Opioid Overdose Hospitalization Trajectories in States With and Without Opioid-Dosing Guidelines
Bibliographic record
Abstract
OBJECTIVES: High-risk opioid-prescribing practices contribute to a national epidemic of opioid-related morbidity and mortality. The objective of this study was to determine whether the adoption of state-level opioid-prescribing guidelines that specify a high-dose threshold is associated with trends in rates of opioid overdose hospitalizations, for prescription opioids, for heroin, and for all opioids. METHODS: We identified 3 guideline states (Colorado, Utah, Washington) and 5 comparator states (Arizona, California, Michigan, New Jersey, South Carolina). We used state-level opioid overdose hospitalization data from 2001-2014 for these 8 states. Data were based on the State Inpatient Databases and provided by the Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality, via HCUPnet. We used negative binomial panel regression to model trends in annual rates of opioid overdose hospitalizations. We used a multiple-baseline difference-in-differences study design to compare postguideline trends with concurrent trends for comparator states. RESULTS: For each guideline state, postguideline trends in rates of prescription opioid and all opioid overdose hospitalizations decreased compared with trends in the comparator states. The mean annual relative percentage decrease ranged from 3.2%-7.5% for trends in rates of prescription opioid overdose hospitalizations and from 5.4%-8.5% for trends in rates of all opioid overdose hospitalizations. CONCLUSIONS: These findings provide preliminary evidence that opioid-dosing guidelines may be an effective strategy for combating this public health crisis. Further research is needed to identify the individual effects of opioid-related interventions that occurred during the study period.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".