Evidence map›Paper›PMID 29554592›Full record

ArticleDrug and alcohol dependence2018

Substance use disorders and medical comorbidities among high-need, high-risk patients with diabetes.

Li-Tzy Wu, Udi E Ghitza, He Zhu, Susan Spratt, Marvin Swartz, Paolo Mannelli

Registry-linked trialAbstract read
In one paragraph

Article in Drug and alcohol dependence, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01966432 (Screening, Brief Intervention, and Referral to Treatment in Primary Care), which is not on this map. Cited by 28 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 3 pooled it
–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.

NCT01966432 nacompletednot on this map

Screening, Brief Intervention, and Referral to Treatment in Primary Care (SBIRT-PC): An add-on Project to "Duke University Southeastern Diabetes Initiative"

TypeinterventionalSponsorDuke UniversityRan2013 to 2016Enrolled134ConditionsDiabetes Mellitus, Type 2, Substance-related DisordersArmsReferral to Treatment, Brief Intervention
3 · Its place in the literature

Who cites it

28 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. The Impact of Opium Abuse on Lipid Profile in Patients with Diabetes: A Systematic Review and Meta-Analysis.International journal of environmental research and public health · 2019
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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.

Li-Tzy WuDepartment of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA; Department of Medicine, Division of General Internal Medicine, Duke University Medical Center, Durham, NC, USA; Duke Clinical Research Institute, Duke University Medical Center, Durham, NC, USA; Center for Child and Family Policy, Sanford School of Public Policy, Duke University, Durham, NC, USA. Electronic address: litzy.wu@duke.edu.
Udi E GhitzaNational Institute on Drug Abuse, Bethesda, MD, USA.
He ZhuDepartment of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA.
Susan SprattDepartment of Medicine, Division of Endocrinology, Duke University Medical Center, Durham, NC, USA.
Marvin SwartzDepartment of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA.
Paolo MannelliDepartment of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC, USA.

Funding

Project-003UG1DA040317 · NIDA · DUKE UNIVERSITY · PI WU, LI-TZY · 2015 to 2019
$9.0M
Substance Abuse & Treatment Gaps in Asians, Pacific Islanders & Multiple-race IndR01MD007658 · NIMHD · DUKE UNIVERSITY · PI WU, LI-TZY · 2013 to 2017
$1.8M
NIDA NIH HHS UG1 DA040317NIMHD NIH HHS R01 MD007658
6 · The paper itself

Abstract

backgroundThe majority of the U.S. healthcare resources are utilized by a small population characterized as high-risk, high-need persons with complex care needs (e.g., adults with multiple chronic conditions). Substance use disorders (SUDs) and mental health disorders (MHDs) are a driver of poor health and additional healthcare costs, but they are understudied among high-need patients.

objectiveWe examine the prevalence and correlates of SUDs and MHDs among adults with high-risk diabetes, who are patients at the top 10% risk score for developing poor outcomes (hospital admission or death).

methodsA risk algorithm developed from Duke University Health System electronic health records (EHRs) data was used to identify patients with high-risk diabetes for targeting home-based primary care. The EHR data of the 263 patients with high-risk diabetes were analyzed to understand patterns of SUDs and MHDs to inform care-coordinating efforts.

resultsBoth SUDs (any SUD 48.3%, alcohol 12.5%, tobacco 38.8%, drug 23.2%) and MHDs (any MHD 74.9%, mood 53.2%, sleep 37.3%, anxiety 32.7%, schizophrenia/psychotics/delusional 14.8%, dementia/delirium/amnestic/cognitive 14.4%, adjustment 9.1%) were prevalent. Overall, 81.7% of the sample had SUD or MHD. Elevated odds of SUD were noted among men (tobacco, alcohol) and those who were never-married (alcohol, cannabis). African-American race (vs. other race/ethnicity) was associated with lower odds of anxiety disorders.

conclusionWhile data are limited to one large academic health system, they provide clinical evidence revealing that 82% of patients with high-risk diabetes had SUD and/or MHD recorded in their EHRs, highlighting a need for developing service models to optimize high-risk care.

Indexed as

AdultAgedAlgorithmsComorbidityDelivery of Health CareDiabetes ComplicationsFemaleHospital MortalityHumansMaleMental DisordersMiddle AgedPrevalenceRiskSocioeconomic FactorsSubstance-Related DisordersComorbidityDiabetes mellitusElectronic health recordsMood disorderSleep disorderSubstance use disorder

Identifiers

PMID29554592
PMCPMC5959045

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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.