Methodology document: Usual intakes from food for energy, nutrients and other dietary components (2004 and 2015 Canadian Community Health Survey  Nutrition)
January 2021
Table of Contents
 Acknowledgements
 List of abbreviations
 1.0 Introduction
 2.0 The 2015 CCHS  Nutrition: Estimation of population usual intake distribution
 2.1 Introduction
 2.2. Rationale for updating estimates for 2004
 2.3 Methodology for estimating usual intakes
 2.3.1 Usual intake estimation with the National Cancer Institute method
 2.3.2 Application of the NCI method in the 2004 and 2015 CCHSNutrition
 2.3.3 Measuring sampling variability with bootstrap replication
 2.3.4 Estimation of iron inadequacy using the full probability method
 2.3.5 Estimation of usual intake distribution for caffeine
 2.3.6 Data source
 2.4. Comparing 2015 and 2004 nutrient intake estimates
 Appendix A  Table footnotes
 Appendix B  List of dietary components
 References
Acknowledgements
Health Canada would like to acknowledge and thank the individuals who have contributed to this work. The production of these intake estimates was a joint venture between Health Canada and Statistics Canada. Subjectmatter experts from the Bureau of Food Surveillance and Science Integration, Food Directorate, and the Office of Nutrition Policy and Promotion at Health Canada and from the Centre for Population Health Data and Health Analysis Division at Statistics Canada produced the usual intake data table and this methodology document.
List of abbreviations
 AI
 Adequate Intake
 AMDR
 Acceptable Macronutrient Distribution Range
 CCHS
 Canadian Community Health Survey
 CDRR
 Chronic Disease Risk Reduction Intake
 CV
 coefficient of variation
 d
 day
 DRI
 Dietary Reference Intake
 EAR
 Estimated Average Requirement
 g
 gram
 IOM
 Institute of Medicine
 kcal
 kilocalories
 mg
 milligram
 n
 sample size
 NCI
 National Cancer Institute
 OC
 oral contraceptive
 SD
 standard deviation
 SE
 standard error
 SIDE
 Software for Intake Distribution Estimation
 UL
 Tolerable Upper Intake Level
1.0 Introduction
Health Canada's Usual intakes from food for energy, nutrients and other dietary components is published on the Government of Canada's Open Government portal. These intake estimates were generated using data collected from Canadians in the 2004 and 2015 Canadian Community Health Survey (CCHS)Nutrition as a joint venture with Statistics Canada. To optimize the usage of the data, it is recommended that users refer to the Table Footnotes (Appendix A) and also read The Reference Guide to Understanding and Using the Data  2015 Canadian Community Health Survey  Nutrition^{Footnote 1} published by Health Canada in June 2017. This reference guide includes an overview of the 2015 CCHSNutrition, including descriptions of the survey sample, how the survey was conducted and survey components. Further, the reference guide introduces the Dietary Reference Intakes (DRI), the nutrient reference standards used to assess diets by agesex groups.
This methodology document is a reference for those who will use the 2004 and the 2015 CCHSNutrition usual intake data to guide nutrition‐related program and policy decisions. It will be of particular benefit to provincial ministries of health, researchers and graduate students, policy makers and analysts, public health professionals, epidemiologists, dietitians, the food industry, and the health media.
The summary data table presents the distribution of usual intakes of 41 dietary components as described in Appendix B. Data are provided for 16 agesex groups at the national, regional and provincial levels. Data used for producing the estimates were obtained from the 2004 and 2015 CCHSNutrition Share Files. The nutrient intakes represent food consumption only. A methodology for combining data on nutrient intakes derived from food with data on vitamin and mineral supplements is being explored by Health Canada. Because supplements may make meaningful contributions to nutrient intakes, inferences about the prevalence of nutrient excess or inadequacy based on intakes from food alone may respectively underestimate or overestimate the prevalence based on total nutrient intakes from both food and supplements.
Results are presented for 13 geographical areas: Canada excluding the territories, the 10 provinces, the Atlantic Region and the Prairie Region. Data from the four Atlantic Provinces and the three Prairie Provinces were combined into the Atlantic Region and the Prairie Region, respectively.
Recognizing that the smoking of tobacco affects vitamin C requirements, estimates are provided for the intake of vitamin C by smoking status.
The next section describes the methodology used to produce the intake estimates and how we addressed computational problems that were encountered. The guide does not provide any interpretation or draw conclusions. Readers are encouraged to consult The Reference Guide to Understanding and Using the Data  2015 Canadian Community Health Survey  Nutrition^{Footnote 1}, for examples of how to interpret the 2015 CCHSNutrition data.
2.0 The 2015 CCHSNutrition: Estimation of population usual intake distribution
2.1 Introduction
