EQ-5D-5L Health-State Values for the Mexican Population
Bibliographic record
Abstract
OBJECTIVE: To generate a value set for the Mexican adult general population to support and facilitate the inclusion of quality-adjusted life years (QALYs) into the health technology assessment process of the Mexican healthcare authorities. METHODS: A representative sample of the Mexican adult population stratified by age, sex and socio-economic status was used. Following version 2.0 of the EuroQol EQ-5D-5L valuation protocol, trained interviewers guided participants in completing composite time trade-off (cTTO) and discrete-choice experiment (DCE) tasks included in the EQ-VT software. Generalized least squares, Tobit and Bayesian models were used for cTTO data. The choice of value set model was based on criteria that included: theoretical considerations, parsimony, logical ordering of coefficients, and statistical significance. RESULTS: Based on quality control criteria and interviewer judgment, 1000 out of 1032 participants provided useable responses. Participants' demographic characteristics were similar to the 2010 Mexican Population Census and followed the socioeconomic structure defined by the Mexican Association of Marketing Research and Public Opinion Agencies (AMAI). The predicted index values in the final cTTO model (a heteroscedastic censored model with Bayesian estimation) ranged from - 0.5960 to 1, with 19.7% of all predicted health state scores less than 0 (i.e., worse than dead). CONCLUSION: This study has generated the first value set representing the stated preferences of the Mexican adult population for use in estimating QALYs. The resulting EQ-5D-5L value set is technically robust and will facilitate health economic analyses as well as quality-of-life studies.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".