Abstract

The Harvard Service Food Frequency Questionnaire (HSFFQ) has been used in the Special Supplemental Nutrition

Program for Women, Infants and Children (WIC) in North Dakota, Missouri, and Massachusetts. This project collaborated with those States to improve HSFFQ output to better facilitate nutrition education, food package decisions, and referrals; to design, implement, and evaluate the use of aggregate nutrition data for local and State practices and policy decisions; and to use prospective data to examine the relationships between diet and childhood

obesity. The project developed a standardized version of the HSFFQ to make collecting and compiling aggregate

data easier and to make data reports more useful. The project demonstrated that aggregating nutrition data at the

State level is feasible. The calibration studies uncovered the need for further analyses to explain the performance of

the tool in the diet assessment of low-income Hispanic and African-American children. Prospective analysis of the

influence of diet on overweight in low-income preschool children, while inconclusive, demonstrated the ability to

use aggregate nutrition data to explore important epidemiological hypotheses.

Dietary Intake and Health Outcomes

Final Report

Contractor and Cooperator Report No. 6

May 2005

By Graham Colditz, Harvard School of Public Health

This study was conducted by the Harvard School of Public Health under a

cooperative research contract with USDA’s Economic Research Service

(ERS) Food and Nutrition Assistance Research Program (FANRP): contract

number 43-3AEM-8-80095 (ERS project representative: Elizabeth Frazão). The

views expressed are those of the authors and not necessarily those of ERS

or USDA.

Final Report: Dietary Intake and Health Outcomes 

Acknowledgements 

Advisory Committee 

Deborah Klein Walker, Ed.D. (co-chair) Associate Commissioner for Programs 

and Prevention, The Commonwealth of Massachusetts, Department of Public 

Health, Boston, Massachusetts 

Walter Willett, M.D., Dr. P.H. (co-chair) Chair, Department of Nutrition, Harvard 

School of Public Health, Boston, Massachusetts 

Elizabeth Barden, Ph.D. Nutrition Projects Manager, Massachusetts Department 

of Public Health, Office of Statistics and Evaluation, Boston, Massachusetts 

Catherine S. Berkey, S.D. Lecturer in Biostatistics, Channing Laboratory, 

Department of Medicine, Brigham and Women’s Hospital, Harvard Medical 

School, Boston, Massachusetts.

Diane Clark, Maternal and Child Nutrition, Nutrition and Physical Activity, National 

Center for Chronic Disease Prevention and Health Promotion, Atlanta, Georgia 

Graham A. Colditz∗, M.D., Dr. P.H. (primary investigator), Channing 

Laboratory, Department of Medicine, Brigham and Women’s Hospital, Harvard 

Medical School, Boston, Massachusetts 

William H. Dietz, M.D., Ph.D., Director, Division of Nutrition and Physical Activity, 

Center for Disease Prevention and Health Promotion, Centers for Disease Control 

and Prevention, Atlanta, Georgia 

Jane D. Gardner∗

, D.Sc., Maternal and Child Health Consultant, Bedford, 

Massachusetts 

Jan Kallio, Massachusetts WIC Program, The Commonwealth of Massachusetts, 

Executive Office of Health and Human Services, Department of Public Health, 

Boston, Massachusetts 

Mary Kelligrew Kassler, Director, Massachusetts WIC Program, The 

Commonwealth of Massachusetts, Executive Office of Health and Human 

Services, Department of Public Health, Boston, Massachusetts 

Jill Leppert, L.R.D., WIC/MCH Nutrition Coordinator, North Dakota WIC 

Program, Bismarck, North Dakota 

 

∗

 Harvard Research Study Group 

iii
Final Report: Dietary Intake and Health Outcomes 

Patricia McKinney, FNS-USDA, Office of Analysis and Evaluation, Alexandria, 

Virginia 

Donna Mehrle, Associate Chief-Nutritionist, Missouri Department of Health, 

Bureau of Nutrition Services and WIC, Jefferson City, Missouri 

Liz Metallinos-Katsaras∗, Ph.D., R.D., Professor of Nutrition Science, Simmons 

College, Boston, Massachusetts 

Helaine Rockett∗, M.S., R.D., Research Nutritionist, Channing Laboratory, 

Brigham and Women’s Hospital, Boston, Massachusetts 

Kelly Scanlon, Maternal and Child Nutrition, Nutrition and Physical Activity, 

National Center for Chronic Disease Prevention and Health Promotion, Atlanta, 

Georgia 

Annie Siu-Norman, Chief, Missouri Bureau of Nutrition Services and WIC, 

Jefferson City, Missouri 

Carol West Suitor, Sc.D., R.D., Nutrition/Maternal and Child Health Consultant, 

Northfield, Vermont 

Jennifer Tuttleman, Bureau of Family and Community Health, The 

Commonwealth of Massachusetts, Executive Office of Health and Human 

Services, Boston, Massachusetts 

John Weimer, Project Manager, Economic Research Service, US Department of 

Agriculture, Washington, D.C. 

Lucy P. Zahler, Missouri Bureau of Nutrition Services and WIC, Jefferson City, 

Missouri 

Staff

Gideon Aweh, Data Manager, Channing Laboratory 

Corey Bergund, Data Manager, North Dakota Department of Health 

Louise Bishop, R.D., L.D., Research Dietitian, Harvard School of Public Health 

Robin Blum*, S.M., Project Coordinator, Harvard School of Public Health 

Hank Dart, S.M., Health Communications Consultant and Project Manager, 

Harvard Center for Cancer Prevention 

Morgan Ford, Research Assistant, Harvard School of Public Health 

Jen Hamilton, Administrative Assistant, Channing Laboratory 

Carol Leighton, Administrative Assistant, Channing Laboratory 

Eleanor Livingston, Director of Administration, Department of Nutrition, Harvard 

School of Public Health 

 

∗

 Harvard Research Study Group 

iV
Final Report: Dietary Intake and Health Outcomes 

P. Kirstin Newby, Candidate for Doctor of Science, Harvard School of Public 

Health 

Arisleyda Veras, Financial Analyst, Department of Nutrition, Harvard School of 

Public Health 

And special thanks to:

Calibration study sites: 

Lynn Community Health Center WIC Program, Lynn, Massachusetts: 

Dawn Belben, On-site Study Coordinator 

Marisa Lucero, Registered Dietitian 

Karla Menger, Registered Dietitian 

Cindy Shore, Registered Dietitian 

Harvard Street Community Health Center WIC Program, Dorchester, 

Massachusetts: 

Jennifer Wallace, On-site Study Coordinator 

Amy Allison, Registered Dietitian 

Eugenia Bodenlos, Registered Dietitian 

Massachusetts, Missouri, and North Dakota State WIC Programs 

Focus group participants from the North Dakota WIC Program 

v
Final Report: Dietary Intake and Health Outcomes 

This report represents the collaborative efforts of many individuals, including the 

advisory committee and staff, and the Harvard study group and staff whose 

names appear at the beginning of this document. Completion of this project was 

complex and required substantial dedication and effort by all those who 

participated throughout its completion. 

The study group wishes to acknowledge the assistance of the Lynn Community 

Health Center and the Harvard Street Neighborhood Health Center WIC 

Programs in collecting the calibration study data. The WIC Program Directors, 

Steve Beauchaine (Lynn WIC) and Marian Gabriel (Dorchester WIC), kindly 

opened the doors of their clinics for the purposes of this project. The on-site 

coordinators, Dawn Belben (Lynn WIC) and Jennifer Wallace (Dorchester WIC) 

and the dedicated registered dietitians who collected the 24-hour recalls worked 

extremely hard to gather information under challenging circumstances. The 

committee would especially like to thank all of the WIC staff and clients who 

dedicated their time to the calibration studies-- this research would not have been 

possible without them. 

We extend our gratitude to the 4 WIC sites in North Dakota that piloted the Client 

Printout preceding the focus groups. Thanks to the WIC nutritionists and clients in 

North Dakota who took the time to participate in both rounds of those focus 

groups. Hank Dart and Morgan Ford worked closely with the study group and 

utilized the focus group input to develop the Client Printout. With the help of the 

focus groups they successfully designed a functional final product for the efficient 

and valuable nutrition education of WIC clients. 

Corey Bergund at the North Dakota Department of Health dedicated much time 

and effort to make it possible to link the Harvard Service FFQ data with WIC 

certification data and vital statistics data for use in the prospective analysis of diet 

and health outcomes. Gideon Aweh, Data Manager at the Channing Laboratory 

made himself endlessly available to support the committee members and staff 

who utilized the linked data over the course of this project. P. Kirstin Newby a 

doctoral student in nutrition at Harvard completed the prospective analysis. The 

committee would like to thank Ms. Newby’s doctoral research committee (advisory 

committee members Drs. Colditz, Willett, and Berkey, and Dr. Karen Peterson 

from the Departments of Maternal and Child Health and Nutrition at Harvard) for 

the time and support they offered to the completion of this analysis. 

Each of the 3 states that were members of the advisory committee contributed 

unique and valuable perspectives to the overall aims of this project. Lucy Zahler 

from Missouri provided great insight into the various uses of the Harvard Service 

FFQ in her state. Jill Leppert from North Dakota offered endless support and 

wonderful examples for each aspect of this project. Deborah Klein Walker and Jan 

Kallio provided perceptive viewpoints around the limitations as well as the endless 

possibilities of utilizing service data for individual assessment, education, and 

population analysis for program and policy planning, and research. All of the state 

vi
Final Report: Dietary Intake and Health Outcomes 

members remained dedicated and optimistic that the concepts explored through 

this project would one day be tangible. 

We are especially grateful to the competent and efficient administrative support 

given by Jen Hamilton, Carol Leighton, Eleanor Livingston, and Arisleyda Veras 

throughout the project. 

Robin Blum and Graham Colditz 

For the Harvard Research Study Group 

Boston, Massachusetts 

August 2002 

vii
Final Report: Dietary Intake and Health Outcomes 

Table of Contents 

EXECUTIVE SUMMARY........................................................................................1

BACKGROUND ...................................................................................................... 1 

PROJECT OVERVIEW ............................................................................................. 2 

AIM 1: (NUTRITION EDUCATION)..................................................................... 12

TASK A: SUMMARY OF FOCUS GROUPS ................................................................. 12 

TASK B: DEVELOPMENT OF STANDARDIZED HARVARD SERVICE FFQ...................... 17 

AIM 2: (COMBINING DATA)............................................................................... 22

TASK C: DEVELOPMENT & IMPLEMENTATION OF AGGREGATE DATA REPORTS ........... 22 

TASK D: CALIBRATION OF THE IN HISPANIC AND AFRICAN AMERICAN CHILDREN....... 35 

AIM 3: (LONGITUDINAL DATA)......................................................................... 54

TASK E: PROSPECTIVE ANALYSIS OF DIET AND HEALTH .......................................... 54 

CONCLUSIONS .................................................................................................. 55

REFERENCES .................................................................................................... 58

APPENDIXES A - H

viii
Final Report: Dietary Intake and Health Outcomes 

1

Executive Summary 

Background 

Diet assessment 

Diet assessment facilitates early detection of asymptomatic children with dietary 

intake problems, creating an opportunity for early intervention before more 

marked effects such as weight loss or obesity become apparent. In a report on 

dietary assessment in the Special Supplemental Nutrition Program for Women, 

Infants & Children (WIC), the Institute of Medicine (IOM) concluded that although 

dietary assessment methods (i.e., 24-hour recalls, food frequency 

questionnaires) are not accurate for assessing diets of individuals, they have an 

important role in WIC for planning or targeting nutrition education for WIC clients 

and, given the less serious nature of the errors in group assessments, provide 

useful population level nutrition information that is helpful in program planning 

and policy evaluation (1). 

Nutrition Education 

Diet information provides a starting point for nutrition counseling. When diet 

assessment is omitted nutrition education tends to be general rather than tailored 

to individual needs (2). The use of a personalized evaluation of dietary quality 

has been shown to enhance awareness and motivation for dietary change. When 

advice is carefully focused, behavior changes have been accomplished (3). In 

our own work in Massachusetts we observed that the majority of WIC visits 

include some 10 to 15 minutes of nutrition education, reflecting the integral part 

this plays in the services that are provided (4). 

Pediatric obesity 

Obesity now affects 1 in 5 children in the US. Many of the consequences of adult 

obesity begin in childhood--for example hypertension, abnormal glucose 

tolerance and hyperlipidemia occur with increasing frequency among obese 

children and adolescents. Questions remain as to whether the onset of obesity in 

early childhood carries greater risk of adult morbidity and mortality. Yet the 

underlying importance of preventing childhood obesity due to its strong relation to 
Final Report: Dietary Intake and Health Outcomes 

2

adult obesity and the associated chronic diseases, mandate a more rigorous 

understanding of the relationship between diet and the development of this 

common childhood condition (5). Further study of diet and obesity and the 

predictors of the onset of obesity in much larger populations are needed. 

The prevalence of obesity has been rising rapidly in the United States, supporting 

the notion that this is not just a genetic condition, but rather a consequence of 

lifestyle, including diet. An interaction between our underlying genetic make up 

and an environment that discourages physical activity and encourages 

consumption of calories, is fueling the growth of obesity (6). Does diet 

composition contribute to the development of obesity above and beyond total 

caloric intake? 

Project overview 

The overall goal of the proposed project was to build on ongoing Harvard 

collaborations with WIC programs in North Dakota, Missouri, and Massachusetts 

to increase the usefulness of the Harvard Service Food Frequency Questionnaire 

(HSFFQ) and its associated dietary data output in improving nutrition education 

and guiding program planning. In addition, prospectively collected state WIC data 

were utilized to examine relations between diet and health outcomes in children 

with obesity at 4 years of age as a prototype. The project aims were to: 

1. Evaluate and improve the HSFFQ output to better facilitate nutrition

education, food package decisions, and referrals. 

2. Design, implement, and evaluate the use of aggregate nutrition data for 

program planning and evaluation at the local and state levels, by aggregating 

diet assessment and administrative data from WIC programs in collaborating 

states. 

3. Utilize prospective data collected through the WIC program to examine 

relations between diet from age 2 to age 4 and childhood obesity, as 
Final Report: Dietary Intake and Health Outcomes 

3

measured by excess adiposity among 4-year-old children. Specifically, test 

the hypothesis that a high-fat diet leads to greater childhood obesity and that 

a diet high in fruits, vegetables, and fiber is associated with lower levels of 

obesity. 

To achieve these objectives we completed the following tasks: 

A. Conducted focus groups on-site in North Dakota. North Dakota was chosen 

as the most appropriate location for the focus groups given their history and 

statewide experience with the Harvard Service FFQ (HSFFQ) (complete 

implementation and regular use of the HSFFQ for nutrition assessment and 

education throughout the North Dakota WIC Program). The first round of 

focus groups were run to learn about how the HSFFQ was used by WIC 

providers to deliver nutrition education to WIC clients. The second series of 

focus groups was conducted following the pilot run of a newly developed 

“Client Printout” to learn about its effectiveness from both WIC clients and 

nutrition counselors. 

B. Developed a standardized version of the HSFFQ under the Nutrition 

Education aim to ease the expansion of the use of the HSFFQ in service and 

research settings. 

