Evaluating the impact of government-led nutrient profiling models on preventable mortality and cardiovascular disease outcomes
Bibliographic record
Abstract
Background: Nutrient profiling (NP) is the science of ranking food according to their nutritional composition with the goal of preventing disease and promoting health and is used in several countries for nutritional policy, such as front-of-package (FOP) labelling. Objective: The objective of this thesis was to evaluate the nutritional quality of Canadian adults’ dietary intakes, as measured by the most commonly used NP models (Ofcom, Nutri-Score and FSANZ), as well as Health Canada’s proposed FOP system, and evaluate the associations of nutritional quality with all-cause mortality and cardiovascular disease (CVD) incidence and death at the national population level. Methods: Data obtained from the nationally representative Canadian Community Health Survey-Nutrition 2004, linked individually with the Canadian Vital Statistics Database (n= 6767) and hospital Discharge Abstract Database (n=6420) to end of December 2017, was used to derive individual NP scores for Ofcom, FSANZ, and Nutri-Score. Weighted Cox proportional hazard models were used to evaluate the multivariate-adjusted associations between diet quality with CVD risk and all-cause mortality. Results: Canadians were found to have “moderately” healthy dietary intakes for each NP system evaluated, and about 38% of energy intake came from items that meet Health Canada’s proposed FOP labelling. After accounting for multiple potential confounders in the multivariable-adjusted models, the association between NP model scores and all-cause mortality was statistically significant for Ofcom model (hazard ratio (HR) in highest quintile: 1.73, 95% CI [1.20, 2.49]), FSANZ model (HR: 1.59, 95% CI [1.15, 2.21]), and Nutri-Score (HR: 1.75, 95% CI [1.18, 2.59]). For CVD incidence and death, the multivariate-adjusted model reached statistical significance only among males, in Ofcom (HR in highest quintile: 2.11, 95% CI [1.15, 3.89]), and FSANZ (HR: 1.74, 95% CI [1.07, 2.84]), and the Nutri-Score (HR: 2.29, 95% CI [1.23, 4.24]). Conclusions: Overall Canadian adults with the lowest diet quality were more likely to experience CVD events and all-cause mortality, as compared to those with higher diet quality. Since NP models have widespread application in regulation of FOP these results can encourage policy makers to use and apply these models for prevention of chronic diseases and health promotion at the population level.
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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 | 0.040 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".