Fourth-generation bike sharing models may also incentivize user based redistribution by employing demand-based pricing where users receive a price reduction or credit for docking bicycles at empty docking locations. A third feature of fourth-generation systems is the seamless integration of bike sharing with public transportation and other alternative modes, such as taxis and car sharing via smart cards, which support numerous transportation modes on a single card. In 2009, the Yélo bike sharing system was launched in La Rochelle, France. This system includes a smart card, which is fully integrated with the public transportation system. This facilitates multi-modal transportation linkages and user convenience, which could lead to greater auto ownership and usage reductions, as more daily trips are supported by alternative modes. However, creating a program that coordinates various forms of transportation on a single card is challenging, as this can be costly and often requires multi-agency involvement. Another area for improvement is bicycle security, which can be supported by ongoing technological advancement, such as the design and integration of GPS units into more robust bicycle frames that further enhance existing locking mechanisms, deter bike theft, and facilitate bike recovery. However, adding GPS units is costly and can potentially increase financial losses, if bikes with built-in GPS are vandalized or stolen. Finally, to target a larger scope of bike sharing users, fourth-generation systems may be more likely to incorporate electric bicycles, which enable longer-distance trips; encourage cycling on steeper hills and slopes; and lessen physical exertion requirements, particularly when users are commuting or making work trips in business attire. Over the past century, California has built an extraordinarily complex water management system with hundreds of dams and a vast distribution network that spans the state. This system generates electricity, provides flood protection, delivers reliable water supplies to 40 million people and sup ports one of the most productive agricultural regions in the world. Yet development of the state’s water manage ment system has come at a price.

Damming waterways,microgreens grow rack diverting water from rivers and streams and altering natural flow patterns have transformed the state’s freshwater ecosystems, leading to habitat degradation, declines of freshwater species and loss of services that river ecosystems provide, including high-quality drinking water, fishing and recreational opportunities, and cultural and aesthetic values. The state aims to accommodate human water needs while maintaining sufficient stream flow for the environment. To support this mission, scientists from the U.S. Geological Survey , The Nature Conservancy and UC have developed new techniques and tools that are advancing sustainable water management in California. At the center of these new advances is the need to understand the natural ebbs and flows in the state’s rivers and streams. Natural patterns in stream flow are characterized by seasonal and annual variation in timing , magnitude , duration and frequency . California’s native freshwater species are highly adapted to these seasonally dynamic changes in stream flows. For example, salmon migration is triggered by pulses of stream flow that follow winter’s first storms, reproduction of foothill yellow-legged frogs is synchronized with the predictable spring snow melt in the Sierra Nevada, and many native fish breed on seasonally inundated flood plains, where juveniles take advantage of productive, slow-moving waters to feed and grow. When rivers are modified by dams, diversions and other activities, flows no longer behave in ways that support native species, contributing to population declines and ultimate extinction. Thus, understanding natural stream flow patterns and the role they play in supporting ecosystem health is an essential first step for developing management strategies that balance human and ecosystem needs. Unfortunately, our ability to assess alteration of natural stream flow patterns, and the ecosystem consequences, is hindered by the absence of stream flow data. California’s stream flow gauging network offers only a limited perspective on how much water is moving through our state’s rivers. In fact, it’s been estimated that 86% of California’s significant rivers and streams are poorly gauged and nearly half of the state’s historic gauges have been taken offline due to lack of funding . Of those gauges that are still in operation, most are located on rivers that are highly modified by human activities and gauge records prior to impacts are limited. These limitations can be partially overcome with modeling approaches to predict the attributes of natural stream flow expected in the absence of human influence.

The predictions can then be compared to measured stream flow at gauging locations, or they can be used to estimate natural flow conditions in ungauged streams. In 2010, Carlisle et al. developed a modeling technique to predict natural attributes of stream flow and assessed stream flow alteration at gauges throughout the United States . Soon after, UC and TNC scientists began using the approach to expand and further refine the technique for applications in California . The models have evolved over time, but all rely on stream flow monitoring data from USGS gauges located on streams with minimal influence from upstream human activities. These are referred to as reference gauges. Some reference gauge data come from historical measurements made before significant modification of flows occurred, such as the years prior to the building of a dam. The remaining data are from reference gauges located in California watersheds that remain minimally altered by human influence. Once reference gauges were identified and flow records obtained from the USGS web-based retrieval system, we used geographic information systems to characterize the watersheds above each reference gauge based on their physical attributes, such as topography, geology and soils . We also assembled monthly precipitation and temperature climate data for the past 65 years for each watershed. The watershed variables and climate data were then compiled and statistically evaluated in relation to observed flow conditions at the reference sites using a machine-learning approach that uses the power of modern computers to search for predictive relationships in large data sets. An advantage of machine-learning techniques is the ability to make predictions from multiple model iterations , which tends to increase accuracy. Once we had developed and evaluated models using observed stream flow data from reference gauges, we could predict stream flow attributes for any portion of a stream or river in California for which the climate and watershed characteristics were known . Additional technical details of the modeling approach are provided in Carlisle et al. 2016 and Zimmerman et al. 2018.In a study led by Zimmerman et al. , we applied the machine-learning technique to assess patterns of stream flow modification in California. We did this by predicting natural monthly flows at 540 streams throughout California with long-term USGS gauging stations and comparing those predictions with ob served conditions. We then assessed how observed flow conditions at the gauges deviated from predictions and recorded the frequency and degree to which flows were either higher or lower than natural expected levels, while considering the uncertainty of model predictions. We found evidence of widespread stream flow modification in California . The vast majority of sites experienced at least 1 month of modified flows over the past 20 years and many sites were modified most of the time .

When stream flows were modified, the magnitude of modification tended to be high. On average, inflated stream flows were 10 times higher than natural expected levels, whereas depleted stream flows were 20% of natural expected levels. Overall, stream flow modification in California reflects a loss of natural seasonal variability by shifting water from the wet season to the dry season and from wet areas of the state to the drier south. Stream flow inflation was most common in dry summer months and for annual minimum flows. Conversely, flow depletion was most common in winter and spring months and for annual maximum flows. Unaltered sites tended to occur in places with relatively low population density and water management infrastructure, such as the North Coast,ebb and flow flood table whereas greater magnitude and frequency of alteration was seen in rivers that feed the massive water infrastructure in the Central Valley and the poulated Central Coast and South Coast regions. A key water management goal in California is to manage river flows to support native freshwater biodiversity. By estimating natural river flows and the degree to which they are modified, our work provides a foundation for assessing “ecological flow” needs, or the river flows necessary to sustain ecological functions, species and habitats. Assessments of ecological flow needs are generally performed at stream reach to regional scales , but rarely for an area as large and geo graphically complex as California. In 2017, a technical team that includes scientists from UC, TNC, USGS, California Trout, Southern California Coastal Water Research Project and Utah State University began developing a statewide approach for assessing ecological flows. The team has identified a set of ecologically relevant stream flow attributes for California streams that reflect knowledge of specific flow requirements for key freshwater species and habitats . Our modeling technique is now being extended to predict natural expectations for these new stream flow attributes. Model predictions of the natural range of variability for these ecologically relevant stream flow attributes will provide the basis for setting initial ecological flow criteria for all streams and rivers in California by the State Water Resources Control Board and other natural resource agencies.

These ecological flow criteria will be based on unimpaired hydro logic conditions, but they can be refined in locations where management and ecological objectives require a more detailed approach. For example, refined approaches would likely be required in rivers that must be managed for species listed under the Endangered Species Act or in rivers where substantial flow and physical habitat alteration makes reference hydrology less relevant for setting ecological flow criteria, such as in the Central Valley or in populated watersheds of coastal California. Our technical team also was involved in establishing the California Environmental Flows Work group of the California Water Quality Monitoring Council . The mission of the Work group is to advance the science of ecological flows assessment and to provide guidance to natural resource management agencies charged with balancing environmental water needs with consumptive uses. The Work group is comprised of representatives from state and federal agencies, tribes, and nongovernmental organizations involved in the management of ecological flows. It serves as a forum to facilitate communication between science and policy development and to provide a common vision for the use of tools and science-based information to support decision-making in the evaluation of ecological flow needs and allocation of water for the environment. The modeling technique described above has also been used to evaluate statewide water allocations. Grantham and Viers analyzed California’s water rights database to evaluate where and to what extent water has been allocated to human uses relative to natural supplies. They calculated the maximum annual volume of water that could be legally diverted according to the face value of all appropriative water rights in the SWRCB’s water rights database. Water rights were distributed according to their location of diversion, and the permitted diversion volumes were aggregated at the watershed scale to estimate a maximum water demand for each of the state’s watersheds. These permitted water diversion volumes were compared with modeled predictions of average annual supplies to estimate the degree of appropriation of surface water resources throughout the state . The study found that appropriative water rights exceed average supplies in more than half of the state’s large river basins, including most of the major watersheds draining to the Central Valley, such as the Sacramento, Feather, Yuba, American, Mokelumne, Tuolumne, Merced and Kern rivers. In the San Joaquin River, appropriative water rights were eight times the volume of estimated natural water supplies . The volume of water rights allocations would be much higher if pre-1914 and riparian water rights had been included, but these data were not available at the time.For example, comparisons of allocations with water use suggest that in most of California only a fraction of claimed water is being used. In a well-functioning water rights system where allocations are closely tracked and verified, an excess of water rights relative to supplies is not necessarily a problem. During water shortages, holders of junior appropriative rights would be required to curtail their water use. When water is abundant, most water rights holders should be able to fully exercise their claims.

An additional issue related to product regulation of marijuana edibles is the high THC potency per package without adequate requirements that these products clearly be demarcated to explicitly communicate the actual size of an individual serving to the consumer.In Colorado and Washington, product regulations allow for each package to contain up to ten 10 mg servings of THC or 100 mg of THC. Poor product labeling in Colorado and Washington contributed to an increase in calls to poison control centers and self-reports of adult intoxication.In Colorado, marijuana-related calls to the poison control center increased from 44 in 2006 to 227 in 2015,while in Washington calls increased by 79% from 111 in 2010 to 199 in 2014.Since commercialization, calls increased by 55% from 129 in 2012 to 199 in 2014.Liquor control boards in charge of approving products prior to market release allowed fruit and candy flavored marijuana products to enter the legal markets in Washington and Colorado. Despite a rule that the Washington State Liquor and Cannabis Control Board not approve any marijuana-infused edible products “especially appealing to children” such as, but not limited to, “gummy candies, lollipops, cotton candy, or brightly colored products” did not block approval of fruit flavored sodas and candy, chocolate and peanut butter flavored cookies and brownies, and chocolate truffles,including Mirth Provisions’ Legal Sparkling Rainier Cherry Soda and Nana’s Secret Soda in Orange Cream and Peach Flavors.Colorado does not have even such nominal restrictions and similar products have entered the market, including Dixie Elixir’s Crispy Cracken and Chocolate Cherry Pretzel. Marijuana edibles may be a safer alternative for adult consumers than marijuana cigarettes because they avoid combustion. However, because edibles are being produced in a wide array of flavors and variations that often are appealing to children, it is questionable whether these products contribute to less harm. Avoiding these harms could be achieved through tight regulation,plant growing stand including low limits on potency, large warning labels, accurate labeling, standardization of dosing, and standardization of packaging to avoid accidental ingestion by children and adults.

There is concern that the high potency of these products as well as their appeal to children may result in adverse health consequences.Indeed, it is likely that such youth appealing products are a major contributor to an increase in accidental childhood ingestion since legalization in Colorado402 and Washington.Prior to legalization in Colorado and Washington there were few cases involving marijuana related accidental poisonings in children. Children admitted to the emergency room for accidental marijuana ingestion increased from 0 to 14 two years following liberalization of medical marijuana laws in Colorado.Following implementation of retail marijuana laws in Colorado in 2013, an additional 14 children were admitted to the hospital for ingestion of edibles,with the first 9 occurring in the first 6 months of legalization.Washington, which modeled its product labeling and potency rules on Colorado’s,experienced a similar increase in adverse outcomes. In 2014, 45% of calls to poison control center were related to marijuana intoxication for those under age twenty – since legalization in 2012, these calls have increased from 50 in 2012 to 90 in 2014. Significantly, the highest number of calls in 2015 were regarding children under the age of five.Of the calls reported for the first nine months of 2015, 51% were in the marijuana/cannabis category, 42% were associated with infused-products, and 7% were related to marijuana oil. Youth accounted for 43% of the statewide calls during this nine-month period in 2015. National data from the United States show similar trends for accidental childhood ingestion. Between 2005-2011 there was an annual 30% increase in marijuana exposure in medical marijuana states while non-medical marijuana states showed no increase.To address the issue of accidental consumption of marijuana edibles, Colorado and Oregon enacted legislation requiring marijuana producers to place a THC warning symbol on their products . Colorado, Washington, and Oregon developed mass media campaigns aimed at preventing youth marijuana use , not general market campaigns designed to minimize overall population use as is done for tobacco. These campaigns were targeted at youth with messages on health risks of impaired memory, developmental delays, increased risk for addiction, depression, anxiety, psychosis, or other mental illnesses.

