Evidence map›Paper›PMID 37124766›Full record

ArticleFrontiers in public health2023

Opioid use and opioid use disorder in mono and dual-system users of veteran affairs medical centers.

Joseph Goulet, Yan Cheng, William Becker, Cynthia Brandt, Friedhelm Sandbrink, Terri Elizabeth Workman, Phillip Ma, Alexander Libin, Nawar Shara, Christopher Spevak and 2 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.2field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 4 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 6 institutions in 1 country.

Joseph GouletVA Connecticut Healthcare System, West Haven, CT, United States.
Yan ChengWashington DC VA Medical Center, Washington, DC, United States.
William BeckerVA Connecticut Healthcare System, West Haven, CT, United States.
Cynthia BrandtVA Connecticut Healthcare System, West Haven, CT, United States.
Friedhelm SandbrinkWashington DC VA Medical Center, Washington, DC, United States.
Terri Elizabeth WorkmanWashington DC VA Medical Center, Washington, DC, United States.
Phillip MaWashington DC VA Medical Center, Washington, DC, United States.
Alexander LibinMedStar Health, Washington, DC, United States.
Nawar SharaMedStar Health, Washington, DC, United States.
Christopher SpevakGeorgetown University School of Medicine, Washington, DC, United States.
Joel KupersmithGeorgetown University School of Medicine, Washington, DC, United States.
Qing Zeng-TreitlerWashington DC VA Medical Center, Washington, DC, United States.
Washington DC VA Medical Center · USGeorgetown University · USVA Connecticut Healthcare System · USGeorgetown-Howard Universities Center for Clinical and Translational Science · USGeorge Washington University · USYale University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Efforts to achieve opioid guideline concordant care may be undermined when patients access multiple opioid prescription sources. Limited data are available on the impact of dual-system sources of care on receipt of opioid medications. Objective: We examined whether dual-system use was associated with increased rates of new opioid prescriptions, continued opioid prescriptions and diagnoses of opioid use disorder (OUD). We hypothesized that dual-system use would be associated with increased odds for each outcome. Methods: This retrospective cohort study was conducted using Veterans Administration (VA) data from two facilities from 2015 to 2019, and included active patients, defined as Veterans who had at least one encounter in a calendar year (2015-2019). Dual-system use was defined as receipt of VA care as well as VA payment for community care (non-VA) services. Mono users were defined as those who only received VA services. There were 77,225 dual-system users, and 442,824 mono users. Outcomes were three binary measures: new opioid prescription, continued opioid prescription (i.e., received an additional opioid prescription), and OUD diagnosis (during the calendar year). We conducted a multivariate logistic regression accounting for the repeated observations on patient and intra-class correlations within patients. Results: Dual-system users were significantly younger than mono users, more likely to be women, and less likely to report white race. In adjusted models, dual-system users were significantly more likely to receive a new opioid prescription during the observation period [Odds ratio (OR) = 1.85, 95% confidence interval (CI) 1.76-1.93], continue prescriptions (OR = 1.24, CI 1.22-1.27), and to receive an OUD diagnosis (OR = 1.20, CI 1.14-1.27). Discussion: The prevalence of opioid prescriptions has been declining in the US healthcare systems including VA, yet the prevalence of OUD has not been declining at the same rate. One potential problem is that detailed notes from non-VA visits are not immediately available to VA clinicians, and information about VA care is not readily available to non-VA sources. One implication of our findings is that better health system coordination is needed. Even though care was paid for by the VA and presumably closely monitored, dual-system users were more likely to have new and continued opioid prescriptions.

Indexed as

Opioid-Related DisordersVeteransAnalgesics, OpioidFemaleHumansMaleRetrospective StudiesUnited StatesUnited States Department of Veterans AffairsAnalgesics, Opioidlogistic modelsobservational studyopioidopioid-related disordersveterans

Identifiers

PMID37124766
PMCPMC10141670
OpenAlexW4362566569

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.