Income inequality trends in cardiovascular health among Canadian children and youth from 2007-2019
Bibliographic record
Abstract
Abstract Background Socioeconomic inequalities in cardiovascular health are well-documented among older adults, but evidence in children and youth is limited. This study assessed income inequalities in cardiovascular health among Canadian children and youth (6-17 years) from 2007-2019, compared to young (18-39 years) and middle-aged adults (40-64 years). Methods This serial cross-sectional study included 25,500 respondents from six biennial cycles of the nationally representative Canadian Health Measures Survey. Equivalized household income quartiles were used to assess socioeconomic position. Sub-optimal cardiovascular health was defined as the presence of obesity, hypertension, diabetes, or dyslipidemia, based on age-specific biomarker thresholds and medication use. Relative and absolute income inequalities in cardiovascular health were quantified using the relative index of inequality (RII) and slope index of inequality (SII), where RII values >1 and SII values >0 indicate pro-rich inequality. Sex-specific analyses and interaction terms were used to evaluate inequality trends. Results The prevalence of sub-optimal cardiovascular health decreased from 2007-2019 for children and youth (22.9%-17.1%, p < 0.01), but remained stable for young (28.4%-27.0%, p = 0.44) and middle-aged adults (55.4%-54.6%, p = 0.88). Income inequalities increased over time for children and youth (RIIs=1.39-3.59, p = 0.01; SIIs=0.07-0.22, p = 0.03). Inequalities were present in young and middle-aged adults, but no significant trends emerged. Among children and youth, inequalities increased over time for males (RIIs=1.09-4.63, p < 0.01; SIIs=0.02-0.29, p = 0.01), while no significant trends were observed for females (RIIs=1.85-2.47, p = 0.27; SIIs=0.13-0.13, p = 0.42). Conclusions Persistent and widening income inequalities in cardiovascular health are evident among children and youth in Canada from 2007-2019. Targeted prevention efforts and policies are needed to address rising cardiovascular health inequalities. Key messages • Persistent relative and absolute income inequalities in cardiovascular health are evident among children and youth, young adults, and middle-aged adults in Canada. • Although the prevalence of sub-optimal cardiovascular health declined from 2007-2019 among children and youth, inequalities widened, with the largest increases observed among males.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".