Association between Neighbourhood Deprivation Trajectories and Self-Perceived Health: Analysis of a Linked Survey and Health Administrative Data
Bibliographic record
Abstract
Life course exposure to neighbourhood deprivation may have a previously unstudied relationship with health disparities. This study examined the association between neighbourhood deprivation trajectories (NDTs) and poor reported self-perceived health (SPH) among Quebec's adult population. Data of 45,990 adults with complete residential address histories from the Care-Trajectories-Enriched Data cohort, which links Canadian Community Health Survey respondents to health administrative data, were used. Accordingly, participants were categorised into nine NDTs (T1 (Privileged Stable)-T9 (Deprived Stable)). Using multivariate logistic regression, the association between trajectory groups and poor SPH was estimated. Of the participants, 10.3% (95% confidence interval [CI]: 9.9-10.8) had poor SPH status. This proportion varied considerably across NDTs: From 6.4% (95% CI: 5.7-7.2) for Privileged Stable (most advantaged) to 16.4% (95% CI: 15.0-17.8) for Deprived Stable (most disadvantaged) trajectories. After adjustment, the likelihood of reporting poor SPH was significantly higher among participants assigned to a Deprived Upward (odds ratio [OR]: 1.77; 95% CI: 1.48-2.12), Average Downward (OR: 1.75; CI: 1.08-2.84) or Deprived trajectory (OR: 1.81; CI: 1.45-2.86), compared to the Privileged trajectory. Long-term exposure to neighbourhood deprivation may be a risk factor for poor SPH. Thus, NDT measures should be considered when selecting a target population for public-health-related interventions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".