A Systematic Review of Interventions and Programs Targeting Appropriate Prescribing of Opioids.
Bibliographic record
Abstract
BACKGROUND: Canada and the United States have the highest levels of prescription opioid consumption in the world. In an attempt to curb the opioid epidemic, a variety of interventions have been implemented. Thus far, evidence regarding their effectiveness has not been consolidated. OBJECTIVES: The objectives of this study were to: 1) identify interventions that target opioid prescribing; 2) assess and compare the effectiveness of interventions on opioid prescription and related harms; 3) determine the methodological quality of evaluation studies. STUDY DESIGN: The study involved a systematic review of the literature including bibliographical databases and gray literature sources. SETTING: Systematic review including bibliographical databases and gray literature sources. METHODS: We searched MEDLINE, Embase, and LILACS databases from January 1, 2005 to September 23, 2016 for any intervention that targeted the prescription of opioids. We also examined websites of relevant organizations and scanned bibliographies of included articles and reviews for additional references. The target population was that of all health care providers (HCPs) or users of opioids with no restriction on indication. Endpoints were those related to process (implementation), outcomes (effectiveness), or impact. Sources were screened independently by 2 reviewers using pre-defined eligibility criteria. Synthesis of findings was qualitative; no pooling of results was conducted. RESULTS: Literature search yielded 12,278 unique sources. Of these, 142 were retained. During full-text review, 75 were further excluded. Searches of the gray literature and bibliographies yielded 49 additional sources. Thus, a total of 95 distinct interventions were identified. Over half consisted of prescription monitoring programs (PMPs) and mainly targeted HCPs. Evaluation studies addressed mainly opioid prescription rate (30.6%), opioid use (19.4%), or doctor shopping or diversion (9.7%). Fewer studies considered overdose death (9.7%), abuse (9.7%), misuse (4.2%), or diversion (5.6%). Study designs consisted of cross-sectional surveys (23.3%), pre-post intervention (26.7%), or time series without a comparison group (13.3%), which limit the robustness of the evidence. Although PMPs and policies have been associated with a reduction in opioid prescription, their impact on appropriateness of use according to clinical guidelines and restriction of access to patients in need is inconsistent. Continuing medical education (CME) and pain management programs were found effective in improving chronic pain management, but studies were conducted in specific settings. The impact of interventions on abuse and overdose-death is conflicting. LIMITATIONS: Due to the very large number of publications and programs found, it was difficult to compare interventions owing to the heterogeneity of the programs and to the methodologies of evaluation studies. No assessment of publication bias was done in the review. CONCLUSIONS: Evidence of effectiveness of interventions targeting the prescription of opioids is scarce in the literature. Although PMPs have been associated with a reduction in the overall prescription rates of Schedule II opioids, their impact on the appropriateness of use taking into consideration benefits, misuse, legal and illegal use remains elusive. Our review suggests that existing interventions have not addressed all determinants of inappropriate opioid prescribing and usage. A well-described theoretical framework would be the backdrop against which targeted interventions or policies may be developed. KEY WORDS: Opioid, prescription, abuse, misuse, diversion, interventions, prescription monitoring programs.
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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 | 0.028 | 0.109 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".