Prescription Opioid Laws and Opioid Dispensing in US Counties: Identifying Salient Law Provisions With Machine Learning.
Journal
Epidemiology (Cambridge, Mass.)
ISSN: 1531-5487
Titre abrégé: Epidemiology
Pays: United States
ID NLM: 9009644
Informations de publication
Date de publication:
01 11 2021
01 11 2021
Historique:
pubmed:
27
7
2021
medline:
26
10
2021
entrez:
26
7
2021
Statut:
ppublish
Résumé
Hundreds of laws aimed at reducing inappropriate prescription opioid dispensing have been implemented in the United States, yet heterogeneity in provisions and their simultaneous implementation have complicated evaluation of impacts. We apply a hypothesis-generating, multistage, machine-learning approach to identify salient law provisions and combinations associated with dispensing rates to test in future research. Using 162 prescription opioid law provisions capturing prescription drug monitoring program (PDMP) access, reporting and administration features, pain management clinic provisions, and prescription opioid limits, we used regularization approaches and random forest models to identify laws most predictive of county-level and high-dose dispensing. We stratified analyses by overdose epidemic phases-the prescription opioid phase (2006-2009), heroin phase (2010-2012), and fentanyl phase (2013-2016)-to further explore pattern shifts over time. PDMP patient data access provisions most consistently predicted high-dispensing and high-dose dispensing counties. Pain management clinic-related provisions did not generally predict dispensing measures in the prescription opioid phase but became more discriminant of high dispensing and high-dose dispensing counties over time, especially in the fentanyl period. Predictive performance across models was poor, suggesting prescription opioid laws alone do not strongly predict dispensing. Our systematic analysis of 162 law provisions identified patient data access and several pain management clinic provisions as predictive of county prescription opioid dispensing patterns. Future research employing other types of study designs is needed to test these provisions' causal relationships with inappropriate dispensing and to examine potential interactions between PDMP access and pain management clinic provisions. See video abstract at, http://links.lww.com/EDE/B861.
Sections du résumé
BACKGROUND
Hundreds of laws aimed at reducing inappropriate prescription opioid dispensing have been implemented in the United States, yet heterogeneity in provisions and their simultaneous implementation have complicated evaluation of impacts. We apply a hypothesis-generating, multistage, machine-learning approach to identify salient law provisions and combinations associated with dispensing rates to test in future research.
METHODS
Using 162 prescription opioid law provisions capturing prescription drug monitoring program (PDMP) access, reporting and administration features, pain management clinic provisions, and prescription opioid limits, we used regularization approaches and random forest models to identify laws most predictive of county-level and high-dose dispensing. We stratified analyses by overdose epidemic phases-the prescription opioid phase (2006-2009), heroin phase (2010-2012), and fentanyl phase (2013-2016)-to further explore pattern shifts over time.
RESULTS
PDMP patient data access provisions most consistently predicted high-dispensing and high-dose dispensing counties. Pain management clinic-related provisions did not generally predict dispensing measures in the prescription opioid phase but became more discriminant of high dispensing and high-dose dispensing counties over time, especially in the fentanyl period. Predictive performance across models was poor, suggesting prescription opioid laws alone do not strongly predict dispensing.
CONCLUSIONS
Our systematic analysis of 162 law provisions identified patient data access and several pain management clinic provisions as predictive of county prescription opioid dispensing patterns. Future research employing other types of study designs is needed to test these provisions' causal relationships with inappropriate dispensing and to examine potential interactions between PDMP access and pain management clinic provisions. See video abstract at, http://links.lww.com/EDE/B861.
Identifiants
pubmed: 34310445
doi: 10.1097/EDE.0000000000001404
pii: 00001648-202111000-00014
pmc: PMC8556655
mid: NIHMS1747107
doi:
Substances chimiques
Analgesics, Opioid
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, P.H.S.
Video-Audio Media
Langues
eng
Sous-ensembles de citation
IM
Pagination
868-876Subventions
Organisme : NIDA NIH HHS
ID : R01 DA048860
Pays : United States
Organisme : NIDA NIH HHS
ID : R01 DA045872
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR003017
Pays : United States
Organisme : AHRQ HHS
ID : R18 HS023258
Pays : United States
Organisme : NIDA NIH HHS
ID : K01 DA049950
Pays : United States
Organisme : NIDA NIH HHS
ID : R01 DA047347
Pays : United States
Informations de copyright
Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
K.M.K. reports personal fees related to consultation in opioid product litigation. All other authors report no conflict of interest.
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