Impact of social determinants of health on executive functions and language: an analysis from the promote study
Bibliographic record
Abstract
Background: Social determinants of health (SDH) are environmental factors linked to increased risk for several conditions, including dementia. However, their impact across different cognitive domains remains unclear. This study aimed to examine how SDH influence distinct cognitive domains in a South American population, addressing a gap in research predominantly based on European and North American cohorts. Methods: Baseline data from the PROMOTE trial, conducted in Brazil between 2022 and 2023, were analyzed. Participants underwent clinical evaluations and the Montreal Cognitive Assessment (MoCA), with scores divided into six cognitive domains: Memory (MIS), Executive (EIS), Attention (AIS), Language (LIS), Visuospatial (VIS), and Orientation (OIS). SDH variables included years of education, ethnicity, family income, and neighborhood income. Regression models, adjusted for age and sex, assessed SDH impacts on total MoCA scores and subscores. Results: Among 147 participants (mean age: 59; mean education: 13.2 years), most were White (n=139). No SDH variable was associated with total MoCA scores. However, distinct associations were found for subscores. Years of education were significantly associated with EIS (β=0.11, p-adjusted=0.002), while lower family income was also linked to lower EIS (β=-0.32, p=0.04). Neighborhood income was significantly associated with LIS (β=0.03, p=0.04). No significant associations were observed between ethnicity and cognitive performance. Conclusion: SDH selectively influence cognitive domains. Education and family income affect executive function, while neighborhood income influences language. These findings highlight the importance of addressing socioeconomic disparities in cognitive health.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".