Evidence map›Paper›PMID 33111239›Full record

Trial reportJournal of general internal medicine2021

Substance Use Disorder Detection Rates Among Providers of General Medical Inpatients.

Kristin L Serowik, Kimberly A Yonkers, Kathryn Gilstad-Hayden, Ariadna Forray, Paula Zimbrean, Steve Martino

Registry-linked trialOpen access · bronzeAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of general internal medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01825057 (Three Strategies for Implementing Motivational Interviewing on Medical Inpatient Units), which is not on this map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.2field-weighted citation impact, top 20% 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.

NCT01825057 nacompletednot on this map

Three Strategies for Implementing Motivational Interviewing on Medical Inpatient Units: See One, Do One, Order One

TypeinterventionalSponsorNational Institute on Drug Abuse (NIDA)Ran2013 to 2019Enrolled1,211ConditionsSee One, Do One, Order OneArmsSee One, Do One, Order One
3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 19 citations in OpenAlex.

  1. Observational
  2. Article
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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

6 authors at 1 institution in 1 country.

Kristin L SerowikDepartment of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 301, New Haven, CT, 06520, USA. kristin.serowik@yale.edu.ORCID 0000-0001-6608-9069
Kimberly A YonkersDepartment of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 301, New Haven, CT, 06520, USA.
Kathryn Gilstad-HaydenDepartment of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 301, New Haven, CT, 06520, USA.
Ariadna ForrayDepartment of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 301, New Haven, CT, 06520, USA.
Paula ZimbreanDepartment of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 301, New Haven, CT, 06520, USA.
Steve MartinoDepartment of Psychiatry, Yale University School of Medicine, 300 George Street, Suite 301, New Haven, CT, 06520, USA.
Yale University · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Three Strategies for Implementing Motivational Interviewing on Medical InpatientR01DA034243 · NIDA · YALE UNIVERSITY · PI MARTINO, STEVE, YONKERS, KIMBERLY A · 2012 to 2016
$3.7M
NCATS NIH HHS UL1 TR001863NIDA NIH HHS R01 DA034243
6 · The paper itself

Abstract

backgroundThe prevalence of substance use disorders is higher among medical inpatients than in the general population, placing inpatient providers in a prime position to detect these patients and intervene.

objectiveTo assess provider detection rates of substance use disorders among medical inpatients and to identify patient characteristics associated with detection.

designData drawn from a cluster randomized controlled trial that tested the effectiveness of three distinct implementation strategies for providers to screen patients for substance use disorders and deliver a brief intervention (Clinical Trials.gov : NCT01825057).

participantsA total of 1076 patients receiving care from 13 general medical inpatient units in a large teaching hospital participated in this study. MAIN MEASURES: Data sources included patient self-reported questionnaires, a diagnostic interview for substance use disorders, and patient medical records. Provider detection was determined by diagnoses documented in medical records. KEY

resultsProvider detection rates were highest for nicotine use disorder (72.2%) and lowest for cannabis use disorder (26.4%). Detection of alcohol use disorder was more likely among male compared to female patients (OR (95% CI) = 4.0 (1.9, 4.8)). When compared to White patients, alcohol (OR (95% CI) = 0.4 (0.2, 0.6)) and opioid (OR (95% CI) = 0.2 (0.1, 0.7)) use disorders were less likely to be detected among Black patients, while alcohol (OR (95% CI) = 0.3 (0.0, 2.0)) and cocaine (OR (95% CI) = 0.3 (0.1, 0.9)) use disorders were less likely to be detected among Hispanic patients. Providers were more likely to detect nicotine, alcohol, opioid, and other drug use disorders among patients with higher addiction severity (OR (95% CI) = 1.20 (1.08-1.34), 1.62 (1.48, 1.78), 1.46 (1.07, 1.98), 1.38 (1.00, 1.90), respectively).

conclusionsFindings indicate patient characteristics, including gender, race, and addiction severity impact rates of provider detection. Instituting formal screening for all substances may increase provider detection and inform treatment decisions.

Indexed as

AlcoholismBehavior, AddictiveSubstance-Related DisordersFemaleHumansInpatientsMaleMass Screeningdetectiondiagnosishospitalizationsubstance use disorder

Identifiers

PMID33111239
PMCPMC7947066
OpenAlexW3095730501

What OpenQuestion holds

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Registered trials

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.