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Performance of statistical methods to correct food intake distribution: comparison between observed and estimated usual intake

  • Eliseu Verly-Jr (a1), Dayan C. R. S. Oliveira (a1), Regina M. Fisberg (a2) and Dirce Maria L. Marchioni (a2)
Abstract

There are statistical methods that remove the within-person random error and estimate the usual intake when there is a second 24-h recall (24HR) for at least a subsample of the study population. We aimed to compare the distribution of usual food intake estimated by statistical models with the distribution of observed usual intake. A total of 302 individuals from Rio de Janeiro (Brazil) answered twenty, non-consecutive 24HR; the average length of follow-up was 3 months. The usual food intake was considered as the average of the 20 collection days of food intake. Using data sets with a pair of 2 collection days, usual percentiles of intake of the selected foods using two methods were estimated (National Cancer Institute (NCI) method and Multiple Source Method (MSM)). These estimates were compared with the percentiles of the observed usual intake. Selected foods comprised a range of parameter distributions: skewness, percentage of zero intakes and within- and between-person intakes. Both methods performed well but failed in some situations. In most cases, NCI and MSM produced similar percentiles between each other and values very close to the true intake, and they better represented the usual intake compared with 2-d mean. The smallest precision was observed in the upper tail of the distribution. In spite of the underestimation and overestimation of percentiles of intake, from a public health standpoint, these biases appear not to be of major concern.

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Corresponding author
* Corresponding author: E. Verly-Jr, email eliseujunior@gmail.com
References
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1. Nusser, SM, Carriquiry, AL, Dodd, KW, et al. (1996) A semiparametric transformation approach to estimating usual daily intake distributions. J Am Stat Assoc 91, 14401449.
2. Freedman, LS, Commins, JM, Moler, JE, et al. (2014) Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake. Am J Epidemiol 180, 172188.
3. Beaton, GH, Milner, J, Corey, P, et al. (1979) Source of variation in 24-hour dietary recall data: implications for nutrition study design and interpretation. Am J Clin Nutr 32, 25462559.
4. Dodd, KW, Guenther, PM, Freedman, LS, et al. (2006) Statistical methods for estimating usual intake of nutrients and foods: a review of the theory. J Am Diet Assoc 106, 16401650.
5. Tooze, JA, Midthune, D, Dodd, KW, et al. (2006) A new statistical method for estimating the usual intake of episodically consumed foods with application to their distribution. J Am Diet Assoc 106, 15751587.
6. Hartig, U, Haubrock, J, Knüppel, S, et al. (2011) The MSM program: web-based statistics package for estimating usual dietary intake using the Multiple Source Method. Eur J Clin Nutr 65, S87S91.
7. Barbosa, FS, Sichieri, R & Junger, WL (2013) Assessing usual dietary intake in complex sample design surveys: the National Dietary Survey. Rev Saúde Pública 47, 171s176s.
8. Souverein, OW, Dekkers, AL, Geelen, A, et al. (2011) Comparing four methods to estimate usual intake distributions. Eur J Clin Nutr 65, S92S101.
9. Goedhart, PW, van der Voet, H, Knüppel, S, et al. (2012) A comparison by simulation of different methods to estimate the usual intake distribution for episodically consumed foods. Supporting Publications 2012:EN-299. http://www.efsa.europa.eu/sites/default/files/scientific_output/files/main_documents/299e.pdf (accessed July 2016).
10. Freedman, LS, Guenther, PM, Krebs-Smith, SM, et al. (2008) A population’s mean healthy eating index-2005 scores are best estimated by the score of the population ratio when one 24-hour recall is available. J Nutr 138, 17251729.
11. Conway, JM, Ingwersen, LA, Vinyard, BT, et al. (2003) Effectiveness of the US Department of Agriculture 5-step multiple-pass method in assessing food intake in obese and non obese women. Am J Clin Nutr 77, 11711178.
12. Verly, E Jr, Castro, MA, Fisberg, RM, et al. (2012) Precision of usual food intake estimates according to the percentage of individuals with a second dietary measurement. J Acad Nutr Diet 112, 10151020.
13. Kipnis, V, Subar, AF, Midthune, D, et al. (2003) Structure of dietary measurement error: results of the OPEN biomarker study. Am J Epidemiol 158, 1421.
14. Verly, E Jr, Fisberg, RM & Marchioni, DML (2013) Is the food frequency consumption essential as covariate to estimate usual intake of episodically consumed foods? Eur J Clin Nutr 66, 12541258.
15. Kipnis, V, Midthune, D, Buckman, DW, et al. (2009) Modeling data with excess zeros and measurement error: application to evaluating relationships between episodically consumed foods and health outcomes. Biometrics 65, 10031010.
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British Journal of Nutrition
  • ISSN: 0007-1145
  • EISSN: 1475-2662
  • URL: /core/journals/british-journal-of-nutrition
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