The Intersection of Socioeconomic and Environmental Factors in Aging: Insights from a Narrative Review
Bibliographic record
Abstract
(1) Background: Socioeconomic conditions and environmental exposures are well-established determinants of health and aging, yet the pathways through which they influence the aging process remain insufficiently understood. Clarifying these mecha-nisms is critical for developing effective, equity-focused public health interventions to support healthy aging; (2) Methods: We conducted a narrative review examining the relationships between socioeconomic conditions, environmental exposures, and ag-ing-related health outcomes. While the scope was intentionally broad to capture diverse exposures and outcomes, we applied a systematic search strategy to identify relevant peer-reviewed studies; (3) Results: The search populated over 4000 articles; 33 relevant papers were selected. The evidence suggests that environmental exposures may mediate or modify the effects of socioeconomic disadvantage on aging. Conversely, socioeconomic conditions can alter the association between environmental factors and aging outcomes. Disadvantaged populations consistently face higher environmental burdens and exhibit poorer aging outcomes, including accelerated biological aging and increased risk of age-related disease; (4) Conclusions: The complex interplay between social and envi-ronmental factors contributes to disparities in aging. Our integrative approach highlights the need for more intersectional, longitudinal research to inform interventions that ad-dress the social and environmental determinants of healthy aging.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".