A participant was classified as a Cannabis User if he or she reported using cannabis monthly or more frequently during the previous year, and as a Cannabis Non-user if they had used cannabis <4 times during the previous year. It should be noted that the majority of participants in the Cannabis User group reported weekly or daily use in the past year. Participants were excluded if they self-reported binge drinking as well as monthly or greater recreational use of other substances . Other exclusionary criteria included any characteristic that would contraindicate magnetic resonance imaging exposure , or a history of traumatic brain injury with loss of consciousness or that occurred in the past year. Participants taking psychotropic medications other than for ADHD were also excluded. It should be noted that few participants reported currently taking stimulant medication to manage their ADHD which is generally consistent with longitudinal studies reporting that young adults who were medicated in childhood often discontinue treatment with stimulant medication in early adulthood . To our knowledge, this is the first study investigating the combined effects of ADHD and cannabis use on EF. We predicted childhood-diagnosed ADHD and cannabis use would be related to worse EF. Instead, for almost all tasks we observed a clear effect for ADHD but not for cannabis use, either contemporaneous or historical. The strongest negative effects of ADHD were on impulsivity, working memory, and verbal memory. Although we also expected individuals with a childhood history of ADHD who used cannabis regularly would demonstrate particularly poor EF performance, we found no significant ADHD by cannabis use interactions. As expected, the ADHD group made significantly more errors of commission and demonstrated worse working memory,vertical grow verbal memory, decision making, and cognitive interference than the LNCG. We also observed non-significant impacts on delayed recall and processing speed with medium effect sizes . Interestingly, we did not observe the expected effect of ADHD on tau.
Since reaction time variability is particularly characteristic of ADHD , at least in children, we were surprised no effect was observed. Some literature suggests reaction time variability is less evident as individuals with ADHD develop so the non-significant finding may be due to maturation. We did not have information to investigate whether participants in the current study still met diagnostic criteria for ADHD. However, at the 8-year follow-up, the original ADHD group in the larger MTA sample demonstrated greater impairment even though only 30% met current ADHD diagnostic criteria suggesting a childhood diagnosis of ADHD is risk factor for continued EF deficits, which is consistent with other studies . We did not observe significant effects of cannabis use except for a small significant effect of cannabis use on decision-making, which should be interpreted with caution given the overall MANCOVA did not indicate a significant main effect for cannabis use. However, the direction of the finding is consistent with the literature and provides modest support suggesting that cannabis use is associated with poorer performance on decision making tasks. Cannabis users may have deficits in the ability to balance rewards and punishments that contribute to drug-taking behavior. This could be cause or effect. Interestingly, this task assesses a ‘hot’ executive function, i.e., one that involves incentives and motivation , which may play a more critical role in the process of addiction than ‘cool’ or more abstract executive functions . It should be noted that studies suggest that dose, persistence, and chronicity of use may impact the effect of cannabis on EF . Cannabis use in our study ranged from monthly to daily over the past year and all were abstinent on the day of testing, which may have affected our ability to detect effects of cannabis use on EF due to recovery of function. Our exploratory analyses investigating age of onset of cannabis use were not significant, potentially because of the much smaller sample size for these analyses. However, review of effect sizes revealed that earlier use of cannabis was associated with poorer performance on cognitive tasks assessing decision-making, working memory, impulsive errors, and response variability than late onset of use. These tasks involve visual attention, which is negatively influenced by early-onset cannabis use . Individuals who initiate use of cannabis before age 16 may be at higher risk for developing persistent neuropsychological deficits because their brain is still developing , especially the prefrontal cortex which is associated with several executive functions including planning, verbal fluency, complex problem-solving, and impulse control, each with its own developmental trajectory .
Thus, adolescence is a particularly vulnerable time for neurocognitive effects of substance use . Still, we clearly found that ADHD diagnosis had a much larger impact on EF than cannabis use. Because ADHD is associated with developmental delays, particularly in the prefrontal cortex , it is possible that the cognitive consequences of ADHD were sufficient that additional impact on EF from cannabis use was difficult to detect. It should be noted that a higher proportion of individuals with ADHD initiated cannabis use early, which may make it difficult to disentangle the independent impact of cannabis on cognition, given larger effect sizes of ADHD. Furthermore, there may be an interaction whereby early onset cannabis use exacerbates ADHD symptomatology through negatively impacting EF. Further investigation is clearly warranted. Our findings must be interpreted in light of several limitations. Sample sizes were small, particularly for the exploratory age of onset analyses. The cross-sectional design makes it difficult to determine causality although the ADHD diagnosis did precede cannabis use for all participants . The measure of cannabis use was based on self-report, which is not the most objective method compared to biological measures. Our results may not generalize to more persistent chronic cannabis users. Excluding regular binge drinkers may also limit generalizability given the high co-occurrence of alcohol and cannabis use . Although we requested participants abstain from prescribed medication and illicit drug and alcohol use prior to the assessment, we did not verify their compliance with this directive. The concern about participants not complying with this directive for cannabis use is somewhat mitigated by the fact that we did not observe an effect of cannabis; if participants indeed did not comply with the requested washout period, we may have observed a false-positive finding based on the negative effects of cannabis on cognitive functioning . It is also possible that discontinuation of stimulant medication may have impaired performance on the cognitive tasks ; however, with such a small proportion of our ADHD sample taking stimulant medication “sometimes” or “always”, it is unlikely that such discontinuation effects would have led to the ADHD group differences.. There are a number of issues needing further investigation. It will be imperative to investigate the effects of regular cannabis use in young adults who continue to meet diagnostic criteria for ADHD, particularly because some studies suggest persistent ADHD is associated with poorer EF and higher rates of comorbid SUD .
