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A population’s mean Healthy Eating Index2005 scores are best
estimated by the score of the population ratio when one 24hour
recall is available1
Laurence S. Freedman, PhDa,2, Patricia M. Guenther, PhD, RDb, Susan M. KrebsSmith, PhD,
RDc, and Phillip S. Kott, PhDd
aGertner Institute for Epidemiology and Health Policy Research, Tel Hashomer, Israel
bCenter for Nutrition Policy and Promotion, U.S. Department of Agriculture, Alexandria, Virginia, U.S.
cNational Cancer Institute, Bethesda, Maryland, U.S.
dNational Agricultural Statistics Service, U.S. Department of Agriculture, Washington, DC, U.S.
Abstract3
The U.S. Department of Agriculture’s (USDA) Healthy Eating Index2005 (HEI2005) is a tool to
quantify and evaluate the quality of diet consumed by the US population. It comprises 12 components,
expressed as ratios of a food group or nutrient to energy intake. The components are scored on a
scale from 0 to M, where M is 5, 10 or 20. Ideally the HEI2005 is calculated on the basis of the
usual dietary intake of an individual. Intake data, collected via a 24hour recall, are often available
for only one day on each individual. In this paper, we examine how best to estimate a population’s
mean usual HEI2005 component and total scores when one day of dietary information is available
for a sample of individuals from the population. Three methods are considered: the mean of individual
scores, the score of the mean of individual ratios, and the score of the ratio of total food group or
nutrient intake to total energy intake, which we call the population ratio. We investigate via computer
simulation which method is the least biased. The simulations are based on statistical modeling of the
distributions of intakes reported by 738 women participating in the Eating at America’s Table Study.
The results show that overall the score of the population ratio is the preferred method. We therefore
recommend that the quality of the US population’s diet be assessed and monitored using this method.
Introduction
The U.S. Department of Agriculture’s (USDA) Healthy Eating Index2005 (HEI2005) is a
tool to quantify and evaluate the quality of the diet consumed by the US population in terms
of its conformity to the 2005 Dietary Guidelines for Americans [1,2]. It comprises 12
components, which are scored on a scale from 0 to M, where M is 5, 10 or 20 according to the
component. The maximum total score is 100. Each component is expressed as a ratio of an
individual’s intake of a specific food or nutrient to their intake of energy, before scoring.
1Appendix Parts AE and Supplemental Tables 12 are available as Online Supporting Material with the online posting of this paper at
http://jn.nutrition.org
3Abbreviations used in text: EATS, Eating at America’s Table Study; HEI, Healthy Eating Index; SoFAAS, Solid Fats, Alcoholic
beverages, and Added Sugars; USDA, United States Department of Agriculture; 24HR, 24 hour recall
Corresponding Author: Laurence S. Freedman.
2Laurence S. Freedman: no conflicts of interest; Patricia M. Guenther: no conflicts of interest; Susan M. KrebsSmith: no conflicts of
interest; Philip S. Kott: no conflicts of interest
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Published in final edited form as:
J Nutr. 2008 September ; 138(9): 1725–1729.
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Ideally, the HEI2005 should be calculated on the basis of the usual dietary intake of each
individual, that is, their mean intake over a specified period (often 1 year). This is in line with
the Institute of Medicine’s emphasis on assessing usual diets. Both the Institute of Medicine
and the Dietary Guidelines for Americans 2005 point out that recommendations are to be met
over the longterm [3,4]. In practice, the usual intake of an individual cannot be observed.
Often, only one day of food intake, collected via a 24hour recall, has been available. In such
circumstances, the HEI2005 component and total scores of each individual’s oneday intake
can be calculated, but this will lead to a biased measure of the individual’s HEI2005 score of
usual intake when the individual’s oneday food/nutrienttoenergy ratio is correlated with his/
her energy intake. More critically, even in the absence of such a correlation, the HEI2005
score on a single day can be a biased measure of the mean HEI2005 score across days because
the scoring system is truncated at 0 on one end and at 5, 10, or 20 at the other. As a result, the
longterm mean of HEI2005 scores on single days of intake differs from the score of the long
term mean intake over those days, which is what we want to measure.
USDA’s most important use of the HEI is to monitor the dietary intake of the population over
time. For this purpose the natural measure of the quality of the population’s diet is the
population’s mean HEI component and total scores, based on the usual intake of each
component. We will call these the population’s mean usual HEI component scores. In this
report, we examine three ways of estimating the population’s mean usual HEI2005 component
scores from data on a series of individuals, each of whom supplied a single 24hour recall
(24HR). With such limited data, no unbiased estimate is available. Our main concern is to
identify which of the three methods has the least bias.
