Neighbourhood built environments as correlates of hospital burden and premature mortality in Canada
Bibliographic record
Abstract
IntroductionThe built environment can shape modifiable risk factors such as obesity, poor diet, and physical inactivity, and could be a policy lever for the reduction of chronic disease. In Canada, the health care costs related to chronic disease continue to rise and there have been few policy options offered. Objectives and ApproachWe examine the role of the built environment in hospital burden and premature mortality, with an emphasis on one of the highest burden diseases, Type 2 Diabetes (T2D). Neighbourhood built environment measures for active living were derived using geographic information systems for respondents of the Canadian Community Health Survey, for whom we have linked hospitalization and mortality records. A combination of ICD codes, self-reported diabetes status, as well as a population-based algorithm identifying those at higher risk of developing diabetes were used to identify cases. Differences in hospitalization frequency, cumulative length of stay, and mortality are investigated. ResultsOver half a million hospitalization records were identified in our cohort of roughly 450,000 survey respondents. Key factors such as age, gender, race, and socioeconomic status are accounted for in modelling the association between neighborhood environment and hospitalization. Hospital burden and mortality in T2D patients are much higher than that of patients who do not report having the condition, and those at elevated risk of T2D display intermediate levels of hospitalization. Two-part hurdle models show evidence of an association between more walkable neighborhoods and lower hospitalization risk in non-T2D patients as well as those at elevated risk of developing T2D. The relationship between neighborhoods and the volume of chronic-disease related episodes as well as mortality is unclear, and under further investigation. Conclusion/ImplicationsElucidating the role of neighbourhood built environments on hospital burden and premature mortality for individuals with diabetes will provide insight as to the full range of clinical and non-clinical interventions that could feasibly address the needs of some the highest health care system users.
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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 | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".