Messages related to the consequences of marijuana use include ineligibility for receiving financial aid and how marijuana-related charges may lead to school suspensions and expulsions. State health departments public awareness messages in Colorado and Washington directed to adults only cautioned adults, particularly new users, to “be safe and sensible” when using newly legal marijuana rather than discouraging use altogether. Colorado contracted with the University of Colorado to evaluate the impact of its mass media campaign on change risk perceptions and use behaviors as well as increasing knowledge of marijuana laws, health impacts of use, safe storage practices, and prevention.Adult exposure to the 2015 “Good to Know Campaign” was associated with an increased likelihood of accurately identifying retail marijuana laws compared to adults with zero exposure, with the proportion adult acute awareness increasing from 62% to 73% at follow up. There were moderate effects on knowledge of harms associated with use and perceptions of risk related to underage use , use around children , and high risk use . The survey did not question respondents whether or not the campaign impacted use behavior or thoughts on quitting, intentions to quit, or quit attempts .Taxation can both be used to raise marijuana prices artificially in order to discourage consumption,and to prevent taxpayers from subsidizing the regulatory, public education, and prevention and control program, and the marijuana education and research program and adjusted periodically for inflation. Additional tax increases could be used as a way to raise the price to reduce marijuana initiation and promote cessation. While all four US states that had legalized recreational marijuana as of October 2016 and Uruguay tax marijuana, these tax rates were not set at levels designed to cover regulatory, public health education, and medical costs associated with marijuana legalization.In Colorado,Washington,and Oregon, state legislators were directed by the ballot initiatives voters enacted to adjust the retail sales tax to make retail marijuana competitive with black market prices. Washington and Oregon ballot initiatives also include criteria for adjusting marijuana taxes to discourage use, and an additional requirement in Oregon to maximize net revenue from the marijuana tax. In Uruguay, officials of the IRRCA have determined that marijuana will be taxed at $1 per gram to compete with black market prices, despite national legislation requiring that government officials develop and fund an enforcement system and education and prevention programs.

Shortly after legal sales went into effect, state legislators in Colorado,Washington,and Oregon reduced marijuana taxes to compete with the black market. Colorado reduced the retail sales tax from 10% in 2014 to 8% in 2015, while Washington consolidated the state’s three-tier tax system to a single ad valorem excise tax of 37% at the retail sales level to reduce the marijuana industry’s federal income tax liability because consumers would pay the tax and so would technically not be considered part of the retailers’ gross income. Oregon also modified its wholesale tax in 2015 to a price-based excise tax of 17% of the retail sale, with up to an additional 3% tax levied at the local level, to increase state revenue through increased sales stimulated by lower prices.In the three states where marijuana taxes were reduced, state legislators were more concerned with short-term gains of competing with the black market and maximizing state revenue than long-term public health impact and costs associated with reduced use through higher taxes. There are no requirements for marijuana to be taxed based on a percentage of tetrahydrocannabinol content, which may in effect provide incentive for manufacturers to increase the THC content of cannabis.Indeed, US marijuana producers have been increasing product potency over the last 20 years.Between 1995 and 2014, marijuana potency increased from 5% to 12%, with a corresponding decline in cannabinol. The result was a THC/CBD ratio increase from a factor of 14 in 1995 to a factor of 80 in 2014.In jurisdictions with legal marijuana sales, edibles and cannabis concentrates, where THC concentration can be as high as 70%,plant grow table has increased in recent years. A weight-based tax, or a tax based on the unit of THC per weight or volume could be a solution to this problem. Another policy worth considering from the alcohol control literature is implementation of minimum unit pricing .Evidence from Canada show that MUP for alcohol is associated with reduced consumption and alcohol-related harms.Longitudinal estimates from British Colombia suggest that a 10% increase in MUP for a given alcohol product would result in a 16.1% drop in consumption.As is the case with most parts of the new regulatory framework for marijuana, implementation of MUP for marijuana should be considered at the same time as legalization in order to avoid potential legal battles with a professionalized marijuana industry. In 2012, Scotland was the first country to pass national legislation requiring MUP for all alcohol products.However, implementation of MUP in Scotland has been met with fierce opposition from the drinks industry, with claims of MUP as a violation of European Union trade law.The US states took varied approaches to regulating restrictions on marijuana business locations, none of which protect those most likely to regularly use marijuana In Colorado, local governments were prohibited by state law from granting a license to a business within 1,000 feet of a school defined as “public or private preschool or a public or private elementary, middle, junior high, or high school or institution of higher education”, alcohol or drug treatment facility, principal campus of college, university or seminary, or a residential child care facility.Although Washington lawmakers prohibited marijuana businesses within 1,000 feet of K-12 schools, recreational center or facility, child care center, public park, public transit center, library, or any game arcade, it allowed local governments to pass rules to reduce the distance requirement to a minimum of 100 feet from areas where children and adolescents are likely to congregate.As of September 2016, four Washington cities reduced the buffer zone for marijuana businesses to 500 feet, and one city reduced its buffer zone to 100 feet for parks, recreational/community centers, libraries, childcare centers, game arcades, and public transit centers.Oregon lawmakers did not prohibit retail store locations within 1,000 feet of colleges or universities despite the fact that many college students are under 21. Retail stores in Alaska were prohibited under the legalization initiative within 500 feet of child-sensitive areas, defined as facilities that provide services for persons under 21, a building in which religious services are regularly conducted, or a correctional facility. Colleges and universities are not explicitly included. Retail outlet density is positively associated with youth and young adult smoking,heavy alcohol consumption,and marijuana use.Despite the fact that use is higher in areas where there are more retail outlets, marijuana regulatory regimes in the four US states have failed to implement licensing systems to control retail density in ways that would protect vulnerable populations . Similar to tobacco and alcohol outlets marijuana businesses appear to be concentrated in low-income, minority communities. By 2016, Colorado marijuana businesses were more likely to be located in census tracts with higher proportions of racial/ethnic minorities , lower proportion of young people, lower median household incomes , higher crime rates, and higher concentrations of alcohol outlets .Similar findings were observed in California neighborhoods with medical marijuana dispensaries.Research on US state implementation of retail marijuana laws has focused on potential impacts of these laws on risk perceptions,use,health harms and stakeholder participation in the regulatory process.There is only a limited literature on the impact of marijuana policies on use and associated harms from the experiences in the Netherlands,Uruguay,and the United States.However, variability in US state medical marijuana laws makes it difficult to make strong generalizations, which likely explains why there is no scientific consensus on how legalization will impact risk perceptions or use patterns.There is limited evidence on the complexities of how a policy is implemented and when it is implemented having a dramatic effect on health-related outcomes. It is important to consider perceptions of risk when assessing the public health impacts of marijuana legalization laws.

Exposure to alcohol advertising is independently associated with initiating drinking, drinking dependence, and binge drinking among young adults .Middle and high school students that own alcohol branded merchandise are more likely to report ever alcohol use. Ownership of alcohol branded merchandise is positively associated with youth perceptions on peer use and peer acceptance of alcohol.Nicotine cravings are enhanced by alcohol use and alcohol cravings are enhanced by nicotine use.Indeed, cigarette companies use imagery of alcohol use in their cigarette advertisements in print media, which disproportionately impacts young adults, particularly college students. Likewise, exposure to television food commercials is an important predictor for unhealthy food choice, brand preference, and high caloric food consumption.Receptivity to television fast food marketing is associated with youth obesity, with a one point increase in marketing receptivity being associated with a 19% increased odds of being obese.Electronic commerce such as internet, mail order, text messaging, and social media sales are difficult to regulate, leading to increased youth sales, tax evasion, and illicit trade compared to traditional tobacco sales.Although tobacco companies advertise on the internet, a substantial amount of tobacco promotion occurs through social media and user-generated promotional media, and the content is predominantly positive. These messages reach both adults and adolescents.In addition, internet sales have provided new avenues for tobacco companies to market their products to youth.A 2002 study that examined cigarette advertising on the Internet in the USA found that nearly 20% of cigarette-selling websites did not include warnings that sales to minors are illegal or prohibited. Among those websites that required some form of age-verification,vertical rack more than half required that a buyer confirm legal purchase age , 15% required that buyers manually type in their date of birth, and 7% required buyers manually enter information from a driver’s license.

At least fifteen US attorneys general have conducted Internet stings and found that children as young as 9 years old were able to purchase cigarettes. For example, a New York sting operation found that 93% of websites observed had sold to children under 18 . A 2004 study found that more than 96% of minors aged 15-16 were able to find an Internet cigarette vendor and place an order in less than 25 minutes, with most completing the order in seven minutes.A study in California found that 101 websites selling tobacco failed to comply with California laws regarding age and ID verification to prevent youth sales. A detailed 22-page summary of the scientific evidence through 2011 on tobacco sales through the Internet submitted to the US Food and Drug Administration to conclude that youth access to tobacco cannot be prevented by existing rules and procedures in the US, including those by which sellers conduct age verification were ineffective at preventing youth access to tobacco products.A 2013 report by the World Health Organization shows that 96 countries banned internet tobacco advertising,141 but enforcing such bans has proven difficult. For example, while the sale of snus is illegal in all European Union countries except Sweden, online vendors in Sweden target online marketing activities toward EU citizens outside of Sweden, including sales promotions, price discounts, and gifts with purchase. A study that made test purchases in ten EU member states reported a 96% success rate . Age-verification relied on self-reports from buyer, and the majority of these sales applied Swedish taxes only, contrary to EU requirements.According to smokers in Western countries, aside from television, the most common source of health information regarding the risks of smoking comes from tobacco product packaging.Indeed, evidence from the Global Adult Tobacco Survey shows that among 12 countries surveyed between 2008 and 2010, more than 90% of men had reported seeing the health warning label on cigarette packages.Large graphic warnings and plain packaging reduce tobacco use, discourage nonsmokers from initiating, and encourage smokers to quit. Large warnings specified in the WHO Framework Convention on Tobacco Control have spread across the world as countries have implemented the FCTC, with slower adoption of effective warning labels in countries that had previously entered into voluntary agreements with the tobacco industry.

The extent to which health warning labels on tobacco packages impact risk perceptions and smoking behavior largely depends upon the size, prominence, position, and design of these messages.Warning labels that cover up to at least 50% or more of principal display areas, and not just limited to the sides of the tobacco package,are associated with increases in health knowledge and motivation to quit. Experimental studies in Canada demonstrate that increasing the warning label from 50% to 75%, 90%, or 100% increased its effectiveness among youth.Studies evaluating graphic, pictorial warning labels in Canada and Australia have shown high levels of cognitive processing and an association between cognitive processing, intentions to quit, and quit attempts.In Brazil and Thailand, countries with strong pictorial depictions on the health impacts of smoking, had the strongest impact on thinking about quitting among current smokers.Nationally representative data from Canada demonstrate that 80% of youth reported pictorial health warning messages decreased the attractiveness of smoking.Compared to small, text-only warning labels, large warning labels that include images in addition to text are more effective at communicating health risks associated with use, evoking an emotional response, provoking thoughts about quitting, increasing motivations and quit attempts among smokers.National data from Canada show that 95% of youth rated pictorial health warnings as more effective at communicating health risks than text-only versions.Large pictorial warnings have longer lasting effects on increasing risk perceptions, encouraging quitting and quit attempts among smokers,and are more likely to be seen by low-literacy adults and children.In contrast, small, text-only warning labels, such as those used for tobacco in the United States, have low impact on youth tobacco use.In addition, these warning labels do not effectively communicate health messages on the specific health risks of tobacco consumption to the public.Young people are less likely to recall seeing text-only warning labels.Among participants that report text-only warning label recall, only one-third were able to accurately recall message content.Additional requirements for effective warning labels include positioning health messages on front and back, and on the top of all principal display areas. Warning labels on tobacco packages are more effective when novel health warnings and messages are used, and the content, layout, and design of the warning label are rotated periodically to avoid “burn out” of stale messages.

