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OCR for page 48
7
Errors in Nutrient Intake
Measurement
All measurements have components of random errors and
systematic bias. Dietary intake measurements are no excep-
tion. In developing an approach to the analysis of dietary
data, it is essential to consider the effect of both types
of error. The estimates of nutrient intake are based on
data from dietary surveys, data in food composition tables,
and the computation of the nutrient intake from these two
data sets. Each data set has sources of random error and
bias; there is also potential for bias in the computation
process. Thus, the subcommittee discussed sources of bias
and variability in the data on dietary intake, food compo-
sition, and computation of nutrient intake. This chapter
presents the results of its analysis of the impact of
random error and systematic bias on the estimates of the
prevalence of inadequate intake.
SAMPLING VARIATION
Random Error
By chance, the persons randomly selected in dietary
surveys may not be representative of the reference popu-
lation. Using statistical theory, one can derive minimum
estimates of this sampling error, which would be increased
by other sources of variability such as those discussed in
this chapter.
Systematic Bias
Systematic bias in sampling can also occur. For example,
in such surveys as the Nationwide Food Consumption Survey
(NFCS), the sample design is based on households. There-
48
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49
fore, the homeless are systematically unde~represented.
Self-selection (i.e., survey participation by consent) can
also produce an unrepresentative sample of the U.S. popu-
lation and result in systematic bias. When there are sev-
eral components to the collection of data, fewer persons
may respond in one component than in another. For example,
fewer persons may complete dietary diaries than respond to
a household interview. This bias would be especially pro-
blematic if those who completed diaries were better orga-
nized and better educated than those who were only inter-
viewed. The magnitude of bias from sample design and from
nonresponse requires special research not usually included
in surveys of this kind. At a minimum, it is important to
determine the probable direction of any such bias and, where
possible, it is preferable to estimate the magnitude as
well.
ERRORS IN ESTIMATING USUAL NUTRIENT rNTAKE
Errors in Reporting Usual Food Intake
Day-to-Day Variation in Intake. The intake of concern
is the average intake of individuals across time. This
usual intake is believed to af feet tissue levels of the
nutrients and body functions. Intake on a particular day
does not reliably portray the usual intake.
The Impact of day-to-day variation in intake is dis-
cussed in some detail in Chapter 4, where the subcommittee
discusses a method to adjust dietary intake to control for
day-to-day variation. Although several statistical tech-
niques might be used for this purpose, the subcommittee
chose to use an analysis of variance procedure for the
analyses presented in this report.
Variability In Reporting and Recording Food Intake. A
.
close examination of errors in reporting, recording, and
coding data from food intake surveys is helpful in ident~-
fying errors that are random within a person and those that
are systematic.
Random error.
A respondent may sometimes overreport
and sometimes underreport in a random fashion. Variation
also occurs between persons when one person underreports
and another person overreports. Even if these errors are
random, they present a problem in the analysis of popu-
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50
ration data. However, systematic bias involving entire
subgroups of the population may have more serious impli-
cations for the analysis of such data. For example, if
all or most members of the lower socioeconomic groups
overestimate intake of such status foods as meat, biased
estimates of iron intake may result.
· Systematic bias. In the NFCS, the importance of
systematic underreporting in the total population has
been recognized. As a result, the U.S. Department of
Agriculture (USDA) has undertaken or funded numerous
studies that address these problems. The literature
relating to these questions is reviewed briefly in the
following paragraphs.
The goal of any dietary survey is to measure what
people eat-more precisely, to measure what foods and
supplements people habitually eat or usually ingest.
Dietary surrey methods can be classified into two general
categories: methods bared on memory (recall) and methods
based on records. In memory-based procedures, subjects
are asked to recall all food and drink consumed over a
'specified time, usually 24 hours. Record-keeping methods
take Several forms: a common procedure is to ark subjects
to keep a diary recording all foods and beverages consumed
during a Specified nether of days, most commonly for per-
iods of up to 7 days. In some cases, respondents are
provided with plastic or paper food models and measuring
devices to aid in estimating portion sizes. In other
cases, they receive only specific written or oral instruc-
tions or both. As mentioned earlier, the recall and
record methods were combined in the collection of 3-day
dietary intake data for individuals in the 1977-1978 NFCS.
