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A combination of statistical methods for the analysis of the relative validation data of the quantitative food frequency questionnaire used in the THUSA study

Published online by Cambridge University Press:  02 January 2007

UE MacIntyre ,
CS Venter ,
HH Vorster  and
HS Steyn
UE MacIntyre*
Affiliation:
Department of Paediatrics and Child Health, PO Box 168, Medical University of Southern Africa, 0204South Africa
CS Venter
Affiliation:
Department of Nutrition and Family Ecology, Potchefstroom University for Christian Higher Education, South Africa
HH Vorster
Affiliation:
Department of Nutrition and Family Ecology, Potchefstroom University for Christian Higher Education, South Africa
HS Steyn
Affiliation:
Statistical Services, Potchefstroom University for Christian Higher Education, South Africa
*
*Corresponding author: Email [email protected]
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Abstract

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Objective:

To apply structural equation modelling (SEM) and estimation of variance components to the relative validation data obtained from the quantitative food frequency questionnaire (QFFQ) used in the Transition, Health and Urbanisation in South Africa (THUSA) study.

Design:

A cross-sectional study.

Setting:

A community-based field study in an African population conducted during 1996.

Subjects:

Residents of the North West Province, South Africa, aged between 15 and 65 years.

Methods:

Relative validity of the QFFQ was tested against 7-day weighed food records, 24-hour urinary nitrogen (UN) excretion and estimated basal metabolic rate (BMR). SEM and estimation of variance components were applied to the log-transformed energy, protein, fat, calcium, iron, vitamin A and vitamin C intakes. UN excretion was used as a biomarker in the application of the SEM to protein and estimated BMR to energy intakes.

Results:

Constant bias (αQ) derived by the SEM varied from 0.85 (vitamin C) to 5.8 (energy). There was significant proportional bias for all nutrients except vitamin C. Validation coefficients (ρ(Q, T )) varied from 0.3 (fat, calcium, iron) to 0.7 (vitamin C). The inclusion of estimated BMR in the SEM for energy increased ρ(Q, T ) from 0.38 to 0.42. The estimation of variance components gave slightly lower correlations for the relationship between intakes from the QFFQ and the unknown true intake.

Conclusions:

Robust statistical methods were successfully applied in a relative validation study for a QFFQ in an African population. Estimated BMR as a biomarker for energy intake produced more meaningful results than UN excretion as a biomarker for protein intake.

Keywords

Dietary surveysRelative validityStatistical methods
Type
Research Article
Information
Public Health Nutrition , Volume 4 , Issue 1 , February 2001 , pp. 45 - 51
Copyright
Copyright © CABI Publishing 2001