While youth perceive health messages on US warning labels for tobacco products to be believable, 186 few reported that these messages were informative or relevant, and that these messages were “vague”, “stale”, and “worn-out”.Warning labels that include messaging with cessation information and a toll-free quitline number are associated with an increase in calls to the quitline,particularly among male smokers and those from low socioeconomic groups,and help to address tobacco-related health disparities. Implementation of comprehensive warning labels for tobacco packaging has been actively opposed by tobacco industry interference in the policy process.Between 1984 and 2003, countries without mandated HWL on tobacco packages transitioned to having either some form of HWL or a voluntary industry HWL passed by the tobacco companies. Countries with voluntary industry HWLs were less likely to adopt comprehensive HWLs, which were compliant with FCTC guidelines than countries with previously enacted mandated HWLs.These findings also point to the importance of implementing at the time of legalization a comprehensive set of demand reduction policies for marijuana before a large marijuana industry develops and works to weaken or defeat public health strategies to control use.Cigarette pack design is a key component to tobacco company marketing techniques.Package design establishes brand identity and promotes brand appeal,seedling grow rack particularly among youth. Tobacco companies design products that are attractive to children while being marketed toward young adult peers. A longitudinal study on youth attitudes toward cigarette brands found a ten-point increase in the proportion of teenage girls reporting a favorite cigarette brand between 2007 and 2008. The study coincided with the launch of RJ Reynold’s campaign for Camel No. 9, a brand that appears to be specifically designed to attract teenage girls, and which accounted for the majority of the increase in brand preference.116 Similar impacts on brand preference were found among young people in Mexico191 that had reported a greater exposure to tobacco marketing and advertising. Tobacco companies use package design techniques to mislead consumers into perceiving their products as less harmful or safer than other tobacco products. Tobacco product packaging with descriptors such as “natural”, “light”, “mild”, and “organic” are associated with false beliefs of the health risks of smoking,and are perceived as less harmful or healthier than tobacco products without these descriptors,likely because the tobacco companies target concerned195 and older smokers at risk of quitting. Indeed, the cigarette companies consider the color of the package as an “ingredient” of the cigarettes that can be used to manipulate users’ perception of the taste of the product in ways interchangeable with changes in the physical product itself.The effectiveness of health warnings may be enhanced through the use of standardized packaging ,a strategy used to reduce attractiveness and appeal of tobacco, to increase the prominence of health warnings,and to correct misperceptions on the health risks of smoking. Plain packaging enhances the effectiveness of health warnings by increasing their notice ability, and has been shown to make smoking less appealing and has the potential to reduce the level of false beliefs about the risks of different brands. Compared to branded packages, tobacco products in standardized packaging are associated with reduced brand awareness and identification,and reduced brand appeal,particularly among young women.

Consistent with previous research in high-income countries, plain packaging in low and middle-income countries have similar impacts on reducing tobacco product appeal.Consistent with adopting a comprehensive tobacco control approach, plain packaging may be useful even if nations have adequately funded mass media campaigns . Unlike media campaigns, packaging changes have almost universal reach and ongoing frequency of exposure. Packaging changes cost little to governments, unlike media campaigns that constantly have to justify their funding allocations against ongoing efforts by tobacco companies to defund media campaigns.As discussed in detail in the next section, plain packs with larger graphic health warning labels complement media campaign messages, amplifying their impact. There is broad scientific consensus that mass media campaigns aimed at the general population are an important element of a comprehensive program to prevent youth initiation of tobacco use and reduce its prevalence.US Surgeon General Report concludes that there is sufficient evidence to infer a causal relationship between the level of funding for anti-smoking media campaigns and reduced smoking prevalence among youth.The effectiveness of well-done anti-tobacco media campaigns is not an argument against other elements of a comprehensive tobacco control policy. Indeed, media campaigns can amplify the effects of other policies, such as plain packaging, advertising restrictions, graphic warning labels and smoke free laws, as well as the other way around, since marketing prohibitions reduce the salience of pro-smoking cues, and increase and reinforce anti-smoking norms. In particular, in Australia, introduction of pictorial health warnings on cigarette packets was supported by a televised media campaign highlighting illnesses featured in two of the warning labels .Between 2005 and 2006, the proportion of smokers aware that gangrene is caused by smoking increased by 11.2 percentage points , and awareness of the link between smoking and mouth cancer increased by 6.6 percentage points . In contrast, awareness of throat cancer decreased by 4.3 percentage points, and this illness was mentioned in the pack warnings but not the advertisements. Smokers who had prior exposure to the warnings were significantly more likely to report positive responses to the advertisements and stronger post-exposure quitting intentions. Thus, anti-smoking television advertisements and pictorial health warnings on cigarette packets reinforced each other to positively influence awareness of the health consequences of smoking and motivation to quit. Analysis of the impact of tobacco control policies and mass media campaigns on smoking prevalence in Australian adults found that stronger smoke free laws, tobacco price increases and greater exposure to mass media campaigns combined to independently explain 76% of the decrease in smoking prevalence from February 2002 to June 2011.For example, youth exposure to anti-tobacco media campaigns reduced the odds of current cigarette use by 15% and smokeless use by 30% compared to students with zero media exposure.

Regarding ketene that has been suggested to be formed by vaping or pyrolytic heating of VEA,it is not clear whether it is identifiable with our methods or is not formed at the temperatures tested here. Products like duroquinone and durohydroquinone are reported to be formed below a vaping coil temperature of 300 °C; however, we do not observe them with the preparation or detection methods used in this work. The selectivity and solubility of GC-MS extraction solvent could be a reason why products like quinones were not observed in the current study. These results underscore the fact that THC oil is a complex mixture, the complexity of which increases with thermal degradation chemistry and the addition of VEA. Further research on individual components is still needed for a better understanding of aerosol composition from vaping cannabis extracts and their mixtures with diluents.Cannabis is the most commonly used federally illicit drug by U.S. adolescents and young adults . One in five 12th grade students reported past-month use of cannabis and 7% reported daily use . In the last few years, there has been an evolving landscape of cannabis products on the market, including various combustible , blunts , vaporized , and edible products . Although smoking cannabis remains the most popular mode of consumption among AYAs , studies indicate a rise in use of non-smoked cannabis products , particularly vaporized cannabis , among AYAs. As cannabis use in this developmental period poses concerns of negative effects on brain development and mental function , preventing AYA cannabis use in all forms is of public health importance. Research shows that AYAs’ perceptions of cannabis is a major driver of use, with low perceived risks associated with initiation and continued use of cannabis . Concurrent with the rise in use, AYAs’ perceived risks of cannabis have steadily declined over the past decade . The expanding legalization of cannabis nationwide may increase acceptability and ease of access among AYAs . Despite AYAs reporting using various forms of cannabis ,grow rack the extant literature predominately focuses on their perceptions of health risks associated with combustible cannabis, or “marijuana” in general.

Lacking are studies concerning AYAs’ perceptions associated with non-combustible products and with blunts. In addition, prior research has asked about perceptions of more general outcomes rather than of specific risk or benefit outcomes . Such nuanced data on specific perceived risks and benefits regarding both health and social impacts across product types could inform the development of tailored messages and educational content for cannabis use prevention . To address these gaps, we analyzed cross-sectional data collected among 433 California AYAs during 2017–2018. According to the 2019 Youth Risk Behavior Survey, California adolescents had lower prevalence of ever and current use of cannabis compared to national estimates of adolescent cannabis use . However, as California is considered the largest legal cannabis market in the US after legalization occurred in January 2018 , cannabis-related data in this state provide important information since policies pioneered in California are often adopted by other states, and so may reflect and inform future use patterns and drivers. This study examined perceptions of not only risks but also benefits related to short-term and long-term use of different cannabis products . Given that blunts are a well-documented form of co-use of cannabis and tobacco among AYAs, and previous research showing different perceptions related to blunts and other combustible cannabis products , we examined perceptions related to blunts and other forms of combustible cannabis separately. We also compared perceptions between participants who had ever used cannabis and those who had never used cannabis. Understanding AYAs’ perceived risks and benefits across different types of cannabis products and by use status is critical to inform public health and education messaging strategies aimed at preventing and reducing use of all forms of cannabis. Means and standard deviations were computed for each perception item. We used generalized linear models to account for correlation of students’ responses clustered within school and unbalanced group sizes. Outcomes were continuous variables indicating perceived chance of having a given health or social risk if using cannabis.

For each perception outcome, we estimated a model comparing means of that outcome among four cannabis products , and another model of that outcome between ever and never cannabis users . All models were adjusted for age and sex. There were six pairwise comparisons among four cannabis products. The statistical significance across pairwise comparison tests was controlled by using the Tukey-Kramer method with p-values adjusted based on the studentized range distribution . All tests were two-tailed with a significance level of α less than 0.05. Analyses were conducted using SAS v9.4. Among the risks assessed , the most common perceived short-term and long-term risks were “get into trouble” and “become addicted,” respectively. Across the cannabis products, perceived percent chance of experiencing the short- and long-term risks was highest for blunts and combustible cannabis, followed by vaporized cannabis, and the lowest for edible cannabis. Almost all of the pairwise comparisons of perceived health risks among the cannabis products were significantly different. Comparisons between combustible cannabis and blunts were not significantly different for most of the perceived health risks. The only significant difference between the two products was that long-term use of blunts was perceived to have a higher likelihood of resulting in lung cancer and heart attack than long-term use of other combustible cannabis products. Combustible cannabis and blunts were perceived to have greater risks than vaporized cannabis for all the health outcomes, except for having a heart attack. Vaporized cannabis was perceived to have greater health risks than edibles, except for having heart attack. For perceived short-term risk of experiencing social problems if used cannabis, the only significant comparison was between blunts and edible cannabis, indicating that blunts were perceived as more likely than edible cannabis to lead to getting into trouble and having friends upset. Compared to never users, ever cannabis users perceived less risk of getting lung cancer and experiencing social problems for the four cannabis products. Likewise, perceived risk of getting oral cancer was lower among ever users for all types of cannabis products, except combustible cannabis. For edible cannabis, ever users perceived lower short-term health risks and addiction than never users. Likewise, ever users perceived lower risk of having trouble catching breath and getting lung disease for vaporized cannabis, and lower risk of becoming addicted for combustible cannabis.Among the benefits assessed , the most common perceived short-term and long term benefits from using cannabis were “feel high or buzzed” and “feel less anxious,” respectively.

For pairwise comparisons of short-term benefits, combustible cannabis was perceived as having greater benefits of all the short-term benefits than vaporized cannabis, and having greater benefits for short-term mental health outcomes than blunts and edibles. Participants thought that smoking combustible cannabis or blunts would be more likely to result in someone “looking cool” than using vaporized or edible cannabis. Participants perceived feeling high or buzzed from using edible cannabis more than from using vaporized cannabis. Regarding long-term benefits, combustible cannabis was perceived as reducing anxiety and depression better than all other cannabis products. Ever cannabis users perceived greater benefits of using cannabis on reducing mental health problems for using all types of cannabis products. Compared to never users, ever users also perceived greater benefits of having better concentration for all types of cannabis, except for vaporized cannabis. In addition, ever users perceived greater benefits of looking cool for combustible cannabis and blunts, while they perceived greater benefits of feeling high or buzzed for edible cannabis. While combustible cannabis and blunts remain the most common forms of AYA cannabis use, increasing rates of use of vaporized and edible cannabis have been observed . However,greenhouse grow tables most studies on perceptions of cannabis focus on combustible cannabis, or “marijuana” more generally. This study extends the literature by examining AYAs’ perceptions of short-term and long-term risks and benefits of cannabis use, with a focus on different types of cannabis products , and between ever and never users of cannabis. The main finding is that AYAs differently perceive risks and benefits across the four cannabis products, and ever cannabis users generally perceive lower risks and greater benefits of cannabis use than never users. Consistent with previous studies among AYAs , the most common cannabis products used in our sample were combustible cannabis and blunts. Interestingly, these products on average were perceived to have greater short- and long-term risks than vaporized and edible cannabis. However, it should be noted that ever cannabis users perceived less risks of using these products than never users. The paradox between perceived risk and actual use of combustible cannabis and blunts may be explained by another finding that AYAs also had greater perceived benefits related to these products. These findings indicate that AYAs’ use of combustible cannabis and blunts are based on a balance of both their perceived risks and perceived benefits of these products, highlighting the role of these sets of opposite perceptions in AYAs’ behavioral decision-making . Prevention and intervention efforts often focus on communicating health risks related to cannabis use rather than social risks , yet such health outcome-focused strategies may not always be effective for young populations , 2021. Indeed, among the perceptions of risks assessed in our study, the most common perceived short-term and long-term risks were social negative outcomes and addiction, respectively. Likewise, a previous qualitative study found that adolescents expressed a concern about addiction and social impacts of cannabis use . This finding suggests that, along with communicating about health risks, increasing AYAs’ awareness of social risks and the addictive nature of cannabis use may be an additional focus to deter AYA cannabis use, as has been shown with other research including both social and health risks for tobacco prevention .

