Neighborhood Deprivation, Subjective and Objective Cognitive Impairment
Bibliographic record
Abstract
Abstract Background Social determinants of health have been associated with disparities in health outcomes, including cognitive impairment and Alzheimer’s disease. While individual‐level disparities have been characterized, more research is needed into structural social determinants of health (SSDoH) and their association with cognitive outcomes, especially for those in midlife. This study aimed to investigate the association between SSDoH and subjective and objective measures of cognitive impairment. Methods English‐speaking adults ages 35‐64 were recruited from an academic general internal medicine practice and federally qualified health network in the Chicagoland area. SSDoH was evaluated using the Area Deprivation Index (ADI), a validated factor‐based index of neighborhood socioeconomic context using participant census block, then divided into terciles with the highest tercile indicating highest neighborhood deprivation. Subjective cognitive concern was measured by a single question assessing concern about cognitive or memory issues as part of the Everyday Cognition Cog scales. Objective cognition was measured using the Montreal Cognitive Assessment (MoCA) with MoCA < 23 (out of 30) indicating cognitive impairment. We used univariate and multivariable logistic regression models to characterize the relationship between SSDoH and cognitive impairment, including a priori covariates of age, sex, education, and number of chronic conditions. Results A total of 596 participants (age 52±8; 64% female; 42% non‐Hispanic Black, 34% non‐Hispanic White, 16% Hispanic; 62% college graduate) were included in the analysis. Younger age, female sex, non‐Hispanic Black race, and lower education and income were associated with higher ADI (Table 1). One‐third of participants (34%) reported subjective cognitive concern, whereas objective cognitive impairment was found in 22.6%. Compared to the lowest ADI tercile (i.e. least deprived), the highest ADI tercile (i.e. most deprived) was significantly associated with objective cognitive impairment (aOR, 3.0; 95% CI, 1.5‐5.9; p = 0.001) but not subjective cognitive concerns (aOR, 1.2; 95% CI, 0.7‐2.0; p = 0.46), after adjusting for a priori covariates (Table 2). Conclusions Neighborhood deprivation is associated with Future research should investigate the possible mechanisms of this association to identify optimal interventions at the neighborhood level.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".