Estimating State Transitions for Opioid Use Disorders
Bibliographic record
Abstract
AIM: The aim was to estimate transitions between periods in and out of treatment, incarceration, and legal supervision, for prescription opioid (PO) and heroin users. METHODS: We captured all individuals admitted for the first time for publicly funded treatment for opioid use disorder (OUD) in California (2006 to 2010) with linked mortality and criminal justice data. We used Cox proportional hazards and competing risks models to assess the effect of primary PO use (v. heroin) on the hazard of transitioning among 5 states: (1) opioid detoxification treatment; (2) opioid agonist treatment (OAT); (3) legal supervision (probation or parole); (4) incarceration (jail or prison); and (5) out-of-treatment. Transitions were conditional on survival, and death was modeled as an absorbing state. RESULTS: Both primary PO (n = 11,733) and heroin (n = 19,926) users spent most of their median 2.3 y of observation out of treatment. Primary PO users were significantly younger (median age 30 v. 34 y), and a higher percentage were female (43.1% v. 31.5%; P < 0.001), white (74.6% v. 63.1%; P < 0.001), and had completed high school (31.8% v. 18.9%; P < 0.001). When compared to primary heroin users, PO users had a higher hazard of transitioning from detoxification to OAT (Hazard Ratio (HR), 1.65; 95% CI, 1.54 to 1.77), and had a lower hazard of transitioning from out-of-treatment to either detoxification (0.75 [0.70, 0.81]) or OAT (0.90 [0.85, 0.96]). CONCLUSION: Our findings can be applied directly in state transition modeling to improve the validity of health economic evaluations. Although PO users tended to remain in treatment for longer durations than heroin users, they also tended to remain out of treatment for longer after transitioning to an out-of-treatment state. Despite the proven effectiveness of time-unlimited treatment, individuals with OUD spend most of their time out of 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".