Evidence map›Paper›PMID 24565311›Full record

ReviewJournal of studies on alcohol and drugs. Supplement2014

Seventy-five years of comorbidity research.

Matt G Kushner

Abstract readReview
In one paragraph

Review in Journal of studies on alcohol and drugs. Supplement, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 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

1 author.

Matt G KushnerDepartment of Psychiatry, University of Minnesota, Minneapolis, Minnesota.

Funding

Applying Latent Variable Modeling to Cormorbidity Treatment ResearchK02AA017886 · NIAAA · UNIVERSITY OF MINNESOTA · PI KUSHNER, MATT G · 2009 to 2013
$573k
NIAAA NIH HHS K02-AA0017886NIAAA NIH HHS K02 AA017886
6 · The paper itself

Abstract

objectiveAs part of the 75th anniversary edition of the Journal of Studies on Alcohol and Drugs, this article reviews research on the relationship between mental disorders and substance use disorders ("comorbidity") from 1940--the journal's inception--to the present.

methodFirst, a survey of the titles and abstracts of all articles published in the journal was used to identify those articles pertaining to comorbidity. Seminal and representative works from this set of articles and a limited selection of articles from other journals were included in the review.

resultsThe early psychosocial research emphasized psychoanalytic formulations of alcohol use as a defensive symptom, which informed the early experimental research on the tension-reducing properties of alcohol. The "cognitive revolution," occurring in the 1970s, enabled an expansion of the tension-reduction theory to include a central role for mental processes (e.g., alcohol expectancies) in promoting drinking to cope with negative affectivity. The early clinical research characterized mental conditions commonly co-occurring with alcohol disorders and considered their etiological relationship to alcohol disorders. The "neo-Kraepelinian revolution" in psychiatry, which also occurred in the 1970s, infused the clinical comorbidity research with a more rigorous diagnostic technology and a range of biomedical research methodologies to study the mechanistic linkages of co-occurring disorders.

conclusionsAlthough a substantial quantity of scientific information on comorbidity has accumulated over the past 75 years, a standard model(s) of comorbidity has yet to congeal. Barriers and opportunities related to achieving this important goal are discussed.

Indexed as

AlcoholismBiomedical ResearchComorbidityHumansMental DisordersSubstance-Related Disorders

Identifiers

PMID24565311
PMCPMC4453502

What OpenQuestion holds

Textmetadata
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Registered trials

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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.