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In Step 1, we trained a classification model that gives the probability of waiting (1\u2009day 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data collection<\/jats:title><jats:p>Client data were collected by states during personal interviews at admission and discharge.<\/jats:p><\/jats:sec><jats:sec><jats:title>Principal findings<\/jats:title><jats:p>Random 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\u2010assisted opioid treatment, and primary drug use frequency augmented disparities beyond race. The identified subgroups had significantly larger racial disparities.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The 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.<\/jats:p><\/jats:sec>","DOI":"10.1111\/1475-6773.13896","type":"journal-article","created":{"date-parts":[[2021,10,17]],"date-time":"2021-10-17T11:18:30Z","timestamp":1634469510000},"page":"411-421","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Using machine learning to advance disparities research: Subgroup analyses of access to opioid treatment"],"prefix":"10.1111","volume":"57","author":[{"given":"Yinfei","family":"Kong","sequence":"first","affiliation":[{"name":"College of Business and Economics California State University Fullerton  Fullerton California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Zhou","sequence":"additional","affiliation":[{"name":"International Institute of Finance, School of Management University of Science and Technology of China  Hefei Anhui China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zemin","family":"Zheng","sequence":"additional","affiliation":[{"name":"International Institute of Finance, School of Management University of Science and Technology of China  Hefei Anhui China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hortensia","family":"Amaro","sequence":"additional","affiliation":[{"name":"Herbert Wertheim College of Medicine and Robert Stempel College of Public Health and Social Work Florida International University  Miami Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3086-5710","authenticated-orcid":false,"given":"Erick G.","family":"Guerrero","sequence":"additional","affiliation":[{"name":"I\u2010Lead Institute Research to End Health Disparities Corp  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,10,24]]},"reference":[{"key":"e_1_2_7_2_1","unstructured":"Centers for Disease Control and Prevention National Center for Health Statistics.National Vital Statistics System: Mortality Statistics. 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