Jacqueline Chan

Validation of nutrient intake using an FFQ and repeated 24 h recalls in black and white subjects of the Adventist Health Study-2 (AHS-2)

OBJECTIVE To validate a 204-item quantitative FFQ for measurement of nutrient intake in the Adventist Health Study-2 (AHS-2). DESIGN Calibration study participants were randomly selected from the AHS-2 cohort by church, and then subject-within-church. Each participant provided two sets of three weighted 24 h dietary recalls and a 204-item FFQ. Race-specific correlation coefficients (r), corrected for attenuation from within-person variation in the recalls, were calculated for selected energy-adjusted macro- and micronutrients. SETTING Adult members of the AHS-2 cohort geographically spread throughout the USA and Canada. SUBJECTS Calibration study participants included 461 blacks of American and Caribbean origin and 550 whites. RESULTS Calibration study subjects represented the total cohort very well with respect to demographic variables. Approximately 33 % were males. Whites were older, had higher education and lower BMI compared with blacks. Across fifty-one variables, average deattenuated energy-adjusted validity correlations were 0.60 in whites and 0.52 in blacks. Individual components of protein had validity ranging from 0.40 to 0.68 in blacks and from 0.63 to 0.85 in whites; for total fat and fatty acids, validity ranged from 0.43 to 0.75 in blacks and from 0.46 to 0.77 in whites. Of the eighteen micronutrients assessed, sixteen in blacks and sixteen in whites had deattenuated energy-adjusted correlations >or=0.4, averaging 0.60 and 0.53 in whites and blacks, respectively. CONCLUSIONS With few exceptions validity coefficients were moderate to high for macronutrients, fatty acids, vitamins, minerals and fibre. We expect to successfully use these data for measurement error correction in analyses of diet and disease risk.

Serum 25-hydroxyvitamin D status of vegetarians, partial vegetarians, and nonvegetarians: the Adventist Health Study-2

BACKGROUND Vegans and other vegetarians who limit their intake of animal products may be at greater risk of vitamin D deficiency than nonvegetarians, because foods providing the highest amount of vitamin D per gram naturally are all from animal sources, and fortification with vitamin D currently occurs in few foods. OBJECTIVE We assessed serum 25-hydroxyvitamin D [s25(OH)D] concentrations and factors affecting them in vegetarians, partial vegetarians, and nonvegetarians in a sample of calibration study subjects from the Adventist Health Study-2. DESIGN Food-frequency questionnaires and sun-exposure data were obtained from 199 black and 229 non-Hispanic white adults. We compared s25(OH)D concentration, dietary and supplemental vitamin D intake, and sun exposure in the different dietary groups. RESULTS We found no significant difference in s25(OH)D by vegetarian status for either white or black subjects. Among whites, dietary vitamin D intake and sun behavior were different between vegetarian groups, but there was no difference in skin type distribution. Among blacks, no significant differences were observed for any of these variables between vegetarian groups. The mean (+/-SD) s25(OH)D was higher in whites (77.1 +/- 10.33 nmol/L) than in blacks (50.7 +/- 27.4 nmol/L) (P < 0.0001). CONCLUSIONS s25(OH)D concentrations were not associated with vegetarian status. Other factors, such as vitamin D supplementation, degree of skin pigmentation, and amount and intensity of sun exposure have greater influence on s25(OH)D than does diet.

Missing data in a long food frequency questionnaire: are imputed zeroes correct?

Background: Missing data are a common problem in nutritional epidemiology. Little is known of the characteristics of these missing data, which makes it difficult to conduct appropriate imputation. Methods: We telephoned, at random, 20% of subjects (n = 2091) from the Adventist Health Study–2 cohort who had any of 80 key variables missing from a dietary questionnaire. We were able to obtain responses for 92% of the missing variables. Results: We found a consistent excess of “zero” intakes in the filled-in data that were initially missing. However, for frequently consumed foods, most missing data were not zero, and these were usually not distinguishable from a random sample of nonzero data. Older, black, and less-well-educated subjects had more missing data. Missing data are more likely to be true zeroes in older subjects and those with more missing data. Zero imputation for missing data may create little bias except for more frequently consumed foods, in which case, zero imputation will be suboptimal if there is more than 5%–10% missing. Conclusions: Although some missing data represent true zeroes, much of it does not, and data are usually not missing at random. Automatic imputation of zeroes for missing data will usually be incorrect, although there is a little bias unless the foods are frequently consumed. Certain identifiable subgroups have greater amounts of missing data, and require greater care in making imputations.