes Mamey sapsapote. 

19013340 Savory weight of leaves and 

flower buds 

Includes winter s

95003350 Seaweed weight of vegetation 

(wet and dry) 

Includes moss, keagar, lav

Attachment 2-51 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

95003351 Seaweed - babyfood weight of vegetation 

(wet and dry) 

Includes moss, keagar, lav

95003360 Sesame, seed dry weight of seed 95003361 Sesame, seed- babyfood dry weight of seed 95003370 Sesame, oil weight of oil 95003371 Sesame, oil- babyfood weight of oil 03003380 Shallot weight of bulb; 

excluding skin 

26003390 Sheep, meat weight of meat; 

excluding weight of bone, total nutrient fat and skin 

26003391 Sheep, meat- babyfood weight of meat; 

excluding weight of bone, total nutrient fat and skin 

26003400 Sheep, meat byproducts weight of meat; 

excluding bones; may include some trimmable fat and/or skin 

Includes tongue. 

Attachment 2-52 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

26003410 Sheep, fat weight of nutrient fat 

only; includes nutrient fat from meat 

26003411 Sheep, fat- babyfood weight of nutrient fat 

only; includes nutrient fat from meat 

26003420 Sheep, kidney weight of organ, 

including nutrient fat 

26003430 Sheep, liver weight of organ, 

including nutrient fat 

15003440 Sorghum, grain dry weight of grain 15003450 Sorghum, syrup weight of syrup 95003460 Soursop weight of pulp; 

excluding peel 

06003470 Soybean, seed dry weight of seed or 

bean 

06003480 Soybean, flour dry weight of flour, 

soy meal, soy protein concentrate and isolate 

Attachment 2-53 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

06003481 Soybean, flour- babyfood dry weight of flour, 

soy meal, soy protein concentrate and isolate 

06003490 Soybean, soy milk total weight of milk 06003491 Soybean, soy milkbabyfood or infant formula total weight of milk 06003500 Soybean, oil weight of oil Includes 06003501 Soybean, oil- babyfood weight of oil Includes 95003510 Spanish lime weight of pulp; 

excluding seeds and pulp 

Includes 

95003520 Spearmint weight of leaves and 

stems 

95003530 Spearmint, oil weight of oil 

Attachment 2-54 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

19023540 Spices, other dry weight See Appen

Spice Lithis lisinclude are uniqulisted sCommodity These lainclude: (seed), (cilantrblack an

19023541 Spices, other- babyfood dry weight See Appen04013550 Spinach weight of leaves; 

juice 

04013551 Spinach- babyfood weight of leaves; 

juice 

09023560 Squash, summer weight of flesh, seeds 

and peel 

Includes squash, squash, squash, vand zucch

Attachment 2-55 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

09023561 Squash, summer- babyfood weight of flesh, seeds 

and peel 

Includes squash, squash, squash, vand zucch

09023570 Squash, winter weight of flesh; 

excluding seeds and peel 

Includes squash, hcalabazaand spagh

09023571 Squash, winter- babyfood weight of flesh; 

excluding seeds and peel 

95003580 Starfruit weight of fruit, 

including seeds and peel 

Also cal

95003590 Strawberry weight of berry; 

excluding leaf cap 

95003591 Strawberry- babyfood weight of berry, 

excluding leaf cap 

95003600 Strawberry, juice weight of juice at 

single strength (or standard dilution) 

Attachment 2-56 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

95003601 Strawberry, juice ­

babyfood 

weight of juice at single strength (or standard dilution) 

95003610 Sugar apple weight of pulp; 

excluding peel 

Include 

95003620 Sugarcane, sugar dry weight of cane 

sugar (sucrose) 

Sucrose obtained and suga

95003621 Sugarcane, sugar- babyfood dry weight of cane 

sugar (sucrose) 

