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Statistics in Nutrition

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

European Journal of Clinical Nutrition volume 77, pages 325–334 (2023)Cite this article

Subjects

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 \(\rho _{{{\mathrm{A}}}} = 0.2\)).

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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.

References

  1. Knekt P, Kumpulainen J, Järvinen R, Rissanen H, Heliövaara M, Reunanen A, et al. Flavonoid intake and risk of chronic diseases. Am J Clin Nutr. 2002;76:560–8.

    Article  CAS  PubMed  Google Scholar 

  2. Hung HC, Joshipura KJ, Jiang R, Hu FB, Hunter D, Smith-Warner SA, et al. Fruit and vegetable intake and risk of major chronic disease. J Natl Cancer Inst. 2004;96:1577–84.

    Article  PubMed  Google Scholar 

  3. Murphy SP, Poos MI. Dietary Reference Intakes: summary of applications in dietary assessment. Public Health Nutr. 2002;5:843–9.

    Article  PubMed  Google Scholar 

  4. United States, Dietary Guidelines Advisory Committee. Dietary guidelines for Americans, 2010. U.S. Department of Health and Human Services, U.S. Department of Agriculture, 2010. Internet: https://play.google.com/store/books/details?id=jbHTXAkN3HYC&rdid=book-jbHTXAkN3HYC&rdot=1 (accessed 19 July 2021).

  5. Institute of Medicine (US) Subcommittee on Interpretation and Uses of Dietary Reference Intakes, Institute of Medicine (US) Standing Committee on the Scientific Evaluation of Dietary Reference Intakes. DRI Dietary Reference Intakes: Applications in Dietary Assessment. Washington (DC): National Academies Press (US), 2000. Internet: http://www.nap.edu/openbook.php?isbn=0309071836 (accessed 19 July 2021).

  6. Palaniappan U, Cue RI, Payette H, Gray-Donald K. Implications of day-to-day variability on measurements of usual food and nutrient intakes. J Nutr. 2003;133:232–5.

    Article  CAS  PubMed  Google Scholar 

  7. Tooze JA, Midthune D, Dodd KW, Freedman LS, Krebs-Smith SM, Subar AF, et al. A new statistical method for estimating the usual intake of episodically consumed foods with application to their distribution. J Am Diet Assoc. 2006;106:1575–87.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Dodd KW, Guenther PM, Freedman LS, Subar AF, Kipnis V, Midthune D, et al. Statistical methods for estimating usual intake of nutrients and foods: a review of the theory. J Am Diet Assoc. 2006;106:1640–50.

    Article  PubMed  Google Scholar 

  9. Guenther PM, Kott PS, Carriquiry AL. Development of an approach for estimating usual nutrient intake distributions at the population level. J Nutr. 1997;127:1106–12.

    Article  CAS  PubMed  Google Scholar 

  10. Carriquiry AL. Estimation of usual intake distributions of nutrients and foods. J Nutr. 2003;133:601S–8S.

    Article  PubMed  Google Scholar 

  11. Nusser SM, Carriquiry AL, Dodd KW, Fuller WA. A semiparametric transformation approach to estimating usual daily intake distributions. J Am Stat Assoc. 1996;91:1440–9.

    Article  Google Scholar 

  12. Hankin JH, Stram DO, Arakawa K, Park S, Low SH, Lee HP, et al. Singapore Chinese Health Study: development, validation, and calibration of the quantitative food frequency questionnaire. Nutr Cancer. 2001;39:187–95.

    Article  CAS  PubMed  Google Scholar 

  13. Johansson I, Hallmans G, Wikman A, Biessy C, Riboli E, Kaaks R. Validation and calibration of food-frequency questionnaire measurements in the Northern Sweden Health and Disease cohort. Public Health Nutr. 2002;5:487–96.

    Article  PubMed  Google Scholar 

  14. Araujo MC, Yokoo EM, Pereira RA. Validation and calibration of a semiquantitative food frequency questionnaire designed for adolescents. J Am Diet Assoc. 2010;110:1170–7.