Methods
The three methods that we compare are:
a.
For each individual, calculate each HEI2005 component score on the basis of his/
her 24HR. Then, for each component score and for the total score, take the (arithmetic)
mean over individuals. We call this the mean score. The HEI2005 total score is
calculated as the sum of these scores over the 12 components.
b.
For each individual and each component, calculate the ratio of the reported intake of
food group or nutrient (relevant to the HEI component considered) to the reported
energy intake. Then take the mean of these ratios over individuals. Finally, calculate
the HEI2005 component score based on this mean ratio. We call this the score of the
mean ratio. The HEI2005 total score is calculated as the sum of these scores over
the 12 components.
c.
Calculate the population’s total intake of food group or nutrient (relevant to the HEI
component considered) and the population’s total energy intake and take the ratio of
these. Then calculate the HEI2005 component score based on this ratio of the totals.
We call this the score of the population ratio. The HEI2005 total score is calculated
as the sum of these scores over the 12 components.
The names given to methods (b) and (c) follow those of KrebsSmith et al [5].
It is not immediately clear which method would be least biased, and one can construct different
numerical examples where each one of the three is the superior method. The methods must
therefore be tested with data that (a) are realistic and conform to typically reported values, and
(b) come from a population with known population mean usual HEI2005 component scores.
Unfortunately, available real datasets do not satisfy condition (b), so we employ instead
computer simulations of data generated from a statistical model that is based on real data.
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The dataset we used as a basis for our statistical model is drawn from the Eating at America’s
Table Study (EATS) [6]. The study was approved by the National Cancer Institute Special
Studies Institutional Review Board. The 738 women we studied were part of a nationally
representative sample. Participants were asked to complete four 24HRs via telephone over a
period of one year (1997–98), with one recall per season. Six hundred and fifty (88%) of these
women completed all four recalls. Foods reported on the 24hour recalls were coded using the
Food Intake Analysis System, version 2., which calculated total daily intakes for energy,
saturated fat and sodium. The food codes, in turn, were linked to the MyPyramid Equivalents
Database, version 1.0, in order to calculate total daily intakes of the food groups of interest.
Summary statistics on the first day’s reported intake of the 12 HEI2005 components (and
energy) were computed (Table 1). Note that the mean ratio is different from the population
ratio (final two columns of Table 1). In most cases, the mean ratio has the larger value; but for
Oils, Saturated Fat, and Solid Fats, Alcoholic beverages, and Added Sugars (SoFAAS), it has
the smaller value.
The statistical model forming the basis of our computer simulations was constructed under a
set of assumptions and calculations. All of the model parameters were estimated from the data
on the women participating in EATS. The details of the estimation procedures are contained
in online Appendix A. A brief description of how the model was formed is given below.
Some food groups are not consumed every day by all individuals. We refer to days on which
a given food group is consumed by a given individual as that individual’s “consumption days,”
the remaining days being the individual’s “nonconsumption days.”
First we made an assumption about the intake distributions. Distributions of intake on
consumption days, both between individuals and within individuals, were assumed to be
normal after a suitable power transformation. The power transformation for each food/nutrient
was individually chosen after inspection of the deciles of the distribution (see second column
of online Supplemental Table 1).
For food groups (but not for nutrients), there is a probability of nonconsumption on a single
day. We examined three assumptions regarding this probability, each of increasing complexity.
Assumption (i)
The probability of consumption is the same for all individuals.
Unfortunately, this assumption was not supported by the EATS data, where too many
individuals report consuming a particular food group on either no days or on all 4 days.
Therefore, we postulated the following.
Assumption (ii)
There are five subclasses of individuals consuming the food on 0%, 25%, 50%, 75% or 100%
of days, respectively. In addition, the distribution of intakes on consumption days is
independent of the probability of consumption.
The second part of assumption (ii), namely, that the distribution of intakes on consumption
days is independent of the individual’s probability of consumption can be readily checked
against data. Unfortunately, this independence assumption was also not supported by the EATS
data. In fact, reported intakes on consumption days have previously been reported to correlate
positively with the probability to consume [7]. This led us to the final assumption.
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Assumption (iii)
There are the same five probability of consumption subclasses as in assumption (ii), but each
has its own mean intake on consumption days.
