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Nutrition and Health (including climate and ecological aspects)

An ensemble method based on marginal-effect models (EMM) for estimating usual food intake from single-day dietary data and internal/external two-day dietary data

Abstract

Background

With collection of repeated 24-hour recalls, there exist challenges in usual intake estimation, including infeasibility of multiple dietary assessments, and shortage of non-zero intakes for episodically consumed foods.

Objectives

We developed an ensemble method based on marginal-effect models (EMM), which estimates usual intake distribution using single-day data with internal or external two-day data.

Methods

The performance of the EMM was evaluated and compared with the National Cancer Institute (NCI) method and NCI 1-d method, via simulations with various scenarios and real data analyses of red meat, fish, and protein from Korea National Health and Nutrition Examination Survey (KNHANES).

Results

Simulations indicated the EMM (maximum bias of 1.67, 3.17, 8.57, 11.63 for average, median, 75%-tile, 95%-tile, respectively) provided more accurate estimation than the NCI method (maximum bias of 4.18, 9.43, 7.56, 37.43) across various scenarios on intake probability and within-person variation. The EMM showed robust estimation when an insufficient number of people have positive consumption on two days. In simulations with various external variance ratios, the EMM showed similar or superior performance to the NCI 1-d method. The EMM produced more stable estimates of usual intake distributions for red meat, fish, and protein than the two NCI methods.

Conclusion

The proposed EMM showed substantial improvement over the NCI methods when data contain a relatively small number of people with positive consumption on two days; is robust when food intake probability is very low or high; and estimates an external variance ratio with relatively low bias.

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Fig. 1: Illustration of (A) overview of the EMM and (B) details of STEP 2 for each resample in the EMM.
Fig. 2: Boxplot of biases calculated by each method for scenarios with various within-person variations under (A) \(\theta = 60\%\) and (B) \(\theta = 90\%\).
Fig. 3: Boxplot of biases calculated by each method for scenarios with insufficient number of people who consumed a given food on two days under the parameters of scenario I-1 (\(n = 10,000\), \(\theta = 60\%\), \(\alpha = 1\), \(\rho _{{{\mathrm{P}}}} = 0.2\), and \(\rho _{{{\mathrm{A}}}} = 0.2\)).
Fig. 4: Boxplots of the bias calculated by each method for scenario II-1 (\(n = 10,000,\;\theta = 90\%\), \(\alpha = 1\), \(\rho _{{{\mathrm{P}}}} = 0.2\), and \(\rho _{{{\mathrm{A}}}} = 0.2\)).

Data availability

The 24-hour recall data and code book in KNHANES described in this manuscript are available upon request from the Korea Disease Control and Prevention Agency (KDCA) (https://knhanes.kdca.go.kr/knhanes/sub03/sub03_02_05.do). The analytic code for the EMM will not be available because it is in the patent application process, but we are open to collaborative research upon request.

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Funding

This work was supported by the Research Program funded by the Korea Centers for Disease Control and Prevention (2019E340200). This research was supported by a grant (17162MFDS026) from Ministry of Food and Drug Safety in 2017.

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Contributions

The author’s contributions were as follows. All authors contributed to the model design and interpretation of the data; KK and IKY designed the research; SAC conducted research, analyzed data, and wrote the paper; JEL, KK, and IKY obtained the funding; KK and IKY had primary responsibility for final content. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Kyunga Kim or In-Kwon Yeo.

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This study was conducted in accordance with the Ethical Guidelines of Epidemiological Research. This study was exempt from the applications of these guidelines because anonymized data was only used in this study.

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Chi, S.A., Lee, H., Lee, J.E. et al. An ensemble method based on marginal-effect models (EMM) for estimating usual food intake from single-day dietary data and internal/external two-day dietary data. Eur J Clin Nutr (2022). https://doi.org/10.1038/s41430-022-01231-1

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