Taken together, it appears that although marijuana use may stimulate appetite and increase caloric intake, it is not associated with increased BMI; on the contrary, it is dose dependently associated with lower BMI and lower hemoglobin A1C levels. Our study has some limitations. Information on marijuana use and other behaviors, including alcohol, smoking, and drug use, were collected via self-report and vulnerable to under reporting due to social desirability bias. Relatedly, there was no biological quantification of marijuana constituents or mode of marijuana use from participants. The imprecise measurement of marijuana use in our study could be a source of measurement error and may have contributed to the wide confidence intervals we observed. Potential confounders, such as physical activity, diet, and waist circumference, were not included in the analysis. Additionally, data from the WIHS were from predominantly black and Hispanic women, while the MACS comprises mostly non-Hispanic white men, which may signal disparities in reliable access to health care. Recent changes in the marijuana environment in the United States include decreased restrictions on use , decreased perception of marijuana as harmful , and increased adult prevalence . As of January 1, 2020, recreational use was legal in 11 states, and decriminalized in 15 others; medical use was legal in 33 . A primary concern about legalization is increased use among youth, but this concern has not been realized to date . However,greenhouse grow tables there is evidence of increased marijuana use and cannabis use disorders among adults that appears to be concentrated among adults aged 26+ .
While extant research suggests that legalization has not led to increased overall use among youth, some recent findings suggest possible increases among young adults . Repeated cross-sectional data suggest increased frequency of use among Oregon undergraduates after legalization in that state in 2015 . Additionally, early marijuana use may lead to cigarette smoking and to poor occupational and educational outcomes in young adulthood. It is also important to note that previous research on the impact of changing marijuana restrictions is largely limited to repeated cross-sectional data. There is a need for longitudinal cohort studies to identify not only trajectories of use but also predictors and correlates. Additionally, it is unclear whether loosening restrictions may have a differential impact on 18–20 year old young adults, for whom use remains illegal, compared with 21–24 year olds. Despite decreasing public apprehension, there are reasonable concerns about young adult marijuana use. Acutely, marijuana increases risk for accidents , emergency department visits , and psychosis . Persistent use predicts poor psychosocial outcomes and neuropsychological and cognitive decline in humans , and animal models suggest cognitive deficits and social anxiety . Persistent use is associated with addiction , including withdrawal . However, continued movement toward increased access makes it important to understand potential public health effects. A further concern is whether legalization modulates tobacco use. Marijuana users appear less likely to quit tobacco , and changes in use of either product are positively associated with changes in the other . Marijuana legalization could send the message that tobacco is also less dangerous than previously believed. In other words, legalization may undermine negative perceptions of tobacco use due to similar routes of administration, use of similar devices, and frequency of co-use . This risk is particularly important during emerging adulthood, when tobacco initiation peaks and patterns of long-term use are established .California was the first state to legalize medical marijuana in 1996.
At that time marijuana use was already high compared with other states, and the impact on prevalence was minimal . However, in 2016 California legalized recreational marijuana use, including possession of up to 1 ounce and individual cultivation beginning in November 2016, with commercial sales permitted as of January 1, 2018 . . State authorities began to plan for regulation of recreational sales in late 2016, and began issuing licenses for cultivation, manufacturing, distribution, testing, and retail sales at the beginning of January, 2018 . Local jurisdictions may still prohibit cultivation and sales. As of February 2018 there were 261 active retail licenses statewide; as of October 2020 that number had increased to 753, including 46 in San Diego County where this study was based . A recent report based on a survey from 2018–19 indicates that young adults generally utilize legal avenues to access marijuana and only rarely acquire it from strangers or dealers . To our knowledge, no research has evaluated changes in frequency of marijuana use in California following legalization of recreational sales, and there are few cohort studies from other states. The current study is one of the first to examine a sample of the same participants before and after legislation was implemented. Participants were at high risk for marijuana use given that all had smoked cigarettes recently at time of enrollment . This study was a secondary analysis of a study of non-daily cigarette smokers in California who were aged 18–24 when they enrolled in 2015–16 and who were followed quarterly for 3 years. Although possession and individual cultivation were permitted during 2017, we focused on legalization of sales in the belief this would have greater impact on availability based on both increased direct sales to young adults and on diversion from peer groups. Based on recent studies among young adults and on the fact that their tobacco use status likely increased risk of marijuana use, we sought to test the hypothesis that frequency of marijuana use would increase followinglegalization. A second goal was to test whether post-legalization changes in marijuana trajectories would be moderated by demographic or other substance use factors. We expected to see greater increases in use among male participants, and among those who used other substances more frequently. Finally, we explored whether changes in marijuana use frequency following legalization were related to cumulative frequency of use prior to 2018.
