Criminal justice measures for economic data harmonization in substance use disorder research - Kathryn E. McCollister; Xuan Yang; Sean M. Murphy; Jared A. Leff; Richard A. Kronmal; Heidi M. Crane; Redonna K. Chandler; Faye S. Taxman; Daniel J. Feaster; Lisa R. Metsch; William E. Cunningham; Frederick L. Altice; et al. (BMC, 2018)
Abstract Background The consequences of substance use disorders (SUDs) are varied and broad, affecting many sectors of society and the economy. Economic evaluation translates these consequences into dollars to examine the net economic impact of interventions for SUD, and associated conditions such as HCV and HIV. The nexus between substance use and crime makes criminal justice outcomes particularly significant for estimating the economic impact of SUD interventions, and important for data harmonization. Methods We compared baseline data collected in six NIDA-funded Seek, Test, Treat and Retain (STTR) intervention studies that enrolled HIV-infected/at-risk individuals with SUDs (total n = 3415). Criminal justice measures included contacts with the criminal justice system (e.g., arrests) and criminal offenses. The objective was to develop a list of recommended measures and methods supporting economic data harmonization opportunities in HIV and SUD research, with an initial focus on crime-related outcomes. Results Criminal justice contacts and criminal offenses were highly variable across studies. When measures grouped by offense classifications were compared, consistencies across studies emerged. Most individuals report being arrested for property or public order crimes (> 50%); the most commonly reported offenses were prostitution/pimping, larceny/shoplifting, robbery, and household burglary. Conclusions We identified four measures that are feasible and appropriate for estimating the economic consequences of SUDs/HIV/HCV: number of arrests, number of convictions, days of incarceration, and times committing criminal offenses, by type of offense. To account for extreme variation, grouping crimes by offense classification or calculating monthly averages per event allows for more meaningful comparisons across studies.