In recent years there have been several reviews of
dietary methods (Bear and Laus, 1982; Becker et al.,
1960; Burk and Pao, 1976; Marr, 1971) as well as many
papers concerned with the quality of specific methods.
The aspect of quality of greatest concern is validity,
especially with regard to systematic bias in data on
dietary intake.
A valid method is one that measures what it intends to
mea';ure--the true intake of subjects. Dietary methods
can be validated only by knowledge of tore intake or by
some Sensitive laboratory measurement of intake. Block
(1982) notes that it is difficult to ascertain true
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51
intake over long periods. Consequently, validation fre-
quently consists of comparing one dietary survey method
with another. Such validation is limited by the lack of
agreement on a "true" or reference method. Similarities
in the two methods may fail to detect common errors when
the two methods are compared. Failure to record accu-
rately all foods eaten during a survey may influence sub-
sequent recall. It may also sensitize the respondent's
power of observation, so that a subsequent recall is more
complete than the initial daily record, as in a survey or
nutritional assessment.
Unobtrusive observations of actual intake have been
made of people in various institutional settings, such as
children in grade schools (Comstock et al., 1981; Graves
and Shannon, 1983; Lachance, 1976; Meredith et al., 1951),
students in college cafeterias (Krantzler et al., 1982;
Mullen _ al., 1984; Raker, 1979), elderly people at con-
gregate meal settings (Gersovitz et al., 1978; Madden et
al., 1976), patients in nursing homes (Caliendo, 1981),
adolescents in a metabolic research unit (Greger and
Etnyre, 1978), young boys consuming food at home for a
1-day period (Stunkard and Waxman, 1981), lactating women
confined to a hospital (Linusson et al., 1975), and
massively obese patients in a clinical research center
(Bray et al., 1978). Settings such as these are optimal
for these studies because portions served are standard-
ized or predetermined. These observations have usually
been made for short periods or even for single meals,
although observations as long as 28 consecutive days were
reported by Mullen et al. (1984) and Krantzler et al.
(1982) in their study of students in a dormitory dining
hall. Institutional routines may, however, alter the
eating patterns and recall as compared to the general
population.
Caveats about validation must be taken into account
before the accuracy and systematic bias in food intake
data are addressed. For example, are there systematic
errors in the dietary intakes reported by some subgroups
of the population, such as young and old subjects, or in
the reporting of particular foods or food groups--biases
that would influence the reported nutrient intakes?
the results of several validation studies indicate that
low intakes may be overreported and high intakes under-
reported, with a resultant flat-slope syndrome (Gersovitz
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52
_ al., 1978; Madden et al., 1976; Stunkard and Waxman,
1981), although mean intakes for groups may be accurately
recalled. However, the flat slope syndrome may be nothing
more than the attenuation of the "lope that arises from
random error in the independent variable, as has been
described by many authors.
Systematic biases may exist in dietary reporting by
selected population subgroups but have not been suffi-
ciently addressed to warrant conclusions. For example,
Campbell and Dodds (1967) used a 24-hour recall to collect
dietary information from elderly and young patients hospi-
talized for various lung disorders. Their purpose was to
test the extent of error when a 24-hour recall was used to
collect information from elderly subjects whose failing
memory may affect recall. The 24-hour recall data were
checked by prompting subjects based on a known menu. In
this study, the elderly subjects underreported calories
more often than did younger subjects, and men underre-
ported more calories than did women. Dietary intakes
obtained without probing were approximately 259 lower than
those obtained with probing.
Marr (1971) concluded that it is difficult to obtain
valid dietary data from young children. For example, it
is necessary to rely on a surrogate, such as a parent, to
provide information about the intake of young children.
In addition, caretakers other than parents frequently
provide food for the child, making it difficult to secure
the necessary data. In the United States, food is increas-
ingly eaten away from home by young children as more and
more preschoolers attend childcare facilities.