C. Implemented common procedures in North Dakota and Missouri for collecting 

statewide WIC data, compiling it and sending it to Harvard for analysis. 

Harvard returned an aggregate report for use in program planning and 

evaluation at local and state levels. 

D. Collected dietary intake data on low-income African American and Hispanic 

children in an attempt to expand the calibration of the HSFFQ and broaden 

the usefulness of the HSFFQ in assessing diet and informing nutrition 

education in multi-ethnic populations. 
Final Report: Dietary Intake and Health Outcomes 

4

E. Compiled longitudinal data from North Dakota and prospectively examined 

the influence of dietary composition and beverage consumption on changes 

in body mass index among low-income preschool children. 

It should be noted that as part of the original proposal the study group 

recommended a review of the dietary assessment tools currently in use or being 

developed by different WIC programs. During the course of exploring this task, it 

was revealed that the Institute of Medicine (IOM) was conducting just such a 

review as part of their review of dietary risk assessment in the WIC program. 

Therefore, we did not pursue this proposed task and refer the reader to the IOM 

report, “Dietary Risk Assessment in the WIC Program” (1). 

A national advisory board comprising representatives from the states, National 

Association of WIC Directors, the Center for Disease Control and Prevention and 

US Department of Agriculture provided guidance throughout the study. 

Findings from focus groups

Use of HSFFQ in nutrition counseling in North Dakota 

North Dakota WIC dietitians do both individual and group counseling. They use 

the HSFFQ printout for individual counseling in conjunction with several nutrition 

handouts. Some dietitians write or circle items on the handouts to personalize 

them for WIC clients. They often use the HSFFQ and its printout to clarify dietary 

intake. 

Client printout 

Four sites in North Dakota piloted the client printout for approximately one month 

prior to the focus group feedback from WIC providers and clients. At all sites the 

client printout was distributed to the women one month after the completion of 

the HSFFQ. The month-long time-period between the completion of the HSFFQ 

and receiving the printout did not concern the clients or the providers. Four of the 

clients said that the printout was not a good indication of what they usually ate 

because they did the HSFFQ when they were in early pregnancy and had 
Final Report: Dietary Intake and Health Outcomes 

5

“morning sickness all day”. The providers reported that they liked the client 

printout and would use it for all clients except those with developmental delays or 

language issues. 

Overall, the reception of the client printout was positive by both providers and 

clients. Many wanted to know when something similar would be available for 

children. 

Standardized HSFFQ

The development and use of the Harvard Service Food Frequency Questionnaire 

(HSFFQ) continues in service settings such as prenatal clinics, Head Start 

Programs, schools, and WIC programs for both service and research purposes. 

The standardized product, although presented differently, contains the majority of 

the core foods found on the HSFFQ used in each of the 3 states. The last page 

of the standardized questionnaire includes questions that examine fiber intake, 

type of fat eaten, fried food consumption, and multi-vitamin and supplement use. 

In addition, the last page will offer the user (practitioner, researcher, etc.) a 

choice of 1 of 3 different modules of questions that ask about physical activity, 

food security or food scarcity, food behavior (see Appendix E). The standardized 

HSFFQ is available in English and Spanish and in a paper or direct-enter 

computer format. 

Explanation of aggregate data reports with reference to examples

Aggregate data reports are currently created and utilized by North Dakota and 

Missouri. Each state compiles the HSFFQ data for their state and sends it to 

Harvard for analysis. Harvard runs specific analyses on the data and returns an 

aggregate report back for use in state WIC program planning and policy decision 

-making. 

The Advisory Board to the ERS/USDA Dietary Intake and Health Outcomes grant 

developed a standard format for the state reports. Each report includes 

population nutrition information for children, pregnant women, and post-partum 

women (lactating and not lactating) broken down into specific age groups. The 
Final Report: Dietary Intake and Health Outcomes 

6

contents include overall nutrition statistics, a list of the top 10 foods that 

contribute to each nutrient, mean consumption per day for 7 food groups and 2 

nutrients, a list of what foods contribute to each food group, a list of the contents 

for the raw nutrient file, and the raw nutrient file for 18 nutrients (see Table 1 in 

the main report for further detail). An example report is included in Appendix F. 

In concept each state receives the exact same information as decided on by the 

Advisory Board. In practice the states’ agendas continue to evolve and in 

response to these changes modifications are made to the aggregate data 

reports. 

North Dakota has used the data to examine differences in dietary intake in 

pregnant women and children 3 to 5 years old by annual family income and 

family size. Missouri used the data from the HSFFQ to evaluate the influence of 

a nutrition education and Farmer’s Market Voucher pilot project on the 

consumption of fruits and vegetables in a small population of women and 

children in the WIC program. Both examples demonstrate the ability of a state to 

use the data from the aggregate data to answer questions deemed important by 

state health departments. 

Calibration study results

In previous studies evaluating the Harvard Service Food Frequency 

Questionnaire (HSFFQ) the term validation has been used to describe studies 

using the same methods employed in the completion of the calibration studies

described in this report. Calibration is another commonly used term to describe 

studies that quantitatively compare values from one method of diet assessment 

(i.e., food frequency questionnaire) to values from a “gold standard” method of 

diet assessment (i.e., 24-hour diet recall) (7). The term calibration is used 

throughout this report. 

African American Children 

For the 108 African American children aged 1 to 5, participating in 

Massachusetts WIC with complete data, feasible daily energy intake (500 – 3500 

kcal per day), and who were not siblings, we compared the first of 2 Harvard 
Final Report: Dietary Intake and Health Outcomes 

7

Service Food Frequency Questionnaires (HSFFQs) collected to the average of 3 

24-hour diet recalls to calibrate the questionnaire. Pearson correlation 

coefficients, adjusted for total energy intake and within and between person 

variation, were calculated for the following 7 WIC nutrients: protein (r = 0.37), 

vitamin A (r = 0.45), vitamin C (r = 0.007), folate (r = 0.27), zinc (r = 0.15), 

calcium (r = 0.13), and iron (r = 0.23). 

For the African American children used in this calibration research, the ratio of 

within to between subject variability is greater than that found in the North Dakota 

calibration for sixteen out of the nineteen dietary nutrients/substances. This 

implies that a greater number than the three 24-hour recalls that were collected 

would be necessary to accurately represent the diet of these children (8). Thus, 

the fact that these correlations were somewhat low, may not necessarily imply 

that the Harvard Service FFQ (HSFFQ) has a poorer validity among African 

American children, but may instead reflect the inadequacy of the three days of 

diet recalls as the gold standard representing the diet of these children. The 

increase in this ratio could be due to the generally increased availability of foods 

in the greater Boston area as compared to North Dakota. 

Hispanic children 

To calibrate the questionnaire for use in Hispanic children aged 1 to 5 years we 

compared the first of 2 Harvard Service Food Frequency Questionnaires 

(HSFFQs) collected to the average of 3 24-hour diet recalls. The final sample 

comprised 45 children after excluding individuals with reported energy intake 

below 500 kcals or above 3500 kcals, siblings, and those missing 1 or more diet 

recall(s). Pearson correlation coefficients, adjusted for total energy intake and 

within person variation, were calculated for the following 7 WIC nutrients: protein 

(r = 0.04), vitamin A (r = 0.07), vitamin C (r = 0.04), folate (r = -0.32), zinc (r = 

0.07), calcium (r = 0.28), and iron (r = 0.16). Given the small final sample of 

Hispanic children who participated in and completed the calibration study (n=45), 

the correlations, as well as the within and between subject variability have very 

wide confidence intervals. Therefore, these results should not be used to draw 
Final Report: Dietary Intake and Health Outcomes 

8

conclusions about the validity of the Harvard Service FFQ (HSFFQ) among lowincome Hispanic children participating in WIC. 

Analysis of the ratio of the within and between subject variability suggests that, 

as with the African American sample, a greater number of 24-hour diet recalls 

may have been necessary to accurately estimate "true intake" and to adjust for 

day-to-day fluctuations in an individual's intake (i.e., within subject variability) for 

the Hispanic children who participated in the present calibration study. This may 

be related to the variety of foods available in an urban area like Greater Boston. 

An increased variety could affect the day-to-day differences in food intake for 

each individual because more choices are available. 

Findings from prospective analysis

Prospective analyses looking at dietary composition, beverage consumption, and 

changes in body mass index among low-income children 2 to 5 years of age 

using linked, longitudinal data (Harvard Service FFQ, WIC Certification data, and 

vital statistics data) from the state of North Dakota were completed. The 

aggregate nutrition data from WIC was linked to vital statistics, birth certificate 

data, in order to obtain additional variables needed to conduct a thorough 

analysis (e.g., birth weight and maternal education). The first analysis tested 

several hypotheses, considering both nutrients (total fat, animal fat, vegetable fat, 

and fiber) and pre-defined North Dakota (ND) food groups1

 (fruits, vegetables, 

breads/grains, and “fat foods”) used for nutrition counseling in the North Dakota 

Special Supplemental Nutrition Program for Women, Infants, and Children. 

(Please refer to the original printout for client nutrition education included in 

Appendix B for food group definitions). After univariate and multivariate 

regression analyses were performed for all nutrients and foods, only vegetable 

intake was significantly related to larger decreases in BMI. Further research is 

 

1

 Nutritionists at the North Dakota WIC program modified the HSFFQ food groups (used to derive daily food serving and 

nutrient intake information) for state-specific uses. These predefined food groups (hereafter referred to as “ND food 

groups”) are used in this study. The ND food groups are similar to the categorization scheme used in the USDA Food 

Guide Pyramid9

. The ND food groups are not related to the WIC federal food package, nor are they federally defined food 

groups. The groups were created for dietary analysis and nutrition education purposes and are used for nutrition 

counseling at North Dakota WIC clinics. 
Final Report: Dietary Intake and Health Outcomes 

9

needed to reproduce these findings when controlling for additional risk factors 

such as familial obesity and physical activity. 

The second analysis explored the hypotheses that fruit juice, fruit drinks, milk, 

soda, and diet soda are positively related to changes in BMI. Changes in BMI 

were not significantly related to intakes of fruit juice, fruit drinks, milk, soda, or 

diet soda in either univariate or multivariate analysis. Results did not change 

when further adjusted for sociodemographic variables. Similar findings were 

seen when intakes of milk and juices were dichotomized into excessive (≥ 12 oz) 

consumption categories. The influence of beverages on body weight may be 

more important for older children who consume a more varied diet. 

Review of diet assessment tools currently in use or being developed by 

different WIC programs

Please refer to the 2002 Institute of Medicine Report: “Dietary Risk Assessment 

in the WIC Program” (1). It can be read on-line and/or ordered at: 

http://www.nap.edu/books/0309082846/html. 

Conclusions

The use of the Harvard Service FFQ (HSFFQ) has been successfully 

implemented and is on-going in all 3 collaborating states. With the input of health 

communication specialists, WIC practitioners, and clients, the HSFFQ Client 

Printout has been tested and refined and is currently used to provide valuable 

individually tailored nutrition education. The newly developed standardized 

version of the HSFFQ will facilitate the expansion of its use to additional settings, 

ease the collection and compilation of aggregate data, and aid the production of 

useful data reports. 

The results of the calibration component make it clear that further analyses are 

necessary to explain the performance of the tool in the assessment of the diet in 

low-income African American and Hispanic children. The poor results may have 

been a result of the study methods, and not a reflection on the performance of 
Final Report: Dietary Intake and Health Outcomes 

10

the HSFFQ in these populations. The previously reported validity of the HSFFQ 

can not be disregarded. The validity reported by Suitor et al., Blum et al., and 

Wei et al. demonstrate that nutrition data for Native American and Caucasian 

children ages 1 to 5 and multi-ethnic pregnant and postpartum women can be 

reliably collected and utilized to provide tailored nutrition education. 

Furthermore, as demonstrated in the state data aggregation component of this 

project, it is feasible to combine nutrition data at a state level to be utilized by 

local, state, and national agencies to answer important questions and inform 

current program practices and policy decisions. 

While inconclusive, prospective analysis of the influence of diet on overweight in 

low-income preschool children demonstrated the ability to utilize aggregate 

nutrition data to explore important epidemiological hypotheses. 

The experiences of the past 3 years have been invaluable in uncovering both the 

significant administrative issues that must be addressed and those that will 

facilitate the process of data aggregation and increase the likelihood of 

successfully sustaining the implemented system. Before any benefits of 

collecting aggregate nutrition data and potential linkages can be reaped on a 

local or state level it is essential that the regulations regarding the use of WIC 

data be clarified to facilitate uniform policy interpretation across states. The 

following steps will facilitate the data aggregation process and increase the 

likelihood of a sustained nutrition data aggregation system: 

1. Designation of a state WIC Program or Department of Public Health staff 

member with understanding of nutrition data structure and other state data 

sources as the official state nutrition data coordinator. The state nutrition 

data coordinator must be given clear responsibility and have an 

appropriate amount of their time allocated to nutrition data collection and 

aggregation to facilitate the use of the nutrition data. 
Final Report: Dietary Intake and Health Outcomes 

11

2. Orientation around the diet assessment method and the collection of 

aggregate nutrition data for state WIC program administrators and 

providers to become acquainted with the utility of the system. 

3. An introductory phase followed with additional orientation and training 

around the diet assessment tool and aggregation of nutrition data. 

4. An approximately six month practice phase of aggregate nutrition data 

collection to resolve issues before annual data compilation officially 

begins. 

5. Successful annual data compilation by Information Systems and state 

appointed nutrition data coordinator sent for analysis and report 

generation. 

Once the above steps are taken and the listed supports are put in place will the 

inherent benefits of these data be reaped by local and state programs and 

policies. 

Future goals should include further calibration of the HSFFQ in Latinos and 

African Americans using our established criteria and methods. Further work is 

needed to better understand the assessment of diet in this subgroup of the 

population.

To support the continued use of these valuable data, it is essential that a funding 

source and functional body be identified. With expansion to additional states the 

data collected through WIC may offer a unique window on evolving patterns of 

diet and child growth. From our experience the management of the compilation 

and use of these data would be best accomplished by an entity residing within a 

state system. This will help regulate the many administrative and practical steps 

that must be completed to successfully link the numerous data sets that various 

agencies may contribute.
Final Report: Dietary Intake and Health Outcomes 

12

Aim 1: (Nutrition Education) 

“Evaluate and improve the output of the HSFFQ to better facilitate nutrition 

education, food package decisions, and referrals.” 

Task A: Summary of focus groups 

At the initial Advisory Committee Meeting on December 9, 1998 (see Appendix A 

for list of Advisory Committee members, meeting attendance records, and 

meeting minutes), focus groups were presented as a method to evaluate the 

education component and the printout performance of the Harvard Service Food 

Frequency Questionnaire (HSFFQ). The committee agreed upon this method 

and discussed what, how, and the direction focus groups should take in the 

evaluation, including reviewing all proposed questions for both rounds of focus 

groups. 

In March 1999 Jane Gardner went to North Dakota’s WIC sites and met with 28 

WIC nutritionists over four days to evaluate the printout that was then being used 

for client education and counseling purposes. North Dakota had been working 

with the HSFFQ since 1993. (See Appendix B for the original printout, the 4 

proposed versions of the client printout, the piloted client printout, and the final 

version of the client printout). 

