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OCR for page 25
5
The Probability Approach
Recognizing the weaknesses of previous efforts to ana
lyze dietary intake, the subcommittee sought an approach
that would take into account the variability both in usual
nutrient intake among individuals and in their nutrient
requirements. To meet this need, it developed and eval
uated a probability approach based on a comparison of two
distributions: nutrient requirement and nutrient intake.
This method takes into account the likelihood that persons
with a particular level of intake would fail to meet their
nutrient requirement. The probability of inadequate intake
would naturally be very low for those with higher nutrient
intakes and would be higher for those with lower nutrient
intake.
Figure S1 shows the distribution of protein require
ments among adult men. The probability curve for the in
take has been plotted as a cumulative distribution of
requirement. At the lower level of intake, below the lower
tail of the original distribution, intake should be inade
quate to meet requirements for everyone. (No persons are
believed to have requirements that low.) At the level of
the mean requirement, assessing a symmetrical distribution,
half the individuals would be expected to have higher needs
and half lower. In the upper tail of the distribution,
probability of inadequacy approaches zero. (No persons are
believed to have requirements this high.) From Figure 51,
then, a probability of inadequacy can be assigned to any
observed level of usual intake. The probability or risk
curve is specific to a particular class of peoplein this
figure, adult men. The application of this concept to
protein is discussed in detail in the FAO/WHO/UNU (in
press) report on energy and protein requirements.
25
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26
100
80
60
40
20
o
it'
l I l


\
\
\
1 1
J
0 10 20 30 40 50 60 70
PROTE IN INTAKE, g/day
FIGURE 51. Cumulative distribution of protein requirement
expressed as a probability curve. The curve
describes the probability, or risk, that an
observed intake would be inadequate for a ran
domly selected male, acing a normal diatri
bution of protein requirements. Based on ARC,
1980.
In Figure 51, the probability of inadequate intake is
plotted against the protein intake for an adult male.
Beyond approximately 30 g/day, the probability of inade
quate intake decreased rapidly and reaches zero at an
intake of about 60 g/day. When applied to the distribu
tion of intakes observed in a population, the curve can
be used to generate a prediction or estimate of the prev
alence of inadequate intakes. The approach does not iden
tify those particular individuals who have inadequate
intakes, only the proportion of the population.
When the approach was applied to iron, Beaton (1974)
demonstrated that predictions of the prevalence of inade
quate iron intakes seemed consistent with estimates based
on h~matologic data on the proportion of women who might
be expected to have increased hemoglobin levels when
treated with iron. In that analysis, approximately 7St of
women in the sample had intakes less than the Canadian
recommended intake of 14 mg/day, but only about 15% were
predicted to have intakes below their own requirements
(Beaton 1971, 1974) . The approach for iron is discussed
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27
in Appendix ~ and is used with data from the Nationwide
Food Consumption Survey (NFCS) later in this section.
More recently, the joint FAO/WHO/UNU (in press) com
mittee that studied protein requirements accepted the
principles of the probability approach and suggested that
it be applied to the interpretation of observed protein
intakes. That committee emphasized that the approach can
not easily be applied to energy. In essence, any logis
tically simple approach depends on the assumption that the
correlation between intake and requirement is approxi
mately known among similar individuals. On the basis of
existing data, this is a reasonable assumption for the
nutrients, at least after body weight or other common
denominator variables of requirement are taken into
account. There is no a priori reason to believe that the
_ .
person with a low (or high) usual intake will necessarily
have a low (or high) requirement. It is possible then to
assign a probability of inadequacy to observed intake of
nutrients. However, much evidence suggests that usual
energy intake and expenditure are closely related in most
people (Beaton, 1983; FAO/WHO/UNU, in press). That is, a
person with a low energy intake is very likely the person
with low energy expenditure. One would have to know the
magnitude of this substantial correlation before a prob
ability approach could be applied to energy with any degree
of confidence. Obtaining this information would be very
dif f icult in the general population.
