Identification of a Syndemic of Blood-Borne Disease Transmission and Injection Drug Use Initiation at the US–Mexico Border
Bibliographic record
Abstract
BACKGROUND: Efforts to prevent injection drug use (IDU) are increasingly focused on the role that people who inject drugs (PWID) play in the assistance with injection initiation. We studied the association between recent (ie, past 6 months) injection-related HIV risk behaviors and injection initiation assistance into IDU among PWID in the US-Mexico border region. SETTING: Preventing Injecting by Modifying Existing Responses (PRIMER) is a multicohort study assessing social and structural factors related to injection initiation assistance. This analysis included data collected since 2014 from 2 participating cohorts in San Diego and Tijuana. METHODS: Participants were 18 years and older and reported IDU within the month before study enrollment. Logistic regression analyses were conducted to assess the association between recent injection-related HIV risk behaviors (eg, distributive/receptive syringe sharing, dividing drugs in a syringe, and paraphernalia sharing) and recent injection initiation assistance. RESULTS: Among 892 participants, 41 (4.6%) reported recently providing injection initiation assistance. In multivariable analysis adjusting for potential confounders, reporting a higher number of injection-related risk behaviors was associated with an increased odds of recently assisting others with injection initiation (adjusted odds ratio per risk behavior: 1.3; 95% confidence interval: 1.0 to 1.6, P = 0.04). CONCLUSIONS: PWID who recently engaged in one or more injection-related HIV risk behavior were more likely to assist others in injection initiation. These results stress the syndemic of injection initiation and risk behaviors, which indicates that prevention of injection-related HIV risk behaviors might also reduce the incidence of injection initiation.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".