One of the goals of the 2015 CCHSNutrition was to estimate distributions of usual intake from food for energy, several nutrients and other food components at the national, provincial and regional levels for 16 DRI agesex groups. To accomplish this, data from two dietary recalls were collected concerning the amount and types of foods consumed in the 24 hours preceding the interview: one recall for all respondents and a second recall only from a representative subsample of the group. Using data from the first dietary recall produces a measure of daily intake (i.e. the quantity of nutrients or food eaten in a given day). Data from both the first and second recalls can be used to produce a populationlevel estimate of usual intake (i.e. the longterm average of the daily intake).
The variability in intakes among a group on a given day reflects both variability in intake within specific individuals (who may have eaten more or less than usual on that day) as well as between different individuals (who habitually have higher or lower intakes). To obtain an estimate of a population's usual intake distribution from daily intake data, one must fit a measurement error model that reduces the effect of the within‐individual variance while measuring the between‐individual variance. Several methods are available to estimate a population's usual intake distributions from daily intake data. Following the release of 2015 CCHSNutrition, Statistics Canada recommended the use of the National Cancer Institute (NCI) method for estimation of usual intakes.
Three main types of estimates can be obtained from usual intake distributions including: (i) the mean usual intake; (ii) the percentage of the population having a usual intake under (or over) a given threshold (cut‐off); and (iii) percentiles of the distribution.
The goal of this document is to summarize the:
 Rationale for providing updated estimates for the 2004 CCHSNutrition
 Methodology used to estimate usual intake distributions for the 2015 CCHSNutrition
 Estimation of iron inadequacy using the full probability method
 Estimation of usual intake for caffeine
 Guidance for comparing intake estimates between cycles 2004 and 2015
 Table footnotes
2.2. Rationale for updating estimates for 2004
Since the release of the data from 2004 CCHSNutrition, novel statistical methods to estimate usual intakes from selfreport dietary assessments have become available. In 2016, a joint technical working group comprised of statisticians from Health Canada and Statistics Canada evaluated existing statistical methods for estimating the usual dietary intake and recommended the use of the NCI method^{Footnote 2} ^{,}^{Footnote 3} for analysis of the 2015 CCHSNutrition data. This was a shift from 2004 CCHSNutrition (Cycle 2.2), where usual intake estimates were calculated using the Iowa State University (ISU) method^{Footnote 4} which uses the Software for Intake Distribution Estimation (SIDE). Usual intake estimates for the 2004 CCHSNutrition have been recalculated using the NCI method in order to facilitate comparisons. The summary data table available on Canada's Open Government Portal presents estimates for both years (2004 and 2015) of the survey. Users are cautioned against comparing the 2015 CCHSNutrition usual intake estimates to those published in the three volumes of the 2004 CCHSNutrition Compendium of Nutrient Intake Tables^{Footnote 5} due to differences in usual intake estimation methodology.
2.3 Methodology for estimating usual intake
2.3.1 Usual intake estimation with the National Cancer Institute method
Estimated distributions of usual nutrient intakes for the 2015 CCHSNutrition were computed using the NCI method^{Footnote 2}^{,}^{Footnote 3}. Despite increased computational time compared with other available methods, the NCI method has advantages as it can be used to estimate intake of both ubiquitously and episodically consumed nutrients and foods, can include covariates in the model, and accounts for the correlation between probability of consumption and amount consumed.
The NCI method was developed on the premise that usual intake is equal to the probability of consumption on any given day multiplied by the average amount consumed on a "consumption day". There are slight differences in how the method is applied for dietary components that are consumed by nearly everyone, nearly every day (i.e. ubiquitously consumed) compared with those that are episodically consumed, on a few days. The approach for ubiquitously consumed components (sometimes referred to as the onepart or amountonly model) assumes a probability of consumption of 1 and requires an estimation of the amount consumed using linear regression on a transformed scale with a personspecific random effect. The more complex estimation of episodically consumed components (referred to as the twopart model) requires a model that estimates (1) the probability of consuming a food component using logistic regression also with a personspecific random effect and (2) the amount consumed using a nonlinear mixed model. Each part of this twopart model may include multiple or no covariates. For the twopart models, if the personspecific random effects of the two parts are correlated, the twopart correlated model is selected. Otherwise, the twopart uncorrelated model is fit. For either the onepart or the twopart models, the next step is to estimate each individual's linear predictor(s), generate random effects using 100 pseudopersons for each individual, add random effects to the linear predictors and backtransform the amount estimate to the original scale and finally estimate mean, standard deviation and percentiles empirically.
