Monitoring opioid addiction and treatment: Do you know if your population is engaged?
Adolescent
Adult
Analgesics, Opioid
/ therapeutic use
Child
Colorado
/ epidemiology
Female
Humans
Male
Middle Aged
Opiate Substitution Treatment
/ statistics & numerical data
Opioid-Related Disorders
/ epidemiology
Patient Participation
/ statistics & numerical data
Research Design
Safety-net Providers
/ statistics & numerical data
Young Adult
Continuity of patient care
Heroin
Opiate substitution treatment
Opioid crisis
Opioid-related disorders
Journal
Drug and alcohol dependence
ISSN: 1879-0046
Titre abrégé: Drug Alcohol Depend
Pays: Ireland
ID NLM: 7513587
Informations de publication
Date de publication:
01 09 2019
01 09 2019
Historique:
received:
18
04
2019
revised:
02
07
2019
accepted:
02
07
2019
pubmed:
16
7
2019
medline:
27
3
2020
entrez:
15
7
2019
Statut:
ppublish
Résumé
Assessment of people affected by opioid-related problems and those receiving care is challenging due to lack of common definitions and scattered information. We sought to fill this gap by demonstrating a method to describe a continuum of opioid addiction care in a large, public safety-net institution. Using 2017 clinical and administrative data from Denver Health (DH), we created operational definitions for opioid use disorder (OUD), opioid misuse (OM), and opioid poisoning (OP). Six stages along a continuum of patient engagement in opioid addiction care were developed, and operational definitions assigned patients to stages for a specific time point of analysis. National data was used to estimate the Denver population affected by OUD, OM and OP. In 2017, an estimated 6688 people aged ≥12 years were affected by OUD, OM, or OP in Denver; 48.4% (3238/6688) were medically diagnosed in DH. Of those, 32.5% (1051/3238) were in the medication assisted treatment stage, and, of those, 59.8% (629/1051) in the adhered to treatment stage. Among that latter group, 78.4% (493/629) adhered at least 90 days and 52.3% (329/629) for more than one year. Among patients who received medication assisted treatment, less than one third (31.3%, 329/1051) were adherent for more than one year. A health-system level view of the continuum of opioid addiction care identified improvement opportunities to better monitor accuracy of diagnosis, treatment capacity, and effectiveness of patient engagement. Applied longitudinally at local, state and national levels, the model could better synergize responses to the opioid crisis.
Sections du résumé
BACKGROUND
Assessment of people affected by opioid-related problems and those receiving care is challenging due to lack of common definitions and scattered information. We sought to fill this gap by demonstrating a method to describe a continuum of opioid addiction care in a large, public safety-net institution.
METHODS
Using 2017 clinical and administrative data from Denver Health (DH), we created operational definitions for opioid use disorder (OUD), opioid misuse (OM), and opioid poisoning (OP). Six stages along a continuum of patient engagement in opioid addiction care were developed, and operational definitions assigned patients to stages for a specific time point of analysis. National data was used to estimate the Denver population affected by OUD, OM and OP.
RESULTS
In 2017, an estimated 6688 people aged ≥12 years were affected by OUD, OM, or OP in Denver; 48.4% (3238/6688) were medically diagnosed in DH. Of those, 32.5% (1051/3238) were in the medication assisted treatment stage, and, of those, 59.8% (629/1051) in the adhered to treatment stage. Among that latter group, 78.4% (493/629) adhered at least 90 days and 52.3% (329/629) for more than one year. Among patients who received medication assisted treatment, less than one third (31.3%, 329/1051) were adherent for more than one year.
CONCLUSIONS
A health-system level view of the continuum of opioid addiction care identified improvement opportunities to better monitor accuracy of diagnosis, treatment capacity, and effectiveness of patient engagement. Applied longitudinally at local, state and national levels, the model could better synergize responses to the opioid crisis.
Identifiants
pubmed: 31302412
pii: S0376-8716(19)30210-8
doi: 10.1016/j.drugalcdep.2019.07.002
pmc: PMC6685741
mid: NIHMS1041991
pii:
doi:
Substances chimiques
Analgesics, Opioid
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
56-60Subventions
Organisme : Intramural CDC HHS
ID : CC999999
Pays : United States
Organisme : NIDA NIH HHS
ID : K01 DA036452
Pays : United States
Informations de copyright
Published by Elsevier B.V.
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