Spatial epidemiology of nonfatal overdose in a community-based cohort of marginalized women in Vancouver, British Columbia (2014–2022)
Bibliographic record
Abstract
BACKGROUND: Given limited data regarding the spatial epidemiology of overdose among women amid the current overdose crisis, we evaluated (1) changes in spatiotemporal clustering of overdose over time, (2) the association between residential proximity to overdose clusters and recent nonfatal overdose, and (3) the association between 'risk environment' features and residential proximity to overdose clusters. METHODS: Questionnaire data were from a merged community-based cohort of marginalized women who use drugs in Vancouver, Canada (09/2014-08/2022). Emerging hotspot analysis was used to classify residential proximity to spatiotemporal clusters of nonfatal overdose and kernel density estimation was used to visualize the spatiotemporal distribution of nonfatal overdose clustering over the 8-year study. Statistical analyses drew on bivariate and multivariable logistic regression using generalized estimating equations (GEE). FINDINGS: Over eight years, among 650 participants (3461 observations), 37·2 % experienced a nonfatal overdose at least once. Annual period prevalence of nonfatal overdose increased from 9·1 % in 2014-15 to 25·6 % in 2021-2022. The highest-density clusters were in Vancouver's Downtown Eastside/Strathcona neighborhoods, where clusters became larger and more dispersed from 2016-onwards. Residential proximity to overdose clusters was associated with higher odds of recent nonfatal overdose. 'Risk environment' features of unstable housing, unsafe sleeping environments, and physical violence were associated with elevated odds of residential proximity to overdose clusters. INTERPRETATION: Marginalized women face a high and rising burden of nonfatal overdose, which is influenced by the 'risk environments' in which they reside. Scale-up of geographically tailored overdose prevention services, harm reduction, and programs addressing violence and housing are needed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".