Measuring the health burden of chronic disease and injury using health adjusted life expectancy and the Health Utilities Index
Bibliographic record
Abstract
OBJECTIVES: To estimate the burden of illness from chronic disease and injury using a population based health survey, which contains both measures of chronic disease and a utility based health related quality of life (HRQOL) measure. DESIGN: An adapted Sullivan method was used to calculate cause deleted health adjusted life expectancies for chronic conditions. SETTING: Ontario, Canada, 1996/97. SUBJECTS: The 1996/97 Ontario Health Survey (n=35 527) was used to estimate the prevalence of chronic conditions. A cause deleted approach was used to estimate the impact of these conditions on the Health Utilities Index (HUI). Cause deleted probabilities of dying were derived with the cause eliminated life table technique and death data from vital statistics for Ontario 1996/97 (n=156 610). RESULTS: Eliminating cardiovascular disease and cancer will cause an "expansion of morbidity", while eliminating mental conditions and musculosketal disorders will result in a "contraction of morbidity". The HUI score varies depending on chronic condition, age, and sex-most of which were assumed not to vary in previous summary measures of population health. CONCLUSIONS: Health adjusted life expectancy estimated for chronic conditions using a utility based measure of health related quality of life from population health surveys addresses several limitations of previous studies that estimate the burden of disease using either a categorical measure of disability or expert opinion and related epidemiological evidence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.145 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".