Literature DB >> 16236196

24-hour national dietary survey data: how do we interpret them most effectively?

Dorothy Mackerras1, Ingrid Rutishauser.   

Abstract

OBJECTIVE: To illustrate the effect of common mistakes when using 24-hour national dietary survey data to estimate the prevalence of inadequate nutrient intakes.
DESIGN: Raw data on nutrient intake from the Australian 1995 National Nutrition Survey were adjusted for within-person variance using standard techniques and corrected for underreporting using the criteria of Goldberg et al. The distributions for six nutrients were compared with current dietary reference values from the UK, USA and Australia.
SETTING: A national sample of the Australian population with a 61.4% response rate.
RESULTS: Adjusting for within-person variance reduced the range of nutrient intakes to 66-80% of the raw data range and the proportion with intakes below the estimated average requirement (EAR) by up to 20%. Excluding underreporters further reduced the proportion below the EAR by up to 10%. Using the dietary reference values from different countries also resulted in some markedly different estimates. For example, the prevalence of low folate intakes ranged from < 1 to 92% for adult women depending on the reference used. Except for vitamin A and protein, the prevalence of low intakes was invariably higher for women than for men.
CONCLUSIONS: Estimates of the prevalence of low nutrient intakes based on raw 24-hour survey data are invariably misleading. However, even after adjustment for within-person variance and underreporting, estimates of the prevalence of low nutrient intakes may still be misleading unless interpreted in the light of the reference criteria used and supported by relevant biochemical and physiological measures of nutritional status.

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Year:  2005        PMID: 16236196     DOI: 10.1079/phn2005720

Source DB:  PubMed          Journal:  Public Health Nutr        ISSN: 1368-9800            Impact factor:   4.022


  5 in total

1.  Nutritional intake of Korean population before and after adjusting for within-individual variations: 2001 Korean National Health and Nutrition Survey Data.

Authors:  Dong Woo Kim; Jae Eun Shim; Hee Young Paik; Won O Song; Hyojee Joung
Journal:  Nutr Res Pract       Date:  2011-06-21       Impact factor: 1.926

2.  Misreporting of energy intake in the 2007 Australian Children's Survey: identification, characteristics and impact of misreporters.

Authors:  Anna M Rangan; Victoria M Flood; Timothy P Gill
Journal:  Nutrients       Date:  2011-02-08       Impact factor: 5.717

Review 3.  Cross-continental comparison of national food consumption survey methods--a narrative review.

Authors:  Willem De Keyzer; Tatiana Bracke; Sarah A McNaughton; Winsome Parnell; Alanna J Moshfegh; Rosangela A Pereira; Haeng-Shin Lee; Pieter van't Veer; Stefaan De Henauw; Inge Huybrechts
Journal:  Nutrients       Date:  2015-05-13       Impact factor: 5.717

4.  The added value of food frequency questionnaire (FFQ) information to estimate the usual food intake based on repeated 24-hour recalls.

Authors:  Cloë Ost; Karin A A De Ridder; Jean Tafforeau; Herman Van Oyen
Journal:  Arch Public Health       Date:  2017-10-30

5.  Comparison of Various Methods to Determine Added Sugars Intake to Assess the Association of Added Sugars Intake and Micronutrient Adequacy.

Authors:  Victor L Fulgoni; P Courtney Gaine; Maria O Scott
Journal:  Nutrients       Date:  2020-09-14       Impact factor: 5.717

  5 in total

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