In addition, our study did not identify whether our participants had experienced any health or social risks related to cannabis. Future research should elucidate actual consequences of specific perceived risks on cannabis use among AYAs. In addition to considering perceived social risks in research and prevention efforts, the perceived benefit construct is also part of many health behavior models , yet rarely included in studies on cannabis use perceptions. We found the most common perceived long-term benefit among AYAs was “feel less anxious.” Beyond recreational purposes, AYAs report using cannabis as self medication to cope with their anxiety and other mental health issues . Our finding that AYAs perceived cannabis use as beneficial to their mental health is particularly important given that recent national data report a concerning trend in poor mental health in this age group . Furthermore, recent reviews indicate that AYA cannabis use is associated with poorer outcomes among those with mood and anxiety disorders and increased risk for developing major depression and suicidality . Our finding highlights a need for correcting AYAs’ misperception regarding benefits of cannabis use and educating AYAs on mental health risks related to use of cannabis. In addition, including screening for mental health problems into routine clinical care and integrating resilience training in prevention programs may offer AYAs better ways to cope with their mental health issues , which in turn may prevent their cannabis use. We also found that AYAs perceive use of edible cannabis as the least harmful to their health compared to other types of cannabis use. Legalization of recreational cannabis use and permission of home cultivation have been associated with a higher likelihood of AYAs using edible cannabis . This changing policy landscape coupled with low perceived risks of edibles call for more attention on preventing use of this product among AYAs. Collectively, our study has implications for public health efforts to prevent cannabis use and its negative health effects on AYAs. Mass media campaigns and educational programs should address both perceived risks and benefits of cannabis use and consider all types of cannabis products. Effective communication strategies may be those that increase perceptions of both health and social risks and correct misperceptions of mental health benefits related to cannabis use. As AYAs’ perceptions differ by cannabis product type, messages should be tailored to specific cannabis products, especially non-combustible products, rather than focus on cannabis use generally. For example, contents on vaporized cannabis could highlight risk of vaping-related lung injuries, while those on edible cannabis could highlight risk for over-consumption and intoxication. This study has several limitations.

A study of Bicing bike sharing user activity in Barcelona found that the average difference in elevation between origin and destination stations for e-bike sharing trips was +6.21 meters, compared to -3.11 meters for conventional bike sharing trips . Although bike sharing offers the opportunity to expand cycling mode share, the evidence from traditional bike sharing ridership suggests that bike sharing users are not socio-demographically representative of the broader population in areas they operate. Existing studies of station-based bike sharing in North America have shown that bike sharing use is strongly correlated with certain user characteristics such as: gender, age, and race. Station-based bike sharing users tend to be younger and upper-to-middle income, with higher levels of educataional attainment than the general population . Station citing has been found to reflect the socio-demographic inbalances in bike sharing ridership, with one study of 42 U.S. bike sharing system reporting that the 60 percent of census tracts with greatest economic hardship contained less than 25 percent of bike sharing stations . Moreover, bike sharing station activity increases in locations with higher percentages of white residents and decreases in relation to older populations . A growing emphasis on transportation equity, particularly with respect to emerging mobiliy services, has motivated many agencies to incorporate equity-focused provisions in their shared micromobility programs . Common approaches to promote equity across station-based bike sharing systems have included offering discounted annual memberships to low income riders, citing stations based on equity reasons,commercial greenhouse supplies providing payment plan options and assistance in obtaining bank accounts, credit, and/or debit cards in order to lower access barriers to bike sharing .

Many cities have required that shared micromobility operators provide such options as a condition for obtaining an operating permit. However, additional barriers to shared micromobility use remain unaddressed. Shaheen et al. introduced the STEPS to Transportation Equity framework to evaluate transportation equity by recognizing the opportunities and limitations of Spatial, Temporal, Economic, Physiological, and Social elements . The STEPS framework can be used to evaluate whether a shared mobility system provides equitable transportation services by identifying specific barriers and opportunities within each category. In particular, spatial factors such as steep terrain and low population density may constrain bike sharing use in certain cities with these characteristics. Temporal factors, which pertain to travel time considerations of travel, may be an issue in cities where shared micromobility demand is unbalanced during peak hours, generating concerns about the reliability of available vehicles. Economic factors include both direct costs and indirect costs that may create hardship for particular groups of travelers. Physiological factors may have posed a serious limitation to bike sharing use that is reflected in the age distribution of riders, though there may be an opportunity to expand shared micromobility use for older and less physically active individuals through electric bike sharing and scooter sharing. Finally, social factors encompass social, cultural, safety, and language barriers that may inhibit an individual’s use of a particular service. Our study consists of three major analytical components: 1) a comparative analysis of bike sharing travel behavior, 2) a discrete choice analysis using a destination choice model, and 3) a geospatial suitability analysis based on the STEPS framework using the DCA coefficients. To inform our analysis, we employed two datasets from February 2018 of Ford GoBike and JUMP, composed of 77,841 docked, conventional pedal bike sharing trips and 24,270 dockless e-bike sharing trips that occurred in San Francisco. We note that February 2018 in San Francisco was slightly warmer than average and relatively dry, with 10 mm of precipitation compared to an average of 112 mm .

The high temperature and low precipitation may have resulted in greater observed ridership than would be expected during this time of year . The trip-level data include trip duration and start and end times. The origin and destination of a trip are docking stations for GoBike and census blocks in which the trip started and ended for JUMP. The age and membership status of GoBike users are also included for each trip. The datasets do not include further information regarding user identification, user characteristics, or the trajectories taken for each trip. Our analysis is thus constrained to the revealed preferences of unidentified, unlinked bike sharing users. Rather than perform a traditional discrete choice model in which individuals’ preferences for specific alternatives among a finite set of choices are modeled, we implemented a destination choice model . We modeled the decision to travel to a particular destination given that a trip originating in a particular location is made using a particular bike sharing service. We supplemented the trip-level data with: tract-level population, job count, employment rate, age, income, and gender distributions from the U.S. Census . From Open Street Map, we used the locations of bike lanes and public bike racks to determine the density of these facilities in each census tract in San Francisco . Finally, we queried the Google Directions and Elevations Application Programming Interfaces for estimates of travel distance, duration, and elevation gain along suggested bike routes for each bike sharing trip . Queries to the Google Directions API used the latitude and longitude of specified trip OD pairs to generate a suggested route that provide a path, estimated travel time, and distance for each query. These paths were then used to query the Google Elevations API for elevation samples at 100 meter intervals, which were used to estimate the total elevation gain of each trip. All unique OD pairs in the activity dataset were used in this querying process, as well as OD pairs for all alternative trips used in the DCA. Alternative GoBike trips included all possible OD pairs starting and ending at a GoBike station in San Francisco, while alternative JUMP trips were generated as the set of all actual origins of JUMP trips paired with the centroid of every census tract in San Francisco. We applied the results of the destination choice model and the STEPS framework in a suitability analysis, which is a geographic information system -based method for determining the ability of a system to meet a user’s needs . In our analysis, we examined the geospatial distribution of bike sharing suitability in San Francisco.

In the following sections, we detail the steps taken to process data, specify a destination choice model, and apply the model and the STEPS framework in a suitability analysis. In this study, observed bike sharing trip destinations are modeled as choices among a discrete set of alternative destinations. Although techniques exist to estimate continuous models , neither the GoBike nor JUMP datasets entail location data on a continuous scale. The GoBike OD locations are constrained to the discrete locations where GoBike stations exist, while the JUMP OD locations are classified by the census block in which the trip started or ended for the purpose of privacy protection. With such discrete spatial data, we took the approach of aggregating trip OD pairs to the census tract level for two reasons to: 1) avoid high correlation between very close OD pairs and 2) simplify the model analysis. Aggregating the data by census tracts also allows for the inclusion of additional attributes to the model such as: demographics, employment rate, job density, and population density,cannabis dry rack all of which can be measured at the census tract level. With aggregation of the data to the census tract level, we note a major limitation in the computability of a model with as many alternatives as there are census tracts in the coverage areas of the two SF bike sharing systems. Forty-six census tracts are serviced by the Ford GoBike system, and 192 census tracts are serviced by JUMP. Discrete choice models generally include Alternative Specific Constants that aim to capture the biases toward each alternative that is not explicitly explained by the other model attributes. To avoid overfitting and aid in the interpretability of our model, we reduced the number of ASCs by clustering the census tracts based on their attributes. We included just one ASC in the model for each of the k clusters, making reasonable assumptions that clustered alternatives have similar unexplained bias. Several techniques can be applied to solve this unsupervised clustering problem. We considered three commonly used techniques for clustering: 1) DBSCAN, 2) Gaussian Mixture Models , and 3) k-means . We decided to work with k-means as it offers two desirable properties: 1) clusters tend to have similar sizes, and 2) clusters are grouped around a centroid. The last property suited our objective of having an average ASC for the entire cluster. K-means is a distance-based algorithm that requires preprocessing of the data to avoid biases due to differences in scale. First, we apply standard normal scaling on every census-level attribute available in our data sets. As our final objective is to determine the relative likelihood of trips destined for a location, we performed a Cross Correlation Analysis between the attributes of a tract and the number of trips that end in the tract. This process produces a projection of the set of attributes so that the clustering analysis favors attributes with a strong correlation to ridership . Figure 2 presents the resulting clusters with an intuitive interpretation of each, based on our a-priori understanding of the neighborhoods they represent. For both systems, we computed elevation gain by summing all increases in elevation observed in the 100 meter intervals sampled.

A complete list of attributes included in the final model are found in Table 1. This model excludes some parameters that were found to be insignificant to the destination choices of bike sharing users. Among them, unemployment measures such as the unemployment ratio or employment to population ratio were not significant when accounting for the log number of jobs. We chose not to include trip cost and membership considerations, as they differed considerably across the GoBike and JUMP systems. GoBike members pay annual membership fees, resulting in variable per-trip costs for each member depending on the frequency with which they use the service. We also did not have information on which of the short-term pass options were used by nonmembers. Though start time has a tremendous impact on destination choice, this choice maker attribute can only be incorporated in the model by interacting it with other relevant features. We chose not to add this refinement for model simplicity. Finally, the distribution of race or ethnicity at trip destinations were found to be highly correlated with economic attributes of destinations thus were not included in the final model. For the JUMP system, we considered every tract in San Francisco County as an alternative, while for GoBike we constrained the choice set to the tracts that contain at least a GoBike station to account for trip feasibility given the service area at the time the data were collected. The sample sizes for each model amounted to 70,779 trips, with 45 alternatives for GoBike and 24,034 trips with 192 alternatives for JUMP. Including all trips and alternatives, our datasets exceeded our computational power to fit the models using the PyLogit Python package. We employed an ensemble method that combines several “weak learners” to divide the workload. In this case, a weak learner is a MNL model trained on a sample of choice experiments. For the GoBike model, each weak learner was trained on 500 choice experiments using all 45 alternatives. However, for JUMP considering all alternatives would result in keeping too few choice experiments. So, we chose to have an approach similar to those employed in stated preference surveys by restricting the number of alternatives for each choice experiment. To fit the JUMP model, we randomly sampled 110 alternatives to use for each weak learner with 500 choice experiments. We chose to use the bootstrap as it measures the variance in the parameters, indicating which parameters are not relevant in the model and can be removed. On the other hand, cross validation is more focused on assessing predictive power . Since we are more concerned with narrowing attributes to those that are most influential in destination choice rather than producing a model that predicts exactly where a bike sharing user will travel to, we considered the bootstrap a more appropriate method for this analysis. Estimating identical models separately on the two datasets required that we keep attributes that happened to be significant for one system but not for the other.

It is notable that we found that those who used marijuana more frequently prior to 2018 reported greater increases in use from 2018 onward. On one hand this is encouraging in that it suggests that lighter and non-users of marijuana were not necessarily encouraged to use as a result of legalization. On the other hand, it appears that those who were already more regular users may have tended to increase consumption, potentially increasing vulnerability to the risks associated with marijuana use. In contrast to previous studies , we found participants who endorsed greater frequency of marijuana use had greater frequency of use of tobacco products. Following legalization this was particularly true for e-cigarettes. The specific mechanism for this association is uncertain, but there are multiple possibilities. First, it may be that relaxing restrictions on a specific substance reduces substance-specific concerns about harm , which then generalizes to other drugs. Alternatively, the association can be explained by use of products that deliver both drugs at the same time , or newer vaporizing devices that may do so separately. It is plausible that innovations in nicotine vaping devices encourages marijuana vaping, promoting diversified marijuana product use and synergistically increasing use of both products. This is consistent with the strengthening association between marijuana and e-cigarette use frequencies post legalization. The possibility that lessening marijuana barriers increases tobacco use is concerning given evidence that co-use is associated with psychosocial distress , health problems , nicotine dependence , and tobacco cessation failure . The present study has several limitations. It is a secondary analysis of a naturalistic study of young adult tobacco users, which limited the specificity of marijuana-related measures and may have yielded a sample with disproportionately frequent marijuana use. There is a strong need for additional studies that include outcomes beyond simply quantity,growers equipment frequency or prevalence of use . The design may limit generalizability to other young adult samples.