    Article  PubMed  Google Scholar 

  15. Haubrock J, Nöthlings U, Volatier JL, Dekkers A, Ocké M, Harttig U, et al. European Food Consumption Validation Consortium. Estimating usual food intake distributions by using the multiple source method in the EPIC-Potsdam Calibration Study. J Nutr. 2011;141:914–20.

    Article  CAS  PubMed  Google Scholar 

  16. Nusser SM, Fuller WA, Guenther PM. Estimating usual dietary intake distributions: adjusting for measurement error and nonnormality in 24-hour food intake data. Center for Agricultural and Rural Development (CARD) Publications 95-sr80, Center for Agricultural and Rural Development (CARD) at Iowa State University, 1995.

  17. Dekkers AL, Verkaik-Kloosterman J, van Rossum CT, Ocké MC. SPADE, a new statistical program to estimate habitual dietary intake from multiple food sources and dietary supplements. J Nutr. 2014;144:2083–91.

    Article  CAS  PubMed  Google Scholar 

  18. Luo H, Dodd KW, Arnold CD, Engle-Stone R. A new statistical method for estimating usual intakes of nearly-daily consumed foods and nutrients through use of only one 24-hour dietary recall. J Nutr. 2019;149:1667–73.

    Article  PubMed  Google Scholar 

  19. Laureano GH, Torman VB, Crispim SP, Dekkers AL, Camey SA. Comparison of the ISU, NCI, MSM, and SPADE methods for estimating usual intake: a simulation study of nutrients consumed daily. Nutrients. 2016;8:166.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Tooze JA, Kipnis V, Buckman DW, Carroll RJ, Freedman LS, Guenther PM, et al. A mixed-effects model approach for estimating the distribution of usual intake of nutrients: the NCI method. Stat Med. 2010;29:2857–68.

    Article  PubMed  Google Scholar 

  21. National Cancer Institute. Usual dietary intakes: the NCI method. Internet: https://epi.grants.cancer.gov/diet/usualintakes/method.html (accessed 19 July 2021).

  22. Zeger SL, Liang KY, Albert PS. Models for longitudinal data: a generalized estimating equation approach. Biometrics. 1988;1049–60.

  23. Overall JE, Tonidandel S. Robustness of generalized estimating equation (GEE) tests of significance against misspecification of the error structure model. Biometrical J: J Math Methods Biosci. 2004;46:203–13.

    Article  Google Scholar 

  24. Carpenter M, Diawara N. A multivariate gamma distribution and its characterizations. Am J Math Manag Sci. 2007;27:499–507.

    Google Scholar 

  25. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, 2019. Internet: https://www.R-project.org/ (accessed 19 July 2021).

  26. SAS Institute. SAS 9.4 Statements: Reference. SAS Institute Cary, NC, 2013. Internet: https://www.sas.com/en_us/home.html (accessed 19 July 2021).

  27. Kweon S, Kim Y, Jang MJ, Kim Y, Kim K, Choi S, et al. Data resource profile: the Korea National Health and Nutrition Examination Survey (KNHANES). Int J Epidemiol. 2014;43:69–77.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Davis KA, Gonzalez A, Loukine L, Qiao C, Sadeghpour A, Vigneault M, et al. Early experience analyzing dietary intake data from the Canadian Community Health Survey-Nutrition using the National Cancer Institute (NCI) Method. Nutrients. 2019;11:1908.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Korea Disease Control and Prevention Agency (KDCA) website. https://knhanes.kdca.go.kr/knhanes/main.do.

  30. Kweon S. Use of secondary and tertiary food code in Korea National Health and Nutrition Examination Survey microdata. Public Health Wkly Rep., KCDC. 2012;5:158–64.