Training materials on the use of the NCI method to estimate usual intake distribution characteristics from the 2015 CCHSNutrition data are available from Statistics Canada (contact Client Services, Health Statistics Division, Statistics Canada at 6139511746 or by email at STATCAN.hdds.STATCAN@canada.ca). The National Cancer Institute has developed SAS macros for implementation of the NCI method, which are available online.
2.3.2 Application of the NCI method in the 2004 and 2015 CCHSNutrition
The NCI method was originally developed for analysis of the United States National Health and Nutrition Examination Survey (NHANES), which has a different design than the one used in the CCHSNutrition surveys. Application of the NCI method to CCHSNutrition was investigated^{Footnote 6} and various statistical considerations were noted. In particular, questions relating to the choice of model (one or twopart), the method to remedy outliers, and the choice of covariates were investigated and are discussed below.
Selection of one or twopart model
The decision of whether to implement the one or two part model was based on the following scenarios^{Footnote 3}:
 If less than 5% of the 24hour recalls (unweighted) had zero intake of a nutrient, then the onepart model was used.
 If greater than 10% of the 24hour recalls had zero intake of a nutrient, the twopart model was fit twice: once including the correlation between personspecific random effects, and another assuming the correlation to be zero. If the correlation is found to be significant, the correlated model is selected, otherwise the uncorrelated model is chosen.
 If between 5% and 10% of the 24hour recalls had zero intake of a nutrient, then both one and two part models were fit and the model with the best fit was chosen for further analysis. The bestfitting model was chosen by examining the significance of the correlation coefficient through implementation of Fisher's ztransformation between the twopart models as in the previous step. If neither of the twopart models converge, the amountonly model is fit. In this case, a warning note may appear stating that the estimated distribution might be rightshifted compared to the true distribution.
Intake estimates for all dietary components, except caffeine, were computed with a onepart model, as less than 5% of 24hour recalls had zero intake of a nutrient. For caffeine, the twopart model was used for individuals under 30 years of age since more than 10% of recalls had zero intake. For adult groups over 30 years of age, the proportion of zero intakes ranged between 6.41% and 10.24%, thus both the one and twopart models were fit for these groups.
Once the model was chosen, the ratio of withinbetween variance components was used to evaluate other statistical assumptions, including choice of covariates and outliers. Large values of the withinbetween variance ratio suggests instability of model parameter estimates, and leads to a larger adjustment of the oneday intakes to the usual intakes. As a result, estimation of percentiles of the usual intake distribution and prevalence of inadequacy may be impacted. To ensure model accuracy, the effect of covariates, outliers and survey weights were evaluated when computing usual intakes.
Covariates
Since estimates of usual intakes by agesex group are desired at the national, regional and provincial levels, province was included as a covariate in the model. Initial analysis using the NCI method indicated nonconvergence of some agesex groups in some provinces, thus data from the 2004 CCHSNutrition (Cycle 2.2) was included to increase the sample size and then provide usual intake estimates using the NCI method for the 2004 survey. As a result, parameter estimates were obtained from a dataset with 2004 and 2015 CCHSNutrition combined, and survey year was also included as a covariate. As per the NCI User Guide, covariates for sequence of recall and weekend/weekday were also included.
Pooling vs. Stratification
Computation was done for usual intakes for each agesex group separately, using a stratified approach. While the NCI method provides the option to pool groups, an initial analysis using the root survey weight indicated large differences in the estimated ratio of withinbetween variance components for different agesex groups. Previous research has noted that pooling is not appropriate in situations where the withinbetween ratios are much different, since usual intake distributions could be biased in such cases^{Footnote 6}. Hence, for the analysis of CCHS  Nutrition data, usual intakes were obtained by stratification of each agesex group, while pooling over survey year and province within each strata.
Outliers
In cases where the difference between Day 1 and Day 2 intakes was abnormally large (i.e. ratio of within to between variation >10), analyses were conducted to look for potential outliers. In such cases, the Day 2 value was removed as Day 1 values are considered more reliable. Day 1 recall is less likely to be biased due to learning curve or change in diet since the respondent is aware that an upcoming recall will take place. The impact of outlier removal on the withinbetween variation was determined on the basis of ±3, ±2.5 or ±2 standard deviations (SD) away from the mean distribution of difference between Day 1 and Day 2 values. The scenario that resulted in the greatest improvement in withinbetween variations with the fewest outliers removed was selected. For those dietary components with outliers identified, the total number of outliers is summarized below:
Dietary Component 
DRI AgeSex Group 
Threshold 
Number of recalls removed 

