Asian American and Pacific Islander substance use treatment admission trends

Sahker, Ethan; Yeung, Chi W.; Garrison, Yunkyoung Loh; Park, Soeun; Arndt, Stephan

doi:10.1016/j.drugalcdep.2016.11.022

Cited by 18 publications

(18 citation statements)

References 34 publications

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“…[125][126][127] These disparities may have accumulated over recent years, leading to increased negative coping behaviors, such as high-risk drinking and the development of AUD. [125][126][127][128][129][130][131] Reasons for the widening of the racial/ethnic gap in alcohol use, high-risk drinking, and AUD are complex, historically rooted in racial/ethnic discrimination and persistent socioeconomic disadvantage both at the individual and community levels. [132][133][134][135][136][137] Future research is warranted to understand the interplay of socioeconomic, psychosocial, cultural, and biological factors that have contributed to the widening of the racial/ethnic gap in alcohol use, high-risk drinking, and AUD in recent years, with particular attention to the development of subracial/subethnic prevention and intervention strategies.…”

Section: Discussionmentioning

confidence: 99%

“…Adversities that disproportionately affect racial/ethnic minorities include family income and educational disparities, unemployment, residential segregation, discrimination, decreased access to health care, and increased stigma associated with drinking . These disparities may have accumulated over recent years, leading to increased negative coping behaviors, such as high-risk drinking and the development of AUD . Reasons for the widening of the racial/ethnic gap in alcohol use, high-risk drinking, and AUD are complex, historically rooted in racial/ethnic discrimination and persistent socioeconomic disadvantage both at the individual and community levels .…”

Section: Discussionmentioning

confidence: 99%

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Prevalence of 12-Month Alcohol Use, High-Risk Drinking, and DSM-IV Alcohol Use Disorder in the United States, 2001-2002 to 2012-2013

et al. 2017

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Prevalence of 12-Month Alcohol Use, High-Risk Drinking, and DSM-IV Alcohol Use Disorder in the United States, 2001-2002 to 2012-2013

et al. 2017

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“…Consequently, only records that indicate the individual had no prior SUD treatment were included in the analyses. Part of our previous work focuses on racial and ethnic minorities [11, 12, 23, 24]. It is known that racial and ethnic minorities vary in their treatment access and success levels [25–27].…”

Section: Methodsmentioning

confidence: 99%

Use of a machine learning framework to predict substance use disorder treatment success

et al. 2017

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There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitates this decision by combining all identified prediction algorithms pertinent for a particular prediction problem. SL generates a final model that is at least as good as any of the other models considered for predicting the outcome. The overarching aim of this work is to introduce SL to analysts and practitioners. This work compares the performance of logistic regression, penalized regression, random forests, deep learning neural networks, and SL to predict successful substance use disorders (SUD) treatment. A nationwide database including 99,013 SUD treatment patients was used. All algorithms were evaluated using the area under the receiver operating characteristic curve (AUC) in a test sample that was not included in the training sample used to fit the prediction models. AUC for the models ranged between 0.793 and 0.820. SL was superior to all but one of the algorithms compared. An explanation of SL steps is provided. SL is the first step in targeted learning, an analytic framework that yields double robust effect estimation and inference with fewer assumptions than the usual parametric methods. Different aspects of SL depending on the context, its function within the targeted learning framework, and the benefits of this methodology in the addiction field are discussed.

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“…Y. Lee, Choi, et al, 2015; Rogers et al, 2020). Although specific opioid-related data for KA is limited, previous research examining a national data set of Asian Americans’ and Pacific Islanders’ (AAPIs) indicated that AAPIs demonstrated a greater increase in admission to substance use treatment facilities than non-AAPIs from 2000 to 2012 (Sahker et al, 2017). The substance reported as the most problematic was prescription opioids with 415% increase during the period (Sahker et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Although specific opioid-related data for KA is limited, previous research examining a national data set of Asian Americans’ and Pacific Islanders’ (AAPIs) indicated that AAPIs demonstrated a greater increase in admission to substance use treatment facilities than non-AAPIs from 2000 to 2012 (Sahker et al, 2017). The substance reported as the most problematic was prescription opioids with 415% increase during the period (Sahker et al, 2017). During the same period, minority races, including African Americans, KA and other AAPIs, Native Americans, and multiracial groups had the highest rates of prescription opioid overdoses (Abudu et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Opioid Literacy of Korean Americans in Rural Alabama: Addressing the Role of Social Determinants of Health

et al. 2021

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Introduction: Opioid crisis has disproportionately affected Alabamians with the highest opioid prescription rate, and it is subjected to affect Korean Americans (KA) negatively based on common predictors of opioid misuse that KA possess. Method: Cross-sectional data of KA in rural Alabama ( N = 230) were analyzed. Opioid literacy was assessed by the Brief Opioid Overdose Knowledge survey. Six social determinants of health factors were considered: financial status, educational attainment, English proficiency, household food insecurity, health literacy, and social contact. Results: Participants had limited opioid literacy ( M = 3.56, SD = 3.06). After adjusting for demographics and health covariates, higher levels of overall opioid literacy were associated with higher household income ( B = .48, p < .01), higher levels of health literacy ( B = .71, p < .01), and less frequent social contact ( B = −.40, p < .01). Significant social determinants of health predictors varied across subdomains of opioid literacy. Discussion: The findings suggest that culturally competent and community-level interventions are needed to increase opioid literacy in KA in rural Alabama.

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Asian American and Pacific Islander substance use treatment admission trends

Cited by 18 publications

References 34 publications

Prevalence of 12-Month Alcohol Use, High-Risk Drinking, and DSM-IV Alcohol Use Disorder in the United States, 2001-2002 to 2012-2013

Prevalence of 12-Month Alcohol Use, High-Risk Drinking, and DSM-IV Alcohol Use Disorder in the United States, 2001-2002 to 2012-2013

Use of a machine learning framework to predict substance use disorder treatment success

Opioid Literacy of Korean Americans in Rural Alabama: Addressing the Role of Social Determinants of Health

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