Individual socioeconomic status, neighborhood disadvantage, and cognitive aging: A longitudinal analysis of the <scp>CLSA</scp>
Bibliographic record
Abstract
BACKGROUND: There are likely many contributors to variation in the rate of cognitive decline in middle and late adulthood, including individual and neighborhood socio-economic factors. This study examines whether individual socio-economic factors, namely income and wealth, correlate with cognitive decline, in part, through neighborhood-level social and material disadvantage. METHODS: Using the three waves of data collection from the Canadian Longitudinal Study on Aging (CLSA), this study included 51,338 participants between the age of 45 and 85 years at baseline (51% female). Individual socio-economic status (SES) was assessed by annual household income and by the current value of savings and investments. Neighborhood disadvantage was measured by area-based material and social deprivation indices. Cognition was measured at each wave using verbal fluency, mental alternations, and delayed word recall. Latent change score models, incorporating direct and indirect pathways, were constructed to estimate the indirect effect of individual SES on cognitive change through area-level disadvantage. Multi-group models were constructed on the basis of age-group (45-64 years; 65-74 years; or 75+ years) to allow for varying estimates across age. RESULTS: Among 45-64-year-olds, income and wealth had indirect effects on initial cognitive level and on rate of cognitive decline through material disadvantage (standardized indirect effects = 0.01, p < 0.001), but only wealth had an indirect effect through social disadvantage (p = 0.019). Among 65-74-year-olds, income and wealth had indirect effects on initial cognitive level (p < 0.01) but not on rate of cognitive decline (p > 0.05), and among 75+ year-olds, no indirect effects were observed (p > 0.05). Wealth and income had direct effects, independent of neighborhood disadvantage, on cognition in all age groups (p < 0.05). CONCLUSIONS: Among middle-aged adults, greater individual SES may mitigate cognitive decline, in part, by allowing individuals to live in more materially and socially advantaged neighborhoods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".