Socioeconomic position and consumption of sugary drinks, sugar-sweetened beverages and 100% juice among Canadians: a cross-sectional analysis of the 2015 Canadian Community Health Survey–Nutrition
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to describe sugary drink (beverages with free sugars), sugar-sweetened beverage (beverages with added sugars, SSB) and 100% juice (beverages with natural sugars) consumption across socioeconomic position (SEP) among Canadians. METHODS: We conducted a cross-sectional analysis of 19,742 respondents of single-day 24-h dietary recalls in the nationally representative 2015 Canadian Community Health Survey-Nutrition. Poisson regressions were used to estimate the prevalence of consuming each beverage type on a given day. Among consumers on a given day, linear regressions were used to estimate mean energy intake. Models included household education, food security and income quintiles as separate unadjusted exposures. Sex-specific models were estimated separately for children/adolescents (2-18 years) and adults (19 +). RESULTS: Among female children/adolescents, the prevalence of consuming sugary drinks and, separately, SSB ranged from 11 to 21 and 8 to 27 percentage-points higher among lower education compared to 'Bachelor degree or above' households. In female adults, the prevalence of consuming sugary drinks and, separately, SSB was 10 (95% CI: 1, 19) and 14 (95% CI: 2, 27) percentage-points higher in food insecure compared to secure households. In males, the prevalence of consuming 100% juice was 9 (95% CI: - 18, 0) percentage-points lower among food insecure compared to secure households. Social inequities in energy intake were observed in female adult consumers, among whom mean energy from sugary drinks was 27 kcal (95% CI: 3, 51) higher among food insecure compared to secure and 35 kcal (95% CI: 2, 67) higher from 100% juice among 'less than high school' education compared to 'Bachelor degree or above' households. CONCLUSION: Social inequities in sugary drink consumption exist in Canada. The associations differed by SEP indicator. Equitable interventions to reduce consumption are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| 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) | 0.002 | 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".