Mortality attributable to poor dietary patterns in Canada: Evidence from the nationally-representative nutrition survey linked with health administrative data
Bibliographic record
Abstract
IntroductionDietary pattern modeling and linkage with health outcomes is essential for development of evidence-based dietary guidelines to support reduction of chronic diseases. National nutrition surveys are not routinely linked with health administrative databases, resulting in a lack of evidence on the health impact of unhealthy diets at the population level. Objectives and ApproachThis study is the first to use a nationally-representative nutrition survey (i.e., Canadian Community Health Survey-Nutrition-2004) linked with health administrative databases to examine the association of 5 key dietary quality indices with mortality risk. In total, 16,212 adults ≥20 years were followed for an average of 7.3 years. Two 24-hour dietary recalls were used to estimate the usual dietary intakes using the National Cancer Institute’s method. Weighted regression calibration was performed to obtain a true parameter relating diet (continuous) to mortality. Population Attributable Fractions were calculated to estimate the burden of all-cause mortality attributable to poor dietary patterns in Canada. ResultsThe 5 dietary quality indices examined were Dietary Approaches to Stop Hypertension(DASH); Dietary Guidelines for Americans Adherence Index 2015(DGAI); Healthy Eating Index-2010(HEI); Alternative HEI-2010(AHEI); and Mediterranean Style Dietary Pattern Score(MSDPS). Having a better diet quality (90%ile vs. 10%ile of index score) was associated with a significant 31-51% reduction in all-cause mortality hazard ratio among adults 45 to 80 years and 10-35% reduction in those ≥20 years (in order of significance: DASH, DGAI, HEI, AHEI and MSDPS). Survival benefit was incrementally greater for higher diet quality scores; however, even the 90%ile scores (Reference) were notably lower than the recommended levels (45.99% of recommended score). On average, 26.42% of all mortality in Canada was attributable to poor dietary patterns (range: 19.01% for MSDPS to 31.39% for DGAI). Conclusion/ImplicationsThe diet-attributable burden of mortality was higher than those reported for other behavioural risks (e.g., smoking). This research informs future formulation of nutrition interventions and policies with a focus on dietary patterns. This project demonstrates the importance of leveraging linked data and analytical capacity to inform future evidence-based nutrition policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".