Evidence map›Paper›PMID 34870679›Full record

ArticleJAMA network open2021

Accessibility to Medication for Opioid Use Disorder After Interventions to Improve Prescribing Among Nonaddiction Clinics in the US Veterans Health Care System.

Eric J Hawkins, Carol A Malte, Adam J Gordon, Emily C Williams, Hildi J Hagedorn, Karen Drexler, Brittany E Blanchard, Jennifer L Burden, Jennifer Knoeppel, Anissa N Danner and 3 more

Abstract read
In one paragraph

Article in JAMA network open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
–field-weighted citation impact
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

27 citing papers in PubMed.

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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

13 authors.

Eric J HawkinsSeattle Center of Innovation for Veteran-Centered and Value-Driven Care, Health Services Research & Development, Veterans Affairs (VA) Puget Sound Health Care System, Seattle, Washington.
Carol A MalteSeattle Center of Innovation for Veteran-Centered and Value-Driven Care, Health Services Research & Development, Veterans Affairs (VA) Puget Sound Health Care System, Seattle, Washington.
Adam J GordonInformatics, Decision-Enhancement, and Analytic Sciences Center, Health Services Research & Development, VA Salt Lake City Health Care System, Salt Lake City, Utah.
Emily C WilliamsSeattle Center of Innovation for Veteran-Centered and Value-Driven Care, Health Services Research & Development, Veterans Affairs (VA) Puget Sound Health Care System, Seattle, Washington.
Hildi J HagedornCenter for Care Delivery & Outcomes Research, Health Services Research & Development, Minneapolis VA Health Care System, Minneapolis, Minnesota.
Karen DrexlerOffice of Mental Health and Suicide Prevention, Veterans Health Administration, Washington, DC.
Brittany E BlanchardDepartment of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle.
Jennifer L BurdenOffice of Mental Health and Suicide Prevention, Veterans Health Administration, Washington, DC.
Jennifer KnoeppelOffice of Mental Health and Suicide Prevention, Veterans Health Administration, Washington, DC.
Anissa N DannerSeattle Center of Innovation for Veteran-Centered and Value-Driven Care, Health Services Research & Development, Veterans Affairs (VA) Puget Sound Health Care System, Seattle, Washington.
Aline LottSeattle Center of Innovation for Veteran-Centered and Value-Driven Care, Health Services Research & Development, Veterans Affairs (VA) Puget Sound Health Care System, Seattle, Washington.
Joseph G LibertoOffice of Mental Health and Suicide Prevention, Veterans Health Administration, Washington, DC.
Andrew J SaxonSeattle Center of Innovation for Veteran-Centered and Value-Driven Care, Health Services Research & Development, Veterans Affairs (VA) Puget Sound Health Care System, Seattle, Washington.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: With increasing rates of opioid use disorder (OUD) and overdose deaths in the US, increased access to medications for OUD (MOUD) is paramount. Rigorous effectiveness evaluations of large-scale implementation initiatives using quasi-experimental designs are needed to inform expansion efforts. Objective: To evaluate a US Department of Veterans Affairs (VA) initiative to increase MOUD use in nonaddiction clinics. Design, Setting, and Participants: This quality improvement initiative used interrupted time series design to compare trends in MOUD receipt. Primary care, pain, and mental health clinics in the VA health care system (n = 35) located at 18 intervention facilities and nonintervention comparison clinics (n = 35) were matched on preimplementation MOUD prescribing trends, clinic size, and facility complexity. The cohort of patients with OUD who received care in intervention or comparison clinics in the year after September 1, 2018, were evaluated. The preimplementation period extended from September 1, 2017, through August 31, 2018, and the postimplementation period from September 1, 2018, through August 31, 2019. Exposures: The multifaceted implementation intervention included education, external facilitation, and quarterly reports. Main Outcomes and Measures: The main outcomes were the proportion of patients receiving MOUD and the number of patients per clinician prescribing MOUD. Segmented logistic regression evaluated monthly proportions of MOUD receipt 1 year before and after initiative launch, adjusting for demographic and clinical covariates. Poisson regression models examined yearly changes in clinician prescribing over the same time frame. Results: Overall, 7488 patients were seen in intervention clinics (mean [SD] age, 53.3 [14.2] years; 6858 [91.6%] male; 1476 [19.7%] Black, 417 [5.6%] Hispanic; 5162 [68.9%] White; 239 [3.2%] other race [including American Indian or Alaska Native, Asian, Native Hawaiian or other Pacific Islander, and multiple races]; and 194 [2.6%] unknown) and 7558 in comparison clinics (mean [SD] age, 53.4 [14.0] years; 6943 [91.9%] male; 1463 [19.4%] Black; 405 [5.4%] Hispanic; 5196 [68.9%] White; 244 [3.2%] other race; 250 [3.3%] unknown). During the preimplementation year, the proportion of patients receiving MOUD in intervention clinics increased monthly by 5.0% (adjusted odds ratio [AOR], 1.05; 95% CI, 1.03-1.07). Accounting for this preimplementation trend, the proportion of patients receiving MOUD increased monthly by an additional 2.3% (AOR, 1.02; 95% CI, 1.00-1.04) during the implementation year. Comparison clinics increased by 2.6% monthly before implementation (AOR, 1.03; 95% CI, 1.01-1.04), with no changes detected after implementation. Although preimplementation-year trends in monthly MOUD receipt were similar in intervention and comparison clinics, greater increases were seen in intervention clinics after implementation (AOR, 1.04; 95% CI, 1.01-1.08). Patients treated with MOUD per clinician in intervention clinics saw greater increases from before to after implementation compared with comparison clinics (incidence rate ratio, 1.50; 95% CI, 1.28-1.77). Conclusions and Relevance: A multifaceted implementation initiative in nonaddiction clinics was associated with increased MOUD prescribing. Findings suggest that engagement of clinicians in general clinical settings may increase MOUD access.

Indexed as

AgedCohort StudiesFemaleHealth Services AccessibilityHumansMaleMiddle AgedOpiate Substitution TreatmentOpioid-Related DisordersPrimary Health CareUnited StatesUnited States Department of Veterans AffairsVeteransVeterans Health Services

Identifiers

PMID34870679
PMCPMC8649831

What OpenQuestion holds

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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.