Evidence map›Paper›PMID 35502437›Full record

ArticleInternational journal of mental health and addiction2022

Logistic Regression Model of Demographic Predictors and Confounders of Binge Alcohol Use Among Adults with Major Depression.

Areen Omary

Open access · bronzeAbstract read
In one paragraph

Article in International journal of mental health and addiction, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 52% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

1 author at 1 institution in 1 country.

Areen OmaryDepartment of Psychology, Sociology and Social Work, Texas A&M University System, West Texas A&M University, 720 S. Tyler, Amarillo, TX USA.ORCID 0000-0002-3985-2011
Texas A&M University System · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying sociodemographic populations with a major depressive episode (MDE) who are at increased risk for binge alcohol use (BAU) is critical for developing focused prevention programs to meet the needs of each population. Thus, the goal of the current exploratory retrospective study is to examine if sex, race, age, education, marital status, and income can significantly predict the risk for BAU among adults with MDE and if the association between MDE and BAU changes after adjusting for demographic variables in question while holding sex, race, and age as constant variables. Data from the Substance Abuse and Mental Health Services Administration's 2018 National Survey for Drug Use and Health were extracted and analyzed to achieve the study goal. The unweighted sample included 6,999 adults representing a weighted population size of 33,900,452.122 in the USA. Results revealed that age and marital status significantly predicted BAU in the past month among adults with MDE. Adults under the age of 50, with a college degree, never married, divorced/separated, and with a high-middle income level or higher were at higher risk for BAU. The study's clinical implications are that understanding demographics of individuals with MDE at increased risk for BAU can inform improved tailored assessment and treatment of alcohol abuse and MDE among at-risk populations. Future research should consider examining additional potential risk factors for BAU among adults with MDE.

Indexed as

Adjustment for confoundersBinge alcohol useComplex survey designDemographic predictorsMajor depressive episode

Identifiers

PMID35502437
PMCPMC9047467
OpenAlexW4225006229

What OpenQuestion holds

Textmetadata
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.