EQ-5D-5L population norms and health inequality for Trinidad and Tobago in 2022–2023 and comparison with 2012
Bibliographic record
Abstract
BACKGROUND: The use of EQ-5D instruments in clinical, policy and economic applications continues to grow internationally. Population norms studies provide baseline values against which demographic and patient groups are compared and inequality is assessed. This study presents updated EQ-5D-5L population norms for 2022-2023, evaluates inequality and compares the results with those of 2012. METHODS: Demographic and EQ-5D-5L data were obtained from mutually exclusive, representative samples of adults in three studies conducted from July 2022 through May 2023. EQ-5D-5L index values, EQ VAS scores, and ceilings (all dimensions at level 1) were calculated for age-sex groups and stratifiers including education, income, ethnicity, marital status, and employment status. For inequality, the Kakwani index was calculated for the EQ VAS scores and index values, and ordered logit models were used to obtain odds ratios for reporting higher levels of problems on each dimension for demographic groups. The results were compared with those from 2012 which included applying the value set that had been used for the 2022-2023 population norms to the 2012 states. RESULTS: Data were obtained form 2,989 respondents. The mean index value was 0.921, EQ VAS was 79.6 and the ceiling was 31.5%. The dimensions with the highest rates of reported problems at any level (2-5) were pain/discomfort (43%) and anxiety/depression (39%). The Kakwani index was 0.113 for EQ VAS and 0.058 for index values, with sex accounting for the largest relative contribution. Mean index values, EQ VAS scores, and ceilings were lower across all demographic groups in 2022-2023 compared to 2012. CONCLUSIONS: This is the first study to investigate how EQ-5D-5L population norms have changed within a country over time. Significant changes were observed in the EQ-5D-5L measures and the relative frequencies of reported problems on the dimensions. Inequality increased, and there were changes in the levels of reported problems on the dimensions for demographic groups. Such changes suggest that national population norms should be updated periodically to capture changes in health status, perceptions of health, and health inequality.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".