First, different trends in availability by country may explain differences in the contribution that perceived availability makes to marijuana use. In Chile, perceived availability generally declined from 2001 to the late 2000’s and then increased until 2016, though at lower rates than use. Local context may affect such trends in perceptions of availability. For example, marijuana availability may depend on where individuals buy, grow, or use marijuana, neighborhood police presence and enforcement of laws, or norms about diversion of marijuana to youth . Second, alternative contributing causes of marijuana use may have arisen in Chile to a greater extent than in Uruguay and Argentina, displacing the contribution that perceived availability make to marijuana use. Such factors may include changes in peer or family substance use , changes in the illegal drug market , or social and cultural changes toward marijuana, influenced by strong lobbying for drug policy reform–particularly for cannabis– in a context of massive social movements among students . While examination of such exposures was outside the scope of the current study, future research should examine whether the contribution of perceived availability and risk to marijuana use is moderated by other potential contributing causes in the local environment. In Uruguay, the increase in perceived availability is not surprising, as the 2013 law created a concrete path to marijuana access for adults. Therefore, greater perceived availability in Uruguay likely corresponds to greater actual availability. For example, it is possible that a surplus of self-cultivated marijuana has tipped over into the streets. Second, even though minors cannot buy it, marijuana may seem more accessible because it sold to adults in pharmacies. Third,rolling benches for growing there may be increased exposure to marijuana use and contact with peers or family members who use marijuana, which could predictably result in a growing sense of availability.

Our findings provide several insights about the availability/use association. First, in this region, where marijuana regulation is becoming progressively liberal and where adolescent marijuana use is increasing, perceived availability may be an increasingly important driver of marijuana use trends . Second, given the strengthening of the availability/use relationship in Argentina and Uruguay, and the high prevalence of perceived easy availability in all three countries, public health professionals in the Southern Cone may consider devoting additional resources towards regulating and intervening on the pathways by which adolescents gain access to marijuana. Relatedly, our findings raise questions about how perceptions of availability relate to real access to marijuana, including how adolescents most often obtain marijuana and whether modes of acquisition have changed over time. Lastly, future studies examining the effect of marijuana legalization on adolescent marijuana use should assess mediation by marijuana availability as a potentially important locus of intervention, particularly as more governments are likely to adopt liberalized marijuana policies. Researchers should seek to better understand this association and its environmental determinants, including in South America with additional years of data, in other legalization contexts, and where perceived availability and marijuana use have not increased, such as the US. The present study has several strengths. We used 1) a large, representative sample, 2) novel methods to analyze time-varying associations, and 3) comparable surveys among secondary school students across multiple years and countries. This is the first study to our knowledge to examine time-varying associations between adolescent risk factors for marijuana use and use of marijuana amid changing marijuana policies in the Southern Cone. This study should also be considered in light of its limitations. First, cross-sectional data preclude assuming temporal relationships between risk factors and marijuana use. Although this was not a study of causal effects, potentially important confounders were not considered. Because of finite sample limitations, our ability to examine covariates was restricted, particularly in Uruguay where the sample size was smallest.

Relatedly, we were not able to model perceived risk and perceived availability together in Argentina, or include all countries in a single model because this would have restricted the analysis to only four years , resulting in insufficient coverage of the time axis for TVEM models . In light of these limitations, we modeled the relationships separately per country, utilizing all years of available data, and note that the strength of associations across countries and within Argentina and Uruguay should be compared with caution. Additionally, these data are self-report, which may contribute to mis-classification, for example by social desirability bias. However, methods of data collection and validation used aimed to minimize these biases . While these data are designed to be representative of secondary school students in urban areas, they are not generalizable to other populations. Lastly, although a strength of our study is that it compares secondary school-attending adolescents across countries, slight differences in student populations between Argentina , Uruguay , and Chile may have limited comparability.The U.S. Cannabis Administration and Opportunity Act introduced in 2021 would decriminalize marijuana federally. California’s policy changes related to tobacco and cannabis use would inform these discussions on the short-term and long-term impact of such policies, especially among young people. The state of California has legalized recreational marijuana use for adults who are aged 21 years or older from January 1, 2018, but the state also fortified a policy environment reducing cigarette and vaping use. Individuals under 21 years old were prohibited to purchase tobacco products and e-cigarettes in California since June 9, 2016; and because e-cigarettes have been counted in California’s smoke-free laws, both tobacco products and e-cigarettes are prohibited in workplaces and many public areas. The cigarette tax was raised by $2 per pack to discourage cigarette smoking in California on April 1, 2017. Thus, all these state-level policies adopted between June 2016 and January 2018 made the state to be a natural experimental ground to investigate young adults’ tobacco and marijuana use behaviors and related risk factors in the context of the adoption of the policies . Young adults are particularly at risk for harm and addiction, as the use of tobacco products in any form and long-term recreational marijuana use can be harmful to their health and well-being .

