Gender differences in the impact of poverty on health: disparities in risk of diabetes‐related amputation
Bibliographic record
Abstract
AIMS: To assess the combined impact of socio-economic status and gender on the risk of diabetes-related lower extremity amputation within a universal healthcare system. METHODS: We conducted a population-based cohort study using administrative health databases from Ontario, Canada. Adults with pre-existing or newly diagnosed diabetes (N = 606 494) were included and the incidence of lower extremity amputation was assessed for the period 1 April 2002 to 31 March 2009. Socio-economic status was based on neighbourhood-level income groups, assigned to individuals using the Canadian Census and their postal code of residence. RESULTS: Low socio-economic status was associated with a significantly higher incidence of lower extremity amputation (27.0 vs 19.3 per 10,000 person-years in the lowest (Q1) vs the highest (Q5) socio-economic status quintile. This relationship persisted after adjusting for primary care use, region of residence and comorbidity, and was greater among men (adjusted Q1:Q5 hazard ratio 1.41, 95% CI 1.30-1.54; P < 0.0001 for all male gender-socio-economic status interactions) than women (hazard ratio 1.20, 95% CI 1.06-1.36). Overall, the incidence of lower extremity amputation was higher among men than women (hazard ratio for men vs women: 1.87, 95% CI 1.79-1.96), with the greatest disparity between men in the lowest socio-economic status category and women in the highest (hazard ratio 2.39, 95% CI 2.06-2.77 and hazard ratio 2.30, 95% CI 1.97-2.68, for major and minor amputation, respectively). CONCLUSIONS: Despite universal access to hospital and physician care, we found marked socio-economic status and gender disparities in the risk of lower extremity amputation among patients with diabetes. Men living in low-income neighbourhoods were at greatest risk.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".