Another limitation is reliance on self-reported substance use data, though evidence suggests self-report tends to be accurate in observational studies, given the lack of strong demand characteristics . Additionally, self-reported data include only some days during 2015–2019 and may not be representative of use during the entirety of this period. Finally, while the study captured self-reported use of marijuana and nicotine/tobacco products before and after legalized sales of recreational marijuana began in California, we did not directly evaluate access to marijuana retail outlets or other methods of product acquisition. In examining marijuana use before and after legalization of recreational sales in California, we found that frequency of use did not change significantly overall, including following legalization. We also found that increases in marijuana frequency tended to coincide with increased tobacco use, and a specific post-legalization association with e-cigarette use. Finally, we found that the most frequent users of marijuana after legalization were those who had used most often prior to 2018. Findings suggest loosening of marijuana restrictions could lead to negative health consequences for young adults. Strengths of the study include the sample size, and the repeated evaluation of a cohort of young adults before and after legalization. Further research is needed to confirm these findings, to understand how risks associated with changes in marijuana policy can be attenuated, and to identify surveillance targets. The continuously evolving marijuana and tobacco landscape also indicates the importance of ongoing evaluation of co-use. Correlation coefficients between environmental and biomarker measurements are widely used in environmental health assessments and epidemiology to explain the exposure associations between environmental media and human body burdens. As a result considerable attention and effort have been given to interpretation of these coefficients. However, there is limited information available on how the variance in environmental measurements, the relative contribution of exposure sources, and the elimination half-life affect the reliability of the resulting correlation coefficients.

To address this information gap, we conducted a simulation study for various exposure scenarios of home-based exposure to explore the impacts of pathway-specific scales of exposure variability on the resulting correlation coefficients between environmental and biomarker measurements. Bio-monitoring data, including those from blood, urine, hair, etc., have been used extensively to identify and quantify human exposures to environmental and occupational contaminants. However, because the measured levels in biologic samples result from multiple sources, exposure routes, and environmental media, the levels mostly fail to reveal how the exposures are linked to the source or route of exposure . Thus, comparison of biologic samples with measurements from a single environmental medium results in weak correlations and lacks statistically significance. In addition, cross-sectional biological sample sets that track a single marker have large population variability and do not capture longitudinal variability, especially for compounds with relatively short biologic half-lives, which can be on the order of days such as pesticides and phthalates. Therefore, in the case where the day-to-day variability of biological sample measurements is large, the use of biomarker samples with a low number of biological measurements in epidemiologic studies as a dependent variable can result in a misclassification of exposure as well as questions of reliability. For chemicals frequently found at higher levels in indoor residential environments than in outdoor environments, it is common to assume that major contributions to cumulative intake are home-based exposure and/or food ingestion. This simplification can be further justified because people generally spend more than 70 percent of their time indoors. Compounds with significant indoor sources and long half-lives in the human body– on the order of years for chemicals such as polybrominateddiphenyl ethers –have been found to have positive associations between indoor dust or air concentrations and serum concentrations in U.S. populations. On the other hand, extant research has not reported significant associations between indoor samples and biomarkers for chemicals primarily associated with food-based exposures, for example, bisphenol-A and perfluorinated compounds.

For chemicals with both home and food-based exposure pathways and short body half-lives , as is the case for many pesticides, a significant association between indoor samples and biomarkers is found less frequently or relatively weak compared to PBDEs. To better interpret these types of findings, we provide here a simulation study for various exposure scenarios to explore the role of the chemical properties and exposure conditions that are likely to give rise to a significant contribution from indoor exposures. We then assess for these situations the magnitude and variance of the associated correlation coefficients between biomarker and indoor levels. The objectives of this study are to generate simulated correlation coefficients between environmental measurements and biomarkers with different contributions of home-based exposure to total exposure and different day-to-day and population variability of intake from both residential environments and food, to interpret the contribution of home-based exposure to human body burden for two hypothetical compounds whose half-lives are on the order of days and years, and to determine how the pattern of variability in exposure attributes impacts the resulting correlation coefficients linking biomarker levels to exposure media concentrations.Because some indoor contaminants are considered potential threats to human health, many studies have applied significant resources to examine the relationship between exposure to indoor pollutants and adverse health effects. However, these studies are potentially limited by the use of a single or a few environmental and biological samples. The significant implications of this situation are reflected in our results. Multi-day, multi-person sample analyses are costly and labor-intensive. In addition, the resulting R2 values from these studies are not interpreted or poorly interpreted in terms of variability and contribution of exposure sources and the biological half-life of a compound. In this regard, the simulation study in this paper provides an important step towards interpreting the relative contribution of home-based exposure to human body burden for two compounds whose biological half-lives are significantly different . Although these two compounds do not cover the full range of chemical substances, bracketing half lives allows us to quantify the significance of source, measurement,plant benches and exposure pattern variability for disaggregating body burden. In particular, it shows that exposure variability and different contributions of exposure sources are more interconnected than commonly considered in many experimental studies. The work also brings to attention the need to understand the impact of a chemical half-life on the relationship between environmental exposures and bio-monitoring data. The sensitivity of day-to-day variability of wipe concentrations and food exposures on the resulting R2 values also points to the importance of understanding variability and contribution of exposure sources. Finally, future work includes computing the relative number of samples needed for various levels of confidence to disaggregate body burden for various types of compounds , environments, and exposure pathways. Despite the lack of experimental data, the simulated results provide key insights on the role of the variability and contribution of exposure sources and biological half-lives in quantifying a relationship between indoor exposure and human body burden. This approach will be useful for designing future exposure and epidemiologic studies that includes indoor environmental samples and bio-monitoring data.In 1996, California became the first state to legalize medical marijuana. Known officially as the Compassionate Use Act, Proposition 215 allowed patients and caregivers to cultivate and possess marijuana for medical use. The campaign in favor of Proposition 215 focused on the benefits for seriously ill patients. Claiming that the Proposition “sends our children the false message that marijuana is safe and healthy,” the campaign against the Proposition focused on anti-drug education .

Neither side addressed potential public health consequences. If Proposition 215 led to an increase in marijuana use, for example, might it also lead to higher rates of all injury deaths , including deaths from assault , deaths from motor vehicle crashes , and—the subject of the present study—deaths from suicide ? Such consequences assume that medical and recreational users are similar. With one exception, the evidence supports this assumption. Since most California medical users were introduced to marijuana as recreational users, for example, it is reasonable to assume that the user-types have similar socioeconomic backgrounds . Compared with recreational users, however, California’s medical users were more likely to report early health problems or disabilities that would warrant medical use . Although Proposition 215 was drafted so loosely that it effectively legalized all uses of marijuana , marijuana use by California juveniles, who were not eligible for medical marijuana certificates, did not increase following Proposition 215 . Nevertheless, at the national level, during a 15-year period when a majority of states loosened their control of medical marijuana, the U.S. suicide rate rose by 24 percent , prompting many to question how legalization and suicide might be linked. The systematic evidence connecting this trend to the availability of medical marijuana is ambiguous, however. Rylander, Valdez, and Nussbaum , for example, find no correlation between a state’s suicide rate and the number of medical marijuana cardholders in the state. Similarly, comparing suicide before and after a state enacts a medical marijuana law, Grucza et al. find no change in a state’s suicide rate. In contrast, Anderson, Rees, and Sabia report a 10.8 percent reduction in suicides averaged across all medical marijuana states. Attributing a suicide trend to the availability of medical marijuana raises questions about the potential mechanisms at play. What theoretical mechanisms could lead us to expect a relationship between the availability of medical marijuana and suicide? Could these mechanisms be more salient for certain types of suicides than others? If the expected relationship is observed, what methodological rules could be used to support a causal interpretation of the relationship? We address these questions in order. responds to changes in opportunity. Holding opportunity constant, risk responds to changes in motivation. Chew and McCleary use motivation/ opportunity mechanisms to explain life course changes in suicide. Kubrin and Wadsworth use motivation/opportunity mechanisms to explain the effects of socioeconomic factors and firearms availability on race-specific suicide. Wadsworth, Kubrin, and Herting use motivation/opportunity mechanisms to explain suicide trends for young Black males. Consistent with this literature, we argue that if medical marijuana affects suicide risk, it must do so through one or both pathways. Mental health theories operate through a motivation pathway. The psychiatric consensus is that suicide is related to depression, anxiety, and other treatable disorders . If marijuana alleviates the acute stress associated with these disorders, then we expect suicide risk to decrease following legalization of medical marijuana.

However, this trend was not significant in the larger subject population presented here, and was further decreased by the additional noise correction steps used in the present study, including removal of motion related and global signal confounds. While we did not find resting-state functional connectivity to be related to vascular differences, it did exhibit a dependence on the magnitude of spontaneous BOLD fluctuations. Previous work has shown that resting-state BOLD connectivity and amplitude also co-vary across behavioral and disease states . If spontaneous BOLD fluctuations are interpreted as faithful measures of intrinsic neural activity, these results may be interpreted as evidence for true variations in both the amplitude and coherence of the underlying neural sources. On the other hand, the results might also reflect variations in the BOLD signal’s sensitivity to spontaneous neural activity. As the BOLD signal becomes more sensitive to underlying neural activity, there can be a relative increase in the signal-to-noise ratio of the resting-state measures, i.e. the magnitude of BOLD signal fluctuations of neuronal origin divided by the magnitude of fluctuations of non-neuronal origin. Since the estimated correlation between two signals tends to increase with SNR, the relation between BOLD functional connectivity strength and fluctuation magnitude may partially reflect the change in SNR with BOLD signal sensitivity. Future experiments with simultaneous electroencephalography and fMRI would be helpful in elucidating the relationship between resting BOLD amplitude and the underlying coherence of intrinsic neural activity. In this study, subjects performed a 5-minute motor task before undergoing resting-state scans. Previous work has shown that spontaneous BOLD fluctuation amplitude and connectivity can be altered by a preceding motor task . However,cannabis drying racks these effects followed a strenuous task causing muscle fatigue, or extended periods of tapping inducing neural plasticity. We expect that these types of effects would be minimal for the relatively short and non-strenuous finger-tapping task used in this study.

Furthermore, as scan order was kept constant for all subjects, it is unlikely that task-related modulation of resting BOLD signals accounted for the inter-subject differences observed here. To conclude, the results presented here indicate that measures of resting-state BOLD fluctuation amplitude and connectivity are relatively insensitive to inter-subject differences in baseline CBF. This lack of dependence on baseline CBF, when compared to the strong inverse dependence exhibited by the task BOLD response, appears to be caused in part by a weakened inverse relationship between relative changes in CBF and baseline CBF during rest. In addition, flow-metabolism coupling is tighter during rest than during a robust motor task, further weakening the relationship between spontaneous BOLD fluctuations and CBF. As resting-state BOLD fluctuations are often used to examine functional connectivity changes in patients, where both disease and medication can alter the vasculature, our findings are very important. However, while we have demonstrated that measures of spontaneous BOLD fluctuations are not affected by vascular differences between subjects, further work is necessary to identify other non-neural confounds before these metrics can be used as reliable estimates of underlying spontaneous neural activity.In the first part of this work, we assessed the effect of a 200 mg dose of caffeine on resting-state BOLD connectivity in the motor cortex across a sample of 9 healthy subjects. As caffeine reduces baseline CBF through adenosine antagonism, and previous work has suggested that vasoconstriction increases the sensitivity of the BOLD signal to neural activity, we expected to find that BOLD fluctuations and functional connectivity were increased. Surprisingly, we found the opposite: that caffeine significantly reduced spontaneous BOLD fluctuations and connectivity. These results suggest that the primary mechanism of caffeine’s action on functional connectivity is probably not through vascular changes, but possibly through caffeine’s direct effect on neural activity. Preliminary work by our group supports this conclusion by directly demonstrating caffeine induced reductions in neural connectivity using MEG . However, the physiological mechanisms behind the caffeine-induced decrease in BOLD signal power remain unknown.

