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Greater exposure reduced the odds of current cigarette use and smokeless use by 30% and 45%, respectively. Anti-smoking media campaigns help to shape social norms and institutional policies around smoking, which in turn change smoking behavior at the population level,including adult quit attempts.Several studies have found that youth are equally likely to report favorable responses to adult-targeted ads as to youth targeted ads,Studies from California,Massachusetts,and Australia demonstrate that exposure to adult-targeted mass media campaigns is associated with reduced smoking initiation and smoking behavior among youth. Even in countries where comprehensive tobacco control policies have been in effect for decades , intensive mass media campaigns have a positive additional influence on smoking behavior outcomes.Tobacco taxes are used to provide an annual revenue stream to support implementation of government media campaigns that consist of paid radio, television, billboard, internet and social media, and print advertising. Media campaigns with greater impact also include public relations campaigns for general market and population-specific communities, including various ethnic populations, young adult, and lesbian, gay, bisexual, transgender, and queer communities.Social norm change has been one of the most effective tobacco control strategies in the United States. The most successful application of the social norm change strategy took place in California, where in 1989 a statewide tobacco control program was implemented to transform the social environment where tobacco use is not socially desirable or acceptable.The key to the success of the California Tobacco Control Program has been its design as a broad-based campaign focused on reinforcing the nonsmoking norm aimed at the population as a whole – not just smokers or youth,for each element of the program, including the statewide hard hitting, evidence-based media campaign. Indeed,greenhouse growing racks by focusing on adults through its comprehensive tobacco control program, California has achieved one of the lowest youth smoking rates in the United States.Advertising bans are another important policy to denormalize tobacco use.

Like large graphic warning labels and plain packaging, they are inexpensive for governments to implement, and generally apply to all products Point of sale tobacco display bans in Ireland and Australia were both followed by reduction in perceived smoking prevalence among youth and young adults, which reflects lower normalization of tobacco use. In contrast to media campaigns, which require regular appropriations and create ongoing opportunities for the tobacco industry to weaken, block, or eliminate funding, advertising bans, once enacted, are legally binding.Promoting understanding of the industry’s predatory behavior has been a central theme of the California Tobacco Control Program since it started in 1989 and the Truth Initiative “truth” campaign. The messaging frame on industry behavior is an important reason for these campaigns’ success at preventing smoking initiation and promoting quit attempts, likely because they reduce the attractiveness of affiliating with the tobacco companies’ brand images. In contrast, programs that focus solely on individual, peer and family influences on youth smoking prevention and understate or ignore the effects of tobacco industry advertising are less effective than campaigns that highlight the role of the tobacco industry. Indeed, when Florida – where the “truth” campaign first originated in 1999– shifted its media messaging away from confronting the tobacco industry to a softer “kids shouldn’t smoke” message, it lost its effectiveness.Tobacco companies use product engineering to maximize consumption and profits.Large corporations have the scientific and technical capacity to undertake research and development programs that aim to identify which characteristics of a product to manipulate, and use sophisticated manufacturing processes to accentuate product features that maximize addictive potential. The cigarette companies invested heavily in their secret internal R&D departments to understand the addiction process, and modified their products to increase their addictiveness.Reviews of internal industry documents show that cigarette companies manipulate nicotine levels, cigarette length, chemical additives to alter nicotine absorption, improve the flavour of the smoke, reduce harshness,and increase puff intensity.

They also use ventilated filters, manipulation of nicotine levels,and other product modifications to attract novice smokers and to increase addictive potential by optimizing nicotine delivery and dosing.Cigarette companies also designed their brands to meet psychological and psychosocial needs of consumers.In addition to attracting youth,product design technology was used to recruit and socially normalize smoking among women,African Americans,Latinos,Asians,LGBTQ,low income groups, and veterans. Cigarette companies have also taken advantage of weak cigarette testing protocols around the world to conceal the actual toxicity of their products to consumers and regulators.In the process of manufacturing cigarettes to enhance nicotine delivery, and so the addictiveness and sales of cigarettes, tobacco companies have reduced particle size and made many other design changes which , while good for the cigarette business, resulted in a more dangerous cigarette in 2014 than in 50 years earlier in 1964. Changes in tobacco blends and curing of tobacco has caused US cigarettes to have higher levels of tobacco specific nitrosamines , a group of carcinogens found in tobacco and nicotine products. Surgeon General Report observed that “[f]or Kentucky reference cigarettes, mutagenicity per mg of total particulate matter was 30–40% lower for unfiltered cigarettes than for the same cigarette with a filter added.”These design changes have not only made cigarettes become more dangerous in terms of rising lung cancer rates,but also contributed to an increase in overall mortality, chronic obstructive pulmonary disease and heart disease. The rising risks correspond to changes in cigarette design – unfiltered to filtered, higher tar to lower tar, introduction of filter vents, among other changes to cigarette design. Deeper inhalation of more dilute smoke increases exposure of the lung parenchyma. These and other design changes in cigarettes may also have contributed to the shift, beginning in the 1970s, in the histologic and topographic features of lung cancers in male smokers, with an increase in the incidence of peripheral adenocarcinomas that largely offset the decrease in squamous-cell and small cell cancers of the central airways.The tobacco companies use menthol and other flavour additives including fruit and candy flavouring as marketing tools to attract young smokers.National survey findings from the United States and Japan confirm that menthol cigarette use is disproportionately common among younger and newer adolescent smokers.

