The association between immigration status and the development of type 2 diabetes in women with a prior diagnosis of gestational diabetes: A population‐based study
Bibliographic record
Abstract
AIMS: The aim of this study was to examine the influence of immigration status and region of origin on the risk of type 2 diabetes in women with prior gestational diabetes (GDM). METHODS: This retrospective population-based cohort study included women with gestational diabetes (GDM) aged 16 to 50 years in Ontario, Canada, who gave birth between 2006 and 2014. We compared the incidence of type 2 diabetes after delivery between long-term residents and immigrants-overall, by time since immigration and by region of-using Cox regression adjusted for age, year, neighbourhood income, rurality, infant birth weight and presence of hypertensive disorders of pregnancy (HDP). RESULTS: Among 38,515 women with prior GDM (42% immigrants), immigrants had a significantly higher risk of type 2 diabetes compared with long-term residents (adjusted hazard ratio [HR] 1.19, 95% confidence interval [CI] 1.13-1.26), with no meaningful difference based on time since immigration. The highest adjusted relative risks of type 2 diabetes compared with long-term residents were found for immigrants from Sub-Saharan Africa (HR 1.63, 95% CI 1.40-1.90), Latin America/Caribbean (HR 1.44, 95% CI 1.28-1.62) and South Asia (HR 1.34, 95% CI 1.25-1.44). CONCLUSIONS: Immigration is associated with a significantly higher risk of type 2 diabetes after GDM, particularly for women from certain low- and middle-income countries. Diabetes prevention strategies will need to consider the unique needs of immigrants from these regions.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".