Age- and sex-related prevalence of diabetes mellitus among immigrants to Ontario, Canada
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The majority of immigrants to Canada originate from the developing world, where the most rapid increase in prevalence of diabetes mellitus is occurring. We undertook a population-based study involving immigrants to Ontario, Canada, to evaluate the distribution of risk for diabetes in this population. METHODS: We used linked administrative health and immigration records to calculate age-specific and age-adjusted prevalence rates among men and women aged 20 years or older in 2005. We compared rates among 1,122,771 immigrants to Ontario by country and region of birth to rates among long-term residents of the province. We used logistic regression to identify and quantify risk factors for diabetes in the immigrant population. RESULTS: After controlling for age, immigration category, level of education, level of income and time since arrival, we found that, as compared with immigrants from western Europe and North America, risk for diabetes was elevated among immigrants from South Asia (odds ratio [OR] for men 4.01, 95% CI 3.82-4.21; OR for women 3.22, 95% CI 3.07-3.37), Latin America and the Caribbean (OR for men 2.18, 95% CI 2.08-2.30; OR for women 2.40, 95% CI: 2.29-2.52), and sub-Saharan Africa (OR for men 2.31, 95% CI 2.17-2.45; OR for women 1.83, 95% CI 1.72-1.95). Increased risk became evident at an early age (35-49 years) and was equally high or higher among women as compared with men. Lower socio-economic status and greater time living in Canada were also associated with increased risk for diabetes. INTERPRETATION: Recent immigrants, particularly women and immigrants of South Asian and African origin, are at high risk for diabetes compared with long-term residents of Ontario. This risk becomes evident at an early age, suggesting that effective programs for prevention of diabetes should be developed and targeted to immigrants in all age groups.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 it