Once the statistical model had been formulated, simulation programs were written in SPlus
(SPlus 2000, Professional Edition for Windows, Release 1, Seattle, 1999) that (a) generated
data from the food/nutrient and energy intake distributions under the three different
assumptions regarding the probability to consume and (b) computed the three estimates of
population mean HEI2005 component scores (mean score, score of the mean ratio, and score
of the population ratio). Each simulation generated a population of 10,000 persons, a single
day of intake (both the food/nutrient for the component of interest and energy) for that
individual to be used in computing the three estimates, and a true usual intake (both for the
food/nutrient and for energy) for each individual. The true usual intakes were used to compute
the true population mean HEI2005 component scores with which the estimates of this
population mean based on a oneday report could be compared. Further details of the
simulations may be found in online Appendix B.
Results
The mean of the true HEI2005 component scores over individuals and the three estimates
(mean score, score of mean ratio, and score of population ratio) were computed (Tables 2–4),
from simulations based on the three statistical models described in Section 2, online
Appendices A and B and online Supplemental Tables 1 and 2. Note that all values in Tables
2–4 are based on means of 10,000 simulated individuals; and that, consequently, standard errors
are very small and all differences are statistically significant.
The results presented in the three tables are qualitatively equivalent; the more sophisticated
modeling of the intake distributions seems to have had little impact. The results indicate that
of the three estimates, the one that usually comes closest to the true population mean HEI2005
component scores is the score of the population ratio. The estimate is not guaranteed to be
accurate, as can be seen from the tables; but for most of the components, this estimate is better
than the alternatives. The exceptions are Saturated Fat and SoFAAS. For Saturated Fat, the
mean score was superior; whereas for SoFAAS, the score of the mean ratio was superior. For
two other components (Total Grains, Meat and Beans), both the score of the population ratio
and the score of the mean ratio attained the maximum, which was closest to the true mean score
for these components. It is notable that for food groups, the mean score was consistently too
low and the score of the mean ratio was consistently too high. For the other components, the
pattern was less clear. For the HEI2005 total score, the score of the population ratio was clearly
superior.
To obtain a summary view of the accuracy of each method we averaged the absolute bias (that
is, the absolute difference between the estimate and the true value) over the 12 components
(final row of Tables 2–4). The maximum and minimum absolute bias taken over the twelve
components is also shown. The mean absolute bias was substantially lower when using the
score of the population ratio, than when using either of the other two estimators.
Discussion
The HEI2005 component scores are based upon ratios of reported intakes of food groups or
nutrients to that of total energy. Estimating distribution properties of ratios is always more
challenging than estimating those of single variables. Ratios may be complicated by
measurement errors and other variation in the denominator values and by correlations between
the denominator values with the ratio of the numerator to the denominator [5]. For example,
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an individual’s energy intake on a specific day is often positively correlated with his/her fat
toenergy ratio on that day.
Such complications are compounded by the HEI2005 component scores themselves, which
are nonlinear functions of a ratio, due to the truncation imposed at the minimum and maximum
scores. This nonlinearity can lead to bias even when the ratio itself is estimated without bias.
For example, consider the Whole Fruits component and imagine an individual who consumes
exactly 2000 kcal (8368 kJ) consistently each day. Suppose this individual consumes 1 cup
equivalent (240 mL) of whole fruit on half the days, but none on the other half. Then the mean
or “usual” ratio for the individual is 0.25 cupequivalents (60 mL) per 1000 kcal (4184 kJ),
leading to a score of 5 × 0.25/0.4 = 3.125, where 0.4 is the truncation point for the maximum
achievable score of 5. If we now determine the mean of the ratios over several days, we will
obtain over the longterm the correct 0.25 cupequivalents (60 mL) per 1000 kcal (4184 kJ)
(since energy intake is constant). If we determine means of the scores on individual days,
however, then over the longterm we will obtain a minimum score of 0 on half the days and a
maximum score of 5 on the other half, giving a mean of 2.5, and not the true value of 3.125.
These complications make it impossible to predict analytically which of the three proposed
estimates is likely to be the least biased. This suggests that the surest way of investigating the
matter is through computer simulation. Based on the results in Tables 2–4, the least biased of
the three methods to estimate population’s mean usual HEI2005 component scores is the score
of the population ratio.
Our conclusion is that one should estimate the population’s mean usual HEI2005 component
scores by calculating the score of the population ratio, that is, by taking the score of the ratio
of the total food/nutrient intake to energy intake. Nevertheless, this conclusion has some
caveats. The conclusion is empirically driven and depends on the US distributions of reported
intakes of the components included in the HEI2005, as well on the standards by which the
HEI2005 component scores are determined.
