Vital and

Health Statistics

Dietary Methodology

Workshopfor the

Third National Health

and Nutrition

Examination Survey

Series 4:

Documents and Committee

No, 27

Reports

The Dietary Survey Methodology Workshop (March 16–1 8, 1986) was sponsored

by the National Center for Health Statistics for the purposes of reviewing,

evaluating, and making recommendations for the selection of dietary

methodologies for the third National Health and Nutrition Examination Survey

(NHANES Ill), Presented are the background papers, consensus statements, and

rationale for the dietary methodologies selected for NHANES Ill,

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Public Health Service

Centers for Disease Control

National Center for Health Statistics

Hyattsvllle, Maiyland

March 1992

DHHS Publication No, [PHS] 92-1464
Copyright Information

Perm}sston has been oota(ned from the copyrtght holders to rep.educe cert~irr

quoted material in llms repot+, Further reproduction of th!s rnaterlal IS p,otl, mtcc!

,wlth out speclf!c permission of the copyright holders All other matertal

contatned In the report IS In the public domain and may be used and reDrln!cd

wthout special permission: cl:at!on as to source, ho’wcver, IS appreclatcc

Suggested Citation

Briefel RB, Sempos CT, eds. Detary methodology workshop for the (h-d

Nat!onal Health and Nutrition Examlnallon Survey. National Center for Hca’ttStatlstlcs Vital Health Stat 4(27) 1992

Library of Congress Cataloging-in-Publication Data

Dietary methodology workshop far !he third Natlo”al Heal!h arid Nu!rlI{or2

Examination Survey (1986: Airlle, Va )

Proceedings of the Dietary Survey Methodology Workshop for the Th, rc

National Health and Nutrition Examination Sutvey/sponsorec by the Nat’onal

Center for Health Statistics.

p cm. – (Vital and health statistics. Series 4, Documents and committee

reports ; no. 27) (DHHS oubllcation ; no. (PHS) 92- 1464)

The Workshop was held in Arlte, Va from Mar. 16 to 18, 1986

Includes b(bllographlcal references

ISBN 0-8406 -0441-6

1. Nutrition surveys-United States Methodology Congresses 2

Diet–Un/ted States–Congresses, 3. Quest\ onnalres. Congresses 4 Food

habits–Unted States–Congresses, 5, National Health and Nutrlt:on

Examination Survey (U. S.) –Congresses 1, Nat!onal Center for Health Stat! st}cs

(U S ) Il. Title Ill. Seres. IV Series: DHHS pubkatlon ; no (PHS) 92 1464.

[DNLM 1, National Health and Nutntlon Exam\ nal!on Survey (US ) 2. D?.la

Collection – methods -- congresses 3 Health Surveys Umted States

congresses 4. Nutrition Survey s- Un!ted States congresses. W2 A N148vd

no, 27]

HA37. U1693 no, 27

[TX359]

362.1 ‘0723 S- dc20

[363 82’0723]

DNLM/DLC

‘or Library of Congress 90 1338

CID
National Center for Health Statistics

Manning Feinleib, M.D., Dr.P.H., Director

Jacob J. Feldman, Ph,D,, Associate Director for AnaZysis

and Epidemiology

Gail F, Fisher, Ph.D., Associate Director for Planning and

Extramural Programs

Peter L, Hurley, Associate Director for Vital and Health

Stat&ics Systems

Robert A, Israel, Associate Director for International

Statistics

Stephen E. Nieberding, Associate Director for

Management

Charles J, Rothwell, Associate Director for Data

Processing and Services

Monroe G. Sirken, Ph.D., Associate Director for Research

and Methodology

David L, Larson, Assistant Director, Atlanta

Division of Health Examination Statistics

Robert S. Murphy, Director

Kurt Maurer, Ph.D., Deputy Director

Ronette R, Briefel, Dr,P,H., Coordinator for Nutrition

Monitoring and Related Research

Clifford Johnson, Chiej Nutrition Statistics Branch

Katherine M, Flegal, Ph.D., Chief Medical Statistics

Branch

Christopher Sempos, Ph.D., ChieJ Longitudinal Studies

Branch

Vicki L. Burt, Chief Survey Planning and Development

Branch

Jean Findlay, Acting Chie$ Survey Operations Branch

Robert Krasowski, ChieJ Computer Systems and

Programming Branch
Foreword

by Catherine E, Woteki, Ph. D., R.D., National Center

for Health Statistics

How to determine what people eat has been a topic of

intense research and debate in recent years, Because the

methods available for use in stuveys rely on the respondent’s

ability to recall and describe what was eaten, they are

subject to errors of omission and commission that can

limit the usefulness of the information for population

estimates and for longitudinal studies of eating habits and

later disease experience.

In 1985, when we at the National Center for Health

Statistics began planning the third National Health and

Nutrition Examination Survey (NHANES III), we took it

as an opportunity to evaluate the methods that had been

used over the preceding 25 years in our various health

examination surveys. We sought the opinions of many

people in the Public Health Service and other government

agencies, in academic research institutions, and in industry

tihout health and nutrition topics to be included in the

survey as well as the specific methods to be used to collect

information, As part of this effort, we sponsored several

workshops to review, evaluate, and make recommendations about what to include in the survey, This report

contains the background papers and consensus statements

from a workshop on dieta~ survey methodology, held in

March 1986, that was a fundamental part of the planning

of the dietary assessment component of the survey,

The workshop participants were faced with the underlying question of choosing dietary methods that would:

● Fulfill the mission of the National Center for Health

Statistics to monitor the nutritional status of the

population.

● Provide baseline data on diet that would be useful in

longitudinal studies of diet and health.

The workshop was organized to address statistical issues

unique to dietary survey design, dietary methods most

appropriate for nutrition monitoring purposes, and those

most appropriate for assessing relationships with energy

balance and three chronic diseases (cancer, cardiovascular

disease, and osteoporosis). Many of the recommendations

stemming from this workshop were incorporated into the

design of NHANES III.

The papers and consensus statements developed for

the workshop and contained in this volume will be useful

to others who are planning surveys or to anyone who is

interested in dietary assessment methods,

..

Ill
Acknowledgments

It is a pleasure for the cochairpersons to acknowledge

the significant contributions of the authors of the papers;

the discussants; Dr. Lenore Kohlmeier, who served as

moderator; and the attendees to the success of the

NHANES III (third National Health and Nutrition Examination Survey) Dietary Survey Methodology Workshop

and to the planning process for NHANES 111, We also

want to acknowledge the contributions of Dr. Manning

Feinleib; the Workshop Planning Committee, namely Dr.

Lenore Kohlmeier, Mr. Robert Murphy, Dr. Catherine

Woteki, Mr. Clifford Johnson, and Dr. Kurt Maurer; and

the many staff of the National Center for Health Statistics

who made the workshop possible, especially Dr. Anne

Looker, Dr. Robert Kuczmarski, Dr. Deborah Winn,

Dr. Stacey FitzSimmons, Mr. Dale Hitchcock, and Ms.

Nancy Hamilton.

We also wish to acknowledge the Publications Branch

of the National Center for Health Statistics for assistance

with the final preparation of this report. Finally, special

acknowledgment is given to Ms. Sharon Stewart for her

patient and professional technical and editorial assistance

during the preparation of this report,

v
Foreword by Catherine E. Woteki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . m““”

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Introduction by Ronette R. Briefel and Christopher T. Tempos,,.....,.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . I

Chapter l, Statistical issues related tothedesign ofdieta~sumey methodolo~ for N~NESIII by Kiang Liz{..,, 3

First discussant's remarks about statistical issues by Hare/d A. Kahn, . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Second discussant’s remarks about statistical issues in design byGeo~e H. Beaten . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Chapter 2. Dietary assessment issues related to cancer for NHANESHI by Gladys Block . . . . . . . . . . . . . . . . . . . . . . . 24

Chapter 3. Dietary methods in cardiovascular disease critique by Patricia J. Elmer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Chapter 4. Dieta~methodolo~ issues related toenerW balance measurement for NmSIII@Dorot@Bbir . . . . . . 43

Chapter 5, Critique ofstudiesof the relationship between diet and osteoporosis by Nancy E. Johnson . . . . . . . . . . . . -5I

Chapter 6. Dietary methodo10gies for food and nutrition monitoring by Elizabeth A. Yetley, Arietta M. Beloian, and

C’hrhtineJ,Lewis . . . . . . . . . . . . . . . . . . . . . . . . ,, .,.,.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

First discussant’s remarks about monitoring by EleanorM. Pao.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

Second discussant’s remarks about State and local perspectives by Susan B. Foerster ..,.,.....,.....,,,. . . . . . 71

Chapter 7, Working group consensus statements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Chapter S. Dietary methodology considerations for NHANES III by Lenore Kohlmeier . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Chapter 9, Process and rationale forselecting dietarymethods for NHANESIII by Christopher T. Sempos, Ronette R.

Briefel, CliffordJohnson, and Catherine E. Woteki . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Appendixes

I. NHANESIII Dietary Survey Methodology Workshop program . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

IL NHANESIII Dietary Survey Methodology Workshop Planning Committee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

111. Directory ofworkshop participants . . . . . . . . . . , !...,,,., . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ,. .,, 93

IV. Outline ofstatistical issues related to NWESIII dietaVsuwey methodolo~ . . . . . . . . . . . . . . . . . . . . . . . . . . 96

v, Outline ofdietary methodology issues for presenters, . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

VI. Prospectus forworkshop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

VH,NHANE SIIIsurveydesign. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

VIII. Measuring dietarypatternsin surveys by Catherine E. Woteki ..,., . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

vii
Symbols

--- Data not available

. . . Category not applicable

Quantity zero

0.0 Quantity more than zero but less than

0.05

z Quantity more than zero but less than

500 where numbers are rounded to

thousands

* Figure does not meet standard of

reliability or precision

,,,

VIII
Dietary Methodology

Workshop for the Third

National Health and

Nutrition Examination

Survey

Introduction

by Ronette R, Briefel, Dr. P.H., R.D., and

Christopher T, Sempos, Ph. D., National Center for

Health Statistics

Background

Planning for the third National Health and Nutrition

Examination Survey (NHANES III) began in 1985 with

the public solicitation of topics to be included in the

survey. Later, as the study’s goals and purposes were

defined and target conditions selected for inclusion in the

study, planning for the nutrition component necessitated

the selection of a methodology or methodologies to be

used for the collection of dietary intake data in

NHANES 111.

In the summer of 1985 initial plans were made to hold

an NHANES III dietary survey methodology workshop for

the purposes of reviewing, evaluating, and making recommendations regarding existing and potential dietary methodologies for NHANES III. Dietary methodologies were

to be evaluated with respect to proposed target conditions

for NHANES III, as well as to nutrition monitoring

activities (l-3) and to the overall objectives of the survey,

which include:

. To assess the health and nutritional status of the

US. population and specific subgroups,

. To monitor changes in health and nutritional status

over time.

o TO provide information on the interrelationship of

health and nutrition variables within population

subgroups,

. To estimate the prevalence of diseases, risk factors,

and health conditions and of changes over time.

. To measure met and unmet care needs related to

target conditions under study.

Initial planning for the workshop involved the selection of major topics or sessions that could feasibly be

covered in a 2–3 day workshop program (appendix 1), The

workshop planning committee (appendix II) identified six

major nutrition areas of interest for the agenda:

. Statistical issues related to nutrition survey design.

● Cancer,

. Cardiovascular disease.

● Energy balance.

● Osteoporosis<

. Nutrition monitoring.

Individual scientists who could provide expertise in one or

more of these areas were identified by the committee and

invited to participate in the workshop (appendix 111).

Although it was not necessary, it was deemed desirable

that invited speakers have some prior knowledge of or

experience with large-scale nutrition surveys such as

NHANES.

During the workshop particular emphasis was placed

on the conceptual, statistical, measurement, analysis, and

research issues that needed to be resolved in order to

design and implement the nutrition component of

NHANES III. Because NHANES III was designed to

produce general descriptive statistics for the U.S. population, it was essential that the nutrition component be

based upon the most current and widely accepted state-ofthe-art dietary methodologies and nutrition assessment.

Thus, experts in the areas of dietary methodologies, nutrition assessment, and statistics were invited to participate

in a workshop designed to discuss, evaluate, and recommend dieta~ methodologies for inclusion in NHANES III.

Structure and scope of work

Each of the six major sessions was designed to have a

working group comprised of a primary presenter, two

discussants, and a National Center for Health Statistics

staff liaison. An outline was provided to each working

group indicating the major issues and nutrients to be

considered during preparation of presentations and papers

(appendixes IV and V). Issues considered by the speakers

and discussants before recommending an NHANES III

methodology included:

. The NHANES III survey design.

. The practicality of the dietary method in terms of

use in a large survey, computer automation, the

length of the interview, and cost.

1
Comparability with previous methods used in

NHANES (4-6) and the Hispanic Health and

Nutrition Examination Survey (7).

The 1984 recommendations of the National

Academy of Sciences’ Coordinating Committee on

Evaluation of Food Consumption Surveys (8) with

respect to linkage with the Nationwide Food

Consumption Survey.

The appropriateness of the method for different

population groups.

Interviewer training requirements,

The ability to provide representative data of an

individual’s usual intake using the method.

The workshop participants were specifically charged

with evaluating dietary methodologies based on the nutrition monitoring and analytic objectives of NHANES III.

The primary presenter was asked to prepare an expert

background paper that would be available for review by

the discussants prior to the workshop. At the conclusion of

the workshop, each working group was asked to prepare a

consensus statement for the group’s particular topic area

that recommended specific dietary methodologies and

strategies based upon the presentations and discussions

that took place during the workshop. In addition,

Dr. Lenore Kohlmeier served as the moderator and guided

discussions throughout the workshop activities.

Dr. Kohlmeier has extensive experience in the planning

and implementation of large-scale surveys and has directed

a national health and nutrition survey “conducted in the

Federal Republic of Germany.

Representatives from government agencies, universities, the food industry, and private scientific foundations

were also invited to attend the workshop. Background

materials for review were sent to all workshop participants

and included an overview of the workshop objectives

(appendix VI), a brief description of NHANES HI (appendix VII), basic outlines used by presenters to prepare their

papers (appendixes IV and V), and a dietary interviewer’s

manual from the Hispanic Health and Nutrition Examination Survey (9), Prior to the workshop background papers

(chapter 6, appendix VIII) were provided to participants

so that they would be aware of nutrition monitoring issues

and past NHANES methods.

This report is organized by each of the six workshop

topic areas on the program’s agenda. The scientific and

technical papers were prepared by the primary presenter.

In addition, formal discussant papers were prepared for

two of the six areas: Statistical issues (chapter 1) and

nutrition monitoring (chapter 6). The consensus statements for each topic area are found in chapter 7. Chapter

8 contains Dr. Kohlmeier’s remarks and commentary on

the state of dietary methodologies.

After the workshop, additional nutrition planning

meetings were held at the National Center for Health

Statistics to take the consensus statements and

recommendations into consideration with respect to the

overall NHANES III planning process. Other publications

by expert panels provided additional recommendations for

consideration during the nutrition and dietary planning

process (10,11). To place these recommendations into

operation involved much planning and testing of dieta~

methodologies and procedures within the NHANES III

framework (2,12). These sumey activities and the resulting

dietary procedures for NHANES III are discussed in

chapter 9.

References

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

U.S. Department of Health and Human Services and U.S. “

Department of Agriculture, Nutrition monitoring in the

United States – a progress report from the Joint Nutrition

Monitoring Evaluation Committee. Washington: Public

Health Service. 1986.

Woteki CE, Briefel RR, KuczmarskiR. Contributions of the

National Center for Health Statistics. Am J Clin Nutr

47:320-8.1988,

Life Sciences Research Office, Federation of American

Societies for Experimental Biology,Nutrition monitoring in

the United States – an update report on nutrition monitoring in the United States, Washington: Public Health

Service, 1989.

Miller HW. Plan and operation of the Health and Nutrition

Examination Survey, United States, 1971-1973. National

Center for Health Statistics. Vital Health Stat l(lOa), 1978,

National Center for Health Statistics. Plan and operation of

the Health and Nutrition Examination Survey,United States,

1971-1973.National Center for Health Statistics,Vital Health

Stat l(lOb), 1977.

McDowell A, Engel A, Massey JT, Maurer K. Plan and

operation of the second National Health and Nutrition

Examination Survey, 1976-80. National Center for Health

Statistics. Vital Health Stat 1(15), 1981.

Maurer KR. Plan and operation of the Hispanic Health and

Nutrition Examination Survey, 1982-84, National Center for

Health Statistics. Vital Health Stat 1(19). 1985,

National Academy of Sciences, National Research Council,

Coordinating Committee on Evaluation of Food Consumption Surveys. National survey data on food consumption:

Uses and recommendations, Washington: National Academy

Press. 1984.

National Center for Health Statistics. Dieta~ interviewer’s

manual for the Hispanic Health and Nutrition Examination

Survey, 1982-84. Instruction manual, part 15f, Hyattsville,

Maryland: National Center for Health Statistics. 1986.

Life Sciences Research Office, Suggested measures of nutritional status and health conditions for the third National

Health and Nutrition Examination Survey, Bethesda, Maryland: Federation of American Societies for Experimental

Biology. 1985.

Life Sciences Research Office. Guidelines for use of dietary

intake data. Bethesda, Maryland: Federation of American

Societies for Experimental Biology. 1986,

Harris T, Woteki C, Briefel RR, ICleinman JC. NHANES

III for older persons: Nutrition content and methodologic

considerations. Am J Clin Nutr 50:1145-9.1989,

2
Chapter 1

Statistical

related to

issues

the design of

dietary survey

methodology for

NHANES Ill

by Kiang IA, Ph.D., Department of Community Health

and Preventive Medicine, Northwestern University

Medical School

Introduction

Several dieta~ factors have been evaluated for their

impact on certain diseases or the risk factors of the

diseases, For example, it is hypothesized that high fat

intake increases, and high fiber intake decreases, the risk

of colon cancer. Saturated fat and dietary cholesterol

intakes have been shown to increase serum cholesterol,

and polyunsaturated fat has been shown to reduce serum

cholesterol, In addition, sodium intake is reported to be

positively, and potassium and calcium intakes negatively,

associated with blood pressure. In studies of these relationships, a major concern is the possible attenuation of

the associations caused by limitations of existent dietary

survey methods and improper uses of dietary data, This

paper discusses the statistical issues associated with the

dietary data collected by different survey methods,

Methods used in specific survey

designs

Many methods are used in epidemiologic studies to

collect dietary information. The dietary survey method is

usually developed to fit the need of the specific study

design, For a case-control study, the purpose is to recall

the usual dietary pattern in the past (before the onset of

the disease, it is hoped), Therefore, frequency or history

methods are commonly used in this type of study (1,2).

For example, Dales et al. compared the frequencies of

consuming fiber-containing foods for colon cancer cases

and normal controls (l), and Lubin et al, compared the

average fat intakes for breast cancer patients and controls

(2).

For a cross-sectional or a prospective study, the goal is to

collect information on the current diet or the dietary habit in

the immediate past, Thus, food records, 24-hour recalls, and

frequency methods are used (3-8). In the second National

Health and Nutrition Examination Survey, both the 24-hour

recall and the food-frequency method were used to collect

dietary information (3), Both the Framingham Study and the

Western Electric study derived a quantitative method from

the Burke Dietary History (4,5). The former method focused

on five specific nutrients related to cardiovascular diseases.

The latter was a very detailed quantitative food-frequency

method based on the usual dietary intake in the past 28 days.

The Hawaii study examined the relationships between several

nutrients and coronary heart disease using single 24-hour

recall data (6). At the second examination, 7-day food records

were also used to collect the dietary information in a subsample (7). The study of Seventh-Day Adventists employed

the frequency method for dieta~ data collection (8),

Accurate assessment of the nutrient composition of

current diet is of importance in clinical trials and dietary

intervention studies. Thus, multiple food records and

24-hour recalls are commonly used in these studies. For

example, the National Diet-Heart Study, the Chicago

Coronary Prevention Evaluation Program, and the Hypertension Control Program used 7-day food records, and the

Multiple Risk Factor Intervention Trial and Lipid Research

Clinic studies used single 24-hour recalls tq collect dietary

data (9–13). These are just a few examples of the dietary

survey methods used in epidemiologic studies. Each of

these methods has many different variations, For example,

24-hour recalls can be done by personal interview with

three-dimensional food models or by telephone interview

with or without paper food models (14,15). The frequency

method is actualIy a family of many different methods

based on the frequencies of consuming certain food items.

Food intake can be assessed by personal interviews or by

self-administered questionnaires (5,16–19), Some of the

methods require information on portion size and the

others assume a standard portion size for each food item

(5,16,17). Moreover, some of the questionnaires include a

very thorough detailed list of food items or groups; the

others may focus only on the frequencies of the food items

or groups containing certain specific nutrients (16,20),

Even though there are many dietary survey methods,

from a statistical point of view, these methods can be

classified into two categories:

1.

2.

Daily consumption methods –This category includes

all the methods that use 1 day as a time unit, for

example, single or multiple food records or 24-hour

recalls.

Frequency methods –This category includes all the

methods that ask participants to recall their usual

3
dietary intakes during a time period in the past –the

last month, the last year, or several years ago.

Statistical issues associated with dietary survey

methods

For most of the studies involving dietary surveys, the

underlying assumption is that people usually maintain

their dietary habits during a target period of time. For

example, a case-control study of the relationship between

a dieta~ factor or factors and a disease assumes that the

disease cases maintained the dietary habits that they had

before the onset of the disease. The study can then test

the hypothesis that the habitual diets of the cases are

higher or lower in the dietary factors of interest than those

of the controls, Cross-sectional studies generally assume

that the current dietary habits have been kept for a long

period of time, and cohort studies further assume that the

dietary habits will be continued in the future. In other

words, during the time period of interest, there is no

change in the mean of each of the dietary factors, and the

day-to-day variation of each dietary factor is just the

random variation about its mean. For most of the studies,

this basic assumption is made implicitly. In some cases, the

assumption may not even be reasonable because changes

in dieta~ habits have occurred, Under this basic assumption, the two types of dietary survey methods have been

used in the past to assess an individual’s habitual dietary

intake. The basic characteristics of these two types of

methods are summarized in table 1.

Daily consumption methods

The daily consumption methods are usually based on

recording or weighing the food consumed in a day or based on

recalling the dietary intake in the previous 24 hours. Thus, the

data are relatively more accurate than data from the other

methods, However, a problem arises if an inadequate number

of records or recalls are used to assess an individual’s habitual

intake. The diet of an individual varies from day to day, It has

been shown that for most dietary factors, such as dietary

cholesterol and fat intakes, the day-to-day variation within an

individual (intraindividual variation) is much larger than the

variation in mean intakes between individuals (interindividual

Table 1, Characteristics of the 2 categories of dietary survey methods

variation) (21–23). Thus, the ideal way to estimate the average

dietary intake of an individual is to randomly select a large

number of days over the target period of time and then

average the intakes of the dietary factors on these days.

Unfortunately, most of the studies used the intake of only

1 day or a few days to estimate the mean intake, As a

consequence, the large intraindividual variation and the inadequate number of daily measurements may attenuate the

potential relationships between dieta~ factors and biological

risk factors or diseases (21–24). As discussed later in the

paper, the attenuation is more serious in analyses based on

individuals and relatively less serious in analyses based on

groups.

Frequency methods

The frequency methods directly estimate the average

intake during the target period, Therefore, intraindividual

variation is not a problem. These methods are generally

easier than the collection of a large number of food

records or recalls. However, the methods may suffer from

the problem of inaccuracy. In some studies, a person has

to report his or her usual dietary pattern during a target

period of time in the remote past, Because of the limitation of a person’s memory, the estimation is likely to

reflect the dieta~ habit in the recent past, It is possible

that the more remote the target period is in the past, the

less accurate the estimation is. Several studies are currently ongoing to examine the ability to recall the dietary

pattern in the remote past. The results of these studies

should provide a better understanding of this problem.

In addition, as it is very difficult for a person to

actually recall the food eaten at each meal, even for the

recent past, the methods generally use questions on the

usual dietary pattern. Thus, the number of meals covered

by the reported survey is very unlikely to be the same as

the number of meals actually taken during the target

period, and the reported food is not exactly the same as

the food actually eaten during the period. As a consequence, the values of each dietary factor generated from

frequency methods may not be very meaningful, Several

studies have reported that frequency methods tend to

overestimate intakes (25,26). When the study goals rely

heavily on the accuracy of the actual value of each dietary

Variable Daily consumption methods Frequency methods

Observation unit Based on recording foods consumed in a day or recalling actual intake in Based on recalling usual dietary pattern

prevloue 24 hours in target period of time

Estimation method Averaging a number of measurements to estimate the true mean intake of a Directly estimating the true mean Intake

dietary factor

Accuracy of data Relatively more accurate Relatively less accurate

Meaningful absolute value Yes No

Preserving rank order Yes, if a large number of measurements are used Maybe

Problems caused by intralndividual Yes No

variation

Seasonal variation taken into Yes, if it is designed properly (usually very difficult) Yes, if it is designed properly

account

Inclusion of infrequently consumed Likely to miss if the number of measurements is small

food

Yes

Feasibility Relatively more difficult if a large number of measurements are needed Relatively easier

Major problem Intralndividual variation and inadequate number of measurements Inaccuracy of the method

4
factor, these methods may not be appropriate. For example,

when there is a predetermined level for the deficiency of a

dietary factor, the use of frequency methods is likely to

have many false negative errors.

Despite these problems, these methods can be a

useful tool in some cases, If the composition of the diet

could be preserved (for example, the reported percentage

of calories from saturated fat were the same as the true

percentage), the analyses using the value of a dietary

factor expressed in terms of percent of calories or per

1,000 calories would still be valid. In addition, if the rank

order of each dietary factor in the population remained

the same as the true rank order, the data analyzed by

ncmparametric techniques would generate valid results.

Therefore, for a frequency method, the most critical issue

is whether it can preserve the composition of each person’s diet or the rank order of the population distribution

of each dietary factor. When a new version of the methods

is developed, it will probably be necessary to demonstrate

the agreement of the rank order of each important dietary

factor between the new method and some other, more

accurate methods, such as an adequate number of food

records or 24-hour recalls.

Effect of intraindividualinterindividual variances

Definition

For the purposes of this paper, terms are defined as

follows, True mean – For the individual, the true mean of a

dietary factor is the hypothetical average from which he or

she varies during a period for which a habitual dietary

pattern is maintained. Intraindividual variation –This is

the variation of the individual’s dietary intake from his or

her true mean intake. Interindividual variation-This is the

variation among the true means of individuals within a

population, Variation caused by inaccurate instrument –

This is the variation of the individual’s observed mean

intake from his or her true mean intake caused by the

inaccuracy of the survey instrument (for example, frequency method),

Basic assumption

It is assumed that the true mean is constant during the

study period for each individual; in other words, that the

variation within each individual’s intake represents random

variation from his or her true mean and is not due to a

change in habitual dietary pattern.

Daily consumption methods – Let X be an individual’s

true mean for a dietary factor during the period of

interest, For i = 1, . . . . k, let Di be the observed value of

this factor at time ti. Because of intraindividual variation,

the observed values are given by

D~=X+ei, i=l, ... ,k (1)

where ei is the departure of the observed value from the true

mean X because of intraindividual variation at time q. Let p

be the mean of X within the population and let q? denote the

variance of X (~ being the interindividual variance). It is

assumed that each individual has the same intraindividual

variation and that ei has mean Oand variance ~ (< being the

intraindividual variance). Furthermore, for i # j, ei, ej, and X

are assumed to be independent random variables. As the

individual’s true mean for the dietary factor is not observable,

X is estimated by

~ = Zik=l Di/k

Frequency methods – Let X be an individual’s true

mean for a dietary factor during the period of interest. Let

D be the estimated value of the dietary factor generated

from a frequency method. Then

D=X+e (l)’

where e is the departure of the estimated value from the

true mean X because of inaccurate estimation. Again, let

u; be the interindividual variance, that is, the variance of

X. It is assumed that e has a mean ~e and variance u: (p.

being the systematic bias caused by inaccurate estimation

of the frequency method and u~being the variance caused

by inaccurate instrument). The variables X and e are

assumed to be independent.

Materials and methods

Some empirical results will be given to illustrate the

ideas. The data used are from two sources: Baseline 7-day

food records from the Chicago Coronary Prevention Evaluation Program (CPEP) and baseline 7-day food records

from the Primary Prevention of Hypertension (PPH) study.

CPEP was a pilot field trial on the primary prevention of

coronary heart disease by nutritional-hygienic means in

519 high-risk, coronary-prone men originally aged 40-59

years (10). The PPH study is an ongoing, randomized,

controlled trial undertaken to test the ability of nonpharmacologic intervention to influence blood pressure in 200

individuals aged 30–44 years assessed to be hypertension

prone (11).

Analyses of individuals

Simple correlation–Let Y be a random variable (such as

a biological risk factor, say, serum cholesterol) and Y and X

have a bivariate normal distribution with parameters pm, I.Ly,

~, LLX,and ~. The variable Yand ei,i = 1,. . . . k, are assumed

to be independent, The correlation coefficient pm has to be

estimated from the data. As the individual’s true mean for the

djeta~kfactor is not observable, X is estimated by

D = xi= ~Di/ k. The actual correlation coefficient estimated

is then

where

C2 = 1

1 + (cr;/kcr:) (3)

5
or

~2 = 1

1 + (u:/ IT;) (3)’

The constant c has different meanings for the two

different types of methods. For the daily consumption

methods, c is the correlation coefficient between the

average value of the k measurements and the true mean

value. For the frequency methods, the value c is the

correlation coefficient between the estimated value (from

the frequency) and the true mean value. A simple example

will help to illustrate the idea, For daily consumption

methods, U:/ u; = 3 and the use of l-day measurement to

characterize an individual’s dietary intake will result in

c =0.5. On the other hand, for frequency methods, c =0.5

means that the correlation between the estimated value

and true mean value is 0,5. In both cases, even though the

sources of the attenuation of association (see details later)

are different, the degrees of the attenuation are exactly

the same.

For simplicity, unless it is specifically stated, the

remainder of the paper will focus on the error caused by

intraindividual variation (based on formula (3)). It should

be understood that the impact of measurement error

caused by an inaccurate instrument on various statistical

analyses is exactly the same as that of intraindividual

variation.

Because c is always less than 1, PYD< p= When the

ratio u;/ ~ is very large and k is small (for example,

k = 1), c can be much smaller than 1 and the correlation

is likely to be seriously underestimated. The ratio of the

intraindividual to interindividual variance can be expressed

as

u; l-p

12 — =—

~z

r P12

where plz is the correlation coefficient between any two

measurements for the dieta~ factor, With use of the

sample correlation coefficient rlz to estimate plz, the ratio

of the variances can be estimated by

;: 1 – rlz

— .—

u; rlz (4)

The constant c can be estimated by substituting formula (4) in formula (3), These formulas have been published previously (21), Table 2 provides the estimated

ratio of the intraindividual to interindividual variances for

several dietary variables, The ratios estimated from the

PPH study are, in general, greater than the corresponding

ratios in the CPEP study, The ratios for protein, total fat,

saturated fat, polyunsaturated fat, and dietary cholesterol

are all greater than 2, with the ratios for the last two being

close to 5.

Based on these ratios, the possible attenuation of the

association between each of these variables and risk factors

6

can be estimated. It can be seen that, using one measurement to characterize an individual’s dietary intake, except

for total calories, c values are all less than 0,60, For

polyunsaturated fat and dietary cholesterol, c values are

about 0,40; in other words, on the average, the correlation

will be reduced by about 60 percent because of the large

,intraindividual variation if only a l-day food record or

single recall is used to assess the individual’s dietary

intake.

Simple linearregression– Let Y, X, and ~ be defined

as in “Simple correlation, ” Let Y be the dependent variable and X be the independent variable. Let PI be the

regression coefficient of Y on X and 13~be the regression

coefficient of Y on ~. As X cannot be observed and has to

be estimated by ~, the actual regression coefficient estimated is

(5)

where c 2 is defined as in “Simple correlation” (27), Again,

the constant c 2 can be estimated by substituting formula

(4) in formula (3). Table 2 provides the estimated value of

c 2 for each of the dietary factors. The values of c 2 range

from 0.16 to 0.41; in other words, the percent reductions

of regression coefficients range from 59 percent to 84 percent if one measurement is used in the study.

Partial correlation–Let Y, X, and ~ be defined as

before and let ZI , . . . . Z,z be random variables such that

Y,x, zl, . . ., Zn have a multivariate normal distribution,

Case 1: Assume that the controlled variables ZI ,, , , ,

Z. do not have intraindividual variation. The partial

correlation

Pfi. z z= Pm. z z“c”cl. ..cn

1

,. .,,” 1

,.. .>)I (6)

where fori= l,,,., n, and Pxzi . ZO= P.Yz,

l_. p2

c? =

Xzi. z,, . . ..zl.l

1._p2 22

Xzi. z,, . . ..zt.c. c .C1. .. l-l (7)

and C2is defined as in “Simple correlation,” It can be seen

easily that if ~ > 0, then Cj c 1, for i = 1, . . . , n, Thus,

if the variables Z 1, . . . . Z,, do not have intraindividual

variation, the partial correlation PYDz, , . .

. >z,, actually

estimated is always weaker than the correlation coefficient

of interest, that is, pm. z, , , . . . z,,. The larger the number

of the controlled variables is, the larger the reduction will

be. The detailed discussion of this type of attenuation was

given in a paper by Liu (27).

Hypothetical example 1: Suppose that the ratio of the

intraindividual to interindividual variances for dietary cholesterol is 3, and only one measurement is used to characterize

an individual’s intake. Thus, by formula (3), c 2 = 0,25, Let Y

denote an individual’s serum cholesterol and X and D denote

the true and the observed mean dieta~ cholesterol intakes,

respectively. Let Z denote the weight of an individual that

does not va~ from day to day. Assume that Pm = 0.6,

Pw = 0.4, and pm = 0.6. It can be computed easily that
Table 2. Estimated ratio of Intraindividual to Interindividual variances

Chicago Coronaiy Prevention Evaluation Program Primary Prevention of Hypertension Study

@

Number of

g

Number of

measurements measurements

Variable T c @ required ~ c &’ required

Total calories (kilocalories per day) . . . . . . . . . 1.5 0.64 0.41 4(6) 2.0 0.57 0.32 5(9)

Protein (mllllgrams) . . . . . . . . . . . . . . . . . . . 2.2 0.56 0.31 6(9)

Total fat(percent ofcalorles) . . . . . . . . . . . . . - 2.6 0.53 0.2s 7(1 1)

Saturated fat(percent ofcalorles) . . . . . . . . . . 2.1 0.57 0.32 5(9) 2.7 0.52 0.27 7(12)

Polyunsaturated fat(percent of calories) . . . . . . 4.4 0.43 0.18 11(19) 5.2 0.40 0.16 14(22)

Dleta~choleeterol (mllllgrams). . . . . . . . . . . . 3.0 0.50 0.25 6(1 3) 4.9 0.41 0.17 13(21)

Dietary calclum (mllllgrams). . . . . . . . . . . . . . 1.9 0.59 0.35 5(8)

NOTE:Onlv 1 measurement used for c. Number of measurements required ars number needed to bmit(l-c) to lessthsn 15 percent snd (shown in parentheses) number needed to timit(1-c) to less

than 10 peicent.

P~x. z=0,491, On the other hand, pm,== Pm. z COCI

=0,491. 0,5.0,935 =0.230. Thus, the true correlation is

reduced by 53,2 percent.

Case 2 Assume that the correlated variables Y and Z and

the controlled variables Zi,i = 1,..., n have intraindividual

variation. The impact of a large ratio of intraindividual to

interindividual variances on partial em-relation does not follow

any pattern, For simplicity,only the first-order partial correlation

coefficientwillbe discussed.Suppose that the variable Z cannot

be observed directly and has to be estimated by ~ = ~=1 Gj,

where forj = 1 ,...,s, Gj =2 +Izr Moreover, assume that for

j=l,..,, s, hj are independent identically distributed normal

random variables with mean Oand variance O& Let ~z denote

the variance of Z. Then,

/

(v P- -=C Pm. z (1-CL) (l-&z) +

YD, G

(1-&d2)(l-&2d2)

P=. Pxz(l -d 2,

~(1-p) (l-p;zc’d’)

where c is defined as before and

~z= 1

1 + (a;z/.$ C&)

(8)

Again, PYE. ~ can be either less than or greater than

P}:Y,~ depending on the other parameters. This property

has been discussed by several researchers (27-30).

Hypothetical example 2: Let pm = pm = f’Xz = 0.5,

C2 =0,9, and d’ =0.5. Then p= . z =0.333 c PYE . E

= 0,403. Let PYX= Pyz = PXZ =0.5, c’ =0.25, and

d2 =0.9, Then, p=. z = 0.333 > PYE. z = 0.161.

Multiple linear regression

Let Y, X, Di, i=l, . . ..k. and Zj, j =1, . . ..nbe

defined as in “Partial correlation.” Let (31be the partial

regression coefficient of Y on X controlled for 21, t . . . Z)!.

Let @~ be the partial regression of Y on D controlled for

z ~,. ,., Zn. Then fori=l, . . ..rz.

PI=P1’C2’C?...C: (9)

where c? is defined by formula (7). Similar to the uartial

correlation coefficient, if 21, . . .‘. 2. do not have i;traindividual variation, the regression coefficient ~; actually

estimated is always smaller than the regression coefficient

of interest, that is, ~ ~,Again, the larger the number of the

controlled variables is, the larger the reduction will be

(28).

Hypothetical example 3: Under the same assumptions

given in “Hypothetical example l,” the reduction of the

regression coefficient 131is 100 (1 – c’ cc:) = 78 percent.

The coefficients of the other independent variables do not

follow any specific pattern. The value can be either

increased or decreased depending on the other parameters. Similarly, if the other independent variables also

have intraindividual variation, the regression coefficient

13~ can be either less or greater than @,, More detailed

discussion was given in the paper by Liu (27).

Multiple logistic regression (linear discriminant

analysis)

The approach suggested by Walker and Duncan uses

the Newton-Raphson Iteration Method to compute the

maximum likelihood estimate of the logistic regression

coefficients (31). Therefore, the potential attenuation

cannot be clearly demonstrated. In this section, only the

approach based on multivariate normal assumptions is

discussed (32). Because the estimates of the coefficients

are the estimates for the linear discriminant function, the

results discussed in this section also apply to linear discriminant analysis. Let XII , . . . . X1l be the dietary and/or

risk factors of the disease patients and X21 ,. ... XX be the

dieta~ and/or risk factors of the nonpatients. Assume that

the vectors (Xl 1, . . . . Xll) and (X2], ..., XX) have multivariate normal distributions with mean vectors and variancecovariance matrices (Ul, E) and (Uz, ~), respectively. Let

W=ul–uz=(lq, ..., WI). Then the regression

coefficients

P = (P1,..., (3,)= WX-l

Suppose that, because of intraindividual variation, XII

and Xzl cannot be observed directly and have to be

estimated by Dll = 2$= 1Dllj I k, and & = ~~= 1Dzlj I k

where forj = 1,. ... k,

7
Dllj = X1l + ellj and D21j = X21 + ezlj

Assume that ellj and ezlj,j =1, . . . ,k are independent

normal random variables with mean Oand variance u: and

are independent of X1l , . . . . X1l and X21, . . . ,X2. Let

(3~ denote the logistic coefficient based on Dll and D21.

Then

p;= fil”cz”c; . ..c. (lo)

where C2is defined as in formula (3) and

l-p72 and c; = l-Pt .2...1-1

c: =

l-p:2 C2 , ~2. ~2. l-p~i .2...i_] 2 , “tl

for i =3 ,. ..,1 and ~,[2...l-l is the partial correlation

coefficient between Xll and Xli given X12 , . . . .Xli _ 1.

Moreover, the odds ratio

is also reduced, It can easily be seen that c 2 < 1 and

c~<lfori =2,..., 1. Thus, the larger the number of

the variables is, the more serious the attenuation will be.

The impact of measurement error on logistic regression

analyses has been discussed in detail elsewhere (33),

Hypothetical example 4 Suppose that in a prospective

study one wants to study the univariate relationship between

the baseline dietary cholesterol intake and 12-year mortality

from coronary heart disease (CHD). Assume that the true

coefficient is 0008 (that is, for every 100-milligram increase in

dietary cholesterol intake, the odds ratio for CHD would

increase 2.2), Assume further that the ratio of intraindividual

to interindividual vatiances for dietary cholesterol intake is 3,

and only one 24-hour recall is done to measure the dietary

cholesterol intake, Thus, C2= 0,25, As a consequence of the

large intraindividual variance, the coefficient reduces to ~ ~=

0.008 . 0.25 = 0002 and the odds ratio for CHD corresponding to a 100-milligram increase in dietary cholesterol

intake reduces to 1.22. Suppose that one wants to adjust for

age in the logistic analysis, Assume that the partial regression

coefficient PI = 0,008 and that the correlation between dietary

cholesterol and age is 0.4. Then C2 = 0.875 and (3~ = 0.008.

0.25 . 0.875=0.00175. The odds ratio corresponding to a

100-milligram increase in dietary cholesterol is reduced to

1 10

.L.l Y.

When the other variables also have intraindividual

variation, P ~ does not follow any pattern. The value ~ ~

can be either less or greater than PI depending on the

other parameters. Consider the case of two variables. Let

Xll and X12 be the two variables for disease patients and

X21 and XZ2be the two variables for nondisease subjects.

Suppose that Xll, X12, X21, and X22 cannot be observed

directly and have to be estimated by Dll, D12,D21, and D22,

respectively, Let C2 be the attenuation factor corresponding to ~1~ and ~zl, and d 2 be the attenuation factor

corresponding to D12 and D2Z,Then

P; =

C2 [Jq – (p.@12(crld2 / 0’2))]

u;(l-p212’c2 “dz)

and

[i-% - (P2P12(U1 / @2))l

& =

cr~(1 – p;2)

where ml and rJ2 are the standard deviations for XII and

X12, respectively.

Hypothetical example 5: Suppose that p12=0,5, C2= 0.9,

d2 =0.25, andul =cr2 =1. If WI =~ =1, @; =0.834 >pl

=0.667, If PI =1 and p,2 =0,5, ~ ; =0.894 < PI =1.

Related issues

In formulas (2) and (6), if Y also has intraindividual

variations, the correlation will be further reduced. The combined impact can be measured by multiplying the attenuation

factor (or factors) from Y to formulas (2) and (6),

It should be pointed out that the attenuation discussed in each anaIysis is the theoretical attenuation on

the parameters (for example, p or ~) caused by a large

ratio of intraindividual to interindividual variances (or by

the measurement error of tfie instrument). The estimated

values (for example, r or P) are also affected greatly by

sampling or other types of errors. Thus, the actual reduction in the correlation or regression coefficients may be

different from the theoretical reduction.

It is important to discuss the impact of the potential

attenuation on each statistical test. For simple correlation

or partial correlation analysis testing whether Pm = O or

PXY” z,... ,

z = O, the test statistic is

‘=&

or

rY5 . 2[ ...2.

t=

l–r~D. z.. ez IS

n–s–2

(12)

(13)

where n is the sample size and s is the number of

covariates. Suppose that the attenuation for r is the same

as the attenuation for p, that is, ry~ = r ~x. c or

r~-. 21. . .ZS=r~. z,. ..z, c.c~. ..c~. (In reality, because

of sampling or other types of errors, this may not be the

case.) Then the corresponding reduction in the t value is

even larger than that for the parameters. As a consequence, the power of the test will be seriously reduced.

When the sample size is very large, the denominators in

formulas (12) and (13) become very small. The statistical

tests still could be significant. However, no matter how

large the sample size is, the correlation is still attenuated.

In this case, unless p is very large, a very small but

significant estimated value is likely to be obtained.

For simple and multiple linear regression analyses,

because the test for Ho: (3 = Ois equivalent to the test for

lYO:p= O, the above discussion can also be applied to

regression analyses.

8
For multiple logistic regression,

where & is the’ estimat~d value for 91; u~l is the

element in the first row and the first column of the inverse

matrix of the variance-covariance matrix V for the vector

(~ ~1,Xlz, , ., Xl, ) and ~ ~lthe corresponding estimate; N]

and N2 are the number of patients and nonpatients,

respectively. The test statistic is

z= b;

@-v(-,++.J

It can be shown that the potential attenuation for the

zvalueisc, c2 .,. CPIf PI is very large and both NI and N2

are very large, the test still can be significant, However,

the regression coefficient remains attenuated. In reality,

the number of patients is likely to be small; thus, the

potential attenuation of the test statistic is likely to be

large.

It is possible to estimate the potential attenuation for

simple correlation and regression analyses and to estimate

the number of measurements needed.

For correlation, one can estimate the number of

measurements by the formula

,_ c’ g ,—

h (1 -c’) U: (14)

where (1-c)1OOis the predetermined amount of attenuation.

Table 2 provides the needed number of measurements for c 20,85 and for c 20.90 based on the CPEP

and PPH data. For most dietary factors, 7 to 14 daily food

records are needed for c > 0.85.

For simple linear regression, the number of measurements can be estimated by

k

~

‘- ‘ *;

where (l-c) 100 is the predetermined amount of attenuation.

Analyses of groups

Ady.sis of variance –A large ratio of intraindividual

to interindividual variances can seriously reduce the power

of the statistical test in analyses of variance. For

i =1, ,.. , s,andj= l,..., ni, let Xv be independent

normal random variables with mean Ki and variance <.

Suppose that Xv cannot be observed and has to be

estimated by ~U, j, where Dy = Z $= ~DUW/ k, the average

of k observed values for Xv, Again, let U: denote the

intraindividual variance. Under a set of the assumptions of

normality and independence, the power for testing

HO:Tj=Lj-I_L, =0, i= 1,,, .J against H~:7i # O,for some

i, is

Power = P{ F*> F(l-a; s-l, ziL1ni -s) +}

where F* has a noncentral F distribution with noncentrality parameter

and degrees of freedom s – 1 and xi 5, ni –s (34).

Because ~ cannot be observed directly and has to be

estimated by Dti., the noncentrality parameter is

+’ =@..

As ~’ < & the power is reduced.

Hypothetical example 6: Suppose that s = 2, xi ~1

ni –s = 10, u = 0,05, @ = 3, and c = 0.5. The power corresponding to @ = 3 is 0.983 (34). The power corresponding to @ = 1.5 is 0.50.

Estimation – A large ratio of intraindividual to interindividual variances can also have an impact on estimation

of parameters, Let Xj and ~j be defined as usual. Again,

assume a set of standard assumptions of normality and

independence. Based on Xj, the 100 “p percent confidence

interval for the mean p is

x?z(l+p)/2vq7yBased on D ; , the confidence interval for p, is

~ ~’ql+p)/2 w

where z(l +P) I z is the 100(1 + p) / 2th percentile of the

standard normal distribution.

Because c <1, the length of the confidence interval is

increased. However, if the sample size n is very large, the

length of the interval will be very small, even with the

factor I/c,

Misclassification – Many statistical problems are caused

by misclassification. If the variable that has intraindividual

variation is used for classi~ing individuals into different

categories, a proportion of the individuals will be misclassified. Based on the same assumptions given in “Simple

correlation,” the probability of misclassifying an individual

whose true mean intake is in the lower 100.p percent of

the distribution of X into the upper 100”p percent of the

distribution based on the observed value ~ is

P (Z2>z~_pz~<Zp)

where ZI and 22 are two standard normal random variables having a joint bivariate normal distribution with

correlation coefficient

PZ,Z2= c

and ZP is the 100.pth percentile of the standard normal

distribution.

Hypothetical example 7: Individuals are classified into

the low and the high level of dietary cholesterol intake

based on one measurement. Suppose that c = 0,5. Then

P(Z2 >0 [ZI < O) = 0.333. That is to say, 33.3 percent

of the individuals in the low level are misclassified into the

high level and 33.3 percent of the individuals in the high

level are misclassified into the low level.

9
Measure of risk in cross-sectionalstudy–Both the risk

ratio (relative risk) and odds ratio can be attenuated by

misclassifying individuals into wrong categories. For i,

J‘= 1, 2, let Pi be the probability that an individual is

classified into the ith category of the dietary factor and the

jth category of the disease status, and P.l, P.2,Pi., and P2.

be the marginal probabilities, wherei= 1 if high group

and i =2 if low group; j =1 if disease and j =2 if no

disease. Let PL be the conditional probability that an

individual is misclassified to the high group given that the

person is in the low group and let PH be the conditional

probability that an individual is misclassified to low given

that the person is in high. Table 3 provides the expected

number in each cell with and without misclassification.

Without misclassification, the risk ratio R and odds ratio O

are

and

With misclassification, the risk ratio RM and odds ratio 0~

change to

[Pll (1-PH) + f’n p L]/pL

RM =

[PIIPH + PZI(1-PL)]/p2.

and

[Pll (l-pH) + P21PL] [P12pH + P,, (1-PL)]

o~ =

[PllPH + P21(l-pL)][P,2(l-PH)+ P22PL]

It can be seen easily that if R >1, RM c R and 0~ c O.

Hypothetical example 8: Consider table 4, which deals

with probabilities. Suppose that c = 0.5. Based on formulas given in “Cross-sectional study,” PL = 0.1418 and

PH = 0.5674. Then RM = 1,25, 0~ = 1.40, R = 2, and

0=3.

Measureof risk in cohortstudy–Let NI a~d Nz be the

numbers of individuals in the high and low groups at

baseline, respectively. Let PI and P2 be the probabilities of

Table 3. Expected number in each cell without and with

misclassification

Disease

D/etafy factor Total Yes No

Without misclassification

Total. ,. ..,. N NP1. N P.2

High . . . . . . . NP,. NP1, NP,2

Low . . . . . . . N P2. N P2, N P22

With misclassification

Total...,... N N P., N P.2

High . . . . . . . NP,. N [P,, (1-PJ + P21PJ N [P,2 (l-PH) + P22PJ

Low . . . . . . . N P2. N [P1lPH + P21 (1-PJ] Iv [P,*PH + P,, (1-PJ]

Table 4. Probability of cross-classification into the cells

Disease

L7ietaiy factor Yes No Total

High . . . . . . . . . . . . . . . . . . . . . 0.1 0.1 0.2

Low. . . . . . . . . . . . . . . . . . . . . . 0.2 0.6 0.6

Total . . . . . . . . . . . . . . . . . . . . . 0.3 0.7 1.0

developing the disease in the high and low groups after t

years of followup. Moreover, let PL and PHbe defined as

before. Then R = PI /P2, O = PI(I –P2) / P2(1 -Pl),

R~ = [Nl PI (l-PH) + N2 P2 P=] /N1

[Nl PI PH +N2 P2 (1-PJ] /N2 (15)

and

[Nl PI (1 -PH) + N2P ~PL]

o&f=

[N,(1-Pl) PH + N2 (1 -P2) (1 -PL)]

[N, PIP* +N, P, (1 -PL)]

[Nl (1 -Pl) (l-PH ) +N, (1 -P2) PL] (16)

It should be pointed out that if, at the tth year, only a

random subsample of the low group is used for calculation

of risk ratio or odds ratio, these expected ratios can still be

computed by replacing the new subsample size for Nz.

Hypothetical example 9: Let NI = 200 and Nz = 800.

Suppose that PI = 0.30 and P2 = 0,10. Furthermore, suppose that PL = 0.1418 and PH = 0.5674. Then R = 3,

0 = 3.86, RM = 1.45, and 0~ = 1.56.

Measureof risk in case-conlrolstudy– Let PI and P2 be

the proportion of the high level of a dietary factor (without intraindividual variation) in the case and the control

groups, respectively. Let N1 and N2 be the number of

people in the case and the control groups and let PHand

PL be the probabilities of misclassification defined previously. Then

PI (1-PJ

o=

(l-P*) P2

and [Pl (l-PH) +(1 -P,) P ~]

0,4=

[P2P~ + (1-P,) (1 -P~)]

,,.

[PlP~ +(1 -Pl) (1 -PL)]

[P2(1 -P~) +(1 -P2) P~]

For most of the case-control studies, the dietary

information is collected by the frequency methods. Thus,

the misclassification is not due to intraindividual variation

but is due to the errors made by the direct estimation of

an individual’s true mean intake of the frequency methods.

Hypothetical example 10: Let PI = 0.3 and P2 = 0.1

and let PH = 0,5674 and PL = 0.1418. Then O = 3.86 and

o~ = 1,44.

Related issues–For analysis of variance and estimation of means, the impact of a large ratio of intraindividual

10
to interindividual variances is generally not very severe if

the sample size in each group is very large.

For a classification variable, a large ratio of intraindividual to interindividual variances and an inadequate

number of measurements will lead to a large proportion of

misclassification. The impact can be very serious. For

example, the reduction in risk ratio and odds ratio can be

very large, and the attenuation will not be changed by

increasing the sample size.

In the analysis of variance or covariance, misclassification between groups will reduce the difference between

the group means and increase the variance. Thus, the

power will be reduced, In some situations, the assumption

of the underlying distribution may become invalid after

misclassification.

Pract[cal problems

Several practical problems related to intraindividual

variation need to be considered in designing a study to

measure dietary intake.

l%wibilityof mult@lerecorak–The number of food records

or 24-hour recalls needed to characterize accurately an individual’s dietary intake may create practical difficulties. For

example, in a large-scale study, collection of 7- to 14-day food

records requires a large amount of time and effort from

trained nutritionists and participants and thus may not be

feasible, This is a common problem faced in such studies.

Unfortunately, there is no perfect solution to this problem.

The following steps may help to partially reduce the

severity of the problem.

1,

2,

Reduction of intraindividual variation caused by other

factors: Usually food records and recalls are converted

into nutrient codes by trained coders. However, in many

cases the coding requires a subjective judgment from the

coders, and coders may not always follow the coding

rules, Thus, a part of the observed intraindividual variation of an individual’s dietary intake may be due to

intracoder and interceder variation in coding food records

or 24-hour recalls. Standardization of coders and their

ongoing training and monitoring for consistency in coding

food records or recalls could reduce the obsemed intraindividual variation.

For 24-hour recalls, intrainterviewer or interinterviewer variations in interviewing techniques can also

contribute to intraindividual variation. However, interviewing techniques can be standardized by training of

the interviewers. For example, Beaton et al. found

that well-trained interviewers contributed very little to

the intraindividual variation (22),

Estimation of the ratio of intraindividual to interindividual variances for the study population: The number of

measurements required may vary from population to

population. For example, the ratio of the intraindividual

to interindividual variances for the 24-hour urine sodium

excretion estimated based on data in Chicago was about 3

(35,36). However, the ratios estimated based on data in

Belgium and China were less than 2 (37,38). Therefore,

the number of measurements needed to characterize an

3,

individual’s sodium intake is probably less for the studies

in Belgium and China than the study for Chicago. Similarly, for different populations the number of measurements needed to characterize an individual’s dietary intake

may not always be the same. It may be worthwhile to

estimate the required number of measurements for the

study population. However, for a study population that

includes different subpopulations, such as the third

National Health and Nutrition Examination Survey

(NHANES III), it may complicate the data analyses and

sometimes even create biases if diflerent numbers of

measurements are used for different subpopulations. Thus,

multiple numbers of measurements are not recommended for a study even though the study population

includes many different subpopulations.

Selection of an optimal number of measurements: It can

be seen in table ~ that when the reduction in correlation

is not very large (that is, c is close to 1), even a small

decrease in reduction requires a large number of measurements. For example, for total calories, five measurements

decrease the reduction in correlation from 43 percent to

15 percent; an additional four measurements decrease

the reduction only from 15 percent to 10 percent. Therefore, one should select an appropriate number of food

records or recalls to best fit the study.

Random versus consecutive measurements –Another

problem that needs to be considered is the choice between

randomly scheduled food records or recalls and consecutive daily records. From a statistical point of view, if the

number of measurements is large, randomly scheduled

food records or recalls during the study period are certainly more appropriate. However, the number of measurements is usually not very large. If for a majority of people

there is a habitual pattern of dietary intakes for different

days in a week, randomly scheduled records or recalls may

not always be the best method. Hankin, Reynolds, and

Margen suggested that dietary intake during weekend

days may be slightly different from the intake during

weekdays (39). If this is the case, the difference in dietary

intakes between weekend days and weekdays should also

be taken into account in the random scheduling of the

measurements. For example, if seven measurements are

needed, five of them can be randomly scheduled on

weekdays and two on weekend days,

Consecutive food records may be easier to do than

randomly scheduled records or recalls. If the day-to-day variation of an individual’s dieta~ intakes on consecutive days is

not independent (that is, the variables ei, i = 1,. . . . k in

“simplecorrelation” are not independent), use of consecutive

food records may be inappropriate. However, this is probably

not the case. For example, in the study done by Hankin,

Reynolds, and Margen (39), the correlation coefficient of each

nutrient between two consecutive days was not any higher

than that between two nonconsecutive days. Thus, even though

use of consecutive food records in recalls is not as good as

randomly scheduled records or recalls, it may still be a

reasonable alternative when random food records or recalls

are not feasible.

11
Seasonal variation –An individual’s dietary intakes may

vary from season to season. From a statistical point of

view, seasonal variation is not a random variation but

rather a change in mean intake. If it is large, it may create

some biases in the study. Sempos et al. conducted a 2-year

dietary survey of middle-aged women (40). The participants were asked to record their diet on 2 randomly

selected days per month over a 2-year period. The results

indicated that the intake of certain food groups varied

from season to season. On the other hand, Potter,

McMichael, and Bonett reported that there was no evidence of seasonal variation (41). Because of rapid development in modern technologies, almost all foods are

available year round. It is possible that seasonal variation

is not as large as before. Unfortunately, the available

literature does not provide adequate information to evaluate how large the seasonal variation is. The diet data

collected by several investigators can be further analyzed

to answer this question (17,40).

