Cognitive impairment and hypertension in older adults living in extreme poverty: a cross-sectional study in Peru
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown that hypertension is a risk factor for cognitive impairment, but whether this association is also present in extremely poor populations in Low Middle Income Countries settings remains to be studied. Understanding other drivers of cognitive impairment in this unique population also merits attention. METHODS: We performed a secondary analysis using data from the "Encuesta de Salud y Bienestar del Adulto Mayor", a regional survey conducted in an extremely poor population of people older than 65 years old from 12 Peruvian cities in 2012. The outcome variable was cognitive impairment, determined by a score of ≤7 in the modified Mini-Mental State Examination. The exposure was self-reported hypertension status. Variables such as age, gender, controlled hypertension, education level, occupation, depression and area of living (rural/urban) were included in the adjusted analysis. We used Poisson regression with robust variance to calculate prevalence ratios (PR) and 95% confidence interval (95% CI) adjusting for confounders. RESULTS: Data from 3842 participants was analyzed, 51.8% were older than 70 years, and 45.6% were females. The prevalence of cognitive impairment was 1.7% (95% CI 1.3%-2.1%). There was no significant difference on the prevalence of cognitive impairment between the group of individuals with hypertension in comparison with those without hypertension (PR = 0.64, 95% CI 0.33-1.23). CONCLUSIONS: The association described between hypertension and cognitive impairment was not found in a sample of extremely poor Peruvian older adults.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".