JSM 2005 - Toronto

Abstract #304645

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 196
Type: Contributed
Date/Time: Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor: Social Statistics Section
Abstract - #304645
Title: Hierarchical Models for a Time Series on Marijuana Abuse among Hospital Emergency Room Admissions
Author(s): Li Zhu*+ and Dennis Gorman and Scott Horel
Companies: Texas A&M University and Texas A&M University and Texas A&M University
Address: School of Rural Public Health, College Station, TX, 77845, United States
Keywords: medical marijuana law ; hierarchical models ; Bayesian methods ; Markov Chain Monte Carlo ; time series
Abstract:

Starting in the late 1990s, electors in eight states have voted in favor of propositions that legalize the medical use of marijuana. The federal government opposes the introduction of medical marijuana laws on a number of grounds. In this study, we test the hypothesis that the introduction of medical marijuana laws is followed by an increase in the use of the drug among a high-risk group: hospital emergency room admissions. We use data from the Drug Abuse Warning Network (DAWN) program to assess prelaw and postlaw trends in marijuana and other drug use. Both loglinear and Poisson regression models are fitted. Bayesian hierarchical structure is built to model the dependence of drug abuse on time, place, medical marijuana law status, and demographic variables. The results suggest cities in states that passed the medical marijuana law experienced a higher increase in drug abuse.

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