It will also be important to investigate whether having a diagnosed cannabis SUD results in more dramatic impact on EF than the regular use defining this sample of users. Another issue that may impact EF outcomes is the age of onset of cannabis use. Future research will need to examine whether there is a critical developmental window when cannabis use more severely affects neuropsychological functioning. Other areas of investigation might include an analysis of whether EF deficits in childhood predict poorer cognitive outcomes, and whether early deficits interact with cannabis use with and without ADHD. Our results should not be taken to indicate that cannabis use carries no risk for cognitive function, only that further investigation is needed. As of November 2021, recreational or “adult-use” cannabis is legal in 18 states and the District of Columbia.1 Cannabis policies regulate the availability of cannabis by legally permitting outlets offering cannabis products for retail sale. Alcohol availability research indicates that higher residential outlet densities make it easier to find, purchase, and use legal intoxicants.Analogously, greater availability of medical cannabis dispensaries has been linked to cannabis use and frequency.4,5 Similar effects are expected for recreational cannabis outlets.Increases in cannabis access and use may have both positive and negative health consequences. Cannabis consumption has been linked to motor vehicle crashes, psychotic disorders, respiratory disease, low birth weight, and cannabis use disorder, but substitution of opioids, tobacco, or alcohol for cannabis may prove beneficial.Outlets may also attract crime, although research on this topic is mixed.State cannabis legalization policies typically defer authority to regulate the density and locations of outlets to local governments. Local governments can limit the number of outlets permitted, establish minimum distances between outlets, and bar their location near sensitive locations such as schools. Local governments also share responsibility with state agencies for abating illegal outlets which are prevalent in California.The impacts of local cannabis policies on outlet densities may have implications for public health by limiting availability. Recreational cannabis outlets are disproportionately located in neighborhoods with high proportions of low-income and racial–ethnic minority residents.Policies that encourage greater reductions in outlets in vulnerable neighborhoods therefore have the potential to promote health equity. Little is known about the impacts of local cannabis policies. Three studies assessed local policies in Colorado, Washington, and California following recreational cannabis legalization.All identified broad variation in local regulatory approaches,vertical outdoor farming ranging from all-out bans to unlimited outlets, with a few jurisdictions allowing outlets while limiting their densities. To our knowledge, no prior study has evaluated how local policies influence outlet densities or socioeconomic and racial–ethnic equity in the distribution of outlet densities within jurisdictions. We addressed these gaps with a spatiotemporal analysis of city and county cannabis policies and cannabis outlets in California.
We evaluated whether specific local policies such as density limits cannabis outlets led to lower outlet densities. We also assessed whether the associations of local policies with outlet densities varied across neighborhoods depending on median income or racial–ethnic composition. We hypothesized that stricter local policies would be associated with lower outlet densities and less disproportionate placement of outlets in less advantaged communities. Cannabis legalization research suggests that provisions enabling outlets are influential for cannabis consumption and related health outcomes.We focus on the local-level policies that determine how many outlets can open and in which communities. Understanding which local policies effectively limit and equalize outlet densities is critical for state and local policymakers seeking to make more informed decisions about which cannabis policies to pursue to protect public health and health equity from potential harms related to legal cannabis.We classified local cannabis policies for 12 of California’s 58 counties representing 59% of the state population. The 12 counties were selected to capture a range of sizes, sociodemographic compositions, political orientations, and approaches to cannabis regulation,20 and included 230 cities and 11 unincorporated county areas . Using a legal epidemiological approach,between November 2020 and January 20021 we systematically identified and coded the characteristics of currently applicable cannabis policies in all 241 jurisdictions. We used a structured data collection instrument to capture the presence or absence and content of pre-specified provisions. Two analysts coded all jurisdictions separately until they achieved >95% agreement. Complete protocols, data collection instruments, and further detail are provided in eAppendices 1-3. However, localities retain considerable discretion. The policy measures we collected were guided by an established taxonomy of all possible cannabis policies.We coded all policies that: were regulated at the local level, varied across jurisdictions, were more restrictive than state law, and were plausibly related to public health given prior evidence, public health best practices, and expert opinion.The outcome was the count of storefront recreational cannabis outlets in each Census block group and year. We web-scraped data on outlets annually between 2018 and 2020 from Weed maps, a high-traffic online promotional cannabis business finder widely used in cannabis research.A prior validation study found that, compared to official license listings or other finders, Weedmaps was the most up-to-date and comprehensive source for capturing cannabis outlets.14 We focused on recreational rather than medical outlets because: following recreational legalization, few medical-only outlets remained; the applicable state laws for medical outlets are distinct; and Weedmaps measures of medical outlets were less valid over the study period. Recreational outlets included both newly opened outlets and outlets that converted from medical to recreational. We focused on storefront outlets, as opposed to home delivery retailers, because this study builds on conceptual models based on physical proximity to outlets offering in-person purchases.3 See eAppendix 3 for detail .