Regional and socioeconomic disparities in cardiovascular disease in Canada during 2005–2016: evidence from repeated nationwide cross-sectional surveys
Bibliographic record
Abstract
INTRODUCTION: The objective of this study is to examine the temporal trends and patterns of regional and socioeconomic disparities in cardiovascular disease (CVD) in Canada during 2005-2016. METHODS: A total of 670 000 adults aged ≥20 years who participated in the Canadian Community Health Surveys between 2005 and 2016 were enrolled for this study. CVD referred to heart disease and stroke in this study. Equivalised household income was used as a proxy of socioeconomic status. Absolute and relative socioeconomic inequalities were measured by slope index of inequality (SII) and relative index of inequality (RII), respectively. RESULTS: In 2015/2016, the overall age-adjusted and sex-adjusted prevalence of heart disease and stroke was 4.80% (95% CI 4.61% to 4.98%) and 1.25% (95% CI 1.13% to 1.36%), respectively. Trend analyses suggested a significant decline in the age-adjusted and sex-adjusted prevalence of heart disease (P for trend <0.001) and a non-significant decline in the age-adjusted and sex-adjusted prevalence of stroke (P for trend=0.058) from 2005 to 2016. Nevertheless, the total number of adults suffering from heart disease and stroke increased by 8.9% and 20.2% over the study period, respectively. Moreover, the age-adjusted and sex-adjusted prevalence of heart disease and stroke varied widely across all health regions, and both of them tended be higher among those with lower income. The SII and RII indicated that there were persistent absolute and relative socioeconomic inequalities in heart disease and stroke across all surveys (eg, SII for heart disease in both sexes, 2005: 0.04 (95% CI 0.03 to 0.04); 2015/2016: 0.03 (95% CI, 0.02 to 0.04); RII for heart disease in both sexes, 2005: 1.99 (95% CI 1.75 to 2.27); 2015/2016: 1.77 (95% CI 1.52 to 2.08). CONCLUSION: Geographical and socioeconomic disparities should be taken into account during the further efforts to strengthen preventive measures and optimise healthcare resources for heart disease and stroke in Canada.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".