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Enregistrement W2942480445 · doi:10.1016/s2214-109x(19)30045-2

Socioeconomic status and risk of cardiovascular disease in 20 low-income, middle-income, and high-income countries: the Prospective Urban Rural Epidemiologic (PURE) study

2019· article· en· W2942480445 sur OpenAlexafffund
Annika Rosengren, Andrew Smyth, Sumathy Rangarajan, Chinthanie Ramasundarahettige, Shrikant I. Bangdiwala, Khalid F. AlHabib, Álvaro Avezum, Kristina Bengtsson Boström, Jephat Chifamba, Sadi Güleç, Rajeev Gupta, Ehimario Igumbor, Romaina Iqbal, Philip Joseph, Manmeet Kaur, Rasha Khatib, Iolanthé M. Kruger, Pablo Lamelas, Fernando Laņas, Scott A. Lear, Wei Li, Chuangshi Wang, Deren Quiang, Yang Wang, Patricio López‐Jaramillo, Noushin Mohammadifard, Viswanathan Mohan, Prem Mony, Paul Poirier, Sarojiniamma Srilatha, Andrzej Szuba, Koon Teo, Andreas Wielgosz, Karen Yeates, Khalid Yusoff, Rita Yusuf, Marjan Walli-Attaei, Martin McKee, Salim Yusuf

Notice bibliographique

RevueThe Lancet Global Health · 2019
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueHealth disparities and outcomes
Établissements canadiensInstitut universitaire de cardiologie et de pneumologie de QuébecOttawa HospitalSimon Fraser UniversityHamilton Health SciencesQueen's UniversityMcMaster UniversityUniversité LavalPopulation Health Research InstituteHealth Sciences Centre
Organismes subventionnairesDeanship of Scientific Research, King Saud UniversityPhilippine Council for Health Research and DevelopmentCanadian Institutes of Health ResearchNational Science Foundation, United Arab EmiratesServierUniwersytet Medyczny im. Piastów Slaskich we WroclawiuUnited Arab Emirates UniversityNorth-West UniversityMinistry of Higher Education, MalaysiaVetenskapsrådetMinistry of Science, Technology and SpaceIndian Council of Medical ResearchUniversiti Teknologi MARABoehringer IngelheimNational Research FoundationOntario Ministry of Health and Long-Term CareHeart and Stroke Foundation of CanadaSwedish InstituteGlaxoSmithKlineNational Center for Research and DevelopmentSouth African Medical Research CouncilSanofiUniversidad de La FronteraAstraZeneca
Mots-clésSocioeconomic statusMedicineDiseaseHousehold incomeProspective cohort studyCohort studyDemographyCohortSocial classGerontologyEnvironmental healthRisk factorPopulationGeographyEconomicsInternal medicine

Résumé

récupéré en direct d'OpenAlex

Background Socioeconomic status is associated with differences in risk factors for cardiovascular disease incidence and outcomes, including mortality. However, it is unclear whether the associations between cardiovascular disease and common measures of socioeconomic status—wealth and education—differ among high-income, middle-income, and low-income countries, and, if so, why these differences exist. We explored the association between education and household wealth and cardiovascular disease and mortality to assess which marker is the stronger predictor of outcomes, and examined whether any differences in cardiovascular disease by socioeconomic status parallel differences in risk factor levels or differences in management. Methods In this large-scale prospective cohort study, we recruited adults aged between 35 years and 70 years from 367 urban and 302 rural communities in 20 countries. We collected data on families and households in two questionnaires, and data on cardiovascular risk factors in a third questionnaire, which was supplemented with physical examination. We assessed socioeconomic status using education and a household wealth index. Education was categorised as no or primary school education only, secondary school education, or higher education, defined as completion of trade school, college, or university. Household wealth, calculated at the household level and with household data, was defined by an index on the basis of ownership of assets and housing characteristics. Primary outcomes were major cardiovascular disease (a composite of cardiovascular deaths, strokes, myocardial infarction, and heart failure), cardiovascular mortality, and all-cause mortality. Information on specific events was obtained from participants or their family. Findings Recruitment to the study began on Jan 12, 2001, with most participants enrolled between Jan 6, 2005, and Dec 4, 2014. 160 299 (87·9%) of 182 375 participants with baseline data had available follow-up event data and were eligible for inclusion. After exclusion of 6130 (3·8%) participants without complete baseline or follow-up data, 154 169 individuals remained for analysis, from five low-income, 11 middle-income, and four high-income countries. Participants were followed-up for a mean of 7·5 years. Major cardiovascular events were more common among those with low levels of education in all types of country studied, but much more so in low-income countries. After adjustment for wealth and other factors, the HR (low level of education vs high level of education) was 1·23 (95% CI 0·96–1·58) for high-income countries, 1·59 (1·42–1·78) in middle-income countries, and 2·23 (1·79–2·77) in low-income countries (p interaction <0·0001). We observed similar results for all-cause mortality, with HRs of 1·50 (1·14–1·98) for high-income countries, 1·80 (1·58–2·06) in middle-income countries, and 2·76 (2·29–3·31) in low-income countries (p interaction <0·0001). By contrast, we found no or weak associations between wealth and these two outcomes. Differences in outcomes between educational groups were not explained by differences in risk factors, which decreased as the level of education increased in high-income countries, but increased as the level of education increased in low-income countries (p interaction <0·0001). Medical care (eg, management of hypertension, diabetes, and secondary prevention) seemed to play an important part in adverse cardiovascular disease outcomes because such care is likely to be poorer in people with the lowest levels of education compared to those with higher levels of education in low-income countries; however, we observed less marked differences in care based on level of education in middle-income countries and no or minor differences in high-income countries. Interpretation Although people with a lower level of education in low-income and middle-income countries have higher incidence of and mortality from cardiovascular disease, they have better overall risk factor profiles. However, these individuals have markedly poorer health care. Policies to reduce health inequities globally must include strategies to overcome barriers to care, especially for those with lower levels of education. Funding Full funding sources are listed at the end of the paper (see Acknowledgments).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil0,021

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,319
Écart entre enseignants0,302 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations644
Publié2019
Routes d'admission2
Résumé présentoui

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