A statistical approach for estimating the distribution of usual dietary intake to assess nutritionally at-risk populations based on the new Japanese dietary reference intakes (DRIs)
The health of individuals is not markedly affected by the nutrients ingested in a single day; rather it is influenced by dietary habits over a long period of time. However, it is impossible to survey usual intake directly because recording diet over a long period of time is burdensome for the subjec...
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| Published in | Journal of Nutritional Science and Vitaminology Vol. 53; no. 4; pp. 337 - 344 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Tokyo
Center for Academic Publications Japan
01.08.2007
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0301-4800 1881-7742 1881-7742 |
| DOI | 10.3177/jnsv.53.337 |
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| Abstract | The health of individuals is not markedly affected by the nutrients ingested in a single day; rather it is influenced by dietary habits over a long period of time. However, it is impossible to survey usual intake directly because recording diet over a long period of time is burdensome for the subjects, so usual dietary intake is rarely measured directly. Instead, we estimated the distribution of selected nutrients in subjects' usual dietary intake using a statistical method (Best-Power method) described previously. And we assessed the proportion of nutritionally at-risk subjects in individual groups based on the new Japanese Dietary Reference Intakes (DRIs), the Estimated Average Requirement (EAR), or the tentative dietary goal for preventing life-style related diseases (DG) as cut-points. We collected the survey data from 208 men and 251 women, aged 50 to 69 y in 2004 and 2005. The survey was carried out on three non-consecutive days four times each year: in spring, summer, autumn, and winter. |
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| AbstractList | The health of individuals is not markedly affected by the nutrients ingested in a single day; rather it is influenced by dietary habits over a long period of time. However, it is impossible to survey usual intake directly because recording diet over a long period of time is burdensome for the subjects, so usual dietary intake is rarely measured directly. Instead, we estimated the distribution of selected nutrients in subjects' usual dietary intake using a statistical method (Best-Power method) described previously. And we assessed the proportion of nutritionally at-risk subjects in individual groups based on the new Japanese Dietary Reference Intakes (DRIs), the Estimated Average Requirement (EAR), or the tentative dietary goal for preventing life-style related diseases (DG) as cut-points. We collected the survey data from 208 men and 251 women, aged 50 to 69 y in 2004 and 2005. The survey was carried out on three non-consecutive days four times each year: in spring, summer, autumn, and winter. The distribution of nutrients in the usual intake was estimated from a dietary survey of 3 d using one-way analyses of variance. We found that the proportion of the population at risk for nutrient deficiency was overestimated in the 1-d intake distribution. On the other hand, the fraction that was nutritionally at-risk in terms of salt intake, according to DG as the cut-point, was underestimated in the 1-d intake distribution: 74.0% of men and 82.5% of women in the 1-d intake, and 90.5% and 93.2%, respectively, for the estimated usual intake adjusted for seasonal variation. The health of individuals is not markedly affected by the nutrients ingested in a single day; rather it is influenced by dietary habits over a long period of time. However, it is impossible to survey usual intake directly because recording diet over a long period of time is burdensome for the subjects, so usual dietary intake is rarely measured directly. Instead, we estimated the distribution of selected nutrients in subjects' usual dietary intake using a statistical method (Best-Power method) described previously. And we assessed the proportion of nutritionally at-risk subjects in individual groups based on the new Japanese Dietary Reference