The role of socioeconomic inequalities in hypertension prevalence in latin america: a national data analysis from five countries
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.
Institutions
- Urgench State University (UZ)
- Bukhara State Medical Institute named after Abu Ali ibn Sino (UZ)
- Samarkand State Medical Institute (UZ)
- Gulistan State University (UZ)
- Andijan State Medical Institute (UZ)
- National University of Uzbekistan (UZ)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-07-19
- DOI
- https://doi.org/10.5281/zenodo.16156964
- Primary Topic
- Model Reduction and Neural Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00