Specifically, the questions asked were: 

 Describe the nutrition education or counseling you currently provide. 

 Do you use the HSFFQ printout for nutrition education? 

 Focusing on the use of the printout of the diet analysis for nutrition

education, what information do you usually use first? Why? 

 What information from the diet assessment do you use the most for 

nutrition education? Why that information? How do you use it? 

 Is there currently information on the printout that you do not use for 

nutrition education? If so, what information? Why is it not used for nutrition 

education? 
Final Report: Dietary Intake and Health Outcomes 

13

 The top two lines provide client information such as the age and for 

women her status related to pregnancy. Do you use this information when 

doing nutrition education? If so, how? What, if anything would you want to 

change in these two lines? Why? 

 Do you use the mean servings by food group for nutrition education? If so, 

how do you use it? What, if anything would you want to change in the 

mean servings by food group category? Why? 

 Do you use the information on the number of food items selected? If so, 

how do you use this information in nutrition education? What, if anything, 

would you want to change about the report on number of food items 

selected? Why? 

 Do you use the graph on nutrient density when doing nutrition education? 

If so, how do you use it? What, if anything would you want to change in 

the use of nutrient density for nutrition education? Why? 

 Do you use the information on estimated calories when doing nutrition 

education? If so, how do you use this information? What, if anything, 

would you want to change about this information? Why? 

 Do you use the information on mean servings per week by food in your 

nutrition education? If so, how do you use this information? What, if 

anything, would you want to change about the listing of mean servings per 

week by foods? Why? 

 Now I would like you to look at the whole format of the printout. What, if 

anything would you want to change? Is there any information you would 

like added? Or deleted? 

 Do you think it would be helpful to send a printout of some kind home with 

the participants? (Show some examples) 

 What training, if any, would you like to have? 

 Present the Missouri printout for comment on the changes that have been 

made that are not yet on the ND printout. (Nutrient density vs. RDA) 

 Are there other comments you would like to make? 
Final Report: Dietary Intake and Health Outcomes 

14

Specific responses to all these questions can be found in Appendix C. From the 

responses we could see that the North Dakota nutritionists used the printout for 

counseling, personalizing it for individual nutrition education. We took the 

comments/information from the nutritionists to propose a new printout. 

Hank Dart, SM, Health Educator, Morgan Ford, SM, Research Assistant, and Dr. 

Jane Gardner, Co-Investigator met and designed 4 potential designs for the new 

client printout (nutrient driven, food group driven (2), and food group and nutrient 

driven) (see Appendix B). North Dakota WIC selected one of the proposed 

formats (with some additional alterations) to pilot in their clinics. Helaine Rockett, 

research nutritionist, and Steve Stuart, programmer, then created the new North 

Dakota version of the HSFFQ direct enter computer program including the 

version of the client printout they selected. The new North Dakota version of the 

HSFFQ was sent to four North Dakota WIC sites to be piloted. 

The second set of Focus Groups held in April 2000 and lead by Dr. Jane Gardner 

was with Providers and Clients to obtain feedback on the new client printout. 

The new printout had been in use for approximately one month prior to our focus 

groups. Each part of the client printout was reviewed and the following questions 

were asked: 

Questions for clients: 

1. At your last visit to the WIC program did you receive a printout (like this 

one) of the analysis of the foods you ate? 

2. How was that printout used at your visit? 

3. Did you take a copy home with you? 

4. What did you do with it when you got home? 

5. Did you refer to it again at home? If so, what did you look at? How many 

times, if any, did you refer to it? 

6. What information did you find most helpful? Why? 

7. What information did you not use? Why? 
Final Report: Dietary Intake and Health Outcomes 

15

8. Is there anything on the printout you would like changed? 

9. Did the printout help you make any changes in your diet? 

10. Do you have any questions for me? 

Questions for providers: 

1. How frequently do you print out an analysis for the client? 

2. How do you use the client printout during the visit? 

3. In which parts of the printout are the clients most interested? Least 

interested? 

4. Do you send that copy home with the client? What do you think she does 

with the printout? 

5. Do clients report that they used, or did not use, the printout? 

6. Do you think there should be a similar printout for children? Would you 

expect any differences in how the child’s versus the pregnant woman’s 

printout would be used? 

7. What problems, or issues, have come up about the client printout? 

8. Do you have anything else you would like to say about the client printout? 

Based on responses obtained in the focus groups the client printout was updated 

to reflect their comments. Brief summaries are provided below with complete 

summaries included in Appendix C. (See Appendix B for all versions of client 

nutrition counseling printouts, including the final version). 

Use of HSFFQ in nutrition counseling in North Dakota:

North Dakota WIC dietitians do both individual and group counseling. They use 

the HSFFQ printout for individual counseling in conjunction with several nutrition 

handouts. Some dietitians write or circle items on the handouts to personalize 

them for WIC clients. They often use the HSFFQ and its printout to clarify dietary 

intake. 

Client printout:
Final Report: Dietary Intake and Health Outcomes 

16

Four sites in North Dakota piloted the client printout for approximately one month 

prior to the focus group feedback from WIC providers and clients. At all sites the 

client printout was distributed to the women one month after the completion of 

the HSFFQ. The month-long time-period between the completion of the HSFFQ 

and receiving the printout did not concern the clients or the providers. Four of 

the clients said that the printout was not a good indication of what they usually 

ate because they filled out the HSFFQ when they were in early pregnancy and 

had “morning sickness all day”. The providers reported that they liked the client 

printout and would use it for all clients except those with developmental delays or 

language issues. 

Comments and Conclusions:

Again, the one-month time-period between the completion of the FFQ and 

receiving the printout was not a concern for either the clients or providers. 

Initially this delay occurred because the North Dakota WIC sites did not have the 

personnel to enter the food frequency questionnaire immediately at the time of 

each visit. Now in North Dakota approximately 50% of the WIC sites give the 

printout to the client at the time of their visit. This low percentage is due to lack 

of time available during each WIC visit. In North Dakota they do not make the 

client’s certification visit 1-2 hours long, but schedule the client to have nutrition 

education more frequently (per conversation with Jill Leppert). 

The HSFFQ is a calibrated self-administered food frequency questionnaire for 

use by low-income women and children (see Appendix D for references: Suitor et 

al. 1989, Wei et al. 1999, and Blum et al. 1999). It is a tool that is designed to 

evaluate a person’s diet in the previous month to evaluate how they usually eat. 

Sometimes our diets change due to sickness or new circumstances. The 

HSFFQ will not reflect a client’s “usual” diet if it is different because of sickness 

or other circumstances. However, it will still be beneficial because the WIC 

nutritionist will review it, realize the woman is not eating correctly, and address 

this immediately. The nutritionist will also review the form the following month 

with the client and inquire how they are feeling and eating now. Given that the 
Final Report: Dietary Intake and Health Outcomes 

17

HSFFQ is sometimes held in the client’s WIC folder and available for discussion 

with the participant albeit 1 month later opens the door for the client and provider 

to continue their talk about diet. 

Now, with the new client printout (a product of the focus groups), many WIC sites 

enter the HSFFQ immediately so the client can go home with an educational 

sheet that has been individualized to them. 

Task B: Development of Standardized Harvard Service FFQ 

The development and use of the Harvard Service Food Frequency Questionnaire 

(HSFFQ) continues in service settings such as prenatal clinics, Head Start 

Programs, schools, and WIC programs for both service and research purposes. 

The food lists and the analyses have been altered for specific projects, each time 

learning more about the characteristics of this tool. Based on these experiences 

and current research projects, standardization of the HSFFQ for use in maternal 

and child health settings was begun as part of this project under the Nutrition 

Education Aim. 

The Harvard Service Food Frequency Questionnaire (HSFFQ) was originally the 

Prenatal Food Frequency Questionnaire (PFFQ). It was designed as a selfadministered tool based on the Nurses’ Health Study food frequency 

questionnaire. The PFFQ was calibrated by Carol Suitor (see reference 1 Suitor, 

et al. in Appendix D). The prenatal questionnaire was later modified and 

renamed the Harvard Food Frequency Questionnaire (HFFQ) to assess dietary 

patterns in children and for use in the WIC program with computerized scoring 

(10), along with the development of an interactive, self-administered, 

computerized format (11). 

In 1998 the HFFQ became the Harvard Service Food Frequency Questionnaire 

(HSFFQ) to identify it more accurately. (Figure 1 depicts the progression of the 

development of the HFFQ previous to the change in name to HSFFQ and the 

development of the standardized Universal HSFFQ). It has been used in North 
Final Report: Dietary Intake and Health Outcomes 

18

Dakota, Missouri and Massachusetts for many years. Within each state, we 

have made changes (mostly to the fourth page non-food items) that a state might 

request. A standardized HSFFQ was discussed at the first and second Advisory 

Committee meetings (see Appendix A for Advisory Committee meeting 

attendees). It was agreed to standardize the questionnaire (list of foods on first 3 

pages and allow for different modules for the fourth page of the standardized 

questionnaire) to make the HSFFQ appropriate for a larger audience. 

Helaine Rockett M.S., R.D., Carol Suitor Sc.D., R.D., and Jane Gardner Sc.D., 

R.N. met several times and developed the standardized questionnaire. The 

specifics were presented at an Advisory Committee Meeting on June 30, 1999. 

The difference between the standardized Universal HSFFQ and the 

questionnaires at that time being used by the states are the following: 

1. Only listing orange juice/grapefruit juice, and other juice 

2. Listing the word melon only, not specifying type of melon 

3. Including fruit cocktail 

4. Separating pasta from pasta and sauce 

5. Separating salad dressing and mayonnaise 

6. Not including pumpkin pie 

7. Combining all beans into one line 

8. Rearranging foods on the form so similar foods follow each other on the 

form (e.g.: tuna, fried fish, other fish) 

9. Adding in burritos/tacos 

These were based on recommendations from the states and the Advisory 

Committee. The rest of the foods remained the same. 

The fourth page of the standardized HSFFQ included specific questions on food 

characteristics (i.e. type of fat) as well as modules of questions recommended by 

the Centers for Disease Control and Prevention that a state could choose from: 

physical activity, food behavior, and food security/food sufficiency. Previously 
Final Report: Dietary Intake and Health Outcomes 

19

the state designed the fourth page with Harvard’s supervision. The standardized 

HSFFQ is offered in English and Spanish, and in a paper or direct-enter 

computer format. 

Appendix E includes copies of the standardized paper HSFFQ for women and 

children in both English and Spanish. The modules for physical activity and food 

security are also found in Appendix E. The food security module used is the 

standardized set of 6 questions developed by USDA (12). This module is treated 

as a state-specific module, not part of the Centers for Disease Control and 

Prevention (CDC) core surveillance system. 

The CDC does not currently have a standard set of questions on food behaviors. 

The module that was piloted in Minnesota on parent-child feeding interactions did 

not prove to be predictive of obesity. Therefore, CDC has not recommended its 

use in other states. Research in this area at CDC continues, but they are not 

currently pursuing another set of questions (per communication with Larry 

Grummer-Strawn on December 18, 2002). A user’s manual is included in 

Appendix E. 

Comments/Conclusions:

Approximately 95% of the foods on the standardized Universal HSFFQ are the 

same (depending on the state 2-6 foods different). The HSFFQ is used in 3 very 

different areas of the United States and has been flexible to adapt to each area. 

If a change was requested by a state, the reasons for change were discussed 

with the WIC liaison to Harvard. The changes to the calibrated HSFFQ included 

name changes such as Fry Bread for donut in North Dakota, if a food was not 

eaten in the area such as greens in North Dakota, or a unique food was 

consumed (e.g. deer, okra). Finally in each of the states they used unique 

wording for juice and cereal that the clients were familiar with instead of using the 

words on the original HSFFQ (WIC juice, Orange juice, Other juice and WIC 

cereal [hot or cold], Other hot cereal, Other cold cereal). 
Final Report: Dietary Intake and Health Outcomes 

20

Below is a diagram that catalogs the specific differences in foods included on the 

different state questionnaires at the time of making the Universal questionnaire. 

Each row represents a row on the HSFFQ and which food was listed in each 

state. For example, while Missouri wanted chocolate milk as one of the milk 

types to choose from, Massachusetts and North Dakota did not have this choice. 

Massachusetts North Dakota Missouri 

 - - Chocolate Milk 

Peaches Peaches Peaches or pears 

Pear Pineapple Pineapple 

- - Okra 

Greens - Greens 

- - Zucchini/yellow squash 

- - Brussel Sprouts 

Ham Liver Liver 

It should be noted that it would require an enormous study to have the statistical 

power necessary to detect a difference in validity with one question (i.e. food 

item) deleted or changed. In particular, if it was an item rarely consumed in the 

population being evaluated (e.g., okra), this would be impractical and an 

insufficient reason to repeat a calibration study (per communication with Walter 

Willett). 

The standardized HSFFQ will facilitate the future expansion of the use of the tool 

to additional settings, ease the collection and compilation of aggregate data, and 

aid the production of useful data reports.
Final Report: Dietary Intake and Health Outcomes 

FIGURE 1. Harvard Food Frequency Development (prior to name change in 

1998 to Harvard Service FFQ and development of the Universal HSFFQ). 

21
Final Report: EDietary Intake and Health Outcomes 

22

Aim 2: (Combining Data) 

“Design, implement, and evaluate the use of aggregate nutrition data for 

program planning and evaluation at the state and national levels by 

aggregating diet assessment and administrative data from WIC programs 

in collaborating states.” 

Task C: Development & implementation of aggregate data reports 

In 1994 North Dakota and Harvard School of Public Health (HSPH) began a 

working collaboration. Beginning in January 1995 North Dakota collected 

aggregate data for the state WIC program and sent the first compiled file to 

HSPH in 1996. In exchange for the use of the data for research purposes, HSPH 

agreed to run specific analyses on the data and send an aggregate report back 

to the state for their use in WIC program planning and policy decision making. 

In 1997 the state of Missouri and HSPH began working together to design a 

version of the Harvard Service FFQ (including a direct enter version of the 

HSFFQ) to meet the needs of their state. The Harvard Service FFQ (with some 

adjustments for the state of Missouri) was piloted in 1997, and since then the 

state and HSPH have worked closely to develop appropriate programs and 

aggregate reports. HSPH has been running aggregate analysis for Missouri 

since 1997. Over the years there have been changes to the information provided 

in order to create an efficient and simple format of data presentation for the 

individual WIC sites. The state also receives aggregate data analysis for all of 

Missouri WIC for use in statewide queries. 

In order for Harvard to generate aggregate data reports for Massachusetts, the 

Harvard Service FFQ (HSFFQ) data must be released to Harvard. This has been 

the primary constraint on Harvard’s ability to generate reports. Based on 

interpretations of the USDA regulations by the legal experts at Massachusetts 

Department of Public Health, it was not felt that they specify directly the use of 

WIC participant data for the purposes of either surveillance or research. While 

the HSFFQ data are not part of the typical WIC certification file, they are defacto 

WIC participant-data, and thus the legal experts felt they were subject to the 
Final Report: Dietary Intake and Health Outcomes 

23

regulations. Although both Harvard and Mass WIC collaborators would like to see 

the data utilized more fully by generating these reports, Mass WIC’s inability to 

release data has precluded Harvard’s ability to do so. 

