The equitable impact of sugary drink taxation structures on sugary drink consumption among Canadians: a modelling study using the 2015 Canadian Community Health Survey-Nutrition
Bibliographic record
Abstract
OBJECTIVE: Estimate the impact of 20 % flat-rate and tiered sugary drink tax structures on the consumption of sugary drinks, sugar-sweetened beverages and 100 % juice by age, sex and socio-economic position. DESIGN: We modelled the impact of price changes - for each tax structure - on the demand for sugary drinks by applying own- and cross-price elasticities to self-report sugary drink consumption measured using single-day 24-h dietary recalls from the cross-sectional, nationally representative 2015 Canadian Community Health Survey-Nutrition. For both 20 % flat-rate and tiered sugary drink tax scenarios, we used linear regression to estimate differences in mean energy intake and proportion of energy intake from sugary drinks by age, sex, education, food security and income. SETTING: Canada. PARTICIPANTS: 19 742 respondents aged 2 and over. RESULTS: In the 20 % flat-rate scenario, we estimated mean energy intake and proportion of daily energy intake from sugary drinks on a given day would be reduced by 29 kcal/d (95 % UI: 18, 41) and 1·3 % (95 % UI: 0·8, 1·8), respectively. Similarly, in the tiered tax scenario, additional small, but meaningful reductions were estimated in mean energy intake (40 kcal/d, 95 % UI: 24, 55) and proportion of daily energy intake (1·8 %, 95 % UI: 1·1, 2·5). Both tax structures reduced, but did not eliminate, inequities in mean energy intake from sugary drinks despite larger consumption reductions in children/adolescents, males and individuals with lower education, food security and income. CONCLUSIONS: Sugary drink taxation, including the additional benefit of taxing 100 % juice, could reduce overall and inequities in mean energy intake from sugary drinks in Canada.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".