Association between neighbourhood poverty and type 2 diabetes risk. Does moving from a high to lower poverty neighbourhood reduce diabetes risk?
Bibliographic record
Abstract
ObjectivesDiabetes, a pressing global public health crisis, Diabetes, a global health crisis, is significantly impacted by social determinants, including neighborhood characteristics. This study aims to to assess whether relocation from a high poverty to lower poverty neighbourhood is associated with a reduction in T2D incidence. Methods and ResultsThis population-based, propensity-matched cohort study will use linked administrative health to examine the association between neighborhood relocation and T2D incidence. The study population will include adults (age ≥20 years) residing in high poverty urban neighborhoods between April 1st, 2002, to March 31st, 2021, as defined by an area Low-Income Measure - After Tax value of 38,730 dollars for a household size 4 based on the Canadian Census. Individuals will be followed for a new diagnosis of T2D using a validated algorithm based on hospitalization and physicians’ claims data. Propensity score matching will used to match individuals who moved from high-to-lower poverty neighbourhoods to one of two comparison groups: those moving from high-to-high poverty areas and those who remain in their original neighbourhood. Time-to-event analysis utilizing Cox proportional hazards regression with a robust variance estimator will be used to compare T2D incidence between matched groups. As a sensitivity analysis, non-propensity score modeling will be conducted using neighbourhood of residence as a time-varying covariate. The results of the aforementioned analyses will be presented during the conference. ImplicationsBy clarifying the link between neighborhood poverty and T2D incidence, the results will guide focused interventions to alleviate health inequalities in socioeconomically disadvantaged areas.
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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 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".