Differences in chronic disease prevalence by ethno-racial identity among Canadians: analyses of nationally representative self-report data
Bibliographic record
Abstract
OBJECTIVE: In 2015, chronic diseases accounted for approximately three-quarters of deaths in Ontario, with the most common conditions being cancer, cardiovascular diseases, diabetes, and chronic lower respiratory diseases. Despite Canada's diversity, there is limited health research on chronic disease prevalence among visible minority populations. This study aimed to examine the relationship between visible minority status and the prevalence of chronic diseases in Canada, with a focus on self-identified ethnoracial identity. DESIGN: = 113,290), accessed through the Statistics Canada Research Data Centre. Each enumerated visible minority group was analysed separately, except for Chinese, Korean, and Japanese participants, who were grouped into a single category due to sample size constraints. Chronic conditions were self-reported with binary 'yes/no' responses, with the exception of obesity, which was derived from reported weight and height data. Logistic regression was used to calculate bivariate and multivariable odds ratios (OR) with 95% confidence intervals (CI). Analyses were stratified by sex (male/female, as measured by the CCHS). RESULTS: Multivariable analyses indicated that visible minority males had higher odds of reporting high cholesterol, type II diabetes, and hypertension, but lower odds of arthritis and cancer, compared white males. Filipino males had the highest odds for hypertension (OR: 2.36; 95%CI: 1.40-3.99), while South Asian males had the lowest odds of cancer (OR: 0.09; 95%CI: 0.04-0.19). Indigenous males and females consistently reported higher odds of most chronic conditions. CONCLUSION: Several ethno-racial groups exhibited elevated odds of specific chronic conditions, though not uniformly across all tested outcomes. These findings underscore the importance for healthcare providers, public health practitioners, and policymakers to consider the nuanced relationship between ethnoracial identity and chronic conditions. Culturally competent care and targeted health interventions should reflect this complexity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".