Immigration and health disparities: insights from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Background: Immigrants experience socio-economic and acculturation stressors when moving to a host country. These stressors may persist and affect their mental well-being throughout their lifetime. Fear of stigma, failure to recognize depressive symptoms, and delays in seeking mental health care at an early stage increase the risk for major depressive disorders in immigrants and impact their physical health. The overall goal of my thesis was to examine the health disparities in Canadian immigrants compared to non-immigrants in terms of depression risk, depression and diabetes dual effect, and the challenge of longitudinal data collection, using the Canadian longitudinal study on aging (CLSA) data.Methods: The CLSA is a national prospective cohort that collects psychological, medical, biological, social, lifestyle and economic data on 51,338 participants since 2012-2015. In my first manuscript, I used the CLSA Comprehensive cohort (in person data collection, 30,097 participants) baseline and Maintaining Contact data at 18 months to evaluate the risk of undiagnosed depression, persistent depressive symptoms, and seeking mental health care for depressive symptoms in immigrants versus (vs) non-immigrants; in my second manuscript, I used the Comprehensive cohort baseline and 3-year (yr) follow-up data (2015-2018) to study the depression and diabetes bidirectional, longitudinal relationship in immigrants and non-immigrants; and in the third manuscript, I used the combined CLSA Tracking (telephone data collection, 21,241 participants) and Comprehensive cohorts baseline and follow-up data to assess the impact of immigrant status, depression, and language (English, French or bilingual) on loss to follow-up (LFU). Results: My first manuscript showed a sex-dependent higher risk of undiagnosed depression in immigrants vs non-immigrants. Female immigrants were more likely to have undiagnosed depression than female non-immigrants (odds ratio 1.50, 95% confidence interval 1.25-1.80), but no difference was observed for men. The risk of persistent depressive symptoms and consulting a mental health care professional for these symptoms at 18 months did not differ between immigrants and non-immigrants. Immigrants who lived in Canada for either less than 20 yrs (0-5 yrs: 3.30, 1.59-6.85; 6-10 yrs 1.84, 1.08-3.11; 11-20 yrs 1.53, 1.02-2.29) or more than 40 yrs (1.21, 1.02-1.43) had higher risks of undiagnosed depression than non-immigrants. Immigrants who arrived in Canada at age > 40 yrs had twice the risk of undiagnosed depression as those who arrived at an earlier age (2.02, 1.43-2.86). My second manuscript revealed a diabetes and depression bidirectional association in non-immigrants and only a unidirectional relationship between depression and diabetes in immigrants. Specifically, diabetes increased depression risk in non-immigrants (1.27, 1.08-1.49), but not in immigrants (1.12, 0.80-1.56), while depression increased diabetes risk in both non-immigrants (1.39, 1.16-1.68) and immigrants (1.60, 1.08-2.37). My third manuscript revealed that immigration ≤ 20 yrs ago (1.84, 1.34-2.35) or arrival at age > 22 yrs (1.32. 1.10-1.58), and depression (1.23, 1.13-1.46) were associated with higher risks of LFU from the CLSA. Language was associated with LFU with no effect modification by depression or immigrant status. Bilingual (vs French) had lower LFU risk outside (0.45, 0.24-0.86) and inside Quebec (0.78, 0.63-0.98). LFU risk was higher in French (vs English) outside Quebec (2.33, 1.19-4.55), but not inside Quebec.Conclusion: My studies showed disparities between immigrant and non-immigrant Canadians in risks of undiagnosed depression, association between depression and diabetes, and risk of LFU at three years. Sex, time of residency, and age at arrival of immigrants were important determinants of their mental health. Longitudinal research addressing mental and physical health of immigrants may be challenged by LFU and data completeness
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".