Migration Patterns from an Open Illicit Drug Scene and Emergency Department Visits among People Who Use Illicit Drugs in Vancouver, Canada
Bibliographic record
Abstract
BACKGROUND: People who use illicit drugs (PWUD) experience various adverse health outcomes leading to increased healthcare service utilization. PWUD are also a highly mobile population which poses challenges to healthcare delivery. The objective of this study was to identify migration patterns from the Downtown Eastside (DTES), an urban illicit drug scene in Vancouver and to estimate the impact of different migration patterns on two outcomes: a) emergency department (ED) visits and b) ED visits resulting in inpatient admission among PWUD. METHODS: Three prospective cohorts of PWUD in Vancouver were linked with regional ED data. We defined the optimal number of trajectory groups that best represented distinct patterns of migration from Vancouver's DTES using a latent class growth analysis. Then, generalized estimating equations were used to estimate the effect of migration patterns on the two ED outcomes. RESULTS: Four distinct migration trajectory patterns were identified among the 1210 included participants: PWUD who consistently lived in the DTES, those who migrated out of DTES early, those who migrated out of DTES late, and those who frequently revisited the DTES. Participants who frequently revisited the DTES had higher odds of an ED visit (adjusted odds ratio = 1.62; 95% confidence interval: 1.28-2.06). There was no significant association between migration patterns and inpatient admission. CONCLUSIONS: We found that PWUD who frequently revisited the DTES were more likely to have utilized the ED, suggesting that there may be a subgroup of PWUD who are at increased risk of experiencing negative health outcomes.Supplemental data for this article is available online at 10.1080/10826084.2021.1958849.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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".