References

1Willett, W. Nutritional Epidemiology. Monographs in Epidemiology and Biostatistics, No. 15. Oxford: Oxford University Press, 1990.Google Scholar
2Buzzard, IM, Sievert, YA. Research priorities and recommendations for dietary assessment methodology. Am. J. Clin. Nutr. 1994; 59 (Suppl.): S275–80.Google Scholar
3Thompson, RL, Margetts, BM. Comparison of a food frequency questionnaire with a 10-day weighed record in cigarette smokers. Int. J. Epidemiol. 1993; 22: 824–33.Google Scholar
4Bingham, SA, Gill, C, Welch, A, et al. Comparison of dietary assessment methods in nutritional epidemiology: weighed records v 24-h recalls, food-frequency questionnaires and estimated-diet records. Br. J. Nutr. 1994; 72: 619–43.Google Scholar
5Lee, J. Alternate approaches for quantifying aggregate and individual agreements between two methods for assessing dietary intakes. Am. J. Clin. Nutr. 1980; 33: 956–8.Google Scholar
6Hebert, JR, Miller, DR. The inappropriateness of conventional use of the correlation coefficient in assessing validity and reliability of dietary assessment methods. Eur. J. Epidemiol. 1991; 7: 339–43.Google Scholar
7Bellach, B. Remarks on the use of Pearson's correlation coefficient and other association measures in assessing validity and reliability of dietary assessment methods. Eur. J. Clin. Nutr. 1993; 47 (Suppl. 2): S42–5.Google ScholarPubMed
8Delcourt, C, Cubeau, J, Balkau, B, Papoz, L and the CODIAB-INSERM-ZENECA PHARMA Study Group. Limitations of the correlation coefficient in the validation of diet assessment methods. Epidemiology 1994; 5: 518–24.Google Scholar
9Bland, JM, Altman, DG. Comparing two methods of clinical measurement: a personal history. Int. J. Epidemiol. 1995; 24: S7–13.Google Scholar
10Beaton, GH. Interpretation of results from diet history studies. In: Kohlmeier, L, ed. The Diet History Method. Proceedings of the 2nd Berlin Meeting on Nutritional Epidemiology. Nishimura: Smith-Gordon, 1991; 15–38.Google Scholar
11Kaaks, R, Riboli, E, Esteve, J, van Kappel, AL, van Staveren, WA. Estimating the accuracy of dietary questionnaire assessments: validation in terms of structural equation models. Stat. Med. 1994; 13: 127–42.CrossRefGoogle ScholarPubMed
12Borrelli, R, Cole, TJ, DiBiase, G, Contaldo, F. Some statistical considerations on dietary assessment methods. Eur. J. Clin. Nutr. 1989; 43: 453–63.Google ScholarPubMed
13Bland, JM, Altman, DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986; i: 307–11.Google Scholar
14Ocké, MC, Kaaks, RJ. Biochemical markers as additional measurements in dietary validity studies: application of the methods of triads with examples from the European Prospective Investigation into Cancer and Nutrition. Am. J. Clin. Nutr. 1997; 65 (Suppl.); S1240–55.Google Scholar
15Schofield, WN. Predicting basal metabolic rate, new standards and review of previous work. Hum. Nutr. Clin. Nutr. 1985; 39C (Suppl. 1): 5–41.Google Scholar
16Bingham, S, Cummings, JH. The use of 4-aminobenzoic acid as a marker to validate the completeness of 24h urine collections in man. Clin. Sci. 1983; 64: 629–35.Google Scholar
17MacIntyre, UE, Venter, CS, Vorster, HH. A culture sensitive quantitative food frequency questionnaire used in an African population 2. Relative validation by 7-day weighed records and biomarkers. Public Health Nutr. 2000 (In press).Google Scholar
18Grant, KI, Langenhoven, ML, Stockton, MA, Day, RS, Bauermeister, P. Foodfinder dietary analysis software. Release 1.10. Parowvallei: Medical Research Council, 1992.Google Scholar
19Maroni, BJ, Steinman, TI, Mitch, WE. A method for estimating nitrogen intake of patients with chronic renal failure. Kidney Int. 1985; 27: 58–65.Google Scholar
20Larkin, F, Metzner, HL, Thompson, FE, Flegal, KM, Guitr, KE. Comparison of estimated nutrient intakes by food frequency questionnaire and dietary records in adults. J. Am. Diet. Assoc. 1989; 89: 215–23.Google Scholar
21Statsoft. Statistica for Windows. Tulsa: Microsoft Corporation, 1995.Google Scholar
22Kaaks, R, Riboli, E. Validation and calibration of dietary intake measurements in the EPIC project: methodological considerations. Int. J. Epidemiol. 1997; 26 (Suppl.): S15–25.CrossRefGoogle ScholarPubMed
23SAS Institute Inc.. SAS/STAT User's Guide, Version 6, Vol. 1, 4th edn. Cary, NC: SAS Institute Inc., 1989.Google Scholar
24Dunn, G. Design and Analysis of Reliability Studies (a Statistical Evaluation of Measurement Errors). London: Edward Arnold, 1989.Google Scholar
25O'Donnell, MG, Nelson, M, Wise, PH, Walker, DM. A computerized questionnaire for use in diet health education. 1. Development and validation. Br. J. Nutr. 1991; 66: 3–15.Google Scholar
26Rothenberg, E. Validation of the food frequency questionnaire with the 4-day record method and analysis of 24-hr urinary nitrogen. Eur. J. Clin. Nutr. 1994; 48: 725–35.Google Scholar
27Black, AE, Jebb, SA, Bingham, SA, Runswick, SA, Poppitt, SD. The validation of energy and protein intakes by doubly labelled water and 24-hour urinary nitrogen excretion in post-obese subjects. J. Hum. Nutr. Diet. 1995; 8: 51–64.Google Scholar
28Bingham, SA, Cassidy, A, Cole, TJ, et al. Validation of weighed records and other methods of dietary assessment using the 24 h urine nitrogen technique and other biological markers. Br. J. Nutr. 1995; 73: 531–50.CrossRefGoogle ScholarPubMed
29Porrini, M, Gentile, MG, Fidanza, F. Biochemical validation of a self-administered semi-quantitative food frequency questionnaire. Br. J. Nutr. 1995; 74: 323–33.CrossRefGoogle ScholarPubMed
30Shetty, PS, Henry, CJK, Black, AE, Prentice, AM. Energy requirements of adults: an update on basal metabolic rates (BMRs) and physical activity levels (PALs). Eur. J. Clin. Nutr. 1996; 50 (Suppl.): S11–23.Google Scholar
31Hayter, JE, Henry, CJK. A re-examination of basal metabolic rate predictive equations: the importance of geographic origin of subjects in sample selection. Eur. J. Clin. Nutr. 1994; 48: 702–7.Google Scholar
32De Boer, JO, van Es, AJH, Voorips, LE, Blokstra, F, Vogt, JE. Energy metabolism and requirements in different ethnic groups. Eur. J. Clin. Nutr. 1988; 42: 983–97.Google Scholar
33Valencia, ME, Moya, SY, McNeill, G, Haggarty, P. Basal metabolic rate and body fatness of adult men in northern Mexico. Eur. J. Clin. Nutr. 1993; 48: 205–11.Google Scholar
34Cole, AH, Taiwo, OO, Nwagbara, NI, Cole, CE. Energy intakes, anthropometry and body composition of Nigerian adolescent girls: a case study of an institutionalized secondary school in Ibadan. Br. J. Nutr. 1997; 77: 497–509.Google Scholar