Evidence map›Paper›PMID 37619959›Full record

ArticleAlcohol (Fayetteville, N.Y.)2024

Healthcare utilization and readiness outcomes among soldiers with post-deployment at-risk drinking, by multimorbidity class.

Joshua C Gray, Mary Jo Larson, Natalie Moresco, Steven Dufour, Grant A Ritter, Patrick D DeLeon, Charles S Milliken, Noel Vest, Rachel Sayko Adams

Open access · greenAbstract read
In one paragraph

Article in Alcohol (Fayetteville, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 21% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Tobacco Product Use and Type by Military Veteran Status: Findings from the National Health Interview Survey, 2021-2023.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2025
    Article
  3. Article
  4. 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

9 authors at 6 institutions in 1 country.

Joshua C GrayUniformed Services University of the Health Sciences, Department of Medical and Clinical Psychology, Bethesda, MD 20814, United States. Electronic address: joshua.gray@usuhs.edu.
Mary Jo LarsonBrandeis University, Heller School for Social Policy and Management, Institute for Behavioral Health, Waltham, MA 02453, United States.
Natalie MorescoBrandeis University, Heller School for Social Policy and Management, Institute for Behavioral Health, Waltham, MA 02453, United States.
Steven DufourUniformed Services University of the Health Sciences, Department of Medical and Clinical Psychology, Bethesda, MD 20814, United States; Naval Medical Center Portsmouth, Portsmouth, VA 23708, United States.
Grant A RitterBrandeis University, Heller School for Social Policy and Management, Institute for Behavioral Health, Waltham, MA 02453, United States.
Patrick D DeLeonWalter Reed National Military Medical Center, Bethesda, MD 20814, United States.
Charles S MillikenArmy's Substance Use Disorder Clinical Care, Office of the Army Surgeon General, Defense Health Headquarters, 7700 Arlington Blvd., Falls Church, VA 22042, United States.
Noel VestBoston University School of Public Health, Department of Community Health Sciences, Boston, MA 02118, United States.
Rachel Sayko AdamsBrandeis University, Heller School for Social Policy and Management, Institute for Behavioral Health, Waltham, MA 02453, United States; Boston University School of Public Health, Department of Health Law, Policy & Management, Boston, MA 02118, United States; Veterans Health Administration, Rocky Mountain Mental Illness Research Education and Clinical Center, Aurora, CO 80045, United States.
Brandeis University · USBoston University · USNaval Medical Center Portsmouth · USUniformed Services University of the Health Sciences · USUnited States Army Medical Command · USWalter Reed National Military Medical Center · US

Funding

Trajectories of non-pharmacologic and opioid health services for pain management in association with military readiness and health status outcomes: SUPIC renewalR01AT008404 · NCCIH · BRANDEIS UNIVERSITY · PI ADAMS, RACHEL SAYKO, LARSON, MARY JO · 2014 to 2023
$7.2M
First Longitudinal Study of Missed Treatment Opportunities Using DOD and VA DataR01DA030150 · NIDA · BRANDEIS UNIVERSITY · PI LARSON, MARY JO · 2010 to 2013
$1.9M
Integrating signals of suicide risk from DoD and VHA data to improve upon suicide risk prevention strategies for combat VeteransR01MH120122 · NIMH · UNIVERSITY OF COLORADO DENVER · PI ADAMS, RACHEL SAYKO, BRENNER, LISA A · 2019 to 2022
$1.9M
Collegiate recovery programming in the U.S.: An implementation science and mixed methods studyK01DA053391 · NIDA · STANFORD UNIVERSITY · PI Noel Adam Vest · 2022 to 2026
$882k
NCCIH NIH HHS R01 AT008404NIDA NIH HHS K01 DA053391NIDA NIH HHS L30 DA056944NIDA NIH HHS R01 DA030150NIMH NIH HHS R01 MH120122
6 · The paper itself

Abstract

Although alcohol use disorder (AUD) regularly co-occurs with other conditions, there has not been investigation of specific multimorbidity classes among military members with at-risk alcohol use. We used latent class analysis (LCA) to cluster 138,929 soldiers with post-deployment at-risk drinking based on their co-occurring psychological and physical health conditions and indicators of alcohol severity. We examined the association of these multimorbidity classes with healthcare utilization and military readiness outcomes. Latent class analysis was conducted on 31 dichotomous indicators capturing alcohol use severity, mental health screens, psychological and physical health diagnoses, and tobacco use. Longitudinal survival analysis was used to examine the relative hazards of class membership regarding healthcare utilization (e.g., emergency department visit, inpatient stay) and readiness outcomes (e.g., early separation for misconduct). Latent class analysis identified five classes: Class 1 -Relatively Healthy (51.6 %); Class 2 - Pain/Tobacco (17.3 %); Class 3 - Heavy Drinking/Pain/Tobacco (13.1 %); Class 4 - Mental Health/Pain/Tobacco (12.7 %); and Class 5 - Heavy Drinking/Mental Health/Pain/Tobacco (5.4 %). Musculoskeletal pain and tobacco use were prevalent in all classes, though highest in Classes 2, 4, and 5. Classes 4 and 5 had the highest hazards of all outcomes. Class 5 generally exhibited slightly higher hazards of all outcomes than Class 4, demonstrating the exacerbation of risk among those with heavy drinking/AUD in combination with mental health conditions and other multimorbidity. This study provides new information about the most common multimorbidity presentations of at-risk drinkers in the military so that targeted, individualized care may be employed. Future research is needed to determine whether tailored prevention and treatment approaches for soldiers in different multimorbidity classes is associated with improved outcomes.

Indexed as

AlcoholismMilitary PersonnelAlcohol DrinkingHumansMultimorbidityPainPatient Acceptance of Health Carealcohol use disorderArmydrinkingmilitarymultimorbiditypost-deployment

Identifiers

PMID37619959
PMCPMC10881892
OpenAlexW4386052715

What OpenQuestion holds

Textmetadata
LicenceTDM
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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.