A preliminary study of 29 children of preschool age was
designed to examine the relative usefulness of the 1-day
food record and the 24-hour recall ( H. Smiciklas-Wright
and P. Hol mberg, Pennsylvania State University, personal
communication, 1985 ) . The investigators observed actual
intake at a lurch consumed by children attending a day-
care center and then asked 14 mothers to provide data on
dietary intake by a 24-hour recall. Seven of the 14 moth-
ers provided no data for the meals consumed while their
children were at the center. The remaining mothers failed
to identify menu items correctly, overestimated portion
sizes, or bath. The 15 parents who provided information
by a l~day record did provide more complete data, although
these parents also tended to m' sidentify foods or over-
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l
53
estimate portion sizes. For data interpretation it is
important to know whether a surrogate reporter was pres-
ent at all meals.
Retrospective diet histories have been compared with
direct measurements of food intake by obese patients
hospitalized in a metabolic unit (Bray et al., 1978).
Three retrospective assessments of diet history were
obtained at Month intervals. In the first history,
energy intake was underestimated. By the third one, esti-
mates of energy intake had risen--particularly because of
improved reporting of alcohol intake. Thus, the third
history probably provided the best correlation between
true and reported energy intake.
Lansky and Brewnell (1982) also examined the accuracy
of food records of applicants in a behavioral weight-
reduction program. Thirty women estimated the quantity
of 10 foods that were displayed in small and large con-
tainers. Container size did not influence quantity
estimates, but the quantities of all 10 foods were over-
estimated. The amount of overestimation varied from a
small overestimation for cola and orange juice to a large
overestimation for potato chips, ham, and turkey. Neither
Brav et al. (1978) nor Lansky and Brownell ( 1982 ) reported
comparable data on subjects of normal weight to determine
whether the errors in reporting intake and recording serv-
ing sizes were restricted to obese persons. A. Blake
(Pennsylvania State University, personal communication,
1985 ) found that serving sizes were underestimated by both
obese subjects and persons within normal weight ranges.
ThiS investigator provided preweighed portions of foods at
a lunch attended by subjects who were contacted the next
day for a 24-hour recall. The majority of subjects, both
obese and normal, underestimated the servings they had
eaten.
Sopko _ al. (1984) found that obese males reported from
78% to 93% of their actual intake in a controlled feeding
experiment. Although the study was not a true validation,
Hallfrisch _ al. (1982) observed that on a 7-day diet
record, men reported 80% and women reported 65% of the con-
sumed calories in subsequent 6-week experimental periods.
These results call into question the validity of recall
or recording of food intake. Reports of food and energy
intakes only slightly higher than basal energy requi re-
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54
meets of adult women are difficult to reconcile with popu-
lation data showing a 40% prevalence of overweight and a
25.7% prevalence of obesity, as indicated by body mass index
(Van Itallie and Woteki, 1985).
Selective underreporting is also suggested by compari-
sons of beer, wine, and distilled spirit consumption, as
reported for adult males and females in the NFCS and
National Health and Nutrition Examination Survey (NHANES),
to estimates of alcohol disappearance, as reported by the
Bureau of Alcohol, Firearms, and Tobacco Control.(Pao et
al., 1982). Similarly, Schnakenberg et al. (1981), studying
the reported food intake of 62 men in a military dining hall
for a 7-day period in comparison to observed and weighed
intakes, found that 13% underestimated their caloric intake
by more than 30% and 34% underestimated their caloric intake
by 10% to 20~. Only 5% overestimated by more than 30~.
In contrast, de St. Jeor (1980), who monitored weight
status to validate the accuracy of energy intake reported by
paid, educated subjects over a 12-week period, concluded
that recorded intakes were an acceptable measure of actual
intake. As reported earlier, A. Blake (Pennsylvania State
University, personal communication, 1985), comparing the
recalled intake of meals to observed and weighed intakes by
obese and nonobese subjects, found no difference between
the two groups, but did confine a tendency to overreport
small intakes and underreport higher intakes. She found
estimated portion sizes to.vary from actual sizes by less
than 10% for most food items.