If the seasonal variation is very large, it should be

taken into account in the study design to avoid any

possible biases. Ideally, multiple records or recalls should

be scheduled for each participant for each season to

account for seasonal variation. Unfortunately, for a largescale study, the collection of multiple records or recalls

can be very difficult. A carefully designed study can help

to partially solve the problem. For example, for the

comparison of the average intakes between different subgroups (such as, age, sex, and race), the possible biases

caused by seasonal variation can be reduced if the diet

surveys for each subgroup are randomly scheduled

throughout the year. For many biological risk factors, the

level is stabilized within a relatively short period of time

after a diet change. For example, serum cholesterol levels

are stabilized 3 to 4 weeks after changing from a high-fat

diet to a low-fat diet (42), Thus, for studying the relationships between dietary factors and biological risk factors,

measuring the biological risk factor on the same day of the

dietary survey may help to prevent the potential attenuation caused by seasonal variation.

Statistical correction – For simple correlation or linear

regression analysis, it is possible to correct statistically for

the attenuation caused by a large intraindividual variation.

However, the correction is not recommended unless the

sample size is very large and the estimated value of the

ratio of intraindividual to interindividual variances is very

accurate. When the sample size is small, the observed

correlation or regression coefficient and the estimated

ratio of intraindividual to interindividual variances may

also be greatly affected by sampling variations. Thus,

statistical correction of an observed coefficient may produce misleading results, Moreover, statistical correction is

not appropriate for the other type of analyses, such as

partial correlation, multiple linear regression, multiple

logistic regression, analysis of variance, and risk ratio

analyses,

Recommendations

Should multiple dietary records be collected in

NHANES Ill?

From a statistical point of view, it is more appropriate

for NHANES III to use as its primary method the daily

consumption method rather than the frequency method. More

specifically,the recommended method is three 24-hour recalls,

with two randomly assigned for weekdays and one for weekend

days. If the three 24-hour recalls using person-to-person

interview are not feasible, the first 24-hour recall should be

done by person-to-person interview using food models, and

the subsequent two should be done by telephone interview

using paper models, This is recommended for the following

reasons.

1.

2,

3.

4.

One of the major purposes of NHANES 111is to estimate

the average dietary intakes of different nutrients for all

subgroups (age, sex, race, socioeconomic, regional, and so

forth), With a large sample size in each of the subgroups,

the estimation can be reasonably accurate even with one

measurement. On the other hand, the frequency methods

cannot provide accurate absolute value for each nutrient.

It is very likely to overestimate or underestimate the

average value. Therefore, the daily consumption method

should be used.

In the two previous NHANES surveys, a single 24-hour

recall was used as the primary survey method, In

order to compare the secular trend of the dietary

intake for each dietary factor, the same survey method

should be used. Otherwise, biases will be created by

the use of different methods.

Food records or 24-hour recalls provide the raw data

on the actual foods eaten by an individual. However,

frequency methods usually group different foods

together. As a consequence, a great deal of information may be lost. For example, many years from now

some nutrients or trace elements may become important. It is possible to estimate the baseline intake for

each individual if the raw data are available. The

information cannot be retrieved if certain foods are

grouped together.

If an inadequate number of 24-hour recalls is used to

characterize an individual’s dietary intake, the association between the dieta~ variable and the biological

risk factor will be attenuated by the intraindividual

variation. On the other hand, if a frequency method is

used to assess an individual’s dieta~ intake, the association will also be attenuated by the inaccuracy of the

method. The attenuation corresponding to most of the

frequency methods is about the same as the attenuation corresponding to three 24-hour recalls,

The use of two 24-hour recalls done by telephone

interview and one 24-hour recall done by person-toperson interview seems to be a possible alternative if

three 24-hour recalls done person to person are not

12
feasible, Two studies had encouraging results on the

accuracy of the telephone interview 24-hour recalls

(14,15). This method should be further evaluated

before use in NHANES III.

Limitations of daily consumption method

The major limitation of the daily consumption method is

the possible attenuation caused by intraindividual variation.The details have been discussed previously.For group

analyses,if the sample size is large, the impact is not very

serious. For analyses of individuals and risk analyses, the

impact can be very serious. If the association between a

dietary factor and a biological variable is strong, the relationship may be attenuated but remains significant. However, if

the association is weak, the relationship will be attenuated and

may become unobservable.

The other limitation is the possible omission of infrequently consumed foods, such as liver and alcohol. These

24-hour recalls may not be adequate to cover the intake of

these foods, The addition of a frequency questionnaire by

Willett et al, or Block et al, should be seriously considered

(17,43).

It should be pointed out that there is no perfect

dietary survey method. Every method has some limitations. Considering the statistical problems and feasibility

of each method the most reasonable choice as the primary

survey method for NHANES III is three 24-hour recalls.

References

1.

2.

3.

4.

-,5

6.

7,

s,

Dales LG, Friedman GD, Ury ~ et al. A case-controlstudyof

relationships of diet and other traits to colorectal cancer in

Americanblacks.Am J Epidemiol 109:132-44.1978.

Lubin JH, Burns PE, Blot WJ, et al. Dietary factors and

breast cancer risk. Int J Cancer 28:685-9.1981.

McDowell A, Engel A, Massey JT, Maurer K. Plan and operation of the second National Health and Nutrition Examination

Survey, 1976-80, National Center for Health Statistics, Vital

Health Stat 1(15). 19S1.

Mann GV, Pearson G, Gordon T, Dawber TR. Diet and

cardiovascular disease in the Framingham Study I. Measurement of dietary intake, Am J Clin Nutr 11:200-25.1962.

Slwtwlle RB, Stamler J, Paul O, et al. Dietary lipids and

serum cholesterol level: Change in diet confounds the cross

sectional association. Am J Epidemiol 115:506-14. 1982.

Tillotson JL, Kato MA, Nichaman MZ, et al. Epidemiology

of coronary heart disease and stroke in Japanese men living

in Japan, Hawaii and California: Methodology for comparison of diet, Am J Clin Nutr 26:177-S4. 1973.

Kato MA, Tillotson JL, Nichaman MZ, et al. Epidemiological studies of coronary heart disease and stroke in Japanese

men living in Japan, Hawaii and California. Am J Epidemiol

97:372-85.1973.

Kdhn HA, Phillips RL, Snowdon DA, Choi W, Association

between reported diet and all-cause mortality: Twenty-oneyear follow-up on 27,530 adult Seventh-Day Adventists. Am

J Epidemiol 119:775-87, 1984.

9.

10.

11.

12.

13.

14.

15.

16,

17,

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

National Diet-Heart Study Research Group: The National

Diet-Heart Study final report. Circulation 37 (suppl 1):1-428.

1968.

Farinaro E, Stamler J, Upton M, et al. Plasma glucose

levels: Long-term effect of diet in the Chicago Coronary

Prevention Evaluation Program. Ann Int Med 86:147-54.

1977.

Stamler R, Grimm R, Gosch FC, et al. Control of high

blood pressure by nutritional therapy Final report of a

4-year randomized controlled trial –The Hypertension Control Program. JAMA 257:1484-91.1987.

Caggiula AW, Christakis G, Farrand M, et al. The Multiple

Risk Factor Intervention Trial (MRFIT). IV. Intervention

on blood lipids. Prev Med 10:443-75.1981.

Lipid Research Clinics Program. The Lipid Research Clinics

Coronary Primary Prevention Trial results: 1. Reduction in

incidence of corona~ heart disease. JAMA 251:351-64,

1984.

Krantzler NJ, Mullen BJ, Schutz HG, et al. Validity of

telephoned diet recalls and records for assessment of individual food intake. Am J Clin Nutr 36:1234-42.1982.

Posner BM, Borrnan CL, Morgan JL, et al, The validity of a

telephone-administered 24-hour dietary recall methodology.

Am J Clin Nutr 36:546-53.1982,

Mahalko JR, Johnson LK, Gallagher SK, Milne DB. Comparison of dietary histories and seven-day food records in a

nutritional assessment of older adults. Am J Clin Nutr

42:542-53.1985.

Willett WC, Sampson L, Stampfer MJ, et al. Reproducibility

and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol 122:51-65.1985.

Chu SY, Kolonel LN, Hankin JH, Lee J. A comparison of

frequency and quantitative dietary methods for epidemiologic studies of diet and disease. Am J Epidemiol 119:323-34.

1984.

Hankin JH, Messinger HB, Stallones RA. A short dietary

method for epidemiologic studies. III. Development of questionnaire. Am J Epidemiol 87:285-98.1967.

Samet JM, Humble CG, Skipper BE. Alternatives in the

collection and analysis of food frequency interview data. Am

J Epidemiol 120:572-81.1984.

Liu K, Stamler J, Dyer A, et al. Statistical methods to assess

and minimize the role of intra-individual variability in

observing the relationship between dietary lipids and serum

cholesterol. J Chron Dis 31:399-418. 1978.

Beaton GH, Milner J, Corey P, et al. Sources of variance in

24-hour dietary recall data: Implications for nutrition study

design and interpretation. Am J Clin Nutr 32:2546-59.1979.

Sempos CT, Johnson NE, Smith EL, Gilligan C. Effects of

intra-individual and inter-individual variation in repeated diet

records. Am J Epidemiol 121:120-30, 1985.

Jacobs DR, Anderson JT, Blackburn H. Diet and serum

cholesterol: Do zero correlations negate the relationship?

Am J Epidemiol 110:77-87.1979.

Young CM, Chalmers FW, Church HN, et al. A comparison

of dietary study methods. J Am Diet Assoc 28:124-8. 1953.

Block B. A review of validations of dietag assessment

methods. Am J Epidemiol 115:492-505. 1982.

Liu K. Measurement error and its impact on partial correlation and multiple linear regression analyses. Am J Epidemiol 127:864–74. 1988.

Snedecor GW, Cochran WG. Statistical methods. Ames,

Iowa:Iowd State University Press. 19740

13
29.

30.

31.

32.

33.

34.

35.

36,

37.

Fleiss JL, Shrout PE. The effects of measurement errors on

some multivariate procedures. Am J Public Health

67:1188-91.1977.

Kupper LL, Effect of the use of unreliable surrogate variables on the validity of epidemiologic research studies. Am J

Epidemiol 120:643-8.1984.

Walker SH, Duncan DB. Estimation of the probability of an

event as a function of several independent variables.

Biometrika 54:167-79.1967.

Truett J, Cornfield J, Kannel W. A multivariate analysis of

the risk of coronary heart disease in Framingham. J Chron

Dis 20:511-24.1967.

Liu ~ Sempos C, Measurement error and its impact on multiple logistic regression analysis, Unpublished manuscript,

Neter J, Wasserman W, Applied linear statistical model.

Homewood, Illinois: Richard D. Irwin, Inc. 1975.

Liu K, Cooper R, McKeever J, et al. Assessment of the

association between habitual salt intake and high blood

pressure: Methodological problem, Am J Epidemiol

110:219-26.1979,

LIUK, Cooper R, Soltero I, Stamler J. Variability in 24-hour

urinary sodium excretion in children. Hypertension 1:631-6.

1979,

Joossens JV, Claessens J, Geboers J, Claes JH, Electrolytes

and creatinine in multiple 24-hour urine collections

38.

39.

40,

41,

42.

43.

(1970-1974). In: Epidemiology of arterial blood pressure,

KesteIoot H, Joossens J, eds. The Hague: Martinus Nijhoff

Publishers bv,

Hsiao CK, Wang SY, Hong ZG, et al. Timed overnight

sodium and potassium excretion and blood pressure in steel

workers. J Hypertens 4:345-50.1986.

Hankin JH, Reynolds WE, Margen S. A short dietary

method for epidemiologic studies II. Variability of measured

nutrient intakes. Am J Clin Nutr 20:934-45. 1967.

Sempos CT, Johnson N, Smith EL, Gilligan C. A two-year

dietary survey of middle-aged women: Repeated dietary

records as a measure of usual intakes, J Am Diet Assoc

84:1008-13.1985.

Potter JD, McMichael AJ, Bonett AZ. Diet, alcohol and

large bowel cancer; a case-control study, Proc Nutr Soc

7:123-6.1982.

Connor WE, Hodges RD, B1eiler RE. Effect of dietary

cholesterol upon serum lipids in man. J Lab Clin Med

57:331-42.1961.

Block G, Hartman AM, Dresser CM, et al, A data-based

approach to diet questionnaire

Epidemiol 124:453-69.1986.

design and testing. Am J
First discussant’s remarks

about statistical issues

by Harold A. Kahn, M.A., Consultant in epidemiology

My one serious disagreement with Dr. Liu’s paper is

in my unwillingness to accept the validity of the 24-hour

dietary recall as established. I do not think that observation of communal meals, comparison of 24-hour recalls

with diet histories, frequency counts, or other diet records

thut themselves have not been validated or the finding of

startistically significant correlations constitute evidence of

validity.

Because it is reasonable to assume that persons who

cannot remember what they ate yesterday are unlikely to

be able to accurately average their diet over the past year

in order to report their usual intake, the situation regarding

validity logically falls under one of the three following

categories:

. All methods have been validated.

. Only daily methods (24-hour recall or food record)

have been validated.

. No methods have been validated

If all methods are valid, because of simplicity and ease

of standardization, the frequency methods would be the

obvious choice.

If only daily methods are valid, it would be appropriate to use them to compute the average diet for groups.

Individual classification by diet could also be attempted by

use of multiple days of data using the adjustments specified by Dr. Liu, An alternative that permits unbiased

estimation of the odds ratio relating diet to disease after

adjustment for multiple covariables is outlined below,

modified from the methods described in the National

Academy of Sciences, National Research Council monographNufrientAdequacy (l).

1,

2,

-.?

4.

Transform the dietary data to approximate normality.

Classify all persons by disease outcome (for example,

sick or well) or by a dichotomous physiologic variable

(for example, serum cholesterol less than 240 milligrams per deciliter or 240 milligrams per deciliter or

more).

Collect two random independent 24-hour dieta~ recalls

(or records) from each subject and for each “disease”

group estimate the mean (x) and variance of individual dietary intakes ( =s~).

From the paired values for all individuals estimate the

variance within individuals ( =s~, assuming the data

are poolable).

5,

6.

7.

8.

9.

Subtract s;/ 2 from s; to estimate the true variance

between individuals, free from the additional variance

of random daily departures from the individual’s true

mean ( = s:, revised).

Use the x together with Sb (revised) to estimate the

proportion of persons with intake above some cutpoint.

Say this is 0.05 for the “sick” and 0.03 for the “well,”

and for convenience assume that the number of sick

and well persons is 100 each.

We can now make a 2 x 2 table:

Sick Well

Risk factor + 5 3

Risk factor – 95 97

100 100

5x97

Estimated odds ratio =— = 1.70

3x95

Carry out the above for each stratum of covariables

for which adjustment is desired (for example, young

men with high serum cholesterol, young men with low

serum cholesterol).

Use the Mantel-Haenszel urocedure .to estimate an

adjusted odds ratio free of bias caused by variance

within individuals.

The last possibility, that no methods have been validated, suggests the urgent need for a validity test, perhaps

using the physiologic fact that for normal adults in our

society a 24-hour urine specimen reflects about 90 percent of the dietary intake of protein, riboflavin, and

thiamine. Because it is not today’s intake that is reflected

in today’s urine specimen but a weighted average of the

intake over the previous 3–4 days, it is not correct to

match urine with daily diet data for each individual;

however, it is possible to match group averages. Obtain

volunteers who agree to be contacted on a random date

during a study period of about 4 months to provide a

24-hour recall interview and begin a 24-hour urine collection on that date. As the 24-hour recalls are collected on a

random date, it is possible to use them to estimate the

15
group average intake during the study period according to

the recall method. Similarly, because the few days preceding a random date are also randomly chosen, the

analysis of 24-hour urine specimens for total nitrogen,

riboflavin, and thiamine can be used to estimate the group

average intake for protein, riboflavin, and thiamine during

the study period according to the urinalysis. If 24-hour

specimens are, in fact, collected and if the averages differ

by more than can be expected from random sampling

error, it would be evidence against the 24-hour recall

procedure, In addition, rank correlation coefficients can

be compared (adjusted as in Dr. Liu’s paper), Failure to

find evidence against the recall procedure using these

nutrients does not prove the case for other nutrients.

However, this is the traditional way science progresses, by

attempting to reject hypotheses, Because these nutrients

are pervasive in the diet, “passing” this validity test would

certainly strengthen the case for using the 24-hour recall

method, In addition, “passing” the test for group means

would justify using multiple 24-hour recalls for more

precise estimates of individual diets.

One other suggestion (if no methods are known to be

valid) is to analyze frequency data only for the “never eat”

and “eat daily” categories, I want to make a final plea for

stressing prospective studies. While we t~ to determine

how diet affects disease, let us not forget that disease

affects diet, Asking someone to report what he ate “before

getting sick” or “2 years ago,” when it is unclear that he

can tell us what he ate yesterday, requires an unjustified

leap of faith.

Reference

1. National Academy of Sciences, National Research Council,

Subcommittee on Criteria for Dietary Evaluation, Coordinating Committee on Evaluation of Food Consumption

Surveys. Nutrient adequacy: Assessment using food consumption surveys. Washington: National Academy Press. 1986,

16
Second discussant’s remarks

about statistical issues in

design

by George H. Beaton, Ph.D., Department of

Nutritional Sciences, University of Toronto

Initial remarks

The paper by Dr. Kiang Liu marks a major contribution to the evolution of thinking about design issues

related to the collection of food intake. But then we have

come to expect his continuing contribution in this area; he

has been leading the way for a decade or more.

Today we know enough of this story to make it

inexcusable to ignore it in designing large studies such as

NHANES 111.The specific moral that we must address is

that one must carefully define the questions before

designing the study. It seems clear that there is no perfect

dietary methodology for inclusion in NHANES III. There

is no methodology that will provide data suitable for

analysis of all of the types of questions that users have

attempted to put to previous NHANES reports.

I think we are in a position to begin building a matrix

of types of questions, analytical approaches, and dietary

methodology requirements – a matrix that would permit a

sensible choice of methods together with a clear declaration of what can and cannot be done with NHANES III

data. It is not my role to try to determine which types of

questions should have priority, but I will try to begin

building the matrix linking question and design.

Technical comments on paper

First, at a level of supplementary information, table 2

of Dr. Liu’s paper (chapter 1) presents some estimates of

the ratio of intraindividual to interindividual variances.

These were based on data from two studies in which he is

involved. Recently a National Academy of Sciences (NAS)

report drew together estimates published in the literature

(l). These are compared in table 1 of this section. The

point to note is that, in some cases, the ratios appear to be

appreciably different across studies; in other cases, the

ratios are similar.

Two important messages arise: (a) The phenomenon

that Dr. Liu discusses is one that has been found in every

data set examined to date and must be recognized in

design and analysis; (b) there is strong indication, as might

be expected, that the variance ratios differ between studies,

perhaps by age, sex, and cultural group and with the

particular methodology applied. The latter is important, as

the intraindividual variation includes not only true biological variation within the individual but also random methodologic variation, The interindividual variation will reflect

both the characteristics of the particular population group

and also sampling effects. These ratios must be established

internally within a data set if they are to be used in

analysis or in interpreting analyses,

Second, Dr. Liu raises the question of consecutive

versus independent days in increasing the number of

observations, Recently Morgan, Johnson, and Goungetas

examined this effect, and they found that it is a persistent

effect and appears to vary with the level of intake (2).

They urge a more extensive examination of these effects.

Table 1, Reported ratios of krtraindivldual to interindividual variance in 1-day data

Adu/t ma/es

Adu/t fema/es, NAS

Nutrient Liu datal NAS report dat$ report dat$

Ratio of variance

Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5, 2.0 1.1,1.0,0.8 1.4,0.8,1.6,1.6,1.1

Protein . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 1.5, 1.2, 1.4

Carbohydrate, ,, .,....,.....,.. . . . . . . .

1.5, 1.3, 2.1, 2.1, 1.4

. . . 1.6, 2.1, 0.6 1.4, 1.2, 1.2

Total fat . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 1.2, 1.2, 1.3

Saturated fatty acids. . . . . . . . . . . . . . . . . . . . .

1.6, 0.9, 2.1

2.1, 2.7 1.1, 2.2, 1.4 1.4, 1.7

Polyunaaturated fatty acids . . . . . . . . . . . . . . 4.4, 5.2 2.8, 3.5, 1.9 4.0, 4.2

Cholesterol . . . . . . . . . . . . . . . . . . . . . . . . . . 3.0,4.9 3.4,5.6,1.6 4.3,4.2

V[lamln A. .,, ,, .,, , ..,,,..., . . . . . . . . .

--- ~), 1.6 24.3, 2.5,7.7, 10.9

Vitamin C,, . . . . . . . . . . . . . . . . . . . . . . . . .

--- 3.5, 2.3 2.0, 2.8,2.3, 2.5

Thiamine, .,..,,,...,,..,.,,,, . . . . . . .

-.. 2.5, 0.9 4.4, 1.6,3.3, 3.9

Riboflavin, . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . 2.4, 0.9 2.2, 1.8, 3.0, 3.3

Nlaclnequlvalent, . . . . . . . . . . . . . . . . . . . . . .

--- 1.6, 2.2 4.0, 2.5

Calcium . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.9 2.2, 1.1 0.9, 1.7, 1.1, 1.2, 1.0

Iron . .,, , .,, ., . .,, ,., .,... . . . . . . . . .

. . . 1.7, 1.6 2.5,1.5,2.7,2.5

‘See chapter 1 of this repot

2NatlonalAcademy OfSGieOCeS(1).

3AIIof “arianca appeared In Intralndivldual component until logarithmicallytransformed.

17
Third, two technical procedures for reducing the true

error component of intraindividual variation are proposed: Standardize the collection of data and standardize

the coding of data. For the record, Beaton et al. (3)

examined the contribution of each of these (not just

interviewer effects) in the Lipid Research Clinic’s Nutrition System and found, in that highly standardized system,

that the contributions were very small, yet intraindividual

variation was very large! In unstandardized systems the

effect would be greater, and the total variance would be

greater. I agree with Dr. Liu’s recommendation, but I

caution that this is not the basic issue,

Finally, Dr. Liu does not discuss issues of the reliability and precision of the food composition data base

and any impact these may have on the estimate of the

intake of an individual. Aspects of this have been discussed in a recent NAS report (1) but in relation to a very

specific use of food intake data. It is certainly another

source of variation that can be either a random variance

(that might be factored out in the same way as true

day-to-day variation) or a source of bias. It is wo”rth noting

that abridged food composition data bases may have an

opposite effect: Interindividual variation may be lost to

some degree.

Comments on implications and recommendations

Dr. Liu’s paper deals almost exclusively with analytical issues arising from the use of proxy estimates of

“usual intake.” As he points out with examples, two

different issues are involved: (a) Observed correlation and

regression coefficients may be attenuated, or in some cases

exaggerated either way; they will differ from realit~ (b)

statistical tests for the existence of any relationship will be

affected such that power is lost. Thus, there is real

potential for an erroneous picture of the nature of the

relationship and indeed for a false negative conclusion

about the existence of any relationship. These issues,

without explicit identification, have plagued the nutrition

literature in the past. I see Dr. Liu’s paper, at this time, as

a plea to avoid this in the future, at least with NHANES III

data,

There would seem to be at least three approaches to

accomplishing this goal:

1.

2.

3.

Increase the reliability of the dietary data through

increased number of days of information or selection

of a dietary methodology that estimates “usual intake.”

Attempt to adjust regression and correlation coefficients through application of estimates of the variance

ratios.

Declare the limitations of the data set and prominently warn users of the errors that must be ex~ected

in certain types of analyses,

Dr. Liu recommends the first possibility and suggests that

the optimal would be 3 days of data collection, His logic

for deriving the optimal number of days would be inadequate for many analyses, Moreover, it is not clear why he

18

is willing to accept telephone interviews in place of faceto-face’ interviews if the goal is to improve reliability and

validity.

Importantly, in his remarks Dr. Liu addressed the

matter of selecting an alternate dietary methodology – the

food frequency or dieta~ history. If I may paraphrase him,

in selecting one of these methods one is trading error

sources, eliminating the probIem of day-to-day variation

but potentially increasing the problem of imprecision of

the estimate, Again, if I understood him correctly, he

suggests that. a good food frequency method might yield

data that are equivalent, for statistical purposes, to three

good l-day recalls or records, but with different types of

error sources. Obviously, “bad” methodology of either

type would yield bad data! (In a paper published long

after this workshop, Beaton (4) demonstrated the apparent

loss of a part of the between-person variation in usual

intake with food frequency methods, At least part of this

10SSappeared to be independent of (not correlated with)

the true variation in usual intakes. This was additional to

any proportional loss of variance that might come from

the grouping of foods,) The point, as I understand it, is

that switching methodologies does not really get us very

far ahead in terms of the statistical issues.

Later in the workshop the question of dietary methodology will be addressed again. However, given the fact

that NHANES designers have felt unable to devote more

than one-half hour of the survey team’s time to the

collection of dietary data (see background statements for

workshop), I am not optimistic that they are now prepared

to invest the time and effort that should be invested to

collect reliable or valid data – at least not unless there is a

very persuading argument that the benefits to be gained

would justify the very real investment that would be

needed. This warrants serious attention. If additional

investment is out of the question, then our role here is to

rule out analyses that cannot be performed. If investment

is possible, then our role is to identify the requirements

that must be met to provide adequate dietary data, We

must have a reading on this if we are to proceed in a

meaningful way.

Dr. Liu dismisses out of hand the second possibility,

using variance estimates to adjust the observed correlation

and regression coefficients, I understand his concerns, but

I believe that this is too easy a dismissal. I would argue

that, at least in some situations, perhaps those involving

only simple correlations or regressions, careful adjustments might be used either to see what the real relationship might look like or even to take into account the

intraindividual variation when testing for statistical significance. (In a paper published after this workshop, Rosner

and Willett (5) presented an approach to estimating

confidence intervals for an adjusted correlation coefficient.) In other applications, in which the distribution of

intakes is the focus of interest (as in assessing the prevalence of inadequate intakes in a population or subpopulation), use of variance information to adjust the distribution

clearly is in order (1), Again, it must be emphasized that,
if this is to be done, the variance estimates must be

derived from the data set at hand. That means that there

must be a sufficient number of replicate observations

distributed in a statistically valid manner to provide reliable estimates of the variances (1), It would not necessarily require three observations of intake to accomplish

this purpose,

The third possibility– avoiding the problematic analyses – Dr. Liu does not discuss. Just as it was argued a

moment ago that we must know the real constraint to

resources for the collection of valid dietary data, we now

must know the real purposes of NHANES 111.Is it really

necessary or cost effective to try to design this survey to

accomplish all of the purposes that would involve the type

of analyses discussed by Dr. Liu? Must those analyses be

addressed in other research data bases collected outside

of NHANES?

I would suggest that a major role of the present

workshop must be to begin building the matrix discussed

earlier, to begin providing the basis for examining questions that might be asked in relation to methodologic,

logistic, and financial demands, Only then can the planners of the survey begin making their cost-benefit

considerations,

Clearly we face some tradeoffs between potential for

use of the data and predictable cost of acquiring the data.

The workshop can lay out a framework for examining

these tradeoffs, but it cannot make the final judgment,

Toward a matrix of methodology versus analysis

potential

Other papers provided as background for the workshop provide a listing of potential applications of NHANES

data, One paper (appendix VI) states the purposes of

NHANES III as:

1,

2,

3.

4.

5,

To monitor changes in health and nutritional status

over time,

To assess the health and nutritional status of the U.S.

population and specific subgroups,

To provide information on the interrelationship of

health and nutrition variables within population subgroups

To measure the prevalence of disease and conditions,

To measure met and unmet care needs related to

target conditions,

Of these purposes, only purpose 3 carries any mandatory requirement for dietary data, Therefore, at face value

it may be fair to state that the purpose of this workshop is

to consider the dietary methodology required to permit

the examination of the interrelationship of health and

nutrition variables within population subgroups, given the

following:

● The other purposes of NHANES III cannot be

compromised.

●

●

There is another national survey that provides

information on dietary intake (but not health

conditions).

There is another set of recommendations that

strongly urges that there be sufficient design and

methodologic comparability to permit linkage of the

two large U.S. data bases – NHANES and the

Nationwide Food Consumption Survey (NFCS).

If we then turn to other papers submitted as background to the workshop, we find that there appears to be

a strong focus on the ability to link diet (or nutrient

intake) to certain specific conditions, notably:

● Cancer.

. Cardiovascular disease.

● Osteoporosis.

● “Energy balance” and obesity.

I think that we will find that linking diet to energy balance

has been thoroughly, and properly, dismissed as inappropriate for NHANES (chapter 4). I believe that we will find

also that linking diet to osteoporosis has been challenged

in terms of any examination of current relationships

between intake and bone mass; apparently, a longitudinal

study in which serial measures of change within individuals can be measured is recommended (chapter 5). In the

case of cancer it appears that what may be wanted is the

opportunity to examine intakes today in relation to health

of the same individual many years from now – a reliable

baseline descriptor of the individual’s current intake of

either foods or food constituents (it could be either or

both). (See chapter 2.) The same could hold for cardiovascular disease; however, here we also face interest in

examining current associations among food-nutrient intake,

serum lipids, blood pressure, and perhaps such individual

traits as smoking, as well as age and sex (chapter 3). This

scope of interest is exemplified as a series of questions

presented by Dr. Patricia Elmer (chapter 3).

In a separate report, another committee has recommended that the NHANES data base, perhaps combined

with the NFCS data base, be used in a joint assessment of

nutritional adequacy and nutritional status – assessments

of prevalence of inadequate intakes and of prevalence of

biochemical and physiological conditions in subpopulations (l). For this purpose the main role of NHANES

dietary data might be to validate the agreement of intake

between surveys for selected population groups. The critical element would be the ability to link population subgroups through sampling design and subject identifiers

and to ensure comparability of at least part of the dietary

methodology and of food composition data bases (6), This

appears to be a recommendation that will find favor with

the Office of Management and Budget and will become a

part of a nutrition-monitoring use of the NHANES data

base,

It has been urged that national data bases, including

NHANES, be used in an epidemiologic mode to attempt

to validate estimates of nutrient requirements in the

19
derivation of future Recommended Dietary Allowances

(RDA’s) (7). This could place special demands on the

food intake methodology of NHANES or might require

only the ability to link NHANES and NFCS as above.

Dr. Woteki (appendix VIII) gave generalized examples of uses of dietary data:

1.

2.

3,

4,

5,

6,

7.

8.

9.

What are the food consumption patterns and nutrient

intakes of subpopulations of the United States by such

characteristics as age, sex, race, income, occupation,

and education?

How do nutrient intakes and food consumption patterns of persons differ by level of education?

What are the regional differences in consumption of

certain food groups?

How do nutrient intakes compare with the RDA’s and

other dietary guidelines?

What dieta~ patterns and nutrient intakes are associated with differing levels of health or health risk?

What are the relationships among dietary intake,

biochemical indicators, and health status for persons

who smoke, use vitamin-mineral supplements, or use

oral contraceptives?

Do diets of subpopulations with high serum cholesterol levels differ from those with lower levels?

What changes in obesity, diet, and activity patterns

will take place in the next 10 years?

Will diet help explain the continuing decline in serum

cholesterol values among men and women?

In her paper, Dr. Woteki describes historical uses of past

cycles of NHANES and other surveys as well as predicts

possible future uses.

I suggest that many uses of NHANES have been

inappropriate in that there has been a more suitable and

underutilized data set available: The NFCS data set. It has

not always been readily or promptly accessible, but there

is at least hope that this will change for the better (1). This

is important. A committee charged to examine the two

surveys concluded that there were a number of reasons

why they could not be combined and why the full scope of

each was needed (6). It was seen as critically important

that steps be taken to ensure that the data bases could be

linked, at least by characteristics of the individuals, so that

some questions could be addressed by using the two data

bases in concert. It was also seen as important that users

be directed to the appropriate data base rather than

insisting that either or both be modified to meet the needs

of all users (6).

If we then ask which of Dr. Woteki’s questions demand

the use of an NHANES-type data base, I suggest that we

find questions 5-9, If we envisage the ability to link

NHANES and NFCS by population characteristics (that

is, link subgroups), then only questions 5 and 6 place a

specific demand on the dietary methodology of NHANES.

My point, by this time, should be obvious. Any major

change in dietary methodology for NHANES 111is going

to be very expensive, We must be sure what questions

20

demand such a change, then ask what change would be

required to address these questions, then finally ask whether

this should be done within the framework of NHANES or

in some other specially designed survey-research study. By

my process of elimination, I am left with the following

area of interest and questions that would place special

demands on NHANES III and cannot be effectively

addressed either by NFCS or by a linking of NFCS and

NHANES, including simple dietary methodology.

Applications requiring use of an NHANES-type data

base

●

●

●

●

●

To provide information on the interrelationship of

health and nutrition variables within population

subgroups.

To provide the opportunity to examine intakes today

in relation to the health of the same individual many

years from now, a reliable baseline descriptor of the

individual’s current intake of either foods or food

constituents. The retrospective or prospective studies

of diet and chronic disease require NHANES 111

only if the studies require that health data be

available at the same time as initial dietary data or if

it is impossible to retain subject identifiers in NFCS.

To be used in an epidemiologic mode to attempt to

validate estimates of nutrient requirements in the

derivation of future RDA’s.

To determine what dietary patterns and nutrient

intakes are associated with differing levels of health

or health risk.

To determine the relationships among dietary intake,

biochemical indicators, and health status for”persons

who smoke, use vitamin-mineral supplements, or use

oral contraceptives.

With the exception of baseline data for prospectiveretrospective studies, we return to several variants on a

similar theme, all concerning the relationships between

diet and a health condition or biochemical-physiologic

measure assessed only in NHANES, However, if we look

at these carefully, not all imply the same demand upon the

dietary methodology,

Some could be addressed in simple correlation or

regression studies within categorical groups, For these, the

possibility of adjusting observed relationships through application of estimates of the variance ratio remains. If so, a

l-day intake data base with appropriate sampling for

replicate intakes remains a possibility.

Some of these could be addressed by classifying individuals by the dependent variable (assuming it is more

stable) and then asking about differences in intake – for

example, in answering the question, “What dietary and

nutrient intakes are associated with differing levels of

health or health risk?” Here, if sample size is adequate,

the l-day intake might suffice.

Others imply multivariate analyses of one type or

another —for example, to ask about the multiplicity of factors
that ma~~influence a variable such as serum cholesterol. These

then are really the questions that drive methodology issues.

These are the questions that must be assessed in terms of

their appropriateness for NHANES III.

Table 2 is a first attempt to begin the categorization

of questions and data demands. To be useful it would have

to be elaborated.

I realize that all of us would wish to be able to conduct

quite elaborate analyses on the data set and hence would

prefer to have dieta~ data that are completely adequate

for all of our purposes. However, I have the temerity to

suggest that NHANES 111is not a research study. Although,

at considerable expense, we may be able to implement a

major change in dietary intake methodology that would

seem to meet our purposes, we must ask whether other

limitations of the design of NHANES would still constrain

the type of analyses we would wish to perform, If so, there

was no point in making the dieta~ methodology change,

The purpose of these remarks, then, is to try to make

us think in a broader perspective of what can be done with

other data bases alone or in combination with

Table 2, Some research questions and applicable data bases

NHANES 111,what can be done with a feasible modification of the NHANES dietary methodology, and what

cannot be done within the framework of any national

survey but must be reserved to the targeted research

study, Only when we address this seriously can we really

offer effective advice on the design of NHANES III.

Followup remarks

I now take this opportunity to add some comments

and suggestions about implications of some of the workshop discussions. I had sincerely hoped that future NFCS

and NHANES studies would have enough in common that

the types of multiple survey analyses that I portrayed

would be feasible, given competent statistical input. I

think that was the sincere hope of the original NAS

committee (6). From remarks made at the meeting, both

formally and informally, I am pessimistic that this will be

very effective, at least in the coming round of surveys. If I

am correct, this is very unfortunate but perhaps understandable; it was never seen as an easy job to try to achieve

Sample question Particular need Appropriate data base

Association of Intake with education,

income, or other traits

Association of Intakes of 2 or more

nutrients

Association of pattern of food use with

nutrient intake

Prevalence of inadequate or

excessive Intakea

Prevalence of biochemical or

physiological indices of Inadequacy or

excess

Nutrition monitoring (blochemlcal and

dietary)

Do subjects with_ have different

dietary intakes?

Role of diet in explaining variance in

biochemical-physiological measure

Baseline nutrient Intake for

prospective or retrospective study

Association of dietary and

biochemical-physiological risk factor in

individual

“Epldemlologlc” validation of nutrient

requirement estimates

Group mean intake: traits of individuals

Reliable estimates of nutrient intake by individuals

Reliable estimates of pattern of food use by and nutrient

intake of individual

Distribution of “usual” intakes;l categorical variables

Distribution of persisting biochemical levels-physiological

markers;l categorical variables

As in preceding 2 categories

Reliable categorical variables; group mean intakes

Reliable estimate of persisting intakel

Reliable estimate of persisting intake

Reliable estimate of pattern of food use

Reliable estimate of persisting intake and of persisting

biochemical or physiological measure

As in nutrition monitoring above-comparability of prevalence

estimates

As In association study above

NFCS; NHANES for some traits

NFCS barely adequate; NHANES

with greatly enhanced intake data

NHANES with greatly enhanced

quantitative intake data; NFCS with

added food frequency

NFCS; NHANES less satisfactory

NHANES (including replicated

measures)

NHANES and NFCS must have

linking categorical variables

NHANES for medical-biochamlcal

categorization; NFCS for household,

etc., categorization

NHANES with greatly enhanced

dietary data

NFCS (barely adequate); NHANES if

initial clinical data needed (enhanced

dietary data needed)

NHANES food frequency data,

perhaps enhanced

NHANES with greatly enhanced

dietary data

NHANES and NFCS (with linking

categorical variables)

NHANES with greatly enhanced

dietary data

lAntlclpates availabilityof replicated values and opportunity to adjust observed distributions to etiminate any biologically significant effects of day.to.day variation in variable. See National Academy

of Scloncoa (NAS) raport (e) for discussion.

NOTES: Dietary nutrient Intake qusstions cannot be asked It food composition data base does not provide reliable astimates of composition of foods with regard to target nutrient. Sea NAS report

(6) for dlscusslon, NFCS Is Nationwide Food Consumption Survey. NHANESis National Haalth and Nutrition ExaminationSurvey,

21
even the minimal levels of integration and coordination

that had been hoped for. However, perhaps more importantly, if this is not achieved in the present round, there

are serious implications for both surveys and for those

who wish to use the information, I think that everyone

stands to lose. Both of the surveys will suffer in terms of

interpretations that can be made. Unless NHANES changes

significantly from the last round, it will potentially suffer

more than NFCS; that is, there is less that can be done

with the NHANES II dietary data than can be done with

the NFCS past dietary data.

My conclusion, then, is that given my current reading,

it is extremely important that NHANES implement at

least minimal enhancement of the dietary data, at least a

capability to do distributional analyses within NHANES 111.

The proposal for sufficient replication to do distribution

adjustment within the cells of NHANES 111 data represents such a minimum. With that, I truly believe that “there

is a very favorable cost-benefit ratio. The cost (dollars and

logistics) will go up quite considerably. However, the

potential gain in information will go up disproportionately!

I hope that all recognized that in the interchange with

Dr. Llu, my minimum enhancement and his were driven

by different analytical objectives. If my minimum were

adopted, you might be able to adjust some of the simple

regressions and correlations, but even that would be

statistically undesirable (and would really do little or

nothing to increase the statistical power to establish the

presence of effects; its main effect would be on the

description of the effects). It really does little to address

the issues put forward by Dr. Liu in his excellent paper.

Dr. Liu is correct. Three days would be better for that

purpose (and of course would be better also for the simple

distribution adjustment), but I am not convinced that 3

days would be good enough for the other analyses to be

worth the additional investment. This, obviously, is something that you and your colleagues will have to address:

What power can you gain, and hence what additional

questions can you answer, for the additional resource

investment (dollars or logistics) that would be required?

My bias is toward a different instrument for those types of

questions. My concern is that, although I can see and have

an intuitive feeling for the obvious gain to be obtained

from reducing intraindividual variance through use of one

of the several variants on a food frequency instrument, I

cannot get any intuitive feeling for the impact of the loss

of precision of identification of foods and amounts involved

in such an instrument. There is a tradeoff in errors and I

cannot assess it. It might be worth contracting for someone

to undertake a paper, such as Dr. Liu’s, or to undertake

“sensitivity analyses” of the type undertaken in the NAS

report (1; 8, pp. 137–159) to try to get a handle on the pros

and cons of those instruments. You are fully aware of the

fact that, although it is difficult to validate the l-day intake

instruments, it is virtually impossible to validate the

frequency-history instruments. (How do you obtain an

independent estimate of “usual” intake?). Perhaps, then,

the only approach is one of examining the potential effect

of adding and/or removing the different types of errors

rather than attempting to establish “truth.”

For the record and based on my empirical work

adjusting the NFCS data sets with 3-day data, the adjusted

and actual distributions are not terribly different. That is,

as many people have shown, the observed variance in

population data falls sharpIy as one adds days from 1 to 3,

but after that the reduction in variance diminishes (the

basis of Dr. Liu’s arguments for 3 days). This does not give

reliable estimates for each individual but does raise the

interesting point that for distributional analyses, one should

consider the cost-benefit ratio of adjusting the distribution

during analysis versus using the observed distribution.

Either way, the critically important point is that, although

you can estimate proportions of the population falling

within the defined intervals, you cannot classify the particular individuals falling in these intervals without appreciable error.

I wanted to emphasize another aspect of the NAS

report (6). Although that report’s approach to adjusting

the distribution of intakes was a necessary prerequisite, it

was really a minor part of the report and its implications.

The major thrust was in relation to the use or nonuse of

cutoff points for dietary (or biochemical) data, I think that

you and your colleagues have been sensitive to the issues

surrounding the cutoffs in biochemical data. I am not sure

that you have really solved the issues. I am certain that

you have not resolved the issues of interpretation of

dietary data, The NAS report was clearly a beginning, not

an end. It provides a construct that I think is meaningful.

However, it now needs a group to address the question of

nutrient requirements or toxicological “requirements”

defining in particular average requirements for defining

states of nutriture. This was never (in recent years, at

least) a goal of the RDA committees; hence, you will not

find the estimates needed within the RDA reports, The

development of such estimates is of importance to both

the U.S. Department of Agriculture and the National

Center for Health Statistics if they are to interpret the

next round of surveys. My message, then, is that there is

merit and mutual benefit if the two agencies can blend

forces in an attempt to have some group address this

specific goal in the next year or two —without getting it

involved in the controversies of meaning and derivation of

the “RDA’s.” These really are two different things.

(Since the workshop, the Food and Agricultural Organization and the World Health Organization have published or initiated three reports dealing with nutrient

requirements and/or population nutrient goals. In these

reports, the committees have been much more explicit in

defining the “what” in “requirement for what,” and indeed

multiple levels of requirements for different nutritional

states have been presented. The committees have been

explicit also in addressing the statistical issues of population intakes versus individual intakes and in differentiating

between the low-risk individual intake (the conventional

RDA) and population mean intakes consistent with a low

prevalence of inadequate or excessive nutrient intakes. It

22
is these types of definitions and specifications that are

needed in the analysis and interpretation of data sets such

as NHANES and NFCS.)

Again, my thanks for inviting me to the meeting; it

was an interesting and informative session. I wish you and

your colleagues every success with the important task that

now lies ahead in designing NHANES 111,

References

National Academy of Sciences, National Research Council,

Subcommittee on Criteria for Dietary Evaluation, Coordinating Committee on Evaluation of Food Consumption

Surveys, Nutrient adequacy Assessment using food consumption surveys, Washington: National Academy Press. 1986.

Morgan KJ, Johnson SR, Goungetas B. An analysis of a

12-d:~ydata series using persistence measures. Am J Epidemiol 126:326-35, 1987.

Beaten GH, Milner J, Corey P, et al. Sources of variance in

24-hour dietary recall data: Implications for nutrition study

design and interpretation. Am J Clin Nutr 32:2546-59.1979.

4.

5.

6.

7.

8.

Beaton GH. Interpretation of results from diet history

studies. In: Kohlmeier L, ed. The diet history method.

Proceedings of the second Berlin meeting on Nutritional

Epidemiology. London: Smith-Gordon. 1991.

Rosner B, Willett WC. Interval estimates for the correlation

coefficient corrected for within person variation: Implications for study design and hypothesis testing. Am J Epidemiol 127:377-86.1988.

National Academy of Sciences, National Research Council,

Coordinating Committee on Evaluation of Food Consumption Surveys. National survey data on food consumption:

Uses and recommendations, Washington: National Academy

Press. 1984.

National Academy of Sciences. Unpublished report of a

workshop to plan a new Recommended Dietary Allowance.

Washington: National Academy of Sciences. 1985,

Beaton GH. New approaches to the nutritional assessment

of population dietary data. In: Proceedings of the Tenth

National Nutrient Data Bank Conference. U.S. Department

of Commerce, 1986.

23
Chapter 2

Dietary assessment

issues related to

cancer for NHANES Ill

by Gladys Block, Ph.D., National Cancer Institute

It has been estimated by Doll and Peto (1) that

35 percent of human cancer maybe associated with diet,

although the range of reasonable estimates is very wide,

from 10 to 70 percent in their estimation. In addition to

suggestive international data on correlations between diet

and disease, numerous epidemiologic studies have found

associations between dietary factors and specific cancers,

The National Health and Nutrition Examination Survey

(NHANES), of the National Center for Health Statistics

(NCHS), offers the unique opportunity to learn what

people are eating, essential to the design of effective

health promotion programs; to study changes over time

and evaluate the success of large-scale interventions

designed to alter dieta~ habits; and to evaluate the

relationship of dieta~ factors to physiological-biochemical

factors and health outcomes, including cancer.

The first two of these functions, assessing dietary

consumption and changes in consumption over time, can

be performed as well by the Food and Drug Administration and the U.S. Department of Agriculture (USDA),

chiefly through the USDA’s Nationwide Food Consumption Survey (NFCS). The existence of that sister survey is

important to bear in mind, for it has important consequences for the methodologic choices that must be made

in the design of the third National Health and Nutrition

Examination Survey (NHANES 111), NFCS, as its name

implies, is designed to assess food consumption; NHANES,

on the other hand, is the only large national survey

designed and mandated to assess not only dietary intake

but the relationship between diet and health, To quote

from the NCHS document on the plan and operation of

the second NHANES (NHANES II): “The essential differentiating characteristic of the health examination surveys is their primary concern with those kinds of healthrelated data obtained only (or at least optimally) from

direct medical examinations. . , , The NHANES surveys in

their present form were designed to permit relating nutritional variables to health measures” (2). This has important methodologic implications for the nature of the

dietary assessment methodolo~ required to achieve this

goal.

Specific dietary constituents and

cancer

There is laborato~ or epidemiologic support for, and

current research interest in, the relationship between

cancer and a number of dietary constituents, including the

following: Total calories; total fat; percent of calories

derived from fat; specific fat fractions such as polyunsaturated fatty acids; cholesterol; dietary fiber and fiber fractions; vitamins A, C, and E; carotenoids; folacin; trace

minerals including selenium and zinc; alcohol; and others,

However, it is important to recognize that a few years ago

the list would have been different; a few years in the

future the list will almost certainly be different. Consequently, it is unwise to focus our attention on a limited list

of a few nutrients. Rather, to be maximally useful even to

the state of knowledge that will exist by the time the

survey itself is completed, the dietary assessment must be

designed to capture, to the best of our ability, the whole

diet rather than a limited number of nutrients. Only by

capturing the whole diet can new hypotheses be generated

and tested, and existing hypotheses tested adequately, and

only by capturing the whole diet can we begin to examine

dietary interactions, a potentially important area about

which our understanding is minimal at best,

Dietary assessment methods

These have been reviewed extensively elsewhere (3,4)

and will only be summarized here.

24-hour recalls

In this method, the detailed dietary intake over the

past 24 hours is reported. It should be used with threedimensional models to enhance portion size accuracy, as

research indicates that individuals of all ages are poor

estimators of their portion sizes, and differing portion size

assumptions also constitute an important source of discrepancies among coders and data bases, In addition, interviewer probes should be detailed enough to preclude “not

further specified” codes. The latter are an important

source of imprecision in group estimates, especially if the

nutrient values for the “not further specified” code are

assigned by the so-called “conservative” approach, which

uses the most extreme value in the data base (the fattiest

cut of meat, for example), regardless of its frequency of

consumption.

The 24-hour recall has been used in previous NFCS

and NHANES surveys for children and infants (by means

of surrogate respondents) as well as adults. It provides

valid data on the mean intake of groups or the intake of an

individual in the prior 24 hours. It does not provide valid

24
estimates of the usual diet of individuals over a longer

period of time (5-7).

Diet records

In this method respondents, in principle at least,

record their diet as they are consuming it, thus minimizing

errors of memory, Conscientiously applied, it can capture

components of diet that might be overlooked by a recall

and affords the opportunity to obtain more accurate

portion size estimation through measuring or weighing. It

clearly cannot be applied to children, whose diet must

again be recorded by a surrogate. It requires respondents

who are reasonably literate and comfortable with the idea

of careful recordkeeping and who are sufficiently committed to the goals of the survey to perform the recordkeeping conscientiously. Like 24-hour recalls, it can provide

valid data on groups; its validity for the individual’s usual

intake depends on the length of the record, discussed

below,

Multiple days of recalls or records

This approach is an attempt to obtain dietary data

more representative of the individual’s usual intake than is

a single day’s intake. This issue is discussed further beiow.

Muitipie-day recalis should be obtained as a series of

24-hour recalls rather than as a single 3-day recall, for

example, as there is a serious decrement in memory

beyond the previous 24 hours (8). Furthermore, some data

indicate that the accuracy and completeness of multiple-day

records suffers after the first 2 of 3 days, at least in some

population groups (9),

Frequency questionnaires

In this approach the respondent is asked to indicate

his or her usual frequency of consumption of each of a list

of foods, or sometimes food groups, This may be quantified in a variety of ways in which a weighting factor such

as nutrient quantity in a “standard portion” is multiplied

by the frequency of consumption to obtain a nutrient score

or estimate of the quantitative nutrient content of the diet.

The quantitation can also be designed so that the “standard

portions” are age- and sex-specific, thereby improving somewhat the precision of the nutrient estimate.

The food iist has, in the past, often been targeted

toward the assessment of a single nutrient, such as vitamin

A, or a few nutrients, and then would include only foods

designed to capture that nutrient. That is not an inherent

feature of frequency questionnaires, however, and food

lists can be designed to capture all of the important

nutrients in an individual’s diet. To do so, the food iist

must reflect the population’s major sources of those nutrients, and the food items must be kept reasonably distinct.

A frequency questionnaire containing an extensive food

list designed to capture a broad range of nutrients in an

individual’s diet might then differ from a list-based diet

history solely in that the list-based diet history is designed

to obtain some measure of the respondent’s usual portion

size of each food, and the frequency

Validations are discussed below.

Diet histories

questionnaire is not.

This term has been applied to a wide variety of

approaches. What I take to be the defining characteristics

of a diet history are that it is designed to elicit quantitative

information about the individual respondent’s usual dietary

intake over an extended period of time, with an attempt at

quantitation of the individual’s usual portion sizes of the

foods in his or her diet. The quantitation may require the

respondent to report the usual portion size of each food in

household measures, or the respondent may select a

three-dimensional model or a photograph that most closely

resembles the usual portion. As it is usually defined, a diet

history involves probing for specific food types, portion

sizes, and seasonal differences, requiring a 1- to lVz-hour

interview with a trained nutritionist, However, if it is list

based and structured, a shorter time and nonnutritionist

interviewers may be used.

Although Burke’s original method (10) was primarily

menu based, with lists and recalls used for cross-checking,

many current approaches to diet history interviews are

primarily list based; that is, the individual is prompted

with a list of specific foods, and then the frequency of

consumption and portion size are obtained for those foods

reported as consumed. Menu-based approaches may be

heavily influenced by the individual’s diet of the past few

days (11) and may be distorted by people’s inclination to

report three “normal” meals per day, when in fact modern

diets are less orderly. List-based approaches, on the other

hand, are dependent on the adequacy of the list of foods.

Validity is discussed under “Critique of methods for

NHANES III,” below.

Appropriateness of methods

For children, all of the methods encounter the same

problems: The questionable ability of the child to respond

accurately, and the questionable ability of a surrogate to

report what the child may have eaten outside the home. If

a diet history or quantified frequency approach were used

in NHANES III, preliminary work would be required to

determine the food list appropriate for capturing the diet

of children. Extensive work on this subject is under way at

the University of Texas for the Women, Infants, and

Children (WIC) program (12). Similarly, an optimal diet

history or quantified frequency questionnaire would require

information on children’s usual portion sizes of each of

the foods; this could be obtained from the NHANES II

data, which used three-dimensional models.

For some nutrients, all dietary methods are probably

inappropriate. The selenium content of grain, for example,

depends on the soil in which it was grown, and soils in the

United States differ markedly in their selenium content.

Such nutrients are probably better assessed in biological

samples,

25
The appropriateness of methods also varies for different study designs and goals, as follows.

1. Determining the adequacy of the dietary intake of the

population and its subgroups –A major function of large

national surveys is to determine the population’s intake of

macronutrients and micronutrients and to assess the adequacy of intake of subgroups defined by age, race, poverty,

or other demographic characteristics. For this purpose,

information is required that is accurate at a group level,

and it must be precise. Whether a group’s mean intake of

vitamin C is 50 milligrams or 60 milligrams has rather

important implications for national health and food policies; it is probably not sufficient simply to know that one

group is higher than another in dietary intake. The most

efficient and valid method for obtaining accurate group

data is a carefully performed 24-hour recall, as done in

previous NHANES.

However, it is important to note that (a) this is not the

only, and perhaps not the primary, role of NHANES, as

suggested in the quotation above; (b) it can be performed

adequately by the other national survey, USDA’s NFCS,

for which this clearly is the primary mandated purpose.

2, Determining temporal trends and comparability with

prior ATL4NES –To be fully comparable with prior

NHANES data, NHANES III and future surveys would

have to use identical methodo10gy-24-hour recalls. However, even if 24-hour recalls were used, the data would still

not be fully comparable, as a decision has been made to

use the USDA food data base rather than the NHANES

data base, The latter included considerable manufacturer

data and food codes for mixed dishes that either do not

exist or are calculated differently in the USDA data base.

Thus, even an NHANES III that used one or more

24-hour recalls would be only partially, not fully, comparable with prior surveys.

If a frequency questionnaire or diet history were used,

there would be uncertainty as to whether apparent changes

in group mean intake were due to real dietary changes or

to methodologic differences. It is possible, however, to

construct these methods so as to provide group mean

estimates that are quite closely comparable to those of

NHANES II (13). This is done by making use of the

food-specific portion sizes reported by NHANES II

respondents using three-dimensional models. Table 1 shows

group mean caloric data for two sex and five age categories, from the NHANES II survey, and from the National

Cancer Institute’s list-based diet history questionnaire

(13), None of the list-based diet history group mean

estimates differs statistically significantly from NHANES 11

estimates, Moreover, the ranking of the age-sex groups

and the large range in intake is well presewed. Similar

data are seen for the other nutrient measures. Thus, it

may be possible to evaluate changes in group means even

if the methodology is changed, provided it is developed

appropriately,

It is important to evaluate the costs, both monetary

and opportunity costs, of attempting to preseme strict

Table 1. Mean calorie values from NHANES II and list-based diet

history (LBDH) (including both self- and interviewer-administered

values), by sex and age

NHAI”JES // LBDH

.9arrdard Standard

Sex and age Number Mean error Mean error

Male

26-34 years . . . . 34 2,746 52 2,624 141

35-44 years . . . . 107 2,434 47 2,328 66

45–54 years . . . . 121 2,371 46 2,237 69

55–64 years . . 146 2,076 26 2,068 54

65–74 years . , . . 125 1,818 24 1,851 59

Female

25–34 years . . . 98 i ,633 25 1,696 49

35-44 years . . 42 1,584 29 1,620 79

45-54 years . . . . 36 1,443 27 1,608 93

55-64 years . . . . 64 1,402 19 1,488 55

65–74 years . . . . 34 1,301 16 1,406 84

1NHANES(National Health and Nutrition Examination Sutvey) II values were calculated using

SESUDAAN to adjuat for sample design.

SOURCE Block et al. (13).

comparability by retaining the 24-hour recall and to consider other sources of time-trend data. This is exactly the

kind of information that is obtainable from the USDA’s

NFCS. Its design and mission allow for the collection of

group mean data comparable from survey to survey. On

the other hand, the use of 24-hour or few-day data in

NHANES 111would vastly diminish the value of all of the

examination and laboratory data and subsequent health

followup, because it would not be possible to relate the

individual’s health or examination and laboratory measurements to dietary intake with anything like the power that

individually valid data would provide. It is exactly considerations such as these that led the NCHS and National

Institutes of Health investigators designing the NHANES I

Epidemiologic Followup Study to employ a focrd frequency approach, so as to have data on an individual’s

long-term usual intake, which is more relevant to health

outcomes. Thus, although precise intake and time-trend

data for groups may be obtainable only with 24-hour or

3-day data, the existence of the other large national

surveys makes it possible to consider other methods for

NHANES III to better serve its unique health assessment

roles.

