The costs of crime during and after publicly funded treatment for opioid use disorders: a population‐level study for the state of California
Bibliographic record
Abstract
BACKGROUND AND AIMS: Treatment for opioid use disorders (OUD) reduces the risk of mortality and infectious disease transmission; however, opportunities to quantify the potential economic benefits of associated decreases in drug-related crime are scarce. This paper aimed to estimate the costs of crime during and after periods of engagement in publicly funded treatment for OUD to compare total costs of crime during a hypothetical 6-month period following initiation of opioid agonist treatment (OAT) versus detoxification. DESIGN: Retrospective, administrative data-based cohort study with comprehensive information on drug treatment and criminal justice systems interactions. SETTING: Publicly funded drug treatment facilities in California, USA (2006-10). PARTICIPANTS: A total of 31 659 individuals admitted for the first time to treatment for OUD, and who were linked with criminal justice and mortality data, were followed during a median 2.3 years. Median age at first treatment admission was 32, 35.8% were women and 37.1% primarily used prescription opioids. MEASUREMENTS: Daily costs of crime (US$2014) were calculated from a societal perspective and were composed of the costs of policing, court, corrections and criminal victimization. We estimated the average marginal effect of treatment engagement in OAT or detoxification adjusting for potential fixed and time-varying confounders, including drug use and criminal justice system involvement prior to treatment initiation. FINDINGS: Daily costs of crime during treatment compared with after treatment were $126 lower for OAT [95% confidence interval (CI) = $116, $136] and $144 lower for detoxification (95% CI = $135, $154). Summing the costs of crime during and after treatment over a hypothetical 6-month period using the observed median durations of OAT (161 days) and detoxification (19 days), we estimated that enrolling an individual in OAT as opposed to detoxification would save $17 550 ($16 840, $18 383). CONCLUSIONS: In publicly funded drug treatment facilities in California, USA, engagement in treatment for opioid use disorders is associated with lower costs of crime in the 6 months following initiation of treatment, and the economic benefits were far greater for individuals receiving time-unlimited treatment.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".