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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".