An analytic approach for describing and prioritizing health inequalities at the local level in Canada: a descriptive study
Bibliographic record
Abstract
BACKGROUND: We present the health inequalities analytic approach used by the Saskatoon Health Region to examine health equity. Our aim was to develop a method that will enable health regions to prioritize action on health inequalities. METHODS: Data from admissions to hospital, physician billing, reportable diseases, vital statistics and childhood immunizations in the city of Saskatoon were analyzed for the years ranging from 1995 to 2011. Data were aggregated to the dissemination area level. The Pampalon deprivation index was used as the measure of socioeconomic status. We calculated annual rates per 1000 people for each outcome. Rate ratios, rate differences, area-level concentration curves and area-level concentration coefficients quantified inequality. An Inequalities Prioritization Matrix was developed to prioritize action for the outcomes showing the greatest inequality. The outcomes measured were cancer, intentional self-harm, chronic obstructive pulmonary disease, mental illness, heart disease, diabetes, injury, stroke, chlamydia, tuberculosis, gonorrhea, hepatitis C, high birth weight, low birth weight, teen abortion, teen pregnancy, infant mortality and all-cause mortality. RESULTS: According to the Inequalities Prioritization Matrix, injuries and chronic obstructive pulmonary disease were the first and second priorities, respectively, that needed to be addressed related to inequalities in admissions to hospital. For physician billing, mental disorders and diabetes were high-priority areas. Differences in teen pregnancy and all-cause mortality were the most unequal in the vital statistics data. For communicable diseases, hepatitis C was the highest priority. INTERPRETATION: Our findings show that health inequalities exist at the local level and that a method can be developed to prioritize action on these inequalities. Policies should consider health inequalities and adopt population-based and targeted actions to reduce inequalities.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".