Projections of Socioeconomic Trends in Obesity and Diabetes in Canada from 2001 to 2021: The Population Health Microsimulation Model (POHEM:CVD).
Bibliographic record
Abstract
INTRODUCTION: Reducing health inequalities is a major public health priority internationally. Social inequalities in obesity and diabetes have been previously reported in Canada. However, it is unclear how these trends will change over time. The objective was to project future trends in obesity and diabetes by socioeconomic position (SEP) from 2001–21. METHODS: All projections were conducted using the Population Health Model for Cardiovascular Disease. This continuous-time microsimulation model uses data from the cross-sectional 2001 Canadian Community Health Survey (CCHS) to simulate a baseline population of 22.5 million “actors” representative of the Canadian population over 20 years. Independent life trajectories are created for each actor, including full risk factor profiles updated each year using predictive algorithms to determine transitions between risk factor states. Obesity (body mass index ≥30kg/m 2 ) and diabetes (physician diagnosed) prevalence were estimated by SEP (education: less than secondary graduation, secondary graduation, some post-secondary and post-secondary graduation). The model was validated by comparing predicted obesity and diabetes prevalence by SEP to observed prevalence in the 2001–11 versions of the CCHS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".