Intakes (DRIs), the Estimated Average Requirement (EAR), or the tentative dietary goal for preventing life-style related diseases (DG) as cut-points. We collected the survey data from 208 men and 251 women, aged 50 to 69 y in 2004 and 2005. The survey was carried out on three non-consecutive days four times each year: in spring, summer, autumn, and winter. The health of individuals is not markedly affected by the nutrients ingested in a single day; rather it is influenced by dietary habits over a long period of time. However, it is impossible to survey usual intake directly because recording diet over a long period of time is burdensome for the subjects, so usual dietary intake is rarely measured directly. Instead, we estimated the distribution of selected nutrients in subjects' usual dietary intake using a statistical method (Best-Power method) described previously. And we assessed the proportion of nutritionally at-risk subjects in individual groups based on the new Japanese Dietary Reference Intakes (DRIs), the Estimated Average Requirement (EAR), or the tentative dietary goal for preventing life-style related diseases (DG) as cut-points. We collected the survey data from 208 men and 251 women, aged 50 to 69 y in 2004 and 2005. The survey was carried out on three non-consecutive days four times each year: in spring, summer, autumn, and winter. The distribution of nutrients in the usual intake was estimated from a dietary survey of 3 d using one-way analyses of variance. We found that the proportion of the population at risk for nutrient deficiency was overestimated in the 1-d intake distribution. On the other hand, the fraction that was nutritionally at-risk in terms of salt intake, according to DG as the cut-point, was underestimated in the 1-d intake distribution: 74.0% of men and 82.5% of women in the 1-d intake, and 90.5% and 93.2%, respectively, for the estimated usual intake adjusted for seasonal variation.The health of individuals is not markedly affected by the nutrients ingested in a single day; rather it is influenced by dietary habits over a long period of time. However, it is impossible to survey usual intake directly because recording diet over a long period of time is burdensome for the subjects, so usual dietary intake is rarely measured directly. Instead, we estimated the distribution of selected nutrients in subjects' usual dietary intake using a statistical method (Best-Power method) described previously. And we assessed the proportion of nutritionally at-risk subjects in individual groups based on the new Japanese Dietary Reference Intakes (DRIs), the Estimated Average Requirement (EAR), or the tentative dietary goal for preventing life-style related diseases (DG) as cut-points. We collected the survey data from 208 men and 251 women, aged 50 to 69 y in 2004 and 2005. The survey was carried out on three non-consecutive days four times each year: in spring, summer, autumn, and winter. The distribution of nutrients in the usual intake was estimated from a dietary survey of 3 d using one-way analyses of variance. We found that the proportion of the population at risk for nutrient deficiency was overestimated in the 1-d intake distribution. On the other hand, the fraction that was nutritionally at-risk in terms of salt intake, according to DG as the cut-point, was underestimated in the 1-d intake distribution: 74.0% of men and 82.5% of women in the 1-d intake, and 90.5% and 93.2%, respectively, for the estimated usual intake adjusted for seasonal variation. |
| Author | Yokoyama, T Saito, K Ishiwaki, A.(National Inst. of Health and Nutrition, Tokyo (Japan)) Yoshita, K Yoshiike, N Fujii, H Nozue, M |
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| Keywords | Reference usual intake dietary assessment Japanese Requirement EAR (Estimated Average Requirement) Vertebrata Need DRIs (Dietary Reference Intakes) Mammalia Food intake statistical method Distribution Risk factor Ear Population |
