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Record W2559819562 · doi:10.1186/s12939-016-0478-6

Wealth and cardiovascular health: a cross-sectional study of wealth-related inequalities in the awareness, treatment and control of hypertension in high-, middle- and low-income countries

2016· article· en· W2559819562 on OpenAlexafffund
Benjamin Palafox, Martin McKee, Dina Balabanova, Khalid F. AlHabib, Álvaro Avezum, Ahmad Bahonar, Noor Hassim Ismail, Jephat Chifamba, Clara K Chow, Daniel J. Corsi, Gilles R. Dagenais, Rafael Díaz, Rajeev Gupta, Romaina Iqbal, Manmeet Kaur, Rasha Khatib, Annamarie Kruger, Iolanthé M. Kruger, Fernando Laņas, Patricio López‐Jaramillo, Fu Minfan, Viswanathan Mohan, Prem Mony, Aytekin Oğuz, Lia M. Palileo‐Villanueva, Pablo Perel, Paul Poirier, Sumathy Rangarajan, Lei Rensheng, Annika Rosengren, Biju Soman, David Stückler, S. V. Subramanian, Koon Teo, Lungiswa Tsolekile, Andreas Wielgosz, Karen Yeates, MO Yong-zhen, Khalid Yusoff, Rita Yusuf, Afzalhussein Yusufali, Katarzyna Zatońska, Salim Yusuf

Bibliographic record

VenueInternational Journal for Equity in Health · 2016
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsHamilton Health SciencesInstitut universitaire de cardiologie et de pneumologie de QuébecQueen's UniversityMcMaster UniversityUniversité LavalPopulation Health Research InstituteMontreal Heart InstituteOttawa Hospital
FundersEconomic and Social Research CouncilCanadian Institutes of Health Research
KeywordsHealth services researchSocial policyInequalityCross-sectional studyPublic healthLow and middle income countriesMedicineControl (management)Quality of Life ResearchHealth policyEnvironmental healthDevelopment economicsDemographic economicsEconomicsEconomic growthDeveloping countryNursingPathology

Abstract

fetched live from OpenAlex

BACKGROUND: Effective policies to control hypertension require an understanding of its distribution in the population and the barriers people face along the pathway from detection through to treatment and control. One key factor is household wealth, which may enable or limit a household's ability to access health care services and adequately control such a chronic condition. This study aims to describe the scale and patterns of wealth-related inequalities in the awareness, treatment and control of hypertension in 21 countries using baseline data from the Prospective Urban and Rural Epidemiology study. METHODS: A cross-section of 163,397 adults aged 35 to 70 years were recruited from 661 urban and rural communities in selected low-, middle- and high-income countries (complete data for this analysis from 151,619 participants). Using blood pressure measurements, self-reported health and household data, concentration indices adjusted for age, sex and urban-rural location, we estimate the magnitude of wealth-related inequalities in the levels of hypertension awareness, treatment, and control in each of the 21 country samples. RESULTS: Overall, the magnitude of wealth-related inequalities in hypertension awareness, treatment, and control was observed to be higher in poorer than in richer countries. In poorer countries, levels of hypertension awareness and treatment tended to be higher among wealthier households; while a similar pro-rich distribution was observed for hypertension control in countries at all levels of economic development. In some countries, hypertension awareness was greater among the poor (Sweden, Argentina, Poland), as was treatment (Sweden, Poland) and control (Sweden). CONCLUSION: Inequality in hypertension management outcomes decreased as countries became richer, but the considerable variation in patterns of wealth-related inequality - even among countries at similar levels of economic development - underscores the importance of health systems in improving hypertension management for all. These findings show that some, but not all, countries, including those with limited resources, have been able to achieve more equitable management of hypertension; and strategies must be tailored to national contexts to achieve optimal impact at population level.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.022
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.120
GPT teacher head0.396
Teacher spread0.276 · 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

Labeled directly by 2 models reading the full record.

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".

Quick stats

Citations116
Published2016
Admission routes2
Has abstractyes

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