Temporal changes in diet quality and the associated economic burden in Canada
Bibliographic record
Abstract
A high-quality diet is associated with a reduced of risk of chronic disease and all-cause mortality. In this study, we assessed changes in diet quality and the associated economic burden in the Canadian population between 2004 and 2015. We used a prevalence-based cost-of-illness approach. We first calculated the diet quality using the Healthy Eating Index-Canada-2010 (HEI-C-2010) and 24-hour recall data from the Canadian Community Health Surveys (CCHS) on nutrition (CCHS 2004 cycle 2.2 and the CCHS-NU 2015). We then retrieved relative risks of HEI-2010 quintiles for chronic diseases from meta-analyses. Based on the proportions of the population following diets of varying qualities and these relative risks, we computed the population-attributable fractions and attributable costs (direct health care and indirect costs) by survey year (2004 and 2015) as well as by age and sex group. Costs were estimated in 2017 Canadian dollars for comparison purposes. We observed that on average the diet quality of Canadians improved between 2004 and 2015: the proportion of the Canadian population that did not eat a diet of high quality decreased from 83% to 76%. This improvement in diet quality translated in a decrease in economic burden of $133 million, down from $13.21 billion in 2004 to $13.08 billion in 2015. The economic burden decreased by $219 million among males but increased by $86 million among females. It also decreased among people under the age of 65 years ($333 million) but increased among those over 65 years ($ 200 million). Our findings suggest that, despite some temporal improvements, the diet of the majority of Canadians is of poor quality resulting in a high attributable economic burden. Policy and decision makers are encouraged to expand nutrition programs and policies and to specifically target the elderly in order to prevent chronic diseases and reduce health care costs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".