ArticleHealth services research2022
Using machine learning to advance disparities research: Subgroup analyses of access to opioid treatment.
Article in Health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed, 20 citations in OpenAlex.
- Intersectionality in AI and Machine Learning for Health Care: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- Examining Ethnic and Racial Variations in Opioid-related Hospitalizations Using Western U.S. Electronic Health Records.Journal of racial and ethnic health disparities · 2026Review
- Subgroup identification of disparities in buprenorphine discontinuation in opioid-use disorder: A Virtual Twins machine learning approach using nationwide United States claims data, 2006-2022.PLOS mental health · 2026Article
- Are Treatment Services Ready for the Use of Big Data Analytics and AI in Managing Opioid Use Disorder?Journal of medical Internet research · 2025Article
- Identifying the Heterogeneity in the Association between Workforce Diversity and Retention in Opioid Treatment among Black clients.Research square · 2024Article
- Factors predicting access to medications for opioid use disorder for housed and unhoused patients: A machine learning approach.PloS one · 2024Article
- Determinants of outpatient substance use disorder treatment length-of-stay and completion: the case of a treatment program in the southeast U.S.Scientific reports · 2023Article
- Development and validation of explainable machine-learning models for carotid atherosclerosis early screening.Journal of translational medicine · 2023Article
- Racial/ethnic residential segregation and the availability of opioid and substance use treatment facilities in US counties, 2009-2019.SSM - population health · 2022Article
- Predicting Patient Wait Times by Using Highly Deidentified Data in Mental Health Care: Enhanced Machine Learning Approach.JMIR mental health · 2022Article
- Using machine learning to advance disparities research: Subgroup analyses of access to opioid treatment.Health services research · 2022Article
- Use of machine learning to examine disparities in completion of substance use disorder treatment.PloS one · 2022Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
objectiveTo operationalize an intersectionality framework using a novel statistical approach and with these efforts, improve the estimation of disparities in access (i.e., wait time to treatment entry) to opioid use disorder (OUD) treatment beyond race. DATA SOURCE: Sample of 941,286 treatment episodes collected in 2015-2017 in the United States from the Treatment Episodes Data Survey (TEDS-A) and a subset from California (n = 188,637) and Maryland (n = 184,276), states with the largest sample of episodes. STUDY
designThis retrospective subgroup analysis used a two-step approach called virtual twins. In Step 1, we trained a classification model that gives the probability of waiting (1 day or more). In Step 2, we identified subgroups with a higher probability of differences due to race. We tested three classification models for Step 1 and identified the model with the best estimation. DATA COLLECTION: Client data were collected by states during personal interviews at admission and discharge. PRINCIPAL
findingsRandom forest was the most accurate model for the first step of subgroup analysis. We found large variation across states in racial disparities. Stratified analysis of two states with the largest samples showed critical factors that augmented disparities beyond race. In California, factors such as service setting, referral source, and homelessness defined the subgroup most vulnerable to racial disparities. In Maryland, service setting, prior episodes, receipt of medication-assisted opioid treatment, and primary drug use frequency augmented disparities beyond race. The identified subgroups had significantly larger racial disparities.
conclusionsThe methodology used in this study enabled a nuanced understanding of the complexities in disparities research. We found state and service factors that intersected with race and augmented disparities in wait time. Findings can help decision makers target modifiable factors that make subgroups vulnerable to waiting longer to enter treatment.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.