Cardiovascular Risk in Middle‐Aged and Older Immigrants: Exploring Residency Period and Health Insurance Coverage
Bibliographic record
Abstract
Abstract Purpose It is reported that while immigrants are, initially, healthier than the native‐born upon resettlement, this advantage erodes over time. In the United States, uninsured aging immigrants are increasingly experiencing severe complications of cardiovascular disease (CVD). The purpose of this study was to compare overall CVD risk and explore the importance of health insurance coverage on CVD risk relative to other health access barriers, from 2007 to 2012, in recent and long‐term immigrants >50 years of age. Methods This study was based on secondary cross‐sectional analysis of the National Health and Nutrition Examination Survey (N = 1,920). The primary outcome, CVD risk category (high or low), was determined using the American College of Cardiology and American Heart Association Pooled Cohort equation. Differences between immigrant groups were examined using independent‐samples t tests and chi‐square analysis. The association between insurance and CVD risk was explored using a hierarchical block logistic regression model, in which variables were entered in a predetermined order. Changes in pseudo R2 measured whether health insurance explained variance in cardiac risk beyond other variables. Results Recent immigrants had lower overall CVD risk than long‐term immigrants but were twice as likely to be uninsured and had higher serum glucose and lipid levels. Based on regression models, being uninsured contributed to CVD risk beyond other health access determinants, and CVD risk was pronounced among recent immigrants who were uninsured. Conclusions Health insurance coverage plays an essential part in a comprehensive approach to mitigating CVD risk for aging immigrants, particularly recent immigrants whose cardiovascular health is susceptible to deterioration. Clinical Relevance Nurses are tasked with recognizing the unique social and physical vulnerabilities of aging immigrants and accounting for these in care plans. In addition to helping them access healthcare coverage and affordable medication, nurses and clinicians should prioritize low‐cost lifestyle interventions that reduce CVD risk, especially diet and exercise programs.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".