Social and health disparities associated with healthy brain ageing in Brazil and in other Latin American countries
Bibliographic record
Abstract
BACKGROUND: Latin American countries present major health-related inequities due to historical, cultural, and social aspects. Recent evidence highlights that factors related to social and health disparities outweigh classic demographic factors in determining healthy brain aging in these populations. However, these analyses have not been conducted with the Brazilian population, the largest and most ethnically diverse population in Latin America. METHODS: Here, we evaluated demographic, social, and health factors for healthy brain ageing using a machine learning model in a Brazilian population-based cohort (n=9412) and in additional cohorts from other Latin American countries, including Colombia (n=23 694), Chile (n=1301), Ecuador (n=5235), and Uruguay (n=1450). FINDINGS: In the Brazilian population and other Latin American countries, social and health disparities were more influential than demographic factors for cognition and functional ability. Uniquely in Brazil, education emerged as the primary risk factor impacting cognitive outcomes, diverging from other Latin American countries where mental health symptoms played more prominent roles. In terms of functional ability, Brazil displayed a distinct pattern, with mental health symptoms identified as the primary contributing factor. INTERPRETATION: Our findings indicate that Brazil converges with other Latin American countries to show that heterogeneous factors impacted more than demographic factors, but also showed a unique set of health factors when compared with other Latin American countries. Therefore, our study emphasises that social and health disparity factors are relevant predictors of healthy brain ageing in Latin America, but population-specific analyses are necessary to identify the specific risk profiles of each country. FUNDING: None. TRANSLATIONS: For the Portuguese and Spanish translations of the abstract see Supplementary Materials section.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".