Systematic mining of patterns of polysubstance use in a nationwide population survey

Abstract

Objectives: To identify patterns of association and transition in polysubstance use based on National Survey of Drug Use and Health (NSDUH) in the United States.

Methods: We developed a new computational platform for PolySubstance Use data Mining for Associations and Transitions (PSUMAnT). It is based on the computation of weighted support, a measure of popularity, for the use of every combination of one or more substances, termed as a drugset, over a period of 5 decades (1965-2014) based on NSDUH data. It uses an efficient bitstring representation with exact and approximate string matching capabilities to search for patterns of association between drugsets and demographics of user groups at different time-intervals. Moreover, it introduces a quantitative definition of a rule of transition between pairs of substances used within a given time-interval, and provides a function for mining them.

Results: We identified the frequent drugsets from individual substance use database, and determined their representation among different demographic groups at different intervals. An interesting pattern of use of pain relievers and tranquilizers was detected for the age-group of 26-34 years. In addition, transition rules for heroin use in the last decade (2004-2015) of the given data were mined.

Conclusions: Computation of weighted supports over time for every possible combination of substances in the survey, and their association with specific user groups, allows PSUMAnT to generate and test novel, interesting hypotheses in polysubstance use. PSUMAnT can be used for mining combinations of substances used among diverse demographic groups including those that have received less attention in this problem.

Keywords: Drug transition; Frequent itemset analysis; Polysubstance use; Transition rule; association rule mining; opioid data analysis.