Health utilities index mark 3 scores for major chronic conditions : population norms for Canada based on the 2013-2014 Canadian Community Health Survey
Bibliographic record
Abstract
Background: Utility scores are frequently used as preference weights when estimating quality-adjusted life-years within cost-utility analyses or health-adjusted life expectancies. Though previous Canadian estimates for specific chronic conditions have been produced, these may no longer reflect current patient populations. Data and methods: Data from the 2013-2014 Canadian Community Health Survey were used to provide Canadian utility score norms for seventeen chronic conditions. Utility scores were estimated using the Health Utilities Index Mark 3 (HUI3) instrument and were reported as weighted average (95% confidence intervals [95% CI]) values. In addition to age and sex-stratified analyses, results were also stratified according to the number of reported chronic conditions (i.e., “none” to “≥5”). All results were weighted using sampling and bootstrapped weights provided by Statistics Canada. Results: Utility scores were estimated for 123,654 (97.2%) respondents (weighted frequency = 29,337,370 [97.7%]). Of the chronic conditions that were examined, “Asthma” had the least detrimental effect (weighted average utility score = 0.803 [95%CI 0.795 – 0.811]) on respondents’ utility scores and “Alzheimer’s disease or any other dementia” had the worst (weighted average utility score = 0.374 [95%CI 0.323 – 0.426]). Respondents who reported suffering from no chronic conditions had, on average, the highest utility scores (weighted average utility score = 0.928 [95%CI 0.926 – 0.930]); estimates dropped as a function of the number of reported chronic conditions. Interpretation: Utility score differed between various chronic conditions and as a function of the number of reported chronic conditions. Results also highlight several differences with previously published Canadian utility norms.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".