Functional and structural social support, substance use and sexual orientation from a nationally representative sample of US adults

Kahle, Erin; Veliz, Philip; McCabe, Sean Esteban; Boyd, Carol J.

doi:10.1111/add.14819

Cited by 37 publications

(31 citation statements)

References 66 publications

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“…The current results provide compelling evidence for gender differences in the multifactorial factors underlying CUD in adults, which had not been previously investigated using a multi-domain approach. We are only aware of one previous study on gender differences in CUD in adults (81). This study specifically investigated gender differences in the role of social support and found a stronger protective relationship of social support in women as compared to men (81).…”

Section: Discussionmentioning

confidence: 99%

“…We are only aware of one previous study on gender differences in CUD in adults (81). This study specifically investigated gender differences in the role of social support and found a stronger protective relationship of social support in women as compared to men (81). Additionally, our results extend previous findings on cannabis use in adolescence that suggest a stronger influence of environmental factors in girls as compared to boys (82–85).…”

Section: Discussionmentioning

confidence: 99%

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Explainable Machine Learning Analysis Reveals Gender Differences in the Phenotypic and Neurobiological Markers of Cannabis Use Disorder

Niklason

Rawls

et al. 2021

Preprint

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Background: Cannabis Use Disorder (CUD) has been linked to environmental, personality, mental health, neurocognitive and neurobiological risk factors. While many studies have revealed gender differences in CUD, the relative importance of these complex factors by gender has not been described. Methods: We conducted a data-driven examination of gender differences in CUD in a community sample of young adults (Human Connectome Project [HCP]; n = 1204, 54% female). We employed state-of-the-art machine learning methods [gradient tree boosting, XGBoost] in combination with novel factor ranking tools [SHapley's Additive exPlanations (SHAP)] as an 'explainable machine learning approach' in the multimodal data collected by the HCP (phenotypic and brain data). Results: We were able to successfully classify both cannabis dependence and cannabis use levels. Previously identified environmental, personality, mental health, neurocognitive, and brain factors highly contributed to the classification. Predominantly-male risk factors included personality (high openness), mental health (high externalizing, high childhood conduct disorder, high fear somaticism), neurocognitive (impulsive delay discounting, slow working memory performance) and brain (low hippocampal volume) factors. Conversely, predominantly-female risk factors included environmental (low education level, low instrumental support) factors. Conclusions: Our data-driven analysis of gender differences in the multimodal risk factors underlying cannabis dependence and use levels demonstrate that environmental factors contribute more strongly to CUD in women, whereas individual factors such as personality, mental health and neurocognitive factors have a larger importance in men. This warrants further investigations, and suggests the importance of understanding how these differences relate to the development of effective treatment approaches.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Explainable Machine Learning Analysis Reveals Gender Differences in the Phenotypic and Neurobiological Markers of Cannabis Use Disorder

Niklason

Rawls

et al. 2021

Preprint

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“…Population-based studies that have examined whether the protective effect of marriage on alcohol use differs by sexual identity have yielded mixed results. For example, one recent U.S. study found marriage was associated with lower odds of alcohol use disorder and drug use disorder among heterosexual women and men, but not among SMW or SMM (Kahle et al, 2020). By contrast, other U.S. studies have found that both same-sex and different-sex married couples reported lower alcohol use than their cohabiting non-married counterparts (Reczek et al, 2014).…”

Section: Ijadr International Journal Of Alcohol and Drug Researchmentioning

confidence: 95%

“…The Official Journal of the Kettil Bruun Society for Social and Epidemiological Research on Alcohol ------IJADR 8(2) ------hazardous drinking or to use marijuana than those who are not, including those who are cohabiting, single, or divorced/separated (Blair & Menasco, 2016;Jang, Patrick, & Schuler, 2018;Kahle, Veliz, McCabe, & Boyd, 2020;Li, Wilsnack, Wilsnack, & Kristjanson, 2010;Liang & Chikritzhs, 2012;Reczek, Liu, & Spiker, 2014). Protective effects of marriage are linked to a variety of factors, including marital partners' influences on each other's health habits, greater social support, and lower general stress (Umberson & Karas Montez, 2010).…”

