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Record W2996354469 · doi:10.1016/s2542-5196(19)30237-2

Explaining the variability in cardiovascular risk factors among First Nations communities in Canada: a population-based study

2019· article· en· W2996354469 on OpenAlexafffundabout
Sonia S. Anand, Sylvia Abonyi, Laura Arbour, Kumar Balasubramanian, Jeffrey R. Brook, Heather Castleden, Vicky Chrisjohn, Ida Cornelius, Albertha Darlene Davis, Dipika Desai, Russell J. de Souza, Matthias G. Friedrich, Stewart B. Harris, James Irvine, Jean L'Hommecourt, Randy Littlechild, Lisa Mayotte, Sarah McIntosh, Julie Morrison, Richard T. Oster, Manon Picard, Paul Poirier, Karleen Schulze, Ellen L. Toth

Bibliographic record

VenueThe Lancet Planetary Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecUniversité LavalUniversity of AlbertaPublic Health OntarioUniversity of British ColumbiaPopulation Health Research InstituteUniversity of VictoriaUniversity of SaskatchewanAssembly of First NationsMcGill UniversityImpactWestern UniversityUniversity of TorontoHamilton Health SciencesQueen's UniversityMcMaster University
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of CanadaSociety for the Study of AddictionCanadian Foundation for Dietetic ResearchUniversity of TorontoHamilton Health SciencesWorld Health Organization
KeywordsMedicineSocioeconomic statusSubclinical infectionLife expectancyDemographyPopulationFramingham Risk ScoreRisk factorGerontologyDiseaseInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.270
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2019
Admission routes3
Has abstractyes

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