It is possible that a caffeine-induced decrease in the coupling between CBF and oxygen metabolism, which has been found during task , could be responsible for the smaller BOLD fluctuations, but this remains to be investigated. Even if vascular confounds are not responsible for the results presented here, caffeine should still be carefully considered in the design and interpretation of resting-state BOLD fMRI studies. In the second part of this work, we employed a non-stationary analysis approach to gain further insight into the mechanisms of caffeine’s effect on functional connectivity. Specifically, we used a sliding window correlation analysis to assess whether caffeine consistently weakens the correlation over time or if transient periods of strong correlation still exist, albeit less frequently. We found that BOLD correlation was significantly more variable over time following a caffeine dose, and that extended periods of strong correlation still existed between periods of lower correlation. Furthermore, the temporal variability of BOLD signal correlation was driven by phase differences between the BOLD signals in the left and right motor cortices. While a consistent decrease in correlation could be caused by an overall change in the vascular system induced by caffeine, it is unlikely that a shift in the state of the vascular system would give rise to the increase in the non-stationarity of the correlations that we found. Instead, the caffeine-induced increase in the temporal variability of functional connectivity tends to support the existence of greater temporal variability in the coherence of the underlying neural fluctuations. In the third part of this work, we investigated the BOLD signal dependence on inter-subject differences in baseline CBF using a sample of 17 healthy subjects. We acquired simultaneous BOLD and CBF measures during a motor task and resting state. Consistent with prior studies, we found a strong dependence of the task-evoked BOLD response on inter-subject variations in baseline CBF, but found a much weaker and not significant dependence of the resting-state BOLD response on baseline CBF. In addition, inter-hemispheric resting-state BOLD connectivity between motor cortex regions did not show a significant dependence on baseline CBF.

The strong inverse dependence of the task BOLD amplitude on baseline CBF appears to be caused by the direct dependence of %∆BOLD on %∆CBF, which is modeled in the Davis equation . This is because %∆CBF is inversely related to baseline CBF, as CBF0 is the denominator in calculating %∆CBF. We find that both of these relationships are weaker during rest than task. The reduced dependence of percent changes in CBF on baseline CBF during rest is caused by significantly smaller absolute CBF changes, which are independent of inter-subject differences in baseline CBF during both task and rest. The weakened relationship between relative changes in BOLD and CBF appears to be caused by tighter flow-metabolism coupling during rest than during a robust motor task. These two factors work together to produce an insignificant relationship between spontaneous BOLD fluctuations and baseline CBF. These findings suggest that differences in both the amplitude and correlation of spontaneous BOLD fluctuations between subjects are probably more reflective of neural activity differences than vascular differences.As the use of fMRI to estimate functional connectivity in the brain is a new and rapidly growing field, work that identifies potential limitations of the technique is very important. In this dissertation, differences in baseline blood flow have been investigated as confounds to interpreting BOLD connectivity changes as neural connectivity changes.While these findings are very encouraging for the field, more work remains to be done before BOLD connectivity measures can be relied on as robust measures of neural connectivity. In particular, future work is necessary to determine whether a caffeine-induced decrease in the ratio of flow-metabolism coupling is responsible for the reduced resting-state BOLD signal amplitude found in this study. If proven to be the case,vertical grow system it would suggest that flow-metabolism coupling changes can influence functional connectivity measures even if changes in CBF alone do not.Empirically determining the ratio of blood flow to oxygen metabolism changes during rest can be challenging because of the inherently low signal to noise ratio in ASL. While previous work has shown that caffeine reduces flow-metabolism coupling in response to a task , it remains to be seen whether this is the case during resting state. An important future study would therefore use the Davis model introduced in Chapter 4 to determine how caffeine affects flow-metabolism coupling during resting-state, and whether this change could be responsible for the diminished spontaneous BOLD fluctuations . This future experiment would also address some of the limitations in the methods applied in Chapter 4 to determine flow-metabolism coupling during rest. For example, in typical Davis model experiments, a model of the task paradigm is used to obtain estimates of BOLD and CBF amplitude changes that stem from neural activation, however this is not possible in resting-state studies. In Chapter 4 of this work we estimated BOLD and CBF amplitude changes using root mean square values, but these values are not necessarily reflections of neural activity. For example, physiological noise or motion may contribute significantly to the variance of the resting BOLD and CBF fluctuations and would confound Davis model estimates using RMS values. A new technique using independent component analysis has shown promise in identifying and removing signal components of non-neural origin from spontaneous BOLD signals . In this method, multi-echo data is acquired and ICA components are examined for a linear dependence on echo time , which indicates that they are truly representative of changes in deoxyhemoglobin content and thus likely to reflect neural activity rather than noise.

It is still unclear how this method can be used on CBF data, which has low sensitivity to deoxyhemoblobin content, but possibly it could be applied to the raw multi-echo ASL data before performing a sliding window difference on the cleaned first echo data set. Another approach that could improve Davis model estimates during resting-state is to acquire simultaneous EEG/fMRI data, which could provide a reference spontaneous neural signal for estimating BOLD and CBF amplitude changes that directly result from neural activity changes . Furthermore, the EEG measurements could provide information on how the amplitude of electrical power fluctuations are altered by caffeine. These findings would shed light on whether the mag-nitude of neural activity is truly reduced by caffeine and then reflected in the decreased power of spontaneous BOLD fluctuations.For each subject and run, the sliding window correlation coefficients between the measured motor cortex BOLD time courses were plotted in a histogram. The sliding window correlation coefficients between the simulated data-derived SNR signal + noise pairs were plotted in a histogram below. For several subjects, the data produced visibly multi-modal histograms in both the pre-dose and post-dose scan sessions, which are not predicted by noise. Representative examples are shown for the pre-dose scan section in Figure B.3 and post-dose scan section in Figure B.4. These findings suggest that true variations in correlation exist between the neural “signal” components of the BOLD signal.In addition to the multi-modal shape of the measured correlation histograms, it can be seen that the variance in measured r-values is visually larger than the variance of the simulated r-values, particularly for the pre-dose subject. To quantify the likelihood that noise is responsible for the measured variability in BOLD correlation present in our data, we simulated correlation variability values. We created a histogram of variability values, which were calculated as the standard deviation of the sliding window correlation time course between each pair of simulated signal + noise time courses. This resulted in 10,000 variability values. Then the actual measured variability was compared to the simulated data to determine a percentile and associated p-value . An example of this procedure is shown in Figure B.5.This was done for each subject and run. Plots of simulated data percentile versus measured variability values are shown for each subject and run during the pre-dose and post-dose scan sessions . Note there are two runs per subject and session. Data points above the dashed line correspond to p-values less than 0.05. These simulations suggest that it is highly unlikely that noise is primarily responsible for the temporal variability in BOLD correlation found in the present study. While noise may still contribute to correlation variability, it is probable that underlying variability in the coherence of neural activity is responsible as well.Since California’s Compassionate Use Act of 1996, cannabis has been legally available — under state but not federal law — to those with medical permission. Until 2018, however, no statewide regulations governed the production, manufacturing and sale of cannabis. Prior to development and enforcement of statewide regulations, there were no testing requirements for chemicals used during cannabis cultivation and processing, including pesticides, fertilizers or solvents .

Because both the global and RVT signals were obtained in arbitrary units, we first normalized the global and RVT signals to their mean values to derive percent changes of these signals. We then computed the energy below 0.08 Hz and the standard deviation of the percent change signals. To assess changes in the finger tapping BOLD response to caffeine, average BOLD block responses were extracted from the combined motor cortex ROI. Each subject’s BOLD block response was interpolated to a time resolution of 0.25s and the following timing parameters were computed: 1) time to reach 50% of the peak response , 2) time at which the response falls to 50% of the peak response , and 3) the full-width half-maximum . In addition, the peak BOLD response amplitudes were calculated for each subject.Figure 2.1 provides a qualitative summary of the results. Resting-state functional connectivity maps obtained by using the average signal from the left ROI as a reference are shown for a representative slice from each subject. The top two rows display each subject’s average pre-dose and post-dose maps for the caffeine session. The extent of significantly correlated voxels in the post-dose connectivity maps has been visibly diminished in most subjects, as compared to the pre-dose maps. In contrast, for most subjects the control session maps in the bottom two rows do not demonstrate an obvious difference in connectivity between pre-dose and post-dose conditions. Metrics of connectivity strength are graphed in Figure 2.2. The scatter plots in the top row show the mean z scores for each subject from the caffeine session and control session, with the solid black lines representing equality between the pre-dose and post-dose sections. The caffeine session plot shows a significant = 3.3, p = 0.01 caffeine-induced decrease in mean z score across subjects. In contrast,rolling benches the mean z scores in the control session are clustered about the centerline, without a significant = -0.51, p = 0.62 change between pre-dose and post-dose conditions.

These results are consistent with a repeated measures two-way ANOVA, which showed that the interaction between period and session is significant = 9.8, p = 0.01. In addition, the baseline mean z scores in panels and are not significantly different = 0.36, p = 0.73. The mean z scores presented here were determined using the average time course from the left motor ROI as a reference. If the average time course from the right ROI is used as a reference instead, the caffeine-induced decline in mean z score is still significant = 2.8, p = 0.02.We have shown that caffeine reduces resting-state BOLD connectivity in the motor cortex. This reduction was apparent in the connectivity maps from a representative subject and in the quantitative metrics of connectivity obtained for all subjects. In addition, we found that baseline CBF and the magnitudes of the spontaneous BOLD fluctuations were decreased by caffeine. Physiological confounds, such as changes in respiration and arterial CO2 levels, can alter BOLD signal fluctuations. Regression-based removal of global and respiration volume per time signals has been shown to reduce the influence of these physiological confounds . When we included the regression of the RVT or global signal in our data analysis we found that the post-dose connectivity metrics were still significantly decreased. In fact, RVT signal regression did not significantly alter the functional connectivity metrics . Furthermore, we found that the variance or energy in the global and RVT signals were not significantly altered by caffeine . These findings suggest that the caffeine-induced reduction in BOLD connectivity is not primarily due to respiration changes. In agreement with the literature, we found that the caffeine-induced reduction in CBF was associated with an acceleration of the BOLD response to a finger tapping task . In addition, vasoconstriction due to either hypocapnia or nitric oxide synthase blockade has been found to increase low-frequency fluctuations in CBF . We have previously presented a bio-mechanical model that explains how vasoconstriction can increase the dynamic compliance of the arterioles and thus increase the responsiveness of the vasculature to neural stimulus and fluctuations .

However, in this study we found that both the spectral amplitude of low-frequency BOLD fluctuations and the coherence between resting BOLD fluctuations were diminished by caffeine, suggesting that an increase in bio-mechanical responsiveness was not a dominant factor. As binding of adenosine to A2 receptors is associated with vasodilation, caffeine-related antagonism may reduce the ability of adenosine to contribute to functional increases in cerebral blood flow. In a recent study, we found that a 200 mg oral dose of caffeine led to a significant decrease in the absolute functional CBF change in response to a visual stimulus but resulted in a significant increase in the percent CBF change . These results indicated that the drop in the absolute functional CBF change was primarily related to a drop in baseline CBF as opposed to reflecting an impairment of neurovascular coupling. Also consistent with our prior work and the results of this study, Liau et al. did not find a significant change in the BOLD response. Taken together, these results indicate that caffeine’s effect on adenosine-related vasodilation does not significantly reduce the task-related functional BOLD response. If task-related and resting-state BOLD activity share a common neurovascular coupling pathway, then the task-related BOLD results suggest that an impairment of adenosine-related vasodilation was probably not the dominant factor in the reduced functional connectivity observed in this study. Further studies elucidating the similarities and differences in neurovascular coupling for task-related and resting-state BOLD signals would be useful. In addition to its vasoconstrictive effects, caffeine directly influences neural activity. Caffeine stimulates the central nervous system by antagonizing adenosine A1 receptors throughout the brain. This blocks the inhibitory actions of adenosine, which include hyperpolarization of membrane potentials and the inhibition of neurotransmitter release . Although caffeine acts as a neurostimulant, previous work has shown that a 200 mg dose of caffeine reduces the power of resting electroencephalography activity in the alpha, beta, and theta bands . In addition, the coherence of anterior cortex neural fluctuations in the alpha and theta bands is decreased by caffeine when compared to periods of caffeine abstinence .

