Trajectories of drug use are usually studied empirically by following over time persons sampled from either the general population (most often youth and young adults) or from heavy or problematic users (e.g., arrestees or those in treatment). The former, population-based samples, describe early career development, but miss the years of use that generate the greatest social costs. The latter, selected populations, help to summarize the most problematic use, but cannot easily explain how people become problem users nor are they representative of the population as a whole. This paper shows how microsimulation can synthesize both sorts of data within a single analytical framework, while retaining heterogeneous influences that can impact drug use decisions over the life course. The RAND Marijuana Microsimulation Model is constructed for marijuana use, validated, and then used to demonstrate how such models can be used to evaluate alternative policy options aimed at reducing use over the life course. 1. Introduction Marijuana is the most widely used illicit substance in the United States, with use rates being the highest among youth and young adults. Average thirty-day use rates among high school seniors in the United States have been about 20% since 1994 and in 2009, and 5.4% of high school seniors reported daily use of marijuana during the past month [1]. Use rates among young adults are also high, with 59% of all individuals between the ages of 19 and 28 reporting having ever used marijuana, 17% of individuals reporting use in the past thirty days, and 5.4% reporting use of marijuana on a daily basis in the past 30 days [2]. Although a large number of studies have examined risk and vulnerability factors associated with the general use and onset of marijuana, only recently have scientists started examining factors associated with marijuana use careers, the duration and severity of marijuana use, and quit behavior. Current work suggests that age of onset, frequency of use at an early age, drug-using peers, and stressful life events are important factors influencing both duration and the probability of quitting [3–6]. Other work suggests that while these various factors are important, there are alternative trajectories of use that people seem to follow because of heterogeneous experiences [7–10]. The vast majority of these trajectory studies have focused only on the periods of late adolescence and young adulthood during which marijuana use peaks [11–13]. None of these analyses have examined the longer-term implications of marijuana use careers, such as the
References
[1]
L. D. Johnston, P. M. O'Malley, and J. G. Bachman, “Monitoring the Future: National Survey Results on Drug Use, 1975-2009. Volume I: Secondary School Students,” (NIH Publication No. 10-7584), National Institute on Drug Abuse, Bethesda, Md, USA, 2009.
[2]
L. D. Johnston, P. M. O'Malley, and J. G. Bachman, “Monitoring the Future: National Survey Results on Drug Use, 1975-2007. Volume II, College Students and Adults Ages 19-45, 2007,” (NIH Publication No. 10-7585), National Institute on Drug Abuse, Bethesda, Md, USA, 2009.
[3]
A. Perkonigg, R. D. Goodwin, A. Fiedler et al., “The natural course of cannabis use, abuse and dependence during the first decades of life,” Addiction, vol. 103, no. 3, pp. 439–449, 2008.
[4]
S. Pudney, “Keeping off the grass? An econometric model of cannabis consumption in Britain,” Journal of Applied Econometrics, vol. 19, no. 4, pp. 435–453, 2004.
[5]
J. C. Van Ours, “Dynamics in the use of drugs,” Health Economics, vol. 15, no. 12, pp. 1283–1294, 2006.
[6]
J. C. Van Ours and J. Williams, “Cannabis prices and dynamics of cannabis use,” Journal of Health Economics, vol. 26, no. 3, pp. 578–596, 2007.
[7]
P. L. Ellickson, S. C. Martino, and R. L. Collins, “Marijuana use from adolescence to young adulthood: multiple developmental trajectories and their associated outcomes,” Health Psychology, vol. 23, no. 3, pp. 299–307, 2004.
[8]
D. B. Kandel and K. Chen, “Types of marijuana users by longitudinal course,” Journal of Studies on Alcohol, vol. 61, no. 3, pp. 367–378, 2000.
[9]
J. E. Schulenberg, A. C. Merline, L. D. Johnston, P. M. O'Malley, J. G. Bachman, and V. B. Laetz, “Trajectories of marijuana use during the transition to adulthood: the big picture based on national panel data,” Journal of Drug Issues, vol. 35, no. 2, pp. 255–279, 2005.
[10]
M. Windle and M. Wiesner, “Trajectories of marijuana use from adolescence to young adulthood: predictors and outcomes,” Development and Psychopathology, vol. 16, no. 4, pp. 1007–1027, 2004.
[11]
W. Hall and R. L. Pacula, Cannabis Use and Dependence: Public Health and Public Policy, Cambridge University Press, Melbourne, Australia, 2003.
[12]
L. D. Johnston, P. M. O'Malley, and J. G. Bachman, “Monitoring the Future: National Survey Results on Drug Use, 1975-2002. Volume I: Secondary School Students,” (NIH Publication No. 10-7584), National Institute on Drug Abuse, Bethesda, Md, USA, 2003.
[13]
D. B. Kandel and J. A. Logan, “Patterns of drug use from adolescence to young adulthood: I. periods of risk for initiation, continued use, and discontinuation,” American Journal of Public Health, vol. 74, no. 7, pp. 660–666, 1984.
[14]
M. L. Dennis, M. A. Foss, and C. K. Scott, Correlates of Long-Term Recovery After Treatment, 2007.
[15]
C. E. Grella, C. K. Scott, M. A. Foss, and M. L. Dennis, “Gender similarities and differences in the treatment, relapse, and recovery cycle,” Evaluation Review, vol. 32, no. 1, pp. 113–137, 2008.
