A scoping review of the association between the built environment and overdose mortality
Bibliographic record
Abstract
BACKGROUND: It is widely known that the built environment affects health outcomes in general, and for marginalized groups, including people who use drugs; however, the relationship between the built environment and overdose mortality is not well understood. METHODS: We present findings from a scoping review of research published between 1996 and 2024 that discuss built environment factors that relate to or affect the likelihood of fatal overdose. RESULTS: In total, twenty-nine papers were included, twenty-seven of which were based in the United States. Various types of built environment factors were identified from the included studies, including public and private locations of fatal overdose (e.g., public bathrooms, decedents' homes), locations positively associated with fatal overdose (e.g., dilapidated buildings, drug houses), micro built environment characteristics (e.g., acceptably clean streets/clean sidewalks, vacant housing), and urban versus rural locations related to fatal overdose. DISCUSSION: Our findings emphasize the critical role that the built environment plays in overdose survivability, which is an important consideration when considering how to reduce overdose mortality across diverse communities. Studies from this review are helpful in understanding the role of the built environment and what types of place-based interventions might need to be implemented across various locations in which fatal overdoses occur to reduce overdose mortality and improve health outcomes. Existing literature largely describes places of overdose but lacks detail regarding positive or negative associations between the location and fatal overdose. There is therefore a need for further research exploring the relationship between built environment and fatal overdose, including qualitative and mixed methods research to provide a more comprehensive understanding of how they intersect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".