Simultaneous EEG/fMRI recordings have shown that resting-state BOLD fluctuations are significantly correlated with EEG power fluctuations in the alpha band , the beta band , and the theta band . These prior findings suggest that the reduction in resting-state BOLD fluctuations and connectivity found in this study may primarily reflect changes in neural power fluctuations. Although the physiological effects of caffeine are often beneficial,rolling grow table such as enhanced mood, attention, wakefulness, and motor speed , a 200 mg dose has been shown to impair several types of memory tasks, including motor learning of a finger tapping task . In light of our findings, this observed decrease in motor learning might reflect a caffeine-induced decrease in resting state neural connectivity. Further experiments with simultaneous EEG/fMRI would be useful to determine if caffeine-induced changes in neural power fluctuations are directly related to the observed reduction of BOLD connectivity. In addition to caffeine, a number of pharmacological agents have been found to alter resting-state BOLD connectivity. Both hypercapnia and cocaine have been shown to reduce the magnitude and coherence of resting-state BOLD fluctuations, while anesthesia appears to have varying effects depending on the specific agent and brain region. Cognitive disorders such as Alzheimer’s disease, schizophrenia, multiple sclerosis, and epilepsy have also been shown to modulate BOLD connectivity . While changes in resting-state BOLD connectivity are typically interpreted as changes in coherent neural activity across spatially distinct brain regions, changes to the neurovascular system may also alter connectivity. For example, as mentioned in the Introduction, hypercapnia appears to decrease BOLD connectivity by weakening the neurovascular coupling between spontaneous neural activity and resting-state BOLD fluctuations. Since many pharmacological agents and diseases are likely to affect both the neural and vascular systems, a greater understanding of the neural and vascular mechanisms that give rise to resting-state BOLD connectivity will be critical for the correct interpretation of changes in connectivity. Similar to a prior study examining the effect of caffeine on baseline oxygen metabolism , we used a control session to examine potential changes in baseline CBF, resting-state functional connectivity, and low-frequency BOLD fluctuations that might have been caused by differences in the subject’s state between the pre-dose and post-dose scan sections. We did not find significant differences between the pre-dose and post-dose results obtained during the control session, indicating that the caffeine-induced decrease in BOLD connectivity was not due to factors, such as subject fatigue, associated with participating in two scan sections. As our protocol did not involve the administration of a placebo dose, it is possible that psychological effects associated with taking a dose could have affected the functional connectivity measures.

Future studies would be useful to assess the effect of a placebo dose on resting-state BOLD connectivity. There was a range of caffeine usage in the current sample of subjects. Prior work has demonstrated variability in the task-related BOLD response due to differences in dietary caffeine consumption. Inter-subject differences in caffeine consumption may also influence the effect of caffeine on resting-state BOLD connectivity. In addition, subjects in this study were asked to abstain from caffeine for at least 12 hours prior to being scanned. Caffeine withdrawal has been shown to alter EEG power in the alpha and theta bands . It is possible that caffeine’s effect on resting-state BOLD connectivity will differ based on the subject’s state of withdrawal during the pre-dose scan section. Further investigation of the effects of dietary consumption and withdrawal on caffeine-induced changes in BOLD connectivity will be helpful. The work presented here shows that caffeine reduces resting-state BOLD connectivity in the motor cortex, most likely by reducing the amplitude and coherence of neural power fluctuations. As the distribution of adenosine receptors varies across the brain , it is possible that the effect of caffeine on functional connectivity will vary with the local receptor concentration. While future work is necessary to determine whether caffeine alters connectivity in other functional networks, the findings of this study indicate that caffeine usage should be carefully considered in the design and interpretation of studies involving resting-state BOLD connectivity.Resting-state functional MRI can be used to assess functional connectivity within the brain through the measurement of correlations between spontaneous blood oxygenation level-dependent fluctuations in different regions. Synchronous BOLD fluctuations have been consistently found at rest within functional networks such as the motor cortex, visual cortex, and default mode network . A growing number of studies have shown that functional connectivity is altered for cognitive disorders such as multiple sclerosis, epilepsy, Parkinson’s, and Alzheimer’s disease , suggesting that resting-state studies can aid in disease diagnosis and improved understanding of disease mechanisms. In addition, inter-subject differences in functional connectivity have been shown to correlate with performance on working memory tasks and intelligence . To date, functional connectivity studies have typically employed stationary metrics obtained with seed-based correlations or independent component analysis computed over an entire resting scan. However, recent work has shown that the correlation strength between different brain regions may vary in time. For example, a study using magnetoencephalography found transient formations of widespread correlationsin resting-state power fluctuations within the DMN and task positive network . This non-stationary phenomenon was particularly apparent when considering nodes in different hemispheres, which exhibited very low stationary correlation. Another study using fMRI found that the phase angle between spontaneous BOLD fluctuations in the DMN and TPN varied considerably over time, with frequent periods of significant anti-correlation . These studies indicate that coordination of spontaneous neural activity is a dynamic process, and suggest that time varying approaches can provide critical insights into functional connectivity. Despite the increasing appearance of resting-state functional connectivity studies in the literature, it remains difficult to interpret the physiological mechanisms behind changes in BOLD signal correlations. The BOLD signal provides an indirect measure of neural activity, and is a complex function of changes in cerebral blood flow , cerebral blood volume, and oxygen metabolism . Factors that alter any part of the pathway between neural activity and the BOLD response can change functional connectivity measurements, making it difficult to decipher the origin of this effect. For example, caffeine is a widely used stimulant that has a complex effect on the coupling between neural activity and blood flow .

Comparing states before and after MML enactment to states without such laws, they find that MML enactment leads to a significant reduction of about 16% in the fatality rate for individuals aged 20-40 from motor vehicle accidents. The authors also find that legalization of medical marijuana leads to a significant 13.2% decrease in fatalities from alcohol-involved accidents. Pacula et al. recognized that heterogeneity in MML policy may result in heterogeneous effects, and they repeated the analysis of Anderson et al. distinguishing between MMLs with and without legal dispensary allowances. Their results confirm that MML enactment is negatively associated with alcohol-involved traffic fatality rates, but they show that this negative relationship is almost entirely offset in states that allow dispensaries. These results are supported by Choi , who uses individual-level data from the National Survey of Drug Use and Health from 2004-2012 and finds that allowing marijuana dispensaries is associated with a 9.1 and 23.9 percent increase in reporting driving under the influence of alcohol and drugs respectively. Why should there be differences in the effects of MMLs on traffic fatalities depending on the allowance of dispensaries? For one, Downey et al. finds that high tetrahydrocannabinol levels significantly increase driving impairment,hydroponics flood tray and MML states that allow dispensaries have been shown to have greater diffusion of high-potency cannabis . Comparing Colorado to non-MML states, Salomonsen-Sautel, Min, et al. find no significant increase in the propor-tion of drivers involved in a fatal motor vehicle crash who tested positive for marijuana until the period when medical marijuana commercialization expanded .The evidence from Table 3.1 can help explain the different findings of past research. There is wide variation in medical marijuana penetration depending on the supply regulations established by state MML policies and the level of federal enforcement.

Using the timing of state MML enactment to identify the effects of medical marijuana legalization on traffic fatalities misses important changes in the availability of marijuana that occur long after initial MML passage. These omitted effects may be of particular importance when assessing the impact on youths, who are more likely to self-report willingness to drive after consuming cannabis or after consuming both marijuana and alcohol . Irrespective of its effects on traffic accidents, marijuana’s role as a substitute for alcohol has important health implications. The question of whether alcohol and marijuana are substitutes or complements has received substantial attention in the literature, but findings have varied. Given the breadth of work, I focus here on studies using marijuana liberalization policies to identify the relationship between marijuana and alcohol use. Empirical evidence on the effect of marijuana decriminalization policies is mixed for a review of the literature, and more recent work specifically examining the effects of MMLs on alcohol consumption has found similarly varied outcomes. Using data from the 2004-2012 National Survey of Drug Use and Health , Wen et al. report a positive effect of MML enactment on frequency of binge drinking for adults over 20 years of age but no effect on alcohol use by individuals under age 21. In contrast, Anderson et al. examine data from the 1993-2010 Behavioral Risk Factor Surveillance System and find that MML enactment has a significant negative impact on past-month drinking and binge-drinking among young adults. Differences in these findings may in part be driven by the fact that the studies cover different years, and hence different state laws are used in the identification of the treatment effects. Pacula, MacCoun, et al. caution against using a binary indicator for marijuana decriminalization laws, as there is substantial variation in how these laws are implemented, enforced, and hence understood by citizens.

As discussed by Pacula et al. and demonstrated in section 3.2, state MML regulations vary greatly and can thus be expected to generate heterogeneous effects. Additionally, the categorical MML measure used in past analyses does not capture the later evolution of medical marijuana markets shown in Figure 3.2. The inclusion of state-specific trends in the empirical specification will thus confound preexisting trends with the dynamic effects of the policy . By using medical marijuana registration rates instead of a binary MML indicator as the policy variable of interest, this paper overcomes these limitations. While there are only a few economic studies of the relationship between marijuana and opioid use, clinical studies by Cichewicz and Welch and Ramesh et al. suggest that smoked cannabis and cannabinoids have opioid sparing properties and may prevent the development of tolerance to opiates. Additionally, studies of opioid dependent patients indicate that moderate use of cannabis or synthetic cannabinoids leads to significantly improved outcomes for medication compliance, opioid withdrawal symptoms, and retention in treatment . The potential for medical marijuana to reduce opioid abuse is supported by Bachhuber et al. , who find that MML enactment is associated with a significant 20% decrease in age-adjusted prescription opioid-related mortality, with these effects strengthening several years post-enactment. Powell et al. find no effect of MMLs on opioid abuse or mortality, but they find that legalizing dispensaries reduces opioid abuse and mortality by about 15%. While these results suggest that increased medical marijuana availability offers the benefit of significantly reducing opioid use, past work has not examined to whom these benefits are accruing. While all age groups have seen significant growth in opioid-related deaths, the rise in mortality rates has been most pronounced for adults aged 45-64 .

Indeed, recent work by Case and Deaton shows that drug poisoning deaths have significantly contributed to the reversal in mortality improvement experienced by US white non-Hispanics aged 45-54 between 1999 and 2013. By estimating the effects of increased medical marijuana availability on opioid poisoning mortality separately by age group, this paper contributes toward further understanding whether medical marijuana can serve to improve the deteriorating mortality outcomes for older individuals. To assess whether increased cannabis use results in more automobile accidents, data was compiled from the Fatal Accident Reporting System for 1990-2013. FARS, collected by the National Highway Traffic Safety Administration ,hydro flood table contains detailed information on the circumstances of the accident, in addition to information on the characteristics of occupants and non-occupants. In order to most precisely identify which age groups experience increased risk of causing fatal accidents,traffic fatalities are analyzed separately by age of the driver involved in single-vehicle accidents only. Summary statistics are given in Appendix H-. It should be noted that the traffic fatality variables used in this analysis differ slightly from that used in Anderson et al. . Their analysis separates traffic fatalities by age of the deceased, while my empirical model analyzes fatalities by age of the driver involved. While these two variables should be correlated, focusing on age of the driver involved provides a better indicator of which individuals are changing their alcohol and cannabis consumption in response to increased medical marijuana availability.To investigate substitution between marijuana and other addictive substances, substance related poisoning mortality data from 1990-2013 was downloaded from the Center for Disease Control’s Wide-ranging Online Data for Epidemiologic Research interface. Alcohol poisonings are defined as deaths with ICD-10 code X45, X65, Y15, or F10.0.5 Opioid analgesic poisonings are defined as those under ICD-10 codes X40-X44, X60-X64, or Y10-Y14, where a prescription opioid was also coded. Heroin-related poisonings are defined as those under ICD-10 codes X40-X44, X60-X64, or Y10-Y14, where heroin was also coded. Summary statistics are given in Appendix H-. Deaths are coded based on multiple-cause reporting instead of the underlying cause, since this can provide a more complete representation of all conditions that contributed to the death . A death is counted if the specified condition is listed on the death certificate as a contributing factor, but the condition need not be the specified as the underlying cause of death. Thus, these counts do not necessarily represent unique deaths on all fatal traffic accidents, but these effects are only statistically significant for daytime accidents. For older adult drivers, increased registration rates do not predict any significant change in fatal traffic accidents. However, while not statistically significant, the effects on drivers aged 45-64 are all negative, with the largest effects for nighttime accidents. While Table 3.2 indicates that greater marijuana availability leads to increased traffic fatalities involving young drivers, it is unclear whether these effects are driven by cannabis use alone or the joint use of cannabis with other substances. To disentangle the role of alcohol and marijuana in generating motor vehicle fatalities, Table 3.3 presents estimates of the effects of registration rate growth on traffic fatalities seperately by substance involvement.Panel A reports estimates of the effects of legal market growth on fatalities in which the driver’s blood alcohol content was tested and found to be equal to zero.

