Little is known about the age of onset of sexual and drug risk and their association with complex patterns of recent drug use among male sex workers (MSW) in a developing country, such as Vietnam. The aim of this study was to determine whether latent class analysis (LCA) would aid in the detection of current individual and polydrug use combinations to predict how different trajectories of sexual and drug initiation contribute to different patterns of current illicit drug use. Data were collected from a cross-sectional survey administered to young MSWs between 2010 and 2011 in Vietnam (N = 710). LCA clustered participants into recent drug use groups, incorporating both the specific types and overall count of different drugs used. Men reported drug use within a 1 month period from an 11-item drug use list. LCA identified three distinct drug use classes: (1) alcohol use, (2) alcohol and tobacco use, and (3) high polydrug use. The current drug use classes are associated with sex worker status, housing stability, income level, educational attainment, marital status, sexual identity, and sexual preferences. High levels of drug use are strongly associated with being a recent sex worker, not having recent stable housing, higher than median income, more than a high school education, less likely to be currently in school and more likely to have non-homosexual preferences and heterosexual partners. An event history analysis approach (time-event displays) examined the timing of the age of onset of drug and sexual risks. Early ages of drug and sexual initiation are seen for all three classes. High current drug users show earlier onset of these risks, which are significantly delayed for moderate and low current drug users. LCA incorporating an overall count of different drugs detected three distinct current drug use classes. The data illustrates that the complexity of drug factors that must be accounted for, both in advancing our epidemiological understanding of the complexity of drug use and the use of drug and sexual risk initiation data to predict current drug use subtypes among high-risk populations.
