The economic burden of not meeting food recommendations in Canada: The cost of doing nothing
Bibliographic record
Abstract
Few studies have estimated the economic burden of chronic diseases (e.g., type 2 diabetes, cardiovascular diseases, cancers) attributable to unhealthy eating. In this study, we estimated the economic burden of chronic disease attributable to not meeting Canadian food recommendations. We first obtained chronic disease risk estimates for intakes of both protective (1. vegetables; 2. fruit; 3. whole grains; 4. milk; 5. nuts and seeds) and harmful (6. processed meat; 7. red meat; 8. sugar-sweetened beverages) foods from the Global Burden of Disease Study, and food intakes from the 2004 Canadian Community Health Survey 24-hour dietary recalls (n = 33,932 respondents). We then calculated population attributable fractions (PAFs) for all relevant food-chronic disease combinations by age and sex groups. These PAFs were then mathematically combined for each disease for each age and sex group. We then estimated attributable costs by multiplying these combined PAFs with estimated 2014 annual direct health care (hospital, drug, physician) and indirect (human capital approach) costs for each disease. We found that not meeting recommendations for the eight foods was responsible for CAD$13.8 billion/year (direct health care: CAD$5.1 billion, indirect: CAD$8.7 billion). Nuts and seeds and whole grains were the top cost contributors rather than vegetables and fruit. Our findings suggest that unhealthy eating constitutes a tremendous economic burden to Canada that is similar in magnitude to the burden of smoking and larger than that of physical inactivity which were estimated using similar approaches. A status quo in promotion of healthy eating will allow this burden to continue. Interventions to reduce the health and economic burden of unhealthy eating in Canada may be more effective if they are broad in focus and include promotion of nuts and seeds and whole grains along with vegetables and fruit rather than have a narrow focus such as primarily on vegetables and fruit.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 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".