1589-P: Trends in Prediabetes Prevalence and Incidence among Immigrants in Ontario, Canada, 2011-2015: A Population-Based Study
Bibliographic record
Abstract
The burden of prediabetes appears to be increasing worldwide. We aimed to examine temporal trends in prediabetes prevalence and incidence among immigrants to Ontario, Canada between 2011 and 2015 using linked administrative health and immigration databases. Comprehensive laboratory data were used to identify individuals without prior diabetes who underwent diabetes screening (N=511,357 in 2011 and 557,081 in 2015). We used logistics multivariate regression and direct age and sex standardization methods with 2011 Canadian census data to calculate prediabetes prevalence and incidence. Using World Health Organization criteria to define prediabetes (fasting plasma glucose (PG) 110-125 mg/dl, 2-hour PG 140-199 mg/dl on a 75 gm OGTT, HbA1c6.0-6.4%), the age- and sex-adjusted prevalence of prediabetes decreased from 15.8% to 12.2% over the 5-year period (p<0.001). These patterns were consistent across all ethnic groups, except immigrants from Sub-Saharan African and the Caribbean, where a rise in prevalence from 14.1 to 15.3% was observed. The largest declines were observed among South Asians and Latin Americans (20.1% to 15.3% and 16.1% to 9.4%, respectively in 2011 vs. 2015, p<0.001). Age- and sex-adjusted prediabetes incidence exhibited similar trends, declining from 11.1 to 9.6 per 100 person-years, with similar patterns by ethnic group as observed for prevalence. These patterns were generally consistent across age groups. In summary, the prevalence and incidence of prediabetes in Canadian immigrants appears to be declining, which may relate to changes in the underlying risk of diabetes in these populations or in screening and prevention practices. Disclosure G. Fazli: None. R. Moineddin: None. B.R. Shah: None. C.F. Wu: None. G. Booth: None.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".