Health Predictors of Neighborhood Selection: A Prospective Cohort Study of Residential Mobility in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Health selection into neighborhoods describes unhealthy people moving disproportionately to lower-income neighborhoods, producing observable socioeconomic gradients sometimes falsely attributed to neighborhood effects on health. We investigated residential mobility outcomes and their relationship to baseline health using population-level data linkages in Ontario, Canada. METHODS: We included Canadian Community Health Survey respondents ages 25 to 64 between 2005 and 2014 (n = 93,235). We assessed baseline health using self-reported health and multimorbidity. We captured moves using health administrative data and the Canadian census. We fit multinomial logistic regression models with a six-category residential mobility outcome: (1) nonmovers from low-income neighborhoods; (2) nonmovers from high-income neighborhoods; (3) movers from low-income to low-income; (4) movers from low-income to high-income; (5) movers from high-income to low-income; and (6) movers from high-income to high-income. We adjusted models for the Canadian Community Health Survey cycle, age, sex, household income, immigrant status, and residential instability. RESULTS: Compared with those with very good or excellent health, respondents reporting fair or poor health at baseline had higher odds of moving from low- to low-income neighborhoods (Adjusted odds ratios [aOR] = 1.73; 95% confidence interval [CI] = 1.46, 2.05), moving from high- to low-income (aOR = 1.64; 95% CI = 1.35, 1.98), moving from low- to high-income (aOR = 1.26; 95% CI = 1.04, 1.54), and not moving within low-income (aOR = 1.36; 1.23, 1.51) relative to not moving within high-income. Results were consistent for objective health measures, comparing respondents with at least four chronic conditions to those with one or none. CONCLUSIONS: In a large, population-based study, both subjective and objective measures of health had a strong relationship with residential mobility outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".