Chronic Disease Development and Multimorbidity Among Immigrants and Refugees in Ontario
Bibliographic record
Abstract
Chronic diseases such as cancer, diabetes, cardiovascular and respiratory diseases are a global concern. In recent decades, Canada has also experienced a major increase in immigration. Yet, a detailed profile of chronic disease and multimorbidity risk patterns across different immigrant populations has been lacking in Canada. The purpose of this dissertation is to identify knowledge gaps in the scientific literature on the development of chronic conditions and multimorbidity across immigrant populations in Ontario, using population-based immigrant and health data housed at ICES. The principal findings of this dissertation indicate that: 1. The risk of developing a chronic condition and multimorbidity was complex and varied by immigrants’ visa category and world region origin since: a. Refugees had the highest risk of developing a chronic condition and multimorbidity (two or more co-occurring chronic conditions) compared to long-term Ontario residents. b. There were differences in the risk of developing a chronic condition and multimorbidity by world regions of origin, when examined across different immigrant categories. 2. Hypertension and diabetes, and in combination with Chronic Obstructive Pulmonary Disease were the leading multimorbidity dyad and triad groups for all immigrant categories and long-term residents of Ontario. 3. The risk of developing a chronic condition increased among immigrants in more recent landing cohorts. The risk was highest among more recent refugees, and lower for family and economic class immigrants, when compared to long-term Ontario residents. These findings provide evidence to inform public health policy and planning by highlighting the complexity and heterogeneity of health outcomes across immigrant populations. Knowledge generated from this work will inform policies and evidence-based decision-making aimed to address the threat of chronic diseases and reduce health disparities.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".