We have found in a sensitivity analysis that our conclusion is robust to the sampling errors
involved when estimating the parameters from the sample of 738 women participating in
EATS. The results are reported in online Appendix C. We have also examined distributions
of intake reported by men in the EATS study and by women in the Continuing Survey of Food
Intakes by Individuals, 1994–96 [8]. Although we have not fully modeled these data in the
same depth as the data on the women in EATS, we obtained a strong impression that the
distributional characteristics were very similar in the three groups (allowing for different levels
of absolute intake) and would lead to the same conclusions presented here.
Nevertheless, we are aware that substantial changes in intake distributions or in the scoring
standards could change the conclusions. For example, while developing the details of this work,
we noticed that changes in the chosen standards for the scores could change the performance
of the three methods that we examined.
It is important to check that the data used for calculating the population’s mean usual HEI
scores are representative of the usual intake of the population, even if usual intake cannot be
assessed in the individual participants. This requires that, in order to make inferences about
the US population, the data come from a nationally representative sample, the dietary reports
are collected for all seven days of the week with proportional representation weekend and week
days and seasons of the year. If probability samples rather than simple random samples are
used, then the appropriate weights must be employed when the population ratios of the total
food/nutrient intake to total energy intake are estimated. It is also advisable that the sample is
quite large, in the order of 1000 individuals or more, to ensure that the standard errors of the
estimates are relatively small.
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As mentioned above, we are confident that our conclusion holds true for currently available
US population data. However, we are not so sanguine with regard to minority subpopulations
of the US, nor with regard to populations in other countries. We recommend that researchers
interested in HEI2005 component scores in these populations carry out a similar exercise to
that reported here, simulating data that follow intake distributions reported in the population
of interest. Until evidence emerges for the superiority of another estimate, the score of the
population ratio would seem to be the best choice in such cases. We also recommend that
periodic checks be carried out to confirm that this measure remains optimal for the US
population because intake distributions may change.
With the caveats mentioned, we recommend estimating the population’s mean usual HEI2005
component scores by the score of the population ratio. Constructing a (twosided) 95%
confidence interval for this measure is recommended over estimating a standard error, as the
sampling distribution may be asymmetric. A 95% confidence interval for a component score
can be constructed using standard survey packages in the following manner. First, determine
the confidenceinterval for the associated population ratio with the package, and then score the
end points of the interval. A precision measure for the total HEI2005 score  the sum of the
12 component scores  is more difficult to develop. An algorithm is given in online Appendix
D.
Our main comparison of the three estimators was based on their biases and not on their standard
errors. We considered the standard error of the estimators to be of secondary importance to the
bias, because in the relatively large samples that we envisage the bias will dominate the error
of the estimate, especially in this case where the biases are often large. To check this further,
we computed from our simulation (under the assumption of a varying probability of
consumption that is correlated with amount of intake on consumption days) the standard error
of the three estimates that would be expected from a sample of 1000 individuals. The mean of
the standard errors taken over the 12 components were 0.09 for the mean score, 0.18 for the
score of the mean ratio, and 0.14 for the score of the population ratio, compared to mean
absolute biases of 0.73, 0.66 and 0.37 respectively. More details may be found in online
Appendix E.
Nutritional survey data sometimes include repeated dietary assessments on all or a subset of
participants. Such repeat assessments allow statistical modeling of withinperson variation and
offer the possibility of reducing the bias in estimating the population distribution of usual
intakes by using statistical modeling [8,9]. A future research aim will be to extend such methods
to estimate the US population distribution of the usual HEI2005 component scores. It is clearly
advantageous to be able to estimate the full distribution rather than just the mean. Furthermore,
if this can be implemented successfully, it would be a short further step to estimate the
population mean directly from these distributions. In principle, estimates of the population
mean derived in this manner should have minimal bias and could, therefore, be an improvement
over the best method when one 24HR is available, namely the score of the population ratio.
Currently, the score of the population ratio should be regarded as the principal method for
estimating the population mean usual HEI2005 component and total scores.
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgements
We thank Lisa Kahle for excellent assistance in data preparation and computing.
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Literature Cited
1. [Blinded for review] Development of the Healthy Eating Index2005. J Am Diet Assoc. in press
2. [Blinded for review] Evaluation of the Healthy Eating Index2005. J Am Diet Assoc. in press
3. Institute of Medicine. Dietary Reference Intakes: Applications in Dietary Assessment. National
Academy Press; Washington DC: 2000.