One reason for the discrepancy between.actual and
reported intake may be the difficulty that respondents have
in estimating portion sizes. Guthrie (1984) found that for
13 food items, from 6% to 75% of adult male and female
subjects reported portion sizes that varied by more than 50%
from the weighed portion sizes. However, only 26% of the
respondents consistently under- or overrepor~ted al1 items in
the meal. H. Smiciklas-Wright and H. Guthrie (Pennsylvania
State University, personal communication, 1984) showed that
the ability of college-age students to estimate within 1 oz
the volume of fluid contained in drinking glasses varied
from 30% to 70%, depending on the size and shape of the
container.
Young et _ . (1952) found that for a variety of foods the
direction of error was generally in overestimating por-
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55
tions. Compensations were made for this bias by using zero
for all omissions of data when calculating means. They con-
cluded that for the group, errors in the estimation of por-
tion sizes for most food types are probably within 20% of
actual portions, except for children's recall, which has a
greater error.
Krantzler et al. (1982) reported on 24-hour recall and 3-
day or 7-day records of students who were observed periodi-
cally over a 28-day period during which they took their
meals in a dormitory dining hall. They reported that foods
eaten regularly, i.e., those contributing the major part of
a meal, were better reported than such foods as condiments,
nuts, and seeds. Estimates of dairy products, meat, and
fish were the most accurate. Guthrie (1984) reported that
few subjects in her study forgot main meal items, but one
in six respondents forgot to mention salad dressing.
Because two-thirds of the respondents used more than 20 g
(about 130 kcal) of salad dressing, that omission alone
could represent a sizable error in daily intake.
The foregoing discussion suggests that errors in
reporting dietary intake undoubtedly exist. The studies
suggest also that the direction of errors varies from study
to study and perhaps from population group to popu- lation
group. In a separate exercise, the committee cam- pared
the distributions of intakes reported in several recent
large surveys. There were differences, suggesting bias in
either estimation of food intake or food composi-
tion data, but these were not consistent across nutrients.
The evidence is not sufficiently consistent to suggest how
these systematic biases affect estimates of individual
nutrients. It is clear, however, that they can do so
markedly. Because the 1977-1978 NFCS did not include data
on nutrient supplements, it certainly underestimated the
intake.
Variability Due to Coding and Analysis of the Nutrient
Content of Foods
Sources of Technical Errors in Food Composition. The
technical errors in food composition data that influence
the interpretation of food consumption surveys fall into
three broad categories: true random variability of the
composition of individual food items, biased food com-
position data, and the differences in bioavailability of
individual dietary nutrients.
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56
· True random variability. Variations in the com-
position of a food item from the population mean as given
in their classification codes reflects the true variation
in nutrient content of foods due to differences in produc-
tion practices and to the effects on raw materials exerted
by such variables as soil, fertilizer application, weather,
pest control, and genetic variation. Postharvest physio-
logical changes, storage, and processing also contribute
to the true variability of the nutrient content of individ-
ual foods.
Estimates of this normal variation were included in the
current USDA food composition data tables, which were used
for the sensitivity analysis of dietary intake information
included later in this chapter.
This analysis indicates
that normal tood composition variation contributes a
significant portion of the total variation in the
estimated intake of given nutrients. However, the sub-
committee suspects that an all-out effort to decrease the
standard error of the mean of the data in the f cod
composition data base is not needed in most cases. Even
though food composition contributes to variation, coeffi-
cients of variation due to errors in the computation of
intakes for most nutrients are generally Small and the
overall error is modest. The subcommittee suggests that
sensitivity analysis be applied to the specific nutrients
to determine the effect of reducing the error of the mean.
The results of the sensitivity analysis should then be
used to develop priorities designating which nutrients
require further refinements either by assaying more sam-
ples or developing more precise analytical methodology.
