Inequalities in Hypertension and Diabetes in Canada: Intersections between Racial Identity, Gender, and Income
Bibliographic record
Abstract
<p>A growing body of research from the United States informed by intersectionality theory indicates that racial identity, gender, and income are often entwined with one another as determinants of health in unexpectedly complex ways. Research of this kind from Canada is scarce, however. Using data pooled from ten cycles (2001- 2013) of the Canadian Community Health Survey, we regressed hypertension (HT) and diabetes (DM) on income in subsamples of Black women (n = 3,506), White women (n = 336,341), Black men (n = 2,806) and White men (n = 271,260). An increase of one decile in income was associated with lower odds of hypertension and diabetes among White men (ORHT = .98, 95% CI (.97, .99); ORDM = .93, 95% CI (.92, .94)) and White women (ORHT = .95, 95% CI (.95, .96); ORDM = .90, 95% CI (.89, .91)). In contrast, an increase of one decile in income was not associated with either health outcome among Black men (ORHT = .99, 95% CI (.92, 1.06); ORDM = .99, 95% CI (.91, 1.08)) and strongly associated with both outcomes among Black women (ORHT = .86, 95% CI (.80, .92); ORDM = .83, 95% CI (.75, .92)). Our findings highlight the complexity of the unequal distribution of hypertension and diabetes, which includes inordinately high risks of both outcomes for poor Black women and an absence of associations between income and both outcomes for Black men in Canada. These results suggest that an intersectionality framework can contribute to uncovering health inequalities in Canada.</p><p><em>Ethn Dis.</em>2017;27(4):371-378; doi:10.18865/ ed.27.4.371. </p>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".