Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-27T08:49:42.529Z Has data issue: false hasContentIssue false
  • English
  • Français

Misclassification associated with measurement error in the assessment of dietary intake

Published online by Cambridge University Press:  02 January 2007

Carl de Moor ,
Tom Baranowski ,
Karen W Cullen  and
Theresa Nicklas
Carl de Moor*
Affiliation:
Departments of Behavioral Science and Biostatistics, The University of Texas M.D. Anderson Cancer Center, 1515 Holcombe Boulevard – Box 243, Houston, TX 77030, USA
Tom Baranowski
Affiliation:
Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
Karen W Cullen
Affiliation:
Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
Theresa Nicklas
Affiliation:
Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
*
*Corresponding author: Email cdemoor@mdanderson.org
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:

Dietary assessment has been used for certification to receive food supplements or other nutrition services and to provide feedback for educational purposes. The proportion of individuals correctly certified as eligible is a function of the amount of error that exists in the dietary measures and the level of dietary intake used to establish eligibility. Whether individuals are correctly counselled to increase or decrease the consumption of selected foods or nutrients is a function of the same factors. It is not clear, however, what percentage of individuals would be correctly classified under what circumstances. The objective of this study is to demonstrate the extent to which measurement error and eligibility criteria affect the accuracy of classification.

Design:

Hypothetical distributions of dietary intake were generated with varying degrees of measurement error. Different eligibility criteria were applied and the expected classification rates were determined using numerical methods.

Setting and subjects:

Simulation study.

Results:

Cut points of dietary intake at decreasing levels below the 50th percentile of true intake were associated with lower sensitivity and predictive value positive rates, but higher specificity and predictive value negative rates. The correct classification rates were lower when two cut points of dietary intake were used. Using a single cut point that was higher than the targeted true consumption resulted in higher sensitivity but lower predictive value positive, and lower specificity but higher predictive value negative.

Conclusions:

Current methods of dietary assessment may not be reliable enough to attain acceptable levels of correct classification. Policy-makers and educators must consider how much misclassification error they are willing to accept and determine whether more intensive methods are necessary.

Keywords

MisclassificationMeasurement errorCertificationNutrition counselling
Type
Research Article
Information
Public Health Nutrition , Volume 6 , Issue 4 , June 2003 , pp. 393 - 399
Copyright
Copyright © CABI Publishing 2003

References

1Institute of Medicine. WIC Nutrition Risk Criteria: A Scientific Assessment. Washington, DC: National Academy Press, 1996.Google Scholar
2Fox, MK, Burstein, N, Golay, J, Price, C. WIC Nutrition Education Assessment Study, Final Report. Cambridge, MA: Abt Associates, 1998.Google Scholar
3Di Clemente, CC, Marinhilli, AS, Singh, M, Bellino, LE. The role of feedback in the process of health behavior change. American Journal of Health Behavior 2001; 25: 217–27.CrossRefGoogle ScholarPubMed
4Gersovitz, M, Madden, JP, Smiciklas-Wright, H. Validity of the 24-hour dietary recall and seven day record for group comparison. Journal of the American Dietetic Association 1978; 73: 48–55.CrossRefGoogle Scholar
5Block, G, Thompson, FE, Hartman, AM, Larkin, FA, Guire, KE. Comparison of two dietary questionnaires validated against multiple dietary records collected during a 1-year period. Journal of the American Dietetic Association 1992; 92: 686–93.CrossRefGoogle ScholarPubMed
6Block, G, Subar, AF. Estimates of nutrient intake from a food frequency questionnaire: the 1987 National Health Interview Survey. Journal of the American Dietetic Association 1992; 92: 969–77.CrossRefGoogle ScholarPubMed
7Willett, W. Nutritional Epidemiology. New York: Oxford University Press, 1990.Google Scholar
8Day, NE, McKeown, N, Wong, MY, Welch, A, Bingham, S. Epidemiological assessment of diet: a comparison of a 7-day diary with a food frequency questionnaire using urinary markers of nitrogen, potassium and sodium. International Journal of Epidemiology 2001; 30: 309–17.CrossRefGoogle Scholar
9Kipnis, V, Carroll, RJ, Freidman, LS, Li, L. Implications of a new dietary measurement error model for estimation of relative risk: application to four contribution studies. American Journal of Epidemiology 1999; 150: 642–51.CrossRefGoogle Scholar
10Scholl, TO, Hediger, ML, Schall, JI, Khoo, CS, Fischer, RL. Dietary and serum folate: their influence on the outcome of pregnancy. American Journal of Clinical Nutrition 1996; 63: 520–5.CrossRefGoogle ScholarPubMed
11Troiano, RP, Flegal, KM, Kuczmarski, RJ, Campbell, SM, Johnson, CL. Overweight prevalence and trends for children and adolescents: The National Health and Nutrition Examination Surveys, 1963–1991. Archives of Pediatrics and Adolescent Medicine 1995; 149: 1085–91.CrossRefGoogle Scholar
12Kipnis, V, Midthune, D, Freedman, LS, Bingham, S, Schatzkin, A, Subar, A, et al. Empirical evidence of correlated biases in dietary assessment instruments and its implications. American Journal of Epidemiology 2001; 152: 394–403.CrossRefGoogle Scholar
13Randall, B, Bartlett, S, Kennedy, S. Study of WIC Participant and Program Characteristics, 1996. FNS 53-3198-3-026. Washington, DC: US Department of Agriculture, Food and Nutrition Service, Office of Analysis and Evaluation, 1998.Google Scholar
14Humbleton, RK, Swaminathan, H, Rogers, HJ. Fundamentals of Item Response Theory. Newbury Park, CA: Sage, 1991.Google Scholar
15Baxter, SD, Thompson, WO, Davis, HC, Johnson, MH. Impact of gender, ethnicity, meal component, and time interval between eating and reporting on accuracy of fourth-graders' self-reports of school lunch. Journal of the American Dietetic Association 1997; 97: 1293–8.CrossRefGoogle ScholarPubMed
16Munoz, KA, Krebs-Smith, SM, Ballard-Barbash, R, Cleveland, LE. Food intakes of US children and adolescents compared with recommendations. Pediatrics 1997; 100: 323–9.CrossRefGoogle ScholarPubMed
17Walker, AM, Blettner, M. Comparing imperfect measures of exposure. American Journal of Epidemiology 1985; 121: 783–90.CrossRefGoogle ScholarPubMed
18Kuhn, DL, Risi, L, Richart, RM, Pollack, A, Kostecki, F, Wright, TC. Two-stage cervical cancer screening: an alternative for resource-poor settings. American Journal of Obstetrics and Gynecology 2000; 183: 383–8.Google Scholar