Factors Contributing to Substance Misuse Treatment Completion among Native Hawaiians, Other Pacific Islanders, and Asian Americans

Godinet, Meripa T.; McGlinn, Lindsey; Nelson, Dawna; Vakalahi, Halaevalu F. Ofahengaue

doi:10.1080/10826084.2019.1657896

Cited by 7 publications

(2 citation statements)

References 46 publications

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“… 10 Methamphetamine and opiate users, particularly among Asian Americans, exhibit lower completion rates than alcohol users. 24 The lack of data disaggregated by Asian ethnic groups has impeded understanding of the nature and prevalence of SUD within Asian-American populations. Based on prior literature revealing social and health disparities by Asian ethnic group (eg, Chinese, Korean, Taiwanese, Pakistani, Vietnamese, Cambodian), we assert that SUD treatment and completion rates differ by subgroup as well.…”

Section: Examining the Asian-american Experiencementioning

confidence: 99%

Urgent need for substance use disorder research among understudied populations: examining the Asian-American experience

Choi,

Lim,

Kwon

et al. 2023

Health Affairs Scholar

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Substance use disorder (SUD) among Asian Americans is understudied. Our review of NIH-funded projects reveals a striking underrepresentation of research focused on SUD in this population, possibly perpetuated by the pervasive societal myth that Asian Americans are a healthy community. Moreover, the limited availability and disaggregation of data on SUD among Asian Americans further hinder our understanding of prevalence rates, treatment utilization, and associated disparities – thereby limiting opportunities for prevention and intervention. In light of these findings, our review serves as a crucial call to action, emphasizing the urgent need for increased research efforts and resources to address the significant gaps in knowledge and inform effective interventions for addressing SUD among Asian Americans.

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Section: Examining the Asian-american Experiencementioning

confidence: 99%

Urgent need for substance use disorder research among understudied populations: examining the Asian-American experience

Choi,

Lim,

Kwon

et al. 2023

Health Affairs Scholar

View full text Add to dashboard Cite

show abstract

“…Godinet et. al [9] adopted a similar approach using TEDS-D data from 2016. Despite including other demographic characteristics as predictors, their focus was on the Asian American and native Hawaiian and Other Pacific Islander populations.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Identify Socio-Economic Factors Responsible for Patients Dropping out of Substance Abuse Treatment

Gautam¹,

Singh²

2020

AJPHR

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In recent years, the subject of substance abuse has drawn considerable attention from researchers and policymakers alike. Researchers have been utilizing the wealth of patient level data available from various agencies to develop prediction models for the relationship between socio-economic factors and substance abuse issues. According to the Substance Abuse and Mental Health Services Administration (SAMHSA), in 2017, 26% of patients admitted to treatment facilities drop out prematurely, which is significant when considering that roughly 1.5 million people are admitted to these treatment facilities every year, thereby revealing the need for an analysis to identify variables associated with such a large number of people not completing treatment. This study applies Multiple Logistic Regression (MLRM) as well as Random Forest Classification (RF) model to determine significant socio-economic factors responsible for patients prematurely dropping out of substance abuse treatment for opioid misuse. A MLRM has its limitations when the dataset has a large number of categorical variables; machine learning methods such as RF have proven more effective and accurate when dealing with such data. Patient level data from the Treatment Episode Dataset -Discharge (TEDS-D 2017) was analyzed and the models were compared using the Area Under the Curve (AUC) operating characteristic. The MLRM was found to have an AUC of .68 while the RF model had an AUC of .89, thereby demonstrating the advantage of machine learning methods. The factors deemed significant from the RF model can provide healthcare professionals as well as administrative officials with the necessary information to help address the issue of patients prematurely dropping out of opioid misuse treatment.

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Does substance use disorder treatment completion reduce the risk of treatment readmission in Chile?

Maturana

Cruz

Rocha-Jiménez

et al. 2023

Drug and Alcohol Dependence

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Factors Contributing to Substance Misuse Treatment Completion among Native Hawaiians, Other Pacific Islanders, and Asian Americans

Cited by 7 publications

References 46 publications

Urgent need for substance use disorder research among understudied populations: examining the Asian-American experience

Urgent need for substance use disorder research among understudied populations: examining the Asian-American experience

A Machine Learning Approach to Identify Socio-Economic Factors Responsible for Patients Dropping out of Substance Abuse Treatment

Does substance use disorder treatment completion reduce the risk of treatment readmission in Chile?

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