Understanding the patterns and distribution of opioid analgesic dependence symptoms using a latent empirical approach

Abstract

Prevalence of extramedical opioid analgesic use in the US is rising, yet little is known about the nature and extent of problems of dependence related to the use of these drugs. This study uses Latent Class Analysis to empirically define classes of past-year extramedical opioid analgesic users based on observed clustering of DSM-IV defined clinical dependence features; multinomial logistic regression is used to describe differences across these groups. The 2002-2003 public data-files of the National Survey on Drug Use and Health were used to identify 7810 extramedical opioid analgesic users in the past-year. The best-fitting four-class 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 (12-17 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.