In the meantime, we have met with Mass WIC to identify alternative solutions or 

mechanisms to surmount this barrier. Any assistance that USDA can provide by 

clarifying the regulations or releasing new regulations would facilitate the ability 

of appropriate experts, outside WIC, to provide the expertise and resources 

needed for the generation of aggregate analyses to WIC that can be used for 

program planning and evaluation. 

The Advisory Board to the ERS/USDA Dietary Intake and Health Outcomes grant 

has since developed a standard format for the state reports. The standard format 

for the state aggregate data reports was proposed and discussed at the Advisory 

Committee meeting on September 13, 1999. The proposed report included the 

following nutrients: calories, protein, total fat, carbohydrates, calcium, iron, zinc, 

vitamin C, vitamin B6, folate, vitamin A, vitamin E, saturated fat, 

monounsaturated fat, polyunsaturated fat, cholesterol, caffeine, and alcohol (see 

Appendix F). The Advisory Committee felt it would be too confusing to list polyand monounsaturated fats separately and chose to combine them into one 

category called “Unsaturated fat”. They also chose to delete caffeine from the 

report. The contents of these reports are summarized in Table 1 and an example 

report as sent to Missouri for children ages 12 – 18 months is included in 

Appendix F (please note: this is just an example that presents raw ASCII data 

that is usually never printed out- the data is meant for statistical analysis and 

therefore has no headers or labels). 

For children we had to make a decision about the cut-point to separate the two 

age groups (1 - 2 years and 3 - 5 years). Since portion sizes are based on age, 

the Advisory Committee wanted the cut-off to be as close to the "correct age" as 

possible. The thought was that 2 years 11 months and 29 days was closer to 3 

years than at 2 years 1 day, but there needed to be a cut-point. Given that, 
Final Report: Dietary Intake and Health Outcomes 

24

statistically, 0.5 is the cut-off, we decided to go with 2.5 or 30 months to be 

rounded down to 2 years and greater than 30 months to be round up to 3 years. 

In concept, each state receives the exact same information as decided on by the 

Advisory Board. In practice the states’ agendas continue to develop and in 

response to these changes alterations to the aggregate data reports are 

implemented. Each state approached the project in a different manner; North 

Dakota has a data manager and Missouri originally sent raw data. 

The states’ handling of the data has had a major impact in Harvard’s receiving 

the data, the quality of the data received (any corrupt records removed), and the 

timeliness of analyzing the data. Over time we have worked out a process that is 

beneficial to each state and efficient in analyzing the data. In North Dakota, the 

data manager sent cleaned raw data with all identifiers removed to Harvard. 

Missouri initially sent just the disks from the individual sites to be analyzed. The 

data then had to be compiled and cleaned. Over the years, Missouri has 

modified their approach and sent the data on tape or CD, and has designated a 

data manager, following North Dakota’s model. This has increased the 

timeliness in the analysis of data (easy to read and accessible). Massachusetts 

working within the confines of USDA requirements has yet to send data to 

Harvard for analysis and reports. They have developed a system of centralized 

compiling of the data from their clinics. We are continuing to work on 

streamlining this part of the program. 
Final Report: Dietary Intake and Health Outcomes 

25

TABLE 1. What is included in an aggregate data report 

1. Each report includes the following information: 

a

a

a

a

a

Overall nutrition statistics (1 page). 

Contribute files (3 pages). Includes the top 10 foods that contribute to a 

specific nutrient total intake for 17 nutrients. 

Mean food group per day (1 page). Includes mean consumption per day 

for 7 food groups and 2 nutrients (meat, bread and cereal, milks, 

vegetables, fruit, vitamin A, vitamin C, sweets, fats). Also includes a list of 

what foods contribute to each food group. 

Data-dictionary for the raw nutrient data file that lists the contents, 

including the name of the nutrients for the file (1 page). 

Raw nutrient data file for 18 nutrients as specified in the data dictionary 

(45 pages). 

2. Each report includes the above information for children, pregnant women, and 

post-partum women (lactating and not lactating) broken down into specific 

age groups as follows: 

Children

12 – 18 months, 19 – 30 months, 31 – 66 months 

Women: Pregnant

Less than 16 years, 16 – 19 years, 20 – 29 years, 30 – 39 years, 40 – 49 

years, 50+ years 

Women: Lactating, and Not-pregnant or lactating (post-partum)

Not broken down by age 

3. Reports are produced for each district or county in the state. The data is sent 

on a CD-ROM. 
Final Report: Dietary Intake and Health Outcomes 

26

Implications for other states and the impact of different approaches on WIC 

costs: 

Per communication with Sharmini V. Rogers, Acting Chief, Bureau of Nutrition 

Research and Surveillance, Missouri Department of Health and Senior Services 

Potential implications for other states:

“Using the HFFQ has allowed Missouri to obtain statewide data on the dietary 

intake of WIC participants. The data obtained are being used to analyze the 

effect of referrals made on dietary intake and to evaluate the impact of selected 

interventions, or the WIC program overall, on improving dietary status and 

related risk factors of WIC participants. Missouri plans to link risk factors, such as 

overweight, with dietary intake by WIC participant to determine if the risk factor 

can be ameliorated by improved dietary intake. For example, for overweight 

children the intake of high sugar drinks, such as cola, may be one of the main 

"culprits" for children in a particular WIC local agency; the HFFQ will provide data 

to support changing the intake of that food item to improve the children's weight 

status. The WIC local agency can then track the impact of their chosen 

intervention on the intake of high sugar drinks. The advantage of using the HFFQ 

is that all foods consumed are related to the food groups as defined by the Food 

Guide Pyramid and over-consumption of food groups can be easily determined, 

plus the individual foods that are contributing to the over consumption can also 

be determined easily. Other factors, such as TV/video time and food insecurity, 

can also be easily compared against risk factor and dietary intake since that 

TV/video time and food insecurity information is also obtained via the HFFQ. 

Other states could use the data to evaluate the impact of their efforts in 

improving the nutritional health of their residents. For states that are automated, 

obtaining and transmitting the data is fairly easy. The implementation of the 

HFFQ statewide in Missouri was not effected as smoothly as it could have been. 

The implication for other states is to plan carefully for this change, assuring that 

the positive reasons for the change are widely distributed in a number of different 

ways. Educating WIC local agencies on the background of the tool and the value 

to them. Technical assistance for each WIC local agency is a must. To use the 
Final Report: Dietary Intake and Health Outcomes 

27

HFFQ tool most effectively, many WIC local agencies need to change their clinic 

flow somewhat; targeted technical assistance would have been very helpful to 

the WIC local agencies in Missouri when implementing the HFFQ in their clinics.” 

Costs impacts:

“In relationship to the overall cost of providing the WIC program to the residents 

of Missouri, the cost of implementing the use of the HFFQ was minimal. The cost 

of continuing the use of the HFFQ is basically non-existent, and may even be 

cost-reducing. When the HFFQ was first piloted in Missouri, computers that 

would have been surplused by the program were used in the pilot. When the 

HFFQ was implemented statewide, other computers that were to be replaced 

were used for the HFFQ. For WIC local agencies which needed additional 

computers, the computers purchased were designed to meet more than one 

need--implementing the HFFQ and Folio, the electronic version of the WIC Local 

Agency Operations Manual (policy and procedures manual). No additional 

staffing was needed to implement the HFFQ. Implementing and maintaining the 

HFFQ was incorporated into the work expectations of the staff, as appropriate. 

While incorporating the HFFQ into the work of the program initially may have 

required additional staff time to be spent on the project, in the long run, staff time 

on other parts of the program were reduced. For example, when monitoring WIC 

local agencies, one of the monitoring items was to assure that foods were 

correctly categorized, by servings, into foods groups. With the HFFQ, the 

calculation of the servings and assignment into food groups is automatic, thus 

eliminating that monitoring item. In addition, the monitoring findings of incorrectly 

calculating serving sizes or incorrectly assigning a food item to a specific food 

group were eliminated for those WIC agencies using the HFFQ. Thus, the 

accuracy of assigning inadequate dietary risk factors improved. Using the HFFQ 

has also resulted in WIC local agency nutritionists being able to spend more time 

counseling participants, instead of completing dietary intake records. Utilized 

correctly, the dietary intake data of the participant can be entered by the 

participant or by staff other than the WIC nutritionist. The time not spent doing 
Final Report: Dietary Intake and Health Outcomes 

28

routine determination may then be spent on counseling the participant from the 

results of the data entered. 

The Missouri WIC Program, as well as other organizations seeking funding, has 

used the data obtained from the HFFQ to support the need for grant funding. In 

addition, the data are often used to evaluate the impact of various interventions 

on the health of Missouri WIC participants. Utilized correctly, the HFFQ could 

result in a lower cost to the program by targeted counseling and interventions. 

The Missouri WIC Program continues to improve the use of the HFFQ by utilizing 

the electronic mail system to disseminate reports to the WIC local agencies, as 

by transmitting "cleaned" records electronically to Harvard University for analysis. 

Future improvements may include submission of HFFQ records from the WIC 

local agencies to the state office by electronic mail.” 
Final Report: Dietary Intake and Health Outcomes 

29

Uses of aggregate data for program planning and evaluation 

To identify counties at higher risk it is possible to use WIC nutrition data (from the 

HSFFQ) and WIC certification data (from the WIC certification form) to run an 

analysis of the intake of a specific nutrient(s) in a specified population by county. 

As an example (Table 2) we used the North Dakota data (they are the state that 

currently links WIC nutrition data and WIC certification data) to look at the intake 

of iron by county in children 3 to 5 years of age. We included the sample size for 

each county, the percent meeting the RDA of 10 mg, the mean intake for each 

county, and the standard error around that mean. This information would help 

program planners in North Dakota identify which counties may be at higher 

nutritional risk. Based on counties with 10 or more data points there is 

substantial variation in the percent meeting the RDA of 10 mg from a low of 44% 

to a high of 90%. 

To evaluate the suitability of the WIC food package for different ethnic/racial 

groups it is possible to use WIC nutrition data (from the HSFFQ) and WIC 

certification data (from the WIC certification form) to run an analysis of the intake 

of a specific nutrient(s) in a specified population by race/ethnicity. 

As an example (Table 3) we again used the North Dakota data (they are the 

state that currently links WIC nutrition data and WIC certification data) to look at 

the intake of iron by race/ethnicity in children 3 to 5 years of age. We included 

the sample size for each race/ethnicity, the percent meeting the RDA of 10 mg, 

the mean intake for each race/ethnicity, and the standard error around that 

mean. This information would help program planners in North Dakota identify 

whether or not a specific ethnic/racial group was at higher nutritional risk and 

possibly not receiving the appropriate supplementation for the specified 

nutrient(s) with the current WIC food package. It appears that there is no 

substantial variation in percent meeting the RDA of 10 mg by race/ethnicity. 
Final Report:Dietary Intake and Health Outcomes 

30

Table 2. Iron (mg) Intake by County for Children 3 to 5 Years of Age 

Participating in North Dakota WIC from July 1997 to July 1998 (n=3766) 

County n 

% Meeting RDA of 

10mg 

Mean Intake 

(mg) 

Standard Error 

Adams 17 82 12.98 0.80 

Barnes 41 90 16.39 0.78 

Benson 114 75 14.10 0.50 

Billings 0 N/A N/A N/A 

Bottineau 33 85 16.66 1.00 

Bowman 19 89 15.48 0.92 

Burke 9 89 17.98 2.63 

Burleigh 257 68 13.91 0.37 

Cass 351 65 13.19 0.31 

Cavalier 49 73 15.37 0.94 

Dickey 28 68 14.43 1.24 

Divide 0 N/A N/A N/A 

Dunn 16 69 14.60 1.70 

Eddy 22 77 15.13 1.21 

Emmons 113 77 14.79 0.58 

Foster 35 77 14.88 1.00 

Golden Valley 14 64 14.31 1.86 

Grand Forks 217 66 12.90 0.35 

Grant 33 82 14.98 0.93 

Griggs 32 66 14.33 1.31 

Hettinger 19 79 14.93 1.81 

Kidder 21 86 14.38 1.03 

LaMoure 18 44 9.94 0.89 

Logan 17 88 17.67 2.59 

McHenry 20 70 14.79 1.33 

McIntosh 0 N/A N/A N/A 

McKenzie 21 62 11.22 0.85 

McLean 36 83 14.77 0.91 

Mercer 39 72 12.71 0.75 

Morton 162 77 14.99 0.47 

Mountrail 13 77 14.39 1.35 

Nelson 5 80 16.87 3.18 

Oliver 0 N/A N/A N/A 

Pembina 40 80 16.09 1.20 

Pierce 51 84 16.57 0.92 

Ramsey 118 74 15.26 0.60 

Ransom 43 70 15.43 1.11 

Renville 15 80 15.22 1.38 

Richland 50 80 14.92 0.91 

Rolette 384 79 15.26 0.30 

Sargent 0 N/A N/A N/A 

Sheridan 16 75 16.00 2.61 

Sioux 12 83 14.99 1.42 

Slope 1 100 17.46 N/A 

 Stark 225 67 13.88 0.45 

Steele 5 80 13.20 1.95 

Stutsman 121 75 14.74 0.55 

Towner 17 71 15.68 1.99 

Traill 36 86 17.77 1.06 

Walsh 163 78 15.79 0.63 

Ward 521 65 13.24 0.27 

Wells 34 71 14.92 1.13 

Williams 143 73 14.12 0.56 
Final Report:Dietary Intake and Health Outcomes 

31

Table 3. Iron (mg) Intake by Race/Ethnicity for Children 3 to 5 Years of Age 

Participating in North Dakota WIC from July 1997 to July 1998 (n=3766) 

Race/Ethnicity n % Meeting RDA (10mg) Mean Intake (mg) Standard Error 

White 2831 72 14.24 0.12 

Native American 667 76 14.73 0.22 

Hispanic 166 69 14.66 0.52 

Black 70 73 14.01 0.65 

Asian/Pacific Islander 32 78 14.40 1.12 
Final Report: Dietary Intake and Health Outcomes 

32

State examples 

North Dakota 

North Dakota and Missouri use the aggregate data reports to ensure that their 

WIC programs are fulfilling the needs of the populations they serve on an 

individual site, county, or statewide basis. They have also used the reports and 

the linked data to examine other patterns of consumption in their clientele. For 

example, in 1996-1997 North Dakota examined the mean nutrient intake of 

pregnant women and children 3 to 5 years old by annual family income and 

family size. They found no significant differences between the different 

categories for each characteristic. See Tables 1-5 in Appendix F for detailed 

outcomes of this analysis. This illustrates the ability of the state to use the 

aggregate data to answer questions deemed important to the state health 

department. 

Missouri 

In 1997 Missouri piloted newly developed nutrition education protocols and the 

distribution of coupons for the local Farmers’ Market. They explored the influence 

of the nutrition education alone, Farmers’ Market coupons alone, and the 

combined nutrition education and Farmers’ Market coupons on the fruit and 

vegetable consumption in a small sample of women and children participating in 

the Missouri WIC program. The HSFFQ was used to compare changes in dietary 

intake of the 33 fruit and vegetable food items included in the HSFFQ between 

baseline and post-intervention (nutrition education and/or Farmers’ Market 

coupons or both). 