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| References | 26) Palaniappan U, Cue RI, Payatte H, Gray-Donald K. 2003. Implications of day-to-day variability on measurements of usual food and nutrient intakes. J Nutr 133: 232-235. 3) Block G. 1982. A review of validations of dietary assessment methods. Am J Epidemiol 15: 492-505. 4) Hartman AM, Brown CC, Palmgren J, Pietinen P, Verkasalo M, Myer D, Virtamo J. 1990. Variability in nutrient and food intakes among older middle-aged men. Am J Epidemiol 132: 999-1012. 9) Ministry of Health and Welfare. 2005. Dietary Reference Intakes for Japanese, 2005. Daiichi Publishing, Tokyo (in Japanese). 30) Goldberg GR, Black AE, Jebb SA, Cole TJ, Murgatroyd PR, Coward WA, Prentice AM. 1991. Critical evaluation of energy intake data using fundamental principles of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. Eur J Clin Nutr 45: 569-581. 13) Dodd KW, Guenther PM, Freedman LS, Subar AF, Kipnis V, Midthune D, Tooze JA, Krebs-Smith SM. 2006. Statistical methods for estimating usual intake of nutrients and foods: a review of the theory. J Am Diet Assoc 106: 1640-1650. 20) Resources Council of the Science and Technology Agency. 2005. Standard Tables of Food Composition in Japan. Fifth Revised and Enlarged Edition. Ministry of Finance Printing Bureau, Tokyo (in Japanese). 22) Buzzard M. 1998. 24-Hour dietary recall and food record methods. In: Nutritional Epidemiology (Willett W, ed), 2nd ed, p 50-73. Oxford University Press, New York, NY. 17) de Lauzon B, Volatier JL, Martin A. 2004. A Monte Carlo simulation to validate the EAR cut-point method for assessing the prevalence of nutrient inadequacy at the population level. Public Health Nutr 7: 893-900. 6) Ogawa K, Tsubono Y, Nishino Y, Watanabe Y, Ohkubo T, Watanabe T, Nakatsuka H, Takahashi N, Kawamura M, Tsuji I, Hisamichi S. 1999. Inter- and intra-individual variation of food and nutrient consumption in a rural Japanese population. Eur J Clin Nutr 52: 781-785. 21) SAS Institute. 1999. SAS OnlineDoc, Version 8, SAS Institute Inc, Cary, NC. 29) Mackerras D, Rutishauser I. 2005. 24-Hour national dietary survey data: how do we interpret them most effectively? Public Health Nutr 8: 657-665. 28) Jahns L, Arab L, Carriquiry A, Popkin BM. 2005. The use of external within-individual variance estimates to adjust nutrient intake distributions over time and across populations. Public Health Nutr 8: 69-76. 5) Oh SY, Hong MH. 1999. Within- and between-person variation of nutrient intakes of older people in Korea. Eur J Clin Nutr 53: 625-629. 32) Fahey MT, Sasaki S, Kobayashi M, Akabane M, Tsugane S. 2003. Seasonal misclassification error and magnitude of true between-person variation in dietary nutrient intake: a random coefficients analysis and implications for the Japan Public Health Center (JPHC) Cohort Study. Public Health Nutr 6: 385-391. 10) Ministry of Health and Welfare. 2004. Kokumin eiyou no genjou (Annual Report of the National Health and Nutrition Survey in 2004). Daiichi Publishing, Tokyo (in Japanese). 14) Carriquiry AL. 2003. Estimation of usual intake distributions of nutrients and foods. J Nutr 133: 601S-608S. 18) The Subcommittee on Interpretation and Uses of Dietary Reference Intakes and the Standing Committee on the Scientific Evaluation of Dietary Reference Intakes. 2000. Dietary Reference Intakes Applications in Dietary Assessment. Institute of Medicine, National Academy Press, Washington DC. 16) Carriquiry AL. 1999. Assessing the prevalence of nutrient inadequacy. Public Health Nutr 2: 23-33. 19) Iwaoka H, Yoshiike N, Date C, Shimada T, Tanaka H. 2001. A validation study on a method to estimate nutrient intake by family members through a household based food-weighing survey. J Nutr Sci Vitamiol 47: 222-227. 23) Guenther PM, Kott PS, Carriquiry AL. 1997. Development of an approach for estimating usual nutrient intake distributions at the population level. J Nutr 127: 1106-1112. 7) Black AE, Cole TJ. 2001. Biased over- or under-reporting is characteristic of individuals whether over time or by different assessment methods. J Am Diet Assoc 101: 70-80. 31) Tokudome Y, Imaeda N, Nagaya T, Ikeda M, Fujiwara N, Sato J, Kuriki K, Kikuchi S, Maki S, Tokudome S. 2002. Daily, weekly, seasonal, within- and between-individual variation in nutrient intake according to four season consecutive 7 day weighed diet records in Japanese female dietitians. J Epidemiol 12: 85-92. 