The use of electronic cigarettes and marijuana among young adults can harm the developing brain, which continues to develop until about age 25 . Use in early adulthood also increases the risk of future addiction to other drugs .The mental health of young adults is especially concerning as smoking and psychological distress are known to co-occur . While there are numerous potential explanations for the co-occurrence, very few studies have looked at the effects of psychological distress and the use of cigarettes, e-cigarettes,vertical cannabis grow and/or marijuana simultaneously. In this study, we focus on the current use of cigarettes, e-cigarettes, and marijuana among Californians ages 18 to 25. We compared the rates in current use between 2017 and 2018 , then examined detailed data from 2018 on patterns of use by sociodemographic characteristics. We further investigated the relationship between the use of cigarettes, e-cigarettes, or marijuana with each of the two products/substance separately, and the use of these products/substance in relationship with psychological distress. This population-based study on young adults gives us an important insight into cigarette, e-cigarette, and marijuana use behaviors in the context of major policy changes. The findings will enhance the understanding of the use and inform the design of programs aimed at curbing the use of these products/substances simultaneously by young adults. All procedures described here were approved by the Institutional Review Boards of the Universities of California, Los Angeles. All participants provided informed consent. The study population was drawn from the 2018 California Health Interview Survey in conjunction with data from the 2017 CHIS annual data file. Starting in 2011, CHIS became a continuous survey, generating an annual household sample of approximately 20,000, enabling the provision of timely population-representative health information for Californians in response to rapidly changing social, economic, public health, and health care environments. CHIS covers dozens of health topics including health and well being, health behaviors, and health insurance coverage. CHIS households were selected through random-digit-dial , and within each household, an adult respondent was randomly selected and interviewed via telephone. To capture the diversity of California populations, CHIS is administered in English, Spanish, Cantonese, Mandarin, Korean, Tagalog, and Vietnamese throughout the state of California. Adjustment factors for the selection mechanisms have been incorporated into the data’s sample weights. Please refer to the CHIS methodology report for details of the design, sampling, and data processing . Our analytical sample was limited to young adults ages 18–25. From the CHIS 2018 adult data file, a total of 3,929 young adults were identified and retained for the main analyses. We also used 2018 CHIS data to compare the rates of current cigarette/e-cigarette/marijuana use with those in 2017.

We used the responses to several questions to define the outcome variables: current users . Specifically, to define current cigarette smoking, respondents who answered yes to the CHIS question, “Altogether, have you smoked at least 100 or more cigarettes in your entire lifetime?” were asked, “Do you now smoke cigarettes every day, some days, or not at all?” If the respondents said they were now smoking every day or some days, they were also asked “In the past 30 days, when you smoked, how many cigarettes did you smoke per day?” For the respondents who had positive responses to these questions or had more than one cigarette in the past 30 days, they were defined as current users. For e-cigarette smoking, CHIS asked adult respondents: “Have you ever used any type of e-cigarette, vape pen, or e-hookah, such as Blu, NJOY, or Vuse, or any larger devices for vaping, sometimes called vapes, tanks, or mods?” Among those who responded positively, a follow-up question was asked: “During the past 30 days, on how many days did you use electronic cigarettes?” For marijuana use, the question was: “Have you ever, even once, tried marijuana or hashish in any form?” Then, to determine marijuana’s current use, CHIS asked the question: “How long has it been since you last used marijuana or hashish in any form?” For the respondents who reported any use within the past month, they were defined as current users.The Kessler 6 scale was administrated to adult respondents to collect self-reports on non-specific psychological distress. It contains six questions on a 5-point scale about the frequency of anxiety and depression symptoms in the past 30 days . The total score of K6 ranges from 0 to 24, K6 scores of 0–4 were usually defined as having no or mild distress, and K6 scores of 5–12 were defined as having moderate psychological distress, and 13 and above were severe psychological distress . Thus, we categorized psychological distress into 3 categories : no/mild psychological distress, moderate psychological distress, and severe psychological distress for this study, as has been done in other studies . Covariates that CHIS 2017represent potential confounding were included if they were known to be related to cigarette or marijuana use behaviors . The fully adjusted model contained individual/household level socio-demographic characteristics collected during CHIS interviews , including age , race/ethnicity , sex , household income standardized by federal poverty level , psychological distress, residence in urban/rural area which is assigned using the Claritas urbanicity model , and seven regions by grouping 58 counties in California according to their geographic locations, such as Greater Bay Area, Sacramento Area, San Joaquin Valley, Los Angeles, other Southern California, and North/Sierra Counties. The Asian, African American, and White race categories were tabulated as non-Latino ethnicity. The “Others” category aggregates non-Latino Native Hawaiians/Pacific Islanders, American Indians/Alaska Natives, and multi-racial individuals. The bivariate analysis chi-squared tests were used to determine if there were significant changes in the rates of using cigarettes, e-cigarettes, and marijuana, separately or any use of the three between 2017 and 2018. We also did the same analyses to examine the differences in the use across the subgroups by socio-demographic and other characteristics using CHIS 2018 data. Logistic models regressing the odds of using cigarettes, e-cigarettes, and marijuana, separately or any use of the three while accounting for sampling weights were conducted. These models have adjusted for covariates, which include age, race/ethnicity, sex, FPL, urban/rural status, region of residence, and psychological distress using CHIS 2018 data.