Cost-effectiveness of a police education program on HIV and overdose among people who inject drugs in Tijuana, Mexico
Bibliographic record
Abstract
Background: Incarceration is associated with drug-related harms among people who inject drugs (PWID). We trained >1800 police officers in Tijuana, Mexico on occupational safety and HIV/HCV, harm reduction, and decriminalization reforms (Proyecto Escudo). We evaluated its effect on incarceration, population impact and cost-effectiveness on HIV and fatal overdose among PWID. Methods: We assessed self-reported recent incarceration in a longitudinal cohort of PWID before and after Escudo. Segmented regression was used to compare linear trends in log risk of incarceration among PWID pre-Escudo (2012-2015) and post-Escudo (2016-2018). We estimated population impact using a dynamic model of HIV transmission and fatal overdose among PWID, with incarceration associated with syringe sharing and fatal overdose. The model was calibrated to HIV and incarceration patterns in Tijuana. We compared a scenario with Escudo (observed incarceration declines for 2 years post-Escudo among PWID from the segmented regression) compared to a counterfactual of no Escudo (continuation of stable pre-Escudo trends), assessing cost-effectiveness from a societal perspective. Using a 2-year intervention effect and 50-year time horizon, we determined the incremental cost-effectiveness ratio (ICER, in 2022 USD per disability-adjusted life years [DALYs] averted). Findings: Compared to stable incarceration pre-Escudo, for every three-month interval in the post-Escudo period, recent incarceration among PWID declined by 21% (adjusted relative risk = 0.79, 95% CI: 0.68-0.91). Based on these declines, we estimated 1.7% [95% interval: 0.7%-3.5%] of new HIV cases and 12.2% [4.5%-26.6%] of fatal overdoses among PWID were averted in the 2 years post-Escudo, compared to a counterfactual without Escudo. Escudo was cost-effective (ICER USD 3746/DALY averted compared to a willingness-to-pay threshold of $4842-$13,557). Interpretation: Escudo is a cost-effective structural intervention that aligned policing practices and human-rights-based public health practices, which could serve as a model for other settings where policing constitutes structural HIV and overdose risk among PWID. Funding: National Institute on Drug Abuse, UC MEXUS CONACyT, and the San Diego Center for AIDS Research (SD CFAR).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".