3. Using cross-sectional studies – An important role of

NHANES is to provide information about the joint distribution of demographic, dietary, and physiologic characteristics in the population and to provide information for

analyses of the relationship between dietary and physiologic factors. Some studies of this sort can be conducted at

the group level and thus could be supported by 24-hour or

few-day data. It is possible, for example, to obtain useful

information from a trend analysis in which the mean

dietary intakes at successive levels of income or age are

examined. However, as Lilienfeld and Lilienfeld have said,

“In studying group characteristics, the results of such

studies provide clues to etiological hypotheses” but “may

26
suflkr from an ‘ecologic fallacy’. . , , Associations established by studying characteristics of population groups

lUWCa greater chance of being indirect than those established by studying characteristics in individuals, In other

words, it is preferable to have an association based on data

chtuincd at the level of the individual and not at the level

of a population or group” (14).

An analysis of dietary and serum cholesterol is a case

in point. Studies of the NHANES I and II data have found

little or no relationship between dietary and serum cholesterol (15,16). However, such a relationship could hardly be

Wcctable in those data because l-day dietary data are

l;trgcly irrelevant to an individual’s longer term intake.

Thus, to be maximally useful for cross-sectional studies,

dietary data for NHANES III should reflect the individuU1’Susual dietary intake; 24-hour or few-day diet information dots not achieve this purpose.

Analytic epidemiology- cohort and

case-control studies

In order to understand and evaluate the relationship

bctwccn a dietary factor and a disease, cohort studies

(prospective) and case-control studies (retrospective) are

performed. In the former, individuals are characterized

with respect to an exposure, and their subsequent disease

outcome is examined. In the latter, individuals are defined

with respect to the presence or absence of a disease and

then characterized with respect to their exposure to a

tlctor at some time in the past. For both types of studies,

it is important to note that individuals must be characterized or ckissified with respect to their exposure to a factor

so thut they can be placed in, for example, quartiles of the

distribution; that is, the individual’s true usual level of

intalw of a dietary factor must be known. Group mean

values are of little value.

Consequently, if the dietary assessment method used

in NHANES III is capable of assessing the individual’s

usual diet, this survey will lend itself to the evaluation of

hypotheses regarding the possible relationship between

dietary factors and disease outcomes or physiologic states.

Dietary methods that cannot be used to at least place

individuals into the correct quartile or so of the distribution of intake of a nutrient will not permit studies of this

sort with any statistical power.

Prospective studies can be conducted within NHANES

111if tho appropriate identifier data are collected; individuals would be categorized with respect to their usual

intcdw at baseline and their subsequent health outcome

tracked. Retrospective studies would be conducted by

wuiting until health outcomes have been tracked at some

time in the future, identifying cases of a disease and

controls, and then looking back at and classifying them

with respect to their usual intake at baseline. Thus,

identical data are needed for the two types of studies.

It is likely that in relation to the initiation or even

promotion of cancer, the diet at some time in the past is

whilt is relevant, Although some methods can provide

moderately good estimates of past diet (17–19), such

methods would be inappropriate in NHANES III. It

would be inefficient to attempt to collect information on

past diet from all respondents, when only a small subset of

cases and controls will be used. Rather, the current usual

diet collected by NHANES III will become the “past” diet

when cases and controls are identified in the future.

Critique of methods for NHANES Ill

24-hour recalls

In my view it would be a grave mistake for NHANES III

to use only a 24-hour recall, either with or without a

subsample in which two recalls were collected. Despite the

benefits of 24-hour recalls with respect to group data and

comparability, the opportunity costs are just too great to

justify their use –costs such as the inability to make

inferences about the relationship of diet to health or to

physiologic factors such as serum cholesterol. All of the

health measures of interest, all of the sociobehavioral

factors, and all disease outcomes are maximally interpretable only in relation to the usual diet of an individual.

There is ample evidence that, although 24-hour recalls can

adequately reflect the mean values of groups (and even

that is questioned by James, Bingham, and Cole (20)),

they are unrepresentative of an individual’s usual intake

(5-7). This failure is not relieved by limiting analysis to the

proportion of various nutrients in relation to reported

caloric intake. Indeed, in Beaton et al. (5) data, the ratio

of intraindividual to interindividual variability actually

increased when nutrients were expressed as a percent of

energy. They indicated that “the elimination of the effect

of energy intake by calculation of the ratio of nutrient: energy would be expected to reduce variability. Nevertheless,

the relatively high intraindividual variability suggests wide

variation, day by day, in the composition of self-selected

diet,” This position is also supported by James, Bingham,

and Cole (20), who concluded that “an individual’s variable intake of protein and energy is sufficient from day to

day to limit the possibility of using nutrient density rather

than absolute intakes as discriminators between

individuals. ”

Multiple days of recalls or records

If 1 day provides an inadequate characterization of an

individual’s usual intake, the next logical suggestion is to

increase the number of days. Thus, NFCS and numerous

other studies use 3 consecutive days of diet, either by

record or a combination of record and recall, in an

attempt to achieve both quantitative precision and greater

representativeness of usual diet. How many days, then, are

required to obtain adequate representativeness of an

individual’s usual diet?

This question has been studied intensively in the last

decade, but it goes back at least as far as Chalmers et al.

27
(21)in 1952.Chalmers et al. came to the same conclusions

as more recent investigators such as Beaton et al. (5,6),

Liu et al. (22), and Sempos et al. (23); it remains to be

seen whether the later conclusions will be heeded any

more than the earlier ones, Their data all indicate that a

small number of days of diet information does not represent an individual’s longer term usual intake with any

precision. Indeed, an individual’s diet is sufficiently variable from day to day that intraindividual variability is

greater than interindividual variability for virtually all

nutrients studied. The results are extremely consistent

across all of these investigators and their different study

populations.

Beaton et al. (5,6) found that to be within f 20

percent of the true usual intake (a rather generous criterion, representing, for example, between 1,600 and 2,400

for a true usual intake of 2,000 calories) would require 7

days for males and 10 days for females. For protein, it

would be 13 days for males and 10 days for females. For

percent of calories from fat, the best case in Beaton’s data,

it would require 3 days for males but 5 days for females.

For most micronutrients the situation is even worse. If one

wanted precision to within & 10 percent, a great many

more days would be required.

Liu et al. (22) examined the ability to classify individuals correctly into quintiles of the distribution. To have

less than a 5-percent chance of grossly misclassifying

someone from the first to the fifth quintile of cholesterol

intake, again a rather generous criterion, would require

7 days of diet information. To avoid misclassi&ing someone

from the first to the third quintile or vice versa would

require 11 days.

Sempos et al. (23) used a somewhat different criterion. Suppose one wanted to ensure that an observed

correlation between a dietary factor and a physiologic

factor such as serum cholesterol was at least as much as

0.9 of the real correlation. This would take 7 days for

calories, 9 days for protein, 6 days for calcium, and at least

15 days for micronutrients such as the B vitamins.

Other investigators have found similar results. Balogh,

Kahn, and Medalie (24) found that to be within k

20 percent for 90 percent of the population would take 9

days for calories and 10 days for cholesterol. James,

Bingham, and Cole (20), in England, found that to correctly classify 80 percent of men into the correct third of

the distribution would require 5 days for calories, 9 days

for fat, and 18 days for percent of calories from fat.

Finally, to give the 1952 paper of Chalmers et al. its due

(21), they calculated that to have a 95-percent confidence

interval equal to & 15 percent of the then Recommended

Dietary Allowance for calories, one would need 14 days

for men and 11 days for women.

As Beaton has asked (25), “What do we think we’re

measuring?” Three days look good because they give us

reasonable means (which even 1 day can do) and because

we have not looked at the intraindividual variability of

such 3-day estimates or their representativeness for a

larger number of days in an individual. Balogh, Kahn, and

Medalie (24) found that the more days of data that were

collected, the more intraindividual variability was revealed.

In data of Willett et al. (26), even two 7-day records were

correlated with each other at only about 0.5.

In most of the above data I have quoted the best

cases, the macronutrients that one would suppose were

reasonably stable from day to day or over a few days. For

most of the micronutrients the situation is worse, and

investigators have found 10, 15, 20, or more days to be

needed. For vitamin A, the situation is totally hopeless.

In addition to the issues of precision and representativeness, multiple-day data, as they would probably have to

be collected in NHANES III, would not relieve the serious

confounding of season and region caused by logistical

necessity, In previous designs, at least, the southern regions

were examined in the winter and the northern regions, in

warmer seasons. The result is that the Northeast, examined in the warmer months, appears to have higher intakes

of vitamins A and C and more consumption of fruit, for

example, than does the South, examined in the opposite

season; and the Midwest and West, spanning both northern

and southern areas, have intermediate values. The result

is that few valid inferences can be drawn about regional

differences in dietary intake, nor can geographic correlations regarding diet and disease be drawn. Several-day

records, collected during the time period of the examination, would only perpetuate this confounding of season

and region.

My reading of the above data is that no number of

days that is within the range of feasibility for NHANES III

is capable of providing yearlong representativeness or

nutrient estimates that one can be confident are within

+ 20 percent of the true usual intake of an individual.

Frequency questionnaires

Although frequency questionnaires have often been

targeted to a few nutrients, they can be designed to obtain

a much broader picture of the individual’s diet. The

questionnaire designed by Dr. Willett at Harvard is an

example of such an instrument.

Are such instruments valid? That is, can they either

categorize individuals along the distribution of intake

and/or provide accurate quantitative estimates? The question should not be given a general answer, because the

performance of a questionnaire depends profoundly on

the exact food list, as well as the portion size and nutrient

content assumptions. Observed correlations may be as

much a function of imperfect portion size assumptions

made by the investigators, for example, as a function of

the respondent’s ability to report his or her usual diet.

Moreover, the appropriateness of the reference method

must be carefully examined. Given the above data on

day-to-day variability, it is unlikely that a 7-day record, for

example, is an appropriate “truth” measure for usual diet,

against which a test instrument is to be judged. (In

Beaton’s data, for example, only percent of calories from

carbohydrate and fat could be obtained from both men

and women in 7 days to an accuracy of * 20 percent.)

28
Only Dr. Willett’s validation study approaches a number

of cloys (four 7-day records) that might adequately represent usual intake for nutrients other than vitamin A. Just

as Willett’s correlations were poorest for dietary vitamin A

(for which the fault might lie with the unrepresentativencss of the “reference” method rather than the questionnaire), so also correlations seen in other studies using

shorter reference periods may reflect unrepresentative

rcfcrcnce periods rather than unreliable questionnaires.

Surprisingly little research has been carried out on the

validity of the nutrient estimates resulting from pure

frq-remcy questionnaires, that is, frequency questionnaires that, although they may be quantified, do not

fittempt to ascertain the respondent’s portion sizes. Virtu-

;dly all of the abbreviated questionnaires usually cited in

this connection actually included some quantitation of the

individual’s usual portion sizes. Jain et al. (27) and Balogh,

Kahn, ond Medalie (24), for example, used pictures of

tliffercnt portion sizes, Browe et al. (28) had the subject

“comptire his usual portion with (a stated standard portion) and make appropriate changes on the questionnaire.”

Rcshef and Epstein (29) also state that they collected “the

size of the usual portion.” Several authors have examined

the reliability of the frequency reports themselves (for

example, number of times consumed carrots) on repeat

administrations or by spouse pairs, It is difficult, however,

to evaluate the meaning of such agreement for the validity

of actual nutrient estimates.

Perhaps the only validation of a pure frequency instrument (that is, one that does not entail variable portion

sizes) aimed at assessing a wide range of nutrients and

using a reasonable reference measure is that of Willett et

al, (26), They administered a 61-item questionnaire and

compared it with the mean nutrient intake from four 7-day

diet records obtained over the course of a year.

The quantitative nutrient estimate was obtained by

multiplying the reported frequency by the nutrient amount

obtained in a portion, which was stated on the questionn:lirc (such as “cream cheese, 1 oz.”). In principle,

respondents could take their own portion size into account

to some extent by modifying their reported frequency.

Thus, if respondents felt that they usually had 2 ounces of

crwrm cheese instead of 1 ounce, they could change their

rwsponse catego~~ from “2-3 per week” to “4-6 per

WL.NA.”As it seems unlikely that very many respondents

uctually would do this carefully, I have included the

instrument under frequency questionnaires rather than

diet histories,

Correlations between the food frequency questionnaire and diet record nutrient estimates for the prior year

w’crc approximately 0,45 without adjustment for reported

caloric intake and about 0.53 after adjustment for calories.

(It should be noted that Willett’s study was done in a

self-administered format. It is likely that correlations would

IMVCbeen somewhat higher if it had been administered by

an interviewer,)

Agreement in this range is greater than chance and

indicates a fair ability to categorize individuals at the

extremes. For example, of individuals in the lowest quintile by diet record, 40–50 percent were in the lowest

quintile by the food frequency questionnaire. Correlations

in this range are also comparable to the degree of precision that could be obtained by a few days of diet records.

However, they also involve a considerable degree of misclassification. The consequence of this misclassification is

that correlations in this range require a fivefold increase in

sample size to detect a statistically significant risk trend

(29), and the observed risk ratio would nevertheless still

be an underestimate of the true risk ratio.

It may be that for some nutrients a pure frequency

questionnaire would be adequate to rank individuals reasonably accurately along the distribution of intake, all that

is really needed for analyses of diet-disease relationships.

Samet, Humble, and Skipper (30) make a reasonable case

that vitamin A is such a nutrient. However, it seems clear

that this is not the case for most nutrients and that some

measure of individual portion size is needed.

Diet histories

Some forms of diet histories require a 1- to 1%.-hour

interview by a nutritionist. Time constraints make it clear

that this form of diet history is not feasible for

NHANES 111.Several investigators, however, have developed brief diet history questionnaires designed to gather

data (a) representative of an individual’s usual diet and

(b) quantified based on information about the respondent’s

usual portion sizes. If such approaches are structured and

list based, they may provide reasonably accurate quantitation about an individual’s usual diet in a shorter interview

time.

The validity of such methods as accurate estimators of

an individual’s usual intake is not easy to evaluate, as

“usual” encompasses a longer period of time than most of

the methods against which it has been tested, Burke et al.

(31) and Reed and Burke (32) evaluated the method

against clinical criteria and found excellent agreement.

When compared with 7-day records, some but not all

investigators have found that the history method gives

higher mean values, and correlations have generally ranged

from 0.4 to 0.8. Some validation studies are summarized in

table 2.

Balogh et al. (11) found correlations ranging from 0.6

(for total carbohydrate) to 0.8 (for fats) behveen the mean

of 8 or more days over a year and a diet history based on

69 food items. Jain et al. (27) validated a different 69-item

questionnaire, quantified by the respondent’s choice among

sets of photographs of different portion sizes, against a

detailed l-hour frequency interview. They reported correlations of 0.6 to 0.7 for nine macronutrients, vitamin C,

and fiber. The interview itself, however, when validated

against multiple diet records among 16 men, achieved

correlations of only 0.24 to 0.61, averaging 0.45 (33).

Hankin, Reynolds, and Margen (34) found similar results

for a short diet history questionnaire, targeted toward

dietary fat, in which respondents selected photographs of

small, medium, and large portions.

29
Table 2. Validationstudies of list-based diet histories (LBDH)

Validation study Pearson correlation

Balogh et al. (11), LBDH vs. 7-day waighed

records, macronutrients. . . . . , . . . , . . . . . 0.7-0.9

Balogh, Kahn, and Medalie (24), LBDH vs. 8+

24-hour recalls, macronutrients. . . . . . . . . . . 0.7-0.8

Jain et al. (19,27), 69-item LBDH vs. interview,

macronutrients, micronutrients . . . . . . . . . . . 0.6–0.7

Hankin, Reynolds, and Margen (34), LBDH vs.

interview,fats, . . . ...<. . . . . . . . . . . . . . 0.6-0.7

Block, et al. (37,39,40), Cummings et al. (38),

macronutrients, micronutrients . . . . . . . . . . . 0.5-0.7

Validations of thk NCI questionnaire have rwcalecl

correlations of approximately 0.6 \vith various refercncc

methods, as well as considerable accuracy in the estimation of absolute nutrient levels (37-39). Excellent corr&ttions with serum carotenoids have also been found (40).

Thus, the work of Hankin, Jain, Balogh, and others, as

well as our work at NCI, all suggest that correlations in

the range of 0.6 to 0.8 can be achieved with Iist-based diet

histories, quantified either with photographs or with a

“small-medium-large” approach.

Other list-based diet histories have been developed by

other investigators. Byers et al. (35), for example, describe

a 128-item questionnaire. Although they usually administer it as an extensive interview, they have developed

shorter versions targeted toward specific nutrients. Similar

extensive instruments have been used by others (36), and

some might lend themselves to modification so as to fit

within the time limit of NHANES III.

We at the National Cancer Institute (NCI) have also

developed a list-based diet history (called “SelfAdministered Diet History” in the methods paper (13) but

capable of being interviewer administered as well). By

using a data-based approach, selecting foods on the basis

of their population contribution to calories and 17 other

major nutrients, and keeping the food items reasonably

distinct, the instrument is designed to capture all of the

major nutrients in the diet with the most efficient food list.

The list contains foods representing at least 90 percent of

U.S. dietary intake for energy and each of the 17 nutrients

in the NHANES II data base. In addition, portion sizes

were developed on the basis of age- and sex-specific data

from NHANES II, thus making use of large-population

data based on three-dimensional models, The effectiveness of this approach can be seen by the good agreement in table 1 and the ability to distinguish groups by

age and sex. Individuals are asked to quantify their usual

portion of each food by indicating whether it is small,

medium, or large with respect to a stated medium portion.

However, other approaches could be envisioned, such as

having the individual select a photograph or threedimensional model for each food, as is done in the

list-based diet histories of Hankin, Reynolds, and Margen

(34), Jain et al. (27), and others.

The dietary intake of special demographic groups or

individuals can be captured more precisely by the inclusion of an open-ended section in which foods frequently

eaten but not on the main list can be reported. The

questionnaire is administered in an average of 25-35

minutes. Furthermore, if the list is developed on the basis

of major nutrient contributors, much shorter lists can yield

correlations of 0.90 to 0.98 with the longer instrument and

correlations with reference data similar to those achieved

by the longer instrument, thus permitting assessment of a

wide range of nutrients in a brief questionnaire (37),

30

Recommendation

The above discussion leads me to the following conclusions, Because NHANES was designed “to permit

relating nutritional variables to health measures” (2),

NHANES HI needs dietary data representative of an

individual’s usual intake over an extended period of time

in order to examine diet-disease relationships. Data bused

on 24-hour recalls are thus totally inadequate for some of

the major goals of NHANES. Multiple days of recall or

records, up to at least 14 days or so, provide the illusion of

precision with representativeness, but not the reality. Pure

frequency questionnaires, in which no individual portion

size information is collected, probably provide less precision in nutrient quantitation than do diet histories and

thus provide less ability to categorize individuals along the

distribution of intake, at least for most nutrients.

I believe the data are quite clear that list-based diet

histories, developed and quantified appropriately, can

provide the individual representativeness needed for the

health goals of NHANES III. For some nutrients, at least,

they can also yield nutrient estimates that accord well with

known diet or more precise group methods (13,17,38,39,

and table 1).

Other important societal goals, such as accurate group

mean estimates and measures of changes in intake over

time, can be provided by the 3-day recall or record format

used in the USDA’s Nationwide Food Consumption Survey,

In this way the large national surveys would complement

rather than duplicate each other, and the major goals of

both could be fully achieved.

I conclude therefore that NHANES III should use the

method that achieves the greatest degree of represcntativeness of the individual respondent’s usual diet so as to

maximize the usefulness of the data unique to

NHANES-the physiologic and health data. If 7 to 14 (or

more) days of diet recalls could be secured reliably from

all respondents in two different seasons, this might bc the

method of choice. Because this is probably not logistically

or economically feasible, I believe there is clear evidence

that a list-based diet history, with some quantitation of

individual portions, can achieve a degree of representativcness that will permit meaningful analytic studies of the

relationship among diet, physiologic measures, and health,
References

1.

-.7

.3.

4,

.,i

6,

7,

L, Y

9,

10,

11.

12.

1~,

14.

Is,

lb.

17.

1s.

lg.

20.

Doll R, Peto R. The causes of cancer: Quantitative estimws of avoidable risks of cancer in the United States

today .New York: Oxford University Press, Inc. 19S1.

McDowI1 A, Engel A, Massey JT, Maurer K. Plan and

opcrtition of the second National Health and Nutrition

Examination Survey, 1976-80, NationalCenter for Health

Statistics. Vital Health Stat 1(15). 1981.

Block G, A review of validations of dietary assessment

methods, Am J Epidemiol 115:492-505. 1982.

Burk MC, Pao EM. Methodology for large-scale surveys of

household and individual diets. Home Economics Research

Report no. 40. Washington: U.S Department of Agriculture.

1976,

Bcaton GH, Milner J, Corey P, et al. Sources of variance in

~~.hour dieta~ recall data: Implications for nutrition study

design and interpretation. Am J Clin Nutr 32:2456-9.1979.

Bcatcm GH, Milner J, McGuire V, et al, Sources of variance

in 24-hour dieta~ recall data: Implications for nutrition

study design and interpretation —Carbohydrate sources, vitamins, and minerals. Am J Clin Nutr 37:9S6-95. 19S3.

Todd KS, Hudes h4, Calloway DH. Food intake measurement: Problems and approaches. Am J Clin Nutr 37:139-46.

1!?83.

Adclsmt S. Some problems in collecting dietary data from

individuals. J Am Diet Assoc 36:453–61. 1960.

Gwsovitz h!, Madden JP, Smiciklas-Wright H. Validity of

the 24-hour dietary recall and seven-day record for group

comparisons. J Am Diet Assoc 28:21S-21. 1952.

Burke BS, Stuart HC. A method of diet analysis. J Pediatr

12:493-503, 193S.

Bulogh M, Medalie JH, Smith H, Green JJ. The development of u dietary questionnaire for an ischaemic heart

disew survey. Isr J Med Sci 4:195-203.1968.

McPhwsmr RS, Nichaman MZ, Kohl HW, et. al, Intake and

food sources of dietary fat among school children in The

wood]ands, Texas. pediatrics 86(4):520-6+ 1990.

Block G, Hartman AM, Dresser CM, et al. A data-based

approach to diet questionnaire design and testing. Am J

Epidemiol 124:453-69, 1986.

Lilienfeld AM, Lilienfeld DE. Foundations of epidemiology.

New York: Oxford University Press Inc. 14,291.1980.

Hwlur WR, Hull AL, Schmouder RP, et al. Dietary intake

:md cardiovascular risk factors: Part II, serum urate, serum

cholesterol, and correlates. National Center for Health

Statistics. Vital Health Stat 11(227). 19S3.

Nichols AB, Ravenscroft C, Lamphiear DE, Ostrander LD

Jr. Independence of serum lipid levels and dietary habits:

The Tccumseh Study. JAMA 236:1948-53, 1976.

Sobell J, Block G, Koslowe P, et al. Validation of a retrospective questionnaire assessing diet 10-15 years ago. Am J

Epidemiol 130:173-87.1989.

Jensen OM, Wahrendorf J, Rosenqvist A, et al. The reliability of questionnaire-derived historical dietary information and temporal stability of food habits in individuals. Am

J Epidemiol 120:281-90. 19S4,

Jain M, Howe GR, Johnson KC, et al. Evaluation of a diet

history questionnaire for epidemiologic studies. Am J Epidemiol 111:212-9.1980.

James WPT, Bingham 5A, Cole TJ. Epidemiological assessment of dietary intake. Nutr Cancer 2:203-12. 19S1.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

35.

36.

37.

38.

39.

40.

Chalmers FW, Clayton MM, Gate LO, et al. The dietary

record —How many and which days? J Am Diet Assoc

28:711-7.1952.

Liu K, Stamler J, Dyer A, et al. Statistical methods to assess

and minimize the role of intra-individual variability in

obscuring the relationship between dietary lipids and serum

cholesterol, J Chron Dis 31:399-418.1978.

Sempos CT, Johnson NE, Smith EL, et al. Effects of

intraindividual and interindividual variation in repeated

dietary records. Am J Epidemiol 121:120-30.1985.

Balogh M, Kahn H, Medalie JH. Random repeat 24-hour

dietary recalls. Am J Clin Nutr 24:304-10.1971.

Beaton GH. What do we think we are estimating? In: Beal

VA, Laus MJ, eds. Proceedings of the Symposium on

Dietary Data Collection, Analysis, and Significance, June

15-16, 1981. Massachusetts Agricultural Experiment Station, Research Bulletin No. 675. Amherst: University of

Massachusetts. 36-49.1982.

Willett WC, Sampson L, Stampfer MJ, et al. Reproducibility

and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol 122:51-65. 19S5.

Jain MG, Harrison L, Howe GR, et al. Evaluation of a

self-administered dietary questionnaire for use in a cohort

study. Am J Clin Nutr 36:931-5. 1982.

Browe JH, Gofstein DM, Medley DM, et al. Diet and heart

disease study in the cardiovascular health center. J Am Diet

ASSOC 48:95-108.1966.

Reshef A, Epstein LM. Reliability of a dieta~ questionnaire. Am J Clin Nutr 25:91-5.1961.

Samet JM, Humble CB, Skipper BE. Alternatives in the

collection and analysis of food frequency interview data. Am

J Epidemiol 120:572-S1, 19S4.

Burke BS, Beal MA, Kirkwood SB, et al. Nutrition studies

during pregnancy. Am J Obstet Gynecol 46:3S-52. 1943.

Reed RB, Burke BS. Collection and analysis of dietary

intake data. Am J Public Health 44:1015–26. 1954.

Jain M, Howe GR, Johnson KC, et al. Evaluation of a diet

history questionnaire for epidemiologic studies. Am J Epidemiol 111:212-9.1980.

Hankin JH, Reynolds WE, Margen S. A short dietary

method for epidemiologic studies. II. Variability of measured

nutrient intakes. Am J Clin Nutr 20:935-45.1967.

Byers T, Marshall R, Fiedler R, et al. Assessing nutrient

intake with an abbreviated dietary interview. Am J Epidemiol 122:41-50.1985.

Van Horn L. Cardia Study. Personal communication. 19S6.

Block G, Hartman AM, Naughton D. A reduced dietary

questionnaire: Development and validation. Epidemiology

1:5S-64. 1990.

Cummings R, Block G, McHenry K, et al. Evaluation of hvo

food frequency methods of measuring dietary calcium intake.

Am J Epidemiol 126:796-S02. 19S7.

Block G, Woods M, Potoslg A, Clifford C. Validation of a

self-administered diet history questionnaire using multiple

diet records. J Clin Epidemiol 43(12):1327-35. 1990.

Block G, Bowen P, Burgess M. Relationship behveen serum

and self-reported dietary measures of carotenoids. Unpublished data.

31
Chapter 3

Dietary methods in

cardiovascular disease

critique

by Patricia J. Elmer, Ph,D,, R.D,, Division of

Epidemiology, University of Minnesota

Introduction

Interest in the effects of nutrients and related dietary

constituents on cardiovascular disease risk factors and

outcomes is constantly increasing. An extensive body of

literature exists, evaluating many nutrition and heart disease relationships. An enormous amount of knowledge is

still to be gained about nutritional influences on the

development and treatment of heart disease as well as the

development of eating patterns, The relationship between

dietary intake of fat and cholesterol and serum cholesterol

and cardiovascular disease mortality has been extensively

evaluated.

A myriad of questions related to dietary intake and

risk factor development, levels of risk factors, and disease

outcomes are currently under study. There is a great deal

of interest in the interrelationships and interactions of

various dietary intakes and other risk factors, health

practices, and behaviors. Examples of current questions

under study include: How does smoking affect nutrient

intake, weight status, and disease outcome? How do

smoking, alcohol, fat, caffeine, and exercise interact to

affect serum lipids? What effect do specific fatty acids,

monounsaturates, and omega three fatty acids have on

serum lipids and coronary heart disease (CHD) mortality?

What effect do dietary fat intake and total calorie intake

have on body fat distribution? What effect do different

minerals have on the etiology of hypertension? Are there

particular ages at which individuals are particularly susceptible to dietary “insult” or dietary modification? How

might the constellation of factors initiated during adolescence – smoking, alcohol use, use of birth control pills,

other drug use, high fat diet, and the growth spurt —affect

future risk factor development? Which individuals are

sodium sensitive, and what effect do minerals other than

sodium have on their blood pressure? What are the effects

of weight loss, exercise, and fiber intake on serum lipids?

What effect does dietary sodium intake have on sodium

taste preference; do other minerals affect sodium taste

preference? What effect do dietary interventions have on

dieta~ intake and risk factor modification?

As can be seen from this very cursory list of current

research questions, a tremendous diversity exists in the

type and scope of investigations. Early work in nutrition

and cardiovascular disease focused on a few macronutrients, particularly fat and cholesterol. The list of nutrients

under investigation in the area of cardiovascular disease

has lengthened considerably and includes not only calories, fat, and cholesterol but also carbohydrates, alcohol,

caffeine, calcium, sodium, potassium, chloride, magnesium, and fiber. There is renewed interest in specific fatty

acids rather than just the classification of individuals by

saturated fat and polyunsaturated fat and there is interest

in the effects of specific fiber components and specific

food items on risk factors. It is likely that this list will

continue to expand in the future, increasing the need for

comprehensive data on nutrients and diet intake.

When choosing a method for the evaluation of dietary

constituents and cardiovascular disease, a wide variety of

factors need to be taken into consideration beyond which

nutrients will be targeted for evaluation. What are the

major questions to be answered through the data obtained?

What is the nature of the dependent variables to be

evaluated in relation to diet, and how are they to be

measured –for example, mortality, risk factors, and physiological, behavioral, and psychological factors? What are

the limitations of the dietary methodologies employed?

Many studies have failed to show correlations between

nutrient intakes and various cardiovascular disease risk

factors or outcomes within populations, although relationships have been seen between populations, A wide variety

of factors have been suggested to contribute to this lack of

findings (1), A large variability in the measures of the diet

or risk factor may obscure the relationship; hence, crucial

emphasis is put on using appropriate, valid, and reliable

methodology. A very narrow range of nutrient intake in

the population under study may exist. There may be a

homogeneously high intake of the nutrient with a concomitant variation in the susceptibility to the nutrient, as is

thought to be the case with sodium intake. Intraindividual

variation in the nutrient intake or physiologic factor may

be very high or even exceed the interindividual variation,

resulting in misclassification of the individual intake or

risk factor level. Are group or individual data needed to

answer the question under study? There may be reporting

bias in certain subgroups of the population, men versus

women, young versus old, and so forth. There may be bias

in the sampling or survey methods; days of the week may

be excluded, potentially underestimating nutrients; or seasonal differences may exist in geographic areas and may

not be reflected in the survey design, The disease effect

(that is, the hyperlipidemic or hypertensiogenic effect)

may begin in childhood or early adulthood, and current

32
adult intake may not affect the factor under study in this

case, retrospective dietary information would be important, Changes in diet, other health practices, or medication may have occurred because of disease diagnosis or

family history, thus obscuring the diet conditions that may

have led to the disease. Major confounders could exist

that have not been accounted for or measured. Examples

include smoking, medication, exercise, and concurrent

disease conditions. Such factors need to be addressed

when choosing a dietary methodology and in the design

and development of the overall survey so that specific

research questions can be adequately evaluated.

Procedural and organizational questions also need to

be addressed when choosing the dietary methodology. Is

the method feasible given the time, staff, or funding

available’? Is appropriate staff available, and can unbiased

interview techniques be developed? What is the nature of

the coding and calculation process for determination of

nutrients; are there adequate guidelines and coding rules?

Do these procedures change over time? What differences

are there between procedures from one survey to another?

What is the nature of the food table (food codes and

composition) to be used for calculation; what are the

sources and vintage of the data; are the values complete

for the nutrients under study?

A wide variety of methods have been used to estimate

dietary intake in cardiovascular disease evaluations,

including 24-hour recalls, food records, food histories,

food frequencies, weighed diets, and observations. These

methodologies have been reviewed and summarized extensively elsewhere, Given the time and logistics of the

National Health and Nutrition Examination Survey

(NHANES), weighed food intakes and observations of

individuals’ intakes would not be appropriate methodologies. The following will be a discussion of methods and

research questions related to the assigned nutrients for

cardiovascular disease, with recommendations for

NHANES 111,

Sodium and potassium

Excessive sodium and salt intake have been implicated

in the development of hypertension in both animals and

humans (2). Potassium intake is believed to have a potentially beneficial effect on blood pressure through a modifying effect on sodium, Dahl originally suggested the

relationship between habitual salt intake in populations

and the prevalence of hypertension (3), He showed that

populations with low salt intakes have a prevalence of

hypertension that is almost zero, compared with populaticms with high salt intakes, such as southern Japan, where

the prevalence is very high. Glieberman further evaluated

salt intake and blood pressure in other populations; he

provided analyses of the regression of salt intake against

diastolic blood pressure for 25 populations, showing a

significantly greater average blood pressure in populations

with greater salt intakes (4). In these studies, salt intake

was estimated by several methods: 24-hour urine collections and estimates of dietary salt and sodium intake.

These studies did not attempt to standardize blood pressure measurement, and the age groups included differed

among populations. In addition, these studies did not

account for other factors affecting blood pressure, such as

weight, alcohol, exercise, levels of stress, or urbanization.

Despite the limitations of such studies, all of which would

tend to obscure any relationship, a relationship between

blood pressure and sodium intake was established.

Sodium restriction in hypertensives has been shown to

lower blood pressure (5-1 1), In a double-blind crossover

trial of the effects of sodium ingestion in hypertensives,

MacGregor et al. demonstrated a significant blood pressure lowering in the low-sodium condition after just 4

weeks of treatment (11), The effect of sodium intake and

blood pressure in infants was studied using a double-blind

design where newborn infants received formula and baby

foods with either the normal amount of sodium or 50

percent less sodium for 6 months, After 25 weeks blood

pressure was significantly different between the two groups,

with a 2.1 millimeters of mercury difference in blood

pressure between the regular and low-salt groups (10).

Recent studies have also shown a blood-pressure-lowering

effect with potassium supplementation in both hypertensive and normotensive groups. However, this effect is less

well established and is being investigated further (9-13).

Lower blood pressures in Western vegetarian groups

have also been related to high potassium intakes (14,15).

Analyses of NHANES data show relationships between

blood pressure and the nutrients sodium, potassium,

alcohol, and calcium and between blood pressure and

weight (16–18). Estimates of dietary sodium intake have

been made by a variety of methods, including data from

production and sale of table salt; estimates of salt and

other sodium ingredients used in food manufacture; and

data from dietary recalls and records and urine excretion

(19-21), Many different sources of sodium contribute to

the total daily intake of an individual. These include

foodstuffs, water, and medications. Most foodstuffs have a

very low naturally occurring sodium content. The majority

of sodium contributed by foodstuffs comes from sodium

that is added either in food manufacture (as sodium

chloride or other sodium-containing flavoring or presewatives) or by the individual (as discretionary salt during

cooking or at the table).

This addition of salt to the food supply in various ways

and at various levels makes the task of quantifying dietary

sodium intake extremely difficult. Although few prescription drugs contain sodium, many over-the-counter drugs

contain significant amounts (22). For example, some antacids can contribute as much as 1,200 milligrams (mg) of

sodium, and a single dose of aspirin can contain 50 mg of

sodium. Estimates of sodium contributed by major categories are 10–20 percent from naturally occurring sodium,

40–60 percent from sodium added in food manufacture,

25–35 percent from “discretionary” salt, and the remainder

from water and medications.

33
Market basket surveys conducted by the Food and

Drug Administration during the period 1976-79 estimated

available sodium content to be approximately 1,740 mg of

sodium per 1,000 kilocalories (20). Food consumption and

dietary survey data suggest that the total daily sodium

intake ranges from 2,300 mg to 6,900 mg (10 to 14 grams

of sodium chloride) per day. Males consume more sodium

than females, and intake in both sexes declines with age.

This decline is probably due to both a decline in calorie

consumption and a reduction in salt use and consumption

of high-sodium foods as a result of the diagnosis of

hypertension or the concern about hypertension. Results

of a recent statewide risk-factor telephone survey in Minnesota indicated that 30 percent of the adults responded

that they did not use salt at the table, Age breakdown of

the data showed that 65 percent of the adults ages 55

years and over did not use salt, compared with only

20 percent of those under age 55 years (23). Several

dietary surveys indicate that children ages 9–12 years may

consume the highest levels of sodium of all age groups.

Data from NHANES I, based on 24-hour recall data, also

showed that sodium intake vaned with age and also by sex,

with males ages 18-44 years and females ages 6-11 years

consuming the maximum amounts (21). Food group analyses

indicate that grain and cereal products are the largest contributor to sodium intake, followed by meat products, dairy foods,

and fats and oils (20),

In the past 15 years a great deal of attention has been

given to developing methods to assess sodium and potassium intake. The widely accepted standard for estimating

sodium is the measurement of electrolytes by a 24-hour

urine collection (24-29), It is believed that this method

avoids the problems and biases related to collecting dietary

data, such as subject memory, accuracy of portion sizes,

coding, and data base concerns, However, even this

standard method is an indirect method and subject to

interpretation error (29). All sodium consumed in a 24-hour

period is not necessarily excreted in that same period.

Balance studies and comparison of analyzed duplicate

portions of food compared with urine indicate that approximately 85–95 percent of the ingested sodium is excreted

in the 24-hour urine collection. The 24-hour urine collections also suffer from the same problems of large intraindividual variability (in sodium intake and daily sodium

excretion rate) that hamper dietary methods. Liu et aI.

estimate that as many as nine 24-hour urine collections

are needed to characterize the individual intake (28,29).

As is the case with dietary methods, investigators have

attempted to develop “shortened” methods for estimating

urine sodium and potassium excretion in epidemiologic

studies, Overnight urine and spot urine collections as well

as chloride titrator strips have been evaluated for this

purpose (30,31). Generally good correlations were found

between the 24-hour and overnight collections for sodium

and potassium. Many believe that overnight urine collections can be used to characterize population means. When

possible, multiple overnight collections are preferable to a

single collection.

Nutrition surveys have often not included estimates of

dietary sodium intake, The belief that it is impossible to

determine sodium, in part because table and cooking salt

cannot be estimated, and concerns over data base limitations in sodium values have limited the use of dietary

sodium evaluations by some investigators, Early dietary

studies in Japan, Koreaj and Polynesia, each using a

different dieta~ method–food history, weighed food intake

calculated by food table values, and a 24-hour recall –

showed good agreement between the dietary sodium values

when compared with 24-hour urine sodium values (29).

More recently a great deal of work has been conducted

with dietary methods for the estimation of sodium and

potassium. Schachter et al. compared 3-day food records

and weighed food portions with 24-hour urine collections,

Calculated food record sodium was 11 percent lower than

the urine values, but the calculated potassium was only

1 percent lower than the urine potassium value (26).

Caggiula et al. compared single- and multiple-day food

records for their ability to estimate sodium, potassium,

and energy intake and assessed the correspondence between

sodium and potassium calculated from food records and

urine analysis (32). A single-day food record approximated the 6-day food record mean closely for sodium,

potassium, and calories. Sixty percent of the group could

be classified correctly into quartiles based on a l-day

record compared with the 6-day mean. Correlation coefficients between the l-day mean and the 6-day mean were

only slightly lower than correlation coefficients between

multiple days and the 6-day mean. Urine excretion of

sodium and potassium were significantly correlated with

the single- and multiple-day record values; correlations

ranged from 0.50 to 0.76. Urinary sodium mean values

were always higher, on average by 40 milliquivalents

(meq), than the food record values, but the potassium

differences between urine and food records were small.

The value of the use of a single well-documented and

calculated food record for estimation of group sodium and

potassium intake was emphasized, but the authors pointed

out that the estimation of individual intake would require

multiple collections.

Elmer et al. evaluated the use of 24-hour recalls and

3-day food records compared with 24-hour urine collections for the estimation of sodium intake in middle-aged

men who were participating in a sodium reduction trial

(33). The food records were carefully collected and documented, salt use was estimated, and a special data base

and calculation system were designed to capture sodium.

At baseline there was excellent correspondence behveen

the urine and 3-day food record average sodium values,

The mean 24-hour urine sodium was 170 meq and the

average 3-day food record intake was 169 meq. During the

intervention period the 3-day food record underestimated

the urine sodium levels by approximately 20 meq (20 percent), It is believed that part of this underestimate was

due to participant desire to show good adherence to the

low-sodium diet intervention advice. The 3-day food record

and 24-hour recall were compared to see if one resulted in

34
a better estimate of sodium intake. The sodium values for

both the recalls and the 3-day food records were similar

during all time periods. In order to compare the effects of

the calculation and data base used for estimating dietary

sodium, the 24-hour recalls were calculated using both the

special sodium data base and the Nutrition Coding Center

(NCC) data base, which did not accommodate sodium

extensively at the time, When the recalls were calculated

using the less sodium-sensitive NCC system, the recalls

underestimated the 24-hour urine sodium levels by 33 percent. The conclusions are that carefully documented and

calculated food records and 24-hour recalls could accurately predict 24-hour urine sodium values and that the

calculation of salt added and the food table used were

extremely important in improving the accuracy of the

sodium estimates,

Simplified methods to estimate sodium intake have

also been used, In a metabolic-ward setting, Frank et al.

found good correlations between a self-administered short

dietary inventory and urine sodium and potassium excretion (34). Pietinen, Tanskanen, and Tuomilehto (35) and

Dcthl (3) both classified individuals based on a few questions about their salt use habits. Pietinen et al. found this

salt index to be significantly correlated with 24-hour urine

sodium excretion. Dahl found classification by his salt

index to be related to blood pressure, but this relationship

wws not replicated by others,

The recommended method for the estimation of sodium

itnd pmossium intake is by use of the 24-hour recall.

Nlultiplc nonconsecutive days should be collected to characterize the individual intake and to reduce the intraindi-

~idual variation. Both weekends and weekdays should be

included, In oddition, it is important to document and

quantify salt and condiment use. Specifically designed

intmvicw probes for sodium and potassium should be

cmployud. The food table utilized for calculation of nutrients needs to be comprehensive in its entries in order to

wicqufitely characterize sodium and potassium intake,

Stanclardized coding rules for sodium capture should also

be developed. For a case control study, information on

previous use would also be recommended, as many individuals muy have modified their intake based on the

ditignosis o; family history of hypertension, Urine collections for the characterization of sodium and potassium

could be considered, utilizing either multiple 24-hour or

overnight collections, The logistics of collecting these

samples may be a potential barrier to obtaining these data,

but substudies could be considered,

Calcium

There is substantive evidence linking cellular calcium

metabolism and calcium intake with the pathophysiology

of hypertension, Serum calcium has been positively correlated with blood pressure. Serum ionized calcium is

reported to be lower in hypertensive persons than normotensive persons, and it has been reported to directly

otfect pw-ipheral vascular tone (36,37). Calcium-regulating

hormones may also affect blood pressure; parathyroid

hormone can be stimulated by low dietary calcium, and

elevated levels have been associated with hypertension in

both humans and animals (37). Several recent investigations have demonstrated associations between dieta~ calcium intake and blood pressure. The observation that

regions with hard water had lower cardiovascular mortality initiated the original epidemiologic interest in relationships between calcium and cardiovascular disease. An

inverse relationship between dietary calcium intake and

blood pressure has been reported for several population

groups (37). Cross-cultural comparisons of the prevalence

of gestational hypertension have shown that countries with

higher calcium intakes have lower prevalence. However,

these studies did not take into account many other variables that may have been related to the development,

detection, and reporting of gestational hypertension, Recent

evidence also suggests a hypotensive effect of calcium

supplementation in some hypertensive persons (38,39).

Several dietary surveys suggest that hypertensive persons as a group consume less calcium than normotensive

persons, Langford et al. report that, based on their surveys, rural black hypertensive women consume diets very

low in calcium (24). In Oregon, 24-hour dietary recalls

were used to assess dietary intake of a small group of

hypertensive and normotensive persons. Hypertensive persons consumed, on the average, 25 percent less calcium

than normotensive persons (40). Cheese consumption was

the major food item of difference, with normotensive

persons consuming larger amounts. It is not clear if this

difference in food consumption was a longstanding pattern

or a recent phenomenon caused by concern over hypertension. The difference in cheese consumption may have been

due to an attempt to reduce sodium or fat intake by a

hypertensive group trying to change their diet.

In a survey of 2,326 men in southern California, a

single question was asked on the usual number of cups of

whole milk consumed daily. Whole milk consumption was

significantly lower for borderline, untreated, and treated

hypertensive men; but among women, consumption was

lower only for treated hypertensive women (41). A

22-percent sample of this population also had a 24-hour

diet recall. Only dairy calcium intake was calculated.

Lower dairy calcium consumption was found in untreated

hypertensive, but not borderline hypertensive, persons,

Total dairy calcium intake adjusted for age, obesity, and

alcohol intake was correlated with diastolic blood pressure, Lower calcium intakes in untreated hypertensive

persons were also reported in analyses of the NHANES I

data which utilized 24-hour diet recalls to quantify calcium

intake (16–18). Calcium intake was higher for men than

women, but the ratio of calcium per 1,000 calories was the

same for men and women. Calcium intake was lower in

the older age groups for both men and women.

As is the case with other nutrients, a single measure of

current calcium intake may not provide the most relevant

information related to cardiovascular disease risk factors

or outcomes. This is also true for the relationship of

35
dietary calcium to bone mineralization and osteoporosis,

which will be reviewed elsewhere in this workshop. Multiple, nonconsecutive, 24-hour recalls can be utilized to

provide an estimate of intake over a l-year period of time.

Use of 24-hour recalls will maintain comparability with

previous NHANES and will provide precise information

so that individuals can be ranked based on their calcium

intake. Multiple days of collection will reduce the intraindividual variability, If long-term calcium intake is needed

for evaluation of a specific question, a food history and

retrospective information would be required. It is important to note that even though many hypertension studies

are focusing on calcium intake, a great many nutrients are

likely to be involved in the development of elevated blood

pressure, including sodium, potassium, chloride, magnesium, vitamin D, fatty acids, and alcohol. The effect of

calcium administration on blood pressure lowering maybe

different in different population groups and may vary by

other dietary conditions, such as sodium and potassium

levels, Collection of dietary data that merely focus on

sources of calcium would be inadequate for the evaluation

of nutrient and blood pressure relationships,

Alcohol

The associations of alcoholic beverage consumption

with heart and other cardiovascular diseases are complex.

Alcohol has diverse effects on the endocrine, gastrointestinal, metabolic, hematopoietic, and necrologic systems

that can affect the heart and circulatory system. Several

epidemiologic investigations have demonstrated that alcohol

intake is positively associated with systolic and diastolic

blood pressure and the incidence of hypertension (16,

17,42-45). However, data also indicate that regular moderate use of alcohol is associated with a lower risk of major

coronary events (42,43). High-density lipoproteins (HDL’s)

have been suggested as possible mediating factors for this

reported protective effect of alcohol consumption (46).

Heavy alcohol consumption and problem drinking are

associated with an increased mortality from all causes,

cardiovascular disease, CHD, and cancer (42–47),

Problems have arisen in comparing studies of the

relationship of alcohol with disease because of differences

in the type of alcohol measure used to evaluate consumption, Measures fall into two distinct groups: Socially defined

measures (alcoholic or problem drinkers based on behavior

patterns) and quantity-frequency measures. A few studies

have used medical diagnosis of alcoholism or liver cirrhosis to classify individual alcohol intake (48). Smoking

status, other dietary components, socioeconomic status,

psychological stress, and personality type have been suggested as potential major confounders in the determination of associations between alcohol and both cardiovascular

risk factors and disease outcomes. Potential misclassification of individuals into consumption groups (particularly

“ever drink” versus “never drink”) may also contribute to

the findings that abstinence is associated with higher

mortality than light or moderate intake,

Alcohol consumption varies by age, sex, socioeconomic status, ethnicity, and religious affiliation. More men

than women drink alcohol, and more men report heavy

consumption. A greater percentage of older adults than

younger adults report no drinking. Approximately 30 percent of all adults classify themselves as nondrinkers and,

using various survey methods, approximately 9 percent of

adults are classified as chronic heavy drinkers, Respondents

who are obese, who smoke, and who do not use seatbelts

are more likely to be chronic heavy alcohol consumers

(16,49).

The Lipid Research Clinics Prevalence Study assessed

alcohol intake in children and adolescents by questionnaire using the past week as the consumption reference

time period. Twenty-two percent of the respondents ages

12–18 years reported consumption in the past week, with

the prevalence increasing from 6 percent for ages 12-13

years to 53 percent for ages 18-19 years (50). The majority

of investigations that have evaluated the relationship

between alcohol consumption and cardiovascular disease

have utilized some type of alcohol intake history for

obtaining data on the quantity and frequency of intake

over some specified period of time. The time period of

reference has varied from 1 week to 1 year. The average ,

number of alcoholic drinks per day and the absolute

amount of ethanol are the two most commonly reported

variables of consumption (41–50). Analyses often classify

individuals into consumption categories or patterns:

Abstainer, light, moderate, heavy, chronic heavy, or alcoholic, These classifications can be extremely important in

the elucidation of the aIcohol-disease relationships. It is

also important to consider not only current classification

but past classification. Several investigations have shown

that light to moderate consumers of alcohol have lower

mortality rates than either heavy drinkers or abstainers;

but when the data are analyzed separately for abstainers

who never drank versus abstainers who were former

drinkers, the former drinkers accounted for most of the

deaths in the aggregate abstainer groups (43),

Currently there is interest in characterizing drinking

patterns further to encompass the concept of “binge

drinking,” cases in which the usual frequency or the usual

average amount consumed may be low to moderate, but a

pattern of excessive consumption exists with some frequency (for example, no alcohol during the week but 7-10

drinks on one or more weekend days). This pattern could

yield an average weekly consumption of 1-4 drinks per

day.

Validity and reliability are major research issues related

to self-reported alcohol intake data. A variety of methods

have been used to evaluate the reliability and validity of

self-reported alcohol intake. It is generally believed that

alcohol intake is underreported and that the underreporting is not random (51). Several investigations indicate

that heavy alcohol consumers or alcoholics markedly underreport intake, Denial of an alcohol problem, cultural

norms, concern over being classified as alcoholic, and

inability to recall alcohol intake during heavy drinking

36
bouts have been cited as reasons for underreporting (51,52).

Other investigations suggest that part of the underreporting might be related to the selection options that

respondents are given as categories of consumption on

questionnaires of alcohol intake. These questionnaires

usually focus on low frequencies and low quantities. Data

suggest that the majority of alcoholic beverages are consumed by a small group of heavy consumers. It is estimated that the heaviest drinking 10 percent of the

population may consume as much as 40-50 percent of all

olcoholic beverages. Providing questions that focused on

heavy consumption yielded higher reported intakes when

two questionnaires were compared in an investigation of

alcoholics’ self-reported intake (53). The reliability of

general population surveys of consumption has been investigated by using alternate forms, test-retest methods, and

X)-day diaries for comparisons. Findings indicated a higher

Icvcl cJf reliability of reported alcohol intake for the

gmmtl population than for the consumption measures

used fur the alcoholic population (54), Klatsky and

Friedman also point out that underreporting would have

the ctlect of dilution of the ability to see an alcoholdisew association, but given the underreporting, associaticms arc still seen (43).

Tlw recommended method for collection of alcohol

intake data is by use of a quantitative beverage frequencyhistory technique, which would cover a minimum of 1

wcc!i’s usual intake and would include questions on maximum omounts used and previous consumption status,

Difficulty in quantifying amounts of alcohol consumed

muy in part be due to the large number and types of

beverage containers and the fact that many individuals

may not actually pour or prepare their own alcoholic

beverages and hence not see the amount of liquor used,

The use of portion-size aids could help to facilitate estimation of consumption. An additional problem related to

alcohol intake evaluation is the potential effect of acute

withdrawal from alcohol, Some investigators suggested

thnt blood pressure may rise during acute withdrawal; the

mmdrinking period that the respondent is spending in the

survey center or clinic in some cases could be considered

in this light, The respondent may report no alcohol consumption in the past 24 hours, and hence be classified as a

low, moderate, or nondrinker, and yet be experiencing the

blood pressure effects of acute withdrawal. Attempts to

classify usual patterns of alcohol consumption and binge

drinking may alleviate some of these difficulties.

Caffeine

Caffeine is found naturally in coffee, tea, and cocoa;

as an additive in other beverages; and in such medications

us untacids, analgesics, and weight loss aids. Coffee is the

gtuatest dietary source of caffeine in the United States,

providing approximately 75 percent of the population

intake. Tea provides 15 percent, and soft drinks, cocoa,

and medications, the remainder (55). Age, sex, and geographic differences exist in the patterns of caffeine consumption. These patterns change with age; the majority of

caffeine for children and adolescents comes from soft

drinks, and coffee is the major source for adults to about

age 60, when many individuals switch to tea. Asian,

Hispanic American, and black persons report consuming

lower amounts of coffee than white persons report, although

it is not clear if the total caffeine consumption is lower

(56). In a report on a survey of substances Generally

Recognized as Safe (GRAS), the distribution of daily

caffeine consumption was estimated. Fourteen percent of

infants 2 years of age consumed some caffeine during a

2-week period, For individuals ages 18 years and over,

82 percent consumed caffeine, with the mean intake being

186 mg per day, a little over the equivalent of two cups of

coffee, Seventy-four percent of the pregnant women surveyed consumed caffeine, with a similar mean intake of

two cups per day (57). Caffeine and coffee consumption

are not routinely reported as part of many general nutrition and food consumption surveys.

An evaluation of the literature on relationships between

caffeine consumption and cardiovascular disease reveals

that most investigators report coffee, not caffeine, consumption. In some instances, only data on coffee intake were

obtained; in other cases, no relationship was found with

beverages other than coffee.

Several studies have shown a link between increased

coffee consumption and an increased incidence of heart

disease (58–61); other investigations have failed to show

this relationship (62–65). The pathophysiologic mechanisms linking caffeine to cardiovascular disease are not

clear, although several hypotheses have been suggested.

The possibility that coffee may produce ventricular arrhythmias has been suggested (66). Associations between coffee

consumption and serum lipids and lipoproteins have been

demonstrated by several investigators (67–69). However,

many of the positive associations between caffeine and

cardiovascular disease have been attributed to the confounding effects of age, smoking, obesity, and alcohol

consumption. Other dietary factors, such as a high-fat

diet, may also confound the coffee and serum lipoprotein

association (66).

The majority of the reports relating caffeine to heart

disease have utilized beverage frequency or a history

technique to obtain consumption data (58–60). Cups of

coffee consumed is the most commonly employed variable

for analysis; milligrams of caffeine may also be calculated.

To date no validation of different methods of obtaining

caffeine intake, such as comparisons between diet histories and frequencies or weighed or measured portions,

have been reported; hence, there are little published data

to evaluate the validity or reliability of self-reported

methods.

When a nutrient or a dietary component has a limited

distribution in the food supply, as caffeine does, or the

consumption patterns are very different for different populations, focused questions about the frequency and quantitation of intake are more useful, in general, than a

single-day intake from a record or recall. The use of a

37
quantitative food frequency record would be recommended in this case; it would be appropriate for the case

control, cohort, and cross-sectional designs. The major

problem associated with collection and quantification of

caffeine intake data is distinguishing between caffeinated

and decaffeinated items. The ability of the respondent to

accurately quantify portions and the choice of the caffeine

values to be used in calculation are other potential problems. Use of portion-size aids and probes on caffeination

should alleviate some of these respondent difficulties.

Because caffeine has potent pharmokinetic properties,

long-term history of use has been of interest in some

investigations of all causes of mortality in addition to

cardiovascular disease and cancer. Some previous studies

have been criticized for the failure to collect information

on the duration of exposure to caffeine (56),

Fat, calories, and cholesterol

Observations of dramatic differences in CHD mortality between geographic areas raised questions about the

possible associations of mortality with differing dietary

habits, Since the 1940’s, the role of nutrition in the

etiology of CHD has been extensively studied in a variety

of epidemiologic settings. Calories, fat, and cholesterol

were the focus in many of the early investigations, and

they continue to be of importance in current studies (70).

These nutrients have been determined by a variety of

methods (24-hour recalls, food records, diet histories, food

frequencies, weighed intakes, and aggregate national food

production and disappearance data). Various prospective

epidemiologic studies have demonstrated significant correlations between dietary fats, especially saturated fats, and

serum cholesterol and CHD mortality. The Seven Countries Study, using weighed food intake for multiple days,

demonstrated correlations of r = 0.87 for percent of

calories and saturated fatty acids with serum cholesterol

and r = 0.84 with CHD mortality (71). The Ni-Hon-San

study of men of Japanese descent in Hawaii, California,

and Japan utilized the 24-hour recall (72). Differences in

CHD incidence paralleled the marked difference in fat

consumption. Residents in Japan had the lowest risk and

those in California, the highest. The Western Electric

Study (73) and the Ireland-Boston Diet Heart Study (74)

utilized the diet history method, In both studies, dieta~

cholesterol consumption, saturated fatty acid intake, and

the Keys dietary score significantly predicted subsequent

20-year CHD mortality. In a 20-year followup of Seventh

Day Adventists, using focused diet frequency questions, it

was observed that for both men and women, meat eating

was associated with an increased risk of CHD and risk

increased as frequency of meat consumption increased

(75), In the Zuthphen study, 20-year CHD was also

obsewed prospectively (76). Dietary intake was estimated

using a food histo~, The investigators found an inverse

association of fish consumption with CHD mortality. Dietary

cholesterol intake was also a significant predictor of CHD

death,

It is generally well accepted that population

comparisons demonstrate strong associations for dietfiry

cholesterol and saturated fat with CHD mortality und

serum lipids. Within-population comparisons have generally showed smaller or no correlations. The homogeneity

of the diet and inadequacies of the dietary methodology to

differentiate correctly an individual’s fat intake have hcen

cited as reasons for the lack of association (77-79). Several studies that found no association utilized 24-hour

recalls. Day-to-day variability in dietary Pdt intake within

an individual’ is larger than between individuals (7!)). .4

single day’s intake is not adequate to charactcrim m

individual’s fat, cholesterol, or calorie intake because of

this large intraindividual variability (79,S0), However, it

should be noted that some prospective studies, utilizing

diet histories, have also failed to demonstrate associations

between dietary fats and CHD mortality. Use of a diet

history method does not guarantee demonstration of association between a nutrient and disease or risk fuctor.