A specific example of the probability approach applied
to protein intake is presented in Table 51. The adjusted
distribution of protein intakes using 19771978 NFCS data
provided to the subcommittee (Figure 42) has been arbi
trarily divided into 11 intake intervals. For each intake
interval, the percentage of the total population expected
to have inadequate intakes is estimated by multiplying the
percentage of the total population in that intake interval
(frequency distribution) by the probability of inadequate
intake for that interval. When the percentages of inade
quate intake at each level are added together, the sum is
2.2the estimated prevalence of inadequate protein
intakes for that population of adult males (Table 51).
The probabilities portrayed in Figure 51 and tabulated
in Table 51 were derived from the description of protein
requirements provided in the Recommended Dietary Allowances
(NRC, 1980). The average protein requirements were stipu
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28
TABLE 51. Predicted Proportion of Adult Males with
Protein Intakes Below Their Individual
Requirements: An Application of the Proba
bility Approacha
Percentage of Estimated
Intake Total Popu Percentage
Interval ration with of Total
(and Observed In Probabil Population
Midpoint), take in That ity of with Inade
g/day Intervalb Z ValueC Inadequacy d quate Intake e
24 0.4 2.85 1.0 0.4
2428 (26) 0.1 2.53 0.995 0.1
2832 (30) 0.2 1.90 0.97 0.19
3236 (34) 0.2 1.27 0.90 0.18
3640 (38) 0.5 0.63 0.74 0.37
4044 (42) 0.9 0 0.5 0.45
4448 (46) 1.1 +0.63 0.26 0.29
4852 (50) 1.3 +1.27 0.10 0.13
5256 (54) 1.8 +1.90 0.03 0.05
5660 (58) 3.5 +2.53 0.005 0.02
60 91.0f +2.86 0 0
Total 2.2g

aBased on 19771978 NFCS data provided to the subcom
mittee.
bBased on frequency distribution.
cz value = interval midpoint  mean requirement/SD.
dProbabilities for each Z value; determined by identi
fying area to right of Z in tables of standard normal dis
tribution.
eObtained by multiplying probability of inadequacy by
proportion of population in each interval.
fPercentages do not add to 100 due to rounding.
gPrevalence of inadequate protein intake in this popu
lation of adult males. Obtained by adding the percent
ages for each interval.
lated as 0.6 g/kg body weight/day with a coefficient of
variation (CV) of 1596. When this was applied to a 70kg
man in the RDA report, the mean requirement was 42 g/day
and the SD, 6.3 g/day. Using these parameters and a table
of areas under the normal distribution curve, one can
derive the proportion of individuals with actual require
ment above a specified level of intake, X. These values
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29
are portrayed in Figure 51. This approach was applied
to population data by Anderson et al. (1984).
The sample analysis in Table 51 is based on relatively
wide ranges of intake. Through the use of computers, very
narrow intervals can be analyzed, thus improving the accu
racy of the estimates. Only 11 intervals were used in
Table S1 because of the constraints of space in this
report. The use of such a small number of intake levels
is not recommended but is unlikely to cause major error.
In its other analyses, the subcommittee analyzed 200
intake intervals. If a parametric approach is used, the
actual distributions rather than points on the
distribution are analyzed. This approach is examined in
Chapter 8 and Appendix C.
The approach can be used with any known distribution
function. For example, the distribution of menstrual
iron losses is a major determinant of iron requirement
in females. This distribution approximates the lognormal
distribution. By logarithmic transformations of the data
on iron intake and use of the logarithmic requirement dis
tribution, a probability approach analogous to the one
described above can be applied. Figure S2 portrays the
probability, or risk, curve for inadequate iron intake by
menstruating women, derived as described in Appendix B.
REQUIREMENT INFORMATION NEEDED FOR THE PROBABILITY APPROACH
A knowledge of, or a reasonable assumption about, the
mean and shape of the requirement distribution for a par
ticular nutrient is necessary for the probability approach
to be applied. As discussed above, the underlying assump
tion of the method is that the correlation between nutrient
intake and requirement is low within reasonably homogeneous
groups of people. Where this is not true, as for energy,
the strength of the correlation must be estimated.
