POPUP: an observational digital study reporting general population norms for the EQ-5D-5-L and HUI-3 in 8 countries
Bibliographic record
Abstract
BACKGROUND: This study aimed to estimate population norms in the US, Canada, UK, Italy, Spain, Germany, The Netherlands, and Belgium for the EQ-5D-5-L with six bolt-on dimensions (vision, breathing, tiredness, sleep, social relationships, self-confidence), and for the Health Utilities Index-Mark 3 (HUI-3). METHODS: A digital study was conducted among 9,000 general population participants, representative of age, sex, education, and region within each country. Data collection included demographics, health conditions, EQ-5D-5-L and bolt-ons, and the HUI-3. National population norms were calculated for each dimension and for utility values. Testing for differences between subgroups was performed with a Generalized Linear Model. RESULTS: The proportion of respondents reporting severe-to-extreme problems at dimension level was highest on the EQ-5D-5-L dimensions pain/discomfort (5.5%) and anxiety/depression (5.6%), and on the HUI-3 dimensions pain (5.7%), emotion (5.4%), and cognition (4.1%). Severe-to-extreme problems on the EQ-5D-5-L bolt-on dimensions were social relationships (8.0%), sleep (7.6%), tiredness (7.4%), self-confidence (5.1%), vision (3.7%), and breathing (2.0%). Mean EQ-5D-5-L utility values for all countries combined displayed a U-shape by age and ranged between 0.819 and 0.871, whereas HUI-3 utility values ranged between 0.717 and 0.768 without a clear pattern. The impact of age by sex on EQ-5D-5-L utility values was country-specific. HUI-3 utilities did not show a linear trend by age, and no difference was found by sex. Italy had the highest mean EQ-5D-5-L utility values, while the Netherlands and Spain had the highest values according to the HUI-3. The lowest utility values were observed in the UK, for both instruments. Utility values differed significantly by education, employment, place of residence, needing a caregiver, being on sick leave and having health conditions such as dementia, MS, depression, rheumatoid arthritis, systemic lupus erythematosus and heart failure. CONCLUSIONS: Important differences in reporting problems and in utility values were found between countries and subgroups, highlighting the need for country-specific population norms.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".