Evidence map›Paper›PMID 30466553›Full record

ArticleJournal of substance abuse treatment2019

Generational trends and patterns in readmission within a statewide cohort of clients receiving heroin use disorder treatment in Maryland, 2007-2013.

K Paul Rezai-Zadeh, Richard N Engstrom, Andy Sharma, Yiming Chen, Jun Chu, Robin Parker Cox, Mei-Ling Ting Lee

Open access · greenAbstract read
In one paragraph

Article in Journal of substance abuse treatment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.2field-weighted citation impact, top 35% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
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

7 authors at 1 institution in 1 country.

K Paul Rezai-ZadehInstitute for Governmental Service and Research, Division of Research, University of Maryland, 4321 Hartwick Road, Suite 208, College Park, MD 20742, United States of America. Electronic address: kprzadeh@umd.edu.
Richard N EngstromInstitute for Governmental Service and Research, Division of Research, University of Maryland, 4321 Hartwick Road, Suite 208, College Park, MD 20742, United States of America.
Andy SharmaInstitute for Governmental Service and Research, Division of Research, University of Maryland, 4321 Hartwick Road, Suite 208, College Park, MD 20742, United States of America.
Yiming ChenInstitute for Governmental Service and Research, Division of Research, University of Maryland, 4321 Hartwick Road, Suite 208, College Park, MD 20742, United States of America; Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, 4200 Valley Drive, Building 255, Room 2234R, College Park, MD 20742, United States of America.
Jun ChuInstitute for Governmental Service and Research, Division of Research, University of Maryland, 4321 Hartwick Road, Suite 208, College Park, MD 20742, United States of America; Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, 4200 Valley Drive, Building 255, Room 2234R, College Park, MD 20742, United States of America.
Robin Parker CoxInstitute for Governmental Service and Research, Division of Research, University of Maryland, 4321 Hartwick Road, Suite 208, College Park, MD 20742, United States of America.
Mei-Ling Ting LeeDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, 4200 Valley Drive, Building 255, Room 2234R, College Park, MD 20742, United States of America.
University of Maryland, College Park · US

Funding

Statistical Methods for Adherence Issues in HIV Prevention ResearchR01AI121259 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI CHEN, YING QING · 2015 to 2018
$2.5M
Methods for Complex Longitudinal Data in HIV/AIDS Prevention ResearchR56AI140953 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI CHEN, YING QING · 2018 to 2018
$657k
NIAID NIH HHS R01 AI121259NIAID NIH HHS R56 AI140953
6 · The paper itself

Abstract

The recent rise in opioid-related overdose deaths stresses the importance of understanding how heroin use disorders persist and what interventions are best suited for treating these illnesses. Trends show that there are diverse pathways leading to heroin use disorder that span multiple generations, but little is known about how different generations utilize and respond to treatment. This study provides insight into treatment utilization for young, middle-aged, and older adults by examination of an unusually rich longitudinal dataset of substance use disorder clients in Maryland who were treated for heroin use. Results show that clear patterns of treatment readmission emerge across generations in treatment-naïve clients with regard to gender, ethnicity, employment, geographical region, and treatment type/intensity. In particular, Millennials comprise the majority of the clients receiving heroin use disorder treatment and are the largest contributor to these readmission patterns. Millennials are also given opioid maintenance therapy (OMT) more frequently than other generations, while exhibiting a strong avoidance to treatment. Generational differences in treatment decisions and outcomes over the course of a treatment career are important for understanding the nature of the current opioid epidemic, and can play an important role in directing heroin use disorder treatment efforts and improving models of care.

Indexed as

AdolescentAdultAgedAge FactorsCohort StudiesFemaleHeroin DependenceHumansMaleMarylandMiddle AgedOpiate Substitution TreatmentPatient ReadmissionYoung AdultGenderHeroinMillennialOMTRaceReadmission

Identifiers

PMID30466553
PMCPMC7779828
OpenAlexW2899358205

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.