The role of socioeconomic inequalities in hypertension prevalence in latin america: a national data analysis from five countries
Bibliographic record
Abstract
Drawing on comprehensive data from Brazil, Mexico, Colombia, Argentina, and Peru, this study exposes stark disparities in both the prevalence and management of hypertension across different social strata in Latin America. The evidence is hard to ignore: individuals in the lowest wealth quintile face a hypertension prevalence of 34.5%, nearly double that of their wealthiest counterparts (18.3%). Similarly, those with only primary education or less are disproportionately affected (33.7%) compared to individuals with university education (18.5%). Rural areas also carry a heavier burden, with a prevalence rate of 29.8% versus 21.7% in urban regions. These health inequalities are further compounded by significant gaps in health care awareness and access. Over half of individuals in the poorest group are unaware of their hypertensive status, in contrast to just a quarter of those in the affluent group. Treatment rates are also alarmingly low among the disadvantaged: nearly half receive no intervention, and three-quarters of cases remain uncontrolled. Geographic barriers only intensify these challenges, with the poor facing nearly six times the difficulty in accessing health care services. Notably, the analysis attributes approximately 42% of the disparity in hypertension rates between socioeconomic extremes to differences in access to health care. The burden of multimorbidity is also unequally distributed; the combination of hypertension, diabetes, and chronic kidney disease is five times more prevalent among the poor. The study also highlights significant gender differences and complex interactions between risk factors in both rural and urban contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".