Non-engagement in substance use treatment among women with an unmet need for treatment: A latent class analysis on multidimensional barriers

Apsley, Hannah B.; Vest, Noel; Knapp, Kyler S.; Santos-Lozada, Alexis R.; Gray, Joy; Hard, Gregory; Jones, Abenaa A.

doi:10.1016/j.drugalcdep.2022.109715

Cited by 12 publications

(2 citation statements)

References 37 publications

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“…While social networks influence individuals with SUDs, the mechanisms for such influence are under-explored among women who use drugs, who are at high risk of adverse outcomes due to gender roles and biology (Apsley et al, 2023; Jones et al, 2019; National Institute on Drug Abuse [NIDA], 2020). Women who use drugs are a unique population and often have different needs than their male counterparts (NIDA, 2020).…”

Section: The Influence Of Family and Social Network On Substance Use ...mentioning

confidence: 99%

The impact of family systems and social networks on substance use initiation and recovery among women with substance use disorders.

Jones,

Strong-Jones,

Bishop

et al. 2024

Psychology of Addictive Behaviors

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Objective: While social networks influence individuals with substance use disorders (SUDs), the mechanisms for such influence are under-explored among women who use drugs. This study triangulates the perspectives of criminal justice professionals, SUD treatment professionals, and women with past and current experiences with substance use to explore these dynamics. Method: We conducted semistructured interviews (N = 42) in 2022 with women with current or past opioid use disorder (n = 20), SUD treatment professionals (n = 12), and criminal justice professionals (n = 10) who work with women with opioid use disorder. Interviews centered around participants' backgrounds, perceived barriers and facilitators to medications for opioid use disorder (MOUD) treatment, and gender-specific issues in MOUD treatment. All interviews were audio-recorded, transcribed, and deidentified. We used a four-step qualitative data analysis process to code transcripts. Results: Across these participants' accounts, we identified mechanisms by which women's social networks influenced their opioid use trajectories: intergenerational substance use, family support and strain, intimate partner influence, and peer support and pressure. Overall, the emergent themes in the present study reflect the embedded nature of support within social systems. Women who had access to and engaged with various forms of social support tended to be those who use/used MOUD and self-identified as in recovery. Conclusions: Combining MOUD treatment with psychosocial interventions allows women to heal from trauma, learn effective coping skills, and receive valuable resources to support recovery. Interventions focusing on family resilience and peer recovery support can disrupt the cycle of addiction and promote MOUD treatment success. Public Health Significance StatementBarriers to SUD treatment and recovery exist at many levels. Interventions focusing on family resilience, fostering a healthy intimate partner relationship among couples in which one or both partners use drugs, and peer recovery support are crucially needed.

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Section: The Influence Of Family and Social Network On Substance Use ...mentioning

confidence: 99%

The impact of family systems and social networks on substance use initiation and recovery among women with substance use disorders.

Jones,

Strong-Jones,

Bishop

et al. 2024

Psychology of Addictive Behaviors

View full text Add to dashboard Cite

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“…The research results regarding the performance of the super learning model have the potential to bring about significant changes, especially in the early detection of women who are at risk of experiencing both mental health and substance use issues simultaneously. Early diagnosis enables proactive intervention, customised support, and trauma-informed treatments [48], which address the fear of social disapproval and encourage more compassionate approaches to women's mental well-being. The research highlights that the model's accuracy in allocating resources effectively could help overcome the obstacles related to stigma, leading to improved health outcomes for both impacted women and their families and communities.…”

Section: Future Prospectsmentioning

confidence: 99%

Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach

Acharya,

Kar,

Ally

et al. 2024

Applied Sciences

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Significant clinical overlap exists between mental health and substance use disorders, especially among women. The purpose of this research is to leverage an AutoML (Automated Machine Learning) interface to predict and distinguish co-occurring mental health (MH) and substance use disorders (SUD) among women. By employing various modeling algorithms for binary classification, including Random Forest, Gradient Boosted Trees, XGBoost, Extra Trees, SGD, Deep Neural Network, Single-Layer Perceptron, K Nearest Neighbors (grid), and a super learning model (constructed by combining the predictions of a Random Forest model and an XGBoost model), the research aims to provide healthcare practitioners with a powerful tool for earlier identification, intervention, and personalised support for women at risk. The present research presents a machine learning (ML) methodology for more accurately predicting the co-occurrence of mental health (MH) and substance use disorders (SUD) in women, utilising the Treatment Episode Data Set Admissions (TEDS-A) from the year 2020 (n = 497,175). A super learning model was constructed by combining the predictions of a Random Forest model and an XGBoost model. The model demonstrated promising predictive performance in predicting co-occurring MH and SUD in women with an AUC = 0.817, Accuracy = 0.751, Precision = 0.743, Recall = 0.926 and F1 Score = 0.825. The use of accurate prediction models can substantially facilitate the prompt identification and implementation of intervention strategies.

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Opioid and stimulant attributed treatment admissions and fatal overdoses: Using national surveillance data to examine the intersection of race, sex, and polysubstance use, 1992–2020

Jones,

Shearer,

Segel

et al. 2023

Drug and Alcohol Dependence

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Non-engagement in substance use treatment among women with an unmet need for treatment: A latent class analysis on multidimensional barriers

Cited by 12 publications

References 37 publications

The impact of family systems and social networks on substance use initiation and recovery among women with substance use disorders.

The impact of family systems and social networks on substance use initiation and recovery among women with substance use disorders.

Predicting Co-Occurring Mental Health and Substance Use Disorders in Women: An Automated Machine Learning Approach

Opioid and stimulant attributed treatment admissions and fatal overdoses: Using national surveillance data to examine the intersection of race, sex, and polysubstance use, 1992–2020

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