Risk of Developing Multimorbidity among Previously Healthy Immigrants and Long-term Ontario Residents
Bibliographic record
Abstract
Background Multimorbidity is an important health issue associated with a greater risk of adverse health outcomes, more frequent hospitalizations, greater healthcare needs, and premature death. This study examined patterns of and the risk of developing multimorbidity between immigrants and long-term residents of Ontario. Methods We used a 1:1 matched retrospective observational open cohort design from 1995 to 2016, using routinely collected population-based administrative data at ICES. Multimorbidity was defined as two or more and three or more co-occurring chronic conditions. Chronic disease frequencies of dyads and triads were examined. Stratified multivariate Cox Proportional Hazard models examined the risk of developing multimorbidity and further by world regions of origin for immigrants compared to long-term residents. Results Hypertension and diabetes, in combination with chronic obstructive pulmonary disease, were the leading multimorbidity dyad and triad groups. After controlling for age, sex, and neighborhood income quintiles, immigrants from the Caribbean and South Asia had a greater risk of developing 2+ multimorbidity, compared to long-term residents. Refugees from North Africa and the Middle East (HR = 1.22 [95% CI: 1.03-1.42]) as well as refugees (HR = 1.78 [95% CI: 1.59 – 1.98]) and family immigrants from South Asia (HR: 1.08 [95% CI: 1.02-1.14]), had a higher risk of 3+ multimorbidity compared to long-term residents of Ontario. Conclusion These findings highlight the importance of routine population-based data collection on immigration status and world regions of origin to inform public health research. Investments in preventive health services and management of multimorbidity are needed for specific population groups.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".