Population Attributable Fraction for Cognitive Impairment in Mexican American Older Adults
Bibliographic record
Abstract
BACKGROUND: The contribution of modifiable risk factors to cases of mild cognitive impairment (MCI) and Alzheimer's disease and related dementias (AD/ADRD) among Hispanic/Latin (a,o,x) adults is unclear. OBJECTIVE: To determine the population attributable fraction (PAF), the proportion of MCI and AD/ADRD cases that would be eliminated with elimination of modifiable risk factors. METHODS: This was a cross-sectional analysis nested within a prospective cohort study (2018 - 2023) of Mexican American older adults. Logistic regression estimated the odds of probable MCI or AD/ADRD (defined by education-specific cutoff scores on the Montreal Cognitive Assessment) associated with age, sex, education, medical comorbidities, cigarette smoking, and alcohol consumption. We calculated the PAF for MCI and AD/ADRD associated with individual and combined risk factors. RESULTS: Among 1,272 participants, the prevalence of MCI was 48% and of AD/ADRD was 23%. Having no more than a high school education was associated with a PAF for MCI of 17% (95% CI, 10 - 24%) and a PAF for AD/ADRD of 52% (95% CI, 40 - 64%). Heart disease and diabetes were each associated with a PAF for MCI of 5% (95% CI, 3 - 7% and 95% CI, 1 - 10% respectively). A history of stroke was associated with a PAF for AD/ADRD of 7% (95% CI, 3 - 11%). The elimination of all modifiable risk factors was associated with 28% of MCI and 55% of AD/ADRD cases. DISCUSSION: Low educational attainment and cardiovascular comorbidities greatly contribute to cases of MCI and AD/ADRD among Mexican American adults.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".