POPUP: an observational digital study reporting general population norms for the EQ-5D-5-L and HUI-3 in 8 countries
Bibliographic record
Abstract
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 | 0.007 |
| 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) | 0.001 | 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".