A quasi-experimental evaluation of marijuana policies and youth marijuana use

Abstract

Background: Marijuana use carries risks for adolescents' well-being, making it essential to evaluate effects of recent marijuana policies. Objectives: This study sought to delineate associations between state-level shifts in decriminalization and medical marijuana laws (MML) and adolescent marijuana use. Methods: Using data on 861,082 adolescents (14 to 18+ years; 51% female) drawn from 1999 to 2015 state Youth Risk Behavior Surveys (YRBS), difference-indifferences models assessed how decriminalization and MML policy enactment were associated with adolescent marijuana use, controlling for tobacco and alcohol policy shifts, adolescent characteristics, and state and year trends. Results: MML enactment was associated with small significant reductions (OR = 0.911, 95% CI [0.850, 0.975]) of 1.1 percentage points in current marijuana use, with larger significant declines for male, Black, and Hispanic (2.7-3.9 percentage points) adolescents. Effects of MML increased significantly with each year of exposure (OR = 0.980, 95% CI [0.968, 0.992]). In contrast, decriminalization was not associated with significant shifts in use for the sample as a whole, but predicted significant declines in marijuana use among 14-year olds and those of Hispanic and other ancestry (1.7-4.4 percentage points), and significant increases among white adolescents (1.6 percentage points). Neither policy was significantly associated with heavy marijuana use or the frequency of use, suggesting that heavy users may be impervious to such policy signals. Conclusion: As the first study to concurrently assess unique effects of multiple marijuana policies, results assuage concerns over potential detrimental effects of more liberal marijuana policies on youth use.

Early evidence of the impact of cannabis legalization on cannabis use, cannabis use disorder, and the use of other substances: Findings from state policy evaluations

“…Less attention has been given to the potential dynamic effects of MCLs. While the few studies ( n = 5) that modeled lagged policy effects tend to show no evidence that including lags alters their overall conclusions (31,37,38,41,44), the common methods for operationalizing delayed policy effects (i.e., linear effects from time of enactment, a set of lagged indicators) assume that implementation delays are homogeneous across heterogeneous policy designs and that the time course of such delays is uncorrelated with both the local and federal context (49), yet time series of medical cannabis patient take-up suggest this is likely not the case (15,50).…”

Associations Between State Laws Governing Recess Policy with Children's Physical Activity and Health

“…A logistic regression model was fit for each of the outcomes of interest (physically active every day, optimal health, no school absences, no school-related problems) with the difference-indifferences estimator and fixed effects for year and state. 34,45 The parameter of interest is the differencein-differences estimator, 32 which is the interaction between a dummy variable indicating whether the state had a recess policy enacted by follow-up and a dummy variable indicating if the observation was from follow-up. The year fixed effect controls for any unmeasured trends in the outcomes over time while the state fixed effect controls for uncontrolled time-invariant differences between states.…”

“…Difference-in-differences analysis is used to estimate the causal impact of policy changes over time as a quasi-experimental alternative when randomized controlled trials are not feasible and works under the assumption that changes in an outcome would be identical between groups except for the impact of the policy implementation. 32 This approach has been used to estimate the effect of marijuana laws, 33,34 texting-while-driving bans, 35 and same-sex marriage laws. 36 Due to changes in the NSCH sampling strategy, survey mode, and other methodological changes, this analysis was limited to the 2003 to 2011/2012 period.…”

“…Because 2011/2012 NSCH data collection started in February 2011, states implementing a policy by the end of 2010 were considered as having a policy at the 2011/2012 follow‐up (Figure 1). A logistic regression model was fit for each of the outcomes of interest (physically active every day, optimal health, no school absences, no school‐related problems) with the difference‐in‐differences estimator and fixed effects for year and state 34,45 . The parameter of interest is the difference‐in‐differences estimator, 32 which is the interaction between a dummy variable indicating whether the state had a recess policy enacted by follow‐up and a dummy variable indicating if the observation was from follow‐up.…”

Effect of cannabis liberalization on suicide and mental illness following recreational access: a state-level longitudinal analysis in the USA

“…In the years since Anderson et al reviewed data from 1990-2007, the advent of recreational marijuana laws has provided unprecedented access to cannabis. Previous research has attempted to measure the consequences of new cannabis liberalization efforts, [21] [22] [23] [24] [25] [26] [27] [28] but there are major disparities in the policy implementation dates among these studies. Recent research has confirmed that suicidal behaviors and other adverse mental health outcomes are correlated with marijuana use disorder (MUD), [29] [30] which can be exacerbated by increasing access to cannabis, [31] but these correlations are also prevalent with other substance use disorders.…”