[16]
C. K. Scott, M. A. Foss, and M. L. Dennis, “Pathways in the relapse-treatment-recovery cycle over 3 years,” Journal of Substance Abuse Treatment, vol. 28, supplement 1, pp. S63–S72, 2005.
[17]
M. T. Childress, A System Description of the Marijuana Trade, Santa Monica, CA, USA, RAND Report MR-235-A/DPRC, 1994.
[18]
S. S. Everingham and C. P. Rydell, Modeling the Demand for Cocaine. MR-332-ONDCP/A/DPRC1994, Santa Monica, CA, USA, RAND.
[19]
C. P. Rydell, J. P. Caulkins, and S. S. Everingham, “Enforcement or treatment? Modeling the relative efficacy of alternatives for controlling cocaine,” Operations Research, vol. 44, no. 5, pp. 687–695, 1996.
[20]
C. P. Rydell and S. S. Everingham, Controlling Cocaine: Supply Versus Demand Programs1994, Santa Monica, CA, USA, RAND Report MR-331-ONDCP/A/DPRC.
[21]
T. G. K. Bentley, K. M. Kuntz, and J. S. Ringel, “Bias associated with failing to incorporate dependence on event history in Markov models,” Medical Decision Making, vol. 30, no. 6, pp. 651–660, 2010.
[22]
L. Mitton, H. Sutherland, and M. Weeks, Microsimulation Modelling for Policy Analysis, Cambridge University Press, Cambridge, UK, 2000.
[23]
A. T. McLellan, D. C. Lewis, C. P. O'Brien, and H. D. Kleber, “Drug dependence, a chronic medical illness implications for treatment, insurance, and outcomes evaluation,” Journal of the American Medical Association, vol. 284, no. 13, pp. 1689–1695, 2000.
[24]
M. Dennis, S. H. Godley, G. Diamond et al., “The Cannabis Youth Treatment (CYT) Study: main findings from two randomized trials,” Journal of Substance Abuse Treatment, vol. 27, no. 3, pp. 197–213, 2004.
[25]
S. H. Godley, B. R. Garner, L. L. Passetti, R. R. Funk, M. L. Dennis, and M. D. Godley, “Adolescent outpatient treatment and continuing care: main findings from a randomized clinical trial,” Drug & Alcohol Dependence, vol. 110, no. 1-2, pp. 44–54, 2010.
[26]
T. F. Babor, “Brief treatments for cannabis dependence: findings from a randomized multisite trial,” Journal of Consulting and Clinical Psychology, vol. 72, no. 3, pp. 455–466, 2004.
[27]
D. D. Simpson, “Drug treatment evaluation research in the United States,” Psychology of Addictive Behaviors, vol. 7, no. 2, pp. 120–128, 1993.
[28]
R. A. Johnson and D. R. Gerstein, “Initiation of use of alcohol, cigarettes, marijuana, cocaine, and other substances in US birth cohorts since 1919,” American Journal of Public Health, vol. 88, no. 1, pp. 27–33, 1998.
[29]
R. A. Johnson and D. R. Gerstein, “Age, period, and cohort effects in marijuana and alcohol incidence: United States females and males, 1961-1990,” Substance Use and Misuse, vol. 35, no. 6–8, pp. 925–948, 2000.
[30]
W. C. Kerr, T. K. Greenfield, J. Bond, Y. Ye, and J. Rehm, “Age-period-cohort influences on trends in past year marijuana use in the US from the 1984, 1990, 1995 and 2000 National Alcohol Surveys,” Drug and Alcohol Dependence, vol. 86, no. 2-3, pp. 132–138, 2007.
[31]
Y. M. Bishop, S. E. Fienberg, and P. W. Holland, Discrete Multivariate Analysis: Theory and Practice, M.I.T. Press, Cambridge, Mass, USA, 1975.
[32]
E. Arias, United States Life Tables, 2003, vol. 54 of NVSR, National Center for Health Statistics, Hyattsville, Md, USA, 2006.
[33]
J. Gfroerer, D. Wright, and A. Kopstein, “Prevalence of youth substance use: the impact of methodological differences between two national surveys,” Drug and Alcohol Dependence, vol. 47, no. 1, pp. 19–30, 1997.
[34]
W. R. Miller and J. K. Manuel, “How large must a treatment effect be before it matters to practitioners? an estimation method and demonstration,” Drug and Alcohol Review, vol. 27, no. 5, pp. 524–528, 2008.
[35]
F. R. Levin and J. J. Mariani, “Dronabinol treatment for cannabis dependence,” European Psychiatric Review, vol. 3, no. 1, pp. 62–64, 2010.
[36]
A. J. Budney, S. T. Higgins, K. J. Radonovich, and P. L. Novy, “Adding voucher-based incentives to coping skills and motivational enhancement improves outcomes during treatment for marijuana dependence,” Journal of Consulting and Clinical Psychology, vol. 68, no. 6, pp. 1051–1061, 2000.
[37]
J. P. Caulkins, “Mandatory Minimum Drug Sentences: Throwing Away the Key or the Taxpayers' Money?” RAND Report MR-827-DPRC: Santa Monica, CA, USA.
[38]
J. P. Caulkins, R. L. Pacula, S. Paddock, and J. Chiesa, “What we can- and cannot-expect from school-based drug prevention,” Drug and Alcohol Review, vol. 23, no. 1, pp. 79–87, 2004.