Design and methodologic protocol are extremely important. In many reports the methodologies are not w’cll

described. There is very little mention of how food items

are coded and what type of data bases were utilized for

calculation, what the interview procedures were, how long

the interview lasted, and so forth. More recently, stand:lrdization of interviews, coding procedures, special methods

to estimate fat, and more extensive food tables have

enhanced the ability to estimate fat intake across all

methods.

Extensive work has been conducted to evaluate the

validity and reliability of the various dieta~ intake measures

(S1-91). Generally the diet histog tends to produce higher

estimates for all nutrients, including fat, cholesterol, and

calories, When diet histories have been repeated, good

agreement has been found between the repeat measures

(77), The diet history is considered by many to provide

better information on the individual’s usual intake and

does not suffer from the problems of large intraindividual

variation (S1–S3), However, if the history is collected for a

long period of time, such as 1 year, the respondent may

have considerable difficulty recalling intake. If the tirnc

period of collection is 1 month, seasonal variation is still

not accounted for. Weighed food portions have been

compared with 24-hour recalls, with good agreement esccpt

for calories (90). Studies of 24-hour diet recalls demonstrate the considerable variability of individual intakes of

fat, calories, and cholesterol. It has been estimated that

from 4 to 15 recalls would be needed to classify cm

individual within 5 to 20 percent of his or her true mum

for calories, fat, and cholesterol (77,S0,SS,91).

There has been considerable interest recently in the

development of quantitative food frequencies and itcmbased diet histories for epidemiologic and clinical studim

(S4,S6,92,93), In a recent report on the validity of a

semiquantitative food frequency, Willett et al. found good

correlations between food record values and the questionnaire values for cholesterol, fat, saturated fat, polyunsaturated fat, and total calories (86).The food records provided

38
slightly higher values for these nutrients than did the food

frequencies, He found that the frequency questionnaire

based on an abbreviated list of foods was able to classify

individuals into quartiles of intake with good correspondence to the food records, Byers et al. used an abbreviated

interview based on 12S foods and compared intake values

to a more extensive food history with good results for fats

und calories; classification by quintiles showed good agreement with the longer method (S4). These studies indicate

[hat, at least in some populations, use of shortened methods

to estimate fat, cholesterol, and calories can provide valid

uncl reliable results. The method of coding and calculating

the nutrients, fat, calories, and cholesterol can produce

clilfcring results based on the procedures and data base

used, It is important to utilize standardized methods and

hilv~ specific rules as well as a current data base that

contains manufactured food product data so as to ade-

~tli~t~lj~reflect the fatty acid composition of the diet and

~iiptttr~ fat und calories that could be omitted without

standardized procedures (94).

The continued theme of the review of dietary methods

is that u single day’s intake is inadequate to characterize

correctly an individual’s usual intake and to allow the

correct placement of that individual into some range of

thu nutrient distribution (such as quintiles). Multiple days

of diet information will be needed for nutrients, fat,

chckstcrol, and calories if individual comparisons are to

bc miidu for nutrient and disease or risk factor associations, hlultiplc 24-hour recalls would provide accurate

d;ttu and maintain comparability with the previous

NHANES, The days should be randomly selected and

nonconsecutive, This type of methodology would necessitate telephone administration. Information on usual types

of fitts usual, such as soft margarine and olive oil, and

consumption of fish would be useful for characterizing

spccitic fatty acid intake patterns. If multiple 24-hour

rcwalls cannot be completed, additional food history questions should be utilized,

Summary

It is planned that a wide variety of questions related to

nutrient inti~ke and cardiovascular disease will be addressed

in NHANES 111,Given the scope of these questions and

tlw number of different types of designs that may be

utilizcd —cross-sectional, cohort, and case control —sev-

~riddifferent dietary methodologies will probably be needed

within NHANES III. Assessment and ranking of current

intake is a primaq objective to the overall NH.ANES III

design, The 24-hour recall will provide this assessment and

maintain comparability with the previous extensive

NHANES.

In many instances, however, information on usual

intake or past intake will be necessary to better elucidate a

nutrient-disease relationship, either because of temporal

exposure concerns or because of the variability of the

nutrient intake, A food history or quantitative frequency

method for the specific nutrient(s) would best address

these needs. Information on specific foods or other diet

constituents (such as fish, dairy foods, alcohol, or water

intake) in addition to nutrient levels will be of interest not

only for cardiovascular disease but, with some food items,

for cancer and osteoporosis as well. A history or quantitative frequency method would be appropriate for this

purpose.

The question of serial and multiple measures needs to

be addressed. For adequate classification of individuals,

several nonconsecutive measures would be most appropriate. The actual number of measures would be determined by the estimate of the most acceptable reduction in

variance that could be achieved with multiple measures,

balanced by the feasibility and cost of such measures. The

use of multiple measures would necessitate the use of

alternative interview techniques, most likely by telephone.

It is clear that, if this type of methodology is adopted,

further validation and extensive pretesting would need to

be conducted. A subsample approach could also be utilized,

Automated coding of dietary data and computerprompted immediate data entry interview techniques may

facilitate the use of these dietary data collection methods.

Whichever methods are utilized, standardization of the

coding procedures and additions to the nutrient data base,

particularly for sodium, are essential.

Finally, there is considerable interest in evaluating

any deleterious effects of dietary modification on health

and mortality outcome. For example, there is concern that

a fat-modified diet may increase cancer mortality in some

or that a low-sodium diet may be unpalatable and lead to

a lowered dietary intake and potential nutrient deficiencies. It would be useful in this regard for NHANES III

to be able to ascertain if altered dietary levels were due to

self-selected dietary practices, to prescription of a therapeutic diet, or to a disease state or illness that lowered

overall food intake.

References

1.

2.

3.

4.

5.

6.

7.

Prineas R, Blackburn H. Clinical and epidemiological relationships behveen electrolytes and hypertension. In: Horan

M, Blaustein M, Dunbar J, et al., eds. NIH Workshop on

Nutrition and Hypertension. New York: Biomedical Information Corp. 1985.

Porter G. Chronology of the sodium hypothesis in hypertension. Ann Intern Med 98(5, pt 2):720-4. 19S3.

Dahl L. Salt intake in hypertension. In: Genset J, ed.

Hypertension. New York: McGraw-Hill Book Co., 54S-58.

1977.

Glieberman L. Blood pressure and dietary salt in human

populations. Ecol Food Nutr 2:143-6.1973.

Parijs J, Joossens J, Linden L, et al. Moderate sodium

restriction and diuretics in the treatment of hypertension.

Am Heart J 85:22-34.1973.

Carney S, Morgan T, Wilson M, et al. Sodium restriction

and thiazide diuretics in the treatment of hypertension. Med

J Aust 1:803-7.1975.

Morgan T, Adam W, Gilles A, et al. Hypertension treated

by salt restriction. Lancet 1:227-30.1978.

39
8. Fagerberg B, Anderson O, Isaksson B, et al, Blood pressure

control during weight reduction in obese hypertensive men:

Separate effects of sodium and energy restriction. Br Med J

288:11-14.1984.

9. Parfrey P, Goodwin F, Evans S. Blood pressure and hormonal changes following alteration in dietary sodium and

potassium in mild hypertension. Lancet 1:56-63, 1981.

10. Hofman A, Hazebroek A, Valkenburg H. A randomized

trial of sodium intake and blood pressure in newborn

infants. JAMA 250:370-3. 1983.

11. MacGregor G, Markandu N, Smith S, et al. Moderate

potassium supplementation in essential hypertension. Lancet

2:567-70, 1982.

12, Khaw K, Them S. Randomized double blind cross-over trial

of potassium on blood pressure in normal subjects. Lancet

2:1127-9.1982.

13. Khaw K, Barrett-Conner E. Dietary potassium and blood

pressure in a population. Am J C1in Nutr 39:963-8.1984.

14. Ophir O, Peer G, Gilad J, et al. Low blood pressure in

vegetarians: The possible role of potassium. Am J C1in Nutr

37:755-62.1983.

15. Meneely G, Battarbee H. High sodium-low potassium environment andhypertension. Am JCardio13:318-26. 1976.

16. Gruchow H, Sobocinski K, Barboriak J. Alcohol, nutrient

intake, and hypertension in U.S. adults. JAMA

253(11):1567-70. 1985.

17. Harlan W, Hull A, Schmouder R, et al. Blood pressure and

nutrition in adults. Am J Epidemiol 120:17-28. 1985.

18. McCarron D, Morris C, Henry H. Blood pressure and

nutrient intake in the United States, Science 224:1392-8,

1984.

19. Dresser CM, Carroll MD, Abraham S. Selected findings:

Food consumption profiles of white and black persons 1-74

years of age in the United States, 1971-74. AdVance data

from vital and health statistics; no 21. Hyattsville, Maryland:

National Center for Health Statistics. 1978.

20. Shank FR. Recent data on the amounts of sodium and

sodium being consumed. In: White PL, Crocco SC, eds.

Sodium and potassium in food and drugs, Chicago: American Medical Association. 1980.

21. Abraham S, Carroll MD. Fats, cholesterol, and sodium

intake in the diet of persons 1-74 years: United States.

Advance data from vital and health statistics; no 54. Hyattsville, Maryland: National Center for Health Statistics. 1979.

22. Fregly M, Attempts to estimate sodium intake in humans.

In: Horan M, Blaustein M, Dunbar J, et al., eds, NIH

Workshop on Nutrition and Hypertension. New York: Biomedical Information Corporation. 1985,

23. Healthy people. The Minnesota Experience MDH Center

for Health Statistics. 1983.

24, Watson R, Langford H, Abernethy J, et al. Urinary electrolytes, body weight, and blood pressure: Pooled cross sectional results among four groups of adolescent females.

Hypertension 2(suppl 1):93-8, 1980.

25, Pietinen PI, Fkdley TW, Clausen JD, et al. Studies in

community nutrition: Estimation of sodium output. Prev

Med 5:400-7.1976.

26. Schachter J, Harper PH, Radin ME, et al. Comparison of

sodium and potassium intake with excretion, Hypertension

2:695-9.1980.

27. Holbrook JT, Patterson KY, Bodner JE, et al. Sodium and

potassium intake and balance in adults consuming selfselected diets. Am J Clin Nutr 40:786-93.1984.

28. Liu K, Cooper R, McKeever J, et al. Assessment of the

association between habitual salt intake and high blood

pressure: Methodologic problems. Am J Epidemiol

110:219-26.1979.

29. Liu K, Cooper R, Soltero I, Stamler J. Variability in 24-hour

urine sodium excretion in children. Hypertension 1:631-6.

1979.

30. Liu K, Dyer AR, Cooper R, et al. Can overnight urine

replace 24-hour urine collection to assess salt intake? Hypertension 1:529-35.1979.

31. Watson R, Langford H. Usefulness of overnight urines in

population groups. Am J Clin Nutr 23(3):290-304. 1970.

32. Caggitda AW, Wing RR, Nowalk MP, et al. The measurement of sodium and potassium intake. Am J Clin Nutr

42:391-8.1985.

33. Elmer PJ, Jacobs DR, Gillum R, Prineas RP. Methods for

accurate assessment of dietary sodium intake in mild hypertension. Circulation 9:107.1983.

34. Frank G, Nicolich J, Voors AW, et al. A simplified inventory

method for quantifying dietary sodium, potassium and energy,

Am J Clin Nutr 38:474-80.1983.

35. Pietinen P, Tanskanen A, Tuomilehto J. Assessment of

sodium intake by a short dietary questionnaire. Stand J Soc

Med 10:105-12.1982.

36. McCarron D, Morris C. Calcium and hypertension. In:

Horan M, Blaustein M, Dunbar J, et al. eds. NIH Workshop

on Nutrition and Hypertension. New York: Biomedical

Information Corporation. 1985.

37. Henry H, Morris C, Parrott-Garcia M. Increasing calcium

intake lowers blood pressure: The literature reviewed. J Am

Diet Assoc 85:182-5.1985.

38, Johnson NE, Smith E, Freudenheim J. Effects on blood

pressure of calcium supplementation of women. Am J Clin

Nutr 42:12-7.1985.

39. McCarron D, Morris C. Blood pressure response to oral

calcium in persons with mild to moderate hypertension. Ann

Intern Med 103:825-31.1985.

40. McCarron D, Morris C, Cole C. Dietary calcium in human

hypertension. Science 217:267-9.1982,

41. Barrett-Conner E, Suarez L. Dairy products, calcium and

blood pressure. Am J Clin Nutr 38:457-61.1983,

42. Kagan A, Yano K, Rhoads G, McGee D. Alcohol and

cardiovascular disease: The Hawaiian experience. Circulation 64(2):27-31, monograph 81.1981.

43. Klatsky A, Friedman G. Alcohol and hypertension, In:

Horan M, Blaustein M, Dunbar J, et al., eds. NIH Workshop on Nutrition and Hypertension. New York: Biomedical

Information Corp. 1985.

44. Dyer A, Stamler J, Paul O, et al. Alcohol, cardiovascular

risk factors and mortality: The Chicago experience, Circulation 64(2):20-30, monograph 81.1981,

45. Wallace R, Lynch C, Pomrehn M, et al. Alcohol and

hypertension: Epidemiologic and experimental considerations. The Lipid Research Clinics Program, Circulation

64(2):41-7, monogrdph 81.1981,

46. Gordon T, Ernst N, Fisher M, Rifkind B. Alcohol and high

density lipoprotein cholesterol. Circulation 64(2):63-6, monograph 81.1981.

47. Caha]an D. Quantifying alcohol consumption: Patterns and

problems. Circulation 64(2):7-13, monograph 81.1981.

40
4S, Celentano D. Martinez RM. McCXreen D. The association

of alcohol and hypertension; Prev-Med 10:590–602. 1981.

49, Gentry E, Kalsbeek W, Hogelin G, et al. The risk factor

surveys. II. Design, methods, and estimates from combined

state data. Am J Prev Med 1(6):9-14. 1985.

50, Glueck C, Heiss G, Morrison J, et al. Alcohol intake,

cigarette smoking and plasma lipids and lipoproteins in

12-19 year old children: The Collaborative Lipid Research

Clinics Study. Circulation 64(2) :4S-56, monograph 81.1981.

51. Watson C, Tilleskjor C, Hoodeshedk-Schow E, et al. Do

alcoholics give valid self-reports? J Stud Alcohol 45(4):344-8.

19s4,

52, Pernanen K, Validity of survey data on alcohol use. In:

Gibbins R, Israel Y, Kalant H, eds. Research advances in

alcohol and drug problems, vol. I. New York: John Wiley &

Sons, Inc. 1974.

53, Poikolainen K, Karkkainen P. Nature of questionnaire options

affects estimates of alcohol intake. J Stud Alcohol

46(3):219-22. 1985.

54. Williams G, Aitken S, Malin H. Reliability of self-reported

alcohol consumption in a general population survey. J Stud

Alcohol 46(3):223-7, 1985.

S5. Roberts H, Barone J. Caffeine: History and use. Food Tech

9:32-9. 19S3.

56. Pozniak P. The carcinogenicity of caffeine and coffee: A

review. J Am Diet Assoc 85:1127-33. 19S5.

57, G$aham D, Caffeine – its identity, dieta~ sources, intake

and biological effects, Nutr Rev 36:97-102.1978.

5S, Jick H, Miettinen OS, Neff RK, et al. Coffee and myocardial

infarction. N Engl J Med 289:63-7.1973.

59, Wilhelmsen L, Tibblin G, Elmfeldt D, et al. Coffee consumption and coronary heart disease in middle-aged Swedish

men. Acts Med Stand 201:547–52. 1977.

60. Report from the Boston Collaborative Drug Surveillance

Program: Coffee drinking and acute myocardial infarction.

Lancet 2:1278-81, 1972.

61. LaCroix A, Mead L, Liang K, Pearson T. Coffee consumption and 25 year incidence of coronary heart disease: Effects

of coffee drinking and smoking habits during adulthood.

Circulation 72(pt 2):53. 1985.

62, IUatsL~ A, Friedman G, Sieglaub A. Coffee drinking prior to

acute myocardiai infarction: Results from the KaiserPermanente epidemiologic study of myocardial infarction.

JAMA 226:540-3.1973.

63, Dawber T, Kannel W, Gordon T. Coffee and cardiovascular

disease: Observations from the Framingham study. N Engl J

Med 291:S71-4. 1974.

64. Yano K, Rhoads G, Kagan A, Coffee, alcohol and risk of

coronary heart disease among Japanese men living in Hawaii.

N Engl J Med 297:405-9.1977,

6S, Rosenberg L, Slone D, Shapiro S, et al. Coffee drinking and

myocardial infarction in young women. Am J Epidemiol

111:675-S1. 1980.

66, Prineas R, Jacobs D, Crow R, et al. Coffee, tea and VPB. J

Chron Dis 33:67-72.1980.

67. Thelle DS, Aneson E, Forde DH. The Tromso Heart Study:

Does coffee raise serum cholesterol? N Engl J Med

3QS:1454-7, 1983,

6S, Hafner S, Knapp A, Stern M, et al. Coffee consumption,

diet and lipids. Am J Epidemiol 122:1-11. 19S5.

69. Williams P, Wood P, Vranizan K, et al. Coffee intake and

elevated cholesterol and apolipoprotein B levels in men.

JAMA 2.53:1407-11, 1985.

70. Stamler J. Population studies. In: Levy R, Rifkind B, Dennis

B, Ernst N, eds. Nutrition lipids and coronary heart disease.

New York: Raven Press, 25-S8. 1979.

71. Keys A. Seven countries: A mtdtivariate analysis of death

and coronary heart disease. Cambridge, Massachusetts: Harvard University Press. 1980.

72. Kato H, Tillotson J, Nichaman M, et al. Epidemiologic

studies of corona~ heart disease and stroke in Japanese

men living in Japan, Hawaii and California. Am J Epidemiol

27:345-64.1973.

73. Shekelle RB, Shryock AM, Paul O, et al. Diet, serum lipids

and death from coronary heart disease: The Western Electric Study. N Engl J Med 304:65–70. 1981.

74. Kushi LH, Lew RA, Stare FJ, et al. Diet and 20-year

mortality from coronary heart disease: The Ireland-Boston

Diet Heart Study. N Engl J Med 312:811-S. 1985.

75. Snowdon DA, Phillips RL, Fraser GE. Meat consumption

and fatal ischemic heart disease. Prev Med 13:490-500.

19s4.

76. Kromhout D, Bosschieter EB, Coulander CD. The inverse

relationship between fish consumption and 20 year mortality

from coronary heart disease. N Engl J Med 312:1205-9.

19s5.

77. Liu K, Stamler J, Dyer A, et al. Statistical methods to assess

and minimize the role of intra-individual variability in

observing the relationship between dietary and serum cholesterol. J Chron Dis 31:399-41S. 1978.

78. Jacobs DR, Anderson JT, Blackburn H. Diet and serum

cholesterol: Do zero correlations negate the relationship?

Am J Epidemiol 110:77-S7. 1979.

79. Beaton GH, Milner J, Corey P, et al. Sources of variance in

24-hour dietary recall data: Implications for nutrition study

design and interpretation. Am J Clin Nutr 32:2546-59.1979.

80. Sempos CT, Johnson NE, Smith EL, Gilligan C. Effects of

intra-individual and inter-individual variation in repeated

diet records. Am J Epidemiol 121:120–30. 1985.

S1. Block G. A review of validations of dietary assessment

methods. Am J Epidemiol 115(4):492-505. 19S2.

82, Fehily A. Epidemiology for nutrition: Survey methodology.

Hum Nutr Appl Nutr 37A:419-25. 1983.

83. Lyon J, Gardner J, West D, Mahoney A. Methodological

issues in epidemiologic studies of diet and cancer. Cancer

Res 43(suppl):2392s-96s. 1983.

S4. Byers T, Marshall J, Fiedler R, et al. Assessing nutrient

intake with an abbreviated dietary interview. Am J Epidemiol 122(1):41-50. 19S5.

85. Mullen BJ, Krantzler NJ, Grivetti LE, et al. Validity of a

food frequency questionnaire for the determination of individual food intake. Am J Clin Nutr 39:136-43. 19S4.

S6. Willett W, Sampson L, Stampfer M, et al, Reproducibility

and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol 122:51–65. 1985.

S7, Hankin J, Messinger H, Stallones R. A short dietary method

for epidemiological studies. IV: Evaluation of a questionnaire. Am J Epidemiol 91:562-7. 1970.

SS. Graham S. Diet and cancer. Am J Epidemiol 112:247-52.

19s0.

89. Marshal J, Priore R, Haughey B, et al. Spouse-subject

interviews and the reliability of diet studies. Am J Epidemiol 112:675–S3. 19S0.

90. Sorenson A, Calkins B, Connolly M, et al. Comparison of

nutrient intake determined by four dietary intake methods.

J Nutr Ed 17:92-9. 19S5.

41
91. Balogh M, Medalie JH, Smith H, Green JJ. The develop- 93. Block G, Dresser C, Hartman A, Carroll M. Nutrient

ment of a dietary questionnaire for an ischemic heart sources in the American diet: Quantitative data from the

disease survey, Isr J Med Sci 4:195-203, 1968. NHANES II survey, L Vitamins and minerals. Am J Epidc92. Block G, Dresser C, Hartman A, Carroll M, Nutrient miol 122:13-26, 1985,

sources in the American diet: Quantitative data from the 94. Jacobs DR, Elmer PJ, Gorder DD, et al. Comparison of

NHANES II survey, II. Macronutrients and fat, Am J nutrient calculation systems. Am J Epidemiol 121:580-92.

Epidemiol 122:27-40, 1985. 1985,

42
Chapter 4

Dietary methodology

issues related to

energy balance

measurement for

NHANES Ill

by Dorothy Blair, Ph.D., Nutrition Program, College of

Human Development, Pennsylvania State University

Introduction

The question most relevant to a discussion of dietary

rncthodologies for measuring energy balance in the National

Health and Nutrition Examination Survey (NHANES) III

is not which method is most appropriate, but rather

whether an attempt to measure energy balance is an

appropriate aim for a large survey such as NHANES HI.

TCJanswer this question we must first have a basic understanding of what energy balance is, what affects it, and

who may be defined as being in energy balance. Then we

should ask with which methods and under what conditions

energy balance has been successfully measured and whether

those methods and conditions can be duplicated or approximated in NHANES 111,

This paper attempts to answer these questions and

ends with a discussion of dietary methodologies that are

most appropriate for the estimation of total energy intake

for NHANES 111,

Definition of energy balance

Energy balance can be defined as a close agreement

between caloric intake and caloric expenditure, Garrow

(1) defines energy balance as an agreement between

energy intake and energy expenditure within & 50 kilocalories per day (kcal/day), Although this is somewhat arbitrary, it demarcates the limits of our accuracy of

measurement under very controlled circumstances. An

imbalance of lesser magnitude would nevertheless have

physiological consequences. A positive imbalance of 30

kcnl/day could lead to an accumulation of 1 kilogram (kg)

of body fat per year.

The test of energy balance in an individual is the

maintenance of stable body fat stores. Fluctuations in

carbohydrate stores may cause variations in body weight as

great as 2 kg (l), with a 2,000 kcal fluctuation in body

energy stores, Such changes in carbohydrate stores invalidate body weight as a measure of energy balance (2). The

choice of stable body fat as the test for energy balance

puts limits on who can be considered to be in energy

balance, Growth- and reproductive-related changes in

body fat stores rule out large segments of the population

from the possibility of being in energy balance. The use of

percent body fat is less valid in the context of energy

balance than is total body fat in kilograms, as the percent

body fat may fluctuate without a necessary change in total

fat stores. An example is the aging-related loss of lean

tissue, which leads to an increased percent of body tissue

as fat without an increase in body energy stores.

Beaton makes a point worth noting: Energy balance is

maintained in some cases at the expense of optimal

physiological and mental functioning (3). An example is

the adaptation of children to severely reduced energy

intake through the cessation of growth and reduced activity.

More commonly observed in affluent societies is the adaptation to low caloric intakes with severe and long-term

dieting. Thus, energy balance is not of itself a desirable

state but may be an adaptive response of the body to

adverse circumstances.

Components and effecters of

energy intake and expenditure

Table 1 shows the components of energy intake and

energy expenditure and lists those factors that influence

either side of the equation. Energy expenditure is under

much tighter bounds than energy intake. It is a function of

the fat-free mass (4,5), of short- and long-term caloric

intake (6,7) and food composition, of illness and growth,

and of movement (3). Movement would seem to be a

discretionary category, but for most of us the caloric

demands of the day are somewhat fixed. Leisuretime

activity is the one dimension of energy expenditure we are

free to define, Energy expenditure may vary from slightly

greater than 1 x basal metabolic rate (BMR), assuming

complete bed rest without food intake, to greater than 2

x BMR, Most of us live in the range of 1.3 to 1,5 x BMR

(sedentary to light activity). Not even a 6-mile jog would

hoist us into the heavy activity categoxy. Mean caloric

intake estimates of population groups that are under 1.4

x BMR must be viewed with suspicion.

Effecters of energy intake are much more varied and

may be situational as well as under cognitive, emotional,

or somatic controls. Physiological feedback mechanisms

described by Van Itallie and Kissileff (8,9) and Booth (10)

may be overridden by external stimuli – palatability, other

people’s behavior, and so forth. Conscious controls maybe

overridden by emotionality, alcohol, or drugs. To most,

eating is a social, emotional, and sensual activity. It is

synchronized with physiological needs only by dint of

constant learned attention to physiological cues (10). For

43
Table 1. Components and effecters of energy balance

htake effecter

Caloric intake Body energy

component store

Caloric

expenditure

component

Expenditure

effecter

Conscious restraint or augmentation of intake Fat Body energy losses: Basal needs Fat-free mass

Palatability Carbohydrate Feces

Mood, stress, illness, drugs

Adaptive thermogenesis: Long-term

Protein Urine Food induced

Time and financial constraints

kilocalorie Intake

Alcohol Nonspecific Short- and long-term Intake;

Weekend, weekday, season, holiday Immune response, fever

Somatic controls:

meal composition

Growth, reproductive

Energy expenditure and exercise

Smoking, tension, drugs,

function; body building

Physiological feedback

thermal regulation

and training

Cyclical variations (menstrual cycle)

Cyclic variations

Movement and exercise Fat-free mass: weight, day,

Alcohol season

those who are less finely in tune with their body’s needs,

the salient cue may be the body composition change that is

the negative outcome of ignoring earlier physiological

cues.

The propensity toward obesity may be a function of

the degree to which cognitive and somatic controls can be

overpowered by external cues and emotional needs (11).

The obese also appear to differ from “normal” persons in

their exercise-induced eating. Increased exercise does not

appear to influence the caloric intake of obese women,

although normal persons increase intake in relation to

exercise intensity (12), Intake may therefore be less tightly

coupled to energy expenditure in obese than normal

persons.

Most single effecters of both intake and energy

expenditure have a coupled influence, so the chance for

energy balance is heightened. For example, severe decrease

in intake decreases both basal metabolism (6,7) and foodinduced thermogenesis, which is approximately 10 percent of caloric intake (1) depending on meal composition.

The energy expended in exercise drops as weight is lost

(6). Smoking increases metabolism and increases consumption (13,14), although the increase in intake maybe due to

a relaxation of necessary restraint. The menstrual cycle

causes a coupled variability in energy expenditure and

intake (15). Exercise also increases intake over the long

term in normal persons (12), though the relationship is not

closeIy coupled within a 2-day period (16).

On the other hand, some effecters uncouple energy

intake and expenditure. Illness increases metabolic requirements but reduces caloric intake through reduction in

appetite (17). In some, stress has an appetite-depressing

effect while muscular tension increases energy expenditure (18), A yearly cycle of weight loss and gain may occur

because of seasonal and holiday overeating and exercise

being reserved for the warmer months. Restrained eating

and the conscious augmentation of energy expenditure

could cause a particularly sharp energy imbalance.

Thus, although there are some physiological and cognitive reasons for a close correlation between energy

intake and expenditure, there are other forces at work

that disrupt this coupling and make a close match between

energy intake and expenditure unlikely. It also seems to be

true that the time required for energy balance to occur

may differ between obese and lean because of a difference

44

in intake cue responsiveness. In a sedentary society where

food is plentiful, varied, and easily attained, energy balance on the short, measurable term may be the exception

rather than the rule.

Precise estimations of energy

balance and categorization of

individuals

The point of measuring energy balance is not only to

determine with some acceptable degree of reliability and

validity those individuals who are in energy balance and

those who are not. The broader goal would seem to be to

increase our understanding of the mechanisms of body fat

maintenance and the effects of body fat fluctuations on

disease processes and outcomes. It must be stressed that

energy balance, unless broadly defined and appropriately

measured, is not the same as body fat maintenance.

It can be assumed that fat-stable individuals, whether

normal or obese, fluctuate with some regularity around a

set body composition. We do not know the periodicity of

this fluctuation or the height of the arc, that is, the degree

to which body composition change is tolerated by the

individual before corrective mechanisms are employed,

There are probably three basic types of body composition

maintenance cycles, with many variations on these themes:

(a) a close coupling of intake and output, resulting in

slight weight fluctuations from day to day but no substantive change in body fat stores —in other words, true energy

balance; (b) a short-term but measurable fluctuation of

body fatness around a long-term stable body composition;

and (c) a seasonal cycle of imbalance with an increase in

winter holiday eating and cold-related decrease in exercise, followed by a reverse summer trend. These three

styles are shown in figure L By definition, those individuals

whose body composition fluctuates (types b and c) cannot

be considered to be in energy balance, nor will they he

measured as such by precise estimates of intake, output,

or body composition assessments. Thus, those individuals

whose body composition is stable by any other criteria will

necessarily be considered out of energy balance if their

body composition fluctuates regularly. The time period

chosen for measurement will also have a profound effect

on who is considered to be in energy balance. Those who
— True energy balance

I I oI I I Response to weight change

=- Seasonal response

5 ~

Winter Spring

Figure 1. Three styles of long-term body composition

maintenance

balance over a longer period and who are caught on the

upswing or downswing will be considered out of balance,

Thus, many individuals who are fat stable in the long

term will be designated as out of balance by precise,

short-term studies with low tolerances for body compositicm change. The more stringent are the criteria for energy

balance, the more false negatives will result. Positive and

negative energy imbalances will exclude an individual

from the maintenance criteria regardless of the outcome

of that imbalance. Short-term energy balance studies have

neither the sensitivity nor specificity to put individuals in

ctitegories that can serve a useful predictive function, The

ccmccpt of body fat maintenance is more easily operation-

;dizcd via serial measurements of body composition, which

would be a more direct measure of the phenomenon of

interest than energy intake and expenditure.

Attempts to measure energy

balance: Realities of imprecision

The literature on energy balance can be divided into

two categories: Those studies performed on small samples

under fairly rigorous field conditions and those studies

using methods more applicable to epidemiological studies

such as NHANES, Metabolic chamber studies are not

included in this review because of their impact on habitual

behavior. The more rigorous field studies will be examined

first to see if any techniques for measuring energy balance

can yield results that are both valid and useful for the

purposes of NHANES.

The most exacting field studies have obtained activity

records and food records or weighed intakes over 7 days or

longer, Energy expenditure has typically been calculated by

the factorial method (19). Generally group means for energy

intake have been very close to energy expenditure, but some

individual subjects appear to be considerably out of balance

over the timeframe of a week, Durnin and Brockway’s review

of six 7-day studies shows that, of 69 individuals studied, only

6 percent had daily intakes that correlated positively with

daily energy expenditure (19). Twenty-sL~percent had significant differences in the mean energy intake minus energy

expenditure after 7 days. Harries, Hobson, and Hollingsworth

(20), reviewing studies done between 1955 and 1962, felt that

1week was not a sufficient time to estimate energy balance

because of the day-to-day variability in energy intake. The

variability was considerably lower for energy expenditure than

for intake.

None of the early estimates of ener~ balance used a

body composition methodology more sophisticated than

body weight. In an exceedingly long study of energy

balance on a captive group of men in Antarctica, Acheson

et al. (21) used a four-site skinfold estimate of body fat,

repeated every week over a period of 6 to 12 months.

However, none of their varied techniques for measuring

energy balance was sufficiently accurate to predict individual fat gains or losses over the study period,

Both Acheson et al. (21) and Borel, Riley, and Snook

(22) have utilized long-term caloric intake data along with

long-term data on body composition to estimate the caloric

intake required for energy balance. This method, the

intake balance technique, corrects caloric intake for the

caloric cost of changes in body composition. The technique has the advantage of correcting for body composition changes that may be the result of altered intake

caused by the self-monitoring of food consumption. However, the correction factor is only as good as the method

for estimating body composition, Certainly, skinfolds are

too imprecise to be used in this context, but even

densitometry could result in substantial errors in the

estimation of the caloric cost of body composition change.

The technique does have the advantage of eliminating the

matching of two messy methodologies – dietary intake and

energy expenditure —as a criterion for success.

Over a week, energy balance may be within

k 10–20 percent. The error of field energy expenditure

techniques is at best * 10 percent (1,23). Assuming

accurate reporting, 3 weeks of dietary data via 24-hour

recall or food record are necessary to estimate usual

energy intakes with confidence limits of * 10 percent

(24). Poor subject cooperation and recall abilities, along

with errors in food composition estimates and coding, can

further decrease validity. In both cases, recording may

change behavior. If randomness of error can be assumed,

then use of group data would be possible, although the

findings would be weak. If randomness cannot be assumed,

then an error of this magnitude could obscure even a

group comparison.

Thus, 7 days or more of continuous data collection of

a type requiring intense subject cooperation and not a

small intrusion on people’s daily routine – methods totally

unsuitable for NHANES —are not sufficiently valid to

classify individuals into energy balance categories that are

consistent with changes in their body fat stores.

The second area for examination in this review of the

literature on energy balance studies is whether estimations

of energy intake and expenditure more applicable to a

45
large-scale epidemiological survey such as NHANES yield

both valid and useful results. Because energy balance

techniques are tedious for the subject and intrude on

normal behavior, many investigators have looked for easier

methods to measure one or both sides of the energy

balance equation, with a resulting increase in the error of

estimation, Usually the energy expenditure side of the

equation suffers the most from approximation. The subject

may spend tedious hours recording dietary intake over

many days, although energy expenditure is estimated by

use of an average daily heart rate and one heart rateenergy expenditure regression equation obtained in a

laboratory under controlled conditions (6,25,26), More

frequently, activity levels have become the surrogate

measure for energy expenditure, presumably as the one

variable in the energy expenditure equation under individual control, However, no attempt has been made to

convert activity into calories expended, which leaves the

impression that a given activity has an equal metabolic

cost for all persons. The unconverted activity levels are

then compared with caloric consumption.

A number of small- and large-scale studies have

examined dietary intake and activity compared to body

fatness. The dietary methods have ranged from 1 day or

repeat 7-day food records and food histories to 24-hour

recalls. Activity levels have been estimated through diaries, observation, or a mechanical device such as a pedometer. The results of these studies are as inconsistent as the

methods are varied but can be grouped into three categories of results:

1,

2.

3.

Body fat is related to decreased caloric intake and

decreased activity–

a, Johnson, Burke, and Mayer (1956): Diet histories,

teenagers (27),

b, Rose and Mayer (1968): Diet histories, infants (28).

c, Hutson et al, (1965): 24-hour recall, adult males

(29),

d, Montoye et al. (1976):

(30).

e. Baecke, van Staveren,

food records, adult males

24-hour recall, adult males

and Burema (1983): 2-day

(31).

Body fat is related to decreased caloric intake but not

to differences in reported activity–

a, Hutson et al, (1965): 24-hour recall, adult females

(29).

b. Baecke, van Staveren, and Burema (1983): 2-day

food records, adult females (31).

c. Huenemann (1967): 7-day food records, teenagers

(32).

d. Stefanik, Heald, and Mayer (1959): Diet histories,

teenage boys (33).

e. Bradfield, Paulos, and Grossman (1971): 3-day records,

teenage girls (34),

Body fat is unrelated to caloric

a. McCarthy (1966): 7-day diet

(35).

intake or activity —

record. adult females

b. Maxfield and Konishi (1966): 7-day diet record,

adult females (36).

The methods used in these studies are so imprecise

that biases in measurement between obese and lean subjects could easily be the major source of significant results,

other errors being random. There was a tendency, at least

in adults, to find no energy intake differences behveen

obese and normal persons using a 7-day diet record,

although 24-hour recalls routinely categorized the obese

as eating less. This tendency was not true for obese

children and adolescents, who were found to eat less than

normal persons by every dietary method employed,

The temptation is strong to use very short-term dietary

data from cross-sectional studies to draw conclusions

relevant to energy balance and weight maintenance or

even to categorize obese and lean individuals. We are ever

hopeful that some relationship between caloric intake and

body fat will be found, even if the meaning of those

relationships is equivocal. The problems of drawing conclusions from such data can be illustrated by the analyses

of the Tecumseh, Michigan, data by Montoye et al. (30)

and of the NHANES I data by Braitman, Adlin, and

Stanton (37). Both studies employed a 24-hour recall to

approximate energy intake, The Tecumseh study employed

a rigorous 30–60-minute recall of the preceding year’s

activities to classify 1,000 men into active, moderately

active, or sedentary categories. NHANES I employed

more modest estimates of perceived recreational and

vocational activity on a scale from O-2. These two activity

scales were summed for an activity index ranging from Oto

4.

Montoye et al. (30) found an inverse relationship

between a year’s average for work and leisure activity

expressed as a work-to-basal ratio and a l-day estimate of

caloric intake, A diet history would have provided a more

appropriate comparison to a year’s worth of activity data,

The authors state that their work-to-basal ratio is an

estimate of energy expenditure, but in fact it tends to

equalize the relationship between energy expenditure and

activity between groups of different body compositions,

The ratio penalizes the obese because the greater caloric

cost of their exertion is balanced by their higher metabolic

rate. It is not surprising that inactivity so defined was

found to be related to a higher sum of skinfolds (table 2).

Table 2. Caloric intake and sum of 4 skinfolds by activitylevelin

males: Tecumseh, Michigan

Sum of

Kilocalories/ skinfo/dsl

Activify categoty kilogram N in millimeters N

Most active . . . . . . . . . . . . 38.2 192 83.6 275

Intermediate . . . . . . 37.4 516 6e.7 813

Least active, . . . . . . . . . . . 33.9 175 92.4 273

lTriceps, subscapular, suprailium, and juxtaumbificus.

Reprinted with permission from Journalof ChronicD;sease,29, Montoye HJ, Block WO,

Metzner HL, Keller JB, Habitual physical activity and serum lipids Males age l&64 In a total

community, 1976, Pergamon Press, Elms ford, New York.

46
Caloric intake was standardized by kilograms body weight.

A comparison based on caloric intake per fat-free mass

would have kept the comparison congruous. An even more

congruent comparison would be caloric intake compared

with an estimate of average daily energy expenditure,

based on an estimate of metabolism and the average

basal-to-work ratio.

Montoye et al,’s sample ranged from 16 to 64 years of

age, Decreasingactivityand decreasingcaloric intake are

expected trends with aging, as is an increase in adipose

tissue, at least until age 40, in men (38). Thus, the strength

of these trends may be accounted for by aging itself and

not any unique differences between obese and normal

individuals. The lack of consistent significance across age

groups strengthens this possibility.

The methodological difficulties, the standardization

problems, and the comparison of a year’s activity to 1 day

of caloric intake all serve to make the data of Montoye et

al, very difficult to interpret and accept at face value. Even

if the comparisons were valid, could we infer from these

data that increasing fatness is associated with decreasing

caloric needs? No, because activity is only a fraction of

energy needs, and because we know nothing about the

variability in caloric intake in this sample. There is reason

to believe that this variability is greater for the obese than

the normal persons, as was stated earlier. The study tells

us nothing at all about dietary variability and nothing

about energy balance.

If more days of dietary intake were assessed or a diet

history for the past year were performed, if the activity

and metabolic data were converted to kilocalories, would

the data then give us information on energy balance? The

energy expenditure data might give an estimation of energy

needs that, by one estimate, is accurate within * 200 kcal

(39), These are useful data. The estimate of caloric intake

would be considerably more valid and useful if variability

over time were considered (40). However the difference

between the two estimates would not be of sufficient

validity and reliability to determine the direction of imbalance, to predict weight maintenance, or to predict changes

in body weight over time.

The data of Braitman, Adlin, and Stanton from

NHANES I (37) are presented in table 3 for females and

males, Reported caloric intake in females by 24-hour

recall was inversely related to the percent optimum weight

category, This relationship remained significant after adjustment for age and the perceived activity index. The relationship between caloric intake and weight category was

not significant for men.

Can it be inferred from these data that obese females

require fewer calories than normal females to maintain

their weight? Is caloric intake in males unrelated to weight

maintenance? The authors state that “there are two possible interpretations of these data: either obese adults eat

no more than nonobese and therefore maintain their

greater weight without excessive energy intake, or the

estimates of food intake in this study differ in accuracy

between obese and nonobese adults, either being

Table 3. Unadjusted and adjusted caloric intake at 5 levels of

percent of optimum weight, by sex United Statea, first National

Health and Nutrition Examination Survey, 1971-75

Caloric intake

Sex and percent Number of Unao@ted adjusted for physical

of optimum weight subjects ca/oric intake activity and age’

Female

Less than 101 . . . . . . 1,246 1,743 1,689

101–119 . . . . . . . . . 1,321 1,591 1,595

120–134 . . . . . . . . . 453 1,480 1,550

135–149 . . . . . . . . . 252 1,411 1,488

1490r more . . . . . . . 245 1,478 1,525

Male

Less than 101 . . . . . . 708 2,423 2,359

101–119 . . . . . . . . . 1,241 2,368 2,375

120–134 . . . . . . . . . 512 2,264 2,310

135–149 . . . . . . . . . 155 2,264 2,310

1490r more . . . . . . . 79 2,406 2,411

1For the analysis of covariance, for femslea, F = 9.269 and p < 0.001; for males, F = 0.795

and p = 0.53 > 0.05.

Reprintedwith permission from Journalof ChronicDisease,9, Braitman LE, Adlin N, Stanton

JL Jr., Obesity and caloric intake: The National Health and Nutrition Examination Survay of

1971-1975 (HANES l), 1985, Pergamon Press, Elmsford, New York.

underestimated by the former or overestimated by the latter.”

At least two other hypotheses are possible. One is that the

obese are restrained eaters for the most part. Thus, a 24-hour

recall may underrepresent the impact of their unrestrained

days and underestimate true consumption. A second hypothesis is that the assumption of weight maintenance in each

group is false. The tendency in cross-sectional studies is to

assume that weight fluctuations in each group are equal,

canceling each other, and that the caloric intake reported is

for weight or energy store maintenance. There is crosssectional evidence from the Health Examination Survey of

1960-62 and NHANES I, 1971–74, that over a 10-year time

span, female age cohorts have increased energy stores in the

form of body fat in all age groups but 55-64 years and 65-74

years. Men increased their subscapular skinfold in each age

catego~, but triceps did not increase after age 35-44 years

(41). Increases in internal body fat stores would be expected

with increasing age. Other studies have shown significant

changes in body composition over time (3). Obviously, 10-year

changes are not in a league with the short-term fluctuations,

which also could cause erroneous data interpretation, but

both types of changes do lend credibility to the fourth hypothesis. We do not know which group, the obese or the normal,

has a more consistent upward trend in body fat.

The degree of energy imbalance and the distribution

of energy imbalance within a range of body fatness would

be crucially important for the interpretation of the data of

Braitman, Adlin, and Stanton. However, if longer term,

valid data were available on energy intake and expenditure for NHANES III, we still could not determine energy

balance with the degree of accuracy necessary to estimate

the direction of true fat fluctuation. Only serial body

composition estimates, the test of energy balance, would

provide the information needed to establish both rapidly

and unequivocally if a meaningful energy imbalance exists.

47
Appropriate caloric intake

methodology for NHANES Ill

The precise and valid measurement of caloric consumption is an appropriate goal for NHANES III, but the

limitations of available methods must restrain the uses to

which the data are put. No method available is ideal and

some are not even possible within the context of NHANES.

Short- and long-term diaries and weighed records are not

applicable because of their reliance on subject cooperation and recording abilities. They also tend to interfere

with normal eating patterns, NHANES III must rely on

methods that put the burden of work on the nutritionist

rather than the interviewee. Rigorous quality control will

have to be maintained in interviewer selection and training

and through the collection of replicate data.

The following criteria should be considered when

choosing the best dietary methodology for estimating

energy consumption:

1. The data should reflect the full range of variability in

the individual’s diet, including weekdays, weekends,

and other points of variability.

Dietary histories obtain a subject’s estimate of habitual

intake (42), although the method has also been used to

obtain weekend and weekday estimates, which can then be

weighted (43), The 24-hour recall has the advantage of

giving data for discrete days so that group variability can

be estimated if a suitable number of days are sampled

(24,40).

NHANES 11 performed 24-hour recalls on Tuesdays

through Saturdays (44), so the weekend variability in

intake observed in other studies (45,46) was not recorded.

Information on weekend-weekday patterns must be part

of the dietary method. Each subject should have at least

one 24-hour recall from a weekend day. If a weekendweekday eating pattern is shown to exist, the intake

estimate should be a weighted average of weekends and

weekdays.

2, The method should minimize reporting bias, particularly differences between obese and normal subjects.

Twenty-four-hour recalls may underestimate caloric

intake in obese versus normal subjects (47–49), but diet

histories are subject to the same dependence on subject

recall (50). When compared with a caloric intake that had

maintained subject weight within t 2 percent over at

least 90 days, the diet history technique overestimated the

normal subject’s needs and underestimated the obese

subject’s needs (39). Whatever dietary method is employed,

it should use special probing techniques aimed at uncovering the intake of high-calorie food items, such as snack

foods, desserts, and alcohol, foods frequently underestimated (49,50), A food frequency cross-check designed to

elucidate the consumption of these particular high-calorie

food items may provide a useful probing tool,

3. The situation should maximize the subject’s ability to

remember both the types of foods consumed and the

amounts.

Unless most meals are consumed away from the

home, the home environment would provide the best site

for the initial dietary interview. Here the setting and the

contents of the refrigerator and cupboards provide memory

cues. The size of portions can be approximated directly

from the actual serving utensils. If interviews were computerized, information on usual container and plate sizes

could be retained on the computer for subsequent interviews in the mobile examination centers and perhaps by

telephone (51,52).

Because intakes on discrete days can help provide the

estimates of day-to-day variability necessary to generate a

statistical approximation of the distribution of usual intakes

(24), repeat 24-hour recalls should be the method of

choice for a cross-sectional comparison of group data.

However, 2–3 days of dietary intake data cannot give a

precise estimate of individual intake. As a result of the

large intraindividual variation in caloric consumption, correlations and regressions of caloric intake with relevant

physiological parameters are unlikely to yield more than

marginally significant results (53). The probability of type

two error – that is, the probability of accepting the null

hypothesis when indeed the null hypothesis is false – is

high.

In the right hands and with cooperative subjects, the

diet history technique would seem to be a better choice

than the 24-hour recall for prospective studies, Several

authors have shown its repeatability over time, especially

for energy intake (54-56). Changes in energy intake as

estimated by diet history have been reasonably congruent

with changes in body weight (57,58). A similar correlation

using a 24-hour recall technique could not be expected.

Diet histories give a higher estimate of caloric intake than

24-hour recalls (59,60), especially in the obese (48). One

negative aspect of diet histories is the possible difficulty of

obtaining information from school-age children (61). Diet

histories are more time consuming to code and to convert

to nutrient intake; therefore, they are more expensive to

perform than 24-hour recalls. However, if an important

part of an already expensive survey is to have comparable

energy consumption data over time, the extra expense of

thorough baseline dietary data would be compensated for

by usable data.

Conclusion

NHANES can never hope to achieve the level of

precision required for the measurement of energy intake

and expenditure to estimate energy balance, nor is it clear

that energy balance data would provide information more

useful than body composition measured over time, The

attempt to measure energy balance is an academic exercise more suited to testing the validity of methodologies

than to meaningfully assessing individuals.

48
The accurate measurement of total energy intake is an

qqxwpriate and useful goal for NHANES III. Although

no existing method is perfectly suited to the measurement

of energy intake, NHANES could considerably improve

the prwisicm with which it presently measures this variuhlc. Repeat 24-hour recalls with improved cross-checks

w rwammended for cross-sectional design, A diet history technique is recommended if a Longitudinaldesign is

:\doptcd.

Acknowledgments

ThwAs are due to Dr. Helen S. Wright for our

wmwrsutions about dietaty methodology and to Dr. John

Bwtrd for his comments on the manuscript.

References

1.

94.

.1.

4.

.,5

L1.

7,

s,

(l,

10.

11,

12.

13,

14.

1~.

1(.).

Garmw JS, Energy balance and obesity in man. 2d ed.

Amstmiam: Elsevier/North-Holland BiomedicalPress. 1978.

Achesmt KJ, Campbell IT, Edholm OG, et al, A longitudinal study of body weight and body fat changes in Antarctim. Am J Clin Nutr 33:972–7.1980.

Bwston G. Energy in human nutrition: Perspectives and

problems, Nutr Rev 41:325-40.1983.

Cunningham JJ, A reanalysis of the factors influencing basal

mcti~bolic rate in normal adults. Am J Clin Nutr 33:2372-4.

1’1s0,

Ravussin E, Burnand B, Schutz Y, et al. Twenty-four-hour

cnwgy expenditure and resting metabolic rate in obese,

modcmitely obese, and control subjects. Am J Clin Nutr

35:566-73. 19S2,

Wamo]d I, Carlgren G, Krotkiewski M. Energy expenditure and

bwiy composition during weight reduction in hyperplastic obese

women, Am J Clin Nutr 31:750-63. 197S.

Licbel RL, Hirsch J, Diminished energy requirements in

rcduccd-obese patients. Metabolism 33:164-70, 1984.

v;~n Itallie TB, Kissileff HR. The physiological control of

energy intake: An econometric perspective. Am J Clin Nutr

3s:97s-s8, 1!3s3,

Vim Itallie TB, Kissileff HR. Physiology of energy intake:

An inventory control model. Am J C1in Nutr 42:914–23.

19s5,

Booth DA, Acquired behavior controlling energy intake and

output. In: Stunkard AJ, ed. Obesity. Philadelphia: WE

Saunders. 19S0.

Rodin J. The externality theory today. In: Stunkard AJ, ed.

CNwsity, Philadelphia: WE Saunders. 1980,

Pi-Sunyer TX, Woo R. Effect of exercise on food intake in

hum[m subjects. Am J Clin Nutr 42:9S3–90. 19S5.

Hcdktuttcr A, Schutz Y, Jequier E, et al. Increased 24-hour

energy expenditure in cigarette smokers. N Engl J Med

314:79-S2, 1986,

.TaccJas DR Jr, Ciottenborg S. Smoking and weight: The

hlinnesota Lipid Research Clinic. Am J Public Health

71:391-6. 19S1.

Changes in nitrogen and energy metabolism during the

menstrual cycle. Nutr Rev 41:1 16-S. 1983.

Durnin JVGA, “Appetite” and the relationships between

cspenditure and intake of calories in man. J Physiol

156:294-306.1961.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

2s.

29.

30.

31.

32.

33.

34.

35.

36.

37’.

38.

Chandra RI<. Nutrition, immunity, and infection: Present

knowledge and future directions. Lancet 1:6SS-91. 19S3.

Miller DS, Mumford P. Obesity: Physical activity and nutrition. Proc Nutr Soc 25:100-7. 1966.

Durnin JVGA, Brockway JM. Determination of the total

daily energy expenditure in man by indirect calorimetW

Assessment of the accuracy of a modern technique. Brit J

Nutr 13:41-53.1959.

Harries JM, Hobson EA, Hollingsworth DF. Individual

variations in energy expenditure and intake. Proc Nutr Soc

21:157-6S. 1962.

Acheson KJ, Campbell IT, Edholm OG, et al. The measurement of daily energy expenditure —an evaluation of some

techniques. Am J Clin Nutr 33:1155-64.1980.

Borel NJ, Riley RE, Snook JT. Estimation of energy expenditure and maintenance energy requirements of college-age men

and women. Am J Clin Nutr 401264-72. 19S4.

Passmore R, Energy balances in man. Proc Nutr Soc

26:97-101.1967.

National Academy of Sciences, National Research Council,

Subcommittee on Criteria for Dietary Evaluation, Coordinating Committee on Evaluation of Food Consumption

Surveys. Nutrient adequacy: Assessment using food consumption surveys. Washington: National Academy Press. 19S6.

Bradfield RB, Jorden M. Energy expenditure of obese

women during weight loss. Am J Clin Nutr 25:971–5. 1972.

Griffiths M, Payne PR. Energy expenditure in small children

of obese and non-obese parents. Nature 260:69S-700. 1976.

Johnson ML, Burke S, Mayer J. Relative importance of

inactivity and overeating in the energy balance of obese high

school girls. Am J C1in Nutr 4:37-44. 1956.

Rose HE, Mayer J. Activity, caloric intake, fat storage, and

energy balance of infants. Pediatrics 41:18-28.1968.

Hutson EM, Cohen NL, Kunkel ND, et al. Measures of

body fat and related factors in normal adults. J Am Diet

i%SOC 47:179-S6. 1965.

Montoye HJ, Block WD, Metzner HL, Keller JB. Habitual

physical activityand serum lipids: Males age 16-64 in a total

community. J Chron Dis 29:697-709. 1976.

Baecke JAH, van Staveren WA, Burema J. Food consumption, habitual physical activity, and body fatness in young

Dutch adults, Am J Clin Nutr 37:27S-S6. 1983.

Huenemann RL. Teen-agers’ activities and attitudes toward

activity. J Am Diet Assoc 51:43340.1967.

Stefanik PA, Heald FP, Mayer J. Caloric intake in relation

to energy output in obese and nonobese adolescent boys.

Am J Clin Nutr 7:55–61. 1959.

Bradfield RB, Paulos J, Grossman L. Energy expenditure

and heart rate of obese high school girls. Am J Clin Nutr

24:1482-S. 1971.

McCarthy NC. Dietary and activity patterns of obese women

in Trinidad. J Am Diet Assoc 48:33-7. 1966.

Maxiield E, Konishi F. Patterns of food intake and physical

activity in obesity. J Am Diet Assoc 49:406-S. 1966.

Braitman LE, Adlin EV, Stanton JL Jr. Obesity and caloric

intake: The National Health and Nutrition Examination

Survey of 1971-1975 (HANES I). J Chron Dis 9:727-32.

19s5.

Blair D, Habicht JP, Sims EAH, et al. Evidence for an

increased risk for hypertension with centrally located body

fat and the effect of race and sex on this risk. Am J

Epidemiol 119:526-40. 19S4.

49
39. Mahalko JR, Johnson LK. Accuracy of predictions of longterm energy needs. J Am Diet Assoc 77:557-61.1980.

40. Beaton GH, Milner BA, Corey P, et al. Sources of variance

in 24-hour dietary recall data: Implications for nutrition

study design and interpretation. Am J Clin Nutr 32:2546-59.

1979.

41. Bowen PE, Custer PB. Reference values and age related trends

for arm muscle area, arm fat area and sum of skinfolds for

United States adults. J Am Coil Nutr 3:357-76.1984.

42. Beal VA. The nutritional history in longitudinal research. J

Am Diet Assoc 51:426-32.1967.

43, van Staveren WA, deBoer JO, Burema J. Validity and

reproducibility of a dietary histo~ method estimating the

usual food intake during one month. Am J Clin Nutr

42:554-9.1985.

44. McDowell A, Engel A, Massey JT, Maurer K. Plan and

operation of the second National Health and Nutrition

Examination Survey, 1976-80, National Center for Health

Statistics. Vital Health Stat 1(15). 1981.

45. Beaton GH, Milner J, McGuire V, et al. Source of variance

in 24-hour dietary recall data: Implications for nutrition

study design and interpretation. Carbohydrate sources, vitamins and minerals. Am J Clin Nutr 37:986-95, 1983.

46. Richard L, Roberge AG. Comparison of caloric and nutrient

intake of adults during week and weekend days. Nutr Res

2:661-8, 1982,

47. Beaudoin R, Mayer J. Food intakes of obese and non-obese

women. J Am Diet Assoc 29:29-33. 1953,

48. van den Berg AS, Mayer J. Comparison of a one-day food

record and research dietary history on a group of obese

pregnant women. J Am Diet Assoc 30:1239-44.1954.

49. Linusson EEI, Sanjur D, Erickson EC. Validating the 24-hour

recall method as a dietary survey tool. Arch Latinoam Nutr

24:277-94.1975.

50. Bray GA, Zachary B, Dahms WT, et al. Eating putterns of

massively obese individuals: Direct vs. indirect meusurwments. J Am Diet Assoc 72:24-7.1978.

51. Krantzler NJ, Mullen BJ, Schutz HG, et al. Validity of

telephoned diet recalls and records for assessment of individual food intake. Am J Clin Nutr 36:1234-42. 1982.

52. Posner BM, Berman CL, Morgan JL, et al. The validity of ii

telephone administered 24-hour dietary recall methodolo~~.

Am J Clin Nutr 36:546-53. l_982.

53. Sempos CT, Johnson NE, Smith EL, Gilligan C. Effects of

intraindividual and interindividual variation in repeated

dietary records. Am J Epidemiol 121:120-30.1985.