For many nutrients, precise descriptions of mean require
ment are not available. Indeed, presumably reliable descrip
tions are available only for protein and vitamin A in adults
(NRC, 1980) and for iron in menstruating women (FAD/WHO,
1970; Health and Welfare, Canada, 1983). For other age and
physiological groups and for other nutrients, there may be
reasonable estimates of the mean requirement, the range of
requirements, or perhaps both, but little or no direct knowl
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30
100
80
60'
40
20
o
'1 ~
\
\
\
\
\
\
\
l I I I I ~
0 2 4 6 8 10 12 14 16 18
IRON INTAKE, mg/day
FIGURE 52. Probability curve for iron intake in men
struating females. The curve describes the
probability, or risk, that an observed level
of iron intake would be inadequate to meet
the needs of a randomly selected female.
This curve is based on the assumption that
that menstrual loss follows a logno~=al
distribution. Based on 19771978 NFCS data
provided to the subcommittee. (Analysis
discussed in Appendix B.)
edge about the shape of the requirement distribution. In
this situation, it is instructive to determine through
sensitivity analysis the effect of a particular assumption
on the outcome of the analysis. This is done in the next
section by testing the effect of changing the mean and the
parameters of the requirement distribution on the estimate
of prevalence.
EFFECT OF REQUIREMENT DISTRIBUTION ON ESTIMATES OF THE
PREVALENCE OF INTAKE ADEQUACY
Influence of Mean and Standard Deviation of Requirement
NFCS data on vitamin C intake of adult men can be used
to illustrate how changes in the mean and the variability
of the requirement distribution can affect estimates of
the prevalence of inadequate intake. To account for changes
in the mean requirement, several hypothetical estimates of
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31
the mean requirement of vitamin C are used, ranging from
10 mg/day to 60 mg/day. To explore the effect of changes
in variability of requirement on the estimates, standard
deviation estimates of 2, 4, 6, 8, and 10 mg/day were used,
corresponding to coefficients of variation of 59s, 109,
1S9e, 20%, and 259 where the mean requirement is 40 mg/day.
Cable 52 presents the results of probability analyses
using each standard deviation estimate for each estimate
of the mean requirement.
TABLE 52. Estimated Prevalence of Inadequate Vito n C
Intakes by Adult Males: An Illustration of
the Sensitivity of the probability Approach to
the Mean and Variability of the Requirement
Distributiona
. . .
Estimated Predicted Prevalence of Inadequate Intakes ( % ) D
Mean of the by SD of Requirement
Requirement SDC SD SD SD SD
(mg/day) 2 mg/day 4 mg/day 6 mg/day 8 mg/day 10 mg/day
10 1.4 1.7 2.3 2.9 3.7
15 4.0 4.4 5.0 5.7 6.4
20 8.4 8.6 9.0 9.5 10.2
25 13.5 13.7 14.0 14.4 15.0
30 19.5 19.5 19.7 20.1 20.5
35 25.6 25.8 26.1 26.3 26.5
40 32.8 32.8 32.8 32.9 32.9
45 40.2 39.9 39.7 39.4 39.2
50 46.4 46.2 46.0 45.7 45.3
55 S1.9 51. 9 51. 7 S1 .4 51. 0
60 57.2 57.0 56.8 56.6 56.3
_
"Based on 19771978 NFCS data provided to the subc~ttee.
bInadeguate in defined as an intake below requirement.
CAss~ed standard deviation of the requirement distribu
tion.
When mean requirement is held constant and the standard
deviation is increased (across rows in Table 52), the pre
dicted prevalence estimates do not change substantially.
For examples for the estimated mean of 10mg/day, the prev
alence estimates range from 1. 4% to 3. 7~. For the highest
mean of 60 mg/day, the prevalence estimate only changes
from 57. 296 to 56. 396 at standard deviations of 2 and 10
mg/day, respectively. However, the prevalence estimates
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32
are greatly affected by changes in the mean requirement,
as shown by comparing the estimates down the columns of
the table. For a standard deviation of 2 mg/day, the esti
mated prevalence of inadequate intake increases from 1.4%
for a requirement of 10 mg/day to 57.2% for a requirement
of 60 mg/aay. This is generally true, regardless of the
estimated standard deviation. Emus, the prevalence of
inadequate intake is sensitive to the mean of the require
ment distribution but is not greatly affected by the vari
ance of the distribution.
