Quebec Health-Related Quality-of-Life Population Norms Using the Health Utilities Index Mark 3
Bibliographic record
Abstract
OBJECTIVES: To provide population utility norms from the Health Utilities Index Mark 3 (HUI3) for the province of Quebec, Canada. METHODS: This study used data from the Care Trajectories Enriched Data (TorSaDE) cohort, which combines data from the Canadian Community Health Survey (CCHS) and the Quebec Provincial Insurance Board [Régie de l'assurance maladie du Quebec (RAMQ)]. The CCHS is a multiround health-related survey conducted by Statistics Canada since 2007. For each round spanning over 2 years, respondents were randomly selected and completed an online questionnaire. Quebec data for the HUI3 were available in the CCHS for rounds 2007, 2009, and 2013. The RAMQ database is an administrative database that contains information on health care services use and medical diagnostics. HUI3 scores were stratified by sociodemographic variables, as well as by self-reported health problems in the CCHS and by medical diagnostics from the RAMQ. Medical diagnostics were retrieved for the CCHS completion year and the year before and identifiable with the ICD-9 code in the RAMQ database. RESULTS: A total of 55,656 individuals were considered in this analysis. The mean (95% CI) and the median interquartile range of HUI3 were respectively 0.919 (0.918-0.919) and 0.973 (0.905-1) for the entire population. Individuals with lower scores were females, those aged 75 and over, divorced or widowed, unemployed during the last 12 months, less educated, or with a lower annual household income. Individuals born abroad and with normal weight of body mass index had higher utility scores. HUI3 score decreased with the number of diagnosed diseases from 0.946 (0.946-0946) for individuals without diagnosed disease to 0.682 (0.678-0.686) for individuals diagnosed with up to 18 diseases. Regardless of the number of diagnosed diseases in the RAMQ database, individuals who self-reported suffering from a single health problem presented a significantly lower HUI3 ranging from 0.944 (0.943-0.944) for Asthma to 0.789 (0.782-0.796) for Alzheimer compared with 0.956 (0.956-0.957) for individuals with no reported health problems. The same pattern was observed when considering individuals regardless of the diagnosed and self-reported diseases. CONCLUSION: Utility score norms for HUI3 were produced in the general population of Quebec. Significant differences among various health problems were identified and norms can be used to compare populations in studies that do not have a control group.
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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 | 0.023 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".