Does the Relationship Between Neighborhood Socioeconomic Status and Health Outcomes Persist Into Very Old Age? A Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this article is (a) to extend previous research on the relationship between neighborhood socioeconomic status (SES) and health by considering a wide range of health-related measures derived from administrative health care records and (b) to explore whether this relationship persists into old age. METHOD: The study involved a complete cohort of community-dwelling residents in Winnipeg, Canada, who were 65 years or older in 2004/2005 (N = 77,930). Health measures were derived from administrative claims data. Census data were used to derive neighborhood-level SES. RESULTS: Multilevel logistic regressions indicated that, relative to individuals living in the most affluent areas, those in the poorest areas had significantly higher odds of having arthritis, diabetes, hypertension, congestive heart failure, ischemic heart disease, chronic obstructive pulmonary disease, depression, and stroke. Significant neighborhood income effects tended to be evident among individuals age 65 to 75 as well as those age 75+. DISCUSSION: A wide range of health conditions among older adults are disproportionately clustered into the poorest areas. Programs and services should be designed to meet the needs of older adults of any age in such neighborhoods.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".