At the conclusion of the pilot project it was apparent that the nutrition education 

methods developed for and tested in this evaluation may have contributed to the 

increase in fruit and vegetable consumption of the participants. Providing 

Farmers’ Market coupons to WIC participants in areas participating in a Farmers’ 

Market Nutrition Program led to increased consumption of fruits and vegetables. 

Providing Farmers’ Market coupons along with intensive nutrition education 
Final Report: Dietary Intake and Health Outcomes 

33

appeared to be effective in increasing fruit and vegetable consumption among 

WIC participants. 

In this example, the aggregate data reports would offer Missouri the opportunity 

to continue to evaluate changes in the fruit and vegetable consumption of women 

and children participating in WIC, following the more extensive implementation of 

the piloted intensive nutrition education and Farmer’s Market coupon programs. 

Massachusetts 

Massachusetts has not yet contracted with Harvard to receive the aggregate data 

reports. If they were receiving the analyzed data, they would be able to use it to 

evaluate the changing needs of their WIC clients with the current recession. 

Given tough economic times, they might find that they have a heavier client 

caseload while at the same time a tighter program budget. Thus, justifying the 

provision of more targeted services for specific sub-populations (i.e., counties, 

cities) within their clientele. 

Conclusions 

While the above examples depict a few of the potential uses of the HSFFQ data 

and WIC certification data, there are data management issues that must be 

considered before the data’s full potential is reached. Before any benefits of 

collecting aggregate nutrition data and potential linkages can be reaped on a 

local or state level it is essential that the regulations regarding the use of WIC 

data be clarified to facilitate uniform policy interpretation across states. The 

following further steps will facilitate the data aggregation process and increase 

the likelihood of a sustained nutrition data aggregation system: 

1. Designation of a state WIC Program or Department of Public Health staff 

member with understanding of nutrition data structure and other state data 

sources as the official state nutrition data coordinator. The state nutrition 

data coordinator must be given clear responsibility and have an 
Final Report: Dietary Intake and Health Outcomes 

34

appropriate amount of their time allocated to nutrition data collection and 

aggregation to facilitate the use of the nutrition data. 

2. Orientation around the diet assessment method and the collection of 

aggregate nutrition data for state WIC program administrators and 

providers to become acquainted with the utility of the system. 

3. An introductory phase followed with additional orientation and training 

around the diet assessment tool and aggregation of nutrition data. 

4. An approximately six month practice phase of aggregate nutrition data 

collection to resolve issues before annual data compilation officially 

begins. 

5. Successful annual data compilation by Information Systems and state 

appointed nutrition data coordinator sent for analysis and report 

generation. 

Once the above steps are taken and the listed supports are put in place will the 

inherent benefits of these data be reaped by local and state programs and 

policies.
Final Report: Dietary Intake and Health Outcomes 

35

Task D: Calibration of the Harvard Service Food Frequency Questionnaire 

(HSFFQ) in Hispanic and African American children 

History of calibration study methods and data collection 

We have previously reported on the validity of the Harvard Service Food 

Frequency Questionnaire (HSFFQ) for use among children 2 to 4 years old (13). 

This followed the design used by Suitor and colleagues in their evaluation of the 

HSFFQ (formerly the prenatal food frequency questionnaire (PFFQ)) among lowincome pregnant women in Massachusetts (14, 15). Briefly, we conducted a 

calibration study among 300 children 1 to 4 years of age participating in the North 

Dakota WIC program (13). We compared the nutrient intake assessed by the 

HSFFQ over the previous month with the average of 3 24-hour recalls conducted 

over a month. After statistical adjustment for energy intake and within-person 

day-to-day variation in diet, we observed an average correlation between 3 24-

hour recalls and the HSFFQ of 0.55, which is comparable to the performance of 

our adult FFQ evaluated by our group in ongoing cohort studies. 

We examined the performance of the HSFFQ among Native American and 

Caucasian children. The average correlation was comparable in each group. We 

also examined performance in those children 1 to 2 years of age and those 3 to 4 

years of age. Again, the average correlation varied little between each of these 

subgroups. 

To expand the use of the HSFFQ this project proposed additional calibration 

studies of the tool to be conducted among 150 African American and 150 

Hispanic participants in WIC programs using our established procedures. The 

HSFFQ is compared to the average of 3 24-hour diet recalls conducted 

approximately ten days apart over one month. We aim to have one weekend day 

included in the three recalls. The HFFQ is administered at the end of the month 

and compared to the average dietary intake from the three 24-hour recalls that 

serve as the gold standard for the calibration. (Figure 2 details the time sequence 

of data collection). 
Final Report: Dietary Intake and Health Outcomes 

36

To insure that study sites had overcome the challenges in adopting a new tool 

and were therefore in “regular practice,” we set the following criteria for study site 

selection: 1) state consultation identified WIC and/or Head Start Programs that 

would serve as a good location, 2) site regularly using the HSFFQ (for 3 months 

or longer), and 3) a meeting with specified location assured their willingness and 

the feasibility of conducting study at the site. Following site selection, local 

dietitians were trained by our research nutritionist Helaine Rockett to administer 

the 24-hour dietary recalls. We used existing notebook computers that have the 

Minnesota diet system loaded for direct entry during the administration of the 

recall. The study coordinator maintained regular contact with study-site 

coordinators to monitor progress of implementation and accrual of subjects. 

Tables 4a through 4e include a detailed chronicle of events during the expansion 

of the validity. Recruitment and implementation proved to be far harder than our 

previous experience indicated it would be. 

Figure 2. Time sequence of data collection 

First HFFQ<--------------------1 month------------------>Final HFFQ 

↑ ↑ ↑

Recall 1 Recall 2 Recall 3 
Final Report: Dietary Intake and Health Outcomes 

37

Table 4a. First Attempt: Truman Medical Center WIC Program 

October ‘98: Funded by ERS/USDA

a

a

Planned to run calibration study in Missouri WIC sites with at least 3 

months experience with HSFFQ 

Human Subjects approval 

December ‘98: Established relationship with Truman Medical Center WIC 

Program

a

a

Study was to begin in spring ’98 

Developed contracts, scopes of work, memoranda of understanding, & 

Human Subjects application for Truman Medical Center 

March ‘99: Presented study protocol to Truman Medical Center administration

a Due to administration change (merger) Truman Medical Center decided 

not to take on additional research project. 
Final Report:Dietary Intake and Health Outcomes 

38

Table 4b. Second Attempt: Kansas City Medical Center Head Start Program 

April/May ‘99: Established relationship with Kansas City Medical Center Head 

Start

a

a

Recruited coordinator 

Prepared documents (study protocol, consent forms, etc.) for Kansas City 

Medical Center executive board meeting in June ‘99 

June/July ‘99:

a Recruited and trained 7 nutritionists 

Projected timeline:

a

a

a

a

July ‘99: Begin participant recruitment 

August ‘99 - January ‘00: Collect data 

February - March ‘00: Review HSFFQs & diet recalls 

April - June ‘00: Analyze data and write-up results 

August ’99:

a Given administrative delays in the implementation of statewide use of the 

HSFFQ, the study site had not been using HSFFQ for at least 3 months. 

Therefore, after discussion with the site, it was decided that it was too 

soon to use this site to complete the study. 
Final Report: Dietary Intake and Health Outcomes 

39

Table 4c. Third Attempt: St. Louis Family Health Center WIC Program 

November ‘99:

a

a

a

Established relationship with St. Louis Family Health Center WIC Program 

to do African-American calibration study (150 kids) 

Recruited on-site coordinator 

Retained RDs previously trained and collecting data for a different project 

December ‘99 - February ‘00: 

a Data collection 

March ‘00:

a

a

Complete clinic staff turnover, not all using HSFFQ for client diet 

assessment 

After discussion with site coordinator, decided to pull-out of St. Louis 

Family Health Center WIC Program 
Final Report:Dietary Intake and Health Outcomes 

40

Table 4d. Fourth Attempt: Hispanic Calibration Study 

March - April ‘00:

a

a

Established relationship with Lynn Community Health Center WIC 

Program in Lynn, Massachusetts 

Recruited and trained on-site coordinator and 3 RDs to collect 24-hour 

recalls 

May ‘00 – September ‘00: 

a

a

Data collection 

75 participants recruited, 20 complete 

September ’00 – March ’01:

a Continued recruitment and data collection 

March ’01:

a

a

a

a

Met with health center director, health center nutrition director and on-site 

study coordinator 

Given difficulty in retaining study participants, expanded recruitment to a 

satellite site of Lynn WIC located in the neighboring town of Salem, 

Massachusetts 

Set recruitment goal of 8 new participants per week 

Goal never met; dropped from recruiting on average 3 new participants 

per week to less than 1 new participant per week 

July ’01:

a

a

Given lack of new recruits despite expanded recruitment area, stopped 

recruitment and data collection 

53 participants with complete FFQ1 and 3 complete diet recalls 

July ’01 to July ‘02:

a Data sorted, entered, cleaned, and nutrient analysis run and written-up for 

small number of participants with usable data 
Final Report: Dietary Intake and Health Outcomes 

41

Table 4e. Fifth Attempt: African American Calibration Study 

August ‘00:

a Established relationship with Blue Hill Corridor Health Center WIC 

Program in Dorchester, Massachusetts 

September ‘00:

a

a

Recruited and trained on-site coordinator 

Recruited and trained RDs to collect 24-hour recalls 

October ‘00 - September ‘01:

a

a

a

Recruitment and data collection: 

Total recruited, n = 135 

Total completed, n = 117 

June ’01 – July ‘02:

a

a

a

a

a

a

Entering and reviewing HSFFQs 

Printing and reviewing 24-hour recalls 

Following closure of Lynn Hispanic calibration, discussed possibility of 

recruiting and following Hispanic clients in Dorchester with clinic 

Dorchester began recruiting Hispanic participants 

Ended up with only 2 additional Hispanic participants because there was 

only 1 dietitian dedicated to recruiting 

Analyze data and write-up results (see Table 4 for results) 
Final Report: Dietary Intake and Health Outcomes 

42

Calibration Study Results 

Calibration in low-income Hispanic children 1 to 5 years of age

Study site selection: 

As per usual practice, to insure that the study site had overcome the challenges 

in adopting a new tool and was therefore in “regular practice,” we consulted with 

Massachusetts WIC in our study site selection. They recommended a clinic and 

we then verified that the site was regularly using the HSFFQ (for 3 months or 

longer), and met with the clinic director and nutrition education director at the 

identified site to assure their willingness and the feasibility of conducting the 

study at that site. 

Recruitment and follow-up: 

During the summer months recruitment slowed. We were told by the study-site 

that in the fall recruitment would pick-up again once kids returned from summer 

vacation. It did not pick-up as expected. There were some underlying issues

that caused this. We encountered some obstacles while setting up the subject 

payment system. The system supported by Harvard University Accounts 

Payable (AP) (newly installed at the end of the fiscal year 1999) was inflexible in 

the distribution of subject payments. These unyielding policies are outlined 

below. 

Problem #1: 

Initially AP would not pay participants without obtaining a Social Security Number 

or other Tax ID number. Given the mobility of the study population it was 

impractical to require these identifiers to pay subjects such a small sum ($20). 

Action #1: 

AP agreed to accept a mailing address in lieu of a SSN or other Tax ID number. 

Problem #2: 
Final Report: Dietary Intake and Health Outcomes 

43

Given the check processing and distribution system, AP would mail checks for 

the $20 study payment to a population with frequently changing addresses. 

Action #2: 

We proposed sending all participant study payments to the study coordinator at 

the WIC clinic for distribution there. 

Problem #3: 

Given the cost of the special processing necessary to complete the payments 

using this method, AP would not accommodate the special circumstances and 

mail checks in batches to the study coordinator for distribution at the WIC clinic. 

Action #3: 

We proposed setting up a petty cash account to distribute cash to the study 

subjects upon the completion of the study. 

Problem #4: 

AP would not allow for a petty cash account to be opened for distributing cash 

payments to study subjects upon completion of their participation. 

Study participants who experienced these problems told other WIC participants 

that it was a hassle to participate and not worth their time. This contributed to the 

drop-off in recruitment numbers. 

Action #4: 

After such poor recruitment and follow-up encountered during the calibration in 

Hispanic children, AP accepted the need to set up a petty cash account. They 

allowed this to happen for the majority of the duration of the calibration study in 

African American children. 

It was also challenging to follow study participants for the collection of the 24-

hour recalls. Given the mobility of this population of WIC participants, (see 
Final Report: Dietary Intake and Health Outcomes 

44

a

a

Appendix H for the spreadsheet of study participant recruitment and follow-up 

that includes this information in the ‘Comments’ column) dietitians had trouble 

contacting subjects to administer the diet recalls. They would make multiple 

attempts (e.g., up to 1 call per day for 7 to 10 days) without any success, and 

therefore the participant would be dropped from the study. 

Throughout the study we continued to have regular contact with the study site, 

and were told that despite the difficulties in recruiting and following study 

participants, they were dedicated to the project and would continue to do their 

best to complete the project as it was proposed. It became obvious after almost 

six months without any new recruits that there were real problems with this 

calibration study. In March 2001 we met with the clinic director, nutrition 

education director, and the study-site project coordinator to discuss the future of 

the Hispanic calibration study. Following the implementation of the tactics 

agreed upon at that March meeting (including the expansion of recruitment to a 

satellite clinic and limiting data collection to the 1st HSFFQ & 3 diet recalls) 

recruitment still did not pick-up by July 2001. At this point in time we decided to 

stop recruiting and work with the data we had collected thus far (as noted in the 

timeline of Table 4d). 

The spreadsheet of study participant recruitment and follow-up for the calibration 

study among Hispanic children is included in Appendix G. This information was 

maintained throughout the time frame of the study. The spreadsheet includes 

dates for the first HSFFQ, any diet recalls collected, and any completed second 

HSFFQs for each subject (see the below overview for a summary). 

Subject recruitment and follow-up overview for calibration among Hispanic 

children: 

total number of subjects recruited, n = 98 

subjects that completed the 1st HSFFQ and 3 diet recalls, n = 52 
Final Report: Dietary Intake and Health Outcomes 

45

a

a

a

a

a

subjects that completed the 1st HSFFQ, 3 diet recalls, AND the 2nd

HSFFQ, n = 20 

Sample: The sample on which the analyses were conducted were those 

subjects who had the first Harvard Service FFQ (HSFFQ) and three 24-hour 

recalls. 98 caregivers/parents of children receiving WIC and who were of 

Hispanic origin completed the HSFFQ. The final sample, after exclusions, with 

completed first HSFFQs and three 24-hour recalls was 45. The following were 

the reasons why subjects were excluded: 

2 were siblings 

44 were missing 1 or more 24-hour recalls. 

5 subjects had daily energy estimates from the HSFFQ that did not meet 

the requirement of 500 to 3,500 kcals. 

2 were missing data for the first HSFFQ. 