Biased food composition data. Errors in food
composition Data result wnen one aaCa are cons~snen='y
incorrect due to the methods of data collection or an~l-
ysis. Often neither the direction nor extent of bias is
known for individual foods. Common causes of biased data
are incorrect identification of the food its, the use of
inappropriate analytical methodology, and the use of imputed
values. Incorrect identification of the food being anal-
Yzed, despite the precision of the analytical technique
used in the assay, leads to biased data. Thus, the subcom-
mittee encourages the USDA to continue with its ongoing
efforts to improve the food nomenclature system.
The nutrient values in the current food composition data
bases for food categories are weighted averages of the con-
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~7
tents of the individual foods that fall within the category
of the listing. Such data are acceptable for calculating
nutrient intake if the nutrient content of each food is ran-
domly distributed about the mean for the food category. In
such cases the estimated nutrient intakes can be reasonably
calculated from the mean content of the food category. How-
ever, the use of such mean values to estimate nutrient in-
take can lead to significant bias when the nutrient content
of the food item is not randomly distributed about the mean
of the food group.
For some categories of processed and manufactured foods,
means are not representative of values for certain brands
because their nutrient contents are controlled by recipes
or processes with unique formulations significantly dif-
ferent from the category mean. Formulations vary to this
extent for only some foods and even then for only some
nutrients in those foods. For example, wide variations in
nutrient content could be found in vit~min-fortified fruit
juices and breakfast cereals, the sugar content of break-
fast cereals, the fat content of bakery products, and the
sodium content of many different prepared foods and meals.
When brand loyalty is considerable, that is, when consumers
consistently use the same brand rather than randomly
selecting similar products, and when the mean nutrient con-
tent of the brand item differs from that of the category
mean, biases will occur in the calculated estimations of
nutrient intake. Such problems can be corrected by using
brand-specific composition data. Since the addition of
brand name identifiers may increase the complexity, and
therefore the cost, of the food composition data and food
consumption surveys, the subcommittee suggests that sensi-
tivity analysis be used to determine which brands need to
be identified by brand name to improve estimates of the
intake of particular nutrients.
The analytical methods constitute a recognized source of
bias in food composition data. In some cases, the methods
do loot measure all chemical forms of the nutrient, thus
leading to underreporting for foods that contain the unmea-
sured chemical forms. Other nutrient assays are inhibited
by some food components, and when such components are pres-
ent, the nutrient content will be underreported. Certain
assays respond to components other than the nutrient of
interest, and when such substances are present, the nutri-
ent content will be overreported.
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~8
Where there are no direct data on the nutrient composi-
tion of a particular food, nutrient values can be calcu-
lated by sting the contributions of each component of the
formulation, if the food item is formulated from several
other food components with known values. Although the
resulting figures usually give acceptable estimates of the
nutrient content of the foods, in some cases a value is
imputed for the nutrient by using the nutrient content of a
similar food and bias may be introduced. For example,
lacking analytical values for corn syrup, known composi-
tion values for molasses might be imputed under the assump-
tion that corn syrup had the same composition. This
results in an overestimation of iron intake, because molas-
ses contains a good deal of iron and commercial syrup has
none. Imputed and calculated values in the current USDA
food composition tables can be identified because the entry
for the number of samples is left blank. However, there is
no way for the users of the tables to determine directly if
the other nutrient values are imputed or calculated.
The subcommittee recommends that the practice of
imputing food composition values be avoided. When sen-
sitivity analysis shows that imputing the content of cer-
tain foods may result in significant errors in estimating
the prevalence of inadequate intake, the components of
those foods must be analyzed. ~~
However, no person's dietary
information should be omitted because there are no analyzed
data for converting foods to nutrients. Thus, values for
many foods must be imputed. It is important therefore that
these imputations be reasonable estimates. For instance,
assigning zero as an imputed value simply because the data
are not available is not a reasonable estimate. If imputed
values are used, they should be flagged so that their
impact on the data analysis can be considered.
Biases due to food composition data--incorrect
assessment of differences in bioavailab~ bed ~ u.r=~.