4. US Department of Health and Human Services and US Department of Agriculture. Dietary Guidelines
for Americans 2005. US Government Printing Office, Stock Number: 001000047191, 2005.
Available at website: http://www.healthierus.gov/dietaryguidelines
5. KrebsSmith SM, Kott PS, Guenther PM. Mean proportion and population proportion: two answers to
the same question? J Am Diet Assoc 1989;89:671–676. [PubMed: 2723291]
6. Subar AF, Thompson FE, Kipnis V, Midthune D, Hurwitz P, McNutt S, et al. Comparative validation
of the Block, Willett, and National Cancer Institute food frequency questionnaires: The Eating at
America’s Table Study. Am J Epidemiol 2001;154:1089–1099. [PubMed: 11744511]
7. Tooze JA, Midthune D, Dodd KW, Freedman LS, KrebsSmith SM, Subar AF, Guenther PM, Carroll
RJ, Kipnis V. A new statistical method for estimating the usual intake of episodically consumed foods
with application to their distribution. J Am Diet Assoc 2006;106:1575–1587. [PubMed: 17000190]
8. Tippett, Katherine S.; Cypel, Yasmin S., editors. U.S. Department of Agriculture, Agricultural
Research Service, Nationwide Food Surveys Report No. 96–1. 1997. Design and Operation: The
Continuing Survey of Food Intakes by Individuals and the Diet and Health Knowledge Survey, 1994–
96.
9. Dodd KW, Guenther PM, Freedman LS, Subar AF, Kipnis V, Midthune D, et al. Statistical methods
for estimating usual intake of nutrients and foods: a review of the theory. J Am Diet Assoc
2006;106:1640–1650. [PubMed: 17000197]
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Table 1
Summary of distributions of intake of selected foods, nutrients and energy, as reported on first day by 738 women participating in EATS
Dietary component
Mean (SD)
% zeros
Percentiles
Mean ratio to
energy ×103
Ratio of mean to
mean energy ×103
10
50
90
Energy, kcala
1753 (777)
0
892
1656
2679


Total fruit, cup equivb
0.94 (1.15)
20
0
0.56
2.60
0.58
0.54
Whole fruit, cup equivb
0.54 (0.81)
44
0
0.07
1.56
0.34
0.31
Total vegetables, cup equivb
1.62 (1.34)
4
0.22
1.34
3.31
1.00
0.92
Dark green, orange vegetables/legumes,
cup equivb
0.26 (0.46)
49
0
0.01
0.86
0.17
0.15
Total grains, oz. equivc
5.82 (3.33)
<1
2.09
5.44
10.02
3.39
3.32
Whole grains, oz.equivc
0.75 (1.18)
43
0
0.12
2.23
0.45
0.43
Milk, cup equivb
1.25 (1.25)
10
0
0.96
3.00
0.72
0.71
Meat/beans, oz. equivc
4.71 (3.77)
3
0.96
4.01
8.83
2.74
2.69
Oils, g
12.38 (14.96)
12
0
8.05
31.96
6.81
7.06
Saturated Fat, g
21.05 (15.11)
0
6.71
18.26
37.37
11.55
12.01
Sodium, g
3.10 (1.61)
0
1.45
2.81
5.06
1.83
1.77
Solid Fats, Alcoholic beverages, and
Added Sugars, kcala
651 (453)
0
203
574
1132
359
371
a1 kcal = 4.184 kJ
b1 cup equivalent = 240 mL
c1 oz. equivalent = 28g
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Table 2
Comparison of three estimates with the true mean HEI component scores, based on computer simulation*: model with
constant probability of consumption#
Component
(Range of Score)
True mean score Mean of individual
scores
Score of mean of
individual ratios
Score of the
ratio of the
means
Total Fruit (0–5)
Whole Fruit (0–5)
Total Vegetables (0–5)
Dark Green and Orange Vegetables
and Legumes (0–5)
Total Grains (0–5)
Whole Grains (0–5)
Milk (0–10)
Meat and Beans (0–10)
Oils (0–10)
Saturated Fat (0–10)
Sodium (0–10)
Calories from SoFAASb (0–20)
3.30
3.79
3.90
2.02
2.39
2.03
3.30
1.41
4.07
5.00
4.58
2.25
3.49 a
4.15 a
4.10 a
1.90 a
4.72
1.49
5.31
9.13
6.14
6.64
2.45
10.17
4.21
1.25
4.69
7.42
4.71
6.57 a
3.33
10.70
5.00 a
1.68
5.61
10.00 a
6.29
7.70
1.87
10.58 a
5.00 a
1.42 a
5.33 a
10.00 a
6.09 a
6.96
2.23 a
9.67
Total (0–100)59.0652.0164.63
60.34 a
Absolute bias**
mean (min, max)
 0.82
(0.07, 1.76)
0.56
(0.15, 1.21)
0.27 a
(0.02, 0.87)
*10,000 simulations for each component.
