Evidence map›Paper›PMID 32685573›Full record

ArticleJournal of health economics and outcomes research2018

Identifying Priority Areas for Increasing the Supply of Medication-Assisted Treatments for Opioid Use Disorder: A Geospatial Approach.

Michael Topmiller, Peter J Mallow, Aaron T Vissman, Jene Grandmont

Abstract read
In one paragraph

Article in Journal of health economics and outcomes research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 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

4 authors.

Michael TopmillerHealthLandscape LLC, American Academy of Family Physicians, Cincinnati, OH, USA.
Peter J MallowXavier University, Cincinnati, OH, USA.
Aaron T VissmanCenter for Health and Human Services Research, Talbert House, Cincinnati, OH, USA.
Jene GrandmontHealthLandscape LLC, American Academy of Family Physicians, Cincinnati, OH, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe opioid epidemic has disproportionately affected several areas across the United States (US), with research indicating that these areas may be underserved and lack access to sufficient medication-assisted treatment (MAT) options. The objective of this study was to introduce a geospatial analytical framework for identifying sub-state priority areas to target federal allocation of MAT training and resources.

methodsWe used a geospatial analytical framework, which integrated multiple substance use measures and layers of geographic information. Measures included estimates of illicit drug dependence and unmet treatment need from the National Survey on Drug Use and Health (NSDUH), opioid-related admissions from the Treatment Episode Data Set: Admissions (TEDs-A), and Drug Enforcement Agency (DEA) waiver practitioner data from the Substance Abuse and Mental Health Services Administration (SAMHSA). Analyses included standard deviation outlier mapping, local indicators of spatial autocorrelation (LISA), and map overlays.

resultsWe identified twenty-nine opioid dependence priority areas, eleven unmet treatment need priority areas, and seven low MAT capacity priority areas, located across the US, including southeastern Ohio, western Indiana, the District of Columbia, New England, and northern and southern California.

conclusionsThis study identified several areas across the US that have unmet need for MAT. Targeting these areas will allow for the most effective deployment of cost-effective MAT resources to aid the greatest number of patients with opioid use disorders.

Indexed as

Geospatial AnalysisIllicit Drug DependenceMedication-Assisted TreatmentOpioid Use DisorderSubstance AbuseUnited States

Identifiers

PMID32685573
PMCPMC7309950

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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.