O4-2.2 Healthpaths dynamics: using functional health trajectories to quantify impacts on Health-Adjusted Life Expectancy (HALE) in Canada
Bibliographic record
Abstract
There is significant dispersion in health status in populations, as well as a strong correlation between health status and socio-economc status. However, there remains considerable uncertainty as to the quantitiative importance of various causal factors in accounting for these health inequalities. Since health status is intrinsically a reflection of co-evolving dynamic processes, it is essential to employ an analytical framework that brings together robust estimates of individuals' health status as functions of health determinants dynamics in order to assess realistically the sources of health inequalities. This analysis is based the Health Utilities Index (HUI), where the index is computed as a non-linear function of eight distinct categorical attributes—vision, hearing, speech, mobility, dexterity, cognition, emotion, and pain. The complex dynamics of HUI in a representative sample have been observed with Statistics Canada' National Popultation Health Survey every 2 years since 1994. The analysis begins with estimates of multivariate functional health trajectories, conditional on co-evolving risk factors. It then uses longitudinal microsimulation, drawing on the estimated system of equations for the dynamic relationships among the eight HUI components and major health determinants. The microsimulation process is used to synthesise a realistic base case representative longitudinal population sample, and then a series of carefully constructed counter-factual populations. Comparisons of the distributions of health-adjusted life lengths and summary HALE measures between counter-factuals and the base case are then used to estimate the quantitative importance of the major factors in accounting for HALE in Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".