#Parameters of model estimated from data on 738 women participating in EATS
**Mean of the absolute differences between the estimated mean HEI2005 component scores and the true mean HEI2005 component scores, taken over
the 12 components.
aThe best of the three estimates
bSolid Fats, Alcoholic beverages and Added Sugars
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Table 3
Comparison of three estimates with the true mean HEI component scores, based on computer simulation*: model with
variable probability of consumption model, independent of intake on consumption days#
Component
(Range of Score)
True mean score Mean of individual
scores
Score of mean of
individual ratios
Score of the
ratio of the
means
Total Fruit (0–5)
Whole Fruit (0–5)
Total Vegetables (0–5)
Dark Green and Orange Vegetables
and Legumes (0–5)
Total Grains (0–5)
Whole Grains (0–5)
Milk (0–10)
Meat and Beans (0–10)
Oils (0–10)
Saturated Fat (0–10)
Sodium (0–10)
Calories from SoFAASb (0–20)
3.22
3.38
3.86
2.02
2.40
2.02
3.31
1.42
4.09
4.97
4.58
2.28
3.50 a
4.10 a
4.10 a
1.92 a
4.70
1.54
5.26
9.05
6.09
6.64
2.45
10.17
4.20
1.30
4.64
7.37
4.65
6.57 a
3.33
10.70
5.00 a
1.75
5.50
10.00 a
6.30
7.70
1.87
10.58 a
5.00 a
1.47 a
5.25 a
10.00 a
6.05 a
6.96
2.23 a
9.67
Total (0–100)58.3851.9164.62
60.25 a
Absolute bias**
mean (min, max)
0.77
(0.07, 1.68)
0.62
(0.21, 1.59)
0.31 a
(0.01, 0.95)
*10,000 simulations for each component.
#Parameters of model estimated from data on 738 women participating in EATS
**Mean of the absolute differences between the estimated mean HEI2005 component scores and the true mean HEI2005 component scores, taken over
the 12 components.
aThe best of the three estimates
bSolid Fats, Alcoholic beverages and Added Sugars
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Table 4
Comparison of three estimates with the true mean HEI component scores, based on computer simulation*: model with
variable probability of consumption, correlated with intake on consumption days#
Component
(Range of Score)
True mean scoreMean of
individual scores
Score of mean of
individual ratios
Score of the
ratio of the
means
Total Fruit (0–5)
Whole Fruit (0–5)
Total Vegetables (0–5)
Dark Green and Orange Vegetables
and Legumes (0–5)
Total Grains (0–5)
Whole Grains (0–5)
Milk (0–10)
Meat and Beans (0–10)
Oils (0–10)
Saturated Fat (0–10)
Sodium (0–10)
Calories from SoFAASb (0–20)
Total (0–100)
Absolute bias**
mean (min, max)
3.03
3.12
3.86
1.96
2.29
1.95
3.30
1.43
4.01
4.95
4.55
2.27
3.54 a
4.24 a
4.06 a
1.95 a
4.70
1.52
5.17
9.09
6.03
6.64
2.45
10.17
57.84

4.19
1.29
4.67
7.47
4.61
6.57 a
3.33
10.70
51.10
0.73
(0.07, 1.62)
5.00 a
1.82
5.60
10.00 a
6.19
7.70
1.87
10.58 a
64.54
0.66
(0.16, 1.83)
5.00 a
1.56 a
5.39 a
10.00 a
6.07 a
6.96
2.23 a
9.67
60.67 a
0.37 a
(0.01, 1.12)
*10,000 simulations for each component.
#Parameters of model estimated from data on 738 women participating in EATS
**Mean of the absolute differences between the estimated mean HEI2005 component scores and the true mean HEI2005 component scores, taken over
the 12 components.
aThe best of the three estimates
bSolid Fats, Alcoholic beverages and Added Sugars
J Nutr. Author manuscript; available in PMC 2008 November 10.