Social disparities explain differences in hypertension prevalence, detection and control in Colombia
Bibliographic record
Abstract
OBJECTIVE: Hypertension is the principal risk factor for cardiovascular diseases. The global Prospective Urban Rural Epidemiology study showed that the levels of awareness, treatment and control of this condition are very low worldwide and show large regional variations related to a country's income index. The aim of the present analysis was to identify associations between sociodemographic, geographic, anthropometric, behavioral and clinical factors and the awareness, treatment and control of hypertension within Colombia - a high-middle income country which participated in the global Prospective Urban Rural Epidemiology study. METHODS AND RESULTS: The sample comprised 7485 individuals aged 35-70 years (mean age 50.8 years, 64% women). Mean SBP and DBP were 129.12 ± 21.23 and 80.39 ± 11.81 mmHg, respectively. The overall prevalence of hypertension was 37.5% and was substantially higher amongst participants with the lowest educational level, who had a 25% higher prevalence (<0.001). Hypertension awareness, treatment amongst those aware, and control amongst those treated were 51.9, 77.5 and 37.1%, respectively. The prevalence of hypertension was higher amongst those with a higher BMI (<0.001) or larger waist-hip ratio (<0.001). Being male, younger, a rural resident and having a low level of education was associated with significantly lower hypertension awareness, treatment and control. The use of combination therapy was very low (27.5%) and was significantly lower in rural areas and amongst those with a low income. CONCLUSION: Overall Colombia has a high prevalence of hypertension in combination with very low levels of awareness, treatment and control; however, we found large variations within the country that appear to be associated with sociodemographic disparities.
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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) | 0.003 | 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".