Percentage of total energy intake from fats 
19 to 30 years, females 
3 SD 
4 
Percentage of total energy intake from monounsaturated fats 
19 to 30 years, females 
3 SD 
8 
Sodium (mg/d) 
19 to 30 years, males 
2 SD 
39 
Potassium (mg/d) 
31 to 50 years, males 
2 SD 
63 
Percentage of total energy intake from linolenic fatty acid 
9 to 13 years, females 
3 SD 
11 
14 to 18 years, males 
3 SD 
18 

Data source: Statistics Canada, 2015 Canadian Community Health Survey  Nutrition; 2004 Canadian Community Health Survey Nutrition (cycle 2.2) Share files 
SAS macros
Analyses were completed using SAS Macros Version 2.1, specifically the MIXTRAN and DISTRIB macros. General documentation on the NCI method, including user guides and specific examples, is also available online.
To perform the usual intake calculations and model selection steps mentioned previously, the NCI univariate macros MIXTRAN and DISTRIB were implemented in a systematic way, as described below.
The MIXTRAN macro transforms the data and fits the nonlinear mixed model. This macro permits the use of covariates in the model fitting procedure and outputs the parameter estimates needed to calculate distributions of usual intake.
The DISTRIB macro uses the parameters estimated by MIXTRAN to estimate the usual intake distributions through simulation. This macro can also provide the estimated percentage of the population whose usual intake falls below or above a certain value, a feature used to provide estimates above or below DRI values (i.e. EARs, AIs and ULs).
The MIXTRAN macro was used extensively to evaluate the ratio of withinbetween variances, to determine the presence of outliers and to evaluate differences in pooling and stratification. Once the final model was chosen, the DISTRIB macro was used to calculate percentiles and prevalence of inadequacy for a particular dietary component. The exception to this procedure was iron, whose estimation procedure is summarized in Section 2.3.4.
Convergence criteria for the MIXTRAN macro
As the NCI method uses a numerical optimization method to find a solution, the default convergence criteria (gconv = 1e8) was originally used for the analysis of all dietary components (Appendix B). In most cases, the default criteria provided feasible solutions with the shortest amount of computational time. For 2% of DRI age/sex groups, the default convergence criteria did not provide a feasible solution, thus more stringent convergence criteria (gconv = 1e12 for Potassium with Males 9 to 13 years old, and 1e10 for the remaining) were used for certain dietary components (Table 2), at the expense of additional run time. In general, it is recommended to use the default convergence criterion in the MIXTRAN macro when computing usual intakes.
Dietary Component 
AgeSex Groups 