54. Dawber TR, Pearson G, Anderson P, et al. Dietasy msessment in the epidemiologic study of coronary heart disease:

The Framingham study. II. Reliability of measurement. Am

J Clin Nutr 11:226-34.1962.

55. Reshef A, Epstein LM. Reliability of a dietasy questionnaire, Am J Clin Nutr 25:91-5.1972.

56. Jain M, Howe GR, Johnson KC, Miller AB, Evaluation of u

diet history questionnaire for epidemiologic studies. Am J

Epidemiol 111:212-9.1980.

57. Trulson NF, McCann NB. Comparison of dietary survey

methods. J Am Diet Assoc 35:672-6. 1959.

58. Block G. A review of validations of dietary assessment

methods. Am J Epidemiol 115:492-505. 19S2.

59. Young CM, Hagan GC, Tucker RE, Foster WD. A comparison of dietary study methods. II. Dietary history vs. seven-day

record vs. 24-hour recall. J Am Diet Assoc 2S:218-21. 1952.

60. Morgan RW, Jain M, Miller AB, et al. A comparison of

dietary methods in epidemiological studies. Am J Epidemiol

107:488-98.1978.

61. Rasanen L. Nutrition survey of Finnish rural children. VI.

Methodological study comparing the 24-hour recall and the

dietary history interview. Am J Clin Nutr 32:2560-7, 1979.

50
Chapter 5

Critique of studies of

the relationship

between diet and

osteoporosis

by Nancy E, Johnson, Ph,D,, Department of Food

Science and Human Nutrition, University of Hawaii

Introduction

Osteoporosis is an age-related disorder characterized

by decreased bone mass and by increased susceptibility to

fractures, especially fractures of the vertebrae, distal radius,

ond proximal femur (1,2). Heaney (3) points out that

fracture and decreased skeletal mass are distinct and

ought not to be confused. The relationship between the

two is still inadequately understood. For most physicians,

frwcture itself is the principal diagnostic evidence of

osteoporosis, with about 1,3 million fractures attributable

to this condition. The risk of osteoporosis developing

increases with age, especially after 45 years, and is higher

in women than in men and in white than in black persons,

The frequency with which age-related fractures occur in

different geographic areas varies enormously. Deaths from

falls are reportedly less common in areas with naturally

fluoridated water than in other regions, but the mortality

data are unreliable. Incidence rates of fractures seem to

be higher among white than among other persons regardless of the geographic area involved (4). The true reasons

for racial differences are unknown, although differences in

bone density have been described, with American black

persons said to have substantially greater levels of bone

density than white persons of the same age and sex.

Melton and Riggs (4) point out that, in general,

people residing in geographic areas where diets are deficient in dietary calcium, protein, and vitamin D have lower

fracture rates, whereas the incidence is highest in developed countries where diets are better, Thus, the observation of lower fracture rates in developing countries is the

opposite of what one would expect. Of course, people

residing in developing countries have lower Iifespans than

those in most developed countries, where environmental

and health conditions are different,

Clinical studies of patients with fractures of the hip,

distill radius, proximal humerus, and vertebra have generally

found some evidence of diminished bone density at the site of

the fracture, It has been shown that fracture rates are greater

for women, who have lower bone density levels than men, and

that the rates increase with advancing age among both sexes

as bone density diminishes (4), Thus, the risk of fracture and

osteoporosis increases as bone density declines. In a geographically defined population of women ages 55-80 years from two

small demographically similar communities in the United

States, the physician-documented fracture rate was 27 percent in women ages 55-64 years and 39 percent in women

ages 65-80 years. The mean bone density of women with

fractures was significantly lower than that of women with no

fractures. Women from the community with low-calcium water

reported more vertebral fractures than women from the

high-calcium community (5),

It has been su~ested that the different fracture syndromes generally associated with osteoporosis might represent separate pathogenetic mechanisms, Crush fractures in

postmenopausal women represent early trabecular bone loss;

hip fractures represent the loss of both cortical and trabecular

bone from the proximal femur. The data obtained from

studying patterns of bone loss from one part of the skeleton

may not apply to all fracture syndromes of interest, Parfitt (6)

proposes two structurally different forms of bone loss. Rapid

bone loss leads to changes in structural elements of trabecular

bone, an increase in the size of marrow cavities, and discontinuity in bone structure. Changes also occur in cortical bone.

These structural characteristics reduce the strength of the

bones to a greater extent than the reduction in the amount of

bone by itself would suggest. Slow bone loss results from

incomplete refilling by osteoblasts of resorption cavities of

normal or reduced size. This leads to simple thinning of

structural elements in both trabecular and cortical bone and

reduces the strength of the bone in proportion to the reduction in the amount of bone, Ruegsegger et al. (7) found that

postmenopausal osteoporosis appears to develop in phases of

relative stability and of acute bone loss, There is a steplike

pattern of trabecular bone 10SS.Also, the yearly trabecular

bone 10SSwas considerably higher in osteoporotic patients

(2.7 percent) than in healthy postmenopausal women

(0,95 percent). These findings suggest that a rate of loss of

trabecular bone that exceeds normal age-related bone loss can

lead to osteoporosis.

Information on bone mineral content is important for

monitoring age-related bone 10SS.Measurements may be

performed to assess cortical or trabecular bone mineral

loss resulting from accelerated bone resorption or decreased

bone formation to provide a quantitative result that can be

used as a predictor of fracture risk. There are differences

in bone loss patterns in different bones, and bone loss

occurs with different rates on different bone surfaces even

within the same bone. Furthermore, these patterns vary in

prominence in different individuals (8).

Some areas of compact bone, particularly the

metacarpal, are poorly correlated with the spine and

areas of trabecular bone, Other areas of compact bone,

51
the radius, for example, provide reasonable prediction of

femoral neck mass, However, with aging and with bone

disease, the degree of association among these areas

decreases and the errors of prediction increase. This is the

result of preferential loss of trabecular bone in the axial

skeleton (9). However, when low bone mass is found at

any measured site, there probably has been a general loss

of bone (9,10). According to Heaney (3), the notion of

osteoporosis implies a causal relationship between

decreased bone mass and increased fragility. It is now

clear that decreased mass is important, and probably the

most important factor in osteoporotic fracture. Bone mass

is a good discriminator between those who are likely to

fracture and those who are not.

Lifestyle factors such as cigarette smoking, high caffeine intakes, and alcoholism play a role in aggravating the

tendency to age-related bone loss. Other factors include

the following (3):

. Slight or slender build,

● Fair skin.

. Family history of osteoporosis or osteoporotic

fracture,

● Small muscle mass.

. Sedentary lifestyle.

. Small peak adult bone mass at age 35 years

. Low calcium intake.

. Early menopause or oophorectomy.

● One or more prior osteoporotic fractures.

It has been suggested that those who have achieved a

larger bone mass by the fourth decade are slower to

evidence clinical expression of bone loss later in life (11).

Thus, the absolute amount of bone mineral present in the

skeleton may dictate the subsequent development of

osteoporosis and fractures.

Dietary factors and bone

There are a number of reviews that provide general

information about nutrients and hormonal influences on

bone (2,3,12–17), The relationship between dietary factors

and bone mineral content or osteoporosis has not been

established unequivocally. Research on the role of dietary

factors in the causation of bone loss leading to osteoporosis

has focused primarily on calcium, although a few other

nutrients have been examined for their role in affecting

calcium balance or calcium metabolism, The nutrients that

generally have been associated with bone include calcium,

vitamin D, phosphorus, protein, sodium, fluoride, vitamin

C, vitamin A, magnesium, zinc, and total calorie intake.

Other nonnutrient components of diet may also affect

bone metabolism,

In an epidemiological study of Japanese-Anerican

men and women living in Hawaii, 1,208 men and 912

women were examined (18). Both 24-hour dietary recalls

and the frequency of eating selected foods containing

large amounts of calcium during a previous week were

collected. An analysis of these cross-sectional data indicated that dietary or supplemental intake of milk, calcium,

magnesium, vitamin A, vitamin C, vitamin D, and caffeine

were significantly related to bone mineral content at onc

or more skeletal sites, even after adjusting for confounding factors such as age, weight, height, exercise,

history of nonviolent fractures, use of thiazides, and usc of

estrogen, However, the effects of diet were modest in

comparison to effects of the other factors. For example,

dieta~ calcium intake could explain only 0.5 percent of

the variation in the bone mineral content of the distal

radius in men, and all dietary variables contributed R2

values of 0.3–0.8 percent. In this study, the sodium-tocalcium ratio was significantly and negatively correlated

with bone mineral content at all skeletal sites only in men.

Caffeine intake was negatively correlated with the radius

and ulna only in women. There was no significant association of supplemental calcium intake with bone mineral

content. The other interesting point of note was that in

women there was no relationship between calcium from

the 24-hour dietary recall and any of the bone measurements, although there was a significant relationship with

milk, and also with weekly calcium intake, For men there

was a significant relationship between bone measurements

and both 24-hour dietary calcium and weekly milk or

calcium intake. However, in both groups there was no

correlation between any of the bone measurements and

either supplemental calcium or dieta~ plus supplemental

calcium.

Similarly, Sowers, Wallace, and Lemke, who examined

midradius bone density in a geographically defined population of 325 women living in Iowa, did not find a correlation between total calcium intake and bone density (5).

However, mean bone density was greater in persons whose

calcium intake was greater than 800 milligrams per day

(mg/day) when this intake was consumed concurrently

with vitamin D in amounts greater than 400 internaticmd

units, The vitamin D intake was significantly correlated

with bone density in this study, but supplemental vitamin

D was negatively correlated with all bones measured in

the Hawaii study (18). There was no significant difference

in bone density between women from the high-calcium

community and those from the lower calcium community

in the Iowa study (5). The level of calcium in the water in

one community was such that long-term daily intakes

could be at a level of 1,200 to 1,500 mg/day. The total

average calcium intake in the low-calcium area was 745 mg

of calcium for food with a total intake of 964 mg: in

high-calcium areas the average from food was 740 mg, and

the high-calcium area averaged 1,329 mg when water was

included, As these average intake levels are relatively

high, it is not really surprising that there is not much

difference between communities because women in both

communities had intake levels well above both the RDA

(Recommended Dietary Allowance) figure and some estimates that have been made of average calcium

requirements.

Freudenheim, Johnson, and Smith recently completed

a 4-year study of bone loss in women 35-65 years of

52
age (19), Diet was monitored for three of the four years,

and usual intakes of individuals were determined. When a

cross-sectional analysis of the data was carried out, the

only nutrients related to bone mineral content at the

beginning of the study were vitamin C and niacin, Examination of bone loss in these women over the 4-year period

revealed that calcium, protein, phosphorus, zinc, folate,

and magnesium were associated with less loss, In a few

cases excessive vitamin C was associated with increased

10ss,

Dietary methods related to study of

osteoporosis

The osteoporosis literature contains a variety of dietary

methods that have been used to associate nutrient intakes

with bone mineral measurements or with incidence of

fracture, These methods include single 24-hour recalls,

3-day records, diet histories, quantitative frequencies for a

week, retrospective frequency at various stages of life, and

serial records. The methods used are sometimes poorly

described. Some of the methods provide an estimate of

usual dietary intakes; the 24-hour recalls and 3-day records

provide an estimate of intake at that particular time and

are likely not a good estimate of usual intake, given the

large amount of intraindividual variance in dietary surveys

(20,21), Studies that have been done are primarily of

adults, often aging adults, and rarely of infants, children,

and adolescents.

The ability of different dietary methods to provide

important information about relationships between diet

and bone depends upon the kind of information sought

about both diet and bone, the statistical methods used to

analyze the data, and the nutrient under study, Retrospective dietary methods that attempt to identify intakes

during childhood, adolescence, and early adulthood need

to reflect usual intake over a specific time period. These

might be the most appropriate methods for cross-sectional

studies and as part of case-control studies. For a cohort

study, in which a series of measurements are made on

relevant bones, if the information desired is rate of bone

loss or changes in bone density during the time of the

study, a different dietary methodology would be desirable.

It would be important to determine the usual diet during

the period of bone changes. This can be accomplished by

monitoring the diet and other related constituents through

a series of observations. The type of dietary method used

needs to be one that describes usual quantitative intake

with reasonable accuracy. For comparative purposes and

also because one method provides slightly different results

from another method, the same method should be used

throughout the study, and some baseline observations

should be made using the identical method.

A single observation on diet might not provide a good

correlation between diet and bone because of the high

intraindividual variance of dietary data that would bias the

coefficient (r ) toward zero. Intraindividual variance is

different for different nutrients, so the number of days, if

single-day records or recalls are used, will be different if

usual diet is estimated (20,21).

Differences between frequency and 24-hour recall

methods can lead to different conclusions. In a recent

study (18) it could be concluded that the frequency method

for estimating calcium obtained through major food sources,

primarily milk or dairy products, was best because it

represented usual milk intake, and likely habitual milk

intake that may have gone back to early adulthood or

adolescent food practices. This is illustrated in data presented by Yano et al. (18) using calcium as an example.

For 912 Hawaiian women with lower bone mineral content

than other comparable groups in the United States, daily

calcium intake estimated by the 24-hour recall was not

correlated with radius, ulna, or os calcis. Morever, neither

daily calcium supplement intake nor the sum of supplement and 24-hour dietary intake was correlated with any

bone measurement, However, daily milk intake was correlated with the distal radius, and weekly calcium intake,

which was estimated using frequency of consumption of

standard portion sizes of foods containing large amounts

of calcium, was significantly correlated with the distal

radius. Because we have developed the habit of equating

calcium with milk and vice versa, we would conclude that

the frequency method is better because it measures usual

intake of calcium and thereby eliminates the problem of

intraindividual variance biasing the correlation coefficient

when only a single observation, the 24-hour recall, is

made. Therefore, in spite of the fact that daily calcium

intake, supplemental calcium intake, and daily plus supplemental calcium intake are not correlated, we might

conclude that we need to use a frequency method to

obtain better estimates of calcium consumption.

There are other possibilities. It is possible that the

correlation between milk and the distal radius is the

critical factor. It is also possible that it is not calcium alone

that is important but either another nutrient found in milk

or a combination of nutrients that enables calcium to be

absorbed more efficiently or bone mineral formation to

take place more rapidly. In our own studies (Freudenheim, Johnson, and Smith) of women 35-65 years of age

whom we followed for a 4-year period, we found that less

bone loss in the radius and improvement in loss was

significantly correlated with phosphorus, magnesium, and

protein, not calcium, in a small number of premenopausal

women and with energy, protein, calcium, phosphorus,

and zinc in 33 postmenopausal women (19). When a 1.5-

gram calcium supplement was added to the usual diet of

half the women, the relationships between diet and radius

bone mineral loss were erased and not even calcium was

significantly correlated with bone. The mixture of nutrients seemed to be important in preventing bone loss. Our

dieta~ method was to obtain two preceded, prevalidated

food records at two times during each month for a 3-year

period. Each of the 2 days was selected using a table of

random numbers; 29 days later the next record was

recorded. Thus, serial records were kept by the women

and usual diet was monitored for three of the four years.

53
Some of these points can be illustrated further with

another widely quoted study that was conducted in Yugoslavia (22). The diet methodology was called a diet history

about usual patterns of eating, with questions on different

food items. One person per household was interviewed,

the information was extrapolated to the rest of the household, and identical household measures and food portions

were used for the questionnaire. Thus, one assumes that a

quantified history was obtained. Hand radiographs were

used to estimate bone mass; femur and forearm fractures

were recorded over a 6-year period. Two different districts, one low calcium and one high calcium, were chosen.

In the high-calcium district, calcium and phosphorus were

both two times as high, protein was about 50 percent

higher, and kilocalories were 10 percent higher than in

the low-calcium district, The fracture rate was higher in

the low-calcium, low-phosphorus district, with good correlation between bone density and fracture. The authors

concluded that the effect was a calcium effect. Because of

high intercorrelations between calcium and phosphorus

(r = 0.961 in the Freudenheim, Johnson, and Smith study

(19)), the effective nutrient could have been phosphorus,

an interaction between both nutrients, or a mixture of two

or more nutrients.

This raises the question of multicollinearity between

nutrients and between calories and almost all nutrients. In

the Freudenheim, Johnson, and Smith study (19), the

correlation between protein and kilocalories was 0.85;

phosphorus and protein, 0,91; and calcium and phosphorus, 0.96. With intercorrelations between nutrients, it

is almost impossible to select one nutrient as the most

important one. In a study of diet and blood pressure,

Reed et al. (23) found that, although the association with

potassium intake was relatively stronger than associations

with other nutrients, the intake of potassium was so highly

correlated with intakes of calcium, milk, and protein that

it was not statistically possible to identify the independent

association of potassium and blood pressure. Although

they could not easily separate the diet items, there was

some support for the idea that the mixture of nutrients

was important. The concept that a food item or specific

mixture of foods is associated with retention of bone mass

needs to be kept in mind for the third National Health

and Nutrition Examination Survey (NHANES III).

There is another problem that needs to be considered:

That is, the present bone mass may be determined by early

dietary habits and the total amount of bone laid down

before adult bone 10SSbegins its relentless course. Therefore, current habits may not be correlated with current

bone mass.

Different dietary methodologies may be necessary to

estimate the different nutrients or foods related to the

development of osteoporosis. The usual intake of nutrients, such as vitamins A and C, with high coefficients of

variation, much of which can be due to high intraindividual variance, may be better estimated using quantified

frequency methods, However, estimates of energy intakes

and the many nutrients highly correlated with energy and

present in small amounts in a wide variety of foods may be

better estimated by using a more precise method and by

monitoring the quantitative consumption on a regular

basis, Thus, for associating bone loss over time, which will

require a series of bone density measurements to determine rates of loss for individuals, quantitative nutrient

intakes over the same time span will be needed. A

combination of serial observations and frequency of consumption of certain food items that are good sources of

vitamins A, C, and D and fluoride may be the best

procedure,

For some dieta~ components such as fluoride, the

analytic procedures are very poor for foods, although

procedures are adequate for water supplies and other

solutions. There are other mineral contributions to water

supplies and these contributions can be substantial. Estimates of usual water intake would be useful, Also, fluid

intakes should be estimated, as tea is a good source of

fluoride; coffee provides caffeine, which increases excretion of calcium; and soda and other beverages may provide

phosphorus and other compounds that may be found to

have an effect on bone.

Dietary vitamin D can be estimated by using a few

foods, but amount of sunlight and supplemental intake

will have an impact on the amount of circulating 25(OH)D3

and 1,25(OH)ZD3. The 1,25(OH)ZD3 circulating in the

blood will have the greatest impact on calcium absorption,

bone formation, and bone resorption,

The factor of exercise and its relationship to bone

mineral content needs to be considered when data are

analyzed. Supplemental estrogen therapy, thiazide ingestion, and other factors need to be considered along with

the dietary assessment. Many women are now taking

various forms of calcium supplements, and a high percentage of people (close to 40 percent) use vitamin or

mineral supplements.

The question of applicability of different dietary

methods to various study designs, such as case-control,

cross-sectional, and cohort, also must include consideration of the bone measurement observations —that is, is

the intent to examine bone mineral content at one point in

time or to determine the rate of bone 10SS?The bone

mineral content measured at a single point in time maybe

determined by early dietary patterns, genetic factors, previous growth retardation periods, and other nonnutritional

considerations. Few relationships have been found between

current diet and cross-sectional measurements of bone. In

the larger population group studied in Hawaii (18) the

nutrients related to bone mineral content in crosssectional analysis were those found in milk, with the

exception of vitamin C in women. In Iowa (5) only

vitamins D and C were positively related to bone, and in

Wisconsin (19) only vitamin C and niacin were related.

For cohort studies the loss of bone mineral over time

(or maintenance of bone density in adults) and formation

of bone in children may be the best measure of potential

for the development of osteoporosis. For these studies,

monitoring both nutrient and energy intakes over time

54
would be desirable. Morgan et al. (24), using survey data that

included 12 daily records per individual from the Explorato~

Study of Longitudinal Measures of Individual Food Intake of

the US, Department of Agriculture (USDA), found that for

food energy estimates and for “small eaters” or low consumers, little additional information is obtained after 6 days.

For 14 larger eaters, the greatest gains in accuracy occurred

prior to day 7, but additional observations did improve the

estimates. Sempos et al, (20) found that 7 repeated records

are needed for estimating the true population correlation Ep,

and 15 are needed for the slope (Ep). For calcium, phosphorus, and magnesium, 6 days are needed to estimate Ep.

Vitamins A and C are more difficult to estimate, as the ratios

of intraindividual variance to interindividual variance are

higher (20).

The development and validation of cost-effective

methods to monitor nutrient and energy intakes are necessary prerequisites to any large-scale study. Morgan et al.

(24) also examined nine methods of assessing dietary

intoke status. They found it necessary to have an interviewer for a l-day recall with either a return to pick up a

2-day diary or telephone recalls after the initial contact. A

mail method provided response rates of less than 50 percent, Personal interviews provided the most consistent

reporting of mean numbers of total food items. Sempos et

al, (20) used an initial instruction method, which was

ultimately taped, and mail return of the structured forms,

with postcard and phone reminders. The return rate was

S0 percent in a highly motivated group of women. A

first-day interview and recall by telephone provided the

most consistent reporting of food energy intake according

to Morgan et al, (24), Telephone interviews provided the

best food energy intake data with the most reasonable

effort,

Methods that work for total calories will work for

nutrients that are highly correlated with total calories. For

those nutrients not associated so directly with calories,

such as vitamins A and C, frequency methods need to be

used.

As noted previously, it may be important to associate

not only nutrients but also a food or a group of foods with

the bone measurements, Recent studies have focused on

examining different ways to assess not only nutrient intakes

but food patterns and food item and food group intakes.

Shortened intake estimation procedures have been developed for the purpose of determining the relative consumption of dietary components, These procedures make use of

a limited data base and are based on the concepts that a

few food items that are frequently used by a population

constitute “core” foods (25,26) and can be used to assess

nutrient intakes or food intakes. For most of the frequency

studies a short list of foods or food groups can be developed, Willett et al, (27) used 99 foods, Johnson et al. have

used short forms of from 68 to 1S3 foods (25), and Byers

et al. (26) used 128 foods in an abbreviated interview

form, Variability in nutrient intake can be explained by a

small number of foods in some cases (26).

The ability of a nonquantitative frequency to substitute for

quantitative methods is limited. In one study (28) there was

reasonable agreement for food items, but intakes of food

groups and nutrients did not provide good relative agreement,

and group absolute agreement was found even less frequently.

For individuals there was no absolute agreement for nutrient

intakes, and agreement for food groups and food items was

rare. Frequency data could be used to rank groups of individuals according to mean intake of food items but not of food

groups or nutrients. Where effects are large, group dietary

methods can tolerate a larger error component. However,

when dietary and physiological relationships are weakly associated, as they have been in almost every study of diet and

disease, the dietary estimate must accurately reflect an individual’s intake. Individual contribution of dietary variables to

multiple regression models of diet and bone mineral content

can be small, from 0.3 to 0.8 percent (18).

For cross-sectional studies that include measures of current bone mineral content, some estimate of usual diet could

be important. With studies of bone, these data may need to be

analyzed in quantiles and using group means. Frequency

methods involving specified foods and portion sizes have been

used by several investigators (27,29). However, comparisons of

these methods with those obtained using more precise methods

have yielded variable results, Early studies by Johnson et al.

(25,30) showed some overestimation using frequency and

some but not all good food group comparisons. Thus, a single

observation, or at least three measurement periods using a

method appropriate to other studies, maybe desirable.

Osteoporosis occurs when there is either rapid bone

loss during the adult and aging years or slow bone loss

during an extended lifespan. Although previous diet may

dictate total skeletal mass and bone mineral content, if

one could maintain that skeletal mass, one would be

unlikely to develop osteoporosis. Thus, it would seem

reasonable in case-control and cohort studies to examine

rates of bone loss over time. Because the loss rates are

usually small, a study interval of from 3 to 5 years is

needed to determine loss rates.

For monitoring diet during periods of loss, single

observations, whether they are recall or structured records,

could be used. Morgan et al. (24) and others have shown

that telephone interviews are feasible, and this may be the

best way to collect these data for most of the nutrients,

and especially for calories and those nutrients highly

correlated with calories.

Since 1974 Johnson, Nitzke, and VandeBerg have used

preceded, structured dietary record forms that provided good

estimates for the specific nutrients under study (30). The

forms were based on frequency of consumption of food items

by the population group being studied. For a study of nursing

homes (25), Johnson et al. used the cycle menus to develop a

data base of 288 foods most frequently seined to residents and

validated the form against weighed food intake measurements. We found the forms reasonably accurate on a percent

paired-dfierence basis, although there were a number of

statistically significant differences. The advantage of using

55
these forms is that they can be processed rapidly and inexpensively. They do not serve to provide good estimates of all

nutrients, but for calcium and protein there was no significant

difference between weighed intake and the form; and for

calories, vitamin A, vitamin C, magnesium, and zinc, differences were less than 5 percent. To include foods consumed

but not on the form, we have a catchall “other foods” category

where any item consumed can be written on the form,

Recommendations

It is likely that information about current diet will not

provide information relevant to bone mineral content as

measured at the same time, with a few exceptions. Consumption of any item, food, beverage, or vitamin-mineral

supplement currently consumed on a regular basis could

affect bone mineral content. However, this information

can be obtained by asking about a usual diet with frequencyof-consumption questions, Contributions of minerals from

current and past water supplies need to be determined.

Early dietary patterns, particularly those related to

milk, dairy products, and other calcium-contributing foods,

may be useful in association with current bone mineral

content,

In order to make NHANES III data comparable with

past NHANES and USDA data, at least some common

measurement method should be used. Because the 24-hour

recall method has been the one consistently used, that

could be considered. I prefer a day’s record to minimize

forgetting errors, and this has also been used in the USDA

surveys, but not to my knowledge in NHANES.

Although 24-hour recalls on single-day records could

be used to classi~ individuals into quantiles, estimates of

the classification errors involved need to be made. Attempts

to correlate single measurements with bone mineral content have not proved very fruitful.

Because dietary and bone mineral relationships have

not been clearly and conclusively shown with crosssectional analysis and because the rate of bone loss is as

important as total bone mass, especially for women, it is

important to monitor changes in both bone mineral content and diet. Therefore, a series of observations on an

individual should be made over the same time period that

bone is being monitored. For this, either a 24-hour recall

or record observation for a series of randomly selected

days is recommended, From the studies that have been

conducted, it seems that 6 days would be best for best

estimates. The best response seems to be obtained with an

original contact and telephone followups, probably with

the same interviewer contacting an individual to take

advantage of the rapport established during the first

interview, Of the six recalls or records, data from the first

3 days can be used to classify individuals into quantiles

with more accuracy than a single record or recall.

Preceded, shortened food record forms have been

found to facilitate data processing, decrease processing

costs, and increase the rapidity with which statistical

analysis of the nutritional status indicators and diets can

be accomplished, With computer equipment currently

56

available, field data could be instantly input to a central

file, and the central file could be immediately converted to

food group and nutrient data, with the ultimate form of

the data in measures that are immediately amenable to

statistical procedures.

In summary, methods not too different from those

previously used would be the best considering all constraints. In addition, serial measurements should be made

to monitor intake. If new frequency methods are used,

especially shortened forms based on fewer foods or nutrients, these methods and forms should be pretested with

standard recording and recall methods to determine where

the

are

1.

2.

3.

4.

5.

6.

errors are likely to be and how great the differences

likely to be,

Information about specific food items, previous dietary

patterns, and composition of the water supply could

occur at the first interview. As there is a need to

collect information about the history of vitamins A

and C intakes for cancer risk factors, this information

could be used for the osteoporosis component as well.

Retrospective questions about dairy food consumption may provide information that can be associated

with bone mineral content.

To develop baseline data for monitoring, a series of

random observations would need to be made about

the time of the initial measurement. The exact number

of observations depends on the error or discrepancy

between those observations and an estimate of the

usual intake. The methodology for comparative purposes must be consistent. The “apples and oranges”

effect manifests itself when two different methods are

used within a single study.

The quantitative method selected for baseline

measurement and for subsequent observations should be

validated by testing the instrument against weighed food

intakes and by chemical analysis of the weighed foods.

This does not deal with the bioavailability problem in

diets, but it does provide an idea of how accurately the

methods portray the true intake of the specified nutrients,

Accuracy of reporting and using the questionnaire should

also be tested in a study population.

For classification of individuals into quantiles, at least

three observations (and at least six observations for

nutrients with relatively small variance) are recommended to obtain reasonable estimates of intakes over

time. For high-variance components of the diet, quantified frequency questions may be the only way to

obtain reasonable estimates of consumption.

Providing some structure to the 24-hour recall procedures may facilitate data collection. Of course, one

must be careful not to lead the answers or bias the

response toward anticipated answers.

A system of immedia[e data input to a large data file

using computer terminals in the field would facilitate data

processing. As the system for collection and analysis is

designed, it would be possible to immediately convert

field data to food categories and nutrients so the statistical work could proceed rapidly.
References

1,

2,

3,

4.

50

6.

7.

&

9,

10.

11.

12,

13,

14.

15,

Osteoporosis Consensus Conference, JAMA 252:799-802.

1984,

Johnston CC Jr. Studies on prevention of age-related bone

loss. In: Peck WA, ed. Bone and mineral research.

Amsterdam: Elsevier. 1985.

Heaney RP. Prevention of age-related osteoporosis in women.

In: Avioli LV, ed. The osteoporotic syndrome. New York:

Grune & Stratton. 1983.

Melton LJ, Riggs BL. Epidemiology of age-related fractures, In: Avioli LV, ed. The osteoporotic syndrome. New

York: Grune & Stratton, 1983.

Sowers MR, Wallace RB, Lemke JH. Correlates of midradium bone density among postmenopausal women: A

community study, Am J Clin Nutr 41:1045-53.1985.

Parfitt AM. Age-related structural changes in trabecular

and cortical bone: Cellular mechanisms and biomechanical

consequences. Calcif Tissue Int 36:5123-8. 1984.

Ruegsegger P, Dambacher MA, Ruegsegger E, et al. Bone

loss in premenopausal and postmenopausal women. J Bone

Joint Surg 64A.1015-23. 1984.

Wahner HW, Dunn WL, Riggs BL. Non-invasive bone

mineral measurements. Seminars Nuclear Med 13:282-9.

1983,

Mazess RB. Non-invasive methods for quantitating trabecular bone. In: Avioli LV, ed, The osteoporotic syndrome.

Detection, prevention, and treatment. New York: Grune &

Stratton, 1983.

Johnston CC Jr. Non-invasive methods for quantitating appendicular bone mass. In: Avioli LV, ed. The osteoporotic syndrome, Detection, prevention, and treatment. New York Grune

& Stratton. 1983.

Darn SM, Rohmann CG, Wagner B. Bone loss as a general

phenomenon in man. Fed Proc 26:1729-36.1967.

Chinn HI. Effects of dietary factors on skeletal integrity in

adults: Calcium, phosphorus, vitamin D and protein.

Bethesda, Maryland: Federation of American Societies for

Experimental Biology. 1981.

Heaney RP, Gallagher JC, Johnston CC, et al, Calcium

nutrition and bone health in the elderly. Am J Clin Nutr

36:986-1013.1982,

Avioli LV, Calcium and osteoporosis. Ann Rev Nutr 4:471-91.

1984,

Darn SM, Hawthorne VM, Calcium intake and bone loss in

population context. In: Rubin RP, Weiss GB, Putney JW,

eds, Calcium in biological systems. New York: Plenum

Press, 1985.

16.

17,

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

Raisz LD, Kream BE. Regulation of bone formation. N

Engl J Med 309:29-35, 83-9.1983.

Parfitt AM. Dietary risk factors for age-related bone loss

and fractures. Lancet 2(8360):1181-4. 1983.

Yano K, Heilbrun LK, Wasnich RD, et al. The relationship

between diet and bone mineral content of multiple skeletal

sites in elderly Japanese-American men and women living in

Hawaii. Am J C1in Nutr 42:877-88.1985.

Freudenheim JL, Johnson NE, Smith EL. Relationships

between usual nutrient intake and bone-mineral content of

women 35–65 years of age: Longitudinal and cross-sectional

analysis. Am J Clin Nutr 44:863-76.1986.

Sempos CT, Johnson NE, Smith EL, et al. Effects of

intraindividual and interindividual variation in repeated

dietary records. Am J Epidemiol 1121:120-9.1985.

Beaton GH, Milner J, McGuire V, et al. Source of variance

in 24-hour dietary recall data: Implications for nutrition

study design and interpretation. Carbohydrate sources, vitamins, and minerals. Am J Clin Nutr 37:986-95.1983.

Matkovic V, Kostial K, Simonovic I, et al. Bone status and

fracture rates in two regions of Yugoslavia. Am J Clin Nutr

32:540-9.1979.

Reed D, McGee D, Yano K, et al. Diet, blood pressure and

multicollinearity. Hypertension 7:405-10.1985.

Morgan KJ, Johnson SR, Rizek RL, et al. Collection of food

intake data: An evaluation of methods. J Am Diet Assoc

87:888-96.1987.

Johnson NE, Sempos CT, Elmer PJ, et al. Development of a

dietary intake monitoring system for nursing homes. J Am

Diet Assoc 80:549-57.1982.

Byers T, Marshall J, Fiedler R, et al. Assessing nutrient

intake with an abbreviated interview. Am J Epidemiol

122:41-50.1985.

Willett WC, Sampson L, Stampfer MJ, et al. Reproducibility

and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol 122:51-65.1985.

Chu SY, Kolonel LN, Hankin JH, Lee J. A comparison of

frequency and quantitative dietary methods for epidemiologic studies of diet and disease. Am J Epidemiol 119:323-34.

1984.

Hankin JH, Kolonel LN, Hinds MW. Dietary history methods

for epidemiologic studies: Application in a case-control

study of vitamin A and lung cancer. J Natl Cancer Inst

73:1417-22.1984.

Johnson NE, Nitzke S, VandeBerg D. A reporting system

for nutrient adequacy. Home Econ Res J 2:210–21. 1974.

57
Chapter 6

Dietary methodologies

for food and nutrition

monitoring

by Elizabeth A, Yetley, Ph. D., R.D., Arietta M.

Beloian, and Christine J, Lewis, Ph. D., R.D., Center

for Food Safety and Applied Nutrition, Food and

Drug Administration

Introduction

Since the early 1900’s, when the existence of frank

nutritional diseases was identified in US. population

groups, the American public and national policymakers

alike have been concerned with the role of nutrition and

dietary intakes in public health (1), National monitoring of

food consumption began in 1936 with implementation of

the continuing series of Nationwide Household Food

Consumption Surveys by the U.S. Department of Agriculture (USDA), In the 1960’s nutrition monitoring was

expanded to include data on individuals with completion

of the first Nationwide Food Consumption Survey (NFCS)

by USDA and the launching of the Ten State Nutrition

Survey by the Department of Health, Education, and

Welfare, supported by recommendations from the 1969

White House Conference on Food, Nutrition, and Health,

The development of such new programs as the Food

Stamp program; food delivery programs for the elderly;

the Women, Infants, and Children (WIC) supplemental

food program; and nutrition labeling had their genesis

from these new concerns and data bases. Nutrition monitoring activities continued through the 1970’s, with the

completion of the second NFCS by USDA and two National

Health and Nutrition Examination Surveys (NHANES) by

the National Center for Health Statistics (NCHS). During

the 1980’s monitoring activities were more focused, with

the conduct of Hispanic HANES (HHANES) by NCHS

and the Continuing Survey of Food Intakes by Individuals

(CSFII) conducted by USDA,

Recently, Federal agencies have paused to evaluate

and learn from the flurry of data collection activities of the

previous two decades. Critical evaluations of issues involved

in the collection and use of dietary assessments were made

by several expert committees under the auspices of the

National Academy of Sciences (2-4). Currently, an expert

panel has been convened under a Food and Drug Administration contract with the Life Sciences Research Office

of the Federation’ of American Societies for Experimental

Biology to provide guidelines on the appropriate uses of

dietary survey data. Ongoing legislative activities to formalize a comprehensive national nutrition monitoring

system also provide evidence of the continuing interest

and perceived need by Congress to develop a strategy for

improving the nutrition monitoring system in the United

States.

When considering nutrition monitoring activities, it is

important to recognize that their orientation and the role

of dieta~ data are shifting. Initial concerns with frank

nutritional deficiencies have been overshadowed by issues

of food safety, questions about the potential for adverse

effects of excessive consumption of food components and

nutrients, increasing interest in identifying the possible

role of dietary factors in prevention of chronic diseases,

and new emphasis on developing educational programs

relative to health promotion and disease prevention.

Although continued monitoring for nutritional inadequacies is needed because of the constantly changing nature

of the food supply and concerns about the availability of

food for certain populations such as low-income groups,

there is a general sense that nutritional deficiencies tend

to be less severe than in the past and largely are limited to

a few target groups. Furthermore, as the national nutrition

monitoring system has expanded its data base to include

other types and sources of data (for example, medical and

biochemical data, surveys on vitamin-mineral supplement

use, screening programs in State health units), the role of

dietary intake data is changing. Dietary data are often no

longer the primary source of information but are now one

of many types of data used for evaluating nutritional and

food safety risks and for developing programs and policies.

Characteristics of food and

nutrition monitoring

To provide a framework for discussion and recommendations on food and nutrition monitoring, we will borrow’

concepts presented by Habicht (5–7). We will consider the

topic of nutrition monitoring in its broadest sense, which

includes not only the traditional assessment of inadequate

and normal intakes of food components that protect

health but also information on imbalances among food

constituents and excessive intakes of food components

deleterious to health. This definition expands the use of

food intake data from its traditional application for evaluation of the potential risk of nutritional inadequacies to

encompass the rapidly increasing need for assessments of

dieta~ exposures to contaminants, toxins, food additives,

58
and nonfood sources of nutrients as well as newer concerns of’excessive intakes of food components, as described

bJ,the Government pamphlet“Nutritionand Your Health:

Dietary Guidelines for Americans” (8), For these reasons,

we must consider the topic at hand to be one of food and

nutrition monitoring rather than simply nutrition monitoring.

Habicht also notes that the collection of food and

nutrition data has a number of public health uses. Each

use has its own requirements for data collection and

analysis activities. Several of these purposes will be briefly

discussed to: (a) clarify how use of dietary data for food

find nutrition monitoring compares and contrasts with

data needs for epidemiological applications and with other

public health data collection activities in nutrition, discussed in previous workshops, and (b) emphasize the need

for linkages to national food and nutrition policy and to

program activities.

Five basic types of data collection and end-use activities can be described, as follows.

Assessment – The measurement and description of the

nutritional status and food intake of a population; the

description and prevalence of nutritional or food intake

problems; and identification of the subgroups at greatest

risk of inadequate or excessive intakes of food components.

Mcmikwing-The estimation of changes in “true” prevalence of food or nutritional intake problems over time.

Monitoring is linked to assessment in that monitoring

consists of repeated assessments over a period of time,

This type of data use imposes the difficult requirement

that repeated assessments be comparable; that is, sampling and measurement methods used in different surveys

must be identical or be capable of being adjusted. Then

ditierences between surveys can be attributed with confidence to differences in food or nutrient intakes and not be

confounded by artifactual differences resulting from varying

methodologies, Ideally, monitoring activities are tied to

the development and evaluation of national public health

policies and programs; however, they are not designed to

promote immediate actions or interventions.

Surveillance –A survey of nutritional or food-related

disorders of public health concern for which rapid and

effective intervention is needed. This type of data collecticm is carried out in an ongoing, continuous system

intimately linked to an active public health program and is

usually focused on high-risk groups and temporally close

or well-defined associations. It typically includes a mechanism for rapid intervention and evaluation. Surveillance is

often linked to assessment and monitoring through their

identification of the food components and nutritionrelated disorders appropriate for inclusion in a surveillance system,

Screenbzg-A system that identifies specific individuals

for nutritional or public health intervention, often at the

community level.

Epidemiological investigation –A comparison of differences in dieta~ intakes among population groups or

subgroups and the relationship of these dietary differences

to different risks of health problems, This type of analysis

attempts to extrapolate findings of diet-health relationships from one population group to another. Whereas

monitoring compares prevalence over a period of time to

identify changes in similar population groups, epidemiological research compares differences in dietary intakes

between populations with differences in their prevalence

or risk of disease. As a consequence, relative differences,

rank-ordering, or categorizing techniques for classifying

persons as having high or low dietary intakes and high or

low risk or rate of disease often are adequate for epidemiological comparisons. In contrast, the prevalence estimates

based on “true” or actual intakes are needed in assessment

and monitoring activities. However, true prevalence can

become a concern for epidemiologists attempting to estimate

the public health importance of a diet-health relationship or

evaluating how a change in risk factors resulting from intervention has affected the prevalence of a disease. In these

cases, the data needs of the epidemiologist are similar to those

needed for monitoring purposes.

From here on, we have limited our discussion to the

assessment and monitoring types of activities. Surveillance

and screening activities, although important, are generally

outside the scope of a survey such as NHANES and are

more appropriately handled by programs such as those

conducted by the Center for Chronic Disease and Health

Promotion, Centers for Disease Control, and by local,

State, and Territorial public health agencies. Epidemiological applications have been well described by several

others participating in this workshop.

In addition to discussing the data needs for assessment and monitoring activities, we will also outline the

linkages of these data bases to public health programs.

Implicit in food and nutrition monitoring is the concept

that the design and evaluation needs of public health

programs should be a planned component of these surveys

(5). To accomplish this, we need not only information on

demographic and descriptive factors to identify subgroups

at need but also adequate information on patterns of food

use (including information on usual and extreme dietary

intakes) and information on how foods are combined

within eating occasions, as well as appropriate and sensitive measures for determining a program’s progress toward

meeting its objectives.

Issues in assessment and

monitoring

As outlined above, the primary purposes of assessment and monitoring of data activities are to estimate the

prevalence of persons at risk because of inadequate,

excessive, or imbalance intakes of foods or food components and to compare changes in these prevalence over

time. The most recent and comprehensive review of issues

involved in food and nutrition monitoring can be found in

the 1986 report from a National Academy of Sciences

(NAS) subcommittee (4), convened for the purpose of

evaluating the assessment of nutrient adequacy from food

consumption surveys. Using this report and other sources

59
as a basis, we have selected certain key issues for more

detailed discussion.

Systematic bias

AI1 measurements have components of random error

and systematic bias. Of course, all errors should be minimized, but systematic errors are more serious than random

types of error when estimating the prevalence of high or

low dietary intakes (4), Systematic error shifts population

distribution curves to the left or right, Therefore, estimates of prevalence are directly affected, Furthermore,

correction of systematic error from one survey to the next

can produce a false and misleading appearance of a real

change from one time period to another.

Examples of several potential sources of systematic

error with relevance to the NHANES dieta~ data collection procedures are given below in order to better illustrate this problem. These examples are not comprehensive

and are, by necessity, taken out of context relative to the

overall survey data base.

Inadequate laboratory methodologies and procedures –A major potential source of systematic error is in

food composition data bases and data handling procedures, Significant errors in estimates of intake of food

components can occur when laboratory methods for analysis are not good or were not properly performed. An

example of the potential bias introduced by this problem

can be shown by data from Wolf (9). In this study, the

analyzed nutrient content of diets of 22 adults was compared with calculated results using USDA Handbook

Number 8. Good agreement was observed for calculated

versus collected food weights and estimates of calcium

intakes, suggesting that the collection and recording procedures were consistent and that reference table values

for calcium were accurate. However, results for other

nutrients were quite variable. Iron estimates were biased

toward high values for the method by calculation compared with the method by analysis; zinc estimates were

biased toward lower values for the method by calculation,

Using the iron content of beef and pork from several data

bases (10–12) as an additional example, the impact on

standard reference values when food composition data are

corrected is shown in table 1. Different iron values were

used for the same food item in several surveys, depending

upon the date of the available food composition data base,

Thus, the prevalence of persons with low intakes of iron

will appear to change over time (assuming that no other

changes in intake patterns or composition values have

occurred). In evaluating monitoring data, it is important,

therefore, that changes resulting from corrections in the

original reference composition data base not be misconstrued as resulting from real changes in nutrient intakes in

subsequent surveys.

Food desrmjxors –The completeness and adequacy of

food descriptions, the appropriate coding of data, and the

changes in descriptors used over a period of time constitute a second major potential source of systematic bias,

Again, several examples can be given,

Table 1. Iron content of beef and pork from data bases used

during National and Hispanic Health and Nutrition Examination

Surveys

Milligrams of Iron

per 100 grams of

Food and suwey Food code food

Beef, ground, cooked, lean

NHANES 1, 1971–741 . . . . . . . . . . 00368 3.50

NHANES II, 1976–802 . . . . . . . . . . 00368 3.50

HHANES, 1982–843 . . . . . . . . . . . 215-0120 2.18

Pork chops, cooked, lean and fat

NHANES 1, 1971–741 . . . . . . . . . . 01717 3,40

NHANES II, 1978-802 . . . . . . . . . . 01717 3.40

HHANES, 1982-843 . . . . . . . . . . . 221–0101 0.93

i First National Health and Nutrition Examination .%rvay. SOURCE: National Center for Health

Statialica. National Health and Nutrition Examination Survey 1971-75 public uae data tapa

documentatiorx Modal gram and nutrient composition. Tapa nos 4702 and 4703. Hyattsville,

Maryland. 1981.

2Second National Health and Nutrition Examination Survey. SOURCE National Center for

Health Statistics. National Health and Nutrition Examination Surveyl 976-1980 public uae data

tape documentation: Model gram and nutrient composition. Tape nos 5702 and 6703.

Hyattaville, Maryland. 1982.

3~~Panic Health and Nutrition Examination Survey. SOURCE: Human Nutrition kIfOrMatiOn

Service. Manual of food codes for individual intake. Hyattsville, Maryland: U.S. Department of

Agriculture. 1985.

First, specificity and consistency in the descriptors of

foods are important, particularly for those processed foods

in which nutrient and ingredient compositions vary significantly by brand, fortification or food processing practices

may result in significant differences among brands or

within a given brand over time, and brand loyalty by

consumers is likely to occur. For instance, several sweetened cola drinks are listed under a single food code in the

manual of food codes now used jointly by USDA and

NCHS (12). Although these beverages have similar nutritional profiles, their profiles for other food components

may vary considerably, As shown in table 2, caffeine

content (13) or a theoretical food additive (“Compound

X’) could represent such food components. Loss of the

Table 2. Food component content of cola soft drinks

Components per 369-gram serving

Soff drink, cola type Caffeine Compound

(924-1031)1 Kilocalories 2 (milligrams)z “X’ (micrograms)

Coca-Cola . . . . . . . . . . . . . 144.0 45.6 . . .

Canned . . . . . . . . . . . . . . . . . . . 92

Notcanned . . . . . . . . . . . . . . . . .

Pepsi Cola . . . . . . . . . . . . 157.2 38.4

Canned . . . . . . . . . . . . . . . .

. . .

. . .

Not canned . . . . . . . . . . . . . . . . .

RC Cola, . . . . . . . . . . . . . 155.8 36.0 .,.

Canned . . . . . . . . . . . . . . . . . .

Not canned . . . . . . . . . . . . . . .

Jamaica Cola . . . . . . . . . . . 185.0 30.0

Canned . . . . . . . . . . . . . . . .

. . .

. . . 129

Notcanned . . . . . . . . . . . .,, . . .

Average value . . . . . . . . . . 155.4 37.5 28

“Maximum” value . . . . 165.0 45.6 129

1Code numbers 924-1031. SOURCE: Human Nutrition information SewIce. Manual of food

codes for individual intake. Hyattaville, Maryland: U.S. Department of Agrlcultura. 1985.

2National Soft Orink Association. Washington, O.C.: personal communication. lgal.

31na~tute of Food Technologists> Experl Panel on Food Safety and Nutrition. Caffaine. Food

Technol 37:87-91.1983.

60
specific brand and packaging identities for the individual

food items in this coding category will require that assumptions be made in using these data for estimating intakes of

caffeine or the theoretical Compound X, Averages for the

components can be used; however, averages will obviously

underestimate intakes of “heavy” consumers who not only

consume the product several times a day but may also

have a brand loyalty to the product with the highest

concentrations. Furthermore, bias will be introduced if

some population subgroups use brand products in differing proportions from other groups, An occasionally

used “conservative” alternative for food safety evaluations

is to assume that all foods within the food category contain

the component of interest and that its concentration is at

maximum level. This approach would clearly overestimate

intakes (table 2). Yet, although the incorporation of more

specific food descriptors into assessment and monitoring

activities is desirable, it would also cause a “correction” in

the data base. Therefore, comparison of intakes based on

detailed brand information in subsequent surveys with the

grouped data used in the past would confound the ability

to differentiate between true changes in intake and changes

because of the artifact of improving the reference data base.

A second example of potential sources of systematic

error caused by changes in data handling can be seen by

reviewing the coding structure for green beans from the

first NHANES survey to the most recent one, based on the

current coding system used jointly by NFCS-CFSII and

HHANES. Changes in assumptions about the processing

of green beans were apparently made from NHANES I to

NHANES II, as evidenced by changes in sodium content

of codes 183 and 184 (table 3). (In general, canned

Tabla 3. Descriptions and composition of green beans used during Health and Nutrition Examination Surveys

SurVey, food code, and description

NHANES 1, 1971-741

182: Beans, snap, green, raw,,..., . . . . . . . . . . . . . . . . . . .

183: Beans, snap, cooked In small amount of water, short time. .

184: Beans, green, cooked (drained) , . . . . . . . . . . . . . . . . . . .

185: Beans, snap, canned, regular pack, solids and liquids . . . . .

186: Beans, green, canned, solids only . . . . . . . . . . . . . . . . . .

le7: Beans, snap, canned, regular pack, drained liquid . .

188: Beans, snap, special dietary pack, solids and liquids. . . . .

189: Beans, snap, special dietary pack, drained solids , . . .

190: Beans, snap, special dietary pack, drained liquid . . . . . . . . .

191: Beans, snap, frozen, cut, not thawed. . , . . . . . . . . .

192: Beans, snap, frozen, cooked, boiled, drained. . . . . . . .

193: Beans, French atyle, not thawed. . . . . . . . . . . . .

194: Beans, snap, French style, cooked, boiled, drained . . .

204: Beans, snap, frozen, cooked, boiled, drained. . . . . . .

NHANES M,1976-802

182: !3eane,snap, green, raw.... . . . . . . . . . . . . . . . . . . . . .

183: Beans, snap, cooked in small amount of water, short time. . . .

184 Beans, green, cooked In large amount of water, long time

185: Beans, snap, canned, regular pack, solids and liquids . . .

186: Beans, green, canned, solids only ., . . . . . . . . . . . . . .

1E17:Beans, snap, canned, regular pack, drained liquid .

192: Beans, snap, frozen, boiled, drained , . . , . . . . . . .

194: Beans, snap, French style, boiled, drained , . . . . . . . .

204: Beans, snap, frozen, boiled, drained , ., , . . . . . . . . . . . . .

HHANES, 1982-843

752-0500: Beans, string, NS as to color or added fat. . . , . . . , . .

752-0501: Beans, string, green, cooked, NS as to fat added in

cooking .,,,,,,.....,,,.,,,., . . . . . . . . . . . . . . . . .

752-0502: Beans, string, green, cooked, fat not added in cooking.

752-0503: Beans, string, green, cooked, fat added in cooking , . .

752–051 1: Beans, string, green, canned, low sodium, cooked, NS

astofat added incooklng . . . . . . . . . . . . . . . . . . . . . . . . . . .

752-0512: Beans, string, green, canned, low sodium, cooked, fat

notadded lncooklng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

752-0513: Beans, string, green, canned, low sodium, cooked, fat

added lncooklng .,..... . . . . . . . . . . . . . . . . . . . . . . . . .

Fat Sodium

Grams per 100 grams

of food

Milligrams per 100

grams of food

0.2

0!2

0,2

0.1

0.2

0.1

0.1

0.1

0,1

0.1

0.1

0.1

0.1

0.1

—

—

3.1

3.1

0.3

3.1

3,0

0.1

3.0

7

4

23;

236

236

2

2

2

1

1

2

2

1

24;

240

236

236

236

236

236

237

253

253

234

253

35

2

35

1First National Health and Nutrition Examination Survey, SOURCE. National Center for Health Statiatica. National Health and Nutrition Examination Survey 1971-75 pubfic uas data tape

documentation: Model gram and nutrlsnt composition, Taps nos 4702 and 4703. Hyatfsville, Maryland, 19el.

‘Second National Health and Nutrition Examination Survey. SOURCE National Center for Health Statistics. National Haalth and Nulrition Examination Survey 1976-80 pubfic uae data tape

documentation: Model gram and nutrisnt composition. Tape nos 5702 and 5703. Hyattsville,Maryland. 1982.

‘Hispanic Health and Nutrition Examination Survey. SOURCE Human Nutrition Information Service. Manual of food codes for individual intake, Hyattsville, Marylend: U.S. Department of Agriculture.

1985.

NOTE NS Is not specified.

61
3,600

r

3,400

3,200

3,000

2,800

2,600

2,400

2,200 I

●✏✼

✼✌❊ ““. Married males

\ n=59 \

. ,/-’ -”\ ,/

““v’” “V*’

Sng!efemales

2,000

[

n.s6 .$●

.*.

.

“.- .~. .’ “.

.**b

1,800 “

.“ “. ‘

“. ..

.“ “*, ,

“.. ●. ●“,* “., ●** .

~9* ●

I,6CX3 TFSSMTWTFSSM TW /

of I I I I I I I I I 1 I I I I

012 345678910111213 14

Duration of study (days)

SOURCEechnakmbarget al.(14,p, 188). Reprintedwith parmlaaionfromAsee$skg

~-aw~ FaUem$,1981,NationalAcademy Praas,Washington, D.C,

Figure 1. Energy intakes by sex and marital status

vegetables tend to be high in sodium, as salt is added

during processing; fresh or frozen forms tend to be naturally low in sodium.) Codes 183 and 184 in NHANES I

and II illustrate the difficulty in estimating both the

intakes of an added nutrient like sodium and the changes

in sodium intake over a period of time, because it was

apparently assumed in NHANES II that sodium is added

by all respondents prior to eating the product (unless a

low-sodium canned product is specified). Thus, a

respondent who uses fresh or frozen green beans without

added salt will appear to have consumed the salted product.

In contrast, for NHANES I, no added salt was assumed.

Additional changes are seen as we move from

NHANES 11 to HHANES, which used the joint coding

system with USDA, Here, not only are food descriptions

different from those used in previous HANES, but also fat

added during cooking is included in the green bean code

rather than listed separately. If such a change in code is

followed for other vegetables, there will be an apparent

change in sources of fat between NHANES II and future

surveys that use the new coding system.

Specific population groups – Another concern in

attempting to identify changes in prevalence of risk over

time is the possibility of systematic bias in reporting food

intake for an entire subgroup, There are insufficient data

to evaluate this issue directly, but several pieces of evidence suggest that this may be a problem. For example,

suggestions of possible sex and age differences in reliability of reporting of dietary results have been noted (4),

Schnakenberg et al, (14) found quite different consistencies in reporting patterns between married and single

persons for both males and females over a 14-day collection of dietary records. Specifically, as shown in figure 1,

respondent fatigue is suggested by decreasing levels of

reported energy intake for single men; married men and

62

Table 4. Estimated intakes of calcium and iron by Infants from

3 surveys

SMIF, NHANES //, NFGS,

Variable 1974+301 1976–6@ 1977-783

Age (months) . . . . . . . . . . . . . . . . 6 6-11 0-12

Kilocalories . . . . . . . . . . . . . . . . . 880 860 880

Calcium (milligrams) . . . . . . . . . . . 827-1,009 S23 877

Iron (milligrams) . . . . . . . . . . . . . . 4.7-10.2 11.4 19.3

1SMIF= Selected Mineralain Foods.

2NHANES = NationalFleslth and Nutrition Examination%_Vey,

3NFCS = Nationwide Food Consumption Survey.

SOURCE Pennington JAT, Wilson DB, Newell RF, et al. Selected Mlnarals in Foods Suweya,

1974 to 1981/82. J Am Diet Asaoc 84771-80, 1984.

single women showed less attenuation over time. In another

study, plate waste was compared with recall data in

hospitalized patients (15). Results showed that elderly

subjects were more likely than young adults to underreport their caloric intake, Probing by interviewers resulted

in increased estimates of reported intakes. Hallfrisch,

Steele, and Cohen (16) found that adult males and females

both tended to underreport their caloric intakes, but the

deficit was more severe in females than in males (35 percent and 15 percent, respectively). In short, this evidence

suggests that there may be systematic reporting errors that

vary from group to group, but the data are limited and

difficult to interpret,

Bias in estimating dieta~ intakes of specific subgroups

is more likely to occur when the population group of

interest consumes a limited variety of foods and when

highly processed foods comprise a large portion of the

diet. An example of this potential problem can be illustrated by comparing estimates of dietary intakes of infants

obtained from three surveys (table 4). The Selected Minerals in Foods (SMIF) data provide, for foods purchased

in 1974-80, nutrient values based on a diet representing

infant eating patterns in the 1965 NFCS (17). After

adjusting the NHANES 11 (1976-80) and the 1977-78

NFCS results to the kilocalorie level of the SMIF diets, it

can be seen that estimates of calcium intake among all

three data bases were similar, but iron intakes varied

considerably. Because a limited number of foods are

available for infants (that is, infant formulas and infant

cereals constitute the major source of nutrition in infant

diets), intakes are particularly sensitive to variations in

fortification practices for the foods used and also to the

particular foods selected. Estimates of total intakes can

thus vary widely. Because the three data bases in table 4

differed in their original sample population, the specificity

of food descriptions used, the assumptions as to whether

fortified or nonfortified products were chosen, and composition values for the iron content for supposedly similar

infant foods, it is not surprising that quite different intake

estimates were obtained,

Underreporting of nutrients, foods, or food groups – Finally, if accurate estimates of intakes are to be made, it is

extremely important that all sources of consumed food

and nutrients be identified, quantified, and incorporated
into the intake estimates. Alcoholic beverages, minerals

and electrolytes supplied by water and medications, organ

meats, vitamin-mineral and other food supplements, and

seasonal foods such as melons should be included. The

low estimates of consumption of alcoholic beverages compared with sales data have been noted (4) and raise

ccmccrns not only about the accuracy of intake estimates

for this beverage group but also about the potential for

underestimating energy intakes, Water can be a significant

source not only of minerals and electrolytes but also of

contaminants. Adequate intake estimates and home use of

water for reconstituting beverages are not available from

any source at the present time. The potential for medicaticms to be a significant source of nutrient minerals is

suggested by recent advertisements for using a popular

antacid as a calcium source. Use of organ meats, although

important both nutritionally and toxicologically, is hard to

accurately estimate with a limited number of days of

dietary data because of infrequent use, An advantage of

obtaining information on vitamin-mineral supplement use,

Iwsides the obvious one of more complete intake estin~iit~s, is suggested by empirical data from Sempos et al.

