A New statistical method for estimating usual intakes of nearly-daily consumed foods and nutrients through use of only one 24-hour dietary recall

Abstract

Background: To estimate usual intake distributions of dietary components, collection of nonconsecutive repeated 24-h dietary recalls is recommended, but resource limitations sometimes restrict data collection to single-day dietary data per person. Objectives: We developed a new statistical method, the NCI 1-d method, which uses single-day dietary data and an external within-person to between-person variance ratio to estimate population distributions of usual intake of nearly-daily consumed foods and nutrients. Methods: We used NHANES 2011-2014 data for men (n = 4938 and n = 4293 for the first and second 24-h recalls) to compare nutrient intake distributions of vitamin A, magnesium, folate, and vitamin E generated by the 1-d method (with use of only the first recall per person) with those from the NCI amount-only method (with use of all days of dietary intake per person). The within-person to between-person variance ratio from the amount-only model was used as the unbiased "external" estimate for the 1-d method. We also examined the effect of mis-specification of variance ratios on usual intake distributions. Results: The amount-only and 1-d methods estimated statistically equivalent median (25p, 75p): 647 (459, 890) compared with 648 (461, 886) μg retinol activity equivalents/d, 338 (268, 420) compared with 334 (266, 417) mg magnesium/d, 595 (458, 762) compared with 589 (456, 758) μg dietary folate equivalents/d, and 9.7 (7.3, 12.6) compared with 9.6 (7.3, 12.7) mg vitamin E/d. As the external variance ratios increased from 25% to 200% of the unbiased ratios, the prevalence of inadequate intake ranged from 53% to 43% for vitamin A, 57% to 55% for magnesium, 16% to 2% for folate, and 70% to 73% for vitamin E. Conclusions: The 1-d method is a viable statistical method for estimating usual intakes of nearly-daily consumed dietary components when the variance ratio is unbiased. Results are sensitive to variance ratio selection, so researchers should still collect replicate data where possible.