Hostname: page-component-745bb68f8f-grxwn Total loading time: 0 Render date: 2025-01-08T08:00:31.308Z Has data issue: false hasContentIssue false
  • English
  • Français

The use of external within-person variance estimates to adjust nutrient intake distributions over time and across populations

Published online by Cambridge University Press:  02 January 2007

Lisa Jahns ,
Lenore Arab ,
Alicia Carriquiry  and
Barry M Popkin
Lisa Jahns
Affiliation:
Department of Nutrition, University of Tennessee, Knoxville, TN, USA
Lenore Arab
Affiliation:
Department of Epidemiology, School of Public Health, University of North Carolina, Chapel Hill, NC, USA
Alicia Carriquiry
Affiliation:
Department of Statistics, Iowa State University, Ames, IA, USA
Barry M Popkin*
Affiliation:
Department of Nutrition, School of Public Health, University of North Carolina, Chapel Hill, NC, USA Carolina Population Center, University of North Carolina at Chapel Hill, CB #8120 University Square, 123 W. Franklin Street, Chapel Hill, NC 27516-3997, USA
*
*Corresponding author: Email [email protected]
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Objective

To examine the utility of using external estimates of within-person variation to adjust usual nutrient intake distributions.

Design

Analyses of the prevalence of inadequate intake of an example nutrient by the Estimated Average Requirement (EAR) cut-point method using three different methods of statistical adjustment of the usual intake distribution of a single 24-hour recall in Russian children in 1996, using the Iowa State University method for adjustment of the distribution. First, adjusting the usual intake distribution with day 2 recalls from the same 1996 sample (the correct method) second, adjusting the distribution using external variance estimates derived from US children in 1996; and third, adjusting the distribution using external estimates derived from Russian children of the same age in 2000. We also present prevalence estimates based on naïve statistical analysis of the unadjusted distribution of intakes.

Setting/subjects

Children drawn from the Russia Longitudinal Monitoring Survey in 1996 and 2000 and from the 1996 Continuing Survey of Food Intakes by Individuals.

Results

When the EAR cut-point method is applied to a single recall, the resulting prevalence estimate in this study is inflated by 100–1300%. When the intake distribution is adjusted using an external variance estimate, the prevalence estimate is much less biased, suggesting that any adjustment may give less biased estimates than no adjustment.

Conclusions

In moderately large samples, adjusting distributions with external estimates of variances results in more reliable prevalence estimates than using 1–day data.

Keywords

ChildrenEstimated Average RequirementIntake distributionPC-SIDERussiaWithin-person variation
Type
Research Article
Information
Public Health Nutrition , Volume 8 , Issue 1 , February 2005 , pp. 69 - 76
Copyright
Copyright © CABI Publishing 2005

