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Record W2154467145 · doi:10.1001/jama.2013.184182

Prevalence, Awareness, Treatment, and Control of Hypertension in Rural and Urban Communities in High-, Middle-, and Low-Income Countries

2013· article· en· W2154467145 on OpenAlexafffund
Clara K Chow

Bibliographic record

VenueJAMA · 2013
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsHamilton Health SciencesPopulation Health Research InstituteMcMaster University
FundersMedical Research CouncilCanadian Institutes of Health ResearchUniversiti Kebangsaan MalaysiaVetenskapsrådetUniversiti Teknologi MARANational Research FoundationSvenska Forskningsrådet FormasNorth-West UniversityFaculty of Community and Health Sciences, University of the Western CapeAstraZenecaSouth Africa Netherlands research Programme on Alternatives in DevelopmentSerbian Academy of Sciences and ArtsUniwersytet Medyczny im. Piastów Slaskich we WroclawiuSanofiForskningsrådet för Arbetsliv och Socialvetenskap
KeywordsMedicineBlood pressureEpidemiologyLow incomePediatricsInternal medicineDemography

Abstract

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IMPORTANCE: Hypertension is the most important preventable cause of morbidity and mortality globally, yet there are relatively few data collected using standardized methods. OBJECTIVE: To examine hypertension prevalence, awareness, treatment, and control in participants at baseline in the Prospective Urban Rural Epidemiology (PURE) study. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional study of 153,996 adults (complete data for this analysis on 142,042) aged 35 to 70 years, recruited between January 2003 and December 2009. Participants were from 628 communities in 3 high-income countries (HIC), 10 upper-middle-income and low-middle-income countries (UMIC and LMIC), and 4 low-income countries (LIC). MAIN OUTCOMES AND MEASURES: Hypertension was defined as individuals with self-reported treated hypertension or with an average of 2 blood pressure measurements of at least 140/90 mm Hg using an automated digital device. Awareness was based on self-reports, treatment was based on the regular use of blood pressure-lowering medications, and control was defined as individuals with blood pressure lower than 140/90 mm Hg. RESULTS: Among the 142,042 participants, 57,840 (40.8%; 95% CI, 40.5%-41.0%) had hypertension and 26,877 (46.5%; 95% CI, 46.1%-46.9%) were aware of the diagnosis. Of those who were aware of the diagnosis, the majority (23,510 [87.5%; 95% CI, 87.1%-87.9%] of those who were aware) were receiving pharmacological treatments, but only a minority of those receiving treatment were controlled (7634 [32.5%; 95% CI, 31.9%-33.1%]). Overall, 30.8%, 95% CI, 30.2%-31.4% of treated patients were taking 2 or more types of blood pressure-lowering medications. The percentages aware (49.0% [95% CI, 47.8%-50.3%] in HICs, 52.5% [95% CI, 51.8%-53.2%] in UMICs, 43.6% [95% CI, 42.9%-44.2%] in LMICs, and 40.8% [95% CI, 39.9%-41.8%] in LICs) and treated (46.7% [95% CI, 45.5%-47.9%] in HICs, 48.3%, [95% CI, 47.6%-49.1%] in UMICs, 36.9%, [95% CI, 36.3%-37.6%] in LMICs, and 31.7% [95% CI, 30.8%-32.6%] in LICs) were lower in LICs compared with all other countries for awareness (P <.001) and treatment (P <.001). Awareness, treatment, and control of hypertension were higher in urban communities compared with rural ones in LICs (urban vs rural, P <.001) and LMICs (urban vs rural, P <.001), but similar for other countries. Low education was associated with lower rates of awareness, treatment, and control in LICs, but not in other countries. CONCLUSIONS AND RELEVANCE: Among a multinational study population, 46.5% of participants with hypertension were aware of the diagnosis, with blood pressure control among 32.5% of those being treated. These findings suggest substantial room for improvement in hypertension diagnosis and treatment.

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)0.003
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.023
GPT teacher head0.233
Teacher spread0.210 · 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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Citations2,025
Published2013
Admission routes2
Has abstractyes

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