( 1S),who found that when dietary intake estimates included

intdws from both supplements and foods, the ratio of

intraindividwd to interindividual variation in variance estim:ttcs decreased significantly and in the desired direction

for reducing the measurement error in estimating “usual”

intakes,

Usual intake

Another problem in attempting to identify population

groups at greatest risk is that in order to interpret dietary

assessments, one needs to be able to estimate usual

inttikcs us accurately as possible, This issue has been

ruvicwed by others (3,4,18) and discussed by previous

speakers in this workshop, Estimations of usual intakes

must be derived from multiple days of data in order to

reduce the effect on population distributions of normal

day-to-day variabilities in intakes by individuals. An example

of how failure to account for intraindividual variation in

food safety evaluations may affect findings can be illustrated by the data in tables 5 and 6. These results (19) for

children ages 2–5 years show that quite different intake

estimates for the “heavy” consumer (90th percentile) can

he obttiined, depending on whether results are expressed

as single-day estimates or as 14-day averages for each

respondent (table 5), Furthermore, estimates of the perccntoge of children ages 2–5 years exposed to the food

component of interest may vary, depending on the number

of days of information per subject (table 6).

As noted by Hegsted (20) and as discussed by the

NAS subcommittee (4), when determining usual intakes in

populations, the need is not for reliable estimates of

dietary intake for each person but rather for reliable

estimates of the distribution of usual intakes for the

population.

The NAS subcommittee indicated that, unlike individual intakes, the distribution of usual dietary intakes for

Table 5. Single-day and 14-day average estimates of

monosodium glutamate consumption among children ages 2-5

years who consume monosodium giutamate

Estimate Mean 90th percentile

Milligrams per day of

monosodium glutamate

.Single day . . . . . . . . . . . . . . . . . . . . . . . . . 99 379

14-day average . . . . . . . . . . . . . . . . . . . . . . 87 176

Extracted from ref. (19), p.187, by courtesy of Marcel Dekker Inc.

Table 6. Singie-day and 14-day average estimates of the percent

of children ages 2-5 years consuming chondrus extract

Estimate Parcent consuming chondrus extract

Single day . . . . . . . . . . . . . . . . . 53

14-day average . . . . . . . . . . . . . . 95

Extracted from ref. (19), p.1SS, by courtesy of Marcel Dekker Inc.

the population can be approximated from a modest number

of repetitions of the l-day intake data. However, they

pointed out that seasonal variations in intake and variation between weekdays and weekend days must also be

taken into account in the data collection and that the

replicated observations should be independent of one

another in time rather than taken on consecutive days.

The subcommittee further suggested that 3 days of observation may be more than is required for the derivation of

distribution of usual intakes. Whether this recommendation hoIds for nonnutritionaI food components that are

not ubiquitously distributed or consumed at least in some

minimal amount on a daily basis is not clear.

Links to public health programs

Clearly, a major role for food and nutrition monitoring is its linkage to the development and evaluation of

national public health policies and programs (figure 2).

At a minimum, these data should be capable of describing

the adequacy and safety of the U.S. diet and of identifying

problem intakes and population groups at greatest risk. In

addition, the monitoring data bases should provide enough

information to aid in developing strategies for resolving

public health problems, for predicting program effectiveness, and for evaluating program impacts, All of these

uses require, in addition to information on total intakes of

food components, data on eating habits and types of food

consumed. Supportive evidence is also needed to assure

that the definition of the problem and intervention are

appropriate, for instance, that a perceived public health

problem is in fact related to food habits and not caused by

other factors. Evidence is also needed to show that recommended changes in food habits will not carry the risk of

unintended adverse health outcomes.

Certain specific program needs (both design and evaluation needs) requiring food and nutrition monitoring

have been described elsewhere (3) and are shown in

table 7 in order to illustrate the diversity of need for

dietary data. In addition, programs such as food assistance

and nutrition education also have important applications

63
Nutritionalstatus

Availability Food z x

and quality + consumption

37

Health

Offood SUppiy patterns

II

yh

h\

I I

I /’1=11 I

1/

II

I

I Nutrition and food policy I I

!

I

/

Research

/

. naejs /

results

NOTE: Solld Iinss Indicate direct imps@ dotted Ilnes IndicateIndirectImpact,

Figure 2. Linkages: Monitoring and national policy

for dietary data but are not listed in the table because

traditionally their design and evaluation have been more

closely linked to NFCS than to NHANES.

The linkage of public health initiatives to the food and

nutrition monitoring system requires, in many cases, an

additional burden for data collection over and above the

capability of assessing intakes of food components.

Specifically, it requires identification of food and meal

patterns, of how much and which types of food are

consumed together in single eating occasions, and of

changes in eating patterns that reflect both quantitative

and qualitative modifications. An example of the last case

is the ability to detect a change in usage of types of foods

that differ only in processing, for example, switching from

canned to frozen vegetables with a concomitant change in

sodium or other food additive intake. Identification of the

types of foods consumed together by eating occasion has

relevance to issues of bioavailability of food components

(nutrients, toxins, and so forth) and other possible interactions, such as that of aspartame with high-carbohydrate,

low-protein meals or snacks,

Data on eating habits and patterns areas important as

assessment of total dietary intakes, The uses of such data

require detailed and complete information on types of

foods consumed, on foods combined within an eating

occasion, and on sources of food components. Similar

statistical and data collection issues arise as with the

estimation of “usual” dietary intakes of food components,

As noted by the recent NAS subcommittee (4), the impact

of individual day-to-day variations is a problem in descriptive data on patterns of food use, just as it is for estimates

of total dietary intakes of food components, In fact,

64

Table 7. Selected program needs requiring food and nutrition

monitoring

Program or activity Agency /nvo/ved

Nutrient fortification of the U.S. food supply. . . . . , , .,

Health education and information programs:

Cholesterol Education Program . . . . . . . . . . . . . . ,

Blood Pressure Education Program . . , . . . . . , . . .

Nutrition labeling initiatives . . . . . . , . . . , . . . . . .

The 1990 Nutrition Objectives for the Nation . . . . . . . .

Iron status in pregnancy

Growth and development in children

Overweight and obesity in adults

Serum cholesterol

Dally sodium intakes

Review of the Recommended Dietary Allowances . . . . .

Safety of the food supply . . . . . . . . . . . . . . . . . . . .

Food additives, contaminants, toxins, other

nonnutrient components (includes Total Diet Study)

Acute adverse reactions to foods

Interaction among food components

Excessive intakes of nutrients

Monitoring of prevalence of specific diet-related

diseases and related dietary intakes. , . . . . ., . . . . .

Osteoporosis

Cancer

Cardiovascular disease

Autoimmune deficiency disease syndrome

Alzheimer’s disease

Hypertension

FDA

NIH

NIH

FDA

DHHS

NAS

FDA, EPA, USDA

NIH, FDA, DHHS

NOTE DHHSis U.S. Departmentof Health and Human Servicss. EPA la Environmental

Protection Agency. FDA Is Food and Drug Administration. NAS IS National Academy of

Scisnces. NIH is National Institutes of Health. USDA is U.S. Department of Agriculture.

estimations of food patterns may require more replications (number of days of dietary data) per individual than

is needed for estimating “usual” distributions of intakes

for population groups.

Recommendations

To this point, we have discussed the characteristics of

a food and nutrition monitoring program and have identified data needs and potentially serious sources of systematic bias. By definition, the monitoring system measures

changes over time. Such measurement requires baseline

data and comparable assessments with subsequent surveys, As summarized in table 8, both NHANES and

NFCS have used 24-hour recall methodologies to collect

dietary data. These two data systems, however, have

differed both within one system from survey to survey and

between the two systems. Such differences could affect

results, although there is no way to adequately or easily

assess the magnitude or direction of such effects if they

have occurred, Our recommendations, therefore, are oriented not only to improving the types and program utility

of information from NHANES, but are also influenced by

concerns that the data collection process be more thorough

so as to better monitor time trends in the eating habits of

the U.S. population and to compare results across surveys,

Methodology: 24-hour recall

We recommend that the 24-hour recall methodology

be continued, This methodology provides continuity with
Table 8. Major characteristics of food consumption surveys: National Health and Nutrition Examination Survey (NHANES) and Nationwide

Food Consumption Survey (NFCS)

Characteristic NHANES /, 1971–75 N/-fANES //, 1976-60 NFCS, 1965 NFCS, 1977–78

Sampling frame Individuals Individuals Household

Sample size

Household

30,000 20,000 15,000

Season Follows sun

30,700

Follows sun Spring

Days of week

All days

Sunday–Thursday Sunday-Thursday All days All days

Dietarymethodology 24-hour recall plusfood 24-hour recall plus food 24-hourrecall

frequency

24-hourrecall plus

frequency 2-day record

Number of days of data collected 1 1 1 3

Collection method Interview Interview Interview Interview plus self-administered

Intordew environment

record

Mobile examination center Mobil examination center Home Home

Scwlng size estimatea Food models Food models Household measures Household measures

Number of foods In data base 3,400 2,600 1,900 3,700

past NHANES as well as with NFCS and other surveys,

within the caveats discussed above. It also has the potential for providing the types of information on eating

behaviors needed for program design and evaluation,

Determination of “usual” intake

Multiple and independent replications of dietary

intakes m-e needed to adjust distributions of population

groups to represent “usual” intakes. Although the NAS

subcommittee (4) recommended 2 days of dietary intake

data as being adequate for adjusting total dietary intake

values, it also noted that more days may be needed for

examining eating patterns. Certainly, 3 days of intake data

would be preferable to 2, but questions of cost efficiency

and logistics must also be considered.

Replications of days of dietary intake collection would

optimally be done by using the same technique on all

respondents –for example, bringing all respondents back

to the mobile examination center for additional dieta~

interviews. However, such an approach may pose too great

u burden on the survey. It is preferable that the second

and subsequent days of dieta~ intake collection not be

conducted in the home of the respondent because, unlike

the mobile examination center, the home may have an

“audience” of family members who could exert social

pressures and possibly influence the information provided

by the respondent.

Several alternatives can be proposed, Multiple days of

intake on a subsample of the total sample could be

obtained, This subsample should be representative of the

sex, age, and socioeconomic groups included in the survey

and could return to the mobile examination center for all

dictmy interviews. Results from this subsample could be

used to estimate the intraindividual variation in dietary

intakes and to adjust the population distributions accordingly. Another possibility is the use of a telephone interview to obtain subsequent 24-hour recalls on subjects, This

procedure has the advantage of cost and time effectiveness

but does not meet the recommendations for a common

duta collection method. If this approach is used,

respondents need to receive some training at the mobile

uxarnination center on how to use paper or household

food models for estimating serving sizes during the telephone interview, A third alternative approach would be to

use calculations of intraindividual variation from NFCS to

adjust the distribution of intakes. However, differences in

sampling and data collection between NHANES and NFCS

raise serious questions as to the appropriateness of extrapolation based on such corrections.

In addition to providing corrections for intraindividual

variations in food intakes, the recall procedures should be

based on “independent” rather than consecutive days of

intake, be representative of the entire week including

weekends, and include seasonal variations as much as

possible.

Detailed food descriptors

Detailed and accurate descriptions of food items in

the survey data base will improve the usefulness of the

data bases. Additionally, evaluation of comparability of

data for making time trend estimates will be facilitated.

Because of the complexity of the food supply and

eating habits, we recommend that an automated interactive

computer system be used by the interviewer. Such a

system not only would provide immediate coding of information and timeliness of data release but also could be

designed to provide the prompts needed to thoroughly

probe for all relevant and obtainable information and to

standardize interviewing techniques. The importance of

such a system’s ability to standardize the interview should

not be overlooked. As suggested by Campbell and Dodds

(15), differences in probing techniques and followup questions used by interviewers can greatly influence the information provided by the respondent.

NFCS and HHANES are currently using a common

reference food composition data base and food coding

system (12). This approach has been strongly recommended by past NAS subcommittees (3,4) to provide

linkages between the two data systems. We support continued use of a common reference system but recommend

that additional data fields be added for each food item to

allow, where appropriate and feasible, information on

processing and packaging, on brand names, on sources of

foods, and on use of home water supplied in food preparation. (This assumes, of course, a computerized automated interview system.) Judgment and reasonableness

are needed to determine these extra data fields. For

65
example, most persons can accurately identifi the brand of

soft drink they consume and indicate whether it comes

from a can, a plastic bottle, or a soda fountain, but they

are not likely to be able to provide similar information for

meats. Vegetables consumed may not be identifiable by

brand name, but consumers can usually distinguish between

canned and untanned products, In another example, brands

of margarine and cooking fats tend to vary greatly in fatty

acid composition. Information on brands of fats and

margarine for home use of products could be obtained

either in the initial home interview or in followup interviews, For estimating food additive intakes, it is important

to know whether the apple pie eaten was prepared from

“scratch” at home, purchased frozen in the supermarket

and then cooked at home, or eaten at a restaurant, Flavor

and color additives vary depending on whether red- or

green-colored gelatin desserts are chosen, Such information can be easily obtained during the interview and would

greatly expand the usability of the dietary intake data.

Complete intake data

As noted previously, some types of foods and beverages tend to be underreported or are difficult to estimate

accurately because of either infrequent use or the need for

special probes. These items include, but are not necessarily limited to, alcoholic beverages, water, organ meats,

vitamin-mineral and food supplements, sugar and salt

substitutes, seasonal foods, and medications that can make

mineral contributions to the diet. It is important that

sufficient probing be conducted so these items are reflected

in the 24-hour recall. In addition, a special frequency

questionnaire is needed to specifically estimate intakes

over a recent and specified time period. As consumption

of some of these items is known to vary depending upon

day of the week (higher alcohol consumption on weekends, for instance), such considerations need to be reflected

in the frequency questionnaire in order to provide a

complete picture of intake, Additionally, because water

can be a significant source of minerals and electrolytes as

well as certain contaminants, it would be useful to obtain

some estimate of frequency and quantity of water consumption as well as to identi~ foods that are reconstituted with

water or have home supplies of water added to them. To

provide composition data on drinking water, an aliquot of

tap water could be obtained from each of the homes

during the initial interview and analyzed for mineral,

electrolyte, and contaminant content.

Reliability

The ability to extrapolate results of reliability and

validity estimates from small, well-controlled studies to a

field survey in which all age and socioeconomic classes are

represented is questionable, Also, in comparing two different methodologies for the same individuals, there is the

possibility of similarities between methods because of

multiple systematic biases that are directionally the same

and occur at the same time, Therefore, as much as

possible, internal checks should be incorporated into the

survey to evaluate the reasonableness of relative intake

differences among subgroups. Several approaches could

be investigated, including use of such statistical techniques

as split half reliability measures to estimate reproducibility

of types of information for subgroups and reproducibility

of specific foods or food groups. Biological markers are

theoretically ideal to check validity of intake data, but in

reality such measures are probably not feasible under field

survey conditions because they frequently involve multiple

and complete 24-hour urine or stool samples, However,

similar patterns of differences among subgroups between

intake estimates and biochemical indicators that reflect

recent intakes (such as serum ascorbic acid) would provide some assurance that dietary intakes are reliable,

However, this approach must be used cautiously, as cornparisons could be confounded by genetic (racial) and life

cycle differences in metabolism of nutrients and by differences among socioeconomic or age groups in infection

rates. Also, for many of the serum or plasma levels of

nutrients, there is a threshold effect; thus their use is

limited largely to checks for low intake rather than high

intake, Furthermore, these approaches can provide only

qualitative information; they cannot be used to validate

the accuracy of quantitative estimates of intakes,

To some extent, the reasonableness of estimates can

also be checked externally using other data bases and

surveys. For instance, the per capita disappearance data

for various food commodities compiled by USDA, as WCI1

as food marketing data gathered by the business sector

and private organizations, can be compared with the

intake values obtained in NHANES. However, the data

collection methodologies and final form of the data can

differ a great deal from those of NHANES; therefore, the

“truth” of the comparison, again, is limited.

Documentation

As mentioned earlier, incompleteness in food description, food intake records, and food composition information can lead to systematic bias. To aid in identifying these

types of biases, adequate documentation for dietary data

is needed. This documentation should be appropriate for

evaluating the completeness and accuracy of the reference

food data base. We recommend the following documentation strategies:

● Imputed intakes should be flagged for both food

code and serving size information.

● Information provided by a surrogate or pro,xy should

be flagged,

● The environmental condition for subsequent 24-hour

recalls should be identified to indicate such pertinent

factors as who other than the respondent was

present or listening during the interview.

● Imputed values in the food composition referrmce

tables should be flagged so that there is an

indication of the completeness of the composition

data base and the impact of imputed values on data

analysis can be considered.

66
●

●

Within reason, documentation as to the adequacy and

accuracy of the analytical laboratory methods used to

determine food composition values should be available

to users and should specify (where appropriate) the

precision, bias, sensitivity, specificity, levels of contamination or interference, and degree of confidence relative

to the analytical method.

Because NHANES III data will undoubtedly be used

for comparisons with past surveys in order to

evaluate changes over time, the survey documentation should, at a minimum, incorporate a

warning to users as to the need to check compatibility, completeness, and the nature of assumptions

for the reference food composition data bases and

food descriptors from survey to survey.

References

1,

2.

?.,

4.

.5.

6,

7,

YL,

Ostenso GL. National nutrition monitoring system: A historical perspective. J Am Diet Assoc 84(10):1181-5. 1984.

National Academy of Sciences, National Research Council,

Committee on Food Consumption Patterns. Assessing

changing food consumption patterns. Washington: National

Academy Press. 1981.

National Academy of Sciences, National Research Council,

Coordinating Committee on Evaluation of Food Consumption Surveys. National survey data on food consumption:

Uses and recommendations. Washington: National Academy

Press, 19S4.

National Academy of Sciences, National Research Council,

Subcommittee on Criteria for Dietary Evaluation, Coordinating Committee on Evaluation of Food Consumption

Surveys, Nutrient adequacy Assessment using food consumption surveys. Washington: National Academy Press. 1986.

Habicht JP, Lane JM, McDowell AJ. National nutrition

surveillance, Fed Proc 37(5):1181-7. 1978.

Habicht JP, Some characteristics of indicators of nutritional

status for use in screening and surveillance. Am J C1inNutr

33:531-5.1980.

Habicht JP, Meyers LD, Brownie C. Indicators for identifying and counting the improperly nourished. Am J Clin

Nutr 35:1241-54, 1982.

U.S. Department of Agriculture and U.S. Department of

Health and Human Services. Nutrition and your health:

9.

10.

11.

12,

13.

14.

15.

16.

17.

18.

19.

Dieta~ guidelines for Americans. Home and Garden Bulletin no 232, 2d ed. Washington. 1986.

Wolf WR. Assessment of inorganic nutrient intake from

self-selected diets. In: Beecher GR, ed. Beltsville Symposium in Agricultural Research: Human Nutrition Research.

London: Attanheld, Osmun Co. 19S1.

National Center for Health Statistics. National Health and

Nutrition Examination Survey 1971-75 public use data tape

documentation: Model gram and nutrient composition. Tape

nos 4702 and 4703. Hyattsville, Maryland. 1981.

National Center for Health Statistics, National Health and

Nutrition Examination Survey 1976-80 public use data

tape documentation: Model gram and nutrient composition.

Tape nos 5702 and 5703. Hyattsville, Maryland. 19S2.

Human Nutrition Information Service. Manual of food

codes for individual intake. Hyattsville, Maryland: U.S.

Department of Agriculture. 1985.

Institute of Food Technologists’ Expert Panel on Food

Safety and Nutrition, Caffeine. Food Technol 37:87-91.

1983.

Schnakenberg DD, Hill TM, Kretsch MJ, et al. Diaryinterview technique to assess food consumption patterns of

individual military personnel. In: Assessing changing food

consumption patterns. Washington: National Academy Press.

1981.

Campbell VA, Dodds ML. Collecting dietary information

from groups of older people. J Am Diet Assoc 51:29-33.

1967.

Hallfrisch J, Steele P, Cohen L. Comparison of seven-day

diet record with measured food intake of twenty-four subjects, Nutr Res 2:263-73.1982,

Pennington JAT, Wilson DB, Newell RF, et al, Selected

minerals in foods surveys, 1974 to 1981/82. J Am Diet Assoc

84:771-80.1984.

Sempos CT, Johnson NG, Smith GL, et al, Effects of

intraindividual and interindividual variation in repeated

dieta~ records. Am J Epidemiol 121:120-30. 19S5.

Yetley EA, Hanson EA. Data sources and methods for

estimating consumption of food components, J Toxicol Clin

Toxicol 21(1 and 2):181-200. Marcel Dekker Inc., N.Y.

1983–84.

20. Hegsted DM. Problems in the use and interpretation of the

Recommended Dietary Allowances. Ecol Food Nutr

1:255-65.1972.

67
First discussant’s remarks

about monitoring

by Eleanor M. Pao, Ph. D., Human Nutrition

Information Service, U.S. Department of Agriculture

The paper by Yetley, Beloian, and Lewis is excellent.

The point that “food” as well as “nutrition” monitoring

should be a topic under consideration is very appropriate. As stated, alleviation of deficiency diseases and

introduction of food programs, such as the Food Stamp

program, introduced in 1961, did not eliminate hunger nor

ensure that all persons consumed adequate diets, Thus,

even in an affluent country such as the United States, the

ability of residents to be well nourished cannot be taken

for granted, Consequently, in recent years, the nutrition

monitoring approach has gained support as a valuable

adjunct to the conduct of large-scale, periodic assessment

surveys. The latter are too cumbersome to discover nutritional problems which may turn up suddenly in particular

population groups. However, the development of the envisioned National Nutrition Monitoring System (NNMS) to

include multiple types and sources of data in addition to

dietary intake data presents some real challenges.

Linkage among surveys and other

data bases in NNMS

First, it would seem that a much higher priority must

be assigned to development of linkages among the various

data sets or surveys in the monitoring system, In fact, I

would emphasize and extend the mention of necessity for

linkage between the data bases (including surveys) and the

public health programs by urging linkage among the surveys and the data bases as well, A system of linkage among

surveys of agricultural production, food consumption, nutritional status, health, and disease prevalence and other

data bases should begin at the planning and design stages

in order to obtain all the necessary information for linking.

Comparability between NHANES

and NFCS

A National Health and Nutrition Examination Survey

(NHANES) and Nationwide Food Consumption Survey

(NFCS) working group recently studied a number of

variables common to both surveys and found a few that

were identical. However, modification of a few more

questions and definitions and addition of some new

questions would contribute further to linkage between the

two surveys and thus provide for increased use of both sets

of data. These include mostly socioeconomic, demographic, household, and personal characteristics. Some

variables seem to be locked into dissimilar definitions, For

example, NHANES and NFCS designate a reference

person and a head of household (male head, if present;

otherwise female head), respectively, around whom relationships in the household are specified. In practice, the

reference person and the head of household often are the

same person, But if the household organization does not

permit ready identification of this person, the two surveys

differ in defining to whom that role should be assigned.

One of the most important underlying reasons for

differences is the uniqueness of purpose of each survey.

This is manifested in the sample designs, For example, the

samples of households in NHANES and NFCS differ on

the definitions of eligible group-quarter households, with

NHANES including some which NFCS excludes.

A common core of questions on food programs would

appear to be a reasonable goal for the two surveys in 1987

and 1988. It would be useful in both surveys to have an

identical question to determine the respondent for children under 12 years and other persons not answering for

themselves. Surrogate reporting needs better documentation and examination. Definitions and protocols for acquisition of information on food intakes, dietary practices,

and other dietary factors have not yet been studied for

possible linkage.

The possibility of one or two questions about health

that could be used to further link NHANES and NFCS

might be explored in order to expand the usefulness of the

two data bases in the nutrition monitoring program. Both

surveys now include a general question on the respondent’s

assessment of his or her own health – excellent, very good,

good, fair, or poor. However, with the public’s interest in

and media coverage of the role of dietary intake in the

development of chronic diseases, it would seem likely that

respondents could self-report whether or not they have

certain conditions, such as heart conditions, hypertension,

cancer, diabetes mellitus, osteoporosis, and dental problems. The respondent-reported prevalence could be compared with the physician-determined prevalence, which

could then be compared with self-reports in subsequent

surveys.

68
Age groups to be surveyed

The age groups to be included in NHANES III may

not have been determined yet, but there seems to be a

significant lack of data on the elderly beyond the age of 74

years. The participation of these individuals in some

phases of NHANES 111might be limited, but there seem

to be many reasons for including this older generation in

the dietary intake part of the survey. Foremost, this age

group is a steadily increasing percentage of the population, Health care costs of the elderly appear to be higher

than those for any other age group. Diet is important in

maintaining vigor and health. Dietary management is

often part of health management and containment of

health costs, The elderly are vulnerable to chronic diseases that are related to dietary factors. They may have

problems with obesity. They may also be on reduced

incomes, which means that diets must be planned more

carefully to maintain good nutrition, People in this age

group may be vulnerable to food fads or fad diets in a

search for better health as well as being heavy users of

vitamin, mineral, and other dietary supplements. For these

and other reasons, the relationships of diet to measures of

physical well-being and other relevant factors in persons

over 74 years need to be explored.

The reasons for excluding infants under 6 months of

age from NHANES are unclear. However, there seems to

be a need for updating the baseline dietary data on a

national sample for this age group, including the extent of

breastfeeding and feeding of milk and other foods and

supplements,

“Usual” intakes

Consensus on an operational definition of “usual”

dietary intake by an individual or group has not yet been

reached, In practice, a number of researchers use a

specified number of days of actual intake spaced over a

stipulated time period. From methodological studies

recently carried out at the Nutrition Monitoring Division

(NMD) of the Human Nutrition Information Service

(HNIS) at the U.S. Department of Agriculture (USDA),

there appears to be a limit to the number of replicated

dietary reports many people in a survey are willing to

provide, In one study of data collection methods, an initial

in-person interview was followed by a telephone interview

to obtain a l-day recall in each subsequent quarter. This

study resulted in a borderline or, in the eyes of some

researchers, an unsatisfactory final response rate, This

brings up the subject of response burden,

Respondent burden

The difficulty in collecting multiple days of dietary

reports from an individual leads to consideration of ways

to lighten the burden or ease the demands on the

respondent, In the previous discussion of systematic bias,

respondent reporting is mentioned as a likely source of

bias in several instances. However, some of the blame for

poor quality data may be the fault of survey designers and

planners, who are asking for information that is beyond

the respondent’s ability to provide. Forcing a respondent

to give an answer is not solving the problem. Some of the

burden in obtaining better quality information could be

transferred to more highly skilled and trained interviewers

provided with better techniques, questions, and probes

eliciting information in a comfortable, nonthreatening

setting. This may require much more testing to improve

the various elements of the dietary interview. Determining

who is the best source of information is important. For

example, packaging and brand information might best be

asked of the household respondent in the home interview.

Also, could the differences in alcohol consumption by

males in NHANES and NFCS be a result of males’

answering for themselves in NHANES, whereas in NFCS

the household respondent may be the source of information but be unaware of the male’s total alcohol

consumption?

Alternative and multiple methods

with possible validation study

The NHANES protocol provides for a home visit

about 2–4 weeks prior to the visit to the mobile examination center. This would seem an opportune time to administer a l-day dietary recall somewhat similar in method to

that used in NFCS. Use of model glasses, mugs, cups,

bowls, and spoons would not be necessary, as those used

by the sample person would be available for actual

measurement of capacity or of customary amounts consumed. This might eliminate some of the guessing that is

required in relating to dishes, glasses, or spoons foreign to

the respondent. Results from the home interview (using a

minimum set of aids such as standard measuring cups and

spoons, a ruler, and some mounds and shapes) could be

compared with results using the full NHANES III set of

modek to determine validity of results when the full set is

used in the clinic setting, I do not recall such a validation

study having been made with a suitably large sample. If

conducting the dietary interview in the home is not considered feasible, the usual measures of portions eaten in

the home cups, glasses, bowls, and spoons could be obtained

during the home interview to validate the model measurements indicated during a clinic interview.

Added to these advantages of home interviewing

would be the opportunity to obtain multiple dietary reports

(including weekend days) and dietary reports more comparable to those from NFCS. It might be possible to

conduct an experiment using the NHANES setting and an

NHANES interviewer with the NFCS method versus an

NFCS home location and an NFCS interviewer with the

NHANES method. This might reveal whether methodological differences now considered to be trivial are more

serious.

69
Meal and snack patterning

Meal and snack patterning of combinations of foods

eaten together, and of foods eaten in a day and across

days, has long been an area of interest and analytical work

at the NMD of HNIS at USDA. Some methodological

studies have been reported, but more research is certainly

needed. Nutrition education, guidance materials, and

USDA food plans are described in terms of foods in meals

and snacks or budgets. Market analysts and the food

indust~ need information about uses of foods, Increased

numbers of ethnic groups have brought their food customs

with them; how they adapt or select from among the U.S.

foods available is important for their health and important

to know in order to provide guidance for planning nutritious meals for their families. Knowledge of low-income

and elderly persons’ meal patterns as well as those of

other subpopulation groups is important for understanding how to deal with their particular problems,

Regional differences in dietary practices also must be

taken into account,

Internal validity checks

In addition to the methods for checking the internal

validity of estimates in a survey previously mentioned,

another method is being considered by the NMD. This is a

question on frequency of use of calcium-rich foods over a

period in the recent past to determine whether designation of frequent and nonfrequent users in the frequency

question is similar to that obtained from the NFCS

3-consecutive-day method.

Another internal validity check– a quantified frequency question concerning alcoholic beverage consumption – could serve a dual purpose. If it were included in

both NHANES III and NFCS 1987, it would serve as a

linkage question that could help unravel why alcoholic

beverage consumption reports are higher in NHANES

than in NFCS.

Recommendations

● Retain the 24-hour recall in the mobile examination

center for comparability and the study of trends and

patterns of changes.

● Try to collect multiple days of data (minimum of

two) using either an in-home interview or a

telephone recall. (Pretesting is needed.)

● Include the frequency question on alcoholic

beverage consumption as an internal check and for

linkage between surveys.

70
Second discussant’s remarks

about State and local

perspectives

by Susan B, Foerster, M.P.H., R.D,, Association of

State and Territorial Public Health Nutrition Directors

(M behalf of the Association of State and Territorial

Public Health and Nutrition Directors (ASTPHND), I

would like to express our appreciation for the opportunity

to participate in this very stimulating and important working

conference,

First, I would like to define what ASTPHND is and what

it represents relative to these proceedings, Unlike most persons here, we are not researchers; rather, we are planners of

intervention programs, implementors, and evaluators. With

one of our most important roles being that of identifying

unmet needs in our States, we are “consumers” of the project:

National Health and Nutrition Examination Survey

(NHANES) HI research information.

Sticcmd, we are historically “new” consumers. The

original purpose of NHANES was to provide the intellectual data with which to set national health and nutrition

policies. With the advent of the “New Federalism,”

including block grants, relaxed performance requirements,

and minimal programmatic oversight as well as decreased

Federal funds for domestic programs, the Federal Government no longer uses its intervention arm as it did in the

past. Rather, it is left to States and localities to establish

policies and priorities, to target funds and high-risk groups,

and to compete with other sectors of government for

public dollars. In other words, the intellectual data base

mwdcd for policy setting and competition for sources has

now been separated from the levels of government with

the capacity and acute need to use the information.

For these reasons, ASTPHND has identified as its top

priority the need to develop and strengthen the data and

epidemiologic underpinnings available for use by States

and localities. NHANES is one of the data bases central to

this priority.

State nutrition priorities

Throughout this workshop, there has been extensive

discussion about hypotheses. Let me share with you,

visually in figure 1, what we believe the “hypotheses” to

be, At the Federal level, hypotheses are proposed by the

1990 Objectives for the Nation (l), the various dietary

targets set recently by the National Institutes of Health

(blood cholesterol, osteoporosis, hypertension, cancer)

(2-5), and, of course, the Recommended Dietary Allowances (RDA’s) (6). At the State level, States set their own

priorities, but ASTPHND is now working on a consensus

I

● National Institutes of

I

Health dietary objectives

● Recoin mended Oletary

● Model State Objectives

for Nutrition3

● Local Model

Core data needs

1Blood pressw pregnanq and Infant heaith; improvednutrition.

2Red!xd fat, cholesterol, sodium, alcohol; increased fibe~ focds Mgh In vltamlns A and

C, crvclferws vegetables, and calcium.

3 Maternal and child healttv adult health; environmental nutrition and consumer

protecflon; and admlnistratlcm and finance.

4 Chronic dlseasq aging; dental healtil maternal and child healtm primafy care; =hool

healtfl others

Figure 1. Public health priorities

recommendation, Model State Objectives for Nutritionj

which will be available in May 19S6 (7). At the local level,

the document Model Standards: A Guide for Communi&

Preventive Heolth Services has just undergone its second

revision and presents a roadmap of priorities agreed upon

by six major bodies of public health policymakers (8).

Thus, for us, the “hypotheses” are fairly well established.

Let us look for a moment at dietary hypotheses. In the

new Model State Objectives, we will probably adopt the

recommendations for nutrient intake in the RDA’s as well as

the important new food and nutrient recommendations of the

National Institutes of Health, including the National Cancer

Institute (2–5). The list and groups will, therefore, include

fiber, vitamin A- and C-rich fruits and vegetables, cruciferous

vegetables, calcium, fat, dietary cholesterol, sodium, alcohol,

and certain food practices, such as choosing specificlow-fator

high-fiber foods, low-fat food preparation methods, and

avoiding certain foods, such as those with naturally occurring

carcinogens. Note here that we begin to need information on

awareness, knowledge, attitudes, and skillsas well as actual food

71
practices. Parenthetically, note that as presently written, neither the 1990 Objectives nor the Local Model Standards

contain adequate dietary objectives, although they are somewhat stronger in setting nutrition education targets.

Roles appropriate for NHANES and

emerging issues

Throughout the workshop, there has been discussion

about what is or is not an appropriate role for NHANES

with regard to national data needs, From a State perspective, I would recommend the following,

Nutrient adequacy of diets, especially among children,

the elderly, and the poor, will remain a priori~. There is

no other data system that can provide normative data on

diet related to health status,

Health promotion, that is, what individuals can do to

keep themselves healthy, is a vital new strategy in public

health. We must have a data base that connects health

status with the prerequisite education-related population characteristics of awareness, knowledge, attitude,

skill, behavior, and service utilization, As there is a real

need to describe the relationship between these characteristics and services, NHANES could make a very important

contribution toward pinpointing unmet service needs.

Environmental nutrition and consumer protection are

traditional public health program areas in which new

dimensions are being defined for public health nutrition to

address. The growing presence of agricultural and industrial contamination in the food supply is making it increasingly important to know the diversity of the food supply

and who consumers of specific foods or types of foods are.

One example would be consumers of “unregulated” foods

that do not enter the commercial market (for example,

wild game and home-grown foods) and the amounts of

these foods such individuals are likely to consume. It is

upon such estimates that States can use established tolerance levels, assess risk, and determine whether public

health action is needed, Besides establishing population

norms, NHANES can assume the critical role of providing

methodologic expertise to States that need to have suitable survey techniques for handling such problems within

their own borders,

Consumer protection includes nutrition labeling of

foods, health claims for foods, and nutrition fraud.

NHANES is the only data base that can connect these

characteristics, not only with dietary intakes —the Nationwide Food Consumption Survey of the U.S. Department

of Agriculture (USDA) can do this —but also with nutritional and health or disease status,

Problems of short-term food availability, hunger,

emerging malnutrition, and episodic biologic or toxic contamination require rapid response and are therefore not

appropriate concerns of NHANES as it now operates.

Other surveys’ methods and data bases are necessary to

address these matters.

NHANES as, a longitudinal study

An objective of the workshop is to define data elements needed for retrospective and prospective national

population study. Let me preface my remarks by saying

that I had been unaware that NHANES is considering

itself longitudinal, and it would seem that shifting from a

cross-sectional survey has vital policy implications, as

follows:

1. Practically speaking, finding ongoing funds is far harder

for long-term studies than for those with more immediate payoff. One example with which I am familiar is

the Alameda County Human Population Laboratory.

Although the lab does superb and often unique work,

it spends an extremely high percentage of its time

securing ongoing funds. Further, during periods of

budget reduction, long-term investment in studies for

which the payoff is likely to be years in the future is

not likely to be successful. Rather, studies that provide short-term results are likely to be viable. Study

results are usually needed yesterday!

2, Historical objectives and uniaue characteristics of

3,

NHANES h;ve included its ~eing a true national

cross-sectional snapshot of diet and health, its ability

to show national trends over time, and its collection of

raw or totally new dietary and health data. If NHANES

becomes longitudinal, how will the sample be refreshed

so that a true national cross section is maintained in

the future? I note that from February to March 1986,

the projected sample size of NHANES III was reduced

from 60,000 to 40,000. How can 40,000 cover the

former participants and new ones? Second, does not

the mere exposure to the NHANES examination

introduce participant bias in the future examinations,

thereby jeopardizing the principal objectives of

NHANES related to monitoring national population

characteristics? Third, NHANES provides a major

source of new or raw information. If for the sake of

consistency with previous surveys, the same questions

and methods must always be used, does that not erode

NHANES’ ability to meet changing needs for

information?

NHANES’ contribution to science as a longitudinal

study needs examination. Some have stated that a

longitudinal NHANES would be superior to clinical

trials such as the Multiple Risk Factor Intervention

Trial or the Lipid Research Clinics in unraveling

multifactorial relationships between diet and health.

How likely is it that a study with totally uncontrolled

“interventions,” such as secular trends, only a fraction

of which would be captured in NHANES, would be

better at elucidating disease causality than would

cohort studies? Rather, NHANES retains its unique

value in characterizing and measuring population trends

and establishing norms that aid in interpreting findings

of clinical trials.

72
In conclusion, I would urge that perspective be maintained

in shifting from a cross-sectional survey. Right now NHANES

occupies the unique and absolutely essential position of being

the only survey that links health status with an array of other

characteristics that States as interventionists are required to

know in order to do their job toward improving the public’s

health. A natural tension exists between the data base

demanded by academicians before they recommend action

and that expected by public health policymakers; this tension

has been well described recently by Miller and Stephenson

(q). NHANES is the data base of public health, and it must

retfiin its flexibility and innovative capacities to continue

serving its principal users by providing cross-sectional, contemporary, and descriptive population statistics.

States’ needs for nutrition-related

data

Now let me provide an overview of how States as

program planners view nutrition data needs. As depicted

in figure 2, achieving optimal nutritional status in the

population is a chain of sequential events with multiple

intervention points, and data are needed at each step,

Consecutively, we might list them as follows. Community,

information, food, and social environments support and

reinforce the desired health behaviors; these environments provide activities and services that introduce and

promote the desired health behaviors, These services lead

to accomplishment of educational prerequisites that enable

and motivate the individual to choose and practice the

desired behaviors. Environment, service, and education

together result in the desired dietary behaviors, including

fxtors of preferential food choices, actual selection among

various food options, the multiple stages of food preparation, and at-table behavior, such as adding seasonings or

trimming fats. These new behaviors lead to the reduction

of nutritional risk factors and improvements of nutritional

status. In the short term disease incidence, and later

diseose prevalence, will be modified. Also decreased will

bc morbidity, including the cost of associated health

services and eventually premature mortality. In program

planning terms, activities related to intervening in the

environment would be called “structure” objectives; activities and services are called “process” objectives; and

educational, dietary, and health factors are called

“outcome” objectives.

Structure+ Process +Awareness + Knowledge

c

>

Attitude ~Skill > Dietary behavior>Food intake

c

D

Nutritional status + Nutrition-related risk factors

>

Figure 2. Chain of events influencing diet, nutrition, and health

Table 1. Inventory of current and potential data sources useful in

nutrition monitoring

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

16,

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

Health and Nutrition Examination Survey

Health Interview Survey

Ambulatory Medical Care Survey

Hospital Discharge Survey

Birth records

National Death Index

Nationwide Food Consumption Survey

Continuing Survey of Food Intakes by Individual

National Nutrient Data Bank

Food and Drug Administration Total Diet Study

Multi-Purpose Consumer Survey

Biennial Food Product and Labeling Survey

Dietary Supplement Survey State-National Data Bases

Center for Chronic Disease and Health Promotion, Centers for

Disease Control School Health Survey

Center for Chronic Disease and Health Promotion, Centers for

Disease Control Prenatal Surveillance

Center for Chronic Disease and Health Promotion, Centers for

Disease Control Pediatric Surveillance

Center for Chronic Disease and Health Promotion, Centers for

Disease Control Telephone Behavioral Risk Factor Surveillance

System

Multiple I%k Factor Intervention Trial/Lipid Research Clinics

Hypertension Detection, Treatment and Follow-Up Program

National Public Health Reporting System Categorical Program Data

Sets

Women, Infants, and Children Program

Perinatal Regionalization/High 13sk Prenatal

Early and Periodic Screening, Detection and Treatment

Adult Programs

Aging programs, miscellaneous data sets

Death records

Genetic Diseases Reports

National Institutes of Health statistics-blood pressure, cholesterol,

diabetes

Hospital discharge reports

State registries-tumor, blood pressure, diabetes

ROSS Labs

National Institutes of Health Community Trials-Stanford,

Minnesota, Pawtucket

SOURCE Adapted from data sources cited in: Public Health Sswice. Promoting healtht

preventing disease Public Health Sewice implementation plans for attaining the Objectives for

the Nation. Public Health Rep Sept.-Ott. SUPPI., 19S3.

Viewing all available sources that may provide necessary information about these objectives, we can Iist data

bases that have been cited in the 1990 Nutrition Objectives for the Nation (1) as components of the National

Nutrition Monitoring System (10) and add to them other

data bases that are commonly available in States and

Localities. This Iist appears in table 1.

Scattergrams are popular in this workshop; let us look

at the question of data categories this way. By comparing

the contents of figure 2 with those of table 1, we can

identify clusters of relative data strength with other areas

of data weakness, Thus, table 2 reveals clearly that major

categories of data collection that need to be strengthened

include structural, process, and educational parameters.

Inasmuch as NHANES is the only national data source

that can link these parameters with health status, it

becomes imperative for NHANES HI to add more data

elements to these fields.

Vertical integration and use of data

Implicit in this discussion is the assumption that data

collected in NHANES are to be selected with consideration to their utility to States and localities. Thus, the

73
Table 2. Sources for and gaps in nutrition data needed by States

and localities for policy, planning, and evaluation

Data co//ecfirrg agency Structure Process Education D/etary Health

Data base

National Center for

Health Statistics. . . . 1-5 2,3,5 1-5 1 14,6

U.S. Department

of Agriculture . . . . . 1,2 --- 7-9 7-9 . . .

FoodandDrug

Administration. . . . . 10–12 --- 10,11 10,13 . . .

Centers for

Disease Control. . . . 14 14 --- --- 15-17

National Institutes

of Health . . . . . . . . --- --- --- 18,19 16,19

State categorical

programs. . . . . . . . 20 20-25 --- --- 15-17,26-30

Other, miscellaneous,

and proprletay . . . . 31 32 31,32 ---

. . .

selection of actual data elements that are consistent with

those already in use or selected as priority for collection is

a fundamental recommendation from the States. It is,

therefore, strongly recommended that NHANES avail

itself of the work now underway within ASTPHND to

identify a “national core data set for nutrition,” which is

based in large part on existing 1990 Objectives and Local

Model Standards but also encompasses new parameters,

such as those called for by the National Public Health

Reporting System and those that are needed for new

national initiatives, such as the National Cancer Institute’s

Year 2000 Dietary Objectives (5,11). The deadline within

ASTPHND for completion of a preliminary “core data

set” is fall 1986. Compatibility among these three levels of

users would immeasurably maximize the usefulness of

NHANES HI in public health.

Optimizing NHANES Ill

A central question posed at the workshop has been

that of how to increase the value of the NHANES III

investment. From a State and local perspective, a “macroview” of NHANES, the following specific recommendations seem to be the most important.

Select data elements that are vertically integrated, as

discussed above.

Maintain comparability of methods to assure comparability of results, For example, the information that the

American population has been consuming about 37 percent of its total calories from fat since NHANES I is, for

those of us who are designing intervention programs, vital

and surprising information. Such statistics not only dictate

the content of the programs we design but also dispel the

perception that nutrient intake is improving through secular trends alone. If, indeed, major reduction in heart

disease and cancer are likely at 30-percent fat levels, then

it is clear that public health authorities must reassess the

priority they place on nutrition as a prevention strategy.

Lateral interdigitation with other Federal data bases is

necessaty in order to fully utilize each survey, avoid

duplication, and prevent incompatibilities that introduce

“noise” rather than clarity into policy questions. For all

the agencies that are part of national nutrition monitoring,

linked data elements need to be identified that correspond

to the priority data element selected by State and local

users. For example, USDA and the National Center for

Health Statistics ought to be able to agree on 24-hour

dietary recall methods and other collection of raw dieta~

data within the Nationwide Food Consumption Survey

that would, in turn, generate an updated and periodically

revitalized List-Based Food Frequency for use in NHANES.

Sample design should consider the needs of non-Federal

users, Drawing a sample that allows only national projections fails to meet the needs of the vast majority of users,

Major target groups such as Hispanics, Asian-Pacific

Islanders, Native Americans, and the very old need to be

added to the traditional population segments, Perhaps

more important is sample design to permit subnational

estimations according to regions that make sense in terms

of health or lifestyle rather than regions selected arbitrarily by the Department of Health and Human Services,

USDA, or the Department of Commerce, Imagine the

value of NHANES reports labeled the “New England”

region or “Pacific Northwest” region! In addition, it would

seem reasonable to design NHANES interviews and analyses such that States or regions could add participants to

the sample or optional data elements to the standard

examination, even if this were done on a costreimbursable basis. Not only would this add to the subnational value of the data, but also more localized “ownership”

of NHANES would result.

Technology transfer is a subject that has been little

mentioned. Survey methods selected for NHANES are

scientifically superior and technologically advanced, Therefore, States and localities look to NHANES for prototype

approaches and methodologic guidance, Of course, it is

vital to continually include new methods, such as the

collection of “matter,” as proposed earlier in the workshop. I am surprised that no one has yet mentioned the

inclusion of “functional parameters” of nutritional msessment, especially for children or the elderly, Tests of

cognition, manual dexterity, and memory related to nutritional status would seem to be farsighted new dimensions

for NHANES. However, it is of greater importance to

consider the potential for broad public health application

in the development of survey methods. Total automation

of dietary interviews, particularly if suitable for microcomputers, and shortened questionnaires or validated telephone followup interviews would be extremely valuable to

public health practitioners. In addition, provision should

be planned to speed the dissemination of new technology

within the public health and research communities.

Finally, attention should be paid in advance to a data

delivery system for NHANES. In the Hispanic HANES

major strides have been made to release data promptly

and into the hands of university and other non-Federal

users. Although this represents major progress over the

lengthy delays of the past, it could be improved by

74
construction of a planned program of data analysis in

partnership with non-Federal experts to assure that major

policy questions are investigated. Otherwise, such examinations are serendipitous, and there are well-known

instances of data misuse, unauthorized publication, and

subsequent public confusion. In addition, the release of

intermittent and timely reports of findings would greatly

enhance the usability of NHANES. As has been done in

Hispanic HANES, it would seem desirable to convene an

advisory mechanism to aid the NHANES staff in setting

ongoing priorities for data analysis and in disseminating

information about NHANES.

It has been my pleasure to participate in this workshop representing State perspectives. We shall watch with

acute interest the progress of NHANES III and look

forward to working together toward achieving mutual data

objectives.

References

1.

2.

.,3

4.

U.S. Department of Health and Human Services. Promoting

health/preventing disease: Objectives for the Nation. Washington: U.S. Government Printing Office. 19S0.

National Institutes of Health. Lowering blood cholesterol to

prevent heart disease. Consensus Development Conference

Statement 5(7). Bethesda, Maryland. 1984.

National Institutes of Health. Osteoporosis: Cause, treatment, prevention. NIH Publication No. 86-2226. Bethesda,

Maryland, 19S6.

Subcommittee on Nonphwrnacological Therapy, 1984 Joint

Nuticmal Committee on Detection, Evaluation, and

5.

6.

7.

8.

9.

10,

11.

Treatment of High Blood Pressure. Nonpharmacological

approaches to the control of high blood pressure. Hypertension 8(5):444-67. 1986.

National Cancer Institute. Cancer control objectives for the

Nation: 1985-2000. NCI Monographs 2. Bethesda, Maryland. 1986.

National Academy of Sciences, Subcommittee on the Tenth

Edition of the Recommended Dietary Allowances. Recommended Dietary Allowances. 10th rev. ed. Washington:

National Academy Press. 1989.

Foerster SB, Lee S, Bonam S, et al. Nutrition goals for three

levels of government: Recommended model objectives for

States [Abstract]. Paper presented at the American Public

Health Association, Oct. 1, 1986.

Model Standards Work Group: Model standards: A guide

for community preventive health services. 2d ed. American

Public Health Association, Association of State and Territorial Health Officials, National Association of County Health

Officials, U.S. Conference of Local Health Officers, and the

Centers for Disease Control. 1985.

Miller SA, Stephenson MG. Scientific and public health

rationale for the Dieta~ Guidelines for Americans. Am J

Clin Nutr 42739-45.1985.

Interagency Committee on Nutrition Monitoring, Nutrition

monitoring in the United States –The directory of Federal

nutrition monitoring activities. DHHS pub. no. (PHS)

89-1255-1. Washington: Public Health Service. 1989.

U.S. Department of Health and Human Services. Healthy

people 2000: National health promotion and disease prevention objectives. DHHS pub. no. (PHS) 91-50212. Washington: Public Health Service. 1991.

75
Chapter 7

Working group

consensus statements

Working Group Number 1:

Statistical issues related to the

design of dietary survey

methodology for NHANES Ill

Kiang Liu, Ph.D., Northwestern Universip, Harold A. Kahn,

MA., consultant in epidemiolop, and Geoige H, Beaton,

Ph,D,, University of Toronto

1.

2.

The recommended methodologies are as follows.

A 24-hour recall should be collected from each individual to maintain comparability with the Nationwide

Food Consumption Survey.

Some type of food frequency data should also be

collected.

Concerning multiple 24-hour recalls, several points

should be made.

1<

2,

3.

Multiple 24-hour recalls should be collected, preferably from each individual, If that is not possible,

multiple 24-hour recalls should be collected in a

randomly selected subsample.

Professors Beaton and Kahn felt that a minimum of

two 24-hour recalls should be collected either from

each individual or in a randomly selected subsample.

Dr. Liu felt that the minimum number of replicate

recalls should be three.

Multiple recalls of the number being recommended

will p&mit the adjustment of distribu~ions for withinperson variability. This procedure, as outlined in

Nutrient Adequacy: Assessment Using Food Consumption Surveys,”would permit more accurate estimates of

prevalence. Dr. Liu felt that the correction of distributions based on two 24-hour recalls should be under-

‘National Academy of Sciences, National Research Council, Subcommittee on Criteria for Dietary Evaluation, Coordinating Committee on

Evaluation of Food Consumption Surveys. Nutrient adequacy Assessment using food consumption surveys. Washington: National Academy

Press. 1986.

4$

5.

taken with great caution. He was not confident that a

good enough estimate of the ratio of within-person to

between-person variability could be obtained with two

replicates; he would have more confidence in an

estimate of the ratio that was based on three replicate

24-hour recalls.

Although multiple recalls will allow for more accurate

estimates of distributions, the number of replicate

recalls recommended will not permit the identification

of particular individual who are above or below a

particular level of nutrient intake,

For comparisons between a dietary variable with a

significant nonzero ratio of within-person variability to

between-person variability and a nondietary variable

with a zero or essentially zero ratio of within-person to

between-person variability:

a. When the dietary variable is the dependent variable, variability is controllable by sample size,

b. When the dieta~ variable is the classification

(independent) variable, the key issue is the misclassification error associated with the dietary

variable’s large ratio of within- to between-person

variability. It should be recognized that the number

of replicate measures recommended may result in

only minimal enhancement of the ability to do

hypothesis testing between or among the dietary

variable’s classification groups.

Two points are made concerning validation of dietary

methods:

1.

2.

Professor Kahn recommended that the 24-hour recall

be validated for estimating group means. This would

be accomplished by comparing mean urinary excretion

of a few selected nutrients (such as nitrogen excretion

for protein) with mean intake assessed by the 24-hour

recall. The study is outlined in his discussant’s remarks

on Dr. Liu’s paper,

Drs. Beaton and LIu felt that any study designed to

assess the validity of the 24-hour recall should be

undertaken to estimate intake for individuals rather

than groups. Professor Kahn does not disagree with

this, but he knows of no natural method for validating

individual intake in a free-living population.

76
Working Group Number 2:

Recommended dietary methods for

cancer

Gladys Block, Ph,D., National Cancer Institute, Walter C.

WillC’tt, ill. D., Dr.P.H., Harvard Universi~, and Gail

i\lcKca\rn-Eyssen, Ph.D,, Ludwig Institute for Cancer Research

NHANES HI could provide potentially important

duttt for the prospective analysis of the relationship between

diet and risk of cancer, as well as for diet and other

diseases, if appropriate dietary methods are used. Although

Iargcr cohorts exist, none provides the opportunity, as

NHANES III could, to examine the role of diet in adolescence or early adulthood in the development of disease

m:my years later, In addition, cross-sectional studies on

the rckttionship of baseline diet to biochemical parameters would be useful, NHANES III is not a data set in

which it would be desirable to attempt case-control studies

involving the collection of retrospective dietary data for all

respondents at baseline, although future studies on a

subset of this population might shed useful light on

whuther observed age differences in intake reflect cohort

ditIcrences or changes associated with aging.

In order to make prospective approaches fruitful,

long-term followup and reassessment at 6- to 10-year

intu-wds would be required. Potential confounding variables must also be assessed at baseline and reassessed at

those intervals, in addition to the collection of the dietary

datti. The collection and storage of biological samples for

the subsequent analysis of nutritional-biochemical factors

ond discuse and the collection of urine and stool as

potential indicators of actual dietary intake could also

provide important data,

The usefulness of NHANES III data for the prospective or cross-sectional analysis of the relationship between

diet and disease or between diet and biochemical parameters is predicated on the use of a dietary method that is

valid for the assessment of an individual’s usual diet, Data

from 2 or 3 days, or from the sort of frequency questionnaire used in previous NHANES surveys, are not adequotc for this purpose, nor will they permit substantial

:idv;mcements in the level of our understanding of the

relationship behveen diet and disease or diet and biochemical parameters. For many of the nutrients that have been

examined, the data are quite clear that extensive quantified frequency and/or list-based diet history methods

prcwide a representation of an individual’s usual diet at a

level of precision equal to or better than that provided by

7 or more days of recall or records. Furthermore, a

combined frequency-history method will provide the opportunity to examine disease relationships at the level of

foods and food groups as well as at the level of nutrient

intake.

An extensive frequency-history method is defined as

ccmt:lining 75-100 items and taking approximately 25

minutes for completion. As list-based instruments are

relatively simple to complete and generally do not require

equipment (such as food models), assessments of changes

in diet over time can be made by readministering the

questionnaire by mail. To this should be added some brief

questions on food preparation, water consumption, and so

forth. Information on vitamin supplement use is essential.

Unless NHANES III uses an extensive frequencyhistory or many (7–21) multiple days of recalls, analyses of

the survey will be limited to descriptive studies of population

characteristics and will not allow analytic study of dietdisease relationships at the individual level because of the

substantial misclassification and the attendant loss of

statistical power. Descriptive studies are the least powerful way of examining etiologic relationships and the one

most subject to hidden and “ecologic” confounding. Analytic studies require data valid for an individual’s usual

diet.

Data from 24-hour recalls can provide internal validation, accurate population means, and comparability with

prior surveys (although note should be taken of the

existence of such data from other national surveys). Replicate 24-hour recalls should be performed on randomly

assigned days, including weekends, approximately 6 months

after the first measure to eliminate seasonal bias.

Therefore, it is recommended that NHANES III use

an extensive 75–100 item frequency-history as the individual measure of nutrient intake, in conjunction with a

single 24-hour recall on every participant and replicate

measures on either everyone or a subset. If respondent

burden or fiscal considerations require a choice between

the two methods, it is recommended that NHANES III

use the extensive frequency-history, if the goal is to use

the health and biologic data for meaningful analytic studies

of diet and disease.

Working Group Number 3:

Recommended dietary methods for

cardiovascular disease

Patricia J. Elmer, Ph.D., R.D., University of Minnesota,

Arlene Caggiula, Ph.D., R.D., University of Pittsbuqh, and

Nancy Ernst, M. S., R.D., National Heart, Lung and Blood

Institule

As NHANES moves from a cross-sectional design to a

longitudinal design (for research and hypothesis testing),

the hypotheses and goals for future data collection need to

be clearly identified and the timeframe for evaluation

stated, Dietary intake information is extremely important

for questions related to the etiology of cardiovascular

disease (CVD), particularly as NHANES III data will

serve as a baseline for longitudinal studies.

