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This study uses Latent Class Analysis to empirically define classes of past\u2010year extramedical opioid analgesic users based on observed clustering of DSM\u2010IV defined clinical dependence features; multinomial logistic regression is used to describe differences across these groups. The 2002\u20132003 public data\u2010files of the National Survey on Drug Use and Health were used to identify 7810 extramedical opioid analgesic users in the past\u2010year. The best\u2010fitting four\u2010class model identified classes that differed quantitatively and qualitatively, with 2% of the users in Class 4 (most severe) and 84% in Class 1 (least severe). Classes 2 and 3 had parallel symptom profiles, but those in Class 3 reported additional problems. Adolescents (12\u201317 year olds) were at higher odds of being in Class 3 versus older age groups; females were two times as likely to be in Classes 2 and 4, and those with mental health problems were at higher odds of belonging to the more severe classes. Differences by type of past year opioid users were also detected. This study sheds light on the classification and distribution of extramedical opioid analgesic dependence symptoms in the US general population, identifying subgroups that warrant immediate attention. 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