Increases in housing rules and surveillance during COVID-19: impacts on overdose and overdose response in a community-based cohort of sex workers who use drugs in Vancouver, BC
Bibliographic record
Abstract
INTRODUCTION: Since the beginning of the COVID-19 pandemic, COVID-19 risk mitigation measures have expanded to include increased rules and surveillance in supportive housing. Yet, in the context of the dual public health emergencies of COVID-19 and the unregulated drug toxicity crisis, we have not evaluated the unintended health and social consequences of such measures, especially on criminalized women. In order to address this dearth of evidence, our aim was to assess the association between increased housing rules and surveillance during COVID-19 and (a) nonfatal overdose, and (b) administration of naloxone for overdose reversal among women sex workers who use drugs in Vancouver, BC. METHODS: This study is nested within An Evaluation of Sex Workers Health Access (AESHA), a community-based prospective cohort of women sex workers in Metro Vancouver (2010-present). Using cross-sectional data collected during the first year of COVID-19 (April 2020-2021), we developed separate multivariable logistic regression confounder models to examine the independent associations between experiencing increased housing rules and surveillance during COVID-19 on (a) nonfatal overdose, and (b) administration of naloxone for overdose reversal in the last 6 months. RESULTS: Amongst 166 participants, 10.8% reported experiencing a recent non-fatal overdose and 31.3% recently administered naloxone for overdose reversal. 56.6% reported experiencing increased rules and surveillance within their housing during COVID-19. The prevalence of non-fatal overdose and administering naloxone was significantly elevated among those exposed to increased housing rules and surveillance during COVID-19 versus those who were unexposed (83.3% vs. 52.1%; 75.0% vs. 48.2%, respectively). In separate multivariate confounder models, exposure to increased housing rules and surveillance during COVID-19 was independently associated with increased odds of administering naloxone [AOR: 3.66, CI: 1.63-8.21], and marginally associated with non-fatal overdose [AOR: 3.49, CI: 0.92-13.27]. CONCLUSION: Efforts to prioritize the right to safe, adequate and affordable housing must avoid reinforcing an overly coercive reliance on surveillance measures which, while often well-intended, can negatively shape residents' well-being. Furthermore, public health responses to pandemics must include criminalized populations so that measures do not exacerbate overdose risk. Implementation of a regulated drug supply is recommended, alongside housing policies that promote residents' rights, safety, and health.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".