Explaining the variability in cardiovascular risk factors among First Nations communities in Canada: a population-based study
Bibliographic record
Abstract
BACKGROUND: Historical, colonial, and racist policies continue to influence the health of Indigenous people, and they continue to have higher rates of chronic diseases and reduced life expectancy compared with non-Indigenous people. We determined factors accounting for variations in cardiovascular risk factors among First Nations communities in Canada. METHODS: Men and women (n=1302) aged 18 years or older from eight First Nations communities participated in a population-based study. Questionnaires, physical measures, blood samples, MRI of preclinical vascular disease, and community audits were collected. In this cross-sectional analysis, the main outcome was the INTERHEART risk score, a measure of cardiovascular risk factor burden. A multivariable model was developed to explain the variations in INTERHEART risk score among communities. The secondary outcome was MRI-detected carotid wall volume, a measure of subclinical atherosclerosis. FINDINGS: The mean INTERHEART risk score of all communities was 17·2 (SE 0·2), and more than 85% of individuals had a risk score in the moderate to high risk range. Subclinical atherosclerosis increased significantly across risk score categories (p<0·0001). Socioeconomic advantage (-1·4 score, 95% CI -2·5 to -0·3; p=0·01), trust between neighbours (-0·7, -1·2 to -0·3; p=0·003), higher education level (-1·9, -2·9 to -0·8, p<0·001), and higher social support (-1·1, -2·0 to -0·2; p=0·02) were independently associated with a lower INTERHEART risk score; difficulty accessing routine health care (2·2, 0·3 to 4·1, p=0·02), taking prescription medication (3·5, 2·8 to 4·3; p<0·001), and inability to afford prescription medications (1·5, 0·5 to 2·6; p=0·003) were associated with a higher INTERHEART risk score. Collectively, these factors explained 28% variation in the cardiac risk score among communities. Communities with higher socioeconomic advantage and greater trust, and individuals with higher education and social support, had a lower INTERHEART risk score. Communities with difficulty accessing health care, and individuals taking or unable to afford prescription medications, had a higher INTERHEART risk score. INTERPRETATION: Cardiac risk factors are lower in communities with high socioeconomic advantage, greater trust, social support and educational opportunities, and higher where it is difficult to access health care or afford prescription medications. Strategies to optimise the protective factors and reduce barriers to health care in First Nations communities might contribute to improved health and wellbeing. FUNDING: Heart and Stroke Foundation of Canada, Canadian Partnership Against Cancer, Canadian Institutes for Health Research.
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How this classification was reachedexpand
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".