Sucrose obtained and suga

95003630 Sugarcane, molasses weight of molasses Includes 

molasses

95003631 Sugarcane, molasses ­

babyfood 

weight of molasses 

95003640 Sunflower, seed dry weight of seeds 95003650 Sunflower, oil weight of oil 95003651 Sunflower, oil - babyfood weight of oil 01033660 Sweet potato weight of roots, with 

or without peel; juice 

01033661 Sweet potato- babyfood weight of root; juice Likely w

Attachment 2-57 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

04023670 Swiss chard weight of leaves and 

stalks 

95003680 Tamarind weight of pulp, 

including seeds; excluding peel; juice 

10003690 Tangerine weight of pulp; 

excluding seeds and peel 

Include m

10003700 Tangerine, juice weight of juice at 

single strength (or standard dilution) 

Include m

01013710 Tanier, corm weight of corm Also cal95003720 Tea, dried dry weight of tea 

leaves 

95003730 Tea, instant dry weight of powder 08003740 Tomatillo weight of fruit; 

excluding outer husks 

08003750 Tomato weight of pulp, seeds 

and skin; tomatoes without skin but with seed (example canned whole tomatoes) 

Attachment 2-58 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

08003751 Tomato- babyfood weight of pulp, seeds 

and skin; tomatoes without skin but with seed (example canned whole tomatoes) 

08003760 Tomato, paste weight of concentrated 

tomato pulp from food described as paste; excluding seeds and skin 

08003761 Tomato, paste- babyfood weight of concentrated 

tomato pulp (from food described as paste); excluding seeds and skin 

08003770 Tomato, puree weight of concentrated 

tomato pulp (from food described as puree or sauce); excluding seeds and skin 

08003771 Tomato, puree- babyfood weight of concentrated 

tomato pulp (from food described as puree or sauce); excluding seeds and skin 

Attachment 2-59 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

08003780 Tomato, dried weight of dried tomato 

(may include skin and/or seeds) 

08003781 Tomato, dried - babyfood weight of dried tomato 

(may include skin and/or seeds) 

08003790 Tomato, juice weight of juice at 

single strength (or standard dilution) 

95003800 Tomato, Tree weight of the tree 

tomato, excluding peel 

Also cal

15003810 Triticale, flour dry weight of flour 15003811 Triticale, flour- babyfood dry weight of flour 50003820 Turkey, meat weight of meat; 

excluding bone, all nutrient fat, and skin 

50003821 Turkey, meat- babyfood weight of meat; 

excluding bone, all nutrient fat, and skin 

50003830 Turkey, liver weight of organ, 

including nutrient fat 

Attachment 2-60 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

50003831 Turkey, liver- babyfood weight of organ, 

including nutrient fat 

50003840 Turkey, meat byproducts weight of meat; 

excluding bone (may contain some fat and/or skin) 

Includes neck, an

50003841 Turkey, meat byproductsbabyfood weight of meat; excluding bone (may 

contain some fat and/or skin) 

Includes neck, an

50003850 Turkey, fat weight of nutrient 

fat only; includes nutrient fat from meat and skin 

50003851 Turkey, fat- babyfood weight of nutrient 

fat only; includes nutrient fat from meat ans skin 

50003860 Turkey, skin weight of skin only 

(0 grams nutrient fat) 

50003861 Turkey, skin - babyfood weight of skin only 

(0 grams nutrient fat) 

19023870 Turmeric weight of roots 

Attachment 2-61 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

01013880 Turnip, roots weight of roots 02003890 Turnip, tops weight of leaves and 

stems 

95003900 Vinegar Weight of vinegar made 

from apple, grape or rice juice in recipe 

14003910 Walnut weight of nutmeat 86003920 Water, dilution, source NS water to dilute or 

reconstitute a juice, beverage, or soup 

This FC EPA, but by a sep

86003930 Water, tapwater- direct 

(drinking) 