Results: The results are presented in Table 5. Given the small final sample of 

Hispanic children who participated in and completed the calibration study (n=45), 

the correlations, as well as the within and between subject variability, have very 

wide confidence intervals. Therefore, these results should not be used to draw 

conclusions about the validity of the Harvard Service FFQ (HSFFQ) among lowincome Hispanic children participating in WIC. The correlations for both the 

macro and micronutrients are generally low. They ranged from -0.32 (for folate) 

to 0.42 for pyridoxine. Table 6 shows the ratio of the within and between subject 

variability for each nutrient for the Hispanic children in comparison to the sample 

of Native American and Caucasian children who participated in a similar 

calibration study of the HSFFQ in North Dakota. What becomes evident is that 

the ratio of the within to between subject variability is higher for 12 of the 19 

nutrients among the Hispanic children than among the children in North Dakota. 

This suggests that a greater number of 24-hour diet recalls may have been 

necessary to accurately estimate "true intake" and to adjust for day-to-day 

fluctuations in an individual's intake (i.e. within subject variability) for the Hispanic 
Final Report: Dietary Intake and Health Outcomes 

46

children who participated in the present calibration study. This may be related to 

the variety of foods available in an urban area like Greater Boston, as compared 

to a more rural area like North Dakota. An increased variety could affect the dayto-day differences in food intake for each individual because more choices are 

available. 
Final Report: Dietary Intake and Health Outcomes 

47

Table 5. Results of calibration study in low-income Hispanic children aged 

1 to 5: Energy and nutrient intake and Pearson correlation coefficients 

between one food frequency questionnaire and three 24-hour recalls (n = 

45). 

Energy and Nutrient Intake 

Correlations 

3 recalls and 

1st HSFFQa

Recalls Harvard Service FFQ 

Nutrients and Energy Mean (SD) Mean (SD) 

Energy (kcal) 1538 (404) 2078 (609) N/A 

Protein (g) 65 (23) 75 (23) 0.04 

Carbohydrates (g) 210 (55) 304 (97) 0.16 

Total Fat (g) 51 (17) 67 (23) 0.15 

Sucrose (g) 26 (15) 45 (20) -0.25 

Dietary fiber (g) 12 (5) 22 (8) -.12 

Calcium (mg) 953 (415) 1148 (485) 0.28 

Iron (mg) 11.1 (4.1) 14.2 (5.7) 0.16 

Vitamin C (mg) 120 (46) 255 (119) 0.04 

Thiamin (mg) 1.4 (.4) 1.8 (.6) 0.02 

Riboflavin (mg) 2.0 (.7) 2.2 (.9) 0.18 

Pyridoxine (mg) 1.6 (.5) 2.1 (.7) 0.42 

Folate (mcg) 298 (89) 331 (131) -0.32 

B12 (mg) 3.7 (1.6) 4.7 (2.0) 0.24 

Vitamin A (re) 901 (626) 1228 (584) 0.07 

Vitamin E (te) 4.4 (2.0) 9.5 (3.7) -.12 

Cholesterol (mg) 306 (146) 241 (97) 0.39 

Magnesium (mg) 241 (78) 333 (111) 0.02 

Zinc (mg) 8.2 (2.8) 11.1 (3.8) 0.07 

Niacin 14.6 (5.2) 17.8 (5.9) -0.03 a

Pearson Correlation Coefficients adjusted for energy intake and within person variation.
Final Report: Dietary Intake and Health Outcomes 

48

Table 6. Comparison of within and between subject variability among lowincome Hispanic children aged 1 to 5 participating in a calibration study of 

the HSFFQ to the variability ratio of low-income children aged 1 to 5 who 

participated in a calibration study of the HSFFQ in North Dakota. 

Hispanic (n = 45) North Dakota (n = 233) 

Within Between Ratio Within Between Ratio 

Protein (g) 101.1 49.6 2.04 134.7 41.4 3.25 

Carbohydrate (g) 986.9 258.1 3.82 818.0 217.2 3.76 

Total Fat (g) 128.1 35.2 3.64 105.8 29.1 3.63 

Sucrose (g) 200.0 67.0 2.98 283.2 71.1 3.98 

Dietary Fiber (g) 12.4 4.1 3.01 11.0 3.3 3.30 

Calcium (mg) 58823.7 47909.8 1.23 80448.1 49735.7 1.62 

Iron (mg) 9.70 4.3 2.27 29.6 22.4 1.32 

Vitamin C (mg) 5099.4 507.1 10.1 4414.5 2142.2. 2.1 

Thiamine (mg) 2.51 0.59 4.32 0.24 0.16 1.54 

Riboflavin (mg) 0.13 0.07 1.95 0.31 0.26 1.18 

Pyridoxine (mg) 0.22 0.005 46.77 0.37 0.25 1.52 

Folate (mg) 12327.9 1273.2 9.68 15259.9 10634.1 1.43 

B12 (mg) 1.60 0.4 4.19 7.20 2.8 2.5 

Vitamin A (re) 460936.8 22174.4 20.8 446345.0 141471.7 3.16 

Vitamin E (te) 6.0 0.26 23.3 25.6 20.0 1.3 

Cholesterol (mg) 24043.7 4169.4 5.77 16689.7 3548.5 4.70 

Magnesium (mg) 1675.4 639.2 2.62 2362.7 569.5 4.15 

Zinc (mg) 4.0 0.3 12.96 42.3 10.9 3.87 

Niacin (mg) 16.4 4.1 4.02 39.1 30.1 1.3 
Final Report: Dietary Intake and Health Outcomes 

49

Calibration in low-income African American children 1 to 5 years of age

The sample size numbers for the calibration study among African American 

children reflect an improvement in the subject payment system (petty cash 

account was established with cash payment at time of study completion) and in 

having a study-site coordinator dedicate more time to following study 

participants. 

The spreadsheet of study participant recruitment and follow-up for the calibration 

study among African American children is included in Appendix G. This 

information was maintained throughout the timeframe of the study. The 

spreadsheet includes dates for the first HSFFQ, any diet recalls collected, and 

any completed second HSFFQs for each subject (see the below overview for a 

summary). 

Subject recruitment and follow-up overview: 

a

a

a

total number of subjects recruited, n = 148 

subjects that completed the first HSFFQ and 3 diet recalls, n= 124 

subjects that completed the first HSFFQ, 3 diet recalls, and the second 

HSFFQ, n = 113 

Please note: These numbers are prior to data entry, cleaning, and exclusions. 

Sample: One hundred and forty eight children were enrolled in the calibration 

study via their caregiver/parents' completion of a Harvard Service Food 

Frequency Questionnaire (HSFFQ). Analyses were conducted on the sample of 

subjects who had the first HSFFQ and three 24-hour recalls (n = 124). The final 

sample was 108 children. Subjects were excluded for the following reasons: 

a 17 subjects of the 124 with the first HSFFQ and 3 diet recalls had daily 

energy estimates from the HSFFQ that did not meet the requirement of 

500 to 3,500 kcal. 
Final Report: Dietary Intake and Health Outcomes 

50

a 24 subjects of the 148 recruited were missing one or more 24-hour recalls. 

Analyses: Analyses were run on the entire sample and on a sub-sample on 

which more stringent criterion was applied. In the latter sample, analyses were 

run only on those subjects in whom not more than 2 months elapsed between the 

first HSFFQ and the third 24-hour recall (n=96). The results were not 

substantively different and thus only the former analyses on 108 children are 

presented here. The correlations presented are adjusted for within and between 

person variation. 

Results: The results are shown in Tables 7 and 8. The correlations ranged from 

a low of -0.006 for Vitamin E to a high of 0.51 for Cholesterol. The highest 

correlations were for the following: protein (0.37), carbohydrates (0.38), dietary 

fiber (0.39), vitamin A (0.45), cholesterol (0.51) and magnesium (0.43). 

To assess the basis for lower correlations than those previously observed in 

other low income children receiving WIC (Blum et al. included in Appendix D), the 

amount of variation within each subject (as indicated by variability of the three 

24-hour recalls for each subject) and between subjects was examined. The ratio 

of within to between subject variation was compared to that found in a similar 

calibration study among children participating in North Dakota WIC (Blum et al. 

included in Appendix D) (See Table 8). The higher the ratio, the greater the 

within subject (i.e., day-to-day variability in nutrient intake for each individual) to 

between subject variability, the greater the number of days needed for the gold 

standard (8). For the African American children used in this calibration research, 

the ratio of within to between subject variability is greater than that found in the 

North Dakota calibration for sixteen out of the nineteen dietary 

nutrients/substances. This implies that a greater number than the three 24-hour 

recalls that were collected would be necessary to accurately represent the diet of 

these children. Thus, the fact that these correlations were somewhat low, may 

not necessarily imply that the Harvard Service FFQ (HSFFQ) has a poorer 

validity among African American children but may instead reflect the inadequacy 
Final Report: Dietary Intake and Health Outcomes 

51

of the three days of diet recalls as the gold standard representing the diet of 

these children. The increase in this ratio could be due to the generally increased 

availability of a variety of foods in the greater Boston area as compared to North 

Dakota. 
Final Report: Dietary Intake and Health Outcomes 

52

Table 7. Results of calibration study in low-income African American 

children aged 1 to 5 years: Energy and nutrient intake and Pearson 

correlation coefficients between one food frequency questionnaire and 

three 24-hour recalls (n=108). 

Energy and Nutrient Intake 

Correlations 

3 recalls and 

1st HSFFQa

Recalls Harvard Service FFQ 

Nutrients and Energy Mean (SD) Mean (SD) 

Energy (kcal) 1464 (323) 1914 (659) N/A 

Protein (g) 53 (13) 76 (25) 0.37 

Carbohydrates (g) 202 (45) 259 (96) 0.38 

Total Fat (g) 51 (14) 68 (27) 0.29 

Sucrose (g) 30 (13) 35 (19) -0.16 

Dietary fiber (g) 9 (3) 17 (8) 0.39 

Calcium (mg) 844 (271) 1212 (482) 0.13 

Iron (mg) 11 (4) 16 (7) 0.23 

Vitamin C (mg) 114 (53) 211 (111) 0.007 

Thiamin (mg) 1.3 (.4) 1.8 (.7) 0.07 

Riboflavin (mg) 1.7 (.4) 2.4 (.9) 0.14 

Pyridoxine (mg) 1.3 (.4) 2.1 (.7) 0.009 

Folate (mcg) 204 (76) 316 (137) 0.27 

B12 (mg) 2.9 (2.1) 5.7 (2.1) 0.20 

Vitamin A (re) 676 (407) 1345 (692) 0.45 

Vitamin E (te) 4.4 (1.9) 8.4 (3.5) -0.006 

Cholesterol (mg) 197 (85) 264 (112) 0.51 

Magnesium (mg) 201 (48) 316 (111) 0.43 

Zinc (mg) 7 (2.1) 12 (4.3) 0.15 

Niacin 14 (4) 19 (7) 0.13 a

Pearson Correlation Coefficients adjusted for energy intake and within person variation. 
Final Report: Dietary Intake and Health Outcomes 

53

Table 8. Comparison of within and between subject variability among lowincome African American children aged 1 to 5 years participating in a 

calibration study of the HSFFQ to the variability ratio among low-income 

children aged 1 to 5 years who participated in a calibration study of the 

HSFFQ in North Dakota. 

African American (n = 108) North Dakota (n = 233) 

Within Between Ratio Within Between Ratio 

Protein (g) 89.6 21.1 4.1 134.7 41.4 3.25 

Carbohydrate (g) 574.7 89.7 6.4 818.0 217.2 3.76 

Total Fat (g) 69.3 14.8 4.7 105.8 29.1 3.63 

Sucrose (g) 126.0 71.1 1.8 283.2 71.1 3.98 

Dietary Fiber (g) 9.2 2.5 3.6 11.0 3.3 3.30 

Calcium (mg) 51030.0 40027.1 1.3 80448.1 49735.7 1.62 

Iron (mg) 22.3 3.7 6.0 29.6 22.4 1.32 

Vitamin C (mg) 4018.3 1171.2 3.4 4414.5 2142.2. 2.1 

Thiamine (mg) 2.0 0.30 6.8 0.24 0.16 1.54 

Riboflavin (mg) 0.12 0.06 1.8 0.31 0.26 1.18 

Pyridoxine (mg) 0.13 0.03 4.9 0.37 0.25 1.52 

Folate (mg) 6525.5 2045.1 3.2 15259.9 10634.1 1.43 

B12 (mg) 3.5 0.76 4.6 7.20 2.8 2.5 

Vitamin A (re) 325305.1 48640.2 6.7 446345.0 141471.7 3.16 

Vitamin E (te) 2.0 0.83 2.4 25.6 20.0 1.3 

Cholesterol (mg) 11588.2 1388.1 8.3 16689.7 3548.5 4.70 

Magnesium (mg) 1232.9 780.7 1.6 2362.7 569.5 4.15 

Zinc (mg) 2.3 0.54 4.3 42.3 10.9 3.87 

Niacin (mg) 13.7 4.5 3.1 39.1 30.1 1.3 
Final Report: Dietary Intake and Health Outcomes 

54

Aim 3: (Longitudinal Data) 

“Using prospective data collected through the WIC program, examine 

relations between diet from age 2 to age 4 and childhood obesity as 

measured by excess adiposity among 4-year-old children. Specifically, we 

hypothesize that a high-fat diet leads to greater childhood obesity and that 

a diet high in fruits, vegetables, and fiber is associated with lower levels of 

obesity. Building on the food pyramid, health outcomes from this study, 

and other sources, we will define diet quality for this age range.” 

Task E: Prospective analysis of diet and health

Summary & Abstracts 

Prospective analyses looking at dietary composition as well as beverage 

consumption and changes in body mass index among low-income preschool 

children using longitudinal aggregate nutrition data from the state of North 

Dakota was completed. Much of the initial effort included data management and 

matching of records. The North Dakota Department of Health staff provided 

invaluable assistance to make this possible. The abstracts included in Appendix 

H were presented at the April 2001 FASEB meeting as well as at the 2001 

Childhood Obesity Conference in San Diego, California. Subsequently, 2 

complete manuscripts based on the work presented in these abstracts are under 

peer review. 
Final Report: Dietary Intake and Health Outcomes 

55

Conclusions 

The use of the Harvard Service FFQ (HSFFQ) has been successfully 

implemented and is on-going in all 3 collaborating states. With the input of health 

communication specialists and WIC practitioners and clients the HSFFQ Client 

Printout has been tested and refined, and is currently utilized to provide valuable 

individually tailored nutrition education. The newly developed standardized 

version of the HSFFQ will facilitate the expansion of its use to additional settings, 

ease the collection and compilation of aggregate data, and aid the production of 

useful data reports. 

The results of the calibration component make it clear that further analyses are 

necessary to explain the performance of the tool in the assessment of the diet in 

African American and Hispanic children participating in WIC. The poor results 

may have been a result of the study methods, and not a reflection on the 

performance of the HSFFQ in these populations. The previously reported validity 

of the HSFFQ can not be disregarded. The validity reported by Suitor et al., 

Blum et al., and Wei et al. demonstrate that nutrition data for Native American 

children ages 1 to 5 and multi-ethnic pregnant and postpartum women can be 

reliably collected, and combined at a state level and utilized to provide tailored 

nutrition education. Furthermore, as demonstrated in the state data aggregation 

component of this project, it is feasible to combine nutrition data at a state level 

to be utilized by local, state, and national agencies to answer important questions 

and inform current program practices and policy decisions. 

