Determinants of Health Preferences Using Data from the Egyptian EQ-5D-5L Valuation Study
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to explore the impact of sociodemographic characteristics and illness experience on time trade-off (TTO)-based utility scores using data from the EQ-5D-5L Egyptian valuation study. METHODS: Data were from the Egyptian valuation study that was conducted using the adapted translated version of the EQ-VT to develop the Egyptian Tariff for the EQ-5D-5L based on preferences of the Egyptian population. Data were analysed using a series of univariate and multivariable censored linear regression models adjusted for severity of health states where the dependent variable was the TTO scores and the independent variables included age, sex, education, geographical region, dwelling, marital status, number of people in the household, employment status, having health insurance, number of chronic conditions, previous experience with illness, and self-rated health. RESULTS: Age, sex, education, marital status, dwelling, region of residence, health insurance and multimorbidity were significantly associated with health state valuations, while employment status, number of people in a household, religion, and previous experience with illness had non-significant associations. CONCLUSION: Age, sex and marital status are the main determinants of health state valuation in the Egyptian population, a finding consistent with those from other countries. Knowing these factors will help tailor health services provided and improve patient-centered care.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".