Specific dietary assessment tool

Questions arise about the use of multiple 24-hour

recalls versus food frequency lists in relation to solving the

77
problems of intraindividual variance and misclassification

and preserving some information about specific foods. For

cardiovascular disease, the dietary assessment tool needs

to obtain information about nutrients associated with

blood pressure regulation and blood cholesterol levels.

These nutrients include sodium, potassium, and specific

fatty acids, There is concern that the use of a food

frequency list could result in misclassification of individuals at high or low intakes of some nutrients of interest

because of systematic biases, in particular those related to

the construction of the instrument, but also the choice of

food composition values and even food portion assumptions.

Some of these questions or problems could be alleviated by lengthening the list of the food frequency instrument or using differential portion sizes, but a resulting

problem then becomes a long interview and a considerable

increase in respondent burden, (It is important to note

that in some situations food frequency questionnaires

have been used as short, quick dietary assessment methods

for epidemiologic studies, and in many cases they can still

provide meaningful dietary intake data for groups.)

Nutritional epidemiology

Among CVD nutritional epidemiologists, reservations

related to the use of a food frequency questionnaire relate

to several specific areas. There is concern that there

should be further analyses of data, examining reliability

and validity among different populations (age-ethnicity)

before this method is used as the only dietary method.

Questions to be answered include the following: Is this a

valid and reliable method to assess dietary intake of

children? Should the food list be different for specific age

and ethnic groups? If so, what is the implication of

interpretation if different lists are used?

Important unresolved etiological questions related to

CVD and hypertension could be addressed in proposed

prospective analyses planned for NHANES III. In particular, NHANES 111could make a unique contribution from

the data obtained by the oversampling of black persons

and other minorities, the elderly, and the low-income

groups. Important in these etiological evaluations is information on the interaction of nutrients and such variables

as body weight and physical activity, NHANES III then

may be able to address questions such as whether there

are critical ages at which interactions affect specific disease outcomes.

If a longitudinal component is added to NHANES III,

the dietary assessment tools must facilitate measurement

of dietary change, If the impact of specific health promotion programs (for example, the National Cholesterol

Education Program) is of interest, information on dietary

attitudes, knowledge, and eating behaviors might also

need to be assessed,

In consideration of the above-mentioned factors, information presented in the manuscript by Dr. Patricia Elmer

(chapter 3), and discussions at the NHANES III Dietary

Survey Methodology Workshop, both the 24-hour recall

and the food frequency list are recommended as being

78

suitable for NHANES 111,Improvement and modification

of the food frequency methodology is further recommended,

Working Group Number 4:

Recommended dietary methods for

energy balance

Dorothy Blair, Ph.D., Pennqlvania State Universip, Johcmna

DwyeG D.SC., R.D., New England Medical Cenler, and

Theodore Van Itallie, M.D., Columbia University

There is general agreement that an attempt to measure

energy balance is an inappropriate aim for NHANES 111.

Energy balance is an issue of quantifying energy input and

energy expenditure. Balance tends to occur over varying

periods of time within individuals, as indicated by peaks

and valleys that negate each other when a long-term

stable, but short-term fluctuating body weight is plotted

over time. Even very precise measurements of energy

intake and expenditure would not adequately categorize

individuals who achieve energy balance over a longer time

than is possible to measure in a cross-sectional survey.

Furthermore, such precise measurements are not even

within the realm of possibility for NHANES III.

A change in fat stores is the measurable outcome of

an energy imbalance and may be more precisely approximated in a survey than either energy intake or energy

expenditure. Serial measurements of body fat are suggested if useful information on the degree of energy

imbalance in the population is a priority. Such information

would be useful for interpreting energy intake data. Weight

histories and more specific information on restrained

eating behavior would also be useful,

Although energy balance is not considered to be an

appropriate aim for NHANES 111,precise, accurate, and

valid measurements of total usual energy intake and

habitual activity levels are appropriate aims. There is

concern that possible estimate biases resulting from obesity be recognized and minimized. With regard to dietary

assessment, probing for information on snack and dessert

food items, increased attention to techniques for eliciting

accurate portion size estimates, and increased frequency

and variety of days assessed would possibly reduce

respondent bias resulting from obesity, To this end, the

home interview could be used to collect dietary data,

particularly on serving container sizes, portion sizes, and

specific brands and snack food items, The food frequency

could be used specifically to probe for snack food and

alcohol use.

It is recommended that random repeat 24-hour recalls

accompanied by a semiquantitative food frequency be

considered, Both weekday and weekend dietary intakes

should be assessed. Continuing to obtain one 24-hour

recall in the mobile examination center would presewe

historical comparability. Subsequent telephoneadministered 24-hour recalls are a possibility, especially to

make weekend data collection more practical. This
technique requires further validation, particularly with

regard to portion size estimations. Ancillary data that

should be collected include use of medications that affect

enerb~ intake (such as anorexic agents), cigarette smoking,

and other drugs, such as antidepressants, that act as

orexigenic agents.

In summary, energy balance cannot be effectively

assessed under conditions outside of the laboratory setting

(that is, a metabolic ward). Even if energy balance could

be precisely measured, its utility would remain highly

questionable because fluctuations about the mean may

render the concept of energy balance meaningless in the

short term,

Working Group Number 5:

Recommended dietary methods for

osteoporosis

Nancy E. Johnson, Ph.D., Universip of Hawaii, and Mary

Fran Sowers, Ph.D., Cornell University

To characterize the current diet for all respondents,

the following recommendations were made:

1.

2.

3.

4,

Multiple 24-hour recalls – as many days as feasible (in

the range of 2–3); use of telephone interviews is

acceptable,

A quantitative food frequency that focuses on food

sources of nutrients with high intraindividual variability, such as vitamin A (if time allows).

Current vitamin-mineral supplement use – characterize in as much detail as possible frequency of use and

amount of nutrients in each supplement taken.

Current water intake, consumption of water and waterbased beverages, use of water softener or charcoalbased water conditioner, and analysis of community

water supply for all minerals possible, including calcium, fluoride, lead, and cadmium.

To characterize the past diet for all respondents, the

following recommendations were made:

1,

2.

Diet: Ask about daily milk consumption separately

(“usually” or “always,” “daily” versus “never” or

“rarely”) and daily cheese, yogurt, and ice cream

consumption for four age groups —O–5 years, 10–14

years, 15–24 years, and 25-44 years.

Vitamin-mineral supplement use: Ask about use of

supplements and type (for example, multimineral versus

simple nutrient) or obtain as much detail as possible,

but not brand information, for 5 years ago, 10 years

ago, and 15 years ago.

Working Group Number 6:

Recommended dietary methods for

nutrition monitoring

Elizabeth A. Yetky, Ph.D., R.D., Food and Drug Administration, Eleanor M. Pao, Ph.D., U,S. Department of Agriculture, and Susan B. Foerster, M.P.H., R.D., Association of

State and Temitorial Public Health Nutrition Directors

Surveillance of food intakes, nutritional status, and

nutrition-related conditions is important for policymaking

and program planning, Nutrition monitoring requires the

measurement of changes in food and nutrient intake over

time; therefore, both baseline and comparable followup

data must be available. Estimates of “actual” intakes

rather than relative intakes among groups are important.

Reasonable estimates of frequency distributions of intakes

are also needed to identify prevalence of marginal or

unsafe intakes in population subgroups. NHANES is a

cornerstone of the National Nutrition Monitoring System

(NNMS) because it contains a series of surveys in which

unique data are collected, including biochemical and medical measures of nutritional and health status as well as

data on dietary intakes and vitamin-mineral supplement

use. Another cornerstone of NNMS is the Nationwide

Food Consumption Survey (NFCS).

To meet the measurement needs of a monitoring

system, methodologies among past, current, and future

surveys must be consistent or comparable as well as

documented so that trends in food intake and dieta~

status can be attributed to real changes in the population

and not to changes in methods or data bases. In addition,

some means of correcting or adjusting for measurement

error resulting from intraindividual variation in daily intakes

is needed in order to obtain population frequency distributions, To facilitate comparability with past NHANES

and with NFCS, and for nutrition monitoring purposes,

the following are recommended:

. Continued use of the 24-hour recall.

● Two l-day independent recalls for all NHANES III

participants,

. Alternative approaches to the latter could involve

obtaining multiple intake days on a subsample,

obtaining subsequent 24-hour recalls by a telephone

interview, or adjusting the distribution of nutrient

intakes using calculations of intraindividual reliability

obtained from NFCS.

Use of the 24-hour recall in NHANES III would provide

consistency with previous data as well as group estimates

of nutrient intake; however, it is noted that the methodology will be different depending on how and where the

24-hour recalls are collected,

79
Additionally, linkage and comparability between

NHANES and NFCS also are important for NNMS.

NHANES III and NFCS should aim to develop common

definitions for key variables and to use the same core food

composition data base and coding system. Whenever possible, the interviewing techniques, level of probing, and

interviewer training also should be consistent. An automated, interactive computerized system to be used in the

interview process is strongly recommended. Increased use

of the household interview is also recommended as a way

to obtain supplementary dietary information. Quantitative

information on vitamin-mineral supplement use, water

intake, medication use, and alcohol consumption is important for the estimation of complete nutrient or food

component intake (as differentiated from food intake).

In addition, information on meal patterns (foods typically eaten together) provides useful data on interactions

among food components and on consumer food use behaviors. A focused questionnaire to obtain information on

infrequently eaten foods that are important, nutritionally

or toxicologically (organ meats, seasonal fruits and vegetables, and so forth), is also valuable.

80
Chapter 8

Dietary methodology

considerations for

NHANES III

by Lenore Kohlmeier, Ph. D., Department of

Epidemiology of Health Risks, Federal Health Office,

Federal Republic of Germany

As in any nutrition survey, selection of the proper

dietary assessment tool for NHANES (National Health

and Nutrition Examination Survey) III depends on the

study goals, resources, and limitations. A number of potential goals for NHANES were presented, argued, and

reevaluated during the workshop, The ability of various

\\,idely utilized dietary methods to meet these goals partially or completely was discussed, The accuracy, precision, and validity of these methods to measure that which

was desired (mostly typical eating behavior) were the

overriding topic of deliberations. The need for information cm dietary behavior in the study of the etiology of

specific diseases resulted in conclusions for specific diseases ranging from realization that the hypotheses cannot

be tested in this study to the expectation that acute and

long-term trends will be examinable within this study

design. Finally, the practical aspects of dietary methodology were discussed, including the need for automation

of information collection and calculation and for further

development along these lines, even though new methods,

which will need validation, are required.

Study goals

The scientists utilizing NHANES III information

expect: (1) to assess cross-sectionally diet and risk for

disease interactions; (2) to look at the effects of prior

behavior on current disease or risk; (3) to look at the

impact of current dietary behavior on future disease states

through longitudinal sampling; (4) to evaluate the effect of

change in the behavior on change in risk of disease

through longitudinal sampling. It should be mentioned

that not all foods, nutrients, or dimensions of dieta~

behavior that will be important to future epidemiological

hypothesis-exploration are identifiable in advance.

Other purposes, such as those described by Woteki

(appendix VIII) in the area of monitoring dietary behavior

of the population, are not exclusively NHANES tasks.

Each of the four above-mentioned purposes of NHANES

needs to be considered in the selection or rejection of

dietary methodologies for NHANES III. The responsibility for currently limiting the future resource data base

that will result from NHANES 111is considerable. Decisions about dietary methodology need to be made in

consideration of the strengths and weaknesses of individual methods and the study design. Full knowledge of

the consequences of selection or rejection of potential

methods is necessa~, and adequate cost-benefit argumentation needs to underline the final decision. In addition,

characteristics of individual methods can be fatal flaws for

certain assessment purposes and must therefore be critically examined in the context of each study goal.

Fatal flaws

Fatal flaws are limiting characteristics of a particular

dietary method, making it inappropriate for one or more

of the goals of information collection. Wrong timeframes,

response bias, invalidity of a method, misclassification of

individuals or groups, population compression (or its opposite), high interindividual variation, atypical behavior, and

expense may be fatal flaws of a particular method in a

study incorporating dietary assessment. Some areas of

study may concern themselves with current eating behavior,

others are interested in projection of future eating behavior,

and still others are acutely interested in behavior that took

place in the distant past. Although projections are often

made assuming that current behavior is reflective of prior

behavior, this may be a misassumption and result in

inappropriate and inaccurate conclusions.

If a method results in response bias, with the subjects

reporting their dietary behavior differently because of the

method of questioning, or different individuals within a

population responding differently to the same question, the

result may be unusable for the comparisons of population

groups that are desired. There are numerous aspects of

validity, one of the most important being whether the

method measures what it sets out to. For example, 24-hour

dietary recalls set out to collect information on the typical

short-term cross-sectional eating behavior of a group.

Recalls provide valid results only if the individuals can

accurately report what was consumed the previous day

and if the day selected represents a period of usual

behavior. It is invalid when quantity information is required,

and it is false when inappropriate numbers of the population have eaten atypically, for example, because they knew

that the next day they would have an examination,

Identification of groups at risk based on quantifying

dietary behavior depends on accurate placement of the

individual within a population distribution. The measurement desired is the usual intake of certain foods or

81
nutrients, If the persons who, for example, eat the smallest

quantities or are the least frequent consumers of meat

wind up in a higher percentile of the population because

of the type of questioning, analyses of risks will be false.

Misclassification becomes less when fewer groups are

used for categorization. Compressing the breadth and

diversity of consumption into a few broad categories can,

however, result in a vast majority of individuals falling into

one rubric. For example, asking about the frequency of

consumption of a particular food in the categories of many

times a day, daily, several times weekly, several times

monthly, or rarely may cause very uneven distributions for

foods such as coffee, milk, or certain types of fish. The

danger is that the vast majority of the population falls into

the very high frequency group in the former cases or a very

low frequency group in the latter case.

The method of assessment may in itself create atypical

dietary behavior and this, in turn, maybe a fatal flaw. For

example, food diaries, if kept over a series of days, tend to

result in reduced consumption (or reporting) of complex

meals and recipes. This change in behavior, caused by the

methodology, may defeat the purpose of assessment,

If the interest is in information on individual intakes

for correlation purposes, selection of a method that

measures a very short cross section of dietary behavior

may result in intraindividual variations that are as great as

or greater than interindividual variations. This purpose

may also be a fatal flaw. If the ideal method for a certain

purpose is selected but is too costly to be carried out

completely in the number of individuals required, this too

is a fatal flaw. Fortunately, this fatal flaw can be prevented

or corrected.

Dimensions of dietary behavior

This workshop addressed measurement of dietary

behavior directly through questioning and indirectly through

body fluid measurements and other biological markers, It

was agreed that the area of biological markers is not fully

developed, and many satisfying solutions of dietary assessment problems may be resolved through future developments, However, for the range of foods and nutrients of

interest in NHANES, biological markers will never be

exclusive reflectors of dietary behavior; the breadth of

interests in nutrients, foods, and processes is too extensive.

Direct questioning was discussed in terms of obtaining

information from individuals and groups regarding the

quality of their food intake and converting it into daily

nutrient intake per person. Dietary nutrient intake is of

particular interest to researchers in the field of cancer and

cardiovascular disease epidemiology. Not discussed at this

workshop were other dimensions of dietary behavior such

as food preparation (grilling, baking, boiling, microwaving), methods of presewation (salting, smoking, deep

freezing, packaging), the necessity of collecting information on brand names of certain food items consumed, and

specific information on the meal itself, such as what was

eaten together at a sitting, how frequently meals are

consumed, and the meal location (where it was prepared,

82

where it was consumed, and so forth). The extent to which

recipes and ingredients in mixed dishes need to be collected was also not discussed. The following list summarizes these factors.

What was eaten (food groups, recipes, items, brand

names)?

How much was consumed?

What was discarded?

How was it prepared and where served?

How was it previously presewed?

How was it packaged prior to consumption?

What was eaten together at the same meal?

Which snacks were consumed?

What ingredients and how much comprised “mixed

dishes”?

Were the items consumed “convenience foods” or

“homemade”?

What was the fluid intake, including water?

What was the current and prior consumption of

vitamins or mineral supplements?

What was the use of “food drugs,” such as garlic pills,

pollen, protein powders, and lecithin preparations?

Aside from direct measurement of previous eating behavior,

the assessment of nutrition-related and health-related

histories and dietary knowledge or ignorance were

mentioned.

Dietary constituents (nutrients)

Special disease-related interests Ied to a focus on

particular constituents of foods to be quantitatively

measured in NHANES HI. This list included the basic

macronutrients, vitamins, minerals, cholesterol, and fiber.

They can be found in table 1.

Table 1. Dietary constituents of interest for particular diseases

Constituent Disease of hterest

Total energy consumption . . . . . . .

Macronutrients . . . . . . . . . . . . . .

Fat . . . . . . . . . . . . . . . . . . . .

Carbohydrates . . . . . . . . . . . . .

Protein . . . . . . . . . . . . . . . . . .

Alcohol . . . . . . . . . . . . . . . . . .

Vitamins:

Vitamin A and carotenoids . . . . . .

Vitamin C . . . . . . . . . . . . . . . . .

Vitamin D . . . . . . . . . . . . . . . . .

Vitamin E . . . . . . . . . . . . . . . .

Cancer, cardiovascular diseases,

obesity

Obesity

Cancer, cardiovascular diseases

Obesity

Obesity

Obesity, cancer

Cancer

Cancer

Osteoporosis

Cancer

Minerals and elements:

Calcium . . . . . . . . . . . . . . . . . . OsteoDorosls

Potassium . . . . . . . . . . . . . . . . Cardiovascular diseases

Phosphorus . . . . . . . . . . . . . . . 0ste0r30r0sis

Magnesium . . . . . . . . . . . . . . . Osteoporosis

Fluoride . . . . . . . . . . . . . . . . . Osteoporosis

Selenium . . . . . . . . . . . . . . . . . Cancer

Other constituents:

Fiber Cancer

Choles\er;l: lI::nl::lll::Il Cardiovascular diseases
Ideal dietary methodology

The following was proposed as a definition of the

ideal method for dietary assessment for scientific purposes:

This ideal method would provide exact and valid

information about typical food items and amounts

consumed (their preparation and previous preservation) per person prior to the onset of disease. It

should be inexpensive, requiring no specially skilled or

trained personnel, little or no personal contact, and

provide immediate results.

Unfortunately, all experts agreed that it does not yet

exist, rind compromises and stylization of methods to goals

are required, Discussions of methods were limited to

those methods with which adequate experience was available, Furthermore, they were based upon the strengths

and weaknesses of individual methods, interest study constraints, and fatal flaws.

24-hour recall

Weaknesses of the 24-hour recall included cost of

collection and data processing, altered eating behavior on

the day before examination, and the large daily intraindividual variation in eating behavior. For individual analyses

and subgroup analyses, the recommendation was for multiple 24-hour records —at least two, to allow the estimation of within-person to between-person variability. This is

important for individual results as well as for accurate

estimation of the population distribution of food or nutrient

intakes. A more complete discussion of the statistical

issues can be found in chapter 1.

At least a single 24-hour recall is strongly desired to

allow NHANES III to continue to be used as a longitudinal measure of national behavior and to ensure some

consistent and comparable methodology with the Nationwide Food Consumption Survey.

Food frequencies

Food frequencies were also intensively discussed as an

inexpensive approach toward collecting more long-term

information on the intake of particular foods, as needed

for studying the dietary component of specific diseases

(such as cancer and osteoporosis). Although assumptions

are generally made that food frequency responses are

valid, prepublication data were presented showing misclassification of up to 30 percent of individuals into wrong

quintile extremes for a number of foods. Quantitative use

of i’ood frequency information was generally not recommended, The validity, usefulness, and comparability of

responses among different ages and ethnic groups was

questioned.

Food frequency questions have little basic commonality; they differ in the foods or food groups addressed, the

range of response possibilities, their inclusion of portion

size components, the underlying timeframe, and the way

the questions are posed, The right food frequency

questionnaire for NHANES III will need to be tailored to

the diseases of interest, their etiological-developmental

frame, and logistic considerations. For feasibility reasons,

an unwillingness to rely solely on 24-hour recalls, and a

range of opinions about the usefulness of food frequency

questionnaires, it was recommended that some form of

food frequency be included in NHANES III. No detailed

discussions of what the food frequency should include

were undertaken.

Dietary histories

Clear distinctions behveen what some persons call

food frequencies and diet histories were not made at this

meeting, The diet history method is generally recognized

as desirable for its breadth and quality of information

collected, but it is considered too costly in time and

resources for the information collected and too cumbersome in the coding, entry, and analyses to be recommended within a large-scale study.

It was noted that the diet history is more widely

applied in Europe, and 24-hour recalls, more in the

United States. Full automation of the diet history method

could make it an attractive alternative to the other methods

discussed. Although technically possible, this would require

a generation of development and validation. Thus it cannot

be recommended for NHANES III, in which a complete

methodology should be established and made final in the

middle of 1987.

Combinations

More than one method of dietary assessment will be

needed in NHANES III to ensure that most of the wide

variety of goals are served: Comparability with the past,

high-quality data for future longitudinal studies, good

quantitative data on individual and group intakes, and

clear classification of persons into groups of high or low

consumption levels. One 24-hour recall and a food frequency questionnaire represent’ a minimal program, Multiple 24-hour recalls, at least in subsamples, are strongly

recommended by all statisticians for addressing many

questions of diet-health interactions. Whether these should

be conducted on particular days of the week, in certain

seasons, or systematically or randomly requires further

discussion.

Automation

An underlying theme of the workshop was the extent

to which automation of dietary assessment should and

must be undertaken in NHANES III. Automation can

take place in a number of individual steps, from the

questioning of the subject to the coding of the food

information. The entry and checking of codes, the calculations of quantities consumed–taking household measures,

losses, conversions, and waste into account – and the conversion of food intake into nutrient intake are all steps

that would greatly benefit from automation. Most experience gained in automation has been acquired in the

83
later steps of dietary assessment – the conversion to nutrients. Very few attempts have been made at fully automating dietary assessments, particularly the data capture

steps, However, financial strains, positive (albeit limited)

experiences with the automation of dietary methods in

population studies, and time factors make enhanced automation inevitable, The advantages of full automation are

as follows:

● No between-interviewer variability (standardization).

● No response bias introduced by the interviewer

(objectivity).

● No change in dietary behavior induced by the

method of assessment.

● Scientists decide on exact phrasing, depth of probe,

and sequences of questioning,

● No hand coding of food information into machine.

● Built-in and immediate editing and checking

(validation) routines (automated quality control).

● Immediate results.

● Better comparability of results.

● Reduced costs of collecting and assessing dietary

information.

The most potent of these advantages currently are the

elimination of costly and error-prone procedures, the

immediacy of results, and reduced costs of operating the

study.

Telephone interviews

Longitudinal followup of individuals as well as multiple 24-hour recalls require multiple contacts with subjects. To reduce costs the application of telephone

interviewers for either repeat 24-hour recalls or repeat

food frequencies entered into the discussions. These would

imply new methodologies, requiring prior investigation

and validation, but they are considered worth pursuing,

84
Chapter 9

Process and rationale

for selecting dietary

methods for

NHANES Ill

by Christopher T. Sempos, Ph.D., Ronette R. Briefel,

Dr,P.H,, R.D,, Ciifford Johnson, M.S.P.H., and

Catherine E, Woteki, Ph.D., R.D., National Center for

Health Statistics

Since the first National Health and Nutrition Examination Survey (NHANES I), which took place in the early

1970’s, the National Center for Health Statistics (NCHS)

has conducted periodic national nutrition surveys (table 1,

(l-6), and appendix VIII). A goal of these surveys has

always been the production of national estimates of the

intake of individual foods, nutrient intake from those

foods, and the total nutrient intake by Americans, The

nutrient intake data from NHANES, along with the dietary

data from the surveys conducted by the Human Nutrition

Information Service, U.S. Department of Agriculture

(USDA), serve as the principal sources of national intake

data for individuals (3–5,7). As such, nutritional intake

data from NHANES are a key element in the National

Nutrition Monitoring System (1,3,4). Selection of a dietary

survey methodology or methodologies for use in an

NHANES is, therefore, of fundamental importance.

Following the workshop, as stated in the introduction,

detailed consideration was given to the workshop papers,

presentations, and consensus statements. During several

months following the workshop, additional planning sessions were held at NCHS that included survey planning

staff and invited guests from NCHS, USDA, and other

Public Health Service agencies. These included a weekly

seminar series over 4 months that examined in detail

individual topics related to the selection of a dietary

survey methodology for use in the third NHANES

(NHANES 111). Additionally, the 1984 recommendations

of the National Academy of Science’s Coordinating Committee on Evaluation of Food Consumption Surveys were

seriously considered, particularly the recommendations

for linkage with USDA’s Nationwide Food Consumption

Survey (7). The recommendations of other expert panels

were also reviewed and considered (8-10).

The results of this process were a series of decisions

about which dietary assessment methods would be included

in the survey. Food frequency instruments would be used

to collect information on water and alcohol consumption,

the intake of selected foods and food groups, food sources

of calcium and vitamins A and C, and historical milk

intake; the primary dietary survey instrument in

NHANES III, as in past NHANES, would be the 24-hour

recall. The decision to use the 24-hour recall as the

prima~ dietary survey instrument was not a foregone

conclusion, nor was it an easy decision.

In this chapter we will briefly explain the logic behind

the decisions. Toward that goal we will discuss: Major uses

of national nutrition data, specific information needs and

constraints for NHANES 111, types of dietary survey

methods available, and a comparison of the availability of

specific information from the 24-hour recall and the food

frequency. Finally, we will describe in more detail the

dieta~ survey methods being used in NHANES 111.

Uses of nutrition data

In general, there are four major uses of national

nutrition data: assessment and monitoring, regulato~ uses,

epidemiologic research, and commercial uses. The topic

areas of the workshop were chosen with these uses in

mind. Cancer, cardiovascular disease, energy baIance (obesity), and osteoporosis were chosen because of their public

Table 1. Description of National Center for Health Statistics examination surveys

Primary

dietary survey

Survey Dates Ages surveyed instrument

NHES I, . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1960-62 18–79 years (’

NHES II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1963-65 6-11 years (’ 1

NHES III. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1966–70 12–17 years (’)

NHANES I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1971-75 1-74 years 24-hour recall

NHANES II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1976-60 6 months–74 yesrs 24-hour recall

HHANES, . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1982-84 6 months-74 years 24-hour recall

NHANESI FOIIOWUP. . . . . . . . . . . . . . . . . . . . . . . . . . . . 1962-present 25-74 years Food frequency

NHANES III. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1988-94 2 months and over 24-hour recall

1NOdietary intake Informationwas collected.

NOTES: NHESIs National Health ExamlnatlonSutvay. NHANESIs National Health and Nutrition ExaminationSurvey. HHANESIs HLspenicHealth and Nutrition ExaminationSurvey.

85
health importance, Moreover, they are especially related

to nutrition. Generally, they are also highly prevalent

diseases or conditions that can be detected both crosssectionally and longitudinally. Although the topics appear

to be weighted toward specific diseases and epidemiologic

research, it must be remembered that none of the major

uses are mutually exclusive. Epidemiologic data are an

important component of the government’s health and

regulato~ policy. Accordingly, for diet, the focus is on the

ability to estimate absolute levels of intake rather than

relative risk. Absolute levels of intake are essential for

setting health guidelines or policy and for evaluating the

magnitude (prevalence) of a purported health problem or

outcome,

In the category “assessment and monitoring” the data

collected wouId be used to produce national reference

data ((1,2) and chapter 6). Questions to be addressed

include: What is the mean intake for the population at

large and for various selected subgroups (for example, sex,

ethnic groups, and age)? What is the range of intakes?

The dietary data will also be used to estimate the prevalence of intakes above or below some fixed cutpoint. in the

past, for example, the Recommended Dietary Allowances

(11) and the recommendations of the National Cholesterol Education Program (12) have been used to set these

cutpoints. The results of such analyses can then be used to

identi& the groups within the country who are at greatest

risk of overnutrition and undernutrition, With repeated

NHANES, it will be possible to monitor trends in dietary

intake over time. The trend data are especially useful both

in evaluating the need for national nutrition programs and

in monitoring the progress in meeting national nutrition

and health objectives,

The principal reason for conducting NHANES is to

supply US. Government agencies with information that

will be useful in formulating and evaluating health policy

and that will be the basis for regulatory actions. Government regulato~ agencies ((13) and chapter 6) use survey

data to determine and document the need for regulatory

action, design a regulatory action, and evaluate the effectiveness of a particular regulatory action,

The impact of diet on the risk of certain conditions,

disease, or even death can be evaluated using NHANES

data. Such epidemiologic research can be cross-sectional

in nature, as is typified by the role of NHANES data in

exploring the association between dietary calcium intake

and blood pressure (14-17), It can consist of an examination of aggregate trends over time (18), and with the

completion of the NHANES I Epidemiologic Followup

Survey (19,20), NHANES data have been used to examine

the longitudinal relationships of diet to the subsequent

risk of disease or death (21).

For the first time in the history of NHANES,

NHANES III was designed to be both a cross-sectional

and a longitudinal survey (1,5). At a minimum,

NHANES III data will be linked to the National Death

Index (22,23) in order to eventually ascertain the cause of

death for all decedents, The objective will then be to

relate baseline dietary intake and nutritional status to risk

of death. The survey was also designed to have the

potential for reinterviews and reexamination, Thus,

NHANES can no longer be thought of as only a crosssectional survey.

There is a great deal of overlap in the uses of

NHANES data. Assessment and monitoring uses are not

distinct from regulatory or epidemiologic uses. A little

recognized use of NHANES data that cuts across all three

areas is the use of national prevalence data to make

estimates of attributable risk (24).

Commercial interest in national nutrition data is, in

theory, similar to the Government’s nutrition monitoring

interests. For example, What are the nutritional contributions of specific food items? When, where, what, how, how

much, and with whom are meals consumed? Are there

specific classes of foods which predominate in the food

consumption patterns of specific subpopulations? Such

information can then be used, for example, to develop

advertising campaigns or new food products.

Information needs and constraints

In designing a survey like NHANES III, specific

information must be collected if the uses outlined above

are to be realized; very real logistical constraints limit

what can be collected.

●

●

●

●

●

Q

●

●

●

●

●

Information is needed about:

The consumption of separate food items served

individually, as components of mixed dishes, and

together with other foods as meals,

Food preparation methods, including type and

amount of fat and salt added in cooking or at the

table,

Type of food –for example, fresh, frozen, canned, or

dried.

Brand names whenever possible (such as for infant

formula, snacks, and prepared foods and meals).

Sources of food – home, work, commercial, and so

forth.

Time of day.

Water consumption.

Usage of vitamin-mineral supplements, including

brand name, dose, and frequency of use,

Alcohol consumption.

“Usual” or average intake of foods by individuals,

Meaningful absolute values for intake, requiring the

collection of exact portion size information.

Our objective was to select the dietary survey methodologies that would supply as much of this spscific information

as possible while also fitting within operational and methodological constraints. Perhaps the greatest constraints

were the operational ones. In all cycles of NHANES, the

participant is interviewed in the home approximately 2 to

3 weeks prior to his or her appointment at the mobile

examination center (MEC) to undergo the examination

procedures. The MEC appointment has historically included

86
the dietary interview, Because of the time lag between

contacts with the participant and the considerations mentioned above, the 24-hour recall has been used as the

primary dietary survey methodology in NHANES,

Once in the MEC, the dietary interview can take no

lcmger than 30 minutes before it impinges on the time

available for the other tests and procedures included in

the examination. To reduce data-processing errors, the

information collected needed to be amenable to computerized collection. Indeed, NHANES III was planned to”be

the first survey in which an onboard computer system

would be used to facilitate household sampling as well as

to record the data collected during the examinations,

Consideration was also given to the ability Qf the

different dieta~ methods to produce data comparable

with that collected in past surveys, the method’s appropriateness with respect to the research questions, its ability to

serve as the basis for longitudinal followup studies, and

the feasibility of administering questionnaires during the

household interview to provide information for nonresponse bias analyses.

Types of dietary survey methods

There are two basic classes or types of dietary survey

methods (table 2) available to choose from: daily food

consumption methods and recalled “usual” or average

food consumption methods (9,10),

The food record and the 24-hour recall are the most

commonly used daily food consumption methods, Food

records are considered by many to be the standard survey

method for estimating dietary intake (25). Food records,

however, have never been used in NHANES for several

reasons, First, they require the training and active participation of the study participant regardless of whether food

is weighed or merely recorded, Because of the level of

effort required by the participant, there is concern about

response rates and the comparability of recording skills

among participants. The level of effort required can also

lead participants to change their dietary patterns during

the recording period, Recording abilities and completeness of the record can then affect dietary intake results,

Moreover, the amount of time required to check the food

records and to review them with the participant as well as

the amount of effort required to code and process 30,000

Table 2. Types of dietary survey methods

Dally food consumption methods

Food record:

Weighed food record

Estimated portion sizes

24-hour recall

Recalled “usual” or average food consumption methods

Diet history:

Food frequency

24-hour recall

“Usual” meal patterns

Food frequency

Portion sizes unspecified or not noted

Portion elzee speclfled (also called “semlquantitative food frequency”)

food records, would be prohibitive in a survey the size of

NHANES III.

Recalled “usual” or averagefood consumption methods

comprise the other possible class of methods available

(table 2). The dieta~ history as originally proposed by

Bertha Burke (26,27) requires an extensive interview,

which may take 1-2 hours to complete (25,27-30). In

NHANES 111about 20-30 minutes were allotted for the

dietary interview in the MEC. Of the “usual” food consumption methods, the food frequency was the most likely

choice based on time considerations in the MEC. There

are two basic types of food frequencies, depending upon

whether portion size information is collected during the

interview (25,31). Because the NHANES III goal is to

estimate both food and nutrient intakes, portion size

information would also need to be specified on the

recording form of the food frequency. Accordingly, the

semiquantitative food frequency wasthe most likely choice

of method from the “usual” class of methods.

Contrasting the availability of the specific information

needed from the 24-hour recall and the semiquantitative

food frequency, it was our judgment and the consensus

recommendations of the expert consultants who participated in the workshop that a 24-hour recall be retained as

the primary instrument for measuring dietary intake in

NHANES HI (chapter 7).

The availability of the necessary specific information

from both the 24-hour recall and the food frequency is

shown in table 3. This table is an adaption of a similar

table given in the Life Sciences Research Office Report

(9, p. 4).

Food frequency instruments, regardless of whether

portion size information is collected, are usually preceded

forms, consisting of a few selected “core” foods that are

grouped into food groups of similar nutrient composition

for a few selected key nutrients (25,32,33). Preceded

forms are not new to the nutrition field, The concept was

originally introduced by Donelson and Leichsenring in

Table 3, Availability of specific information, by dietary survey

methodology

Semiquantitative

Specific information 24-hour recall food frequency

Individual foods . . . . . . . Yes No meals, few

mixtures

Food preparation

methods . . . . . . . . . . . Detailed General

Type of food . . . . . . . . . Yes Sometimes

Brand names . . . . . . . . Some Rare

Source . . . . . . . . . . . . Yes No

Time of day . . . . . . . . . Yes No

Water consumption . . . . Yes Yes

Supplements . . . . . . . . Yes Yes

Alcohol . . . . . . . . . . . . Less accurate More accurate

“Usual” intake. . . . . . . . Possible with Less accurate

replicates

Absolute intake . . . . . . . More accurate Less accurate

lAsked as separate questionnaire items and not as part of 24-hour raCall or food fWJenCY in

the third National Health and Nutrition Examination Survey.

SOURCE Adapted from Life Sciences Research Office Guidelines for use of dietary intake

dete. Bethesde, Marylend Federation of American Societies for Experimental Siology. 19S6.

87
1942 as an effort to develop short-form methods for

recording dietary records (34). Since that time their concept of the short form has been used to develop preceded

food record forms (32,33,35,36) and the semiquantitative

food frequency forms (37,38). Because the list of foods

contains only a limited number of “core” foods and

because foods are grouped together, food frequency instruments do not, as a rule, yield information comparable with

that from a 24-hour recall.

Food frequencies do not generally yield intakes of

individual foods, as mixtures or food eaten together as

meals, They also do not supply information about food

preparation methods, type of food, brand names, source of

food, or time of day when the food was consumed.

Although both instruments have equivalent potential

to estimate intake from water and vitamin-mineral

supplements, food frequencies are likely to provide a

better representation of alcohol consumption (chapter 3).

Randomly collected multiple 24-hour recalls will provide,

in our opinion, a better estimate of “usual” intake than

food frequencies (chapter 1 and (9)),

Because food frequency forms often contain only a

limited list of “core” foods, an open-ended section for

recording foods not listed is desirable. If there is a great

deal of cultural diversity, it will be impossible to develop a

food list that includes all of the essential “core” foods

eaten by different ethnic groups but at the same time is

kept reasonably short. If there are too few foods listed, a

great deal of effort will be needed to code other foods to

an existing food group on the form or to create new food

groups not listed on the form.

The food lists on a semiquantitative food frequency

form are generally composed of single foods; as a result,

an individual filling out a preceded form must separate

most if not all salads and mixed dishes into their component foods, People must convert a recipe amount to a

fraction of the standard portion size listed on the form,

multiply that by the frequency with which that amount of a

particular food is usually consumed, and then add to it the

amounts of that food from other sources in order to

estimate the usual consumption of the particular food.

Having estimated the frequency of consumption of a single

food, the results from all of the foods in the particular

food group must then be added together to determine the

total “usual” consumption of the food group. This is an

extremely cumbersome and difficult process for most people

to perform.

Once a food is recorded on a food frequency form, all

the information about food preparation, foods cohsumed

together as meals or mixed dishes, type of food, brand

name, source of food, time of meals and snacks, ingredients, and portion size is lost. This point cannot be overemphasized or overstated, If there is ever a need to

calculate the intake for a new dietary factor that was not a

“key nutrient” in the development of the food groups on

the food frequency form, it will be impossible to recombine foods into more appropriate food groups. Accordingly, food frequencies, and preceded records of any form,

by definition must be used to meet only limited objectives.

Many of the uses to which NHANES data have been put

would have been impossible if a preceded semiquantitative food frequency – or food record, for that matter –

based on a list of “core” foods had been used (see chapter

6 and (13)).

Methods selected for NHANES Ill

For the reasons outlined above, it is also our opinion

that the 24-hour recall provides a more accurate estimate

of the previous day’s intake than a semiquantitative food

frequency provides of the “usual” intake over the period

of several months or a year (9,39). Accordingly, we accepted

for NHANES III and have recommended to other countries planning national nutrition surveys the following:

● Use a daily food consumption method as the primary

method for achieving “major use” needs.

. Collect at least 2 days of dietary intake information

on at least a random sample of participants.

. Use a food frequency instrument to estimate water

and alcohol intake, the frequency of consumption of

selected foods and food groups of special interest,

and information on historical intake of particular

foods.

In NHANES III, a 24-hour recall interview will be

conducted with every participant who comes to the MEC

to undergo the examination procedures, Examinations will

not be limited to a Tuesday–Saturday schedule, as in past

Health and Nutrition Examination Surveys; therefore,

24-hour recall data will be collected on all days of the

week. This will allow for the comparison of weekend and

weekday dieta~ data and provide a more representative

distribution of dietary intake data for all days of the week.

We plan to collect two additional 24-hour recalls by

telephone for all examined sample persons 50 years of age

and over, The National Institute on Aging of the National

Institutes of Health has supported the followup telephone

24-hour recalls. Unfortunately, funding does not allow us

to obtain multiple 24-hour recalls for all NHANES III

participants, but a subsample of the examinees return for

a replicate MEC examination and receive a second 24-hour

recall.

A targeted food frequency is colIected in NHANES 111

as part of the household interview. In addition, other

questions on water and alcohol intake are asked in the

MEC. The targeted food frequency will focus on food

sources of calcium and vitamins A and C. The food

frequency instrument does not ask the respondent to

estimate “usual” portion size; accordingly, the food frequency data will not be used to produce nutrient intake

estimates. Because the food frequency information is

collected in the household, it will serve as a source of

dietary information for all interviewed persons and will be

used in followup analyses, The information will also be

used as a further means to investigate response bias by

comparing intakes of those participants who are

88
intmviewed only with intakes of those who are interviewed

and examined, Finally, we are collecting historic frequency

information on the intake of milk as a part of the

osteoporosis component of NHANES III.

In summary, the key feature of any national dietary

survey is flexibility in order to serve the multiplicity of data

needs both within and outside the government. This point

cannot be overstated. A current example of the need for

flexibility is the introduction of new ingredients such as fat

substitutes into the food supply, NHANES and the National

Nutrition Monitoring System are designed first and foremost to meet the policy decisionmaking needs of the

Federal Government. If NHANES dietary data are to

serve a central role in nutrition assessment and monitoring, developing food policy and regulations related to

food safety and fortification, and epidemiologic research

for the Federal Government, it is essential to maintain as

much flexibility as possible. In general, we believe that

daily food consumption methods, in particular the 24-

hour recall, provide this flexibility in NHANES HI.

References

1,

7

4,

.3.

4,

.5.

(i

7,

s,

9,

10.

11.

12,

Woteki CE, Briefel RR, Kuczmarski R, Contributions of the

N~ticmal Center for Health Statistics. Arrr J Clin Nutr

47:320-S. 198S.

Murphy RS, Michael GA. Methodologic considerations of

the National Health and Nutrition Examination Survey.Am

J Clin Nutr 35:1255-S, 1982.

Woteki CE. Methods for surveying food habits: How do we

know what Americans are eating? Clin Nutr 5:9-16. 19S6.

Woteki CE, Dietary survey data: Sources and limits to

interpretation, Nutr Rev suppl 204-13. 19S6.

Murphy RS, Sempos C. Design considerations and dimensions of diet in the third National Health and Nutrition

ExaminationSurvey(NHANES 111),In: KohlmeierL, Helsing

E, eds. Epidemiology, nutrition, and health. London: SmithGordon. 19S9.

Yetley E, Johnson C. Nutritional applications of the Health

and Nutrition Examination Surveys (HANES). Annu Rev

Nutr 7:441-63, 1987.

National Academy of Sciences, National Research Council,

Coordinating Committee on Evaluation of Food Consumption Surveys. National survey data on food consumption:

Uses and recommendations. Washington: National Academy

Press. 19S4.

Life Sciences Research Office, Suggested measures of nutritional status and health conditions for the third National

Htxdth and Nutrition Examination Survey. Bethesda, Maryland: Federation of American Societies for Experimental

Biology. 19S5.

Life Sciences Research Office. Guidelines for use of dietary

intake data. Bethesda, Maryland: Federation of American

Societies for Experimental Biology. 19S6.

Anderson SA. Guidelines for use of dietary intake data. J

Am Diet Assoc SS:1258-60, 19SS,

National Academy of Sciences, Subcommittee on the Tenth

Edition of the RDAs. Recommended Dietary Allowances,

tenth rev. ed. Washington: National Academy Press. 19S9.

Expert Panel on Detection, Evaluation, and Treatment of

High Blood Cholesterol in Adults. Report of the National

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23,

24.

25.

26.

27.

2s.

29.

30.

31,

32.

33.

34.

Cholesterol Education Program Expert Panel on Detection,

Evaluation, and Treatment of High Blood Cholesterol in

Adults. Arch Intern Med 14S:36-69. 19S8.

Yetley EA, Hanson EA. Data sources and methods for

estimating consumption of food components. J Toxicol Clin

Toxicol 21(1 and 2):1S1-200. 1983-S4.

Sempos C, Cooper R, Kovar MG, et al. Dietary calcium and

blood pressure in National Health and Nutrition Examination Surveys I and II. Hypertension S:1067-74. 19S6.

Harlan WR, Hull A, Schmouder RL, et al. Blood pressure

and nutrition in adults: The National Health and Nutrition

Examination Survey. Am J Epidemiol 120:17-2S. 19S4.

McCarron DA, Morris CD, Henry HJ, Stanton JL. Blood

pressure and nutrient intake in the United States. Science

224:1392-8.1984.

Feinleib M, Lenfant C, Miller SA. Hypertension and calcium, Science 226:384-6. 1984,

Fulwood R, Rifkind B, Havlik R, et al. Trends in serum

cholesterol levels among U.S. adults aged 20 to 74 years:

Data from the National Health and Nutrition Examination

Survey, 1960 to 1980. JAMA 257:937-42, 1987.

Cornoni-Huntley J, Barbano HE, Brody JA, et al. National

Health and Nutrition Examination Survey I –Epidemiologic

Follow-up Survey. Public Health Rep 9S:245-51. 1983.

Cohen BB, Barbano HE, Cox CS, et al. Plan and operation

of the NHANES I Epidemiologic Followup Study, 1982-S4.

National Center for Health Statistics. Vital Health Stat

1(22). 1987.

Jones DY, Schatzkin A, Green SB, et al. Dietary fat and

breast cancer in the National Health and Nutrition Examination Survey I Follow-up Study. J Natl Cancer Inst

79:465-71.1987.

Edlavitch SA, Baxter J. Comparability of mortality follow-up

before and after the National Death Index. Am J Epidemiol

127:1164-78. 198S.

Patterson BH, Bilgrad R. Uses of the National Death Index

in cancer studies. J Natl Cancer Inst 77:877-S1, 1986.

Kahn HA, Sempos CT. Statistical methods in epidemiology,

New York: Oxford University Press. 1989.

Becker BG, Indik BP, Beeuwkes AM. Dietary intake methodologies— a review. Ann Arbor: University of Michigan

Research Institute. 1960,

Burke BS, Stuart HC. A method of diet analysis: Application in research and pediatric practice. J Pediatr 12:493-503.

1938.

Burke BS. The dietary history as a tool in research. J Am

Diet Assoc 23:1041-6.1947.

Truswell AS. How do we know what Australians eat? Med J

Aust 149:2-4. 198S.

Marr JW. Individual dietary surveys: Purposes and methods.

World Rev Nutr Diet 13:105-64.1971.

Block G. A review of validations of dietary assessment

methods. Am J Epidemiol 115:492-505, 1982.

Sampson L. Food frequency questionnaires as a research

instrument. Clin Nutr 4:171-8. 1985.

Johnson NE, Sempos CT, Elmer PE, et al. Development of

a dietary intake monitoring system for nursing homes. J Am

Diet Assoc 81:35-40.1982.

Sempos CT, Johnson NE, Smith EL, et al. A ~vo-year

dietary survey of women. J Am Diet Assoc 84:100S-13, 1984.

Donelson EG, Leichsenring JM. A short method for dietary

analysis. J Am Diet Assoc 1S:429-34. 1942.

89
35. Johnson NE, Nitzke S, VandeBerg DL, A reporting system 38. Block G, Hartman AM, Dresser CM, et al. A data-based

for nutrient adequacy. Home Econ Res J 2:210-21.1974. approach to diet questionnaire design and testing. Am J

36. Hankin JH, Huenemann R, A short dietary method for Epidemiol 124:453-69, 1986,

epidemiologic studies. J Am Diet Assoc 50:487-92. 1967. 39. Liu K. Consideration of and compensation for intra37. Willett WC,Sampson L, Stampfer MJ, et al, Reproducibility individual variability in nutrient intakes, In: Kohlmeier L,

and validity of a semiquantitative food frequency question- Helsing E, eds, Epidemiology, nutrition and health. London:

naire, Am J Epidemiol 12251-65.1985. Smith-Gordon, 1989.

90
Appendix I

NHANES Ill Dietary

Survey Methodology

Workshop program

Sunday, March 16, 1986

5:00 p.m.

6:30 p.m.

&OO porn,

8:1.5 p.m.

S:30 p.m.

8:45 p.m.

Registration

Dinner

Welcome – Robert Murphy, M.S.P.H.

Introduction to Planning for NHANES III –

Kurt Maurer, Ph,D,

Nutrition Statistics and the Mission of

NHANES III – Catherine E. Woteki, Ph.D.,

R.D.

Purpose of Workshop – Christopher T.

Sempos, Ph.D.

Workshop Schedule and Procedures – Lenore

Kohlmeier, Ph.D.

Break for the evening

Monday, March 17, 1986

8:00 a.m. Breakfast

Topic Number 1: Statistical issues related to survey design

S:45 a.m.

9:15 a.m.

9:30 a.m.

9:45 a.m.

10:15a.m,

Presenter: Kiang Liu, Ph.D. (Northwestern

University)

First Discussant: Harold A. Kahn, M.A. (Consultant in epidemiology)

Second Discussant: George H. Beaton, Ph.D.

(University of Toronto)

Open discussion

Break

Topic Number 2: Cancer

10:45 a.m.

11:15 a.m.

ll:30a.m,

11:45 a.m.

12:15p.m.

Presenter: Gladys Block, Ph.D. (National Cancer Institute)

First Discussant: Walter C. Willett, M.D.,

Dr. P,H. (Harvard University)

Second Discussant: Gail McKeown-Eyssen,

Ph,D. (Ludwig Institute for Cancer Research)

Open discussion

Lunch

Topic NundYx 3: Cardiovascular disease

1:30 porn,

2:00 p.m.

2:15 p.m.

Presenter: Patricia J, Elmer, Ph.D., R.D. (University of Minnesota)

First Discussant: Arlene Caggiula, Ph. D., R.D.

(University of Pittsburgh)

Second Discussant: Nancy Ernst, M.S., R.D.

(National Heart, Lung, and Blood Institute)

2:30 p.m. Open discussion

3:00 p,m, Break

Topic Number 4: Energy balance

3:30 p.m.

4:00 p.m.

4:15 p.m.

4:30 p.m.

5:00 p.m.

6:00 p.m.

7:00 p.m.

Presenter: Dorothy Blair, Ph.D. (Pennsylvania

State University)

First Discussant: Johanna Dwyer, D.SC., R.D,

(New England Medical Center)

Second Discussant: Theodore Van Itallie, M.D.

(Columbia University)

Open discussion

Adjournment

Social hour

Dinner

Tuesday, March 18, 19S6

S:00 a.m. Breakfast

Topic Number 5: Osteoporosis

S:45 a.m.

9:15 a.m.

9:30 a.m.

10:OOa.m.

Presenter: Nancy E. Johnson, Ph.D. (University of Hawaii)

First Discussant: Mary Fran Sowers, Ph.D.

(Cornell University)

Open discussion

Break

Topic Number 6: Nutrition monitoring

10:30a.m. Presenter: Elizabeth A. Yetley, Ph.D., R.D.

(Food and Drug Administration)

ll:OOa.m. First Discussant: Eleanor M. Pao, Ph.D. (US.

Department of Agriculture)

11:15 a.m. Second Discussant: Susan B. Foerster, M.P.H.,

R.D. (Association of State and Territorial

Public Health Nutrition Directors)

11:30 a.m. Open discussion

12:15p.m, Lunch

Dieta~ survey methods –An overview

1:30 p.m.

2:15 p.m.

3:00 p.m.

3:10 p.m.

Presenter: Lenore Kohlmeier, Ph.D. (Federal

Health Office, Federal Republic of Germany)

Open discussion

Thank you – Robert S. Murphy, M.S.P.H.

(National Center for Health Statistics)

Adjournment

91
Appendix II

NHANES Ill Dietary

Survey Methodology

Workshop Planning

Committee

Christopher T, Sempos, Ph.D.

Cochairperson

Nutritional Statistics Branch

National Center for Health Statistics

Ronette R. Briefel, Dr.P.H., R.D.

Cochairperson

Survey Planning and Development Branch

National Center for Health Statistics

Lenore Kohlmeier, Ph,D,

Moderator

Institute for Social Medicine and Epidemiology

Federal Health Office

Federal Republic of Germany

Clifford Johnson, M.S.P.H.

Chief, Nutritional Statistics Branch

National Center for Health Statistics

Kurt R. Maurer, Ph.D.

Chief, Survey Planning and Development Branch

National Center for Health Statistics

Robert S. Murphy, M.S.P.H,

Director, Division of Health Examination Statistics

National Center for Health Statistics

Catherine E. Woteki, Ph. D., R.D.

Deputy Director, Division of Health Examination

Statistics

National Center for Health Statistics

92
Appendix Ill

Directory of workshop

participants’

Presenters and discussants

Lenore Kohlmeier, Ph.D.

Department of Epidemiology of Health Risk

Institute for Social Medicine and Epidemiology

Federal Health Office

General Pape Strasse 64-66

D-1OOO,Berlin 42

Federal Republic of Germany

George H. Beaton, Ph.D.

Department of Nutritional Sciences

F~culty of Medicine

University of Toronto

Toronto, Ontario

Canodu M5S 1A8

Dorothy Blair, Ph.D.

Nutrition Program

College of Human Development

Pennsylvania State University

Unviersity Park, PA 16802

Gladys Block, Ph.D.

National Cancer Institute, DCPC

EPN, Room 313

9000 Rockville Pike

Bethesda, MD 20892-4200

Arlene Caggiula, Ph.D., R,D.

Department of Epidemiology

Graduate School of Public Health

University of Pittsburgh

Pittsburgh, PA 15261

Johanna Dwyer, D.SC., R,D.

Department of Medicine

Tufts University School of Medicine and

New England Medical Center Hospitals

Frances Stern Nutrition Center -

171 Harrison Avenue

Boston, MA 02111

lR~~r.~s~nts affiliation at the time of th~ workshop.

Patricia J. Elmer, Ph. D., R,D.

Division of Epidemiology

University of Minnesota

Stadium Gate 27

611 Beacon Street, SE

Minneapolis, MN 55455

Nancy Ernst, M.S., R.D,

Division of Epidemiology and Clinical Applications-OD

National Heart, Lung, and Blood Institute

National Institutes of Health

Federal Building, Room 2A04

7550 Wisconsin Avenue

Bethesda, MD 20892-4200

Gail McKeown-Eyssen, Ph.D.

Ludwig Institute for Cancer Research

Toronto Branch

Toronto, Ontario

Canada M4Y 1M4

Susan B, Foerster, M.P.H., R.D.

California Department of Health Services

President, Association of State and Territorial Public

Health Nutrition Directors

714 P Street

Sacramento, CA 95814

Nancy E. Johnson, Ph.D.

Department of Food Science and Human Nutrition

University of Hawaii

Henke Hall, Room 224

1800 East-West Road

Honolulu, HI 96822

Harold A. Kahn, M.A.

Consultant in Epidemiology

3405 Pendleton Drive

Silver Spring, MD 20902

Kiang Liu, Ph.D.

Department of Community Health and Preventive

Medicine

Northwestern University

303 East Chicago Avenue

Chicago, IL 60611

93
Eleanor M. Pao, Ph.D.

Food Consumption Research Branch

Human Nutrition Information Service

US. Department of Agriculture

6505 Belcrest Road

Hyattsville, MD 20782

Mary Fran Sowers, Ph.D.

Division of Nutritional Sciences

Cornell University

302 MVR Hall

Ithaca, NY 14853

Theodore B. Van Itallie, M.D.

Department of Medicine

Columbia University College of Physicians and Surgeons

St. Luke’s Roosevelt Hospital Center

Amsterdam Avenue and l14th Street

New York, NY 10025

Walter C, Willett, M.D., Dr.P.H,

Department of Epidemiology

Harvard School of Public Health

677 Huntington Avenue

Boston, MA 02115

Elizabeth A. Yetley, Ph. D., R.D,

Center for Food Safety and Applied Nutrition

Food and Drug Administration

HFF-265

200 C Street, SW

Washington, DC 20204

Attendees

Melody Bacha, M.P.H., R.D,

Analysis and Evaluation

Food and Nutrition Service

U.S. Department of Agriculture

Room 1024

3101 Park Center Drive

Alexandria, VA 22302

Marilyn Buzzard, Ph. D., R.D,

Nutrition Coordinating Center

University of Minnesota

Suite 526

2829 University Avenue, SE

Minneapolis, MN 55414

Eva Callmer, Ph.D.

Department of Medical Nutrition

Karolinska Institute

Huddinge University Hospital F69

S-14186 Huddinge

Sweden

Carolyn Clifford, Ph.D.

National Cancer Institute

National Institutes of Health

Blair Building, Room 623

7550 Wisconsin Avenue

Bethesda, MD 20892-4200

Marcie Cynamon, M.S.

Division of Health Interview Statistics

National Center for Health Statistics

6525 Belcrest Road

Hyattsville, MD 20782

A1ta Engstrom, Ph.D.

Director, Nutrition Marketing Service

General Mills

Box 1113

Minneapolis, MN 55440

Donald Everett, M.S,

National Institute on Aging

National Institutes of Health

612 Federal Building

7550 Wisconsin Avenue

Bethesda, MD 20892

Marilyn Farrand, M.S., R.D.

National Heart, Lung, and Blood Institute

National Institutes of Health

6A08 Federal Building

7550 Wisconsin Avenue

Bethesda, MD 20892

Richard Havlik, M.D.

Office of Program Planning, Evaluation, and Coordination

National Center for Health Statistics

6525 Belcrest Road

Hyattsville, MD 20782

John Heybach, Ph.D.

Director, Scientific Affairs

NutraSweet Group

4711 Golf Road

Skokie, IL 60076

Joan Cornoni-Huntley, Ph.D.

National Institute on Aging

National Institutes of Health

612 Federal Building

7550 Wisconsin Avenue

Bethesda, MD 20892

Mary Grace Kovar, Dr.P.H.

Office of Interview and Examination Statistics Program

National Center for Health Statistics

6525 Belcrest Road

Hyattsville, MD 20782

94
Virginia Laukaran, Dr,P.H,

Food and Nutrition Board

National Research Council

2101 Constitution Avenue (NAS-340)

Washington, DC 20018

Christine Lewis, Ph. D., R.D.

