Association Between Neighborhood‐Level Income and the Incidence of Cardiovascular Events Varies by Immigration Status: A Population‐Based Cohort Study
Bibliographic record
Abstract
Background Neighborhood‐level income is inversely associated with cardiovascular events; however, it is uncertain whether this association varies with immigration status. Methods and Results We conducted a population‐based cohort study of 5.2 million (53% women, 19% immigrants) urban‐dwelling people aged ≥40 years without a prior history of cardiovascular disease in Ontario, Canada. Neighborhood‐level income was measured in quintiles from quintile 1 (lowest) to quintile 5 (highest), and immigrants were defined as those born outside of Canada who moved to Canada after 1985. We estimated the association between neighborhood‐level income and the rate of incident cardiovascular events (hospitalization for stroke or myocardial infarction, or cardiovascular death) using multivariable cause‐specific hazards models and added an interaction term to see if the association varies by immigration status. The absolute difference in the rate of cardiovascular events across income quintiles was less pronounced in immigrants than in long‐term residents: age‐ and sex‐adjusted rate per 1000 person‐years in quintile 1 versus quintile 5: 5.69 versus 4.10 in immigrants and 8.37 versus 5.87 in long‐term residents. In adjusted models, the interaction between immigration status and neighborhoodl evel was significant ( P interaction <0.001). The hazard of cardiovascular events declined with increasing income among long‐term residents (hazard ratio [HR] Q1vsQ5 , 1.46 to HR Q4vsQ5 , 1.10) and immigrants, albeit with a smaller gradient (HR Q1vsQ5 , 1.43 to HR Q4vsQ5 , 1.20). Conclusions The association between neighborhood‐level income and cardiovascular disease incidence varies by immigration status. Understanding the social and structural factors associated with residing in low‐income neighborhoods can help with the development of prevention programs that improve health for all.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".