Abstract MP50: Regional Income and Relative Individual Income, but Not Income Inequality, is Associated With Cardiovascular Health
Bibliographic record
Abstract
Background: Result from many studies support that the associations between income, income inequality, and mortality, including CVD mortality, are very complex. Given the urgent need for greater CVD prevention in populations, it is essential to understand how income inequality and income, both in individuals and the regions in which they live, can affect cardiovascular health (CVH). Objective: To examine the associations between regional income and income inequality and individual relative income and individual CVH. Setting: This study was carried out in a nationally representative sample of Canadian adults aged 20 years and older residing in 113 health regions (HR) across Canada. Data and Methods: This study is a cross-sectional design using data from the Canadian Community Health Survey (CCHS) 2015-2016 database. The CCHS is a nationwide, nationally representative survey that collects information on the health status, health care utilization, and health determinants of the Canadian population. The study outcome was individual CVH, defined using the AHA CVH Index (CVHI) and determined using self-reported responses in CCHS. Regional income inequality was measured as the Gini coefficient of the HR. Regional income was measured as the median household income in the HR. Individual income was measured as relative, not absolute, income representing the individual’s household income compared to those in the HR. Multilevel models were used to examine the associations between regional income and income inequality and individual relative income and individual CVH, controlling for individual age, sex, race and education. Analyses were conducted using SAS 9.4 software. Results: The majority of the population were males (51%), aged 40-60 (37%), with tertiary education (64%) and of the White race (79%). Overall, mean CVH for individuals was 4.5. The national average Gini coefficient across HRs was 0.4. The average individual fell within the 6 th decile for relative household income. Living in a HR with greater income inequality was not associated with lower individual CVH (β= -0.04 p-value=0.91), though living in an HR with higher median household income was associated with better individual CVH (β= 0.32 p=0.004). Finally, having higher relative household income was associated with better individual CVH (β= 0.05 p<0.0001), regardless of the median income of the region of residence. Conclusion: While the inequality of income within a HR did not significantly affect CVH, higher regional income and relative individual income was associated with better CVH. Results of this study contribute to the growing body of evidence attempting to disentangle the true associations between income, income inequality, and CVH.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".