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Collection of food intake data: a reappraisal of criteria for judging the methods

Published online by Cambridge University Press:  09 March 2007

Renato Borrelli
Renato Borrelli
Affiliation:
Institute of Internal Medicine and Metabolic Disease, 2nd Medical School, Via Sergio Pansini 5, Nuples 80131, Italy
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Abstract

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The relationship between diet and the development of chronic disease still remains a controversial area. One major difficulty is to obtain a valid estimate of habitual pattern and level of food consumption for each individual. There is, in fact, a voluminous and largely negative literature on the validity of dietary assessment methods. In the present paper the utility of the most frequently used dietary assessment method in epidemiological studies is discussed in terms of precision and accuracy.

Keywords

Food intakeEpidemiologyStatistics
Type
Food Intake, Nutritional Epidemiology
Information
British Journal of Nutrition , Volume 63 , Issue 3 , May 1990 , pp. 411 - 417
Copyright
Copyright © The Nutrition Society 1990

References

REFERENCES

Armitage, P.& Berry, G. (1987). Statistical Methods in Medical Research. Oxford: Blackwell Scientific Publications.Google Scholar
Bazzare, T. L. & Yuhas, J. A. (1983). Comparative evaluation of methods of collecting food intake data for cancer epidemiology studies. Nutrition and Cancer 5, 201–214.CrossRefGoogle Scholar
Beaton, G., Milner, J., McGuire, V., Feather, T. E. & Little, J. A. (1983). Source of variance in 24-hour dietary recall data: implications for nutrition study design and interpretation. Carbohydrate sources, vitamins and minerals. American Journal of Clinical Nutrition 37, 986–995.Google ScholarPubMed
Bingham, S. A. (1987). The dietary assessment of individuals: methods, accuracy, new techniques and recommendations. Nutrition Abstracts and Reviews 57, 705–742.Google Scholar
Black, A. E., Ashby, D. R., Day, K. C., Bates, C. J. & Paul, A. A. (1983). Analytical versus food table values for vitamin C foods: the effect on calculated vitamin C intake of elderly subjects. Human Nutrition: Applied Nutrition 37A, 9–22.Google Scholar
Block, G. (1982). A review of validations of dietary assessment methods. American Journal of Epidemiology 115, 492–505.CrossRefGoogle ScholarPubMed
Borrelli, R., Cole, T. J., Di Biase, G. & Contaldo, F. (1989). Some statistical considerations on dietary assessment methods. European Journal of Clinical Nutrition 43, 453–463.Google Scholar
Box, G. E. P. & Cox, D. R. (1964). An analysis of transformation. Journal of the Royal Statistical Society B 26, 211–252.Google Scholar
Burke, B. S. (1957). The diet history as a tool in research. Journal of the American Dietetic Association 23, 1041–1046.CrossRefGoogle Scholar
Cole, T. J. & Black, A. E. (1983). Statistical aspects in the design of dietary surveys. In The Dietary Assessment of Populations. MRC Environmental Epidemiology Unit Scientific Report no. 4, pp. 5–7. Southampton: Southampton General Hospital.Google Scholar
Gardner, M. & Heady, J. (1973). Some effect of within person variability in epidemiological studies. Journal of Chronic Diseases 26, 781–795.CrossRefGoogle Scholar
Hebert, J. R. & Miller, D. R. (1988). Methodological considerations for investigating the diet-cancer link. American Journal of Clinical Nutrition 47, 1068–1077.CrossRefGoogle ScholarPubMed
James, W. P. T., Bingham, S. A. & Cole, T. J. (1981). Epidemiological assessment of dietary intake. Nutrition and Cancer 2, 203–212.Google Scholar
Karkeck, J. M. (1987). Improving the use of dietary survey methodology. Journal of the American Dietetic Association 87, 869–871.CrossRefGoogle ScholarPubMed
Lawton, W. H., Sylvestre, E. A. & Young-Ferraro, B. J. (1979). Statistical comparison of multiple analytic procedures, applications to clinical chemistry. Technometrics 21, 397–409.CrossRefGoogle Scholar
Liu, K. (1988). Measurement error and its impact on partial correlation coefficient and multiple linear regression analyses. Americun Journal of Epidemiology 12, 864–874.CrossRefGoogle Scholar
Liu, K. (1989). Consideration of and compensation for intraindividual variability in nutrient intake. In Epidemiology, Nufrition and Health, pp. 87–97 [Kohlmeier, L. and Helsing, E., editors]. London: E. Smith-Gordon.Google Scholar
Liu, K., Stamler, J., Dyer, A., McKeever, J., & McKeever, P. (1978). Statistical methods to assess and minimize the role of intraindividual variability in obscuring the relationship between dietary lipid and serum cholesterol. Journal of Chronic Diseases 31, 399–418.Google Scholar
McHenry, E. W., Ferguson, H. P. & Gurland, J. (1945). Sources of error in dietary surveys. Canadian Journal of Public Health 36, 355–361.Google Scholar
Marr, J. W. (1971). Individual dietary surveys, purposes and methods. World Review of Nutrition and Dietetics 13, 105–164.Google Scholar
Marr, J. W. & Heady, J. A. (1986). Within and between person variation in dietary surveys, number of daysnecded to classify individuals. Human Nutrition: Applied Nutrition 40A, 347–364.Google Scholar
Morgan, K. J., Johnson, S. R., Rizek, R. L., Reese, R. & Stampley, G. L. (1987). Collection of food intake data: an evaluation of methods. Journal of the American Dietetic Association 87, 888–896.Google Scholar
Nie, N. H., Hull, C. H., Jenkins, J. G., Steinbrenner, K. & Bent, D. H. (1975). SPSS Statistical Package for the Social Sciences. New York: McGraw-Hill.Google Scholar
Prentice, A. M., Black, A. E., Coward, W. A., Davies, H. L., Goldberg, G. R., Murgatroyd, P. R., Ashford, J., Sawyer, M. & Whitehead, R. G. (1986). High levels of energy expenditure in obese women. British Medical Journal 292, 983–987.CrossRefGoogle ScholarPubMed
Rosner, B. & Willett, W. C. (1988). Interval estimates for correlation coefficient corrected for within personvariation: implications for study design and hypothesis testing. American Journal of Epidemiology 127, 377–386.CrossRefGoogle ScholarPubMed
Westgard, J. O., Carey, R. N. & Wold, S. (1974). Criteria for judging precision and accuracy in method development and evaluation. Clinical Chemistry 20, 825–832.CrossRefGoogle ScholarPubMed