The health and financial impacts of a sugary drink tax across different income groups in Canada
Bibliographic record
Abstract
BACKGROUND: Overconsumption of sugar-sweetened beverages (SSBs) contributes to childhood and adult obesity and numerous related diseases, including heart disease, strokes, cancers, and type 2 diabetes. It also increases healthcare costs. Sugary drink taxes have been implemented in several countries to curb sugar intake. However, there is a concern that sugary drink taxes are regressive. This study assessed the health and financial impacts of a simulated sugary drink tax across different income groups in Canada. METHODS: A proportional multi-state life table-based Markov model simulated the 2016 Canadian population by income quintile. The model applied a 20 % tax on sugary drinks and determined the effects on type 2 diabetes and BMI-related diseases compared to no intervention. The income-specific parameters modelled included: population demographics; cross- and own-price elasticities; mean BMI; sugary drink consumption; mortality; and disease epidemiology. RESULTS: A 20 % sugary drink tax was estimated to reduce the consumption of sugary drinks by an average of around 15 %, with a greater reduction in the lowest income quintile. The estimated mean reduction in BMI ranged from 0.21 to 0.33, dependent upon sex and income quintile; these reductions were greater among the lower income quintiles for both females and males. The 20 % sugary drink tax was estimated to avert approximately 690,000 DALYs over a lifetime among the 2016 Canadian adult population; estimated DALYs averted were approximately 156,000, 140,000, 137,000, 134,000, and 125,000 for the lowest through to the highest income quintile, respectively. Lifetime health care savings were estimated to be $2.27bn, $2.16bn, $2.17bn, $2.12bn, and $1.98bn for the lowest through to the highest income quintile, respectively. The estimated annual tax burden for the 2016 Canadian population was $1.4bn. The average absolute tax burden was estimated to be $39.00 to $44.30 per person, with the middle-income quintile bearing the heaviest absolute tax burden. The lowest income quintile would pay the highest proportion of income in tax, implying that the tax is regressive. CONCLUSIONS: Low-income Canadians would gain the most health benefit from a sugary drinks tax. However, the lowest income quintile would also pay the largest proportion of income in tax. A tax on sugary drinks is therefore financially regressive but forecast to reduce health disparities across 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".