Healthy Immigrant Effect or Under-Detection? Examining Undiagnosed and Unrecognized Late-Life Depression for Racialized Immigrants and Nonimmigrants in Canada
Bibliographic record
Abstract
OBJECTIVES: Immigrants to Canada tend to have a lower incidence of diagnosed depression than nonimmigrants. One theory suggests that this "healthy immigrant effect (HIE)" is due to positive selection. Another school of thought argues that the medical underuse of immigrants may be the underlying reason. This unclear "immigrant paradox" is further confounded by the intersecting race-migration nexus. METHODS: This population-based study analyzed data of participants (n = 28,951, age ≥45) from the Canadian Community Health Survey (2015-2018). Multivariable logistic regression was employed to examine associations between race-migration nexus and mental health outcomes, including depressive symptoms (Patient Health Questionnaire [PHQ-9] score ≥10). RESULTS: Compared to Canadian-born (CB) Whites, immigrants, regardless of race, were less likely to receive a mood/anxiety disorder diagnosis (M/A-Dx) by health providers in their lifetime. Racialized immigrants were mentally disadvantaged with increased odds of undiagnosed depression (Adjusted odds ratio [AOR] = 1.76, 99% Confidence interval [CI]:1.30-2.37), whereas White immigrants were mentally healthier with decreased odds of PHQ depression (AOR=0.75, 99%CI: 0.58, 0.96) and poor self-rated mental health (AOR=0.56, 99% CI=0.33, 0.95). Among the subpopulation without a previous M/A-Dx (N = 25,203), racialized immigrants had increased odds of PHQ depression (AOR = 1.45, 99% CI: 1.15-1.82) and unrecognized depression (AOR = 1.47, 99% CI: 1.08-2.00) than CB Whites. Other risk factors for undiagnosed depression include the lack of regular care providers, emergency room as the usual source of care, and being home renters. DISCUSSION: Despite Canadian universal health coverage, the burden of undiagnosed depression disproportionately affects racialized (but not White) immigrants in mid to late life. Contingent on race-migration nexus, the HIE in mental health may be mainly driven by the healthier profile of White immigrants and partly attributable to the under-detection (by health professionals) and under-recognition of mental health conditions among racialized immigrants. A paradigm shift is needed to estimate late-life depression for medically underserved populations.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".