Inequalities in health. Analytic approaches based on life expectancy and suitable for small area comparisons
Bibliographic record
Abstract
STUDY OBJECTIVE: Simple measures of inequalities in health are proposed to facilitate the work of health policy makers and to build on the understanding of health differences between populations. In addition, it is aimed to make these measures applicable for comparisons of small populations and subgroups. METHODS: Inequalities in health or health deficiencies were quantified as the difference between the life expectancy of the subgroup of interest and that of the national population. Health deficiencies were divided into disease specific components by partial application of cause eliminated life table methods. To manage small numbers and to depict time trends, locally weighted regression smoothing was applied. Confidence intervals were constructed through Monte Carlo simulations. APPLICATIONS AND COMPARISONS: The proposed approaches were applied to the health situation in Cape Breton County, Nova Scotia, Canada, and disclosed the significance of different diseases and distinct patterns between communities. The proposed measures were also compared with the traditionally used standardised mortality rates and ratios. Here, the proposed measures appeared beneficial in that they are easier to comprehend and that they provide time trends and more robust estimates. CONCLUSIONS: The above advantages make the proposed approaches beneficial to health policy makers and epidemiologists. The approaches may also be incorporated in economic evaluations as well as in more sophisticated public health models.
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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 | 0.042 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".