Predictors of needle exchange program utilization during its implementation and expansion in Tijuana, Mexico
Bibliographic record
Abstract
OBJECTIVE: Until the early 2000s, there was only one needle exchange program (NEP) offered in Mexico. In 2004, the second Mexican NEP opened in Tijuana, but its utilization has not been studied. We studied predictors of initiating NEP during its early expansion in Tijuana, Mexico. METHODS: From April 2006 to April 2007, people who inject drugs (PWID) residing in Tijuana who had injected within the last month were recruited using respondent-driven sampling. Weighted Poisson regression incorporating generalized estimating equations was used to identify predictors of initiating NEP, while accounting for correlation between recruiter and recruits. RESULTS: NEP uptake increased from 20% at baseline to 59% after 6 months. Among a subsample of PWID not accessing NEP at baseline (n = 480), 83% were male and median age was 37 years (Interquartile Range: 32-43). At baseline, 4.4% were HIV-infected and 5.9% had syphilis titers >1:8. In multivariate models, factors associated with NEP initiation (p < .05) were attending shooting galleries (Adjusted Relative Risk [ARR]: 1.54); arrest for track-marks (ARR: 1.38); having a family member that ever used drugs (ARR: 1.37); and having a larger PWID network (ARR: 1.01 per 10 persons). NEP initiation was inversely associated with obtaining syringes at pharmacies (ARR: .56); earning >2500 pesos/month (ARR: .66); and reporting needle sharing (ARR: .71). CONCLUSIONS: Uptake of NEP expansion in Tijuana was vigorous among PWID. We identified a range of factors that influenced the likelihood of NEP initiation, including police interaction. These findings have important implications for the scale-up of NEP in Mexico.
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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 | 0.003 |
| 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 | 0.001 |
| 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".