References

1Dwyer, J, Picciano, MF, Raiten, DJ. Future directions for the integrated CSFII–NHANES: What We Eat in America–NHANES. Journal of Nutrition 2003; 133: 576S–81S.CrossRefGoogle ScholarPubMed
2Woteki, CE. Integrated NHANES: uses in national policy. Journal of Nutrition 2003; 133: 582S–4S.CrossRefGoogle ScholarPubMed
3Nusser, SM, Carriquiry, AL, Dodd, K, Fuller, WA. A semiparametric transformation approach to estimating usual daily intake distributions. Journal of the American Statistical Association 1996; 91: 1440–9.CrossRefGoogle Scholar
4Basiotis, 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 desired confidence. Journal of Nutrition 1987; 117: 1638–41.CrossRefGoogle Scholar
5Carriquiry, AL. Estimation of usual intake distributions of nutrients and foods. Journal of Nutrition 2003; 133: 601S–8S.CrossRefGoogle ScholarPubMed
6Dwyer, J, Picciano, MF, Raiten, DJ. Estimation of usual intakes: What We Eat in America–NHANES. Journal of Nutrition 2003; 133: 609S–23S.CrossRefGoogle ScholarPubMed
7Gay, C. Estimation of population distributions of habitual nutrient intake based on a short-run weighed food diary. British Journal of Nutrition 2000; 83: 287–93.CrossRefGoogle ScholarPubMed
8Guenther, PM, Kott, PS, Carriquiry, AL. Development of an approach for estimating usual nutrient intake distributions at the population level. Journal of Nutrition 1997; 127: 1106–12.CrossRefGoogle ScholarPubMed
9Kohlmeier, L, Bellach, B. Exposure assessment error and its handling in nutritional epidemiology. Annual Review of Public Health 1995; 16: 43–59.CrossRefGoogle ScholarPubMed
10Willett, W. Nutritional Epidemiology. New York: Oxford University Press, 1998.CrossRefGoogle Scholar
11Beaton, GH, Milner, J, Corey, P, McGuire, V, Cousins, M, Stewart, E, et al. . Sources of variance in 24-hour dietary recall data: implications for nutrition study design and interpretation. American Journal of Clinical Nutrition 1979; 32: 2546–9.CrossRefGoogle ScholarPubMed
12 Institute of Medicine. Dietary Reference Intakes. Applications in Dietary Assessment. Washington, DC: National Academy Press, 2000.Google Scholar
13Arab, L, Carriquiry, A, Steck-Scott, S, Gaudet, MM. Ethnic differences in the nutrient intake adequacy of premenopausal US women: results from the Third National Health Examination Survey. Journal of the American Dietetic Association 2003; 103: 1008–14.CrossRefGoogle ScholarPubMed
14Pobocik, RS, Benavente, JC, Boudreau, NS, Spore, CL. Pregnant adolescents in Guam consume diets low in calcium and other micronutrients. Journal of the American Dietetic Association 2003; 10: 611–4.CrossRefGoogle Scholar
15Jahns, L, Arab, L, Carriquiry, A. Dietary Reference Intakes still used incorrectly in journal articles. Journal of the American Dietetic Association 2003; 10: 1292–3.CrossRefGoogle Scholar
16Barr, SI, Murphy, SP, Poos, MI. Interpreting and using the dietary references intakes in dietary assessment of individuals and groups. Journal of the American Dietetic Association 2002; 102: 780–8.CrossRefGoogle ScholarPubMed
17Murphy, SP, Poos, MI. Dietary Reference Intakes: summary of applications in dietary assessment. Public Health Nutrition 2002; 5: 843–9.CrossRefGoogle ScholarPubMed
18Murphy, SP. Collection and analysis of intake data from the integrated survey. Journal of Nutrition 2003; 133: 585S–9S.CrossRefGoogle ScholarPubMed
19Popkin, BM, Baturin, A, Kohlmeier, L, Zohoori, N. Russia: monitoring nutritional change during the reform period. In: Wheelock, V, ed. Implementing Dietary Guidelines for Healthy Eating. London: Blackie A&P, 1997.Google Scholar
20Zohoori, N, Mroz, T, Popkin, BM, Glinskaya, E, Lokshin, M, Mancini, DJ, et al. . Monitoring the economic transition in the Russian Federation and its implications for the demographic crisis – the Russian Longitudinal Monitoring Survey. World Development 1998; 26: 1977–93.CrossRefGoogle Scholar
21Tippett, KS, Cypel, YS. Design and Operation: The Continuing Survey of Food Intakes by Individuals and the Diet and Health Knowledge Survey, 1994–96. Washington, DC: US Department of Agriculture, Agricultural Research Service, 1997.Google Scholar
22Stang, J, Story, MT, Harnack, L, Neumark-Sztainer, D. Relationships between vitamin and mineral supplement use, dietary intake, and dietary adequacy among adolescents. Journal of the American Dietetic Association 2000; 100: 905–10.CrossRefGoogle ScholarPubMed
23 Institute of Medicine, Food and Nutrition Board. Dietary Reference Intakes for Vitamin C, Vitamin E, Selenium, and Carotenoids. Washington, DC: National Academy Press, 2000.Google Scholar
24Carriquiry, AL. Assessing the prevalence of nutrient inadequacy. Public Health Nutrition 1999; 2: 23–33.CrossRefGoogle ScholarPubMed
25Murphy, SP, Barr, SI, Poos, MI. Using the new Dietary Reference Intakes to assess diets: a map to the maze. Nutrition Reviews 2002; 60: 267–75.CrossRefGoogle Scholar
26Dodd, KW. A Technical Guide to C-SIDE. Dietary Assessment Research Series Report 9. CARD Technical Report 96-TR32. Ames, IA: Center for Agricultural and Rural Development, Iowa State University, 1996.Google Scholar
27Dodd, KW. A User's Guide to C-SIDE: Software for Intake Distribution Estimation Version 1.0. CARD Technical Report 96-TR31 Ames, IA: Center for Agricultural and Rural Development, Iowa State University, 1996.Google Scholar
28Hoffmann, K, Boeing, H, Dufour, A, Volatier, JL, Telman, J, Virtanen, M, et al. . Estimating the distribution of usual dietary intake by short-term measurements. European Journal of Clinical Nutrition 2002; 56(Suppl. 2): S53–62.CrossRefGoogle ScholarPubMed
29Andrews, M, Kantor, LS, Lino, M, Ripplinger, D. Using USDA's Thrifty Food Plan to assess food availability and affordability. Food Review 2001; 24: 45–53.Google Scholar
30Lokshin, M, Popkin, BM. The emerging underclass in the Russian Federation: income dynamics 1992–96. Economic Development and Cultural Change 1999; 47: 803–29.CrossRefGoogle Scholar
31Carriquiry, AL, Fuller, WA, Goyeneche, JJ, Jensen, HH. Estimated Correlations among Days for the Combined 1989–91 CSFII. Dietary Assessment Research Series Report 4. CARD Staff Report 95-SR77. Ames, IA: Center for Agricultural and Rural Development, Iowa State University, 1995.Google Scholar
32 SAS Institute Inc. SAS/STAT Software: Version 8.0. Cary, NC: SAS Institute Inc, 1999–2001.Google Scholar
33 StataCorp. Stata Statistical Software: Release 8.2. College Station, TX: StataCorp LP, 2003.Google Scholar
34Bandini, LG, Must, A, Cyr, H, Anderson, SE, Spadano, JL, Dietz, WH. Longitudinal changes in the accuracy of reported energy intake in girls 10–15 y of age. American Journal of Clinical Nutrition 2003; 78: 480–4.CrossRefGoogle ScholarPubMed
35Fisher, JO, Johnson, RK, Lindquist, C, Birch, LL, Goran, MI. Influence of body composition on the accuracy of reported energy intake in children. Obesity Research 2000; 8: 597–603.CrossRefGoogle ScholarPubMed
36Chang, HY, Suchindran, CM, Pan, WH. Using the overdispersed exponential family to estimate the distribution of usual daily intakes of people aged between 18 and 28 in Taiwan. Statistics in Medicine 2001; 20: 2337–50.CrossRefGoogle ScholarPubMed