Influence of the Shape of Requirement Distribution
Several nutrients were analyzed by the subcommittee to
determine how the shape of the distribution influences prev
alence estimates. These analyses demonstrated that the
estimated prevalence is similar whether a normal assumption
is used or whether the probability of inadequacy is assumed
to increase in a linear manner. In this case, a probabil
ity of O was assigned to the mean requirement +2 SD, and a
probability of 1.0 was assigned to 2 SD.
The results of these analyses demonstrate empirically
that the model is not particularly sensitive to either the
variance of the requirement distribution or the shape of
the distribution, assuming that the requirement distribu
tion is approximately symmetrical. For all nutrients
studied, the variability of the requirement appears to be
much smaller than the variability in the observed data on
nutrient intake. 'ibis is illustrated in Figure 53 in
which the probability curve is superimposed on the distri
bution of intakes. Generally, therefore, the errors of
overestimation and underestimation of the prevalence of
inadequate intake tend to cancel out, and except at the
ends of the intake distribution, the model is not sensi
tive to changes in the shape of the requirement distribu
tion, i.e., its symmetry.
These empirical findings seem to apply to all the intake
distributions examined by the subc~'littee (ascorbic acid,
protein, and vitamin A for adult males and females and
iron, thiam~n, and thiamin/l,OOO local for adult males),
assuming that requirements are distributed symmetrically
around the mean. When this condition is present, the
simplest empirical approach to estimating the prevalence
might be to determine the proportion of the study popula
tion with usual intakes below the mean requirement.
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33
123
11.1
98
z 86
o
~ 74
cat
a
cat
O—
3.7 i 7.\.
t2
o
USUAL INTAKE
1DAY INTAKE
e e ~ ~ e e RISK OF INADEQUATE
INTAKE
0 4080
8160 12240 16320 204
V ITAM I N A I NTAK E, I U/day
FIGURE 53. Oneday and adjusted distributions of vitamin A
intake of adult females and superimposed proba
bility curve. Note that the range in varia
tion of requirements is small in comparison to
range of intakes. It is assumed that require
ments are normally distributed with a 15% coef
ficient of variation. Based on 19771978 NFCS
data provided to the subcommittee.
Although this procedure will introduce some error into the
results, especially when the mean requirement is close to
the end of the intake distribution, the error is likely to
he within the confidence limits of the estimate. For exam
ple, in the first row of Table 52, it would be difficult to
assert that 1.4% is different from 3.7%. Conversely, one
might say with confidence that both are very low. Thus, the
use of any reasonable standard deviation of requirement will
improve the estimate of prevalence.
The subcommittee also explored the use of the probability
approach when the requirement distribution is highly asym
metrical, e.g., for iron requirements of menstruating women.
(See Appendix B for the details of this analysis.) The
relative shape of the curve is fixed by the known distribu
tion of menstrual iron losses. However, changing the
assumption about the upper lout of iron absorption will
also change the mean of the requirement distribution. In
Table 53, prevalence has been estimated with the proba
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34
bility approach. It has also been estimated by using the
logarithmic distribution of iron requirements and estimating
the proportion of cases falling below the median require
ment. To change the position of the requirement di~tri
bution in this table, various iron absorption rater have
been assumed. Iron absorption among women ingesting typical
NorthA~erican mixed diets and maintaining iron stores has
been estimated to be approximately 20% (FAD/WHO, 1970).
Thus, the table compares two approaches to assessment across
a family of requirement distributions.
