A Rapid Review of the Impact of Systems-Level Policies and Interventions on Population-Level Outcomes Related to the Opioid Epidemic, United States and Canada, 2014-2018
Bibliographic record
Abstract
OBJECTIVES: In the United States, rising rates of overdose deaths and recent outbreaks of hepatitis C virus and HIV infection are associated with injection drug use. We updated a 2014 review of systems-level opioid policy interventions by focusing on evidence published during 2014-2018 and new and expanded opioid policies. METHODS: We searched the MEDLINE database, consistent with the 2014 review. We included articles that provided original empirical evidence on the effects of systems-level interventions on opioid use, overdose, or death; were from the United States or Canada; had a clear comparison group; and were published from January 1, 2014, through July 19, 2018. Two raters screened articles and extracted full-text data for qualitative synthesis of consistent or contradictory findings across studies. Given the rapidly evolving field, the review was supplemented with a search of additional articles through November 17, 2019, to assess consistency of more recent findings. RESULTS: The keyword search yielded 535 studies, 66 of which met inclusion criteria. The most studied interventions were prescription drug monitoring programs (PDMPs) (59.1%), and the least studied interventions were clinical guideline changes (7.6%). The most common outcome was opioid use (77.3%). Few articles evaluated combination interventions (18.2%). Study findings included the following: PDMP effectiveness depends on policy design, with robust PDMPs needed for impact; health insurer and pharmacy benefit management strategies, pill-mill laws, pain clinic regulations, and patient/health care provider educational interventions reduced inappropriate prescribing; and marijuana laws led to a decrease in adverse opioid-related outcomes. Naloxone distribution programs were understudied, and evidence of their effectiveness was mixed. In the evidence published after our search's 4-year window, findings on opioid guidelines and education were consistent and findings for other policies differed. CONCLUSIONS: Although robust PDMPs and marijuana laws are promising, they do not target all outcomes, and multipronged interventions are needed. Future research should address marijuana laws, harm-reduction interventions, health insurer policies, patient/health care provider education, and the effects of simultaneous interventions on opioid-related outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.028 | 0.033 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".