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Estimation of the Usual Food Intake Distribution Reflecting the Consumption Frequency and a Comparison of the Proportion of Non-consumers: Based on the KNHANES 2009

섭취빈도가 반영된 식품의 일상섭취량 분포의 추정 및 비섭취자 비율의 비교 연구: - 국민건강영양조사 자료(2009년) 활용 -

  • Ham, Su Ji (Major of Food and Nutrition, Department of Human Ecology, Korea National Open University) ;
  • Kim, Dong Woo (Major of Food and Nutrition, Department of Human Ecology, Korea National Open University)
  • 함수지 (한국방송통신대학교 대학원 생활과학과 식품영양학전공) ;
  • 김동우 (한국방송통신대학교 생활과학부 식품영양학전공)
  • Received : 2021.07.23
  • Accepted : 2021.08.29
  • Published : 2021.08.31

Abstract

Objectives: The objective of this study was to estimate the distribution of the usual dietary intake of foods with respect to the probability of consumption derived from the Food Frequency Questionnaire (FFQ) of the 2009 Korea National Health and Nutrition Examination Survey (KNHANES). Methods: The intake quantity and frequency of 63 food items were assessed from the 2009 KNHANES which was completed by 7,708 participants. The participants completed one or two 24-h dietary recalls and one FFQ. The usual intake distribution was estimated using the multiple source method (MSM), and the proportion of non-consumers was calculated through the usual intake distribution. This was then compared with the proportion of non-consumers from the 24-hour recall method. Results: The difference in the proportion of non-consumers ranged from 2% to 82.9%, indicating that there is a very large difference based on food groups. The food groups in which the proportion of non-consumers did not differ was composed of foods consumed daily, such as 'rice', 'cereal and barley', and 'Chinese cabbage and kimchi', or foods with distinct palatability such as 'coffee' and 'alcohol'. On the other hand, in the case of the food groups with a high difference in the proportion of non-consumers, most comprised fruits that emphasized seasonality. Conclusions: In the case of foods or food groups that are occasionally consumed, it is desirable to use 2 recalls with additional FFQ data by combining the consumption frequency and the quantity consumed.

Keywords

Acknowledgement

This research was supported by Korea National Open University Research Fund.

