Hypertension in seven Latin American cities: the Cardiovascular Risk Factor Multiple Evaluation in Latin America (CARMELA) study
Bibliographic record
Abstract
BACKGROUND: Little information is available regarding hypertension, treatment, and control in urban population of Latin America. OBJECTIVE: We aimed to compare blood pressure (BP) distribution, hypertension prevalence, treatment, and control in seven Latin American cities following standard methodology. METHODS: The Cardiovascular Risk Factor Multiple Evaluation in Latin America (CARMELA) study was a cross-sectional, epidemiologic study assessing cardiovascular risk factors using stratified multistage sampling of adult populations (aged 25-64 years) in seven cities: Barquisimeto (Venezuela; n = 1848); Bogotá (n = 1553); Buenos Aires (n = 1482); Lima (n = 1652); Mexico City (n = 1720); Quito (n = 1638); and Santiago (n = 1655). The prevalence of hypertension and high normal BP were determined based on 2007 European Society of Hypertension and European Society of Cardiology definitions. RESULTS: BP increased with age in men and women; pulse pressure increased mainly in the upper age group. The hypertension prevalence ranged from 9% in Quito to 29% in Buenos Aires. One-quarter to one-half of the hypertension cases were previously undiagnosed (24% in Mexico City to 47% in Lima); uncontrolled hypertension ranged from 12% (Lima) to 41% (Mexico City). High normal BP was also evident in a substantial number of each city participants (approximately 5-15%). Majority of population has other cardiovascular risk factors despite hypertension; only 9.19% of participants have no risk factors apart from hypertension. CONCLUSION: From 13.4 to 44.2% of the populations of seven major Latin American cities were hypertensive or had high normal BP values. Most hypertensive patients have additional risk factors. Public health programs need to target prevention, detection, treatment, and control of total cardiovascular risk in Latin America.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".