Immigration Status and Sex Differences in Primary Cardiovascular Disease Prevention: A Retrospective Study of 5 Million Adults
Bibliographic record
Abstract
Background We evaluated whether immigration status modified the association between sex and the quality of primary cardiovascular disease prevention in Ontario, Canada. Methods and Results We used a population‐based administrative database‐derived cohort of community‐dwelling adults (aged ≥40 years) without prior cardiovascular disease residing in Ontario on January 1, 2011. In the preceding 3 years, we evaluated screening for hyperlipidemia and diabetes in those not previously diagnosed; diabetes control (HbA 1c <7%); and medication use to control hypertension, hyperlipidemia, or diabetes in those with previous diagnosis. We calculated the absolute prevalence difference (APD) between women and men for each metric stratified by immigration status and then determined the difference‐in‐differences for immigrants compared with long‐term residents. Our sample included 5.3 million adults (19% immigrants), with receipt of each metric ranging from 55% to 90%. Among immigrants, women were more likely than men to be screened for hyperlipidemia (APD, 10.8%; 95% CI, 10.5–11.2) and diabetes (APD, 11.5%; 95% CI, 11.1–11.8) and to be treated with medications for hypertension (APD, 3.5%; 95% CI, 2.4–4.5), diabetes (APD, 2.1%; 95% CI, 0.7–3.6) and hyperlipidemia (APD, 1.8%; 95% CI, 0.5–3.1). Among long‐term residents, findings were similar except poorer medication use for diabetes (APD, −2.8%; 95% CI, −3.4 to −2.2) and hyperlipidemia (APD, −3.5%; 95% CI, −4.0 to −3.0]) in women compared with men. Conclusions The overall quality of primary preventive care can be improved for all adults, and future research should evaluate the impact of observed equal or better care in women than men, irrespective of immigration status, on cardiovascular disease incidence.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".