Section: Ijadr International Journal Of Alcohol and Drug Researchmentioning

confidence: 99%

Investigating Differential Protective Effects of Marriage on Substance Use by Sexual Identity Status

Trocki

Mericle

Drabble

et al. 2020

IJADR

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Background: Research suggests that marriage is protective against substance use. However, few studies have examined whether this protective effect differs for sexual minorities, a population at increased risk for substance use. Using data from four waves of the cross-sectional U.S. National Alcohol Survey (NAS; 2000, 2005, 2010, and 2015), we investigated whether the protective effects of marriage varied by sexual identity. Methods: Sex-stratified logistic regression models were used to examine independent and interactive effects of current marital status (being married vs. not) and sexual minority status (lesbian/gay/bisexual vs. heterosexual) on high-intensity drinking, alcohol use disorder, and marijuana use in the past year. Results: Among both women and men, sexual minority status was generally associated with higher odds of these outcomes and marriage was consistently associated with lower odds. Differential effects of marriage by sexual identity with respect to marijuana use were found only among men; marriage was significantly associated with decreased odds of marijuana use among heterosexual men but increased odds among sexual minority men. Conclusions: Marriage may be less consistently protective against hazardous drinking and marijuana use among sexual minorities than heterosexuals. Findings underscore the importance of both quantitative and qualitative studies designed to better understand disparities in substance use across both sexual identity and relationship statuses.

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“…Family support, especially for adolescents in long term residential programs has been proven to be necessary for successful recovery from addiction [66]. Studies have also showed that having a strong social and family resource improves the chances of addiction recovery [67][68][69][70]. Self-development efforts encompassing activities that lead to mental and physical well-being, such as regular exercise, meditation, and yoga have been observed to help heal the body and mind [71,72].…”

Section: The Sem Model For Recoverymentioning

confidence: 99%

Analysis of associations between emotions and activities of drug users and their addiction recovery tendencies from social media posts using structural equation modeling

Jha

Singh

2020

BMC Bioinformatics

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Background Addiction to drugs and alcohol constitutes one of the significant factors underlying the decline in life expectancy in the US. Several context-specific reasons influence drug use and recovery. In particular emotional distress, physical pain, relationships, and self-development efforts are known to be some of the factors associated with addiction recovery. Unfortunately, many of these factors are not directly observable and quantifying, and assessing their impact can be difficult. Based on social media posts of users engaged in substance use and recovery on the forum Reddit, we employed two psycholinguistic tools, Linguistic Inquiry and Word Count and Empath and activities of substance users on various Reddit sub-forums to analyze behavior underlining addiction recovery and relapse. We then employed a statistical analysis technique called structural equation modeling to assess the effects of these latent factors on recovery and relapse. Results We found that both emotional distress and physical pain significantly influence addiction recovery behavior. Self-development activities and social relationships of the substance users were also found to enable recovery. Furthermore, within the context of self-development activities, those that were related to influencing the mental and physical well-being of substance users were found to be positively associated with addiction recovery. We also determined that lack of social activities and physical exercise can enable a relapse. Moreover, geography, especially life in rural areas, appears to have a greater correlation with addiction relapse. Conclusions The paper describes how observable variables can be extracted from social media and then be used to model important latent constructs that impact addiction recovery and relapse. We also report factors that impact self-induced addiction recovery and relapse. To the best of our knowledge, this paper represents the first use of structural equation modeling of social media data with the goal of analyzing factors influencing addiction recovery.

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Functional and structural social support, substance use and sexual orientation from a nationally representative sample of US adults

Cited by 37 publications

References 66 publications

Explainable Machine Learning Analysis Reveals Gender Differences in the Phenotypic and Neurobiological Markers of Cannabis Use Disorder

Explainable Machine Learning Analysis Reveals Gender Differences in the Phenotypic and Neurobiological Markers of Cannabis Use Disorder

Investigating Differential Protective Effects of Marriage on Substance Use by Sexual Identity Status

Analysis of associations between emotions and activities of drug users and their addiction recovery tendencies from social media posts using structural equation modeling

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