This FC EPA, but by a sep

86003940 Water- indirect (cooking) This FC 

EPA, but by a sep

86003950 Water, bottled water This FC 

EPA, but by a sep

86003960 Water, commercial beverage This FC 

EPA, but by a sep

Attachment 2-62 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

95003970 Water chestnut weight of tuber 95003980 Watercress weight of leaves and 

stems; juice 

09013990 Watermelon weight of pulp and 

rind; excluding seeds 

To inclupickled w

09014000 Watermelon, juice weight of juice at 

single strength (or standard dilution) 

15004010 Wheat, grain dry weight of grain Include w

cracked wgrain flcouscous

15004011 Wheat, grain - babyfood dry weight of grain Include w

cracked wgrain flcouscous 

15004020 Wheat, flour dry weight of flour 15004021 Wheat, flour- babyfood dry weight of flour 15004030 Wheat, germ dry weight of germ 15004040 Wheat, bran dry weight of bran 15004050 Wild rice dry weight of grain 

Attachment 2-63 
EPA Food 

Commodity (FC) Code 

Food Commodity (FC) Weight Basis of Food 

Commodity (FC): amount=edible portion as consumed, except where otherwise noted (in gm/kg body weight/day) 

Comments 

01034060 Yam, true weight of roots 01034070 Yam bean weight of roots Also cal

Attachment 2-64 
APPENDIX A 

THE FISH LIST 

FISH-SALTWATER Finfish, Tuna 

Tuna 

FISH-SALTWATER Finfish, Other 

Alewife 

Anchovy 

Barracuda 

Bass 

Bluefish 

Bonita 

Butterfish 

Cisco 

Cod 

Croaker 

Dolphinfish 

Drum 

Flatfish 

Flounder 

Grouper 

Haddock 

Halibut 

Herring 

Kingfish 

Mackerel 

Menhaden 

Monkfish 

Mullet 

Perch 

Pollock 

Pompano 

Porgy 

Rockfish 

Roe (herring, sea urchin) 

Roughy 

Sablefish 

Sardine 

Sea Trout 

Scup 

Shad 

Shark 

Skate 

Smelt 

Snapper 

Sole 

Spot 

Swordfish 

Tilefish 

Tomcod 

Whitefish 

Whiting 

Eel 

Attachment 2-65 
APPENDIX A

THE FISH LIST, Continued

FISH-SHELLFISH, Crustacean FISH-SHELLFISH, Mollusc 

Crab Abalone 

Crayfish and Crawfish Clam 

Cuttlefish Conch 

Lobster Mussel 

Shrimp and Prawns Octopus 

Oyster 

Scallop 

Snail 

Squid 

FISH-FRESHWATER, FinfishFISH-FRESHWATER, Finfish Farm Raised 

Carp Bluegill

Catfish (not farm raised) Catfish, Channel

Caviar (Sturgeon) Trout (Rainbow, Brook, Lake)

Pike Tilapia

Salmon (Chinook, Chum, Coho, Sockeye)(not farm raised) Salmon (Chinook, Chum, 

Tilapia (not farm raised) Coho, Sockeye)

Sturgeon

Trout (not farm raised) 

Attachment 2-66 
APPENDIX B

THE HERB LIST

Angelica Woodruff 

Balm Wormwood 

Borage 

Burnet 

Camomile 

Catnip 

Chervil, dried leaves 

Chinese chive 

Clary 

Costmary 

Curry, dried leaves 

Horehound 

Hyssop 

Lavender 

Lovage, leaves 

Marigold 

Nasturtium 

Pennyroyal 

Rosemary 

Rue 

Sage 

Sweet bay 

Tansy 

Tarragon 

Thyme 

Wintergreen 

Note that some herbs are not included in this list as they are unique FCs and are cited in 

this vocabulary separately. These latter include: basil, chives, coriander (cilantro), leaf, 

dill (dillweed), fennel, Florence (Italian and sweet), lemongrass, marjoram (oregano), 

parsley, and savory (summer and winter). 

