Relative Efficiency of the EQ-5D, HUI2, and HUI3 Index Scores in Measuring Health Burden of Chronic Medical Conditions in a Population Health Survey in the United States
Bibliographic record
Abstract
OBJECTIVE: We sought to compare the ability of the EQ-5D, Health Utilities Index Mark 2 (HUI2), and HUI Mark 3 (HUI3) index scores to discriminate between respondents based on the presence or absence of chronic medical conditions in a population health survey. METHODS: Secondary analyses were conducted with data from a probability sample (n = 3480, mean age: 42.5 years, male: 42.4%, Hispanic: 28.6%) of the 2001 noninstitutionalized US general adult population. F-statistic ratios were used to evaluate the relative efficiency of the EQ-5D, HUI2, and HUI3 in differentiating respondents with or without each of 18 chronic medical conditions, and differentiating respondents with low- or high-burden conditions. RESULTS: In comparing respondents with and without chronic medical conditions, the F-statistic values of these 3 indices were not significantly different, except for EQ-5D versus HUI2 [mean F-statistic ratio: 0.79, 95% confidence interval (CI): 0.59-0.98]. In comparing respondents with a low-burden condition with those with a high-burden condition, the F-statistic values of EQ-5D and HUI2 index scores were similar, while those for EQ-5D versus HUI3 (mean: 0.79; 95% CI: 0.66-0.92) and for HUI2 versus HUI3 (mean: 0.83; 95% CI: 0.71-0.95) were significantly less than 1.0. The overall ceiling effects of the EQ-5D, HUI2, and HUI3 index scores were 48.9%, 15.4%, and 15.3%, respectively. CONCLUSIONS: Although the EQ-5D seems to be marginally less informative, the EQ-5D, HUI2, and HUI3 index scores were generally comparable in determining health burden of chronic medical conditions in this population health survey data.
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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 | 0.064 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".