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Background: Polysubstance use among people who misuse opioids (PWMO) is highly prevalent, but understudied. We defined, estimated, and analyzed national polysubstance use patterns among PWMO using National Household Survey on Drug Use and Health data (2017-2019).Methods: We obtained estimates of past-month patterns of polydrug use using cluster analysis and latent class/profile analysis. We considered misuse of prescription opioids and use of heroin, cocaine (including crack), marijuana, alcohol, and "other" substances.Results: We identified a five-cluster solution for binary indicators of past-month use and a six-cluster solution for frequency of use. The largest binary cluster (37%) included misuse of prescription opioids and use of alcohol. The second-largest cluster (15%) included misuse of prescription opioids, alcohol, marijuana, and "other" substances. Among those who used heroin, 36% used methamphetamine. In terms of frequency of use, the largest cluster among people who misuse opioid who used multiple substances (almost 40%) misused prescription pain relievers, alcohol, and marijuana infrequently. The second-largest cluster (23%) used marijuana almost daily and misused prescription pain relievers an average of 6.6 days. PWMO in a cluster of almost daily heroin use indicated use of methamphetamine, marijuana, and prescription opioids. Those who used methamphetamine, were using it more than 15 days a month.Conclusions: We have developed reference measures of polydrug patterns among US household population and estimated their demographic characteristics. We identified clusters of high-risk polydrug use. These findings have implications for the development of prevention and treatment solutions in the United States. [ABSTRACT FROM AUTHOR]

Abstract: Aims: We present methodology to identify statistically distinct patterns of daily alcohol use and classify them into categories that could be further used in monitoring of transitions between patterns such as transitions from regular to problem use. Data: The study analyzed individual patterns of adult alcohol consumption from two datasets containing short (<6 month) and long (up to 2years) daily records of drinking. These data were collected over the period between 1999 and 2003. Results: By using a non-parametric (Kolmogorov–Smirnov) test we have identified distinct drinking patterns and classified them into 8 types according to their means, percentages of non-drinking days and variances of consumed amount during drinking days. For each studied individual we calculated a transition chart that characterizes transitions between the types. Conclusions: Individual daily consumption patterns can be identified, and classified into distinct patterns. Changes between the patterns could be related to life events or environmental trends, and thus provide insights into pathways towards either heavier use or recovery. [Copyright &y& Elsevier]

Abstract: Multilevel modeling techniques have become a useful tool that enables substance abuse researchers to more accurately identify the contribution of multiple levels of influence on drug-related attitudes and behaviors. However, it is difficult to determine the relative importance of the different hierarchical levels because, in the case of dichotomous outcomes, the variance components estimation involves calculations using a log-odds metric at the lowest level of estimation. We present methods introduced by Goldstein and Rasbash [Goldstein, H., Rasbash, J., 1996. Improved approximations for multilevel models with binary responses. J. Roy. Stat. Soc. A 159, 505–513.] to convert the variance components from the log-odds to the probability metric. This method provides a more logical and interpretable way to examine variation for nonlinear outcomes, which tend to be heavily utilized in substance use research. Using data from the National Household Survey on Drug Abuse [Substance Abuse and Mental Health Services Administration (SAMHSA), 2001. 1999 National Household Survey on Drug Abuse. Data Collection Final Report. Office of Applied Studies (OAS), Rockville, MD. Available at http://www.samhsa.gov/oas/nhsda/1999/Collect/toc.htm. Accessed on July 1, 2003.], we partition variation among individual, household, and neighborhood levels for the binary outcome of past year marijuana use to illustrate this approach. We also conduct a stability analysis to examine the robustness across different estimation procedures commonly available in commercial multilevel software packages. Finally, we partition the variance components using a conventional continuously distributed outcome and compare the relative magnitudes across binary and continuous outcomes. [Copyright &y& Elsevier]