    Google Scholar 

  31. Bennett E, Peters SAE, Woodward M. Sex differences in macronutrient intake and adherence to dietary recommendations: findings from the UK Biobank. BMJ Open. 2018;8:e020017.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Hyndman RJ, Athanasopoulos G. Forecasting: principles and practice. OTexts, 2018.

  33. Souverein OW, Dekkers AL, Geelen A, Haubrock J, de Vries JH, Ocké MC, et al. EFCOVAL Consortium. Comparing four methods to estimate usual intake distributions. Eur J Clin Nutr. 2011;65:S92–101.

    Article  PubMed  Google Scholar 

  34. Australian Bureau of Statistics. Catalogue No 4363.0. 55.001-Australian Health Survey: users’ guide, 2011–13, 2013.

  35. Ethiopian Public Health Institute. Ethiopia national food consumption survey. Ethiopian Public Health Institute, Addis ababa, Ethiopia, 2013.

  36. Rivera JA, Pedraza LS, Aburto TC, Batis C, Sánchez-Pimienta TG, González de Cosío T, et al. Overview of the dietary intakes of the Mexican population: results from the National Health and Nutrition Survey 2012. J Nutr. 2016;146:1851S–5S.

    Article  CAS  PubMed  Google Scholar 

  37. Hill RJ, Davies PS. The validity of self-reported energy intake as determined using the doubly labelled water technique. Br J Nutr. 2001;85:415–30.

    Article  CAS  PubMed  Google Scholar 

  38. Trabulsi J, Schoeller DA. Evaluation of dietary assessment instruments against doubly labeled water, a biomarker of habitual energy intake. Am J Physiol Endocrinol Metab. 2001;281:E891–9.

    Article  CAS  PubMed  Google Scholar 

  39. Macdiarmid J, Blundell J. Assessing dietary intake: Who, what and why of under-reporting. Nutr Res Rev. 1998;11:231–53.

    Article  CAS  PubMed  Google Scholar 

  40. Subar AF, Kipnis V, Troiano RP, Midthune D, Schoeller DA, Bingham S, et al. Using intake biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: the OPEN study. Am J Epidemiol. 2003;158:1–13.

    Article  PubMed  Google Scholar 

  41. Krebs-Smith SM, Graubard BI, Kahle LL, Subar AF, Cleveland LE, Ballard-Barbash R. Low energy reporters vs others: a comparison of reported food intakes. Eur J Clin Nutr. 2000;54:281–7.

    Article  CAS  PubMed  Google Scholar 

  42. Basiotis PP, Welsh SO, Cronin FJ, Kelsay JL, Mertz W. Number of days of food intake records required to estimate individual and group nutrient intakes with defined confidence. J Nutr. 1987;117:1638–41.

    Article  CAS  PubMed  Google Scholar 

  43. Kim DW, Kyung Park M, Kim J, Oh K, Joung H, Lee JE, et al. Sources of variation in nutrient intake and the number of days to assess usual intake among men and women in the Seoul metropolitan area, Korea. Br J Nutr. 2013;110:2098–107.

    Article  CAS  PubMed  Google Scholar 

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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.

Author information

Authors and Affiliations

  1. Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Korea

    Sang Ah Chi

  2. Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea

    Sang Ah Chi & Kyunga Kim

  3. Department of Food and Nutrition, College of Human Ecology, Seoul National University, Seoul, Korea

    Heejin Lee & Jung Eun Lee

  4. Research Institute of Human Ecology, Seoul National University, Seoul, Korea

    Jung Eun Lee

  5. Department of Global Healthcare, Korea Health Industry Development Institute, Cheongju, Korea

    Haeng-Shin Lee

  6. Department of Data Convergence & Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Korea

    Kyunga Kim

  7. Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Korea

    Kyunga Kim

  8. Department of Statistics, Sookmyung Women’s University, Seoul, Korea

    In-Kwon Yeo

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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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The authors declare no competing interests.

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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 77, 325–334 (2023). https://doi.org/10.1038/s41430-022-01231-1

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