Trends in COVID-19 testing, infection, vaccination, and housing assistance among people who inject drugs in Los Angeles and Denver
Bibliographic record
Abstract
Introduction We examined COVID-19 trends and the socio-structural correlates of infections and vaccinations among people who inject drugs (PWID).Methods We collected retrospective survey data from PWID (n = 472). We examined changes in COVID-19 testing, infection, and housing assistance for the first year of the pandemic, and then used multivariate logistic regression to examine factors with ever having an infection and receiving at least one vaccination from April 2020-March 2021.Results The sample was mostly male, White and Latinx, and unhoused. Testing by quarter ranged from 46% to 33%. Housing assistance declined from 10% to 7% by the last quarter, while infections ranged from 6% to 4%. Ever being COVID-19 positive was associated with being male, having an income source, and being arrested in the last 3 months. Almost half of participants reported vaccination (47%). Receiving a vaccination was associated with prior U.S. armed forces service, older age, receiving substance use disorder treatment, and having ever received COVID-19 testing.Discussion Efforts to vaccinate PWID fell short and housing assistance declined. Receiving targeted assistance, including better access to housing and medication treatments for opioid use disorder, could be important efforts to mitigate the heightened risks of COVID-19 and the ongoing harms PWID face that predate the pandemic.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".