ArticleNutrients2019
Early Experience Analyzing Dietary Intake Data from the Canadian Community Health Survey-Nutrition Using the National Cancer Institute (NCI) Method.
Article in Nutrients, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 22 citations in OpenAlex.
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- Dietary polyphenol intake in the Canadian population: Findings from the 2015 Canadian Community Health Survey-Nutrition.Canadian journal of public health = Revue canadienne de sante publique · 2024Article
- An ensemble method based on marginal-effect models (EMM) for estimating usual food intake from single-day dietary data and internal/external two-day dietary data.European journal of clinical nutrition · 2023Article
- Estimating the dietary and health impact of implementing front-of-pack nutrition labeling in Canada: A macrosimulation modeling study.Frontiers in nutrition · 2023Article
- The estimated dietary and health impact of implementing the recently approved 'high in' front-of-package nutrition symbol in Canada: a food substitution scenario modeling study.Frontiers in nutrition · 2023Article
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- Nutrient intakes of Canadian adults: results from the Canadian Community Health Survey (CCHS)-2015 Public Use Microdata File.The American journal of clinical nutrition · 2021Article
- Within-Person Variation in Nutrient Intakes across Populations and Settings: Implications for the Use of External Estimates in Modeling Usual Nutrient Intake Distributions.Advances in nutrition (Bethesda, Md.) · 2021Article
- Micronutrient Fortification of Commercially Available Biscuits Is Predicted to Have Minimal Impact on Prevalence of Inadequate Micronutrient Intakes: Modeling of National Dietary Data From Cameroon.Current developments in nutrition · 2020Article
- Differences in Population-Based Dietary Intake Estimates Obtained From an Interviewer-Administered and a Self-Administered Web-Based 24-h Recall.Frontiers in nutrition · 2020Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOne of the underpinning elements to support evidence-based decision-making in food and nutrition is the usual dietary intake of a population. It represents the long-run average consumption of a particular dietary component (i.e., food or nutrient). Variations in individual eating habits are observed from day-to-day and between individuals. The National Cancer Institute (NCI) method uses statistical modeling to account for these variations in estimation of usual intakes. This method was originally developed for nutrition survey data in the United States. The main objective of this study was to apply the NCI method in the analysis of Canadian nutrition surveys.
methodsData from two surveys, the 2004 and 2015 Canadian Community Health Survey-Nutrition were used to estimate usual dietary intake distributions from food sources using the NCI method. The effect of different statistical considerations such as choice of the model, covariates, stratification compared to pooling, and exclusion of outliers were assessed, along with the computational time to convergence.
resultsA flowchart to aid in model selection was developed. Different covariates (e.g., age/sex groups, cycle, weekday/weekend of the recall) were used to adjust the estimates of usual intakes. Moreover, larger differences in the ratio of within to between variation for a stratified analysis or a pooled analysis resulted in noticeable differences, particularly in the tails of the distribution of usual intake estimates. Outliers were subsequently removed when the ratio was larger than 10. For an individual age/sex group, the NCI method took 1 h-5 h to obtain results depending on the dietary component.
conclusionEarly experience in using the NCI method with Canadian nutrition surveys data led to the development of a flowchart to facilitate the choice of the NCI model to use. The ability of the NCI method to include covariates permits comparisons between both 2004 and 2015. This study shows that the improper application of pooling and stratification as well as the outlier detection can lead to biased results. This early experience can provide guidance to other researchers and ensures consistency in the analysis of usual dietary intake in the Canadian context.
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