While inconclusive, prospective analysis of the influence of diet on overweight in 

low-income preschool demonstrated the ability to link aggregate nutrition data 

with vital statistic data to explore important epidemiological hypotheses. 

The experiences of the past 3 years have been invaluable in uncovering both the 

significant administrative issues that must be addressed and those that will 

facilitate the process of data aggregation and increase the likelihood of 

successfully sustaining the implemented system. Before any benefits of 
Final Report:Dietary Intake and Health Outcomes 

56

collecting aggregate nutrition data and potential linkages can be reaped on a 

local or state level it is essential that the regulations regarding the use of WIC 

data be clarified to facilitate uniform policy interpretation across states. The 

following steps will facilitate the data aggregation process and increase the 

likelihood of a sustained nutrition data aggregation system: 

1. Designation of a state WIC Program or Department of Public Health staff 

member with understanding of nutrition data structure and other state data 

sources as the official state nutrition data coordinator. The state nutrition 

data coordinator must be given clear responsibility and have an 

appropriate amount of their time allocated to nutrition data collection and 

aggregation to facilitate the use of the nutrition data. 

2. Orientation around the diet assessment method and the collection of 

aggregate nutrition data for state WIC program administrators and 

providers to become acquainted with the utility of the system. 

3. An introductory phase followed with additional orientation and training 

around the diet assessment tool and aggregation of nutrition data. 

4. An approximately six month practice phase of aggregate nutrition data 

collection to resolve issues before annual data compilation officially 

begins. 

5. Successful annual data compilation by Information Systems and state 

appointed nutrition data coordinator sent for analysis and report 

generation.

Once the above steps are taken and the listed supports are put in place will the 

inherent benefits of these data be reaped by local and state programs and 

policies. 

Future goals should include further calibration of the HSFFQ in Latinos using our 

established criteria and methods. Further work is needed to better understand 

the assessment of diet in this subgroup of the population. 
Final Report: Dietary Intake and Health Outcomes 

57

To support the continued use of these valuable data, it is essential that a funding 

source and functional body be identified. With expansion to additional states the 

data collected through WIC may offer a unique window on evolving patterns of 

diet and child growth. From our experience the management of the compilation 

and use of these data would be best accomplished by an entity residing within a 

state system. This will help regulate the many administrative and practical steps 

that must be completed to successfully link the numerous data sets that various 

agencies may contribute.
Final Report: Dietary Intake and Health Outcomes 

58

References 

1. Institute of Medicine. Dietary Risk Assessment in the WIC Program. 

Washington, DC: Food and Nutrition Board, Institute of Medicine, National 

Academy Press, 2002. 

2. Ammerman AS, Haines PS, DeVellis RF, et al. A brief dietary assessment 

to guide cholesterol reduction in low-income individuals: design and 

validation. J Am Diet Assoc 1991;91:1385-90. 

3. Contento I, Balch G, Bronner Y. The effectiveness of nutrition education 

and implications for nutrition education policy, programs, and research: a 

review of the research. J Nutr Education 1995;27:277-320. 

4. Rodan M. Food frequency questionnaires and nutritional risk assessment 

in low-income populations. Boston, Massachusetts: Harvard University, 

1997:110. 

5. Dietz W. Health consequences of obesity in youth: childhood predictors of 

adult disease. Pediatrics 1998;101:518-25w. 

6. Rosenbaum M, Leibel R. The physiology of body weight regulation: 

relevance to the etiology of obesity in children. Pediatrics 1998;101:525-

39. 

7. Willett WC, Lenart E. Reproducibility and Validity of Food-Frequency 

Questionnaires. In: Willett WC, ed. Nutritional Epidemiology, 2nd Edition. 

New York: Oxford University Press, 1998. 

8. Willett WC. Chapter 12: Corrections for the effects of measurement error. 

In: Willett WC, ed. Nutritional Epidemiology, 2nd ed. New York: Oxford 

University Press, 1998. 

9. US Department of Agriculture. The food guide pyramid. Hyattsville, MD: 

Human Nutrition Information Service, 1992 (Publication HG 252). 

10. Gardner JD, Suitor C, Witschi J, Wang Q. Dietary assessment 

methodology for use in the Special Supplemental Food Program for 

Women, Infants and Children (WIC). Report to the U.S. Department of 

Agriculture. Boston, Massachusetts: Harvard School of Public Health, 

1991. 

11. Suitor C, Gardner J. Development of an interactive, self-administered 

computerized food frequency questionnaire for us with low-income 

women. J Nutr Education 1992;24:82-6. 

12. Bickel G, Nord M, Price C, Hamilton W, Cook J. Guide to Measuring 

Household Food Security: Revised 2000. Measuring Food Security in the 

United States.: USDA, FNS, Office of Analysis, Nutrition, and Evaluation, 

2000. 

13. Blum RE, Wei EK, Rockett HR, et al. Validation of a food frequency 

questionnaire in Native American and Caucasian children 1 to 5 years of 

age. Matern Child Health J 1999;3:167-72. 

14. Wei EK, Gardner J, Field AE, Rosner BA, Colditz GA, Suitor CW. Validity 

of a food frequency questionnaire in assessing nutrient intakes of lowincome pregnant women. Matern Child Health J 1999;3:241-6. 

15. Suitor CJ, Gardner J, Willett WC. A comparison of food frequency and diet 

recall methods in studies of nutrient intake of low-income pregnant 

women. J Am Diet Assoc 1989;89:1786-94.
Final Report: Dietary Intake and Health Outcomes 

Dietary Intake and Health Outcomes: Final Report 

Appendices A – H 
Final Report: Dietary Intake and Health Outcomes 

Appendices Table of Contents 

APPENDIX A (ADVISORY COMMITTEE) ....................................................................................................................................A-1

1. ADVISORY COMMITTEE MEMBERS & MEETING ATTENDANCE .................................................................................................A-2

2. ADVISORY COMMITTEE MEETING MINUTES

Meeting 1: December 9, 1998, Atlanta Georgia............................................................................................................A-4

Meeting 2: June 30, 1999, Boston, Massachusetts ....................................................................................................A-13

Meeting 3: September 13, 1999, Boston, Massachusetts (Task Force on Data and Program Planning)...................A-33

Meeting 4: September, 11, 2000, Boston, Massachusetts..........................................................................................A-37

APPENDIX B (CLIENT NUTRITION EDUCATION).....................................................................................................................B-1

1. ORIGINAL PRINTOUT FOR CLIENT NUTRITION EDUCATION..................................................................................................... B-2

2. 4 PROPOSED CLIENT PRINTOUTS.........................................................................................................................................B-5

3. PILOTED CLIENT PRINTOUT ............................................................................................................................................... B-10

4. FINAL CLIENT PRINTOUT.................................................................................................................................................... B-11

APPENDIX C (FOCUS GROUPS) ................................................................................................................................................C-1

1. FOCUS GROUP QUESTIONS & SUMMARIZED RESPONSES

Summary 1: Summary of North Dakota Nutritionist Focus Groups (March 1999)........................................................C-2

Summary 2: Focus Group Q & A for North Dakota Nutritionists (March 1999).............................................................C-6

Summary 3: Summary of North Dakota Interviews with Providers and Clients on the Client Printout (April 2000).....C-18

Summary 4: Focus Group Q &A for North Dakota Providers and Clients on the Client Printout (April 2000) ............C-20

APPENDIX D (REFERENCES).....................................................................................................................................................D-1

1. SUITOR CJW, GARDNER J, AND WILLETT WC. A comparison of food frequency and diet recall methods in studies of 

nutrient intake of low-income pregnant women. J Am Diet Assoc 89:1786-94, 1989. .....................................................D-2

2. WEI EK, GARDNER J, FIELD AE, ROSNER BA, COLDITZ GA, AND SUITOR CJW. Validity of a food frequency questionnaire in 

assessing nutrient intakes of low-income pregnant women. Maternal and Child Health Journal 3(4):241-6, 1999.......D-11

3. BLUM RE, WEI EK, ROCKETT HRH, LANGELIERS JD, LEPPERT J, GARDNER JD, AND COLDITZ GA. Validation of a food 

frequency questionnaire in Native American and Caucasian children 1 to 5 years of age. Maternal and Child Health 

Journal 3 (3):167-72, 1999. ............................................................................................................................................D-17

APPENDIX E (STANDARDIZED HSFFQ) ..................................................................................................................................E -1

1. STANDARDIZED HSFFQ: WOMEN & CHILDREN’S IN ENGLISH & SPANISH...............................................................................E-2

2. 4TH PAGE MODULES: PHYSICAL ACTIVITY & FOOD SECURITY ..............................................................................................E-18

3. USERS MANUAL ...............................................................................................................................................................E-21

APPENDIX F (AGGREGATE DATA)...........................................................................................................................................F-1

1. ORIGINAL AND CURRENT AGGREGATE DATA REPORT LAYOUTS............................................................................................F-2

2. AGGREGATE DATA REPORT EXAMPLE: CHILDREN 12 – 18 MONTHS OLD IN MISSOURI

 Overall nutrition statistics ............................................................................................................................................F-4

 Contribute files ............................................................................................................................................................F-5

Mean food group per day ............................................................................................................................................F-8

 Data-dictionary for the raw nutrient data file................................................................................................................F-9

 Raw nutrient data file.................................................................................................................................................F-10

3. AGGREGATE DATA USES EXAMPLE: NORTH DAKOTA WIC CLIENT CONSUMPTION PATTERNS ..............................................F-55

APPENDIX G (VALIDATION STUDY RECRUITMENT & FOLLOW-UP).................................................................................. G-1

1. HISPANIC CALIBRATION STUDY SUBJECT RECRUITMENT & FOLLOW-UP CHART.....................................................................G-2

2. AFRICAN AMERICAN CALIBRATION STUDY SUBJECT RECRUITMENT & FOLLOW-UP CHART .....................................................G-4

APPENDIX H (PROSPECTIVE ANALYSIS ABSTRACTS)........................................................................................................H-1

1. ABSTRACT 1: DIETARY COMPOSITION AND CHANGES IN BODY MASS INDEX AMONG LOW-INCOME PRESCHOOL CHILDREN..........H-2

2. ABSTRACT 2: BEVERAGE CONSUMPTION AND CHANGES IN BODY MASS INDEX AMONG LOW-INCOME PRESCHOOL CHILDREN......H-3
Final Report: Dietary Intake and Health Outcomes 

Appendix A: Advisory Committee 

1. Advisory Committee Members & Meeting Attendance 

2. Advisory Committee Meeting Minutes 

Meeting 1: December 9, 1998, Atlanta Georgia 

Meeting 2: June 30, 1999, Boston, Massachusetts 

Meeting 3: September 13, 1999, Boston, Massachusetts (Task Force on Data and 

Program Planning) 

Meeting 4: September, 11, 2000, Boston, Massachusetts 







































































































































































































































Final Report: ERS/USDA-- Dietary Intake and Health Outcomes 

Appendix G: Calibration Study Recruitment & Follow-up 

1. Hispanic Calibration Study Subject Recruitment & Follow-up Chart 

2. African American Calibration Study Subject Recruitment & Follow-up Chart 
Calibration of the HSFFQ in Hispanic Children 1 to 5 Years Old

FFQ1 Recall1 Recall2 Recall3 FFQ2 Comments

6/5/2000 10/12/2000 11/5/2000 11/17/2000 4/10/2001

6/19/2000 7/25/2000 7/30/2000 8/10/2000 9/20/2000

5/20/2000 6/19/2000 7/3/2000 7/23/2000 10/12/2000

5/17/2000 6/16/2000 6/27/2000 7/8/2000 8/21/2000

6/8/2000 8/1/2000 8/14/2000 8/22/2000 12/9/2000

4/11/2000 10/12/2000 11/5/2000 11/17/2000 4/10/2001

5/17/2000 6/16/2000 6/27/2000 7/8/2000 8/21/2000

5/19/2000 7/7/2000 7/17/2000 9/18/2000 10/15/2000

6/27/2000 10/10/2000 10/26/2000 11/12/2000 8/20/2000

5/20/2001 7/5/2000 7/10/2000 7/17/2000 8/20/2000

5/15/2000 6/16/2000 7/27/2000 8/5/2000 9/8/2000

6/8/2000 8/1/2000 8/14/2000 8/22/2000 12/9/2000

5/20/2000 6/19/2000 7/3/2000 7/23/2000 10/12/2000

4/27/2000 6/10/2000 7/3/2000 7/12/2000 9/6/2000

7/17/2000 8/5/2000 8/16/2000 8/22/2000 9/12/2000

6/1/2001 7/19/2000 7/31/2000 8/7/2000 10/16/2000

4/11/2000 6/17/2000 6/30/2000 7/7/2000 8/21/2000

5/8/2000 6/16/2000 7/16/2000 7/27/2000 8/20/2000

6/22/2000 8/22/2000 8/30/2000 9/9/2000 10/16/2000

4/11/2000 6/21/2000 6/30/2000 7/9/2000 8/21/2000

6/16/2000 4/12/2001 4/19/2001 4/23/2001 Incomplete/unwilling to complete.

5/17/2000 6/10/2000 7/3/2000 7/12/2000 Incomplete/unwilling to complete.

6/20/2000 10/18/2000 11/2/2000 3/8/2000 Incomplete/unwilling to complete.

5/16/2000 12/31/2000 1/23/2001 1/31/2001 Incomplete/unreachable

8/1/2000 8/16/2000 9/9/2000 9/22/2000 Incomplete/unwilling to complete.

5/20/2000 6/15/2000 7/1/2000 7/7/2000 Incomplete/unwilling to complete.

8/23/2000 11/5/2000 11/17/2000 12/14/2000 Incomplete/unwilling to complete.

8/24/2000 9/17/2000 9/28/2000 10/26/2000 Incomplete/unwilling to complete.

5/25/2000 7/1/2000 7/7/2000 7/21/2000 Incomplete/unwilling to complete.

5/20/2000 6/15/2000 7/1/2000 7/7/2000 Incomplete/unwilling to complete.

5/19/2000 9/12/2000 9/18/2000 9/22/2000 Incomplete/unwilling to complete.

8/2/2000 8/20/2000 9/15/2000 9/28/2000 Incomplete/unwilling to complete.

5/16/2000 6/13/2000 7/3/2000 7/9/2000 Incomplete/unwilling to complete.

5/12/2000 7/13/2000 9/12/2000 9/22/2000 Incomplete/unwilling to complete.

5/12/2000 10/12/2000 10/16/2000 10/23/2000 Incomplete/unwilling to complete.

7/10/2000 8/31/2000 9/10/2000 9/28/2000 Incomplete/unwilling to complete.

5/16/2000 4/26/2001 4/29/2001 5/3/2001 Incomplete/unwilling to complete.

9/13/2000 10/24/2000 10/30/2000 11/3/2000 Incomplete/unwilling to complete.

5/19/2000 7/16/2000 8/22/2000 9/4/2000 Incomplete/unwilling to complete.

6/21/2000 10/7/2000 11/11/2000 11/20/2000 Incomplete/unwilling to complete.

5/15/2000 6/10/2000 7/3/2000 7/27/2000 Incomplete/unwilling to complete.

6/14/2000 8/31/2000 9/22/2000 11/11/2000 Incomplete/unwilling to complete.

