Evidence map›Paper›PMID 22351489›Full record

ArticleInternational journal of methods in psychiatric research2012

A dimensional approach to understanding severity estimates and risk correlates of marijuana abuse and dependence in adults.

Li-Tzy Wu, George E Woody, Chongming Yang, Jeng-Jong Pan, Bryce B Reeve, Dan G Blazer

Abstract read
In one paragraph

Article in International journal of methods in psychiatric research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.1field-weighted citation impact, top 14% 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

12 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. 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

6 authors at 6 institutions in 1 country.

Li-Tzy WuDepartment of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Duke University Medical Center, Durham, NC 27710, USA. litzy.wu@duke.edu
George E Woody
Chongming Yang
Jeng-Jong Pan
Bryce B Reeve
Dan G Blazer
Duke Medical Center · USDuke University · USSocial Science Research Council · USTreatment Research Institute · USUniversity of North Carolina at Chapel Hill · USVeterans Health Administration · US

Funding

Southern Consortium Node of the Clinical Trials NetworkU10DA013727 · NIDA · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI BRADY, KATHLEEN T. · 2000 to 2015
$34.4M
Delaware Valley Node of the Clinical Trials NetworkU10DA013043 · NIDA · UNIVERSITY OF PENNSYLVANIA · PI WOODY, GEORGE EDWARD · 1999 to 2015
$25.8M
Treatment/Substance Use Disorders/HIV Risk ReductionK05DA017009 · NIDA · UNIVERSITY OF PENNSYLVANIA · PI WOODY, GEORGE EDWARD · 2003 to 2012
$1.3M
MDMA and Other Hallucinogen Use: Onset and Abuse/DependenceR01DA019623 · NIDA · DUKE UNIVERSITY · PI WU, LI-TZY · 2007 to 2013
$1.1M
Classification of Substance Use Severity and Treatment Outcomes: NIDA CTN StudiesR33DA027503 · NIDA · DUKE UNIVERSITY · PI WU, LI-TZY · 2010 to 2012
$1.1M
Epdemiology of Non-Medical Prescription Anagesic UseR01DA019901 · NIDA · DUKE UNIVERSITY · PI WU, LI-TZY · 2007 to 2009
$416k
NIDA NIH HHS HHSN271200522071CNIDA NIH HHS K05 DA017009NIDA NIH HHS K05DA017009NIDA NIH HHS R01 DA019623NIDA NIH HHS R01DA019623NIDA NIH HHS R01 DA019901NIDA NIH HHS R01DA019901NIDA NIH HHS R33 DA027503NIDA NIH HHS R33DA027503NIDA NIH HHS U10 DA013043NIDA NIH HHS U10DA013043NIDA NIH HHS U10 DA013727
6 · The paper itself

Abstract

While item response theory (IRT) research shows a latent severity trait underlying response patterns of substance abuse and dependence symptoms, little is known about IRT-based severity estimates in relation to clinically relevant measures. In response to increased prevalences of marijuana-related treatment admissions, an elevated level of marijuana potency, and the debate on medical marijuana use, we applied dimensional approaches to understand IRT-based severity estimates for marijuana use disorders (MUDs) and their correlates while simultaneously considering gender- and race/ethnicity-related differential item functioning (DIF). Using adult data from the 2008 National Survey on Drug Use and Health (N = 37,897), Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for MUDs among past-year marijuana users were examined by IRT, logistic regression, and multiple indicators-multiple causes (MIMIC) approaches. Among 6917 marijuana users, 15% met criteria for a MUD; another 24% exhibited subthreshold dependence. Abuse criteria were highly correlated with dependence criteria (correlation = 0.90), indicating unidimensionality; item information curves revealed redundancy in multiple criteria. MIMIC analyses showed that MUD criteria were positively associated with weekly marijuana use, early marijuana use, other substance use disorders, substance abuse treatment, and serious psychological distress. African Americans and Hispanics showed higher levels of MUDs than Whites, even after adjusting for race/ethnicity-related DIF. The redundancy in multiple criteria suggests an opportunity to improve efficiency in measuring symptom-level manifestations by removing low-informative criteria. Elevated rates of MUDs among African Americans and Hispanics require research to elucidate risk factors and improve assessments of MUDs for different racial/ethnic groups.

Indexed as

Severity of Illness IndexAdolescentAdultAge FactorsDiagnostic and Statistical Manual of Mental DisordersEthnicityFemaleHealth SurveysHumansLogistic ModelsMaleMarijuana AbuseMarijuana SmokingPopulation GroupsPrevalenceRetrospective Studies

Identifiers

PMID22351489
PMCPMC3370135
OpenAlexW1547681394

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.