The selection of criteria for assessing nutritional intake
is complicated by the variation in absorption of nutri-
ents. For example, evidence indicates that the bioavaila-
bility of iron varies with its chemical form in the diet
(e.g., heme versus nonheme; reduced iron versus iron phos-
phates); the presence or absence of absorption enhancers in
the meal (e.g., ascorbic acid or the "meat factor"); the
presence or absence of compounds in the mea'-, such as
phytates and tannic acid, which reduce the absorption of
iron; and the physiological status of the small intestine
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59
- -
as it relates to the state of the iron stores of the indi-
vidual (Morris, 1983). The assessment of the iron intake
of individuals has several components: the iron require-
ment of different age and sex groups, the food composition
data, and often an assessment of the bioavailability of
iron in the meals. To take into account the bioavailabil-
ity of iron in the diet in the 1980 RDAs, a factor of 10
was applied to the actual requirement to account for absorp-
tion losses. In contrast, data based on food composition
do not usually contain any direct information on the bio-
availability of iron in individual foods. However, this is
not always the case. For vitamin E, most food composition
data bases do have a built-in estimate of bioavailability
for each food. The subcommittee believes that inclusion of
the bioavailability of iron in calculation of requirements
introduces a potential source of bias in the assessment of
nutrient intake. Some forms of iron are available at much
less than the 10% level and some at higher levels. A1-
though the use of a fixed level of absorption does not take
into account the influence of the iron sources on its bio-
availability, a 20% level has been suggested as the upper
limit by an FAD/WHO (1970) committee and some of the cal-
culetions in this report are based on this limit (see
Appendix B).
It is possible to improve the data on bioavailability by
beginning with the actual requirement for absorbed iron for
each age and sex category. This can be done by removing the
correction factor for the lack of iron availability from the
stated iron requirement). The concentrations of each chem-
ical form of the iron in each food in the food composition
data base can then be listed together with the concentra-
tions of each iron absorption enhancer and inhibitor for
each food in the data base. In this way it is possible to
develop algorithms to calculate the biologically available
iron for each meal based on the chemical form(s) of the
enhancers and the inhibitors in the meal. Although this
approach will provide a better estimate of the iron intake
of individuals and thus populations, it requires the devel-
opment of new computer algorithms. It thus requires more
research, which must be guided by sensitivity analysis to
avoid research and analysis of marginal utility.
Nutrient Data with Probable bias
In a recent report of the National Research Council, the
current status of the methodology for nutrient analysis in
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60
foods was discussed (NECK 1984). Methods for assaying sev-
eral of the nutrients of concern to this subcommittee were
found to be less than adequate. In particular, the authors
found that the methods for vitamin A, carotenoids, vit ~ n
B12, vitamin C, and folacin were such that there was only
a fair probability that these methods would produce correct
values. Following are detailed discussions of the status
of these nutrients.
Vitamin A. Data on the composition of foods containing
preformed vitamin A (whether naturally present or added
during processing) appear to be reasonable, since the
methods used to obtain them appear to be reasonable assay
systems. Thus, the subcommittee believes that estimates of
the dietary intake of vitamin A from these data are
reasonable.
Carotenoids. Approximately 50 carotenoids possess vitamin
A activity, each apparently with its own biological potency.
Currently the vitamin A contributions of all these compounds
are lumped together in food composition data bases as retinal
equivalents. The chemical assay of the carotenoids is very
complex (particularly in fruits and vegetables), and the
current techniques used for food assay are not adequate for
the determination of all the carotenoids in foods. Further-
more, there is no agreement on the assignment of biological
potency as vitamin A for each carokenoid isomer. The subcom-
mittee suggests that studies be undertaken to determine the
concentration of each carotenoid isomer in fruits and vegeta-
bles. If such studies are undertaken, consideration should
also be given to the concept of measuring all carotenoid
isomers because of the interest in the apparent protective
effects these may have as anticancer agents. There is no
apparent reason to believe that only the carotenoids with
vitamin A activity may have anticancer activity.
