Prescription Drug Monitoring Programs: Promising Practices in Need of Refinement
Bibliographic record
Abstract
Editorials5 June 2018Prescription Drug Monitoring Programs: Promising Practices in Need of RefinementWilson M. Compton, MD, MPE and Eric M. Wargo, PhDWilson M. Compton, MD, MPENational Institute on Drug Abuse, Bethesda, Maryland (W.M.C., E.M.W.) and Eric M. Wargo, PhDNational Institute on Drug Abuse, Bethesda, Maryland (W.M.C., E.M.W.)Author, Article, and Disclosure Informationhttps://doi.org/10.7326/M18-0883 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Although recent data indicate that overdose deaths involving illicit opioids (including heroin and, especially, synthetic opioids, such as fentanyl and related compounds) have escalated in the past 3 years, widespread overprescription, diversion, and misuse of opioid analgesics started the crisis (1). Prescription opioids remain a major contributor to overdose deaths and serve as an entry point for many persons to become addicted to opioids, even if they switch to illicit opioids later because of lower cost and progression of their opioid use disorder (2–4).Implementation of prescription drug monitoring programs (PDMPs) has been among the many policy-level efforts to curb ...References1. King NB, Fraser V, Boikos C, Richardson R, Harper S. Determinants of increased opioid-related mortality in the United States and Canada, 1990-2013: a systematic review. Am J Public Health. 2014;104:e32-42. [PMID: 24922138] doi:10.2105/AJPH.2014.301966 CrossrefMedlineGoogle Scholar2. Compton WM, Jones CM, Baldwin GT. Relationship between nonmedical prescription-opioid use and heroin use. N Engl J Med. 2016;374:154-63. [PMID: 26760086] doi:10.1056/NEJMra1508490 CrossrefMedlineGoogle Scholar3. Han B, Compton WM, Blanco C, Crane E, Lee J, Jones CM. Prescription opioid use, misuse, and use disorders in U.S. adults: 2015 national survey on drug use and health. Ann Intern Med. 2017;167:293-301. [PMID: 28761945]. doi:10.7326/M17-0865 LinkGoogle Scholar4. Cicero TJ, Ellis MS, Kasper ZA. Increased use of heroin as an initiating opioid of abuse. Addict Behav. 2017;74:63-66. [PMID: 28582659] doi:10.1016/j.addbeh.2017.05.030 CrossrefMedlineGoogle Scholar5. Fink DS, Schleimer JP, Sarvet A, Grover KK, Delcher C, Castillo-Carniglia A, et al. Association between prescription drug monitoring programs and nonfatal and fatal drug overdoses. A systematic review. Ann Intern Med. 2018;168:783-90. doi:10.7326/M17-3074 LinkGoogle Scholar6. Compton WM, Jones CM, Stein JB, Wargo EM. Promising roles for pharmacists in addressing the U.S. opioid crisis. Res Social Adm Pharm. 2017. [PMID: 29325708] doi:10.1016/j.sapharm.2017.12.009 CrossrefMedlineGoogle Scholar7. Volkow ND, Collins FS. The role of science in addressing the opioid crisis. N Engl J Med. 2017;377:391-394. [PMID: 28564549] doi:10.1056/NEJMsr1706626 CrossrefMedlineGoogle Scholar8. Dowell D, Haegerich TM, Chou R. CDC guideline for prescribing opioids for chronic pain—United States, 2016. JAMA. 2016;315:1624-45. [PMID: 26977696] doi:10.1001/jama.2016.1464 CrossrefMedlineGoogle Scholar9. Spoth R, Trudeau L, Shin C, Ralston E, Redmond C, Greenberg M, et al. Longitudinal effects of universal preventive intervention on prescription drug misuse: three randomized controlled trials with late adolescents and young adults. Am J Public Health. 2013;103:665-72. [PMID: 23409883] doi:10.2105/AJPH.2012.301209 CrossrefMedlineGoogle Scholar10. Jones CM, Lurie PG, Compton WM. Increase in naloxone prescriptions dispensed in US retail pharmacies since 2013. Am J Public Health. 2016;106:689-90. [PMID: 26890174] doi:10.2105/AJPH.2016.303062 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: National Institute on Drug Abuse, Bethesda, Maryland (W.M.C., E.M.W.)Disclaimer: The opinions expressed in this commentary are those of the authors and do not necessarily reflect the views of the National Institute on Drug Abuse, the National Institutes of Health, or the U.S. Department of Health and Human Services.Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M18-0883.Corresponding Author: Wilson M. Compton, MD, MPE, 6001 Executive Boulevard, MSC 9581, Bethesda, MD 20892; e-mail, [email protected]nih.gov.Current Author Addresses: Dr. Compton: 6001 Executive Boulevard, MSC 9581, Bethesda, MD 20892.Dr. Wargo: National Institute on Drug Abuse, 6001 Executive Boulevard, Bethesda, MD 20892.This article was published at Annals.org on 8 May 2018. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoAssociation Between Prescription Drug Monitoring Programs and Nonfatal and Fatal Drug Overdoses David S. Fink , Julia P. Schleimer , Aaron Sarvet , Kiran K. Grover , Chris Delcher , Alvaro Castillo-Carniglia , June H. Kim , Ariadne E. Rivera-Aguirre , Stephen G. Henry , Silvia S. Martins , and Magdalena Cerdá Metrics Cited byEffect of a Veterans Health Administration mandate to case review patients with opioid prescriptions on mortality among patients with opioid use disorder: a secondary analysis of the STORM randomized control trialYoung adult opioid misuse indicates a general tendency toward substance use and is strongly predicted by general substance use risk"Nobody Knows How You're Supposed to Interpret it:" End-user Perspectives on Prescription Drug Monitoring Program in MassachusettsMandates are not magic bullets: Leveraging context, meaning and relationships to increase meaningful use of prescription monitoring programs"People need them or else they're going to take fentanyl and die": A qualitative study examining the 'problem' of prescription opioid diversion during an overdose epidemicDeficiencies with the Use of Prescription Drug Monitoring Program in Cancer Pain Management: A Report of Two CasesPolysubstance use in the U.S. opioid crisisThe Importance of Learning Health Systems in Addressing the Opioid CrisisAdvances in prescription drug monitoring program research: a literature synthesis (June 2018 to December 2019)Association between buprenorphine/naloxone and high-dose opioid analgesic prescribing in Kentucky, 2012–2017Prescription drug monitoring programs: Assessing the association between "best practices" and opioid use in MedicareEpidemiology of the U.S. opioid crisis: the importance of the vectorFentanyl and fentanyl-analog involvement in drug-related deathsCurrent Opioid Access, Use, and Problems in Australasian JurisdictionsPrescription Drug Monitoring Programs and drug overdoses 5 June 2018Volume 168, Issue 11Page: 826-827KeywordsDisclosureDrug abuseDrugsHeroinOpioid use disorderOpioidsPatientsPharmacistsSystematic reviewsTherapeutic drug monitoring ePublished: 8 May 2018 Issue Published: 5 June 2018 PDF downloadLoading ...
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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 | 0.031 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".