Cargando…

Use of machine learning to examine disparities in completion of substance use disorder treatment

The objective of this work is to examine disparities in the completion of substance use disorder treatment in the U.S. Our data is from the Treatment Episode Dataset Discharge (TEDS-D) datasets from the U.S. Substance Abuse and Mental Health Services Administration (SAMHSA) for 2017–2019. We apply a...

Descripción completa

Detalles Bibliográficos
Autores principales: Baird, Aaron, Cheng, Yichen, Xia, Yusen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9506659/
https://www.ncbi.nlm.nih.gov/pubmed/36149868
http://dx.doi.org/10.1371/journal.pone.0275054
_version_ 1784796777844572160
author Baird, Aaron
Cheng, Yichen
Xia, Yusen
author_facet Baird, Aaron
Cheng, Yichen
Xia, Yusen
author_sort Baird, Aaron
collection PubMed
description The objective of this work is to examine disparities in the completion of substance use disorder treatment in the U.S. Our data is from the Treatment Episode Dataset Discharge (TEDS-D) datasets from the U.S. Substance Abuse and Mental Health Services Administration (SAMHSA) for 2017–2019. We apply a two-stage virtual twins model (random forest + decision tree) where, in the first stage (random forest), we determine differences in treatment completion probability associated with race/ethnicity, income source, no co-occurrence of mental health disorders, gender (biological), no health insurance, veteran status, age, and primary substance (alcohol or opioid). In the second stage (decision tree), we identify subgroups associated with probability differences, where such subgroups are more or less likely to complete treatment. We find the subgroups most likely to complete substance use disorder treatment, when the subgroup represents more than 1% of the sample, are those with no mental health condition co-occurrence (4.8% more likely when discharged from an ambulatory outpatient treatment program, representing 62% of the sample; and 10% more likely for one of the more specifically defined subgroups representing 10% of the sample), an income source of job-related wages/salary (4.3% more likely when not having used in the 30 days primary to discharge and when primary substance is not alcohol only, representing 28% of the sample), and white non-Hispanics (2.7% more likely when discharged from residential long-term treatment, representing 9% of the sample). Important implications are that: 1) those without a co-occurring mental health condition are the most likely to complete treatment, 2) those with job related wages or income are more likely to complete treatment, and 3) racial/ethnicity disparities persist in favor of white non-Hispanic individuals seeking to complete treatment. Thus, additional resources may be needed to combat such disparities.
format Online
Article
Text
id pubmed-9506659
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-95066592022-09-24 Use of machine learning to examine disparities in completion of substance use disorder treatment Baird, Aaron Cheng, Yichen Xia, Yusen PLoS One Research Article The objective of this work is to examine disparities in the completion of substance use disorder treatment in the U.S. Our data is from the Treatment Episode Dataset Discharge (TEDS-D) datasets from the U.S. Substance Abuse and Mental Health Services Administration (SAMHSA) for 2017–2019. We apply a two-stage virtual twins model (random forest + decision tree) where, in the first stage (random forest), we determine differences in treatment completion probability associated with race/ethnicity, income source, no co-occurrence of mental health disorders, gender (biological), no health insurance, veteran status, age, and primary substance (alcohol or opioid). In the second stage (decision tree), we identify subgroups associated with probability differences, where such subgroups are more or less likely to complete treatment. We find the subgroups most likely to complete substance use disorder treatment, when the subgroup represents more than 1% of the sample, are those with no mental health condition co-occurrence (4.8% more likely when discharged from an ambulatory outpatient treatment program, representing 62% of the sample; and 10% more likely for one of the more specifically defined subgroups representing 10% of the sample), an income source of job-related wages/salary (4.3% more likely when not having used in the 30 days primary to discharge and when primary substance is not alcohol only, representing 28% of the sample), and white non-Hispanics (2.7% more likely when discharged from residential long-term treatment, representing 9% of the sample). Important implications are that: 1) those without a co-occurring mental health condition are the most likely to complete treatment, 2) those with job related wages or income are more likely to complete treatment, and 3) racial/ethnicity disparities persist in favor of white non-Hispanic individuals seeking to complete treatment. Thus, additional resources may be needed to combat such disparities. Public Library of Science 2022-09-23 /pmc/articles/PMC9506659/ /pubmed/36149868 http://dx.doi.org/10.1371/journal.pone.0275054 Text en © 2022 Baird et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Baird, Aaron
Cheng, Yichen
Xia, Yusen
Use of machine learning to examine disparities in completion of substance use disorder treatment
title Use of machine learning to examine disparities in completion of substance use disorder treatment
title_full Use of machine learning to examine disparities in completion of substance use disorder treatment
title_fullStr Use of machine learning to examine disparities in completion of substance use disorder treatment
title_full_unstemmed Use of machine learning to examine disparities in completion of substance use disorder treatment
title_short Use of machine learning to examine disparities in completion of substance use disorder treatment
title_sort use of machine learning to examine disparities in completion of substance use disorder treatment
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9506659/
https://www.ncbi.nlm.nih.gov/pubmed/36149868
http://dx.doi.org/10.1371/journal.pone.0275054
work_keys_str_mv AT bairdaaron useofmachinelearningtoexaminedisparitiesincompletionofsubstanceusedisordertreatment
AT chengyichen useofmachinelearningtoexaminedisparitiesincompletionofsubstanceusedisordertreatment
AT xiayusen useofmachinelearningtoexaminedisparitiesincompletionofsubstanceusedisordertreatment