11) Subcommittee on Criteria for Dietary Evaluation, Coordinating Committee on Evaluation of Food Consumption Surveys, Food and Nutrition Board, National Research Council. 1986. Nutrient Adequacy Assessment Using Food Consumption Surveys. National Academies Press, Washington DC. 12) Nusser SM,. Carriquiry AL, Dodd KW, Fuller WA. 1996. A semiparametric transformation approach to estimating usual daily intake distributions. J Am Stat Assoc 91: 1440-1449. 24) Barr SI, Murphy SP, Poos MI. 2002. Interpreting and using the dietary references intakes in dietary assessment of individuals and groups. J Am Diet Assoc 102: 780-788. 8) Cai H, Shu XO, Hebert JR, Jin F, Yang C, Liu DK, Gao YT, Zheng W. 2004. Variation in nutrient intakes among women in Shanghai, China. Eur J Clin Nutr 58: 1604-1611. 15) Dwyer J, Picciano MF, Raiten DJ. 2003. Estimation of usual intake: What we eat in America—NHANES. J Nutr 133: 609S-623S. 1) Hankin JH, Reynolds WE, Margen S. 1967. A short dietary method for epidemiologic studies. Am J Clin Nutr 20: 935-945. 27) Carriquiry AL, Camano-Garcia G. 2006. Evaluation of dietary intake data using the tolerable upper intake levels. J Nutr 136: 507S-513S. 2) Beaton GH, Milner J, Corey P, McGuere V, Cousins M, Stewart E, de Ramos M, Hewitt D, Grambsch PV, Kassim N, Little JA. 1979. Sources of variation in 24-hour dietary recall data: implications for nutrition study design and interpretation. Am J Clin Nutr 32: 2546-2559. 25) Hoffmann K, Boeing H, Dufour A, Volatier JL, Telman J, Virtanen M, Becker W, De Henauw S. 2002. Estimating the distribution of usual dietary intake by short-term measurements. Eur J Clin Nutr 56 (Suppl 2): S53-S62. 22 24 25 27 28 29 (23) 1997; 127 30 10 32 11 12 13 14 15 16 17 18 19 PALANIAPPAN U (26) 2003; 133 1 2 3 4 5 6 7 8 9 TOKUDOME Y (31) 2002; 12 20 21 |
| References_xml | – reference: 1) Hankin JH, Reynolds WE, Margen S. 1967. A short dietary method for epidemiologic studies. Am J Clin Nutr 20: 935-945. – reference: 14) Carriquiry AL. 2003. Estimation of usual intake distributions of nutrients and foods. J Nutr 133: 601S-608S. – reference: 13) Dodd KW, Guenther PM, Freedman LS, Subar AF, Kipnis V, Midthune D, Tooze JA, Krebs-Smith SM. 2006. Statistical methods for estimating usual intake of nutrients and foods: a review of the theory. J Am Diet Assoc 106: 1640-1650. – reference: 24) Barr SI, Murphy SP, Poos MI. 2002. Interpreting and using the dietary references intakes in dietary assessment of individuals and groups. J Am Diet Assoc 102: 780-788. – reference: 31) Tokudome Y, Imaeda N, Nagaya T, Ikeda M, Fujiwara N, Sato J, Kuriki K, Kikuchi S, Maki S, Tokudome S. 2002. Daily, weekly, seasonal, within- and between-individual variation in nutrient intake according to four season consecutive 7 day weighed diet records in Japanese female dietitians. J Epidemiol 12: 85-92. – reference: 11) Subcommittee on Criteria for Dietary Evaluation, Coordinating Committee on Evaluation of Food Consumption Surveys, Food and Nutrition Board, National Research Council. 1986. Nutrient Adequacy Assessment Using Food Consumption Surveys. National Academies Press, Washington DC. – reference: 5) Oh SY, Hong MH. 1999. Within- and between-person variation of nutrient intakes of older people in Korea. Eur J Clin Nutr 53: 625-629. – reference: 27) Carriquiry AL, Camano-Garcia G. 2006. Evaluation of dietary intake data using the tolerable upper intake levels. J Nutr 136: 507S-513S. – reference: 22) Buzzard M. 1998. 24-Hour dietary recall and food record methods. In: Nutritional Epidemiology (Willett W, ed), 2nd ed, p 50-73. Oxford University Press, New York, NY. – reference: 32) Fahey MT, Sasaki S, Kobayashi M, Akabane M, Tsugane S. 2003. Seasonal misclassification error and magnitude of true between-person variation in dietary nutrient intake: a random coefficients analysis and implications for the Japan Public Health Center (JPHC) Cohort Study. Public Health Nutr 6: 385-391. – reference: 20) Resources Council of the Science and Technology Agency. 