A Nationally Representative Analysis of Trends in Socioeconomic Inequities in Diet Quality Between 2004 and 2015 Among Adults Living in Canada
Bibliographic record
Abstract
Diet quality is a key determinant of chronic disease and shares a similar socioeconomic patterning. Inequities in diet quality are stable or widening in the US, however these trends have not been examined in other nations. Moreover, prior US studies only examined differences in diet quality between the most and least disadvantaged groups in absolute terms. Quantifying trends in relative terms and along the full socioeconomic gradient according to multiple indicators of socioeconomic position (SEP) can provide a more comprehensive perspective to inform optimal points of intervention. The purpose of this study was to quantify nationally representative trends in absolute and relative gaps and gradients in diet quality between 2004 and 2015 according to three indicators of SEP among adults living in Canada. Adults (≥18 years) who participated in the nationally representative, cross-sectional Canadian Community Health Survey - Nutrition in 2004 (n = 20,880) or 2015 (n = 13,970) were included. SEP was classified based on annual gross household income (quintiles), education (5 categories) and neighborhood deprivation (quintiles). Dietary intake data from interviewer-administered 24-hour recalls were used to derive Healthy Eating Index-2015 scores. Dietary inequities were quantified using four indices: absolute gaps, relative gaps, absolute gradients (slope index of inequality) and relative gradients (relative index of inequality). Sex-stratified multivariable linear regression models examined trends in HEI-2015 scores between 2004 and 2015. Mean HEI-2015 scores improved significantly from 55.3 in 2004 to 59.0 in 2015 (maximum 100 points); however these trends were not consistently equitable. While inequities in HEI-2015 scores were stable in females, the absolute gap and gradient in HEI-2015 scores according to household income increased in males, as did the absolute gradient according to education. Absolute and relative gaps and gradients in diet quality remained stable or widened between 2004 and 2015 in Canada. Novel policies are needed to tackle these avoidable inequities. Providing universal access to resources with a scale and intensity proportionate to need (i.e., proportionate universalism) may reduce inequities in diet quality and thus, chronic disease risk. Not applicable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".