Attachment 2-67 
APPENDIX C

THE SPICE LIST

Allspice 

Anise, seed 

Anise, star 

Annatto, seed 

Caper, buds 

Caraway 

Caraway, black 

Cardamom 

Cassia, buds 

Celery, seed 

Clove, buds 

Cumin 

Fennel, common 

Fennel, Florence, seed 

Fenugreek, seed 

Grains of Paradise 

Juniper Berry 

Lovage, seed 

Mace 

Mustard, seed 

Nutmeg 

Poppy, seed 

Saffron 

Vanilla 

Note that this list does not include some spices that are unique FCs and are listed 

separately in this Commodity Vocabulary. These latter spices include: cinnamon; dill, 

seed; coriander (cilantro), seed; black and white pepper. 

Attachment 2-68 
APPENDIX D

SELECTED REFERENCES FOR THE EPA FOOD COMMODITY VOCABULARY 

Arthey, D. and P.R. Ashust. 1996. Fruit processing. Blackie Academic and professional. NY. 248 pp. 

Bebee, C.N. 1990. The Protection of Tropical and Subtropical Fruits. 1979-April 1990. USDA National Agricultural

Library. BLA No. 97.

Chaisson, C., B. Petersen, S. White, and A. Clayton. 1984. Tolerance Assessment System: Crops to Food Map. US

EPA. HED Toxicology branch. 58 pp.

Codex Alimentarius. 1993. Pesticides Residues in Food. Section 2. Codex Classification of Foods and Animal Feeds.

FAO/WHO. Rome, Italy. Vol. 2. 218 pp.

Considine, D.M. and G.D. Considine. 1982. Foods and Food Production Encyclopedia. Van Nostrand Reinhold, Co.

NY. 2305 pp.

Ensminger, M., J. Oldfield, and W.W. Heineman. 1990. Feed and Nutrition Digest. Second Ed. Regu Publication.

Clovis, CA. 795 pp. 

Farrell, K.T. 1990. Spices, Condiments, and Seasonings. Second Ed. AVI Book. VanNostrand Reinhold Co. NY.

244 pp.

Heaton, D.D. 1997. A Produce Reference Guide to Fruits and Vegetables From Around the World. Nature’s Harvest.

Food Product Press. NY. 415 pp. 

Hoseney, R.C. 1994. Principles of Cereal Science and Technology. Second Ed. American Association of Cereal

Chemists. St. Paul, MN. 378 pp.

Janick, J. Editor. 1996. Progress in New Crops. American Society of Horticulture Science Press. Alexandria, VA.

600 pp.

Larkcom, J. 1991. Oriental Vegetables. Kodansha, Int. NY. 232 pp.

Magness, J.R., G. M. Markle, and C.C. Compton. 1971. Food and Feed Crops of the United States. USDA IR-4.

Rutgers, NJ. IR Bulletin No. 1. Bulletin 828. 

Markle, G.M., J.J. Baron, and B.A. Schneider. 1998. Food and Feed Crops of the United States. Second Ed. Meister

Publications. Willoughby, OH. 517 pp.

Morton, J.F. 1987. Fruits of Warm Climates. First Ed. Media Inc., Greensboro, NC. 505 pp. 

Pennington, J.A.T. 1997. Bowes and Church’s Food Values of Portions Commonly Used. Seventeenth Ed.

Lippincot-Raven Publishers. Philadelphia, PA. 481 pp.

Attachment 2-69 
APPENDIX D (continued) 

Randolph, S. and M. Synder. 1993. The Seafood List. US FDA . Washington, DC 68 pp. . 

Raymond, E. 1999. The Packer 1999 Produce and Availability Guide. Vance Publishing Co. Lenexa, KS. 552 pp.. 

Schneider, Bernard A. 1992. Comparison of USDA Human Nutrition Information Service (HNIS) Legume 

Vegetables Vocabulary With OPP Commodity Vocabularies. US EPA. September 4. 

Schneider, B.A. 1993. Response to Commodities, Subcommodities, and Commodity Groups Not Yet Included In 

The New DRES Vocabulary. Part I: A - CN, Entries 1 - 206. US EPA. 

