Self-Reported Health Status of the General Adult U.S. Population as Assessed by the EQ-5D and Health Utilities Index
Bibliographic record
Abstract
OBJECTIVE: This study aimed to describe the self-reported health status of the general adult U.S. population using 3 multi-attribute preference-based measures: the EQ-5D, Health Utilities Index Mark 2 (HUI2), and Mark 3 (HUI3). METHODS: We surveyed the general adult U.S. population using a probability sample with oversampling of Hispanics and non-Hispanic blacks. Respondents to this home-visit survey self-completed the EQ-5D and HUI2/3 questionnaires. Overall health index scores of the target population and selected subgroups were estimated and construct validity of these measures was assessed by testing a priori hypotheses. RESULTS: Completed questionnaires were collected from 4048 respondents (response rate: 59.4%). The majority of the respondents were women (52.0%); the mean age of the sample was 45 years, with 14.8% being 65 or older. Index scores (standard errors) for the general adult U.S. population as assessed by the EQ-5D, HUI2, and HUI3 were 0.87 (0.01), 0.86 (0.01), and 0.81 (0.01), respectively. Generally, younger, male and Hispanic or non-Hispanic black adults had higher (better) index scores than older, female and other racial/ethnic adults; index scores were higher with higher educational attainment and household income. The 3 overall preference indices were strongly correlated (Pearson's r: 0.67-0.87), but systematically different, with intraclass correlation coefficients between these indices ranging from 0.59 to 0.77. CONCLUSIONS: This study provides U.S. population norms for self-reported health status on the EQ-5D, HUI2, and HUI3. Although these measures appeared to be valid and demonstrated similarities, health status assessed by these measures is not exactly the same.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".