The National Survey on Drug Use and Health (NSDUH) is an initiative of the Substance Abuse and Mental Health Services Administration (SAMHSA). It is an annual household-based survey addressing substance use, substance use disorders, mental health problems, and use of treatment and services among civilian, non-institutionalized US household residents aged 12 and older. NSDUH uses a stratified multistage area probability sample to ensure that results are representative at the state and national levels. The survey has been ongoing since 1971 (originally under the name National Household Survey on Drug Abuse, NHSDA) and has included all 50 states and the District of Columbia since 1999 (Center for Behavioral Health Statistics and Quality [CBHSQ] 2017a, b). Both the original NHSDA and its contemporary successor, NSDUH, have undergone several major redesigns (CBHSQ 2014), so careful attention must be paid to analyses of drug use trends over time.

NSDUH has many design strengths and is the most frequently used dataset for research efforts that examine the impact of marijuana policy on change in behavior, norms, attitudes, and related risk factors among adolescents and emerging adults (Hasin 2018; Leung et al. 2018). In addition to being representative at the state and national levels, NSDUH includes several marijuana-related questions that have been assessed consistently over time. The survey includes questions on marijuana use, including lifetime use, age of first use, recency of use, frequency of use in the past 30 days and past 12 months, and symptoms of cannabis use disorder (CBHSQ 2017a, b; SAMHSA 2017). Annual samples include nearly 70,000 participants, and adolescents and emerging adults are oversampled. About 25% of respondents are 12 to 17 years old and another 25% are 18 to 25 years old; therefore, there is a sufficiently large number of youth to conduct stratified analyses to examine the differential impact of changes in marijuana use in association with MMLs and RMLs in specific states and for population subgroups, e.g., age, race/ethnicity, sex/gender, and rural vs. urban status. In the 2016 NSDUH, the prevalence of past-30-day marijuana use was 6.5% for 12- to 17-year-olds, 20.7% for 18- to 25-year-olds, and 7.2% for those aged 26 and older (SAMHSA 2017).

Recent studies using NSDUH data have examined changes in marijuana use among adolescents, emerging adults, and adults aged 26 or older in states with MMLs (e.g., Martins et al. 2016; Mauro et al. 2019; Wen et al. 2019; Wen et al. 2015). Martins et al. (2016) found post-MML increases in marijuana use among those aged 26 and older, but not among 18- to 25-year-olds. Building on that study, Mauro et al.’ (2019) article in this issue examined gender differences in the impact of MMLs on the prevalence of past-month use, daily use, and cannabis use disorder from 2004 to 2013. Although there were no post-MML differences in any of the outcomes among adolescents or emerging adults of either gender, there were statistically significant increases in the prevalence of past-month use and in daily use among those who reported past-year use for men and women past the developmental transition stage of emerging adulthood (i.e., age 26+). The field would benefit from additional studies that examine the impact of MMLs for other demographic subpopulations.

Because all RMLs allow for legal use only among those aged 21 and older (NCSL 2018), the impact of these policies on use and marijuana-related perceptions may differ for youth below age 21 versus adults over age 21. Research by Wen et al. (2015) provides evidence of such a difference. They used NSDUH data and demonstrated post-MML increases in past-month marijuana use, daily or almost daily marijuana use, and marijuana abuse or dependence among those aged 21 or above, but not among 12- to 20-year-olds. Their conclusions about the relevance of legal age are strengthened as they estimated several age cut points—at ages 18, 25, and 30—but only the age 21 cut point produced statistically significant results. More work needs to investigate differential effects among emerging adults who can and cannot legally access marijuana, or studies should conduct sensitivity and robustness checks to evaluate different age cutoffs. This work is particularly important as evaluations of RMLs begin in earnest once more years of data post-RML are available.

NSDUH is particularly useful for prevention science research because it includes measures of several marijuana-specific risk factors, which are strong predictors of future drug use (e.g., Catalano et al. 2018). These include marijuana-related perceptions, including injunctive norms such as perceived wrongfulness of use and peer and parental disapproval, as well as the perceived harmfulness associated with use. Better understanding of trends in the prevalence of these risk factors before and after the changes in marijuana policy would offer important clues about the mechanisms involved in health risk behaviors (CBHSQ 2017a, b; SAMHSA 2017). In this special issue, Wen et al. (2019) compared several pre-and post-MML marijuana-related perceptions (i.e., perceptions about availability of marijuana, parental approval of marijuana use, wrongfulness of recreational marijuana use, and harmfulness of marijuana use) among adolescents (12- to 17-year-olds) and emerging adults (18- to 25-year-olds) but found only two statistically significant changes: an increase in low perceived harmfulness among emerging adults and a decrease in perceived parental approval among adolescents. Other perceptions did not change in either age group. We need to continue to monitor trends in marijuana-related perceptions, as well as their association with changes in marijuana use, but should also expand examinations to a greater range of marijuana-related risk factors in multiple ecological domains, i.e., not only individuals’ attitudes and norms, but also attitudes and norms in the family, among peers, and in the local community. Moreover, Schmidt et al. (2016) using NSDUH data demonstrated that there is a national trend of increasingly permissive views about marijuana among young people. This trend is independent of state-specific influences and has accelerated in the past decade, perhaps due to the growing number of states adopting MML and the potential digital media spillover of legalization debate across states. Therefore, future studies should examine the association between the secular trends and marijuana-related norms and perceptions of harm and the role of these factors as both predictors and consequences of state-level marijuana policies.

As highlighted in the introduction of this special issue (Johnson and Guttmannova 2019), it would also be important to examine changes in other substance use (especially alcohol, tobacco, and opioids) among adolescents and young adults as well as changes in alcohol-, tobacco-, and opioid-specific risk factors. NSDUH is a suitable data source for this work as it covers the use of all commonly used drugs and diverse margins of use, including substance use disorders, and also assesses a number of other substance-use-specific risk factors (Lipari et al. 2017). Because use of one substance is often associated with use of other drugs, marijuana legalization may have unanticipated consequences for alcohol, tobacco, and opioid use. On the other hand, trends in alcohol, tobacco, and opioid use may also impact marijuana use, interacting with marijuana policy changes. For example, a seemingly paradoxical discrepancy has been observed in recent years between a population-level rise in the prevalence of low perceived harm from marijuana use and a lack of a corresponding increase in the prevalence of marijuana use among adolescents (e.g., Fleming et al. 2016; Lipari et al. 2015; Miech et al. 2015; Sarvet et al. 2018b). Using eight waves of statewide survey data collected from high school students in Washington State between 2000 and 2014, Fleming et al. (2016) found no evidence that this divergence in trends was attributable to a weakening in the individual-level association between marijuana-specific risk factors (e.g., perceptions of harm) and marijuana use. Rather, substantial decreases in alcohol and cigarette use may have had spillover effects on marijuana use, dampening increases in marijuana use that might have otherwise occurred due to increases in marijuana-specific risk factors. Better understanding of the trends in risk factors for different substance use (e.g., perception of harm from using alcohol and tobacco vs. marijuana) and their associations with use of different substances could inform cannabis policy evaluation by making it possible to better account for other sources of variation in substance use. It would also provide information about the potential counter-effects of substance use-related prevention and intervention efforts planned for or already on the ground.