For all age groups, the estimates are insignificant. In contrast, for accidents in which the driver had a positive BAC value, Panel B shows that increased medical marijuana availability is associated with a significant 11.6% increase in traffic fatalities involving a driver aged 15-20 and an insignificant 5.3% decrease for drivers aged 45-64. While the results from Panel C are consistent with increased prevalence of cannabis use for drivers of all ages, Panel D suggests that it is the joint use of alcohol and marijuana that generates negative externalities in the form of increased traffic fatalities caused by drivers aged 15-24. The results of Table 3.3 are consistent with the experimental evidence from driving simulator studies, but caution should be taken in interpreting these results. Drivers are not regularly tested for cannabinoids, and the decision to test may well be endogenous with expansion in the medical marijuana market. Also, because THC is lipid-soluble and excreted slowly over time into urine, a positive test for cannabinoids does not necessarily mean the individual has used marijuana recently — let alone that cannabis-impairment caused the accident . Still, the results suggest that differences between youths and older adults in the decision to use marijuana and alcohol jointly may generate different health consequences caused by greater marijuana availability. To further examine substitution behavior, Table 3.4 presents estimates of the effects of registration rates on poisoning mortality involving alcohol , prescription opioid analgesics , and heroin . Panel A shows that higher registration rates predict a large and significant decline in alcohol-related poisoning mortality for adults aged 45-64. With increased medical marijuana access, older adults appear to substitute away from the heavy use of opioids. Registration rates have a significant negative effect on opioid-analgesic poisoning mortality for adults aged 45-64 of 11-15%. These results are smaller but in line with the findings of Powell et al. . In contrast, there is suggestive evidence of complementarity between alcohol and marijuana for youths aged 15-24, consistent with the evidence from Tables 3.2 and 3.3. As stated earlier, the Poisson specification was preferred over the more intuitive loglinear specification and the commonly-used negative binomial regression model. Since in many years, there are relatively few single-vehicle traffic fatalities, a loglinear specification will introduce considerable noise in the analysis, and will result in biased estimates under heteroskedasticity . While the negative binomial regression estimator can account for the over-dispersion apparent in the data, violation of the model’s assumptions about the underlying data-generating process will produce biased coefficients . Still, these alternative models can provide specification checks for the primary analyses. Tables 3.5 and 3.6 thus present coefficients on the registration rate variable from the log-linear and negative binomial specifications for completeness. In line with the estimates from Tables 3.2 and 3.4, these alternative specifications confirm that growth in the legal medical marijuana leads to a significant increase in weekend and nighttime traffic fatalities caused by drivers aged 15-20, and significant declines in alcohol and opioid analgesic poisoning deaths for adults aged 45-64. As the final set of sensitivity analyses, Tables 3.7 and 3.8 estimate the effects of medical marijuana market growth including only states that had enacted an MML as of 2015. The effects are thus estimated from differences in market size within the set of states that presently provide legal protections for medical marijuana. Since all of these states eventually passed laws, they may be considered more similar. For all outcomes, the results are largely unchanged. One difference of note is that the negative effects on traffic fatalities involving a driver aged 45-64 are larger and significant when the sample is restricted to MML states.

And for states that legalized state-licensed dispensaries in their initial MML, there were often substantial implementation lags between law enactment, the licensing of dispensaries, and the opening of dispensaries . Even if the date of first dispensary operation is correctly identified, production-related realities may lead to further lags before the full effects on access and price are realized. These lags are likely not random, but will be correlated with unobservable local attributes as well as enforcement efforts at the federal level. This paper shows that there is substantial heterogeneity both across states and over time in the extent to which users and suppliers have actively participated in state medical marijuana programs. Changes in perceived federal enforcement had far greater effects on medical marijuana take-up than MML enactment alone, and these effects were concentrated in those states that imposed relatively lax restrictions on legal producers. This indicates that medical marijuana participation is largely driven by the expected benefits associated with access to legal supply. A few other findings deserve mention. First, states with MMLs allowing chronic pain as a qualifying condition on average have significantly higher registration rates. This is unsurprising since these states have a larger pool of eligible applicants. However, the fact that the federal memos did not have a differential effect on registration rate trends in these states with laxer qualifying standards suggests the federal policies did not differentially affect physician willingness to recommend a “marginal” patient. Second, higher registration fees significantly reduce medical marijuana participation. This is consistent with anecdotal evidence that registration fees represent a barrier to take-up for many patients ,hydroponic flood table and future work should assess whether there is age or demographic heterogeneity in the elasticity of participation with respect to application costs.

From this paper’s analysis, changes in medical marijuana registration rates seem to follow a pattern largely consistent with economic models of rationality. Overall, both across- and within-state variation in medical marijuana patient registration rates is primarily driven by differences in the costs of obtaining marijuana. Given evidence that supply spillovers from legal medical marijuana markets to illegal markets largely occur through diversion from registered patients to unregistered consumers , changes in registration rates may more precisely reflect the spillover effects of MML policy compared to binary indicators for various dimensions of MML regulations. The conclusions of this paper can be used to inform the methodologies employed by future work studying the effects of MMLs on substance use and other related outcomes. Using a binary measure of MML enactment to identify the effects of marijuana liberalization relies on a coarse measure of the impact of MML policy on the marginal user. By instead evaluating the effect of MMLs through changes in registration rates or the policy aspects that induced such changes, future work can more accurately assess the complex and dynamic effects of liberalization policies on marijuana consumption and its associated health consequences in the general population. Since 1996, growing evidence of the potential medical benefits of cannabis and increasing social acceptance of the drug have led twenty-three states and Washington D.C. to enact medical marijuana laws , which legalize the use and cultivation of marijuana for medical purposes at the state level. Several of these states have also recently expanded legalization to allow the commercial sale of marijuana to adults for recreational purposes. Prior research has sought to estimate the effects of these policies on marijuana consumption by comparing MML states before and after law passage to states without such laws, but findings have varied.However, this approach assumes that the law’s passage had an equal and immediate effect on demand-side or supply-side channels that would increase consumption.

This paper instead uses improved data and a novel instrumental variables approach to directly study the effects of growth in the size of legal markets for medical marijuana on recreational use. Newly collected data on per capita medical marijuana patient registration rates show that changes in market size are driven by policies changing supply costs and not MML enactment alone, and that growth in the legal market has the unintended consequence of significantly increasing recreational marijuana use among both adults and adolescents. Evidence suggests that changes in the supply of medical marijuana, driven by policies changing the costs faced by legal producers, generate these spillovers to adolescent markets. A simple model of supplier behavior argues that if growth in the legal market is driven by lower production costs, then the price and availability of marijuana in the illegal market will track changes in the legal market. Specifically, the model implies that changes in federal enforcement will have larger effects on marijuana availability in states where legal producers are subject to relatively lax production limits. To con- firm the model’s predictions, I exploit two policies that changed federal enforcement against medical marijuana suppliers as cost shifters. Empirical evidence from medical marijuana patient registration rates confirms that supply costs drive changes in the legal market. The Ogden Memo caused an additional 2% of the adult population to register as medical marijuana patients in states with loose supply regulations, compared to an additional 0.2% in states with strict regulations. Similarly, the Cole Memo significantly reduced legal market growth in loosely regulated MML states but had no effect in states that strictly regulated supply. To estimate the causal effect of changes in medical marijuana supply on marijuana consumption, I instrument for market size with the interaction of initial state regulatory laxness and changes in federal enforcement.

Results show that reaching the median state’s legal market size would significantly increase the prevalence of marijuana use in the past month from 7.2% to 7.7% for adolescents aged 12-17 , from 17.3% to 18.9% for 18-25 year-olds, and from 4.4% to 5.2% for adults over age 25. The significant effects found for youths are in contrast to the null findings of prior research.2 This paper contributes a novel approach for estimating heterogeneous effects in markets with limited legal access. However, this approach has some potential limitations. First, the identification strategy relies on the assumption that federal enforcement changes did not impact demand differently in states with different supply regulations; a number of robustness checks address this potential threat to identification. Second, since the measures of marijuana use are self-reported, the estimates may be subject to reporting bias. Evidence from arrest rates is used to support that the results are not driven by changes in reporting behavior. Third, the approach does not account for potential cross-border spillovers of marijuana supply. This would tend to bias the estimates downward, but evidence using Montana as a case-study indicates that this bias is small. The paper proceeds as follows. Section 2.2 explains state variation in medical marijuana policies, outlines a simple model of supplier behavior, and shows that medical marijuana registration rates are a valid measure of legal marijuana market size. Section 2.3 outlines the methodological approach, section 2.4 describes the data, and section 2.5 presents the empirical results. Robustness checks are included in section 2.6, and section 2.7 concludes. With the Compassionate Use Act in 1996, California became the first state to pass a medical marijuana law , removing criminal penalties for the use, possession, and cultivation of medical marijuana. The state law directly contradicts the federal ban on marijuana use and distribution established in 1937,which remains in place today due to concerns cited by the Office of National Drug Control Policy about legalization’s effects on marijuana use and abuse . The potential increase in youth consumption is of particular concern as some research suggests use of marijuana during early adolescence predicts increased risk of dependence,ebb and flood table lower educational attainment, and cognitive impairmentDespite federal prohibition of marijuana, as of 2015, twenty-two additional states and Washington D.C. have enacted laws providing some protections for the use of medical marijuana; five of these states have also legalized the sale of marijuana to adults aged 21 and older for non-medical purposes. Table 2.1 shows a summary of state laws and various dimensions on which they differ.5 These state regulations vary greatly in how medical marijuana can be supplied. Certain states allowed “caregivers” to supply marijuana to an unlimited number of patients. This permitted producers to operate with virtually no quantity limits and little state oversight. Other states had more restrictive supply limits, allowing only home-cultivation by the patient himself or by the patient’s designated caregiver, who was limited to supplying only one patient. Finally, some states legalized state-licensed dispensaries, which could serve many patients but were subject to substantial monitoring and limited production quotas. Different production allowances can be expected to have heterogeneous effects on price, availability, quality, and product variety in the legal market. While laxer production limits should lead to lower access costs for registered medical marijuana patients, they may also have the unintended effect of increasing spillovers to youths. Adolescents largely do not have legal access to medical marijuana,6 but evidence from cigarette markets shows that youth access laws are limited in effectively reducing teenage consumption due to the presence of social markets .

As costs facing legal users in the formal market significantly affect availability to underage users through these secondary markets , changes in the size of the state medical marijuana industry may better predict changes in adolescent cannabis use than the passage of the law alone. Previous work has mostly ignored the wide variation in MMLs and their implementation. Given the heterogeneity in state supply restrictions, differences in the findings of prior work may be partly due to which MML states are used for identification. Pacula et al. recognized the importance of accounting for differences in the specific dimensions of MML policy, but there remains debate over how to categorize these regulatory differences and interpret their effects. For instance, Pacula et al. find evidence that only MMLs that legalized dispensaries saw significant increases in recreational marijuana use. However, their finding that dispensary laws have significant effects even without the existence of operational dispensaries raises questions as to how to interpret these results . And, as shown in Table 2.1, there is heterogeneity even within states that allowed dispensaries in the strictness of production restrictions. Another potential explanation for the varied findings of past work is the incomplete consideration of the role of federal policy in determining the size and structure of medical marijuana markets. While federal law has remained unchanged throughout years of state experimentation with marijuana liberalization, federal enforcement in these states has varied widely. Before 2009, the federal government made direct threats toward MML states, stating that even users and suppliers in compliance with state policy would remain subject to federal prosecution . However, between 2009 and 2012, two federal memos dramatically altered perceived federal enforcement in medical marijuana states. The Ogden Memo, announced on October 19, 2009, formalized guidelines for federal prosecutors in MML states. The memorandum maintained the government’s commitment to prosecuting significant traffickers of marijuana, but emphasized that “prosecution of individuals with cancer or other serious illnesses who use marijuana as part of a recommended treatment regimen consistent with applicable state law, or those caregivers in clear and unambiguous compliance with existing state law who provide such individuals with marijuana, is unlikely to be an efficient use of limited federal resources” . In sum, the Ogden Memo de-prioritized the federal government’s involvement in prosecuting medical marijuana users and suppliers in states with MMLs. On June 29, 2011, the US government reversed this stance by issuing the Cole Memo as a response to the government’s perceived “increase in the scope of commercial cultivation, sale, and distribution and use of marijuana for purported medical purposes” . The Cole Memo stated that individuals involved in the business of medical marijuana sales and distribution would be subject to federal enforcement action. In the months leading up to and following the memo, the Drug Enforcement Administration stepped up raids on medical marijuana producers . If changes in the risk of federal prosecution shift production costs, then both state variation in supply restrictions and time-variation in federal policy will determine the size of the legal market. Moreover, in states where legal and illegal markets for marijuana co-exist, policy changes that shift production costs in the legal market may affect price and availability in the illicit market. To better understand the effects of costs associated with state restrictions and federal enforcement, I outline a simple model of supplier behavior.