Differences in prevalence of diabetes among immigrants to Canada from South Asian countries
Bibliographic record
Abstract
AIMS: The prevalence of diabetes is high in South Asians migrants. However, most previous research has studied South Asians as a collective whole. The aim of this study was to examine diabetes prevalence among immigrants from five South Asian countries living in Ontario, Canada. METHODS: Population-based health care and immigration databases were used to compare crude and adjusted diabetes prevalence on 1 January 2012 between immigrants to Ontario from different South Asians countries and the non-immigrant population. The prevalence of diabetes was also stratified by various sociodemographic factors. RESULTS: There were 431 765 first-generation South Asian immigrants; 68 440 (crude prevalence of 15.9%) of whom had a diagnosis of diabetes. After standardization for age, sex and income, diabetes prevalence was highest among South Asians from Sri Lanka (26.8%) followed by Bangladesh (22.2%), Pakistan (19.6%), India (18.3%) and Nepal (16.5%) in comparison with the non-immigrant population (11.6%). Increased prevalence was evident among men compared with women in each country of South Asia. Sociodemographic indicators including income, education, English proficiency and refugee status were associated with increased prevalence of diabetes in specific populations from South Asia. CONCLUSION: Striking differences in the prevalence of diabetes are evident among immigrants from different countries of South Asia. Awareness of the heterogeneity will help in recognizing priorities for the delivery of primary care for specific South Asian migrant populations with a range of settlement needs that also encompass social determinants of health.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".