References

  1. Getz GS, Reardon CA. Nutrition and cardiovascular disease. Arterioscler Thromb Vasc Biol 2007; 27(12): 2499-2506. https://doi.org/10.1161/ATVBAHA.107.155853
  2. Kim K, Yun SH, Choi BY, Kim MK. Cross-sectional relationship between dietary carbohydrate, glycaemic index, glycaemic load and risk of the metabolic syndrome in a Korean population. Br J Nutr 2008; 100(3): 576-584. https://doi.org/10.1017/S0007114508904372
  3. World Health Organization, Food and Agriculture Organization of the United Nations. Diet, nutrition and the prevention of chronic diseases. Geneva: World Health Organization; 2003.
  4. Kim S, Lee JS, Hong KH, Yeom HS, Nam YS, Kim JY et al. Development and relative validity of semi-quantitative food frequency questionnaire for Korean adults. J Nutr Health 2018; 51(1): 103-119. https://doi.org/10.4163/jnh.2018.51.1.103
  5. Choe JS. Evaluation of long-term dietary intakes of housewives. Korean J Community Living Sci 2004; 15(1): 91-104.
  6. Nusser SM, Fuller WA, Guenther PM. Estimating usual dietary intake distributions: Adjusting for measurement error and nonnormality in 24-hour food intake data. New York: Wiley; 1997.
  7. 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(10): 1640-1650. https://doi.org/10.1016/j.jada.2006.07.011
  8. Zhang S, Midthune D, Guenther PM, Krebs-Smith SM, Kipnis V, Dodd KW et al. A new multivariate measurement error model with zero inflated dietary data, and its application to dietary assessment. Ann Appl Stat 2011; 5(2B): 1456-1487.
  9. Carriquiry AL. Estimation of usual intake distributions of nutrients and foods. J Nutr 2003; 133(2): 601S-608S. https://doi.org/10.1093/jn/133.2.601S
  10. 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(10): 1575-1587. https://doi.org/10.1016/j.jada.2006.07.003
  11. Pereira JL, de Castro MA, Crispim SP, Fisberg RM, Isasi CR, Mossavar-Rahmani Y et al. Comparing methods from the National Cancer Institute vs Multiple Source Method for estimating usual intake of nutrients in the Hispanic community health study/study of Latino youth. J Acad Nutr Diet 2021; 121(1): 59-73. https://doi.org/10.1016/j.jand.2020.03.010
  12. Haubrock J, Nothlings U, Volatier JL, Dekkers A, Ocke M, Harttig U, Illner AK, Knuppel S, Andersen LF, Boeing H; 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(5): 914-20. https://doi.org/10.3945/jn.109.120394
  13. Hoffmann K, Boeing H, Dufour A, Volatier JL, Telman J, Virtanen M et al. Estimating the distribution of usual dietary intake by short-term measurements. Eur J Clin Nutr 2002; 56(2): S53-S62.
  14. Shamah-Levy T, Rodriguez-Ramirez S, Gaona-Pineda EB, Cuevas-Nasu L, Carriquiry AL, Rivera JA. Three 24-hour recalls in comparison with one improve the estimates of energy and nutrient intakes in an urban Mexican population. J Nutr. 2016; 146(5): 1043-50. https://doi.org/10.3945/jn.115.219683
  15. Harttig U, Haubrock J, Knuppel S, Boeing H. The MSM program: Web-based statistics package for estimating usual dietary intake using the Multiple Source Method. Eur J Clin Nutr 2011; 65(1): S87-S91. https://doi.org/10.1038/ejcn.2011.92
  16. Biro G, Hulshof K, Ovesen L, Cruz JA. Selection of methodology to assess food intake. Eur J Clin Nutr 2002; 56(2): S25-S32.
  17. Zhang S, Krebs-Smith SM, Midthune D, Perez A, Buckman DW, Kipnis V, Freedman LS, Dodd KW, Carroll RJ. Fitting a bivariate measurement error model for episodically consumed dietary components. Int J Biostat. 2011; 7(1): 1. https://doi.org/10.2202/1557-4679.1322
  18. Lee JY, Kim DW. Validation of food intake frequency from food frequency questionnaire for use as a covariate in a model to estimate usual food intake. Culin Sci Hosp Res 2017; 23(2): 64-73. https://doi.org/10.20878/cshr.2017.23.2.007
  19. Subar AF, Dodd KW, Guenther PM, Kipnis V, Midthune D, McDowell M et al. The food propensity questionnaire: Concept, development, and validation for use as a covariate in a model to estimate usual food intake. J Am Diet Assoc 2006; 106(10): 1556-1563. https://doi.org/10.1016/j.jada.2006.07.002
  20. Hartman AM, Brown CC, Palmgren J, Pietinen P, Verkasalo M, Myer D et al. Variability in nutrient and food intakes among older middle-aged men: Implications for design of epidemiologic and validation studies using food recording. Am J Epidemiol 1990; 132(5): 999-1012. https://doi.org/10.1093/oxfordjournals.aje.a115743
  21. Carroll RJ, Midthune D, Subar AF, Shumakovich M, Freedman LS, Thompson FE et al. Taking advantage of the strengths of 2 different dietary assessment instruments to improve intake estimates for nutritional epidemiology. Am J Epidemiol 2012; 175(4): 340-347. https://doi.org/10.1093/aje/kwr317
  22. Kipnis V, Midthune D, Buckman DW, Dodd KW, Guenther PM, Krebs-Smith SM et al. Modeling data with excess zeros and measurement error: Application to evaluating relationships between episodically consumed foods and health outcomes. Biometrics 2009; 65(4): 1003-1010. https://doi.org/10.1111/j.1541-0420.2009.01223.x