The Diet Screener in the 2005 California Health Interview Survey
The screener used in the 2005 California Health Interview Survey
(CHIS) was derived from the Five-Factor Screener in
the 2005 NHIS Cancer Control Supplement (CCS). The 2005 CHIS screener asks
respondents for information about how frequently they consume foods in 11 categories. No
portion size questions are asked.
This screener does not attempt to assess total diet. The questions allow researchers to
gather information about intakes of fruits and vegetables and teaspoons of added sugar.
Fruit and vegetable intake is quantified using two different metrics. The Pyramid
servings metric is based on the 1992 definitions of servings from the Food Guide Pyramid.
The cup equivalents metric is based on the 2005 definitions, derived from Dietary Guidelines for Americans.
The 2005 CHIS Diet Screener is composed of QA05_C14 to QA05_C24 of the 2005
CHIS Adult Questionnaire The following variables in the 2005 CHIS Adult data
(available at http://www.chis.ucla.edu/)
were derived by the procedures outlined here: FV, FV_ADJ, FVNB, FVNB_ADJ, FVNF,
FVNF_ADJ, FVNFB, FVNFB_AD, FVCE, FVCAD, FVCNB, FVCNBAD, FVCNF, FVCNFAD,
FVCNFB, FVCNFBAD, SUG, and SUG_ADJ. Note that
the variables SUG, SUG_ADJ, FVNB, and FVNB_ADJ were corrected/modified 2/27/2008.
In CHIS 2005, we applied rules for excluding extreme data responses, described in Definition of Acceptable Dietary Data. The process of scoring
the individual response data is described in Scoring
Procedures. A description and guidelines for the appropriate uses of the
screener-estimated dietary intakes is found in Uses of Screener
Estimates. Validation data for the CHIS 2005 screener are presented in Validity Results.
NOTE: The dietary variables on the CHIS dataset are in their natural units. For
analyses, however, they must be transformed, first, to approximate normal distributions.
For servings of fruits and vegetables and cup equivalents of fruits and vegetables, use
the square root transformation; for teaspoons of added sugar, use the cube root
transformation. After analyses, the result variables can be back-transformed for easier
interpretation.
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