The health and economic benefits of sugar taxation and vegetables and fruit subsidy scenarios in Canada
Bibliographic record
Abstract
A tax on sugar-sweetened beverages (SSB) has been implemented in various jurisdictions. Though research confirmed this tax to reduce sugar consumption and to prevent chronic diseases, it also revealed concerns: one concern relates to the small proportion of sugar in the diet coming from SSBs; and another concern relates to the disproportional tax burden to low-income groups. To inform public health decision makers on alternatives, we examined three 'real world' taxation and subsidy scenarios in Canada: 1) a CAD$0.75/100 g tax on SSBs; 2) a CAD$0.75/100 g tax on free sugar in all foods; and 3) a 20% subsidy on vegetables and fruit (V&F). Using national survey data and a proportional multi-state life table-based Markov model, we simulated the changes in disability-adjusted life years, healthcare costs, tax revenue, intervention costs, and incremental cost-effectiveness ratio for five income quintiles after implementing the three scenarios, over a lifetime of the 2015 Canadian adult population. The first, second and third scenario would prevent 28,921, 262,348 and 551 cases of type 2 diabetes, respectively. They would avert 752,353, 12,167,113, and 29,447 disability-adjusted life years and save CAD$12,942 million, 149,927 million, and 442 million in health care costs, respectively, over a lifetime. Combining the second and third scenarios would lead to the largest health and economic benefits. Although the lowest income quintile would bear a higher sugar tax burden (0.81% of income, CAD$120/person/year), this would be compensated by a coinciding subsidy on V&F (1.30% of income, CAD$194/person/year). These findings support policies that include a tax on all free sugar in foods and a subsidy on V&F as an effective means to reduce chronic diseases and health care costs. Although the sugar tax was financially regressive, the V&F subsidy could compensate for the tax burden of the disadvantaged groups and improve health and economic equity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".