The economic burden of excessive sugar consumption in Canada: should the scope of preventive action be broadened?
Bibliographic record
Abstract
OBJECTIVE: Excessive sugar consumption is an established risk factor for various chronic diseases (CDs). No earlier study has quantified its economic burden in terms of health care costs for treatment and management of CDs, and costs associated with lost productivity and premature mortality. This information, however, is essential to public health decision-makers when planning and prioritizing interventions. The present study aimed to estimate the economic burden of excessive free sugar consumption in Canada. METHODS: Free sugars refer to all monosaccharides and disaccharides added to foods plus sugars naturally present in honey, syrups, and fruit juice. Based on free sugar consumption reported in the 2015 Canadian Community Health Survey-Nutrition and established risk estimates for 16 main CDs, we calculated the avoidable direct health care costs and indirect costs. RESULTS: If Canadians were to comply with the free sugar recommendation (consumption below 10% of total energy intake (TEI)), an estimated $2.5 billion (95% CI: 1.5, 3.6) in direct health care and indirect costs could have been avoided in 2019. For the stricter recommendation (consumption below 5% of TEI), this was $5.0 billion (95% CI: 3.1, 6.9). CONCLUSION: Excessive free sugar in our diet has an enormous economic burden that is larger than that of any food group and 3 to 6 times that of sugar-sweetened beverages (SSBs). Public health interventions to reduce sugar consumption should therefore consider going beyond taxation of SSBs to target a broader set of products, in order to more effectively reduce the public health and economic burden of CDs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".