2005. Standard Tables of Food Composition in Japan. Fifth Revised and Enlarged Edition. Ministry of Finance Printing Bureau, Tokyo (in Japanese). – reference: 30) Goldberg GR, Black AE, Jebb SA, Cole TJ, Murgatroyd PR, Coward WA, Prentice AM. 1991. Critical evaluation of energy intake data using fundamental principles of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. Eur J Clin Nutr 45: 569-581. – reference: 17) de Lauzon B, Volatier JL, Martin A. 2004. A Monte Carlo simulation to validate the EAR cut-point method for assessing the prevalence of nutrient inadequacy at the population level. Public Health Nutr 7: 893-900. – reference: 28) Jahns L, Arab L, Carriquiry A, Popkin BM. 2005. The use of external within-individual variance estimates to adjust nutrient intake distributions over time and across populations. Public Health Nutr 8: 69-76. – reference: 8) Cai H, Shu XO, Hebert JR, Jin F, Yang C, Liu DK, Gao YT, Zheng W. 2004. Variation in nutrient intakes among women in Shanghai, China. Eur J Clin Nutr 58: 1604-1611. – reference: 25) Hoffmann K, Boeing H, Dufour A, Volatier JL, Telman J, Virtanen M, Becker W, De Henauw S. 2002. Estimating the distribution of usual dietary intake by short-term measurements. Eur J Clin Nutr 56 (Suppl 2): S53-S62. – reference: 18) The Subcommittee on Interpretation and Uses of Dietary Reference Intakes and the Standing Committee on the Scientific Evaluation of Dietary Reference Intakes. 2000. Dietary Reference Intakes Applications in Dietary Assessment. Institute of Medicine, National Academy Press, Washington DC. – reference: 6) Ogawa K, Tsubono Y, Nishino Y, Watanabe Y, Ohkubo T, Watanabe T, Nakatsuka H, Takahashi N, Kawamura M, Tsuji I, Hisamichi S. 1999. Inter- and intra-individual variation of food and nutrient consumption in a rural Japanese population. Eur J Clin Nutr 52: 781-785. – reference: 26) Palaniappan U, Cue RI, Payatte H, Gray-Donald K. 2003. Implications of day-to-day variability on measurements of usual food and nutrient intakes. J Nutr 133: 232-235. – reference: 4) Hartman AM, Brown CC, Palmgren J, Pietinen P, Verkasalo M, Myer D, Virtamo J. 1990. Variability in nutrient and food intakes among older middle-aged men. Am J Epidemiol 132: 999-1012. – reference: 19) Iwaoka H, Yoshiike N, Date C, Shimada T, Tanaka H. 2001. A validation study on a method to estimate nutrient intake by family members through a household based food-weighing survey. J Nutr Sci Vitamiol 47: 222-227. – reference: 7) Black AE, Cole TJ. 2001. Biased over- or under-reporting is characteristic of individuals whether over time or by different assessment methods. J Am Diet Assoc 101: 70-80. – reference: 12) Nusser SM,. Carriquiry AL, Dodd KW, Fuller WA. 1996. A semiparametric transformation approach to estimating usual daily intake distributions. J Am Stat Assoc 91: 1440-1449. – reference: 10) Ministry of Health and Welfare. 2004. Kokumin eiyou no genjou (Annual Report of the National Health and Nutrition Survey in 2004). Daiichi Publishing, Tokyo (in Japanese). – reference: 29) Mackerras D, Rutishauser I. 2005. 24-Hour national dietary survey data: how do we interpret them most effectively? Public Health Nutr 8: 657-665. – reference: 9) Ministry of Health and Welfare. 2005. Dietary Reference Intakes for Japanese, 2005. Daiichi Publishing, Tokyo (in Japanese). – reference: 23) Guenther PM, Kott PS, Carriquiry AL. 1997. Development of an approach for estimating usual nutrient intake distributions at the population level. J Nutr 127: 1106-1112. – reference: 21) SAS Institute. 1999. SAS OnlineDoc, Version 8, SAS Institute Inc, Cary, NC. – reference: 16) Carriquiry AL. 1999. Assessing the prevalence of nutrient inadequacy. Public Health Nutr 2: 23-33. – reference: 3) Block G. 1982. A review of validations of dietary assessment methods. Am J Epidemiol 15: 492-505. – reference: 2) Beaton GH, Milner J, Corey P, McGuere V, Cousins M, Stewart E, de Ramos M, Hewitt D, Grambsch PV, Kassim N, Little JA. 1979. Sources of variation in 24-hour dietary recall data: implications for nutrition study design and interpretation. Am J Clin Nutr 32: 2546-2559. – reference: 15) Dwyer J, Picciano MF, Raiten DJ. 2003. Estimation of usual intake: What we eat in America—NHANES. 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| Title | A statistical approach for estimating the distribution of usual dietary intake to assess nutritionally at-risk populations based on the new Japanese dietary reference intakes (DRIs) |
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