We recently described the relationship between marijuana and tobacco use in this sample and there is partial overlap in the data used in these studies. The primary differences are the previous study included data only from subjects’ first two years following enrollment,cannabis growing systems while the current study utilized data from all three years of follow-up, regardless of when subjects enrolled, and the previous study examined trends in use over time without regard to changes in the legal environment, while the current study explicitly examined whether frequency of marijuana use was associated with legalization of recreational sales.We recruited California residents aged 18–24 who had smoked cigarettes at least monthly for 6 months but never daily for 30+ days. Participants were recruited on a rolling basis during 2015–2016 and completed substance use assessments quarterly for three years. The present secondary analysis utilized an “intent-to-treat” approach, in which all enrolled participants were included. Average age at enrollment was 20.4 years , 51.9% were male, and most were full-time students. In terms of race/ethnicity, 41.7% identified as Caucasian, 21.0% Latinx, 19.1% Asian American, 13.1% multi-ethnic, and 5.1% from other backgrounds. Study participants completed assessments every three months for three years. All data were collected between March 2015 and October 2019. Assessments were completed online via SurveyMonkey. Compensation was $25 per annual assessment and up to $40 per quarterly assessment via electronic gift cards. Staff sent individualized survey links via email or SMS. All procedures were approved by the University of California, San Diego Institutional Review Board.. Demographics evaluated at baseline included sex, age, racial/ethnic background, and student status. Because the age range was narrow and our interest was in the potential impact of legalization, age was transformed into a time-varying binary variable reflecting whether or not participants were aged 21 at the time of each assessment. Student status was collapsed into a dichotomous variable comparing full-time students to all others. Marijuana and tobacco frequency were assessed at all 13 timepoints. At annual assessments, participants completed the Timeline Follow Back , on which they reported number of cigarettes, and whether they had used each of e-cigarettes, hookah, cigars, cigarillos, smokeless tobacco, snus, marijuana, and alcohol on each of the previous 14 days. At quarterly assessments, participants completed brief daily surveys, in which they indicated whether they had used each of the same products in the last 24 hours. We created time-varying variables representing constructs of interest. Time reflected the study timepoint, from baseline to year 3 . For all assessments, we calculated the number of days on which participants used marijuana , cigarettes , e-cigarettes , and alcohol . We counted the number of days at each timepoint at which assessment occurred to account for the fact that the maximum number of days differed for annual versus quarterly assessments, and that participants may not have provided data for all days during quarterly assessments. We created a binary legalization variable that indicated whether or not each assessment occurred prior to or after January 1, 2018. We also created a post-legalization slope variable that was coded as 0 for all pre-legalization timepoints, and to reflect time since legalization for post-legalizatin timepoints .
Finally, for each participant we calculated the total number of days prior to January 1, 2018 on which use was assessed , as well as the number of those days on which any marijuana use was reported . We used bivariate tests to evaluate whether demographic variables were related to predictors and outcomes; when associations were significant, we accounted for demographics in hypothesis tests. To test whether frequency of marijuana use changed following legalization we utilized a piecewise or segmented multilevel longitudinal regression model, an approach recommended for evaluating the impact of policy changes . This model included segments for the period prior to January 1, 2018, and for the period from that date onward. The model tested the temporal trend in frequency of marijuana use, the impact of legalization, and changes in the rate of marijuana use over time following legalization by incorporating the time, legalization, and post-legalization slope variables as predictors. Sex, race/ethnicity, and binary age were included as covariates. Second, we used multilevel longitudinal regression models to evaluate the associations of sex, race/ethnicity, binary age and frequency of alcohol, cigarette and e-cigarette use with frequency of marijuana use before and after legalization. We did so by testing for three-way interactions between the predictors of interest , time, and legalization. Significant three-way interactions would indicate that impact of legalization on the trajectory of marijuana use frequency was moderated by the predictor of interest. All demographic interaction terms were included in one model, and all substance use interaction terms in another. In both cases, non-significant interaction terms were removed in a backward manner and models refit. Finally, we used a similar multilevel modeling approach to test whether time-invariant cumulative marijuana days was associated with time-varying marijuana frequency over time post-legalization. All analyses were conducted using Stata 15.0, with alpha = .05; missing data were not imputed.The proportion of data missing was 0% for the first 3 assessments , and increased with each subsequent assessment, with 3.2% of participants missing outcome data at year 1, 10.5% at year 2, and 14.1% at the final year 3 timepoint. Missingness increased over time and was most common among participants identifying as White . Missingness was not significantly associated with sex or with frequency of cigarette, e-cigarette, or marijuana use at the previous assessment. The first 5 assesments occurred prior to legalization for all participants. The proportion of the sample for whom assessment occurred after legalization increased with each subsequent assessment, from 1.8% at the first quarterly timepoint following year 1, to 37.7% at year 2, 80.3% six months after year 2, and 100% at year 3. Bivariate analyses indicated women tended to use e-cigarettes less frequently , younger participants used marijuana more frequently , and non-White participants reported greater cigarette frequency . Consequently, sex, age, and race/ethnicity were included in subsequent analyses. Table 1 details frequency and likelihood of marijuana, alcohol and tobacco use at baseline and at each annual assessment. The proportion of days on which participants used marijuana remained relatively stable, while the number of participants who reported any marijuana use declined modestly from baseline to year 3. Alcohol use was stable across the three years of observation. Proportion of days using e-cigarettes exhibited a 50% increase, while the proportion of participants with any e-cigarette use was relatively stable. In contrast, use of cigarettes, and consequently overall use of tobacco products, decreased over time. The piecewise regression model is shown in Table 2.