Depression, diabetes and immigration status: a retrospective cohort study using the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Background: A bidirectional association between depression and diabetes exists, but has not been evaluated in the context of immigrant status. Given that social determinants of health differ between immigrants and nonimmigrants, we evaluated the association between diabetes and depression incidence, depression and diabetes incidence, and whether immigrant status modified this association, among immigrants and nonimmigrants in Canada. Methods: We employed a retrospective cohort design using data from the Canadian Longitudinal Study on Aging Comprehensive cohort (baseline [2012–2015] and 3-year follow-up [2015–2018]). We defined participants as having diabetes if they self-reported it or if their glycated hemoglobin A1c level was 7% or more; we defined participants as having depression if their Center for Epidemiological Studies Depression score was 10 or higher or if they were currently undergoing depression treatment. We excluded those with baseline depression (Cohort 1) and baseline diabetes (Cohort 2) to evaluate the associations between diabetes and depression incidence, and between depression and diabetes incidence, respectively. We constructed logistic regression models with interaction by immigrant status. Results: Cohort 1 (n = 20 723; mean age 62.7 yr, standard deviation [SD] 10.1 yr; 47.6% female) included 3766 (18.2%) immigrants. Among immigrants, 16.4% had diabetes, compared with 15.6% among nonimmigrants. Diabetes was associated with an increased risk of depression in nonimmigrants (adjusted odds ratio [OR] 1.27, 95% confidence interval [CI] 1.08–1.49), but not in immigrants (adjusted OR 1.12, 95% CI 0.80–1.56). Younger age, female sex, weight change, poor sleep quality and pain increased depression risk. Cohort 2 (n = 22 054; mean age 62.1 yr, SD 10.1 yr; 52.2% female) included 3913 (17.7%) immigrants. Depression was associated with an increased risk of diabetes in both nonimmigrants (adjusted OR 1.39, 95% CI 1.16–1.68) and immigrants (adjusted OR 1.60, 95% CI 1.08–2.37). Younger age, male sex, waist circumference, weight change, hypertension and heart disease increased diabetes risk. Interpretation: We found an overall bidirectional association between diabetes and depression that was not significantly modified by immigrant status. Screening for diabetes for people with depression and screening for depression for those with diabetes should be considered.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".