When the requirement distribution is asymmetrical about
the median, as it is for menstruating women, the propor
tion of persons falling below the median requirement is not
a reliable estimate of the prevalence. (See last two col
umns of Table 53. ) The full probability approach in manda
tory. The reason for this may be that unlike the vitamin A
intake model in Figure 53, the range of the iron require
ment distribution encompasses a substantial part of the
range of the dietary intake distribution. Because of the
asymmetry of the requirement distribution, errors of under
and overest ~ tion on the two sides of the median require
TABLE 53 . Comparison of Probability Estimates of the
Prevalence of Inadequate Iron Intake and the
Proportion of Intakes Falling Below the Mean
Requirementa

Estimates
Derived Using
Median Require
Inferred Estimates
Assumed Median Derived f ram
Limit of Requirement Probability ment a" Cutoff
Absorption (96) (mg/day)b Approach Point (96)
14 9.39 S0.4 44.0
16 8.22 38.7 29.2
18 7.30 29.7 18.2
20 6.57 23.0 12.7
22 5.98 18.0 8.5
24 5.48 14.3 6.1
25 5.26 12.8 5.2
abased on 19771978 NFCS data for menstruating women
provided to the subcommittee.
bIn the requirement distribution model used, the median
physiological loss of iron is 1.32 mg/day.
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35
ment would not be expected to cancel each other our. For
iron, both the mean and the shape of the requirement dis
tribution are important. For this reason, the simplified
empirical approach in which the proportion below the median
is used as an estimate of the prevalence of inadequacy can
not be applied to iron intake in menstruating womenor to
any other nutrient where there is reason to believe that
there is strong asymmetry in the distribution.
Impact of the Mode in Which Requirements Are Expressed
It is generally accepted that nutrient requirement
estimates, and hence approaches to estimating the preva
lence of inadequate intakes, must take into account the
physiological variables of age, sex, pregnancy, and lac
tation. However, other variables that affect nutrient
requirement should also be taken into account.
Current nutrient requirement reports (FAO/WHO/UNU, in
press; Health and Welfare, Canada, 1983; NRC, 1980) recog
nize that body weight affects protein requirement. The
primary requirement estimate is usually expressed as grams
of protein per kilogram of body weight per day. Similarly,
energy intake affects thiamin requirements, which are usu
ally stated as mg/l,OOO kcal/day. In addition, at least
one report (Health and Welfare, Canada, 1983) recognizes
the inf luence of protein intake on vitamin B6 require
ment, which is given as grams of protein intake per day.
In those reports, estimates are often applied to a repre
sentat~ve subject to derive an estimate of recommended
intake per day, without reference to the original variable
that affected the requirement. This practice creates two
potential problems. First, the variance of requirement per
day may have been underestimated in this derivation. For
example, if the variability of requirement for protein per
kilogram of body weight has a CV of 15%, and this estimate
must be extended to adult men, the variability of their
body weights must be considered. The variability of protein
requirement per day must be greater than that for protein
requirement per kilogram of body weight per day (FAD/WHO/
UNU, in press). There are analogous situations for thiamin
and vitamin B6 requirements. To estimate prevalence, this
adjustment of variance should be taken into account. How
ever, since the final estimates of prevalence are not
seriously affected by the magnitude of the variance of the
requirement, this may not be a serious problem.
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36
The second problem is much more important. If the
variable of requirement (body weight, energy intake, or
protein intake, in the examples cited above) is also asso
ciated with dietary intake of the nutrient, then there will
be a spurious correlation between requirement ner dav and
intake per day.
~ ~ . ,
A large man, for example, can be expected
to have a higher protein requirement than a mall ma" and
is likely also to have a higher total food intake. In
addition, protein intake is likely to be larger, result
ing in a correlation between intake and requirement, unless
body size is controlled. This contradicts a basic assump
tion underlying the probability approach, i.e., that there
is a very low correlation between intake and requirement.
The simplest way to avoid this is to express both require
ment and intake in relation to common variables, e,.g., per
kilogram of body weight, per 1,000 koal, or per gram of
protein intake, as appropriate.