Sodium 
Females: 19 to 30 years old Females: 31 to 50 years old 
Potassium 
Females & Males Combined: 1 to 3 years old Males: 9 to 13 years old Females: 14 to 18 years old Females: 19 to 30 years old Males: 31 to 50 years old 
Energy 
Females & Males Combined: 1 to 3 years old 
Phosphorus 
Females & Males Combined: 1 to 3 years old Males: 14 to 18 years old Males: 71 years and over Females: 71 years and over 
Vitamin C 
Females: 9 to 13 years old Males: 14 to 18 years old 
Calcium 
Females & Males Combined: 4 to 8 years old 
Moisture 
Females & Males Combined: 1 to 3 years old 
Total Sugars 
Females: 14 to 19 years old 
2.3.3 Measuring sampling variability with bootstrap replication
The CCHSNutrition surveys have a complex design, implying that no mathematical formula exists to calculate the sampling variability directly. Instead, it is necessary to use a replication method to estimate this variance, and the most convenient method is bootstrap replication. Statistics Canada has provided bootstrap replicate weights to estimate the variance from complex survey sampling designs.
For simple estimates such as totals, ratios or regression parameters, it is possible to estimate the sampling variability by using the bootstrap weights with a survey procedure, such as SUDAAN, STATA, or PROC SURVEYMEANS in SAS. These procedures properly account for the complex survey design in the estimation of standard errors. To obtain an estimate, the parameter of interest is calculated (e.g. total, ratio) for each of the 500 replicates and then the variance between the 500 values is computed. This is the method used to estimate the average dietary component intake using day one recalls only. For estimates related to distributions of usual intake, this process must be repeated when using the NCI method. Thus, it is necessary to estimate the parameters of interest with the NCI method for each replicate (using each bootstrap weight) and then calculate the variance between each of the 500 estimates.
For some survey procedures, the variance of the 500 replicates compares each estimate with the mean of the 500 bootstraps (the bootstrap mean). The root estimate (the estimate calculated using the original survey weight) is also available from the data. Typically, since the number of replicates is large (500), the bootstrap mean will converge to the root mean estimate. However, since the NCI method may fail for some of the 500 replicates, it is possible that not all of the 500 distribution estimates will be available to calculate the bootstrap mean estimates. For this reason, when calculating the variance from the bootstrap estimates, each replicate is compared with the root estimate and not with the bootstrap mean. As such, some of the bias caused by failing replicates is mitigated by the estimation procedure.
More specifically for usual intakes, let denote the estimate of the parameter (e.g. mean, percentile, prevalence of inadequacy) obtained using the root survey weight. Then _{b},b=1,2,…,B (where B=500), represents the estimate of the parameter from each of the B=500 bootstrap replicates. The bootstrap standard error for is then calculated by:
2.3.4 Estimation of iron inadequacy using the full probability method
The distribution of iron requirements for menstruating females and other agesex groups is not normally distributed, nor necessarily symmetric. Therefore, the full probability approach^{Footnote 7} is required for the estimation of iron inadequacy as opposed to the EAR cutpoint method. For all agesex groups, the iron requirement distributions from the Institute of Medicine's (IOM) report on the DRIs: The Essential Guide to Nutrient Requirements^{Footnote 8} Appendix G was used to estimate inadequacy. For the three DRI agesex groups of menstruating females aged between 14 and 50 years, the iron requirement distributions of mixed populations, which assumes 17% oral contraceptive (OC) users and 83% nonOC users, were used to estimate inadequacy^{Footnote 8}. For females 51 to 70 years and 71+ years, the iron requirement distributions for the postmenopausal population were used.
Tables of the risk of inadequate intake for specified ranges of the usual intake of iron, which are provided in the IOM report, were used for calculating iron inadequacy. The following summarizes how the full probability method to estimate iron inadequacy was implemented:
 The NCI method was used to estimate the usual intake distribution for iron. For each DRI agesex group, the MIXTRAN macro was run separately with the covariates survey year (cycle), province, weekend/weekday and sequence of 24hour recall, similar to other nutrients. For females 9 to 13, 19 to 30 and 31 to 50 years old, other covariates pertaining to female health had sufficient sample size and were considered to improve model fit. In particular, for females 9 to 13, the variable "Have you begun having menstrual cycles (periods) yet?" was considered; while for females 19 to 30 and 31 to 50, the covariate "In the past month, did you take birth control pills, including for reasons other than birth control?" was used. For females 31 to 50, the birth control covariate was significant (p=0.0012) and was included in the final model. For females 9 to 13 and 19 to 30 years old, these covariates were not significant (p=0.1552 and p=0.1400 respectively) and were removed from the final model. Individuals with missing covariate values were excluded from the final model for females 31 to 50 years old.
 In all cases, once the model was finalized, the parameter estimates from MIXTRAN were included in the DISTRIB macro to compute usual intake distributions for iron. Within the DISTRIB macro, the dataset corresponding to estimates of the pseudoindividuals (mcsim) was obtained, which considers iron usual intakes for 100 simulated individuals from each respondent.
 From Appendix G of the IOM report^{Footnote 8} on the DRIs for iron, Tables G5, G6 and G7 were used to determine the risk values. For females aged 14 to 18 years and menstruating women, the tables for the mixed adolescent and adult populations were used. Finally, for females 5170 and 71+, tables for the postmenopausal requirements were used.
 As an example, for the mixed adolescent population, intakes below the minimum value of 4.49 mg/d are assumed to have 100% probability of inadequacy (risk=1.0). Those with intakes above or equal to the maximum value of 14.39 mg/d are assumed to have zero risk of inadequacy. For intakes between these two extremes, the risk of inadequacy is calculated as 100 minus the midpoint of the percentiles of the requirement.
 The weighted average of these simulated risk values over all respondents within the DRI agesex group was the estimate of the iron inadequacy for that agesex group.
 Since covariates were included to improve estimates in some agesex groups, a different approach was used to calculate the usual intake distribution for adult males and females 19 years and older. In these two groups, results from each of the four stratified MIXTRAN runs (e.g. females 19 to 30, females 31 to 50, females 51 to 70 and females 71+) were obtained. The 100 simulated pseudoindividuals from each of these DRI agesex groups were found using the DISTRIB macro, and the risk associated with each of the 100 pseudoindividuals was calculated, as outlined in the previous step. Finally, the simulated data from the four genderspecific agesex groups were "stacked" and the prevalence of inadequacy for the entire adult group was estimated by gender.
 Standard errors for the estimates were calculated with the probability approach using the bootstrap method, as described in Section 2.3.3.
 For additional information on iron estimation and the full probability method, consult the Health Canada publication Reference Guide to Understanding and Using the Data  2015 Canadian Community Health Survey  Nutrition, Appendix 4^{Footnote 1}.
2.3.5 Estimation of usual intake distribution for caffeine
The analysis of caffeine differed from other nutrients, primarily since intake varies depending on the age group considered. To be consistent with Health Canada guidance, information on the usual intake of caffeine is provided for individuals aged 4 years and older. Unlike other nutrients, the usual intake of caffeine was analyzed using the twopart NCI model, since caffeine was found to be an episodically consumed nutrient for some Canadians.
The percentage of 24hour recalls with zero intake was larger than 10% for many agesex groups (Table 3), hence the correlated and uncorrelated models were fit (see section 2.3.2). For individuals over 30 years of age, the percentage of zero intake was between 5% and 10% approximately, thus all three NCI models were fit  correlated, uncorrelated and amountonly model. For all models, the same covariates were used: survey year, province, weekend/weekday, and sequential effect of the 24hour recall. In addition, the parameter estimates from MIXTRAN which were obtained using the original survey weight became starting values for the subsequent bootstrap runs. As part of MIXTRAN, BoxCox transformations were used to transform the data to normality. However, for children 4 to 8 years, males 9 to 13 years and females 9 to 13 years old, the logtransformation (lambda = 0) was used. The amountonly model was not considered in the final analysis since all of the twopart models converged.
AgeSex 
Percentage of zero intake (%) 
No. of outliers removed in final model 
Final pvalue for correlated model 
Twopart Model Used 