Clinical Nutrition Branch

Food and Drug Administration, HFF-265

~0() C Street, SW

Washington, DC 20204

Luize Light, Ed,D.

National Cancer Institute

National Institutes of Health

Blair Building, Room 420

7550 Wisconsin Avenue

Bethesda, MD 20892-4200

James T, Massey, Ph.D.

Office of Research and Methodology

National Center for Health Statistics

6525 Belcrest Road

Hyattsville, MD 20782

Linda Meyers, Ph.D.

Office of Disease Prevention and Health Promotion,

OASH, DHHS

Switzer Building, Room 2132

330 C Street, SW

Washington, DC 20201

Susan Pilch, Ph.D.

Federation of American Societies for Experimental Biology

Life Sciences Research Office

9650 Rochwille Pike

Bethesda, MD 20814

William Rand, Ph,D,

INFOODS Network, Room 20A-226

Massachusetts Institute of Technology

77 Massachusetts Avenue

Cambridge, MA 02139

Robert Rizek, Ph.D.

Nutrition Monitoring Division

Human Nutrition Information Service

U.S. Department of Agriculture

Federal Building, Room 368

6S05 Belcrest Road

Hyattsville, MD 20782

Harold Roth, M.D.

Division of Digestive Diseases and Nutrition

Epidemiology and Data Systems Program

National Institute of Arthritis, Diabetes, Digestive and

Kidney Diseases

National Institutes of Health

Federal Bufiding, Room 616

Bethesda, MD 20892

Kathleen Turczyn, M.S.

Division of Epidemiology and Health Promotion

National Center for Health Statistics

6525 Belcrest Road

Hyattsville, MD 20782

John E. Vanderveen, Ph.D.

Division of Nutrition, HFF-260

Food and Drug Administration

200 C Street, SW

Washington, DC 20204

Regina Ziegler, Ph.D.

National Cancer Institute

National Institutes of Health

Environmental Epidemiology Branch

Landow Building, Room 3C07

7910 Woodmont Avenue

Bethesda, MD 20892

Division of Health Examination Statzktics:

Ronette R. Briefel, Dr. P.H., R.D.

Stacey FitzSimmons, Ph.D.

Dale Hitchcock, B.S.

Clifford Johnson, M.S.P.H.

Robert Kuczmarski, Dr.P,H., R.D.

Andrea LaCroix, Ph.D.

Anne Looker, Ph.D., R.D.

Kurt R. Maurer, Ph.D.

Marie Mitchell, B.S.

Robert S. Murphy, M,S.P.H.

Christopher T. Sempos, Ph.D.

Deborah Winn, Ph.D.

Catherine E. Woteki, Ph. D., R.D.

National Center for Health Statistics

6525 Belcrest Road

Hyattsville, MD 20782

95
Appendix IV

Outline of statistical

issues related to

NHANES Ill dietary

survey methodology *

I. Review of dietary survey methodology in the past

A. Methods used in specific study designs

1. Case-control

2. Cross-sectional (prevalence)

3. Cohort (incidence)

B, Problems with dietary surveys in the past as

related to statistical issues

1. Assumptions made in collection of data and

study design

a. Case-control

b. Cross-sectional

c. Cohort

2. Number of records collected

3. Uses of data in analysis

11, Within- and between-person variability in food

consumption

A,

B.

Definitions

1. Usual intake

2. Within-person variability

3. Between-person variability

Effects of large ratios of within- to between-person

variability

1, Case-control

a. Based on or referring to baseline data

b, Dietary history for some specified period

of life prior to NHANES III taken at

baseline

2. Cross-sectional

3. Cohort

c.

D,

Estimation of number of dietary records to be

collected per individual

1. How should number be determined?

2. Should number vary by age, race, sex?

Effects of number of dietary records collected per

individual on later analyses

1. Group comparisons

a. Analysis of variance

b. Correlation

c. Regression

i. Simple

ii. Multiple

d. Measures of risk

i. Odds ratio

ii. Relative risk

2, Individual comparisons

a. Correlation

b. Regression

i, Simple

ii. Multiple

c. Measures of risk

i. Odds ratio

ii. Relative risk

111,Recommendations

A.

B.

Should multiple dieta~ records be collected?

1. How many?

2. When should they be collected?

Limitations of recommendations on study design

for future analyses

96
Appendix V

Outline of dietary

methodology issues

for presenters

1. Review of dietary methods related to (topic area) in

the following groups

A, Adults

B, Adolescents

C. Infants and children

H. Critique of different methods

A, Ability of different dietary survey methods to

estimate various dieta~ constituents of concern

for topic area

Cancer

1. Total calories

2. Fat

3. Vitamin A

4.. Carotenoids

5, Selenium

6. Vitamin E

7, Vitamin C

S. Fiber

Cardiovasculardisease

1 Total calories

2. Fat

3, Cholesterol

4, Sodium

5, Potassium

6, Calcium

7. Caffeine

S. Alcohol

Ene~) balance

1. Total calories

2. Fat

3. Protein

4. Carbohydrate

5, Alcohol

Osteoporosis

1, Total calories

2, Protein

3$ Calcium

4, Phosphorus

5, Fluoride

6, Magnesium

7, Water

S. Vitamin D

III.

Nutrition monitoring

1. Total calories

2. Iron, sodium, and other minerals

3. Vitamin A and other fat-soluble vitamins

4. Folacin and other water-soluble vitamins

5, Vitamin-mineral supplements

6, Nonnutritional dietary components

B. Applicability to various study designs

1. Case-control

2. Cross-sectiona[ (prevalence)

3. Cohort (incidence)

Recommendations

A. Method(s) recommended

1. Case-control

a. Based on or referring to baseline data

b. Dieta~ history for some specified period

of life prior to NHANES III taken at

baseline

2. Cross-sectional

3. Cohort

B. Application to NHANES

1. Time limit for dietary interview of 30 minutes

2. Continuity and comparability with past and

present national nutrition surveys

a. NHANES

b. Nationwide Food Consumption Survey

(U.S. Department of Agriculture)

3. Potential for automation

4. Equipment needed

5. Training for interviewers

C. Validity of method(s) recommended

D. Should multiple records be collected?

1. How many?

2. Using which dietary survey methods? (if recommendation is to use two or more dietary

survey methods)

E. When should multiple records be collected?

1. Prior to medical examination

a. Household interview

b. Other

2. Medical examination

3. After medical examination

F. Method of collection of multiple records

1. Interview in person

2, Telephone interview

3. Mailed interview

97
Appendix VI

Prospectus for

workshop

The third National Health and Nutrition Examination

Survey (NHANES III) will be fielded in 1988, At present,

the National Center for Health Statistics is soliciting

suggestions for survey content and topics to be considered

for NHANES III. It is anticipated that a 3-hour examination consisting of questionnaires, biochemical measurements, and medical procedures will be administered to

approximately 30,000 persons ages 2 months and older

over a 6-year period. The examination phase will address

certain target conditions. Components under consideration for NHANES III include overweight and obesity,

cardiovascular disease, osteoporosis, cancer, and nutrition

monitoring,

Objectives

The major purpose of the workshop is to review,

evaluate, and make recommendations regarding existing

and potential dietary methodologies with respect to target

conditions to be studied in NHANES III. For the purposes of this conference, dietary intake methodologies will

be evaluated with respect to proposed target conditions

that include a nutrition component as well as to the

overall objectives of NHANES III:

1.

2.

3,

4.

5,

To monitor changes in health and nutritional status

over time.

To assess the health and nutritional status of the U.S.

population and specific subgroups.

To provide information on the interrelationship of

health and nutrition variables within population subgroups.

To measure the prevalence of disease and conditions.

To measure met and unmet care needs related to

target conditions.

Data needs

Previous Health and Nutrition Examination Surveys

have utilized various methodologies (24-hour recall, food

frequency, and questionnaires) to describe the dietary

intake and nutritional habits of the U.S. population, The

NOTE: Provided as background information to workshop participants

prior to the workshop.

98

24-hour recall yields quantitative estimates of recent

dietary intake for groups, whereas multiple 24-hour

recalls, food records, or a diet history provide quantitative

intake for individuals. The diet history and the food

frequency methods yield estimates of qualitative intake

over time for both individuals and groups and, therefore,

may be appropriate methods to use for investigating the

relationship between past dietary intake and health. Food

frequencies can be targeted to specific foods, food groups,

or nutrients depending on research needs, Quantitative

data can be estimated from food frequency data using

portion size information, but a standardized food composition data base for use with frequency questionnaires has

not been developed.

Current dietary methodologies used in clinical studies

and surveys are appropriate for different research situations, The traditional dietary methods can be time consuming and costly in terms of respondent time, interviewer

expertise required, and recording and editing of data;

thus, they may not be practical for use in a large survey

such as NHANES HI. In recent years, epidemiologic

research has been aimed at developing and testing other

dietary methods, such as the semiquantitative food frequency, to provide dietary data for both individuals and

groups.

The National Nutrition Monitoring System requires

that national estimates of dietary intake continue to be

provided. The Food and Drug Administration monitors

the nutritional adequacy of the population’s intake for

food fortification policy. In addition to the continuing

need to provide descriptive data (for example, the prevalence of overweight and obesity and the mean dietary

intake of specific population groups), a growing need

exists for information in the applied nutrition area to test

specific research questions. The collection of accurate

dietary intake data for individuals is desirable for comprehensive research analyses relating nutrition to health and

specific target conditions.

In order to examine the interactions of nutritionrelated variables with health conditions or to relate food

consumption patterns and nutrient intake to physiological

and physical indicators of health status, the dietary methodologies used must be appropriate for the target condition, applicable at the individual level, and practical in

regard to the survey design. If NHANES 111 uscs a

longitudinal design, adequate baseline dietary and
nutritional data that relate to the followup of particular

target conditions must be collected in order to test specific

hypotheses regarding nutrition and health status. Therefore, the collection of reliable and valid data for both

individuals and groups of individuals will most likely be

required for NHANES III.

Issues to be considered when determining the dietary

rncthodologies for NHANES III include: (1) the survey

design; (2) the practicality of the dietary method in terms

of use for a large survey, computer automation, the length

of the interview, and cost; (3) the comparability to previous methods used in NHANES; (4) the 1983 recommenduticms of the Coordinating Committee on the Evaluation

of Food Consumption Surveys with respect to linkage with

the Nationwide Food Consumption Survey; (5) the appropriateness for different population groups; (6) interviewer

training; and (7) the representativeness of an individual’s

usu:d inttike.

The survey’s data needs can be interpreted in terms of

whether the analytical research requires precise quantitative data on nutrient intake or the relative classification or

ranking of individuals with respect to nutrient intake.

Consideration must also be given to whether the data

needed relate to cross-sectional estimates of recent group

intake, estimates of past or long-term individual intake, or

both, Conferences on dietary methodology have been

previously conducted, but none has dealt with the issues

that relate specifically to the data needs, limitations, and

objectives of NHANES.

Proposed approach

The NHANES HI planning schedule includes the

solicitation of project proposals and topic review prior to

the dietary conference. Target conditions will be defined

during late 1985. The conference will be held March

16-18, 1986. Each speaker will be asked to prepare a

background paper related to one of the major target

components or statistical issues. In addition, two expert

consultants for each of the components will comment on

the background papers and also participate in the conference, Thus, the conference will involve approximately 1S

outside consultants, as well as NHANES III planning

staff, and representatives from the U.S. Department of

Agriculture, the Food and Drug Administration, and the

National Institutes of Health, for a total of approximately

40 participants. Each speaker will be given an outline to

be used in the preparation of the background papers

explairiing the specific issues to be addressed and requesting

suggestions and recommendations for dietary methodologies to be used in NHANES III. After discussions held

during the conference, a revised background paper will be

prepared by each of the six speakers, in consultation with

the discussants. Finally, each of the six working groups will

prepare a consensus statement recommending dietary

methodologies to be used in NHANES III for the group’s

specific topic area.

99
Appendix Vll

NHANES Ill survey

design

The National Center for Health Statistics is planning

the third National Health and Nutrition Examination

Survey (NHANES 111),NHANES I took place in 1971-75

and NHANES II, in 1976-80. As in previous NHANES

programs, the primary purpose of NHANES III will be to

measure and monitor the health and nutritional status of

the U.S. population. In addition, projects of an analytic

nature are likely to be included in NHANES III, and

serious thought is also being given to the establishment of

a cohort study using NHANES 111 as the baseline. This

cohort study may include reexamination at intervals of

every 6 years, mail followup, and monitoring of deaths

through the National Death Index.

NHANES III, like previous NHANES programs, will

consist of a household medical history interview and a

medical examination conducted in a mobile examination

center 1 to 4 weeks after the household interview. It is

anticipated that the 3-hour medical examination will consist

NOTE: Provided as background information to workshop participants

prior to the workshop.

of questionnaires, biochemical measurements, and medical procedures, In the past the examination has been

administered to approximately 20,000 persons ages

6 months-74 years over a 4-year period. In NHANES III,

we plan to interview 40,000 persons and to examine 30,000

persons ages 2 months and older during a 6-year period to

improve coverage of black and Hispanic persons and to

permit time-trend analysis.

The dietary interview in the past has been conducted

during the 3-hour medical examination. In previous

NHANES it consisted of a 24-hour recall and food frequency interview covering the past 3 months. The entire

dietary interview took approximately 30 minutes,

If we are to survey 40,000 persons over a 6-year

period, it is essential that we plan very carefully the

dietary survey methods to be used in NHANES 111so that

we can participate effectively in nutrition monitoring efforts;

study the relationship of diet to specific target conditions;

maintain comparability with past NHANES and the U.S.

Department of Agriculture’s Nationwide Food Consumption Survey and automate as much of the dietary dfita

collection as possible.

100
Appendix Vlll

Measuring dietary

patterns in surveys

by Catherine E, Woteki, Ph,D,, R,D., National Center

for Health Statistics

Introduction

In nutrition surveys, five types of measurements are

needed to completely characterize the nutritional status of

a person or a population: Food and nutrient intakes, body

measurements, hematological and biochemical tests, physical examination for the presence of clinical signs of

deficiency or toxicity, and medical history. No one method

alone is sufficient for assessing the nutritional status of an

individual or a group of individuals,

In approaching the design of a survey in which nutrition is to be assessed, the selection of which nutritional

assessment methods are to be used depends on the answers

to two questions: What aspects of nutrition are of interest?

How suitable are the available measures? Clear statements of the goals of the survey and of hypotheses to be

tested are helpful in deciding which aspects of nutrition

are of interest and which broad categories of measurements should be included. Consideration as to the practicality, reliability, and validity of available measurements

will determine their suitability for the survey under design.

In a recent article, Ware et al, (1) discuss issues of

practicality, reliability, and validi~ when selecting measures

of health status, Their discussions are also pertinent to

selection of nutritional status indicators. The authors

caution that health status, like nutritional status, cannot

be observed directly and that investigators can only make

inferences about health or nutritional status from fallible

indicators, Whether a given measurement is practical

depends upon the total measurement resources available,

respondent burden, and analytical resources. Total

measurement resources and the priority assigned to nutritional assessment will to a large degree determine the

amount of staff time and resources to be dedicated to

nutritional assessment. The burden on the respondent in

terms of time, inconvenience, and physical discomfort will

affect refusal rates, rates of missing responses, and administration time in followup of nonrespondents. The degree

of reliability needed depends on the purpose of the study,

with more sensitive and specific measures required for

clinical decisionmaking than for the comparison of groups

of people, Validity of nutritional status indicators should

be evaluated within the context of the particular health or

nutritional components of interest to the study. The literature with respect to reliability and validity of nutritional

status measurements is limited, offering opportunities for

future research,

This appendix is a description of the data available

from national nutrition surveys and a review of the

strengths, weaknesses, and limits of interpretation. The

nutrient content of the food supply and the 24-hour recall

of food consumption are discussed in detail.

Data available from national

surveys

The Federal Government collects a broad range of

nutrition-related information under the general heading

of the National Nutrition Monitoring System (NNMS).

The Food and Agriculture Act of 1977 (Public Law

95-113) instructed the Secretary of Agriculture and the

Secretary of Health, Education, and Welfare to submit to

Congress a proposal for a comprehensive nutritional status

monitoring system that would integrate the ongoing nutrition survey activities of both Departments, The Departments’ original proposal was submitted to Congress in

May 1978, and at the request of the Committee on Science

and Technology, the General Accounting Office (GAO)

reviewed it. In June 1978, GAO sent a letter to the

Secretaries of the two Departments recommending the

development of a comprehensive implementation plan.

The Joint Implementation Plan for a Comprehensive

National Nutrition Monitoring System was submitted to

Congress in September 1981. Ostenso (2) has described in

greater detail the history of nutrition monitoring.

The Implementation Plan classified the activities conducted under NNMS into five major categories:

● Health status measurements.

. Food consumption measurements,

. Food composition.

● Dietary knowledge and attitudes.

● Food supply determinations.

The activities in each of these categories and the

agencies responsible for them are described below.

Health status measurements

The Department of Health and Human Services

(DHHS), through the National Center for Health Statistics

(NCHS), the Centers for Disease Control (CDC), and

other agencies, collects a broad array of data on the

101
health status of the American population. Two programs

are specifically oriented to nutrition. These are the National

Health and Nutrition Examination Surveys (NHANES),

conducted by NCHS, and the Coordinated State Surveillance System (CSSS), conducted by CDC, Table 1 summarizes the specific nutrition-related indicators for which

data are available from NHANES and CSSS,

NHANES, conducted periodically by NCHS, is the

cornerstone of Federal efforts to monitor the overall

nutritional status of the American people, NHANES consists of a series of surveys carried out on a representative

sample of the civilian noninstitutionalized U.S. population, comprising more than 20,000 persons in each survey.

The surveys, which include health histories, dietary interviews, physical examinations, and laboratory measurements, provide information on national health and

nutritional status. Two national surveys have been completed –NHANES I (1971-75) and NHANES II

(1976-80) –as well as a survey of the health and nutritional status of Hispanic Americans, conducted in 1982–84.

Descriptions of the plan and operation of these surveys

have been published by NCHS (3-6).

Through NHANES, physical and biochemical

measurements are made that provide information about a

number of nutrition-related conditions, including growth

Table 1. Sources of data on indicators of nutritional status

Natiorra/ h’ea/th and MM- Coordinated State

tion Examination Survey Surve///ar?ceSystem

Indicator /1 //2 Pediatric Pregnant

Height . . . . . . . . . . . . . . .

Weight . . . . . . . . . . . . . . .

Skinfold thickness:

Triceps . . . . . . . . . . . . .

Subacapular. . . . . . . . . .

Head circumference . . , . ,

Hemoglobin . . . . . . . . . . .

Hematocrit . . . . . . . . . . . .

Red blood cell count . . . . . .

White blood cell count. . . . .

Mean corpuscular volume. . .

Mean corpuscular

hemoglobin concentration. .

Red blood cell

protoporphyrin . . . . . . . . .

Serum iron . . . . . . . . . . . .

Serum total iron-binding

capacity . . . . . . . . . . . . .

Transferring saturation. . . . . .

Ferritin . . . . . . . . . . . . . . .

Serum zinc . . . . . . . . . . . .

Serum copper . . . . . . . . . .

Serum vitamin C. . . . . , . . .

Serum albumin ., ., . . . . .

Serum vitamin A. . . . . . . . .

Red blood cell folate . , . .

Serum folate . . . . . . . . . . .

Serum cholesterol. . . . . . .

Serum triglycerides , . . . . . .

High-density lipoprotein . . . .

Serum vitamin B,2 . . . . . . ,

Breastfeeding . . . . . . . .

Low birth weight. . . . . . . . .

Yes

Yes

Yes

Yes

Yes

Yes

—

Yes

---

. . .

. . .

Yes

Yes

Yes

. . .

---

---

. . .

. . .

---

---

---

Yes

.-.

..-

..-

---

.-.

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yea

Yes

Yes

Yes

Yes

3-74 years

(3)

3–74 years

3-74 years

3-74 years

3-74 years

3-11 years

(3

(31

20-74 yaars

20-74 years

20-74 years

(3

(31

. . .

Yes

Yes

Yes

Yes

Yes

.-.

. . .

-..

---

. . .

. . .

. . .

. . .

---

---

---

. . .

---

. . .

---

. . .

. . .

. . .

.-.

Yes

Yes

Yes

---

. . .

. . .

---

---

..-

. . .

---

---

. . .

. . .

---

---

-..

. . .

---

---

. . .

Yes

Yas

lAges SUNeyed ars 1-74 ysars.

Zunleaa othamviae specified, agss aurvayed are 6 months-74 Years.

3Performed on a subsample of persons ages 3-74 years.

102

retardation, anemia, obesity, heart disease, hypertension,

diabetes mellitus, vitamin and mineral deficiency or toxicity, and heavy metal and pesticide exposure. The types of

measurements include physical examinations, anthropometry, hematological assessments, biochemical analyses of

blood and urine, x rays, and functional assessments, as

well as a health history.

The Nutrition Division, Center for Chronic Disease

Prevention and Health Promotion, CDC, contributes to

nutrition monitoring through a nutrition surveillance program, CSSS monitors the nutritional status of high-risk

pediatric and pregnant populations through the collection

of measurements readily available, such as height, weight,

hemoglobin, and hematocrit. The system uses information

from service delivery programs operated by selected State

and metropolitan health jurisdictions to provide data on

the prevalence of major nutritional problems in the targeted groups. The composition of the groups under surveillance is determined by their socioeconomic status,

their proximity to a nonprivate outpatient clinic, and the

fact that selected State health departments are cooperating with the CDC program in obtaining and utilizing

nutrition-related data. The indicators of health status

measured in CSSS are limited to a relatively few indexes

related to nutritional problems identified in the NHANES

activities. CSSS provides information about the prevalence

of overweight, underweight, retarded linear growth, and

anemia among high-risk children. In addition, pregnant

women are kept under surveillance with attention to such

indicators as anemia, abnormal weight changes, fetal survival, birth weight of the child, and whether breast or

bottle feeding is used and for how long. The CDC publishes annual reports on its findings (7).

Food consumption measurements

Four surveys provide information on the food people

consume. They are the Nationwide Food Consumption

Survey (NFCS) and the Continuing Survey of Food Intakes

by Individuals (CSFII), both of which are conducted by

the Human Nutrition Information Service (HNIS) of the

Department of Agriculture (USDA), the NHANES (previously described), and the Food Usage Survey conducted

by the Food and Drug Administration (FDA). Table 2

shows the nutrients for which estimates are made in

NFCS, CSFII, NHANES, and the national food supply

determination (discussed later).

NFCS has been conducted at roughly 10-year intervals

since the mid-1930’s. It is actually two surveys: A survey of

household food use, which has been conducted six times,

and a survey of individuals’ food intakes, which has been

conducted twice. The most recent NFCS was conducted in

1977–78 and consisted of a basic survey of 15,000 households and household members and five additional

surveys – surveys conducted in Alaska, Hawaii, Puerto

Rico and surveys in the continental United States of

low-income and elderly households. In NFCS, the method

of collecting household data is a 7-day recall of food used,
Table 2. Sources of data on nutrients and other food constituents

National Nationwide Food Consumption Survey NHANES Total

food Diet Food

Nutrient or food constituent supply Household Individual CSF112 / // Study composition

Water . . . . . . . . . . . . . . . . . . . .

Encrgy(kcal) . . . . . . . . . . . . . . . .

Protein:

Total. , ..,.,....,....,,. .

Aminoacids . . . . . . . . . . . . . . .

Carbohydrate:

Total . . . . . . . . . . . . . . . . . . . ,

Sugars. . . . . . . . . . . . . . . . . .

Lipids:

Totalfat . . . . . . . . . . . . . . . . . .

Saturatedfat . . . . . . . . . . . . . . .

Olelcacld . . . . . . . . . . . . . . . .

Total monounsaturated . . . . . . . .

Llnolelcacld. , . . . . . . . . . . . . .

Total polyunsaturated . . . . . , . . .

Cholesterol, .,........,,.. .

Vitamins:

Avalue, lnternatlonalunlts . . . . . .

Avalue, retlnol equivalents. . . ., ,

Carotene . . . . . . . . . . . . . . . . .

E

T;larni; &j::::::::::::::

Rlboflavln (Be)....,,....,,..

Niacin (preformed) . . . . . . . . , . .

Pantothenicacld ., , , . . , ., . . .

I%acti:::: :::::::::::::

B.1.........,,...........

c

Mln;ra~; ”’””’’’””””””” ““”

Calcium . . . . . . . . . . . . . . . . . .

Phosphorus, . . . . . . . . . . . . . .

Magnesium, . . . . . . . . . . . . . .

Iron, ...,....,..,...,,,

lodlne. ..,,,...,,....,,. .

Sodium. ..,,....,,..,,,,.

Potassium . . . . . . . . . . . . . . . .

Copper . . . . . . . . . . . . . . . . . .

Zinc . . . . . . . . . . . . . . . . . . . .

Manganese . . . . . . . . . . . . . . .

Selenium..,...,,,....<. .,

Chromium . . . . . . . . . . . . . . . .

Fiber:

Crude . . . . . . . . . . . . . . . . . . .

Dleta~ . . . . . . . . . . . . . . . . . .

Alcohollcbwerages .,., . . . , . . .

..-

Yes

---

Yes

. . .

Yes

Yes

Yes

-..

Yes

Yes

.-.

-..

..-

Yes

Yes

Yes

-..

Yes

---

Yes

Yes

..-

---

---

Yes

Yes

Yes

---

---

..-

---

Yes

Yes

---

Yes

---

. . .

---

---

---

-..

---

---

.-.

-..

---

..-

. . .

Yes

Yes

-..

Yes

---

Yes

Yes

Yes

..-

Yes

. . .

Yes

Yes

---

-..

. . .

Yes

Yes

Yes

-..

.-.

---

-..

Yes

Yes

Yes

Yes

-..

---

Yes

Yes

---

. . .

. . .

. . .

---

. . .

.-.

Yes

. . .

. . .

..-

---

. . .

. . .

---

---

. . .

---

. . .

..-

. . .

. . .

. . .

. . .

..-

-..

---

---

. . .

. . .

-..

-..

..-

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

-..

.-.

Yes

Yes

Yes

Yes

(3

Yes

...

Yes

(3)

?

$(9

1

(9

Yes

(?

...

(9

Yes

Yes

Yes

(9

Yes

(9

Yes

Yes

Yes

Yes

Yes

...

...

Yes

Yes

${

(3)

..-

...

Yes

Yes

Yes

Yes

..-

Yes

.-.

Yes

. . .

Yes

---

Yes

Yes

Yes

Yes

Yes

---

Yes

.-.

Yes

Yes

. . .

Yes

(4)

Yes

---

Yes

-..

..-

---

. . .

..-

. . .

Yes

-..

. . .

..-

. . .

..-

(4)

Yes

Yes

. . .

Yes

. . .

Yes

Yes

Yes

. . .

. . .

..-

Yes

Yes

Yes

$-~

;;!

Yes

Yes

.-.

..-

---

Yes

Yes

Yes

. . .

Yes

. . .

Yes

Yes

Yes

-..

Yes

Yes

Yes

Yes

Yes

Yes

Yes

. . .

Yes

Yes

Yes

Yes

-..

-..

Yes

Yes

Yes

. . .

Yes

.-.

Yee

Yes

Yes

Yes

Yes

..-

. . .

Yes

Yes

. . .

Yes

. . .

. . .

. . .

Yes

Yes

Yes

---

. . .

. . .

-..

. . .

. . .

. . .

-..

. . .

Yes

Yes

Yes

-..

Yes

Yes

Yes

..-

-..

Yes

Yes

Yes

Yes

-..

---

-..

---

Yes

..-

. . .

---

..-

-..

---

Yes

---

. . .

. . .

. . .

Yes

-..

---

..-

Yes

Yes Yes

lAlcohollc beverages are Included.

2CmIllnulng Survey of Food Intakes by Individuals

~Nutrlent data availabla at the complatlon of revialon of U.S. Department of Agricultures Handbook No. 8.

4SOUFlCE: Food Supply and Nationwide Food Consumption Survey data supplied by Dr. Susan Welsh, Human Nutrition Information Service, U.S. Department of Agriculture.

The individual household members recall 1 day’s food

intdwandkeep adiaxyfor 2 additional days. Descriptions

of the survey design and results are published (8,9) and

idso made available as computer tapes for researchers.

HNIS also conducts CSFII as a component of the

National Nutrition Monitoring System (2), The first CSFII

collcctcd I-day food and nutrient intake forwomen 19–50

years of age and their children ages l-5 years in the48

conterminous States duringApril and June 1985(10). This

sample, the ’’core monitoring group,” was selectedas the

group shown tobe at nutritional risk in previous surveys.

The survey will be conducted annually and additional

population groups may beadded inthe future.

Using commercial market research databases (A,C,

Nielsen Company), FDA conducts, on a biennial basis, a

survey of a statistically representative sample of products

representing major food classes from the total packaged

food supply, The Food Usage Survey involves approximately 1,700 individual food brands representing about

44 percent of the packaged food supply in retail dollar

terms, which in turn is generalizable to the total packaged

food supply. The ingredient data are the basis for the

FDA food ingredient data bank and are used for multiple

special studies. Significant changes in aggregated public

purchasing patterns and the food industry’s reactions can

be followed. For example, changes in public purchasing

practices associated with avoiding specific components of

foods (such as sugars and food additives) may be quickly

identified. The same data base is used to measure changes

in aggregate public purchasing practices of nutritionally

modified foods, such as fortified foods, low-sodium and

reduced-sodium foods, low-cholesterol and reducedcalorie foods. Through this means, public responses to

nutrition information and education programs, new labeling

approaches, media coverage, and other societal events can

be measured. This type .of information permits

103
estimates of the impact of programs initiated by government and the private sector in the interest of improving

nutritional health.

Food composition

Assessing the nutritional adequacy of diets reported

in NHANES and NFCS would be impossible without

information on the nutrient content of foods. Four different activities contribute to our knowledge of food

composition: The Nutrient Data Bank, the Total Diet

Study, Labeled Food Surveillance, and research.

The USDA Nutrient Data Bank (NDB) is the major

mechanism for collecting, evacuating, storing, and collating

nutrient composition data for individual foods (11). The

task is substantial, as there are some 10,000 to 15,000 food

items in the U.S. food supply and data are being acquired

for 60 to 100 nutrients or other food components. Data

are collected and entered into the NDB on a continual

basis, but the availability of data on some nutrients is

limited by the lack of suitable methods. Sources of data

include a number of Federal Government laboratories,

including USDA’s Nutrient Composition Laboratory, university research under Government sponsorship, and food

nutrient analyses conducted by industry in support of the

nutrition labeling program. The development of the NDB

is keyed to the process of revising Agriculture Handbook

No. 8, “Composition of Foods: Raw, Processed, Prepared”

(12), which is the standard reference table on food composition.

FDA conducts annually its Total Diet Study to estimate average consumption of important components of

the diet. This study provides a tracking system for specific

indicators of significant changes in the nutritional quality

of the national food supply. For example, through this

study, FDA has documented the existence of a higher

quantity than desirable ok iodine in the food supply (13).

The quantity of iodine is now being monitored, particularly for some of the food classes noted previously to have

high levels, such as milk and cereal grain products (14).

The scientific base for the Total Diet Study has been

updated from food consumption data obtained in 1965 to

the recently available NHANES II and NFCS data (15).

Approximately 200 individual foods are involved, representing about 90 percent of the total foods consumed in

the United States. Sampling is done in 30 urban areas in

the United States, and analyses for dietary content of

seven minerals (iodine, iron, sodium, potassium, copper,

magnesium, and zinc) are performed. This is the only

extant system for annual chemical analytical measurement

of average intakes of pesticides, heavy metals, and environmental contaminants. The study has recently been

converted from the measurement of food composites to

measurements of nutrients, pesticides, and contaminants

in individual foods.

FDA has maintained since 1977 both a surveillance

and a compliance program for nutrition labeling. A

statistical sample of the 40 percent of processed foods

that bear nutrition labels is analyzed for many nutrients on

a continuous basis. Annually, approximately 300 foods are

analyzed for eight nutrients, involving in excess of 2,000

individual analyses. This surveillance program permits

FDA to track the evolution of nutrition labeling in the

food supply, assure necessary levels of accuracy of label

values, and identify segments of the industry that require

encouragement. This activity also permits early identification by FDA of new fortification practices by industry.

When combined with consumer studies, reasonable assessments of the value of nutrition labeling are possible.

USDA’s Nutrient Composition Laboratory, located in

Beltsville, Maryland, provides essential data on the nutrient

content of foods consumed in the United States by analyzing the nutrient content of foods with tested, dependable assay techniques; developing either new or improved

methods for the analysis of nutrients in foods; developing

sound sampling techniques to ensure that representative

samples are analyzed; and conducting research on the

effect of food-processing procedures and of transportation

and marketing methods, as well as home, institution, and

restaurant food preparation procedures, on the nutrient

composition of foods, Data from these studies are used to

update the food composition values in the Nutrient Data

Bank.

Dietary knowledge and attitudes

Annually since 1978, FDA has conducted the Public

Attitudes Survey based on a national probability sample of

food purchasers to measure public attitudes, knowledge,

and practices relating to food and nutrition. About onehalf of the survey content is concerned with such matters

as opinions about nutrition, food quality, and food regulation and is repeated every year for the purpose of tracking

changes over time. The other half involves new areas of

interest or concern to FDA.

This survey involves detailed interviews in the homes

of approximately 1,500 individuals primarily responsible

for household food purchases, of whom about 85 percent

are women and 15 percent men. Studies of this type

permit monitoring of public attitudes and practices about

foods and nutrition, as well as identification of public

concerns and elements of confusion. It is through this

mechanism that the predominance of avoidance practices

in food purchases through use of food labels was identified

(for example, avoidance of fats and sugars). In addition,

assessments are made of the influence of nutrition misinformation and the pubIic’s ability to comprehend food

label information,

Food supply determinations

Each year since 1909, USDA has calculated the nutrients available for daily per capita consumption from

estimates of per capita food availability (retail weight). No

deductions are made for waste of food in the distribution

system or in the home, use for pet food, or loss of

nutrients during the preparation of food, Adjustments arc

104
mode for nutrients added to the food supply through

enrichment and fortification – such as iron, thiamine, riboflavin, and niacin added to flour and cereal products (16).

The estimates of nutrient availability are based on the

quantities of 350 foods that “disappear” into the U.S. food

mtirlwting system. Hence, the term “disappearance data”

is frequently used in referring to these estimates. Levels of

food energy (kilocalories) and 15 nutrients (table 2) are

calculated using food composition data collected and

published by USDA.

Dietary questionnaires used in

previous HANES

The five major components of previous Health and

Nutrition Examination Surveys were a household questionnaire, a medical history questionnaire, a dieta~ questionnaire, examination by a physician, and special procedures

and twsts. The household questionnaire consisted of questions about family relationship; age, sex, and race of family

members; housing; occupation, income, and educational

lCVC1of each family member; and participation in the

Food Stamp program and school breakfast and lunch

programs. Separate medical history questionnaires were

used depending on the age of the sample person, one

questionnaire for children 6 months–n years and another

for persons 12-74 years. Both the household and medical

history questionnaires were administered in the

respondent’s home.

When the sample persons arrived at the mobile examination center, they were scheduled through the dietary

intro-view, physician’s examination, and special procedures

and tests. The procedures and tests for the nutritional

assessment included body measurements, urine tests, and

blood tests, From blood samples taken in the center, a

number of nutrition-related assays were done. These

included serum albumin, serum vitamins A and C, serum

lipids (cholesterol, triglycerides, and high-density lipoproteins), protoporphyrin, serum iron, total iron-binding

capacity, serum zinc, and serum copper. Red cell folates,

serum folates, serum ferritin, and serum vitamin B12were

determined on blood samples with abnormal complete

blood count, hemoglobin, hematocrit, or mean corpuscular volume (MCV) and on a subsample of all other

blood samples.

The dietary questionnaires consisted of a 24-hour

recall, a food frequency, a supplemental dietary questionnaire, and specific questions on medication and vitaminmineral supplement usage. All dietary interviews were

conducted by trained interviewers who held at least a

bachelor’s degree in home economics. Copies of the questionnaires used in NHANES II and the Hispanic Health

and Nutrition Examination Survey (HHANES) are available (5,6),

During the 24-hour recall, respondents were asked to

report all foods and beverages consumed on the previous

day. Respondents estimated the size of the portions

consumed by referring to food models. In addition to

foods and portion sizes, interviewers asked about what

time of day the food was eaten and its source. The time of

day was coded as one of five ingestion periods –morning,

noon, be@veen meals, evening, or total day. The source of

the food was coded as home, school, restaurant, or other.

Each food item was coded by the interviewer within

72 hours of the interview. The food codebook developed

for NHANES II contained 5-digit food codes for approximately 2,500 food items. Each food item was identified by

name, including brand name if appropriate; whether it was

raw, dry, canned, or frozen; how it was prepared; and for

mixed dishes without food codes, the major ingredients. A

food composition data base updated from NHANES I was

used to calculate the energy, vitamin, and mineral content

of the reported foods. Modifications to the NHANES I

data base included new data from USDA’s revised Handbook Number 8 (12) and food composition data from food

companies on new products and brand name products of

unique formulation. The food composition data base for

HHANES and future surveys will be the data base in

current use by the U.S. Department of Agriculture, which

includes 27 dietary components and energy. (See CSFII

listing in table 2.)

The questionnaire used to determine the frequency of

food consumption has changed with each survey. In

NHANES II and HHANES, the food frequency elicited

information about the consumption of 18 food groups over

the previous 3 months. These groups can be related to the

13 major groups in NHANES I but are progressively more

detailed. The frequency was coded as a whole number,

never, or unknown, The interval at which the food was

usually eaten was coded as never, daily, weekly, or less

than weekly. One question was asked about how often the

salt shaker was used at the table. Responses to this last

question could be assigned to one of three codes –rarely

or never, occasionally or seldom, frequently or always.

The supplemental dietary questionnaire contained

questions about whether the respondent was on a special

diet and, if so, what type and for how long. One question

asked about use of medications in the previous week,

These were commonly prescribed medications that might

interfere with test results or affect interpretation of results.

Another question related to problems preventing the

respondent from obtaining needed groceries. The final

question asked about trouble swallowing, pain, nausea and

vomiting following eating, and loss of appetite.

The medication, vitamin, and mineral usage questionnaire has evolved over time. In NHANES II, specific

information was requested about the brand name, manufacturer’s name, and reason for using vitamin or mineral

supplements and medications, Sample persons were left

the form and asked to fill it out and bring it with them to

the examination centers. Because of low response, the

procedures for collecting medication and supplement usage

were changed in HHANES. During the household interview, sample persons were asked to bring their

105
medicines and supplements to the interviewer, who

recorded the label information.

The quality of t&e dieta~ component was controlled

at several levels. Before the survey began, the dietary

interviewers were trained in interview techniques and in

how to code the 24-hour recall. A manual describing the

procedures to be followed was issued to each interviewer.

Periodically, the forms were reviewed and evaluated, and

instructions were issued to the interviewers to promote

consistency. Interviewers exchanged coded 24-hour recall

forms to check each other’s work, and forms were also

reviewed by the field staff before forwarding to headquarters. At every location, each interviewer tape recorded two

interviews with randomly selected subjects, The recordings

were evaluated at headquarters for adherence to procedures. Comparisons were made at headquarters of the

mean values and frequency distributions by stand location

and by interviewer to detect unusual results by location

and systematic errors by interviewers. Foods for which no

appropriate food codes existed were forwarded to headquarters for assignment of new code numbers.

those who take vitamins and other dietary supplements

the persons who need them? Do the diets of subpopulations with high serum cholesterol levels differ from those

with lower levels?

Changes over time in food and nutrient intakes could

be tracked and correlations made with health variables,

Examples of questions that could be posed to the data

include: What changes in obesity, diet, and activity patterns will take place in the next 10 years? Will diet help

explain the continuing decline in serum cholesterol values

among men and women?

Estimates of individuals’ food and nutrient intakes

Of the methods used by Federal surveys, only one

would be appropriate for studies of the association behvccn

diet and health – estimates of food and nutrient intakes by

individuals. National surveys collect information about

food and nutrient intakes by individuals for two purposes– to estimate intakes by groups of individuals who

share some characteristic (age, sex, race, income, education, and so forth) and to relate estimated intakes by

Uses and limitations of survey

individuals to some condition (such as cholesterol intake

methods

and serum cholesterol level).

The national surveys use three methods to collect

Uses of dietarydata

The NHANES dietary data have been put to four

types of uses: Relating diet and demographic characteristics, relating diet and health characteristics, determining

interactions of diet and nutritional status indicators, and

tracking trends in diet and nutrient intakes over time.

In relating diet to demographic characteristics of the

population, the major question to be asked is: What are

the food consumption patterns and nutrient intakes of

subpopulations of the United States by such characteristics as age, race, sex, income, occupation, and education?

The NHANES dieta~ data can answer such questions as:

How do nutrient intakes and food consumption patterns

of persons differ by level of education? What are the

regional differences in consumption of certain food groups?

The NHANES data have been used to relate food

consumption patterns and nutrient intakes of subpopulations of the United States to indicators of health status,

Specific questions that could be addressed include: How

do nutrient intakes compare with the Recommended

Dietary Allowances and other dietary guidelines? What

dietary patterns and nutrient intakes are associated with

differing levels of health or health risk?

Examining interactions among nutrition-related variables, NHANES data could be used to compare among

dietary intake, biochemical status, anthropometry, and

presence or absence of health conditions, Questions that

could be addressed by the data include: What are the

relationships among dietary intake, biochemical indicators, and health status for persons who smoke, use vitaminmineral supplements, or use oral contraceptives? Are

information from individuals about their food intakes:

24-hour recalls (NHANES, NFCS, and CSFII), food diaries (NFCS), and food frequency questionnaires

(NHANES). The 24-hour recall and food diaries attempt

to capture actual dietary intakes during specified periods

of time; frequency questionnaires attempt to capture usual

intakes over longer timespans ranging from a few months

to a year. The 24-hour recall and diaries yield quantitative

estimates of foods and nutrients ingested. This is not yet

possible with food frequencies because a food composition

data base has yet to be developed for this purpose. Several

reviews (17–19) have appeared about the reliability and

validity of these methods, The literature is more extensive

for the 24-hour recall method than for diaries or

frequencies.

The 24-hour recall and diaries can be used in crosssectional surveys to determine the foods ingested and to

calculate nutrient intakes. Estimates can be made of the

mean, median, and distribution of intakes by specific

groups described by age, sex, race, income, or other

characteristics. The average of repeated recalls and

diaries can be used to determine usual food intake by

individuals. The minimum number of days of obsewation

required to characterize usual intake varies with the

nutrient of interest and by the amount of intraindividual

and interindividual variation.

Both the 24-hour recalI and the diaries are limited in

that they rely on the respondent’s abili~ to remember and

describe to an interviewer what he or she ate and drank or

to remember to record what was eaten. Probably more

frequently than one would like to admit, the diary method

becomes a recall when the respondent fails to record what

106
was eaten and relies on memory to complete the diary

bcfcwe it is returned to the interviewer,

The precision of estimates of individuals’ usual intakes

based cm single, l-day obsewation is low, largely because

of high intraindividual variance (20,21). The major components of variance in the 24-hour recall are sex, day of

the week, and interindividual and intraindividual variation, Mcn tend to eat more than women, so most studies

of variability in intakes treat the sexes separately. The day

of the week for which the recall was obtained can be a

significant source of variance for women, with weekend

intakes being higher than weekday intakes, However, the

two largest sources of variance are interindividual and

intmindividual. Interindividual variations of energy and

nutrient intakes are relatively constant, but intraindividual

variations differ with the nutrient under consideration.

Intraindividual variation is usually larger than interindividwd variation and includes methodological errors as well

M the true day-to-day variation in intake within

respondents, The high intraindividual variability noted in

many studies suggests a wide daily variation in the composition of self-selected diets.

The food composition data base used to calculate

nutrient levels from reported foods is a contributor to

error associated with 24-hour recalls and diaries, The

pcrccnt of data from analytic sources, that is, actual

chemical analysis of food, in USDA’s Primary Data Set

(which includes nutrient values for all food items needed

to create a survey nutrient data base) is shown in table 3.

For some dietary components (total dietary fiber, for

example) only scanty data exist,

Table 3. Percent of data from analytical sources in Primary Data

Set: January 1986

All food Major

Component sources sources

Protein. . . . . . . . . . . . . . . . . . . . . . 95 97

Fat, .,, . . . . . . . . . . . . . . . . . . . . . 95 97

Calcium . . . . . . . . . . . . . . . . . . . . . 90 94

Iron .,, ,, .,, ., .,, ,,, .,...,. . 89 88

Magnesium, ,,, . . . . . . . . . . . . . . , 74 72

Phosphorus .,, . . . . . . . . . . . . . . . . 89 92

Potassium . . . . . . . . . . . . . . . . . . . . 89 92

Sodium . . . . . . . . . . . . . . . . . . . . . . 89 86

Zinc . . . . . . . . . . . . . . . . . . . . . . . . 72 79

Copper . . . . . . . . . . . . . . . . . . . . . . 85 70

Vltamln A(lnternatlonal units). . . . . . . . 94

Vitamin A(retlrrol equivalents). . . . , . . . : 73

Camtcno(retinolequivalents). . . . . . . .

Vitamin E. . . . . . . . . . . . . . . . . . . . .

54 90

28 52

V[tamln C . . . . . . . . . . . . . . . . . . . . e4 95

Vitamin B, . . . . . . . . . . . . . . . . . . . . 90 91

VIIamln Be . . . . . . . . . . . . . . . . . . . . 90 92

Niacin. . . . . . . . . . . . . . . . . . . . . . . 89 93

V[tamln Be. .,, . . . . . . . . . . . . . . ,, 73 70

Folacln . . . . . . . . . . . . . . . . . . . . . . 54 68

Vltamln B12 .,,,,.....,,, . . . . . . 63 69

Cholesterol . . . . . . . . . . . . . . . . . . . 80 91

Saturated fatty acids, , . . . , , ., . . . . . 59 74

Monounsaturated fatty acids. , , . . . . . . 58 78

Polyunsaturated fattyaclds. . . . . . . . . . 58 72

Total dletaryfiber, . . . . . . . . . . . . . . . 28 40

SOURCE:Frank Hepburn, US, Department of Agriculture. Calculated from the Primary Data

Sot, which Includes nutrient values for all food items naededto craate a survey.nutrient data

bnso.

Food frequency questionnaires are useful to classify

individuals and groups by food intake characteristics (18).

Frequencies canbe designed to cover the entire diet(as

has been done for NHANES), orthey can be targetedto

oneor more foods or groups offoods of research interest,

Information onthe frequency ofconsumption offoods can

be combined with estimates of usual portion sizes to yield

estimated quantities of foods consumed.

Frequencies suffer from a number of limitations.

Because frequencies cover longer periods of time than

recalls or diaries, they are more difficult to validate.

Usually, validation studies have attempted to compare

frequencies with another questionnaire method. As yet,

frequencies cannot be used to estimate nutrient intakes.

Additionally, like recalls, frequencies rely on the

respondent’s ability to accurately remember and report his

or her usual food intake.

Summary

This paper reviews the strengths, weaknesses, and

limits to interpretation of dietary intake methods used in

national nutrition surveys, including per capita consumption of food and nutrients and the 24-hour recall. These

dietary assessment methods make up but one of five

categories of nutritional assessment techniques. The other

four are body measurements, hematological and biochemical tests, medical examination for the presence of clinical

signs of deficiency or toxicity, and medical history. No one

method alone is sufficient for assessing the nutritional

status of individuals or groups.

In the hypothesis-generating phases of research on

diet and cardiovascular diseases and diet and cancer,

correlations of per capita availability of food and nutrients

with mortality from these diseases in several countries

proved useful. However, the approach is limited to the

extent that similar data are available from several other

countries and that the association observed is true and not

spurious. The 24-hour recall method has proved to provide accurate and reproducible estimates of the mean

intakes of population groups, but multiple-day information is necessa~ for characterizing an individual’s usual

nutrient intake. Food frequency questionnaires are useful

when one is interested in food rather than nutrient consumption of individuals or groups. The 24-hour recall,

food diary, and food frequency methods can be used to

develop a descriptive epidemiology of food intake within

U.S. population groups with specified fitness and activity

characteristics or to monitor the prevalence of dietary risk

factors within the population. They are limited to the

extent that food composition data are available, When

selecting a dietary assessment method to be used in a

nutrition survey, three points must be kept in mind:

Practicality in terms of respondent burden and analysis

resources, reliability, and validity.

107
References

1.

2.

3.

4,

5.

6.

7.

8.

9.

10.

Ware JE, Brook RH, Davis AR, Lohr EN. Choosing measures

of health status for individuals in general populations. Am J

Public Health 71:620-5, 1981.

Ostenso G. National Nutrition Monitoring System: A historical perspective, J Am Diet Assoc 84:1181–5. 1984.

Miller HW. Plan and operation of the Health and Nutrition

Examination Survey, United States, 1971-1973, National

Center for Health Statistics. Vital Health Stat l(lOa). 1978.

National Center for Health Statistics. Plan and operation of

the Health and Nutrition Examination Survey, United States,

1971-1973, National Center for Health Statistics, Vital Health

Stat l(lOb). 1977.

McDowell A, Engel A, Massey JT, Maurer K. Plan and

operation of the second National Health and Nutrition

Examination Survey, 1976-80. National Center for Health

Statistics. Vital Health Stat 1(15). 1981.

Maurer KR. Plan and operation of the Hispanic Health and

Nutrition Examination Survey, 1982-84. National Center for

Health Statistics. Vital Health Stat 1(19), 1985.

Centers for Disease Control. Nutrition surveillance, 1980.

Washington: Public Health Service. 1982,

Human Nutrition Information Service. Food consumption:

Households in the United States, spring 1977, Pub no H-1,

Washington: U.S. Department of Agriculture. 1982.

Human Nutrition Information Service. Food intakes: Individuals in 48 States, year 1977-78. Pub no I-1. Washington:

U.S. Department of Agriculture. 1983,

Human Nutrition Information Service. Nationwide Food

Consumption Survey continuing survey of food intakes by

individuals, women 19-50 years and their children 1-5 years,

1 day, 1985. Pub no 85-1, Washington: U.S. Department of

Agriculture. 1985.

11.

12.

13.

14.

15.

16,

17.

18.

19.

20.

21.

Hepburn FN. The USDA National Nutrient Data Bank, Am

J Clin Nutr 35:1297-1301, 1982.

U.S. Department of Agriculture. Composition of foods:

Raw, processed, prepared. Agriculture Handbooks 8-1

through 8-12, Washington: U.S. Department of Agriculture.

1976-84.

Park YR, Harland BF, Vanderveen JE, et al. Estimation of

dietary iodine intake of Americans in recent years. J Am

Diet Assoc 79:17-24.1981.

Allegrini M, Pennington JAT, Tanner JT. Total Diet Study:

Determination of iodine intake by neutron activation analysis. J Am Diet Assoc 83:18-24.1983,

Pennington JAT. Revision of the Total Diet Study food lists

and diets. J Am Diet Assoc 82166-73. 1983.

Welsh SO, Marston RM. Review of trends in food use in the

United States, 1909 to 1980. J Am Diet Assoc 81:120-5,

1982.

Burk MC, Pao EM. Methodology for large-scale surveys of

household and individual diets. Home Economics Research

Report No. 40. Washington: U.S. Department of Agriculture, 1976.

Block G, A review of validations of dietary assessment

methods. Am J Epidemiol 115:492-505, 1982,

Bazzarre TL, Myers MP. The collection of food intake data

in cancer epidemiology studies, Nutr Cancer 1:22-45. 1979.

Beaton GH, Milner J, Corey P, et al. Sources of variance in

24-hour dietary recall data: Implications for nutrition study

design and interpretation. Am J Clin Nutr 322546-59, 1979.

Todd KS, Hudes M, Calloway DH, Food intake measurement: Problems and approaches, Am J Clin Nutr 37:139-46,

1983.

108

* U.S. GOVERNMENTPRINTINGOFFICE: 1992– 312- 08 2/ 6000 1
Vital and Health Statistics

series descriptions

SERIES 1,

SERIES S.

SERIES 3.

SERIES 4.

SERIES 5.

SERIES 6.

SERIES ICI.

SERIES 11.

SERIES 12.

SERIES 13,

SERIES 14,

Programs and Collection Procedures- Reports

describing the general programs of the National Center for

Health Statlstlcs and its ofhces and dw!slons and the data

collection methods used. They also Include defmmons and

other material necessary for understanding the data.

Data Evaluation and Methods Research – Stud!es of new

statistical methodology includlng experimental tests of new

survey methods, studies of vital statistics collection

methods, new analytical techniques, objective evaluations

of reliability of collected data, and contrtbutlons to statistical

theory. Studies also include comparison of U.S.

methodology w[th those of other countnes.

Analytical and Epidemlological Studies – Reports

presenting analytical or mterpreth’e studies based on wtal

and health statistics, carrying the analysis further than the

expository types of reports in the other series.

Documents and Committee Reports – Final reports of

major committees concerned with vital and health statlst!cs

and documents such as recommended model vital

registration laws and revised birth and death cerlf!cates

Comparative International Vital and Health Statistics

Reports – Anaiyhcal and descnpttve reports comparing

U.S. wtal and health statlst!cs w!th those of other countnes

Cognition and Survey Measurement– Reports from the

National Laboratory for Collaborative Research In Cognmon

and Survey Measurement using methods of cognwie

science to design, evaluate, and test suwey Instruments.

Data From the National Health Interview SurveyStatistics on ]Ilness, accidental mjunes, dlsabdlty, use of

hosptal, medical, dental, and other serwces, and other

health-related topics, all based on data collected In the

continuing national household interview survey.

Data From the National Health Examination Survey and

the National Health and Nutrition Examination Survey –

Data from direct examlnatlon, testing, and measurement of

national samples of the cwdlan nonmstltutlonahzed

population prowde the basis for (1) estimates of the

medically defined prevalence of specific diseases In the

Umted States and the distributions of the population with

respect to physical, phys!olog!cal, and psychological

characteristics and (2) analysts of relatlonsh!ps among the

various measurements without reference to an expliclt flmte

universe of persons,

Data From the Institutionalized Population Surveys

Discontinued in 1975. Reports from these surveys are

included m Series 13.

Data on Health Resources Utilization - Statwtrcs on the

utilization of health manpower and facllmes provldlng longterm care, ambulatory care, hospital care, and family

planning services.

Data on Health Resources: Manpower and Facilities -

Statistics on the numbers, geographic dtstnbutlon, and

characteristics of health resources including physmans,

dentists, nurses, other health occupations, hosplfals,

nursing homes, and outpatient facll]tles.

SERIES 15

SERIES 16

SERIES 20

SERIES 21

SERIES 22

SERIES 23.

SERIES 24.

Data From Special Surveys – Statlsllcs cm health and

health-related topics collected m spec!al surveys that are

not a parl of the conttnung data systems of the National

Center for Heallh Statlstcs.

Compilations of Advance Data From Vital and Health

Statistics –These reports prowde early release of data

from the National Center for Health Statcst!cs’ health and

demographic surveys. Many of these releases are followed

by deta[led reports In the Vital and Health Statlstlcs Series,

Data on Mortality– Various statwtlcs on mortallty other

than as Included n regular annual or monthly reports

Special analyses by cause of dea[h, age, and other

demographic variables, geographic and t!me ser)es

analyses’ and statistics on characteristics of deaths not

ava!lable from the vital ‘ecords based on sample suweys of

those records.

Data on Natality, Marriage, and Divorce –Various

sta!sttcs 07 natalty, marrage, and dworce other than

!hose ncluded m regular anrual or mont’nly reports,

Special ana’yses by de~ographlc variables, geographic

and tme seres ana:yses, sh.ujes of ferlhty, and S!atSllCS

On Characterst!cs of brths not available from the wtal

records based on sample surveys of those records

Data From the National Mortality and Natality Surveys–

Dscontlnued n 1975 Reports from these sample suweys

based on wtal records are included m Series 20 and 21,

respectively.

Data From the National Survey of Family Growth --

Statlstlcs on ferll!ty, faintly formation and dssolutlon, family

plannrng, and related maternal and Infant health topics

denved from a periodic survey of a nationwide probabhfy

sample of women 1544 years of age.

Compilations of Data on Natality, Mortality, Marriage,

Divorce, and Induced Terminations of Pregnancy –

Advance reports of brths, deaths, marriages, and d!vorces

are based on f)nal data from the Nat!onal Wtal Statlstlcs

System and are publshed annually as supplements to the

Monthly Vtta! Stakstlcs Report (MVSR). These reports are

followed by the publl catjon of detailed data In Wal

Statts!lcs of the Umted States annual volumes. Other

reports Inc ud!ng ,nduced terrwnatlons of pregnancy Issued

periodically as supplements to !he MVSR prowde selected

ftnd, ngs based on data from the National Vital Statlsttcs

System and may be followed by detailed reports m !he Wal

and Health S[atlstcs Series

For answers to questions about this repon or for a hst of t)tles of reports

publlshed m these series, contact

Sclentlflc and Techmcal Information Branch

National Center for Heal[h Statlstlcs

Centers for Dsease Control

Publtc Health Sewce

6525 Belcrest Road, Room 1064

Hyattsvllle, Md 20782

301-436-8500
DEPARTMENT OF

HEALTH & HUMAN SERVICES

Public Health Service

Centers for Disease Control

National Center for Health Statistics

6525 Belcrest Road

Hyattsville, Ma@and 20782

OFFICIAL BUSINESS

PENALTY FOR PRIVATE USE, .$300

DHHS Publication No. (PHS) 92-1464, Series 4, No. 27