Early Experience Analyzing Dietary Intake Data from the Canadian Community Health Survey—Nutrition Using the National Cancer Institute (NCI) Method

Abstract

Background: One 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. Methods: Data 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. Results: A 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. Conclusion: Early 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.

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

“…Region-specific estimates of vitamin A intake from breast milk were calculated based on the mean vitamin A content of the breast milk from each geographic stratum in the Cameroon national micronutrient survey ( 38 ) combined with estimates of average breast milk intake at 1–2 y of age ( 39 ) ( Supplemental Table 1 ). The estimated ratio of within-person to between-person variance for vitamin A intake in women was extremely large, even after removing potential outliers as suggested by Davis et al ( 31 ). To obtain reliable estimates in this case, we used a modified approach described in Supplemental Methods .…”

“…NCI recommends person-specific, time-dependent, and so-called “nuisance” factors to be included in the model as covariates. Including covariates helps to make the distribution of random effects more normally distributed and results in much greater precision ( 29 , 31 , 32 ). In this study, usual intake for preschool children was adjusted for age, sex, interviewer ID, sequence of interviews (i.e., first interview compared with subsequent interview), use of translator in the dietary interview, weekend (binary variable indicating weekend compared with weekday), breast-feeding status, maternal education (secondary/higher, primary, or no formal education), socioeconomic status [categorized into quintiles, as described previously ( 24 )], and macroregion.…”

“…The NCI “two-part correlated model” jointly models the probability of consumption on a given day and the usual amount consumed on consumption days. Usual intake is the product of these 2 quantities, which can be correlated with one another ( 31 , 32 ).…”

Differences in Population-Based Dietary Intake Estimates Obtained From an Interviewer-Administered and a Self-Administered Web-Based 24-h Recall

“…For CCHS 2015, the model for “other foods” intakes yielded very high within:between variance ratio (above 50), indicating that between-individual variance could not be distinguished from within-individual variance. Two outliers who had a mean difference between the second and first 24-h recall >2.5 standard deviations away from the mean of differences were identified ( 33 ). Removal of data for these outliers rendered the within:between variance ratio acceptable (i.e., 13.8).…”

“…The majority of 24-h recalls (67%) were collected in 2016; 30 and 3% of dietary intake data were collected in 2015 and 2017, respectively. The quotas were based on 30 strata created on the basis of the five administrative regions (Capitale-Nationale/Chaudière-Appalaches, Estrie, Mauricie, Montréal, and Saguenay-Lac-St-Jean), sex, and three predetermined age groups (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49), and 50-65 years). Each stratum was based on the most recent demographic data from the Institut de la statistique du Québec (2013) at the time of recruitment, aiming for a sample size of 1,000 participants.…”