Females & Males combined: 4 to 8 years 
36.0 
1 
0.75 
Uncorrelated 
Males: 9 to 13 years 
32.3 
0 
0.40 
Uncorrelated 
Females: 9 to 13 years 
30.6 
0 
≤0.0001 
Correlated 
Males: 14 to 18 years 
27.4 
0 
0.29 
Uncorrelated 
Females: 14 to 18 years 
31.2 
0 
0.52 
Uncorrelated 
Males: 19 to 30 years 
20.1 
0 
0.005 
Correlated 
Females: 19 to 30 years 
19.5 
1 
0.61 
Uncorrelated 
Males: 31 to 50 years 
9.9 
8 
0.17 
Uncorrelated 
Females: 31 to 50 years 
10.2 
15 
0.03 
Correlated 
Males: 51 to 70 years 
6.8 
10 
0.003 
Correlated 
Females: 51 to 70 years 
7.0 
11 
0.12 
Uncorrelated 
Males: 71 years and older 
6.4 
6 
0.02 
Correlated 
Females 71 years and older 
6.9 
7 
N/A 
Uncorrelated 
Data Source: Statistics Canada, 2015 Canadian Community Health Survey  Nutrition; 2004 Canadian Community Health Survey Nutrition (cycle 2.2) Share files N/A  Unable to calculate correlated pvalue since the correlated model did not converge: uncorrelated model fitted 
The following procedure was used in choosing which twopart model provided the best fit. When the Fisher's Ztransformation of the estimated correlation between the random effects in the correlated model differed statistically from zero at the 5% significance level, then the correlated model was used. Otherwise, the uncorrelated model was fit. Correlated models were used to estimate usual intakes for females 9 to 13 years, males 19 to 30 years, females 31 to 50 years, males 51 to 70 years and males 71 years and older. For females 71 years and older, the correlated model did not converge using the root survey weight, thus the uncorrelated model was used for analysis. The uncorrelated model was fit for all other DRI groups (Table 3).
By implementing the outlier detection strategy, described in Section 2.3.2, the resulting ratio of withinperson to betweenperson variation was found to be smaller than 10 in all DRI agesex groups. No outliers were removed using this method. Another outlier detection strategy was used to search for possible violations to the normality assumption^{Footnote 9}. In particular, the method computes a BoxCox transformation of the original nonzero intake values and flags extreme values satisfying one of two criteria: i) those below the 25^{th} percentile minus 2.5 times the interquartile range of the transformed distribution; and ii) values which were above the 75^{th} percentile plus 2.5 times the interquartile range. Table 3 lists the number of outliers removed for each DRI agesex group using this method.
In addition to the specific DRI agesex groups, usual intake distributions of caffeine for males 19 years and older and females 19 years and older were also calculated. A distinct approach was used for these combined age groups because each individual agesex group required different models (Table 3). Based on their respective model, for each individual, 100 simulated pseudoindividuals were outputted by the NCI method using the DISTRIB macro. Finally, the simulated pseudoindividuals obtained were "stacked" and the distribution of usual intakes for both adult gender groups was estimated.
Standard errors for the caffeine estimates were calculated using the bootstrap method, as described in Section 2.3.3. For the males 19+ group, 46 bootstrap replicates failed, compared with 80 failed replicates for the females 19+ group.
2.3.6 Data source
The datasets used to generate estimates were the 2004 and 2015 Canadian Community Health Survey  Nutrition Share Files, which consist of all respondents who agreed to share their responses with the survey share partners. About 96% of respondents agreed to share their responses^{Footnote 1}.
Excluded from the dataset were respondents with null intakes (zero total intake from food) or invalid intakes, breastfed children and pregnant or breastfeeding women. Day one and day two recalls were used. Three respondents with day two recalls who did not have a corresponding day one recall were excluded. Analysis was performed on provincial, regional (Atlantic and Prairies) and national levels for all agesex groups other than children aged between 0 and 1 year. Analysis was also performed for the aggregated agesex groups: males 19+ and females 19+ years of age.
2.4. Comparing 2015 and 2004 nutrient intake estimates
One of the objectives of the 2015 CCHSNutrition was to assess whether changes in dietary intake have occurred since the 2004 CCHSNutrition. To meet this objective, the percentage of the population above or below relevant DRI reference values in 2004 and 2015 were compared. This was done using ttests where the mean change between 2004 and 2015 is compared to 0 and where the estimate of variance of that change comes from the bootstrap repetitions. The pvalues presented in the summary data table were not adjusted for multiple comparisons.
When interpreting betweenyear comparisons and before drawing conclusions, it is essential to consider that the data are not adjusted for differences in methodology. Differences in intakes between the two survey years may reflect changes in consumption patterns, changes in the nutrient composition of foods and/or changes in survey methodology among other potential explanations. Please refer to The Reference Guide to Understanding and Using the Data 2015 Canadian Community Health Survey Nutrition Section 4^{Footnote 1} for detailed discussions of what differed between survey years and potential implications. A number of potential differences in data collection and processing for the 2015 CCHSNutrition are likely to have affected intake estimates. Three of the major differences include:
 changes in the nutrient databases beyond reformulation of food products by manufacturers, for example, filling in nutrient values that were 'missing';
 use of an updated model booklet in the interview to estimate amounts consumed;
 the addition of quality checks during the interview when a large amount was entered, thereby allowing any necessary revisions to be made in the presence of the respondent.
Appendix A  Table footnotes
The following footnotes apply to the summary data table:
 The survey excludes from its target population those living in the three territories, individuals living on reserves, residents of institutions, full‐time members of the Canadian Armed Forces and residents of certain remote regions.