Vitamin Big. The current acceptable assay for vitamin
. . . . . . . . _
B12 is the classical microbiological assay using Lacto-
bacillus. The technique appears to work well with raw com-
..
modifies but not for processed foods that contain microbial
growth inhibitors. The recently introduced protein-binding
assays have been shown to respond to B12 isomers, which
have no vitamin activity. Data on the correct vitamin B12
content of foods are therefore available for only a few
foods, but sensitivity analysis will probably show that
better, more complete analyses are only necessary for a few
other foods.
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61
Vitamin C. Both ascorbic acid and dehydroascorbic acid
have vitamin C activity. There are several acceptable
methods for measuring both forms. These appear to be ade-
quate for assaying vitamin C in fresh products. Vit~Tn~n C
is very labile to air oxidation, and the use of improper
sample preparation can lead to an underestimation of the
vitamin C content of fresh products.
In the case of processed foods, the situation is more
difficult. Almost all the methods that measure the two
forms of vitamin C also detect isoascorbic acid, and because
isoascorbic acid is commonly used as an antioxidant in the
food-processing industry, but has no vitamin C activity in
humans, assays for the vitamin C content of processed foods
have the potential for overestimating the vitamin C content.
Folacin. The class of chemical compounds named folacin
consists of a complex mixture of chemical isomers with
various oxidation states and different numbers of glutamate
residues. There is no single chemical, biochemical, or
microbiological assay that will accurately measure all the
forms of this nutrient so they can be related to folacin
activity in humans. Furthermore, the subcommittee does not
know of any acceptable combination of techniques for mea-
suring all the Homeric compounds with folacin activity.
The standard assay is a microbiological one that has
differential responses to different isomers. It is not
known whether or not humans have the same response to the
individual folacin isomers as does Lactobacillus casei;
however, there is no reason to assume that they do. It is
known that there are a number of additional compounds that
interfere with the existing assays or that alter the bio-
availability of each folacin isomer. The interactions of
these assay inhibitors and of compounds that alter the
bioavailability are not well understood nor, apparently,
have all the interfering compounds been identified.
An accurate, sensitive chemical assay for all the folacin
isomers is needed to permit the resolution of the complex
problems associated with assaying the folacin content of
foods. riven the difficulties with assays of the folacin
isomers, the data on the folacin content of food items do
not appear to be reliable and the subcommittee does not
believe that accurate estimates of the dietary intake of
folacin can be obtained from the current food composition
data bases.
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62
IMPACT OF SYSTEMATIC BLAS
The preceding discussion has suggested that sources of
random variation in either the food intake estimate or the
food composition data affect confidence in the estimates of
the prevalence of inadequate intake. For food intake data,
the standard error of the composition data for individual
foods may result in a relatively small under- or overesti-
mation of prevalence. The models do not include systematic
bias, such as that derived from consistent under- or over-
estimation of either total nutrient intake or the nutrient
content of foods. The following paragraphs address the
impact of that type of effect.
As noted, there are no valid estimates of the magnitude
(if any) of bias in estimates of food intake from the NFCS
data. The subcommittee emphasizes the need to determine if
there is such a bias and if so, its extent so that the
methods of estimation can be improved. An analagous situ-
ation holds for food composition data. In one study, Wolf
(1981) estimated nutrient intakes from food records, from
the 1963 USDA food composition data base (USDA, 1963), and
from direct chemical analysis (see Table 7-1).
The analyses suggest that there was a definite bias toward
overestimation of true iron intake. The low regression slope
and correlation coefficient suggest that the bias may apply
to only some classes of foods rather than to all foods. That
is, there may have been a methodological error in the food
composition table for some classes of foods.
USDA's food composition tables have been revised since
those used by Wolf (1981). Major changes were made in the
iron data for some classes of foods. At the request of the
subcommittee, Wolf and his colleagues have recalculated the
data from the original study by using the new food composi-
tion tables.
Table 7-1 indicates that there was a systematic bias
in the earlier food composition tables, a bias that has
now been removed for iron. The comparison illustrates
also that bias in composition data must be investigated
separately for each nutrient in each food composition
table instead of being regarded as generically applicable.
