Low levels of awareness, treatment, and control of hypertension in Andean communities of Ecuador
Bibliographic record
Abstract
The major burden of hypertension (HTN) occurs in low-middle-income countries (LMIC) and it is the main modifiable risk factor for cardiovascular diseases (CVD). Few population studies on HTN prevalence have been carried out in Ecuador where there is limited information regarding its prevalence, awareness, treatment, and control. Thus, the aim of the present study was to determine the prevalence, awareness, treatment, and control of HTN and its association with socio-economic, nutritional, and lifestyle habits in urban and rural Andean communities of Pichincha province in Ecuador. The authors studied 2020 individuals aged 35-70 years (mean age 50.8 years, 72% women), included in the Ecuadorian cohort of the Prospective Urban and Rural Epidemiology (PURE) study, from February to December 2018. The hypertension prevalence (>140/90 mmHg) was 27% and was greater in urban than in rural communities, more common in men, in individuals older than 50 years of age, in people with low monthly income and low level of education. Higher prevalence was also observed in subjects with obesity, and among former smokers and those who consumed alcohol. Only 49% of those with HTN were aware of their condition, 40% were using antihypertensive medications, and 19% had their blood pressure under control (<140/90 mmHg). These results showed low levels of awareness, treatment, and control of HTN in the Andean region of Ecuador, suggesting the urgent necessity of implementing programs to improve the diagnosis and management of HTN.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".