 The table excludes pregnant and breastfeeding females, subject to another set of nutritional recommendations. The sample of pregnant and breastfeeding females is not large enough to allow for reliable estimates at the provincial level.
 Sample size is based on the first 24‐hour recall (first day of interview) only.
 Intakes are based on food consumption only. Intakes from vitamin and mineral supplements are not included. Inferences about the prevalence of nutrient excess or inadequacy based on intakes from food alone may respectively underestimate or overestimate the prevalence based on total nutrient intakes from both food and supplements.
 The intake distribution (percentiles and percentage above or below a cut‐off when applicable) was adjusted using the National Cancer Institute (NCI) Method as described in Tooze JA, Midthune D, Dodd KW, et al.: A new statistical method for estimating the usual intake of episodically consumed foods with application to their distribution. J Am Diet Assoc 2006;106: 15751587 and Tooze JA, Kipnis V, Buckman DW, et al.: A mixedeffects model approach for estimating the distribution of usual intake of nutrients: the NCI method. Stat Med 2010; 29: 28572868
 Bootstrapping techniques were used to produce the coefficient of variation (CV) and the standard error (SE).
 AMDR is the Acceptable Macronutrient Distribution Range, expressed as a percentage of total energy intake. Intakes inside the range (shown in the AMDR columns) are associated with a reduced risk of chronic disease while providing adequate intakes of essential nutrients. For further information on AMDR in assessing population groups, see the Health Canada publication Reference Guide to Understanding and Using the Data  2015 Canadian Community Health Survey Nutrition, Section 2.2.6 page 28^{Footnote 1}.
 EAR is the Estimated Average Requirement. In the context of reporting results in a populationbased survey such as the 2004 and 2015 CCHSNutrition, the primary use of the EAR is to estimate the prevalence of inadequacy of some nutrients in a group. For further information on EAR and how to interpret the prevalence of inadequacy in a population see the Health Canada publication The Reference Guide to Understanding and Using the Data  2015 Canadian Community Health Survey  Nutrition, Section 2.2. 2, page 24^{Footnote 1}.
 AI is the Adequate Intake. The level of intake at the AI (shown in the AI columns) is the recommended average daily intake level based on observed or experimentally determined approximations or estimates of nutrient intake by a group or groups of apparently healthy people that are assumed to be adequate. It is developed when an EAR cannot be determined. The percentage of the population having a usual intake above the AI (shown in the %>AI columns) almost certainly meets their needs. The adequacy of intakes below the AI cannot be assessed, and should not be interpreted as being inadequate. For further information on AI and how to interpret the prevalence of inadequacy in a population, see the Health Canada publication Reference Guide to Understanding and Using the Data  2015 Canadian Community Health Survey  Nutrition, Section 2.2.4, pages 2526^{Footnote 1}.
 UL is the Tolerable Upper Intake Level. The level of intake at the UL (shown in the UL columns) is the highest average daily intake level that is likely to pose no risk of adverse health effects to almost all individuals in the general population. For further information on UL and how to interpret the prevalence of intakes above the UL in a population, see the Health Canada publication The Reference Guide to Understanding and Using the Data  2015 Canadian Community Health Survey  Nutrition, Section 2.2.5, page 28^{Footnote 1}. In 2017, the Guiding Principles for Developing Dietary Reference Intakes Based on Chronic Disease recommended that the UL be retained in the expanded DRI model, but that it should characterize toxicological risk^{Footnote 10}.
 The Chronic Disease Risk Reduction Intake (CDRR) is the lowest level of intake for which there is sufficient strength of evidence to characterize a chronic disease risk reduction. For more detailed understanding of the CDRR and its interpretation when assessing intakes of particular nutrients, consult the 2017 National Academies report, Guiding Principles for Developing Dietary Reference Intakes Based on Chronic Disease^{Footnote 10}.
 For a more detailed understanding of DRIs and their interpretation when assessing intakes of particular nutrients, consult the summary of the series of publications on DRIs published by the Institute of Medicine: Dietary Reference Intakes: The Essential Guide to Nutrient Requirements, (2006)^{Footnote 8}.
 For more detailed understanding of DRIs and their interpretation when assessing intakes of sodium and potassium, consult the Dietary Reference Intakes for Sodium and Potassium, 2019^{Footnote 11}.
 Data on transfat intake cannot be obtained from the 2004 and 2015 CCHSNutrition datasets and therefore are not reported separately. However, the estimates for percent energy from total fat comprise all fats, including transfats. Note that the estimates provided for energy intake from the individual types of fat will not add up to the estimates provided for total fat due to measurement error as well as the lack of data on transfat intake.
 In terms of precision, the estimate 0.0 with a standard error of 0.0 refers to a standard error smaller than 0.1%.
 Data with a coefficient of variation (CV) from 16.6% to 33.3% are identified as follows: (E) use with caution.
 Data with a coefficient of variation (CV) greater than 33.3% with a 95% confidence interval entirely between 0 and 3% are identified as follows: <3 interpret with caution.
 Data with a coefficient of variation (CV) greater than 33.3% were suppressed due to extreme sampling variability and are identified as follows: (F) too unreliable to be published.
 Comparisons between the 2004 and 2015 CCHSNutrition were calculated using paired ttests without adjustment for multiple comparisons.
 Data are not adjusted for differences in methodology between the 2004 and 2015 CCHSNutrition. For additional information on what differed between years and potential implications, please refer to section 2.4 of this methodology document.
Appendix B  List of dietary components
Dietary Components 


