Evidence map›Paper›PMID 23266570›Full record

ArticleJAMA psychiatry2013

Mapping common psychiatric disorders: structure and predictive validity in the national epidemiologic survey on alcohol and related conditions.

Carlos Blanco, Robert F Krueger, Deborah S Hasin, Shang-Min Liu, Shuai Wang, Bradley T Kerridge, Tulshi Saha, Mark Olfson

Abstract read
In one paragraph

Article in JAMA psychiatry, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing 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

40 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

8 authors.

Carlos BlancoCollege of Physicians and Surgeons, Department of Psychiatry, Columbia University, NewYork, NY 10032, USA. cb255@columbia.edu
Robert F Krueger
Deborah S Hasin
Shang-Min Liu
Shuai Wang
Bradley T Kerridge
Tulshi Saha
Mark Olfson

Funding

Adverse childhood experiences, personality psychopathology, and alcohol disordersU01AA018111 · NIAAA · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI HASIN, DEBORAH S · 2009 to 2013
$3.4M
Interpersonal therapy for depression in Breast CancerR01CA133050 · NCI · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI MARKOWITZ, JOHN C · 2009 to 2013
$2.6M
Towards a Comprehensive Developmental Model of Substance Use DisordersR01DA019606 · NIDA · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI OLFSON, MARK · 2006 to 2016
$2.6M
2/2 Gambling Addiction: Treatment Mediators and ModeratorsR01MH082773 · NIMH · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI BLANCO, CARLOS · 2009 to 2012
$1.6M
Epidemiologic Studies of Alcohol Use Disorders (ongoing)K05AA014223 · NIAAA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HASIN, DEBORAH S · 2003 to 2012
$1.5M
SMART: Improving Detection & Outcome of Psychiatric Comorbidity in Drug TreatmentR01DA020783 · NIDA · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI BLANCO, CARLOS · 2006 to 2010
$1.4M
Substance Use Comorbidity Care: Evidence-Based Stepped Strategies for DepressionR01DA023973 · NIDA · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · PI BLANCO, CARLOS · 2009 to 2010
$1.1M
Bridging Efficacy and Effectiveness in Drug Abuse TreatmentK02DA023200 · NIDA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BLANCO, CARLOS · 2008 to 2012
$618k
War Violence and Global Burden of Diseases Related To Substance Use and DisordersF31DA025377 · NIDA · UNIV OF MARYLAND, COLLEGE PARK · PI KERRIDGE, BRADLEY TOWNSEND · 2008 to 2012
$169k
NCI NIH HHS CA133050NCI NIH HHS R01 CA133050NIAAA NIH HHS AA014223NIAAA NIH HHS AA018111NIAAA NIH HHS K05 AA014223NIAAA NIH HHS U01 AA018111NIDA NIH HHS DA019606NIDA NIH HHS DA020783NIDA NIH HHS DA023200NIDA NIH HHS DA023973NIDA NIH HHS F31 DA025377NIDA NIH HHS F31DA025377NIDA NIH HHS K02 DA023200NIDA NIH HHS R01 DA019606NIDA NIH HHS R01 DA020783NIDA NIH HHS R01 DA023973NIMH NIH HHS MH082773NIMH NIH HHS R01 MH082773
6 · The paper itself

Abstract

CONTEXT Clinical experience and factor analytic studies suggest that some psychiatric disorders may be more closely related to one another, as indicated by the frequency of their co-occurrence, which may have etiologic and treatment implications. OBJECTIVE To construct a virtual space of common psychiatric disorders, spanned by factors reflecting major psychopathologic dimensions, and locate psychiatric disorders in that space, as well as to examine whether the location of disorders at baseline predicts the prevalence and incidence of disorders at 3-year follow-up. DESIGN, SETTING, AND PATIENTS A total of 34 653 individuals participated in waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions. MAIN OUTCOME MEASURES The distance between disorders at wave 1, calculated using the loadings of the factors spanning the space of disorders as coordinates. This distance was correlated with the adjusted odds ratios for age, sex, and race/ethnicity of the prevalence and incidence of Axis I disorders in wave 2, with the aim of determining whether smaller distances between disorders at wave 1 predicts higher disorder prevalence and incidence at wave 2. RESULTS A model with 3 correlated factors provided an excellent fit (Comparative Fit Index = 0.99, Tucker-Lewis Index = 0.98, root mean square error of approximation = 0.008) for the structure of common psychiatric disorders and was used to span the space of disorders. Distances ranged from 0.070 (between drug abuse and alcohol dependence) to 1.032 (between drug abuse and dysthymia). The correlation of distance between disorders in wave 1 with adjusted odds ratios of prevalence in wave 2 was -0.56. The correlation of distance in wave 1 with adjusted odds ratios of incidence in wave 2 was -0.57. CONCLUSIONS Mapping psychiatric disorders can be used to quantify the distances among disorders. Proximity in turn can be used to predict prospectively the incidence and prevalence of Axis I disorders.

Indexed as

Alcohol-Related DisordersMental DisordersModels, StatisticalAdultComorbidityDiagnostic and Statistical Manual of Mental DisordersFactor Analysis, StatisticalFemaleForecastingHealth SurveysHumansIncidenceMaleMiddle AgedPrevalencePsychiatric Status Rating Scales

Identifiers

PMID23266570
PMCPMC3777636

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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.