Abstract P468: Geographic and Socioeconomic Inequalities in Poor Cardiovascular Health in Canada
Bibliographic record
Abstract
Background: Poor cardiovascular health (CVH), characterized by clinical risk and unhealthy lifestyle habits, is a leading cause of death and disease worldwide. Despite advances in healthcare and policy, there remains an inequitable burden of poor CVH among Canadian sub-populations based on socioeconomic status and geographic region. Using recently published data and a national toolkit, this study aimed to quantify inequalities in poor CVH across the Canadian population. Methods: We conducted a cross-sectional study on Canadian adults, ≥20 years, from the nationally representative Canadian Community Health Survey 2017. Using the American Heart Association’s CVH Index, CVH was defined for each individual as a summed score of 7 components, where 1 point was awarded for achieving ideal health in each component. A total score of 0-2 points indicated poor overall CVH. The Canadian Institute for Health Information (CIHI) Measuring Health Inequalities Toolkit, a standardized methodological approach to analyzing health inequalities in population-based data using pre-defined stratifications and second-level interactions, was used to quantify inequalities in poor CVH based on Toolkit-defined stratifications in sex, income, urban/rural status, and region of residence. The regional distribution of CVH was mapped using ArcGIS software. Results: Approximately 7% of Canadians had poor CVH, representing 2 million Canadians. Poor dietary habits were noted in 99.0% of the population, with poor body mass index and poor physical activity noted in 58.1% and 42.3%, respectively. The eastern provinces of Newfoundland and Labrador and New Brunswick had the greatest proportion of health regions with poor CVH. An examination of the largest CVH inequalities across provinces revealed that females in the lowest income tercile residing in Prince Edward Island were 8-times more likely to experience poor CVH than females in the highest income tercile (RR 8.43, 95%CI 7.09-10.04). Additionally, females in New Brunswick residing in rural regions were almost 3-times more likely to experience poor CVH than females residing in urban regions (RR 3.07, 95%CI 1.07-4.87). In most provinces, income and urban/rural inequalities among males were observed but were of smaller magnitude than the inequalities among females. Conclusion: The greatest inequalities in poor CVH were experienced by females in the lowest income groups. Urban/rural inequalities in CVH were complex and varied by geographic region. The CIHI toolkit is a robust and systematic approach to understanding health inequalities that will enable comparison between studies and health/disease states, and thus facilitate comparative policy evaluation and population health priority setting.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".