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Estimating Usual Food Intake Distributions by Using the Multiple Source Method in the EPIC-Potsdam Calibration Study1–3

2011; Elsevier BV; Volume: 141; Issue: 5 Linguagem: Inglês

10.3945/jn.109.120394

1541-6100

J Haubrock, Ute Nöthlings, Jean‐Luc Volatier, Arnold Dekkers, Marga C. Ocké, Ulrich Harttig, Anne‐Kathrin Illner, Sven Knüppel, Lene Frost Andersen, Heiner Boeing,

Biochemical Analysis and Sensing Techniques

Estimating usual food intake distributions from short-term quantitative measurements is critical when occasionally or rarely eaten food groups are considered. To overcome this challenge by statistical modeling, the Multiple Source Method (MSM) was developed in 2006. The MSM provides usual food intake distributions from individual short-term estimates by combining the probability and the amount of consumption with incorporation of covariates into the modeling part. Habitual consumption frequency information may be used in 2 ways: first, to distinguish true nonconsumers from occasional nonconsumers in short-term measurements and second, as a covariate in the statistical model. The MSM is therefore able to calculate estimates for occasional nonconsumers. External information on the proportion of nonconsumers of a food can also be handled by the MSM. As a proof-of-concept, we applied the MSM to a data set from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Calibration Study (2004) comprising 393 participants who completed two 24-h dietary recalls and one FFQ. Usual intake distributions were estimated for 38 food groups with a proportion of nonconsumers > 70% in the 24-h dietary recalls. The intake estimates derived by the MSM corresponded with the observed values such as the group mean. This study shows that the MSM is a useful and applicable statistical technique to estimate usual food intake distributions, if at least 2 repeated measurements per participant are available, even for food groups with a sizeable percentage of nonconsumers.