Health inequalities and inequities by age: Stability for the Health Utilities Index and divergence for the Frailty Index
Bibliographic record
Abstract
Successful aging is an important policy goal in an aging society. A key indicator of successful aging of a population is whether health inequalities (differences) and inequities (unfair differences) in the population increase or decrease with age. This study investigates how health inequalities and inequities differ across age groups in the Canadian population within the equity framework of equal opportunity for health, using two popular measures of health, the Health Utilities Index Mark 3 (HUI) and the Frailty Index (FI). We use the 2009-10 Canadian Health Measures Survey. We first quantify the degree of health inequality by calculating the Gini coefficient for the distributions of the HUI and the FI within three age groups (20-44, 45-64, and 65-79 years). We then identify sources of health inequality by using regression models and decomposing inequality into ethically acceptable and unacceptable components. We finally quantify the degree of health inequity by calculating the Gini coefficient for each health measure and each age group after standardizing for fairness. We find that the magnitudes of inequality and inequity in the HUI and the FI in each of the three age groups are policy relevant. The magnitude and age-related dynamics of health inequality and inequity depend on the choice of the health measures. In all three age groups, inequality and inequity in health measured by the HUI are larger than those measured by the FI. Across the three age groups, inequality and inequity are stable in the HUI but divergent in the FI. This study contributes to the methodological development to support policies for successful aging. Examination of alternative notions of health captured by the HUI and the FI contributes to the exploration of how the fair distribution of each aspect of health may characterize a successfully aging population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".