Marr (19713 has presented descriptions of several compar-
isons of calculated and measured intake; the direction of
bias is inconsistent across studies and across nutri-
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63
TABLE 7-1. Comparison of Calculated and Measured Food Intakes
Using Revised Flood Composition Tables for 22
Subj ectsa
Estimated Nutrient Intake
Correla-
tion (R Group Mean (mg/day
square) ~ l~5l=~37URbii67~ic
Regression
Nutrient Equation
Calcium Y = 0.82X + 71 0.89 832 762
Iron
original Y = 0.49X + 4.3 0.25 12.8 10.6
recalcu-
lation Y = 0.83X + 2.09 0.33 lO.S 10.6
Zinc Y = 0.68X + 3.5 0.43 7.4 8.6
Copper Y = 0.lSX + 0.88 0.02 0.89 1.01
Potassium Y = 0.82X + 0.26 0.72 2,680 2,370
Sodium Y = 0.35X + 2.2 0.19 2,800 3,190
aFrom Wolf, 1981.
bSelf-administered intake record.
CChemically measured content of duplicate meal
..
ents. Depending on the study design, such comparisons
may be affected by variation and bias either in the food
composition table alone or in combination with the food
intake record.
When interpreting the results of such studies, one must
recognize that the composition of individual food items
vary and that the food composition table, at best,
presents the average composition of a class of foods.
That is, one should not expect perfect agreement between
calculated and measured composition for an item of food or
for a diet. This effect is demonstrated in Appendix E.
Moreover, the introduction of this variation in composi-
tion (or, if one wishes, random variation) into the esti-
mate of food composition in comparison to the true com-
position of the foods consumed will affect the slope of
regression analyses in which calculated and directly mea-
sured composition are compared. To some degree, the flat
slope syndrome described above and in the nutrition liter-
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64
ature (e.g., Marr, 1971) can be attributed to this type
of effect. (A similar phenomenon will occur if, for
example, 1-day intakes are compared with multiple day
intakes: the intraindividual variations in the data will
be different, and this may affect the regression slope
while leaving group mean comparisons unaffected.)
Although there are no direct estimates of the
magnitude of bias that may exist in computed nutrient
intakes reported in recent NFCS or other large-scale
survey data bases, it is possible to demonstrate the
potential effect of such bias on estimates of the preva-
lence of inadequate intake. This is illustrated for
protein in Table 7-2. Here the observed distribution of
intakes has been systematically increased or decreased by
TABLE 7-2. Potential Impact of Systematic Bias in Either
Food Composition Tables or Food Estimates on
Estimates of the Prevalence of Inadequate
Intakesa
.
Adjustment to
Intake Distribution
(Mock Systematic Bias
in Intake Estimate)
:
Group Mean
Intake Estimated Bias in
(g/day) Prevalence (%) Estimate (%)
Original ~ 20 g 111.2 0. 1 - 2 .1
Original + 15 g 106.2 0.3 - 1.9
Original ~ 10 g 101.2 0.7 - 1.5
Original + 5 g 96.2 1.2 - 1. 0
Original 91. 2 2.2 0
Original - 5 g 86.2 3.6 + 1.4
Original - 10 g 81.2 5.6 ~ 3.4
Original - 15 g 76.2 8.4 + 6.2
Original - 20 g 71. 2 12 .3 +10. 1
.
aData from 1977-1978 NFCS for protein intake by adult
males.
adding a constant amount of protein to each intake. This
process shifts the total intake distribution upward or
downward but does not change the shape of the distribu-
tion.
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65
The magnitude of the effect on estimates of the preva-
lence of inadequate intake will depend on the relative
positions of the distributions of intake and of require-
ment. Nevertheless, it is readily apparent that for any
such estimate of adequacy, systematic bias in either food
intake estimates or food composition data will result in
erroneous estimates of prevalence. This effect is not
specific to the probability approach. It would also occur
if fixed cutoff points had been used.
Representative terms from entire chapter:
composition data
