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Record 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 on 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

Bibliographic record

VenueThe Lancet Global Health · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecOttawa HospitalSimon Fraser UniversityHamilton Health SciencesQueen's UniversityMcMaster UniversityUniversité LavalPopulation Health Research InstituteHealth Sciences Centre
FundersDeanship 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
KeywordsSocioeconomic statusMedicineDiseaseHousehold incomeProspective cohort studyCohort studyDemographyCohortSocial classGerontologyEnvironmental healthRisk factorPopulationGeographyEconomicsInternal medicine

Abstract

fetched live from 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).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.319
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations644
Published2019
Admission routes2
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