The impact of mode of expression on prevalence estimates
for thiamin is shown numerically in Table 54. The two
_ . .
estimates of prevalence presented were both derived with
the probability approach. One is based on an estimate of
thiamin requirement per day. The other is based on an
estimate of thiamin requirement per 1,000 kcal/day. Table
54 demonstrates that there is a substantial difference
between the estimates derived in these two ways because in
the second approach correlation between intake and require
ment is avoided and there is recognition that a person with
a low thiamin intake may also have a low energy intake and,
hence, a low but adequate requirement for thicken. In the
~ ~  ~
1980 Recommended Dietary Allowances, the proposed thiamin
allowance was expressed as 0.5 mg/1,000 kcal/day (NRC,
1980). The average requirement was not explicitly stated,
but the text implies that it is approximately 0.4 mg/l,000
kcal/day, with an implied CV of about 12.5%. In trans
lating these into intakes per day, the It tee on Dietary
Allowances assumed an average energy intake of about 3,000
cal/day for the young adult male and derived an RDA of
1.5 mg/day.
The imputed average requirement would be
approximately 1.2 mg/day. Two different expressions of
requirement distributions can be made: 0.4 + 0.05 mg/1,000
koal/day and 1.2 + 0.15 mg/day. The effects of these two
requirement estimates on the NFCS data are given in Table
54. The Committee on Dietary Allowances suggested that
the relationship of thicken requirement to energy intake
may not be consistent at levels of energy intake, specifi
cally less than 2,000 kcal/day. The modes of expression
given in Table S4 do not take this RDA into account.
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37
TABLE S4. Comparison of Two Approaches to the Assessment
of Thiam; n Intake for Adult Malesa
Mode of Expression
of Intake and
Requirement Data
Percentage of Population
Predicted to Have Intakes
Below Actual Requirements
mg/l, 00 0 koal/day
mg/day
3.4
36.9
aBased on data from 19771978 NFCS provided to the
subcommittee.
As illustrated by the analysis of thiamin, when a known
variable of requirement can be measured and applied in anal
ysis, it is correct to express both intake and requirement
in relation to this variable before applying the probabil
ity approach to assessment, If fully valid prevalence esti
mates are to be obtained.
Impact of Criteria for Requirement Estimate
As discussed earlier, the criteria that serve as the
conceptual framework for the requirement estimate have a sub
stantial effect on estimates of the prevalence of inadequate
intake. Two examples illustrate this principle. After exam
ining the literature, an FAD/WHO committee (FAD/WHO, 1970)
concluded that an ascorbic acid intake of 10 mg/day was more
than minimally adequate to prevent or cure scurvy in adult
men. Since the range of requirements for this criterion of
adequacy appeared to be 6 to 10 mg/day, one might assume a
mean requirement of 8 mg/day and a CV of 15%. The Committee
on Dietary Allowances agreed with this estimate of the
requirement to prevent scurvy; however, it accepted meta
bolic pool size as the basis for deriving the recommended
allowance and suggested that the upper tail of the require
ment range, the recommended intake, was approximately 60
mg/day (NRC, 1980). That c=~ittee's report suggests that
the CV of this requirement might be 15% to 20%. Estimates
of the requirement distribution for the maintenance of an
adequate body pool can be derived as a mean requirement of
45 mg/day with a CV of 15~. Using this logic, one can define
two different criteria for vitamin C requirement distribu
tions, and the results obtained with these two different cri
teria can be compared. To explore their impact on the preva
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38
fence of inadequate intake, these two requirement distribu
tions were applied to NFCS data. The resulting estimates of
inadequate intake for vitamin C are shown in Table 55.
In 1963, a committee (Health and Welfare, Canada, 1964)
reviewed the evidence and concluded that a thicken intake of
O. 2 mg/1, 000 koal/aay was adequate to prevent clinical signs
of beriberi in adults. An FAD/WHO (1967) committee cited
evidence suggesting that 0.23 mg/1,000 kcal was higher than
the requirement that would prevent any functional aberration
for at least 12 weeks. In the United States, the Committee
on Dietary Allowances (NRC, 1980) suggested that even lower
levels might be adequate to prevent beriberi. Making a
judgment based on these three estimates, the present subcom
mittee estimated that the average thiamin requirement for
the prevention of clinically detectable malfunction is
approximately 0.2 mg/1,000 koal per day, with a CV of about
15~. However, none of the committees believed that this
requirement was desirable for maintenance of a suitable
state of health, and all of them estimated requirements on
the basis of metabolic function or implied tissue levels.
