Fruit and Vegetable Screener: Uses of Screener Estimates in CHIS

• Introduction
• Variance-Adjustment Factor
  • What is the variance adjustment estimate and why do we need it?
  • How did we estimate the variance adjustment factors?
  • How do you use the variance adjustment estimates?
  • When do you use variance adjustment estimates?
• Attenuation of Regression Parameters Using Screener Estimates
• References

Introduction

Dietary intake estimates from the California Health Interview Survey (CHIS) Fruit and Vegetable Screener are rough estimates of usual intake of fruits and vegetables. 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.

Variance-Adjustment Factor

What is the variance adjustment estimate and why do we need it?

Data from the CHIS Fruit and Vegetable 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 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 (variance). The variance of the screener, however, is expected to be smaller than the variance of true intake, since 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 itself. As a result, the screener estimates of intake cannot be used to estimate quantiles (other than median) or prevalence estimates of true intake without an adjustment. Procedures have been developed to estimate the variance of true intake using data from 24-hour recalls, by taking into consideration within person variability^1,2. We extended these procedures to allow estimation of the variance of true intake using data from the screener. The resulting variance adjustment factor adjusts the screener variance to approximate the variance of true intake in the population.

How did we estimate the variance adjustment factors?

We have estimated the adjustment factors in an external validation dataset available to us. The results indicate that the adjustment factors differ by gender: 1.2 for men and 1.1 for women. Under the assumption that the variance adjustment factors appropriate to the California Health Interview Survey are similar to those in the Eating in America's Table Study (EATS), the variance-adjusted screener estimate of intake should have variance closer to the estimated variance of true intake than would have been obtained from repeat 24-hour recalls. For a slightly different fruit and vegetable screener (7 rather than 8 items) validated in the Observing Protein and Energy Nutrition Study (OPEN), the variance adjustment factors are quite similar, which gives us some indication that these factors might be relatively stable from population to population.

How do you use the variance adjustment estimates?

To estimate quantile values or prevalence estimates for an exposure, you should first adjust the screener so that it has approximately the same variance as true intake.

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 - mean_{unadj scr.}) + mean_{unadj scr.}

This procedure is performed on the normally distributed version of the variable (i.e. Pyramid servings of fruits and vegetables is square-rooted). The results can then be squared, to obtain estimates in the original units.

A similar variance adjustment procedure is used to estimate prevalence of obtaining recommended 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.

When do you 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 objectives.

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. The "attenuation factor"³ can be estimated in a calibration study and used to deattenuate the estimated regression coefficient (by dividing the estimated regression coefficient by the attenuation factor).

We estimated attenuation factors in the EATS data (see below). If you use these factors to deattenuate estimated regression coefficients, note that the data come from a single study.

If you categorize the screener values into quantiles and use the resulting categorical variable 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 this^5,6, 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 there is only one covariate in the model measured with error; when there are multiple covariates 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.

References

1. National Research Council. Nutrient Adequacy: Assessment Using Food Consumption Surveys. Washington, DC: National Academy Press, 1986.
2. Institute of Medicine. Dietary Reference Intakes: Applications in Dietary Assessment. Washington, DC: National Academy Press, 2000.
3. Rosner B, Willett WC, Spiegelman D. Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error. Stat Med 1989;8:1051-69.
4. Flegal KM, Keyl PM, Nieto FJ. Differential misclassification arising from nondifferential errors in exposure measurement. Am J Epidemiol 1991;134:1233-44.
5. Flegal KM, Brownie C, Haas JD. The effects of exposure misclassification on estimates of relative risk. Am J Epidemiol 1986;123:736-51.
6. Morrissey MJ, Spiegelman D. Matrix methods for estimating odds ratios with misclassified exposure data: extensions and comparisons. Biometrics 1999;55:338-44.