Diet Screener in CHIS 2005: Uses of Screener Estimates in CHIS
Dietary intake estimates from the California Health Interview Survey (CHIS) Diet
Screener are rough estimates of usual intake of fruits and vegetables and added sugar. They are
not as accurate as more detailed methods (e.g. 24-hour recalls). However, validation
research suggests that the estimates may be useful to characterize a population's median
intakes, to discriminate among individuals or populations with regard to higher vs. lower
intakes, to track dietary changes in individuals or populations over time, and to allow
examination of interrelationships between diet and other variables. In addition, diet
estimates from the CHIS could be used to augment national data using similar methods.
What is the variance adjustment estimate and why is it needed?
Data from the CHIS Diet Screener are individuals' reports about their
intake and, like all self-reports, contain some error. The algorithms we use to estimate
servings of fruits and vegetables and added sugar calibrate the data to 24-hour recalls. The screener
estimate of intake represents what we expect the person would have reported on his 24-hour
recall, given what he reported on the individual items in the screener. As a result, the
mean of the screener estimate of intake should equal the mean of the 24-hour recall
estimate of intake in the population. (It would also equal the mean of true intake in the
population if the 24-hour recalls were unbiased. However, there are many studies
suggesting that recalls underestimate individuals' true intakes).
When describing a population's distribution of dietary intakes, the parameters needed
are an estimate of central tendency (i.e., mean or median) and an estimate of spread
(i.e., variance). The variance of the screener, however, is expected to be smaller than
the variance of true intake because the screener prediction formula estimates the
conditional expectation of true intake given the screener responses, and in general, the
variance of a conditional expectation of a variable X is smaller than the variance of X
As a result, the screener estimates of intake cannot be used to estimate quantiles
(other than median) or prevalence estimates of true intake unless it is first adjusted so
that it has approximately the same variance as true intake.
When is it appropriate to use variance adjustment estimates?
The appropriate use of the screener information depends on the analytical objective.
Following is a characterization of suggested procedures for various analytical
|Estimate mean or median intake in the population or within subpopulations.
||Use the unadjusted screener estimate of intake.
|Estimate quantiles (other than median) of the distribution of intake in the population; estimate prevalence of attaining certain levels of dietary intake.
||Use the variance-adjusted screener estimate.
|Classify individuals into exposure categories (e.g., meeting recommended intake vs. not meeting recommended intake) for later use in a regression model.
||Use the variance-adjusted screener estimates to determine appropriate classification into categories.
|Use the screener estimate as a continuous covariate in a multivariate regression model.
||Use the unadjusted screener estimate.
How were the variance adjustment factors estimated?
We developed procedures to estimate the variance of true intake using data from 24-hour
recalls, by taking into consideration within-person
We extended these
procedures to allow estimation of the variance of true intake using data from the
screener. The resulting variance adjustment factors adjust the screener variance to
approximate the variance of true intake in the population.
We used two external validation datasets available to us to estimate the adjustment
Eating at America's Table Study (EATS) and the
Observing Protein and Energy Nutrition Study (OPEN).
The results indicate that the adjustment factors differ by gender
for each dietary variable. Under the assumption that the variance adjustment factors
appropriate to the
California Health Interview Survey
are similar to those in these
external studies, the variance-adjusted screener estimates of intake should have variances
closer to the estimated variance of true intake than would have been obtained from repeat
||Variance Adjustment Factors
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How are the variance adjustment factors applied?
Adjust the screener estimate of intake by:
- multiplying intake by an adjustment factor (an estimate of the ratio of the standard
deviation of true intake to the standard deviation of screener intake); and
- adding a constant so that the overall mean is unchanged.
The formula for the variance-adjusted screener is:
variance-adjusted screener = (variance adjustment factor)*(unadjusted screener
- meanunadj scr.) + meanunadj scr.
This procedure is performed on the normally distributed version of the variable (i.e.,
Pyramid servings of fruits and vegetables is square-rooted; teaspoons of added sugar is
cube-rooted). The results can then be back-transformed to obtain estimates in the original
A similar variance adjustment procedure is used to estimate prevalence of intakes for
the 2000 NHIS in:
Thompson FE, Midthune D, Subar AF, McNeel T, Berrigan D, Kipnis V. Dietary
intake estimates in the National Health Interview Survey, 2000: Methodology, results, and
interpretation. J Am Dietet Assoc 2005;105:352-63.
Attenuation of Regression Parameters Using Screener Estimates
When the screener estimate of dietary intake is used as a continuous covariate in a
multivariate regression, the estimated regression coefficient will typically be attenuated
(biased toward zero) due to measurement error in the screener. This
can be estimated in a calibration study and used to deattenuate the estimated regression
coefficient (by dividing the estimated regression coefficient by the attenuation
We estimated attenuation factors in the EATS and OPEN data (see the following
||Attenuation factors for screener-predicted intake
If the screener values are categorized into quantiles and the resulting categorical
variable is used in a linear or logistic regression, the bias (due to misclassification)
is more complicated because the categorization can lead to differential misclassification
in the screener4.
Although methods may be available to correct for this5,6,
it is not simple, nor are we comfortable suggesting how to do it at this time.
Even though the estimated regression coefficients are biased (due to measurement error
in the screener or misclassification in the categorized screener), tests of whether the
regression coefficient is different from zero are still valid. For example, if one used
the SUDAAN REGRESS procedure with fruit and vegetable intake (estimated by the screener)
as a covariate in the model, one could use the Wald F statistic provided by SUDAAN to test
whether the regression coefficient were statistically significantly different from zero.
This assumes that only one covariate in the model is measured with error; when multiple
covariates are measured with error, the Wald F test that a single regression coefficient
is zero may not be valid, although the test that the regression coefficients for all
covariates measured with error are zero is still valid.
- National Research Council. Nutrient Adequacy: Assessment Using Food Consumption
Surveys. Washington, DC: National Academy Press, 1986.
- Institute of Medicine. Dietary Reference Intakes: Applications in Dietary Assessment.
Washington, DC: National Academy Press, 2000.
- Rosner B, Willett WC, Spiegelman D. Correction of logistic regression relative risk
estimates and confidence intervals for systematic within-person measurement error. Stat
- Flegal KM, Keyl PM, Nieto FJ. Differential misclassification arising from
nondifferential errors in exposure measurement. Am J Epidemiol 1991;134:1233-44.
- Flegal KM, Brownie C, Haas JD. The effects of exposure misclassification on estimates
of relative risk. Am J Epidemiol 1986;123:736-51.
- Morrissey MJ, Spiegelman D. Matrix methods for estimating odds ratios with
misclassified exposure data: extensions and comparisons. Biometrics 1999;55:338-44.
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