For examples the Committee on Dietary Allowances (NRC, 1980)
recommended an intake of 0.5 mg/1,000 kcal/day but did not
specify the underlying requirement distribution. To illus
trate the importance of changes in nutrient requirement, an
TABLE 55. Prevalence of Inadequate Intake Estimated
with Two Different Assumptions About
Requirement Distributions of Adult Mena
Criterion of Adequacy
for Requirement
Estimated Prevalence of
Inadequate Intakes (I)
Ascorbic Acid Thiamine
Avoidance of clinically 0.7 0
detectable malfunction
Maintenance of tissue 39.6 3.4
levels or metabolic
pools

aBased on 19771978 NFCS data provided to the
subcommittee.
bThiamin intakes per 1,000 koal examined. No lower
limit was placed on absolute level of thiamin intake.
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39
average requirement of 0.4 mg/day with a CV of approximately
12.59 can be used. The prevalence of inadequate thicken
intake was determined for each of there two criteria as given
in Table 55.
As shown in Table 55, estimates of the prevalence of
inadequate intake depend on the criterion of nutritional
adequacy underlying the requirement estimate. Agreement
between the dietary assessment and a biochemical or clini
cal anses~ment of the same population depends to a large
extent on both the concordance between the underlying con
cepts of adequacy that have been used to net the dietary
requirement and the biochemical or clinical criterion. Not
surprisingly, current approaches often result in different
estimates of the prevalence of inadequate nutrition when the
some criterion of adequacy has not been used to establish
requirements for dietary intake and criteria for biochemical
mea~ur,nent~.
nparinon with Fixed Cutoff Approach
In Table 56, prevalence estimates derived with the
probability approach are compared with those based on the
fixed cutoff approach. The bases of the probability esti
mates were presented earlier in this chapter. Four arb4
trary cutoff points, expressed as percentages of the RDA
ONE:, 1980), have been used.
TABLE 56. Comparison of Estimates of the Prevalence of
Inadequate Intakes for Adults Using Probabll
ity and Fixed Cutoff Approachesa
Prevalence Estimates ( 9e ), by Approach Used
Nutrient
and Sex Probability
Group Approach___
Fixed Cutoff Approach
1009 RDA 8096 RDA 709 RDA 60% RDA
_
Protein 2.3
(males )
Vitamin C 39.6
(males )
Iron 23.0
( females)
6.S 2.4 1.3 0.8
57.5 44.5 36.3 27.1
98.2 91.2 81. 6 62 .5
aApplied to adjusted data from 19771978 NFCS provided to
the sun; ttee.
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40
It becomes Immediately apparent from the table that a
fixed cutoff approach may or may not give estimates of the
prevalence of inadequate intakes similar to those generated
with the probability approach. However, the cutoff point
that produces the similarity is specific to the character
istics of both the requirement distribution and intake dis
tribution. Thus it may or may not be correct. For this
reason, analysis of prevalence with cutoff points is not
recommended.
The possibility of using the mean nutrient requirement
as a cutoff point was also considered by the subcommittee.
m is would be a possible alternative if the following con
ditions apply: the requirement distribution is reasonably
symmetrical, the mean requirement does not fall in the tail
of the intake distribution, and the variance of dietary
intake is greater than the variance of the requirement for
that nutrient.
SU=A"
When the shape of the distribution of nutrient reguire
ment is known or can be inferred, a probability approach
to the assessment of observed nutrient intakes is the most
efficient and logical analytical approach. If, as is a
reasonable assumption for most nutrients, the requirements
are distributed relatively symmetrically about the mean or
median, the probability approach is sensitive to the esti
mate of the average requirement. It is, however, rela
tively insensitive to the shape and variance of the require
ment distribution in the assessment of population data.