Energy and macronutrients 
Total energy intake Total carbohydrates Percentage of total energy intake from carbohydrates Total sugars Percentage of total energy intake from sugars Total fats Percentage of total energy intake from fats Total saturated fats Percentage of total energy intake from saturated fats Total monounsaturated fats Percentage of total energy intake from monounsaturated fats Total polyunsaturated fats Percentage of total energy intake from polyunsaturated fats Linoleic acid Percentage of total energy intake from linoleic acid Linolenic acid^{Footnote a} Percentage of total energy intake from linolenic acid Protein^{Footnote b} Percentage of total energy intake from protein Total dietary fibre^{Footnote c} Cholesterol 
Vitamins 
Vitamin A^{Footnote d} Vitamin B6 Vitamin B12 Vitamin C Vitamin C  by smoking status Vitamin D Folate Folacin^{Footnote e} Naturally occurring folate Niacin Riboflavin Thiamin 
Minerals 
Calcium Iron^{Footnote f} Magnesium Phosphorus Potassium Sodium Zinc 
Other Dietary Components 
Caffeine Moisture^{Footnote g} 

References
 Footnote 1

Health Canada. Reference Guide to Understanding and Using the Data  2015 Canadian Community Health Survey Nutrition. 2017. Available at: https://www.canada.ca/en/healthcanada/services/foodnutrition/foodnutritionsurveillance/healthnutritionsurveys/canadiancommunityhealthsurveycchs/referenceguideunderstandingusingdata2015.html
 Footnote 2

Tooze JA, Midthune D, Dodd KW, et al.: A new statistical method for estimating the usual intake of episodically consumed foods with application to their distribution. J Am Diet Assoc 2006;106: pp. 15751587.
 Footnote 3

Tooze JA, Kipnis V, Buckman DW, et al.: A mixedeffects model approach for estimating the distribution of usual intake of nutrients: the NCI method. Stat Med 2010; 29: pp. 28572868.
 Footnote 4

Nusser SM, Carriquiry AL, Dodd KW, Fuller WA: A semiparametric transformation approach to estimating usual daily intake distributions. J Am Stat Assoc 1996; 91: pp. 14401449.
 Footnote 5

Health Canada. Canadian Community Health Survey, Cycle 2.2, Nutrition (2004) Nutrient Intakes from Food Provincial, Regional and National Summary Data Tables, Volume 1, 2 and 3.
 Footnote 6

Davis KA, Gonzalez A, Loukine L, Qiao C, Sadeghpour A, Vigneault M, Wang KC, Ibanez D. Early experience analyzing dietary intake data from the Canadian Community Health Survey  Nutrition using the National Cancer Institute (NCI) Method. Nutrients 2019; 11: pp. 1908.
 Footnote 7

National Research Council. The probability approach Nutrient Adequacy: Assessment Using Food Consumption Surveys 1986., Washington, DC: National Academy Press, pp. 2540.
 Footnote 8

Otten JJ, Hellwig JP, Meyers LD. Dietary Reference Intakes. The Essential Guide to Nutrient Requirements. 2006. Washington, D.C.: National Academies Press.
 Footnote 9

KrebsSmith SM, Guenther PM, Subar AM, Kirkpatrick SI, Dodd KW. Americans do not meet federal dietary recommendations. The Journal of Nutrition 2010; 140, pp. 18321838.
 Footnote 10

National Academies of Sciences, Engineering, and Medicine. 2017. Guiding principles for developing Dietary Reference Intakes based on chronic disease. Washington, DC: The National Academies Press.
 Footnote 11

National Academies of Sciences, Engineering, and Medicine. 2019. Dietary Reference Intakes for sodium and potassium. Washington, DC: The National Academies Press.
 Footnote 12

IOM (Institute of Medicine). Dietary Reference Intakes for energy, carbohydrate, fibre, fat, fatty acids, cholesterol, protein, and amino acids (Macronutrients). 2005. Food and Nutrition Board, Institute of Medicine. The National Academies Press, Washington, DC.
 Footnote 13

Health Canada. Proposed Policy: Definition and Energy Value for Dietary Fibre. Food Directorate, Health Products and Food Branch, Health Canada. 2010. Available at: https://www.canada.ca/en/healthcanada/services/foodnutrition/publicinvolvementpartnerships/proposedpolicydefinitionenergyvaluedietaryfibre/consultation.html
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