Evidence map›Paper›PMID 41265164›Full record

ArticleAddictive behaviors2026

Patterns of polysubstance use disorder among human trafficking survivors: A latent class analysis.

Nathaniel A Dell, Jason T Carbone, Theresa Anasti, Lauren Grimes, Kathleen M Preble, Lindsay B Gezinski, Hilary Thibodeau

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Article in Addictive behaviors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

7 authors.

Nathaniel A DellWashington University School of Medicine in St. Louis, St. Louis, MO, USA. Electronic address: delln@wustl.edu.
Jason T CarboneSUNY Upstate Medical University, Syracuse, NY, USA.
Theresa AnastiWashington University in St. Louis, St. Louis, MO, USA.
Lauren GrimesWashington University School of Medicine in St. Louis, St. Louis, MO, USA.
Kathleen M PrebleUniversity of Texas - Arlington, Arlington, TX, USA.
Lindsay B GezinskiUniversity of Utah, Salt Lake City, UT, USA.
Hilary ThibodeauWashington University in St. Louis, St. Louis, MO, USA.

Funding

Transdisciplinary Training in Addictions ResearchT32DA015035 · NIDA · WASHINGTON UNIVERSITY · PI Leopoldo J Cabassa, Patricia A Cavazos-Rehg · 2002 to 2026
$5.3M
NIDA NIH HHS T32 DA015035
6 · The paper itself

Abstract

backgroundSubstance use is commonly documented among human trafficking (HT) survivors in emergency department (ED) settings. Multiple substance use disorders (poly-SUD) are associated with poor health and psychosocial outcomes. This study identified latent classes and demographic covariates of HT-related ED visits by the types of SUDs documented in survivors' medical records.

methodsWe used cross-sectional data from the United States 2019-2021 Nationwide Emergency Department Sample, including visits of patients aged 12-64 years with an ICD-10-CM code documenting either sex or labor exploitation (N = 4,212). A bias-adjusted three-step latent class analysis was conducted, with SUDs documented via ICD-10-CM codes included as indicators in the model.

resultsThe optimal three-class solution had superior fit based on pre-selected indicators, low classification error, and acceptable entropy. The largest class comprised 76.01 % of the sample and showed a lower predicted probability of the SUD classes considered. The second largest class (17.27 %) was characterized by high predicted probability of stimulant use disorder with moderately high predicted probability of opioid use disorder. The smallest class (6.72 %) was characterized by high predicted probability of each SUD considered. Class membership was differentially associated with disposition from the ED, nicotine use disorder, and income.

conclusionsAlthough most ED visits were classified as having relatively low probability of SUD, nearly one quarter of the sample had high risk of either stimulant use disorder or high poly-SUD. Poly-SUD in HT survivors is associated with increased risk of hospitalization. Findings provide direction for tailoring intervention programs to support SUD recovery among HT survivors.

Indexed as

Human TraffickingSubstance-Related DisordersSurvivorsAdolescentAdultChildCross-Sectional StudiesEmergency Service, HospitalFemaleHumansLatent Class AnalysisMaleMiddle AgedOpioid-Related DisordersUnited StatesYoung AdultEmergency departmentHuman traffickingLatent class analysisPoly-substance use disorder

Identifiers

PMID41265164
PMCPMC12720257

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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.