Schneider, B.A. 1993. Response to Commodities, Subcommodities, and Commodity Groups Not Yet Included In 

The New DRES Vocabulary. Part II: CO - E, Entries 207 - 283. US EPA. 

Schneider, B.A. 1993. Response to Commodities, Subcommodities, and Commodity Groups Not Yet Included 

In The New DRES Vocabulary. Part III: F - G, Entries 284 - 379. US EPA. 

Schneider, B.A. 1993. Response to Commodities, Subcommodities, and Commodity Groups Not Yet Included In 

The New DRES Vocabulary. Part IV: H - K, Entries 380 - 419. US EPA. 

Schneider, B.A. 1993. Response to Commodities, Subcommodities, and Commodity Groups Not Yet Included In 

The New DRES Vocabulary. Part V: L - M, Entries 420 - 508. US EPA. 

Schneider, B.A. 1993. Response to Commodities, Subcommodities, and Commodity Groups Not Yet Included In 

The New DRES Vocabulary. Part VIA: O - Pears, Entries 509 - 554. US EPA. 

Schneider, B.A. 1993. Response to Commodities, Subcommodities, and Commodity Groups Not Yet Included In 

The New DRES Vocabulary. Part VIB: Peas - Pumpkins, Entries 555 - 621. US EPA. 

Schneider, B.A. 1993. Response to Commodities, Subcommodities, and Commodity Groups Not Yet Included In 

The New DRES Vocabulary. Part VII: Q - R, Entries 622 - 663. US EPA. 

Schneider, B.A. 1994. Response to Commodities, Subcommodities, and Commodity Groups Not Yet Included In 

The New DRES Vocabulary. Part VIII: S, Entries 664 - 784. US EPA. 

Schneider, B.A. 1994. Response to Commodities, Subcommodities, and Commodity Groups Not Yet Included In 

The New DRES Vocabulary. Part IX: T, Entries 785 - 833. US EPA. 

Schneider, B.A. 1994. Response to Commodities, Subcommodities, and Commodity Groups Not Yet Included In 

The New DRES Vocabulary. Part X: U - Z, Entries 834 - 920. US EPA. 

Schneider, B.A. 1994. Response to Public Comments on Pesticide Tolerance Crop Grouping Regulation: Proposed 

Rule (40CFR §180). Part II: Evaluation of Supporting Data Concerning Commodity Common Names and Proposed 

Additions to Several Crop Groups. CB# 12719. DP Barcode D196080. US EPA. OPPTS, HED, CBTS. June 22. 15 

pp. 

Schneider, B.A. 1996. Uses of Brans of Wheat, Oats, Barley, and Rye As Foods. D220744. US EPA. OPP HED Feb. 

14. 32 pp. 

Schneider, B.A., Yuen-Shaung Ng, H. Jamerson, C. Colledge, C. Lang, I. Hornstein, R. Cromwell. 1999. EPA Food 

and Feed Tolerance Vocabulary. US EPA. OPP. HED. 

Attachment 2-70 
APPENDIX D (continued) 

Schneider, Elizabeth. 1985. Uncommon Fruits and Vegetables: A Common Sense Guide. Harper and Row 

Publishers, Inc. NY. 546 pp. 

Smith, D.S., J.N. Cash, W.K. Nip, and Y.H. Hui. 1997. Processing Vegetables Science and Technology. Technomics 

Publ. Co. 434 pp. 

U.S. EPA. 1993. Pesticide Tolerances: Portion of Food Commodities to Be Analyzed for Pesticide Residues. 

Proposed Rule. OPP-3000243. Federal Register Vol. 58. No. 187. Pp. 50888-50893. September 23. 

U.S. EPA. 1993. Table 1. Raw Agricultural and Processed Commodities and Feedstuffs Derived from Crops. 

Subdivision G: Residue Chemistry Guidelines. OPPTS860.1000 

Attachment 2-71