6/20/2000 10/25/2000 11/5/2000 11/11/2000 Incomplete/unwilling to complete.

6/28/2000 8/16/2000 9/9/2000 11/15/2000 Incomplete/unwilling to complete.

6/7/2000 7/19/2000 9/21/2000 9/28/2000 Incomplete/unwilling to complete.

8/26/2000 3/8/2001 4/20/2001 4/23/2001 Incomplete/unwilling to complete.

5/15/2000 7/6/2000 7/10/2000 7/14/2000 Incomplete/unwilling to complete.

6/19/2000 9/12/2000 10/11/2000 10/18/2000 Incomplete/unwilling to complete.

7/5/2000 8/15/2000 8/27/2000 9/15/2000 Incomplete/unwilling to complete.

5/12/2000 10/12/2000 10/16/2000 10/23/2000 Incomplete/unwilling to complete.

6/8/2000 7/18/2000 8/5/2000 8/15/2000 Incomplete/unwilling to complete.

9/11/2000 Incomplete/unreachable

7/5/2000 Unreachable.

7/21/2000 Incomplete/unreachable

6/23/2000 Incomplete/unreachable

Participant Tracking 
Calibration of the HSFFQ in Hispanic Children 1 to 5 Years Old

7/15/2000 Unreachable.

5/16/2000 Incomplete/unreachable

9/6/2000 4/18/2001 5/30/2001 Incomplete/unreachable

5/12/2000 Incomplete/moved, unreachable/Missing recalls.

7/10/2000 Incomplete/unreachable

8/24/2000 Unreachable. Phone disconnected. 

7/27/2000 Incomplete/unreachable

6/23/2000 Incomplete/unreachable

8/22/2000 Incomplete/unreachable

6/13/2000 Unreachable. Wrong number. ML

8/18/2000 Incomplete/unreachable

6/7/2000 Incomplete/unreachable

7/5/2000 Incomplete/unreachable

8/7/2000 Unreachable. Phone disconnected.

8/30/2000 Incomplete/unreachable

6/1/2000 Incomplete/unreachable

5/11/2000 6/13/2000 Incomplete/unreachable

6/6/2000 Incomplete/unreachable

5/12/2000 Incomplete/moved, unreachable

8/15/2000 Unreachable.

6/6/2000 Incomplete/unreachable

9/11/2000 Unreachable.

6/21/2000 Incomplete/unreachable- phone disconnected.

8/7/2000 Unreachable. No phone #.

5/11/2000 6/13/2000 Incomplete/unreachable

Lost 12/31/2000 1/23/2001 1/31/2001 Lost FFQ1.

8/15/2000 Incomplete/unreachable

8/24/2000 Incomplete/unreachable

5/20/2000 6/16/2000 Incomplete/unwilling

8/30/2000 Unreachable.

5/20/2000 7/6/2000 Incomplete/unreachable. 

8/24/2000 Unreachable.

7/5/2000 8/16/2000 Unreachable.

9/27/2000 Unreachable. Had beeper number.

8/19/2000 Unreachable.

6/21/2000 Incomplete/unreachable

6/14/2000 7/11/2000 Incomplete/unwilling- staying in shelter.

Lost 12/13/2000 1/7/2001 1/24/2001 Incomplete/unreachable

5/22/2000 Unreachable.

5/20/2000 7/6/2000 Incomplete/unreachable. 

5/20/2000 7/6/2000 Incomplete/unreachable. 

Participant Tracking 
Calibration of the HSFFQ in African American Children Ages 1 to 5 

FFQ1 Recall1 Recall2 Recall3 FFQ2 Comments

11/29/2000 12/14/2000 12/21/2000 12/23/2000 1/6/2001

11/7/2000 11/26/2000 12/8/2000 12/13/2000 1/6/2001

11/14/2000 11/28/2000 12/8/2000 12/23/2000 1/6/2001

12/1/2000 12/13/2000 12/17/2000 12/21/2000 1/6/2001

11/15/2000 11/30/2000 12/12/2000 12/23/2000 1/6/2001

11/17/2000 12/11/2000 12/18/2000 12/27/2000 1/7/2001

11/30/2000 12/14/2000 12/18/2000 12/19/2000 1/8/2001

11/16/2000 11/28/2000 12/8/2000 12/23/2000 1/8/2001

11/22/2000 12/13/2000 12/19/2000 12/27/2000 1/8/2001

12/4/2000 12/12/2000 12/21/2000 12/23/2000 1/12/2001

11/29/2000 12/13/2000 12/18/2000 12/23/2000 1/12/2001

11/12/2000 11/28/2000 12/9/2000 12/18/2000 1/14/2001

11/20/2000 12/13/2000 12/28/2000 1/2/2001 1/15/2001

11/6/2000 12/5/2000 12/27/2000 1/3/2001 1/16/2001

11/14/2000 11/29/2000 12/12/2000 12/23/2000 1/17/2001

11/29/2000 12/13/2000 12/20/2000 1/2/2001 1/18/2001

12/4/2000 1/3/2001 1/7/2001 1/17/2001 1/25/2001

11/15/2000 11/30/2000 12/13/2000 12/17/2000 1/26/2001

12/6/2000 1/1/2001 1/7/2001 1/18/2001 1/26/2001

12/2/2000 1/1/2001 1/7/2001 1/16/2001 1/28/2001

11/30/2000 12/13/2000 12/18/2000 12/21/2000 1/29/2001

11/20/2000 12/13/2000 12/27/2000 1/3/2001 1/29/2001

12/18/2000 1/1/2001 1/7/2001 1/16/2001 1/31/2001

12/4/2000 12/19/2000 12/22/2000 12/23/2000 2/1/2001

12/27/2000 1/17/2001 1/20/2001 1/22/2001 2/6/2001

1/9/2001 1/21/2001 1/25/2001 1/30/2001 2/6/2001

1/2/2001 1/15/2001 1/24/2001 1/27/2001 2/6/2001

12/29/2000 1/17/2001 1/25/2001 1/28/2001 2/6/2001

12/20/2000 1/2/2001 1/16/2001 1/27/2001 2/6/2001

12/18/2000 1/2/2001 1/7/2001 1/17/2001 2/7/2001

11/15/2000 11/30/2000 12/11/2000 12/15/2000 2/9/2001

1/29/2001 2/6/2001 2/9/2001 2/12/2001 2/23/2001

1/17/2001 1/31/2001 2/12/2001 2/20/2001 2/24/2001

1/16/2001 1/26/2001 1/30/2001 2/4/2001 2/24/2001

1/26/2001 2/1/2001 2/6/2001 2/12/2001 2/24/2001

1/22/2001 2/5/2001 2/9/2001 2/13/2001 2/26/2001

12/28/2000 1/16/2001 1/24/2001 1/27/2001 2/26/2001

1/17/2001 1/31/2001 2/4/2001 2/15/2001 2/26/2001

1/25/2001 2/5/2001 2/9/2001 2/13/2001 2/26/2001

1/20/2001 1/26/2001 1/31/2001 2/7/2001 2/26/2001

1/17/2001 1/30/2001 2/5/2001 2/8/2001 2/28/2001

1/29/2001 2/1/2001 2/9/2001 2/12/2001 2/28/2001

1/19/2001 1/30/2001 2/5/2001 2/7/2001 3/2/2001

1/22/2001 2/1/2001 2/9/2001 2/12/2001 3/8/2001

1/23/2001 2/5/2001 2/9/2001 2/13/2001 3/9/2001

11/20/2000 1/18/2001 1/22/2001 1/24/2001 3/9/2001

1/11/2001 1/17/2001 1/22/2001 1/27/2001 3/9/2001

1/19/2001 2/3/2001 2/5/2001 2/7/2001 3/9/2001

11/6/2000 2/12/2001 2/14/2001 2/21/2001 3/21/2001

Participant Tracking
Calibration of the HSFFQ in African American Children Ages 1 to 5 

2/12/2001 2/20/2001 2/26/2001 3/11/2001 3/21/2001

2/12/2001 2/21/2001 2/26/2001 3/3/2001 3/21/2001

2/26/2001 3/5/2001 3/10/2001 3/14/2001 3/21/2001

2/28/2001 3/4/2001 3/9/2001 3/14/2001 3/21/2001

2/28/2001 3/5/2001 3/9/2001 3/14/2001 3/21/2001

2/2/2001 3/3/2001 3/5/2001 3/12/2001 3/21/2001

2/28/2001 3/6/2001 3/9/2001 3/14/2001 3/21/2001

3/19/2001 3/26/2001 3/30/2001 3/31/2001 4/5/2001

3/21/2001 3/26/2001 3/30/2001 4/1/2001 4/5/2001

11/8/2000 3/25/2001 3/28/2001 4/1/2001 4/5/2001

11/9/2000 3/25/2001 3/28/2001 3/31/2001 4/5/2001

11/8/2000 3/25/2001 3/28/2001 3/31/2001 4/5/2001

11/9/2000 3/26/2001 3/28/2001 3/31/2001 4/5/2001

11/8/2000 3/25/2001 3/28/2001 3/31/2001 4/5/2001

3/26/2001 4/16/2001 4/20/2001 4/24/2001 5/3/2001

3/29/2001 4/16/2001 4/24/2001 4/26/2001 5/3/2001

3/28/2001 4/20/2001 4/24/2001 4/26/2001 5/3/2001

2/5/2001 2/22/2001 3/1/2001 3/11/2001 5/7/2001

3/22/2001 4/16/2001 4/20/2001 4/24/2001 5/9/2001

4/18/2001 5/11/2001 5/14/2001 5/19/2001 5/15/2001

3/29/2001 4/30/2001 5/2/2001 5/4/2001 5/17/2001

4/12/2001 4/26/2001 4/30/2001 5/4/2001 5/18/2001

4/5/2001 4/26/2001 4/30/2001 5/4/2001 5/21/2001

4/2/2001 4/27/2001 4/30/2001 5/4/2001 5/23/2001

4/5/2001 4/26/2001 4/30/2001 5/4/2001 5/23/2001

4/16/2001 5/11/2001 5/14/2001 5/19/2001 5/25/2001

4/17/2001 5/10/2001 5/15/2001 5/19/2001 5/25/2001

4/17/2001 5/14/2001 5/17/2001 5/19/2001 5/25/2001

4/25/2001 5/11/2001 5/14/2001 5/19/2001 5/25/2001

4/12/2001 5/14/2001 5/17/2001 5/20/2001 5/25/2001

5/10/2001 5/29/2001 6/1/2001 6/3/2001 6/13/2001

3/9/2001 5/16/2001 5/18/2001 5/29/2001 6/13/2001

3/14/2001 5/21/2001 5/25/2001 5/29/2001 6/13/2001

5/9/2001 5/29/2001 6/1/2001 6/3/2001 6/13/2001

5/11/2001 5/29/2001 6/1/2001 6/3/2001 6/13/2001

3/12/2001 5/16/2001 5/26/2001 5/30/2001 6/13/2001

4/3/2001 4/27/2001 5/1/2001 5/4/2001 6/14/2001

3/8/2001 5/21/2001 5/26/2001 5/29/2001 6/14/2001

3/8/2001 5/16/2001 5/18/2001 5/20/2001 6/21/2001

4/16/2001 5/10/2001 5/14/2001 5/19/2001 7/11/2001

6/5/2001 6/18/2001 6/21/2001 6/24/2001 8/13/2001

7/10/2001 7/20/2001 7/23/2001 7/28/2001 8/13/2001

6/5/2001 6/19/2001 6/21/2001 6/24/2001 8/13/2001

7/12/2001 7/19/2001 7/22/2001 7/26/2001 8/19/2001

7/10/2001 7/19/2001 7/26/2001 7/28/2001 8/22/2001

5/8/2001 5/29/2001 6/1/2001 6/3/2001 8/23/2001

6/18/2001 7/20/2001 7/23/2001 7/28/2001 8/23/2001

4/16/2001 5/11/2001 5/15/2001 5/19/2001 8/24/2001

7/17/2001 7/20/2001 7/23/2001 7/26/2001 8/24/2001

7/19/2001 8/3/2001 8/8/2001 8/14/2001 8/30/2001

7/10/2001 7/19/2001 7/23/2001 7/28/2001 9/13/2001

8/7/2001 8/17/2001 8/20/2001 8/27/2001 9/14/2001

Participant Tracking
Calibration of the HSFFQ in African American Children Ages 1 to 5 

8/21/2001 9/4/2001 9/9/2001 9/12/2001 9/18/2001

8/28/2001 9/4/2001 9/9/2001 9/12/2001 9/19/2001

8/23/2001 9/4/2001 9/9/2001 9/12/2001 9/19/2001

8/23/2001 9/4/2001 9/9/2001 9/12/2001 9/19/2001

8/7/2001 8/17/2001 8/22/2001 8/27/2001 9/20/2001

8/14/2001 9/4/2001 9/9/2001 9/12/2001 9/20/2001

7/26/2001 8/8/2001 8/12/2001 8/14/2001 9/20/2001

8/16/2001 9/4/2001 9/9/2001 9/15/2001 9/20/2001

8/9/2001 8/17/2001 8/20/2001 8/27/2001 9/21/2001

7/21/2001 8/1/2001 8/5/2001 8/8/2001 9/28/2001

8/14/2001 9/23/2001 9/25/2001 9/29/2001 10/11/2001

8/28/2001 9/20/2001 9/25/2001 9/29/2001 10/15/2001

8/30/2001 9/20/2001 9/23/2001 9/29/2001 10/15/2001

8/30/2001 9/20/2001 9/25/2001 9/29/2001 10/15/2001

12/14/2000 1/3/2001 1/14/2001 1/19/2001

11/29/2000 12/19/2000 12/21/2000 12/23/2000

8/23/2001 9/12/2001 9/16/2001 9/18/2001

2/4/2001 2/19/2001 2/28/2001 3/2/2001

2/4/2001 12/13/2000 12/25/2000 12/27/2000

11/6/2000 12/5/2000 12/13/2000 1/5/2001

1/11/2001 1/21/2001 1/29/2001 1/31/2001

7/24/2001 8/1/2001 8/5/2001 8/8/2001

6/12/2001 6/21/2001 6/23/2001 6/24/2001

8/7/2001 8/17/2001 8/22/2001 8/27/2001

11/27/2000 12/11/2000 1/3/2001 1/15/2001

11/28/2000 Not interested.

11/6/2000 Difficult to reach.

11/6/2000 Never w/child; doesn't know diet.

11/8/2000 7 tries- never answer

1/27/2001 6 tires- doesn't know what kid eats.

2/4/2001 1/3/2001

2/4/2001 2/1/2001 2/23/2001

3/13/2001 Unreachable.

5/9/2001 Wrong #; tried 5 times

11/9/2000

3/15/2001 8 tires; unreachable.

1/3/2001 1/15/2001

3/2/2001 5 tries; unreachable.

11/7/2000 12/5/2000

12/21/2000 Not interested; 8 tries.

1/19/2001 1/26/2001 2/1/2001

3/12/2001 Unreachable.

11/6/2000 ER in family- no time to participate.

11/30/2000 12/15/2000

12/20/2000 # not in service.

11/8/2000

12/27/2000 1/23/2001 1/26/2001

Participant Tracking