Tobacco products that disguise the taste of tobacco through flavouring agents and palatability enhancers create products that largely appeal to youth and young adults.Menthol is the most popular characterizing flavour of cigarettes in the US, with more than 90% of all cigarettes containing menthol.Tobacco companies use menthol’s analgesic effects to mask acute effects of smoking . Such harsh effects, if experienced by the smoker, could encourage quit attempts and cessation among menthol users.Women perceive the minty aroma of menthol cigarettes to be more socially acceptable than non-menthol cigarettes, which complicates public health efforts to denormalize tobacco use. In the US, the tobacco companies intensely market menthol cigarettes in predominately black communities through price discounts, signage, and through associations of menthol use with hip hop lifestyle and culture.Family and social factors that prevented smoking among African American teens do not seem to carry over into young adulthood likely due to tobacco company targeted marketing.In 2012, teenage smoking prevalence among whites was twice as high as black smoking prevalence .While use rates among young adults remains higher for whites than blacks ,compared to white smokers, menthol cigarettes are disproportionately used among black smokers. National data from the United States show that around 80% of African American smokers use menthol cigarettes compared to around 30% of whites. Tobacco-caused morbidity and mortality rates are disproportionately higher among African Americans compared to whites,and menthol cigarette smoking is disproportionately high among African Americans, which may help to partly explain the disproportionate tobacco-related disease burdens.These rapid changes in medical costs are due to the fact that risks of cardiac events,non-cancer lung disease, complications of pregnancy,and effects on children begin to appear almost immediately when people stop smoking or being exposed to secondhand smoke. Cancer is also affected, albeit more slowly over time. Hospitalizations for heart attacks, other cardiovascular conditions, stroke, and pulmonary conditions drop immediately following implementation of smoke free laws, as do need for treatment of respiratory conditions,plant bench indoor and complications of pregnancy and hospitalizations for childhood illnesses. The fact that marijuana smoke exposure has similar – indeed larger – effects on vascular function73 suggests that there may be similar adverse consequences and medical costs if marijuana use increases following legalization and expansion of the market. Tobacco control policy change in Australia between 2001 and 2011 played a substantial role in reducing smoking prevalence among Australian adults between 2001 and 2011. During that time, the Australian government increased tobacco taxes, adopted more comprehensive smoke free laws, and increased investment in mass media campaigns, which can explain 76% of the decrease in smoking prevalence from 23.6% to 17.3% . Comprehensive tobacco control policies may have an even greater impact on cigarette consumption and demand reduction in low and middle income countries compared to high income countries.For example, there has been a 50% reduction in male and female smoking prevalence in Brazil between 1989 and 2010, which represents a 46% relative reduction compared to the 2010 prevalence under the counterfactual scenario of policies held to 1989 levels.Combined these policies had averted 420,000 deaths by 2010, with estimates of an almost 7 million deaths averted projected by 2050.

Uruguay, an international leader in tobacco control, became one of the first countries to fully implement the Framework Convention on Tobacco Control. In 2006, Uruguay implemented its national smoke free law, and in 2009 the government implemented the largest graphic warning label, covering 80% of the package. In that same year Uruguay prohibited use of false or misleading statements on tobacco packages . There were three tobacco tax increases in June 2007, June 2009, and February 2010, which made tobacco products in Uruguay the highest in the region. In 2012, the Ministry of Health launched an aggressive mass media campaign308 and in 2014 the government prohibited all forms of tobacco marketing including advertising, promotion and sponsorship, product promotion, and point-of sale displays. Since implementation of its comprehensive tobacco control program, tobacco consumption, risk perceptions, and social acceptability of use and the tobacco industry have shifted dramatically. From 2003 to 2011, adult smoking dropped by 3.3 percent each year while youth smoking dropped by 8 percent, from 39% to 31% for males and from 28% to 20% for females. 308 In 2012, 75% of Uruguayans favored a total ban on all tobacco products within 10 years and 60% of the population believed the tobacco companies were unethical. Support for comprehensive smoke free laws among smokers increased from 54% in 2006 to 90% in 2012.After Uruguay implemented its smoke free law, hospital admissions for heart attacks dropped 20%309 and non-hospital emergency visits for bronchospasm dropped by 15%. 303A 2000 study on marketing restrictions in OECD countries found that the effects of marketing bans are cumulative and that partial bans were not associated with reductions in tobacco use. Overall, comprehensive bans on advertising and promotions were associated with a significant reduction in tobacco consumption since implementation, with larger effects for more comprehensive bans. Tobacco companies use market research to understand smoking behaviour among different segments of the population,and, in turn, use such research in future marketing campaign messages.This information can be used to design advertising campaigns that circumvent partial advertising restrictions by shifting expenditures toward other media outlets .For example, after the 1998 Master Settlement Agreement in the United States, in which the tobacco companies agreed to some limitations on their advertising and promotional activities, the tobacco industry shifted marketing expenditures to direct mailings and online marketing.Partial advertising restrictions permit cigarette companies to target young adults through lifestyle magazines created by the industry,event sponsor ships,and low income and less educated women through distribution of coupons with food stamps, direct mail, and bundle offers at the point-of-sale.Following implementation of a 2012 law that prohibited point-of-sale tobacco displays in New Zealand the odds dropped significantly for experimentation with smoking , smoking initiation , and smoking prevalence , among adolescents, consistent with similar studies from Ireland,Norway,and Australia.There was a marginal decrease in perceived peer smoking among New Zealand smokers, which may have been greater if all forms of tobacco marketing had been prohibited simultaneously.Because the tobacco industry continuously seeks to evade any advertising restrictions, the World Health Organization recommends that governments license tobacco manufacturers and retailers, with penalties and sanctions for noncompliance, including license suspension and revocation for repeat violations commensurate on the nature and seriousness of the offence, to assist with enforcement efforts to control tobacco advertising.

Given the exploratory and cross-sectional nature of this study, we could not examine a causal relationship between perceived risks/benefits and cannabis use behavior. Longitudinal studies have found a bidirectional relationship between perceptions and tobacco use, in which perceived risks and benefits predicted adolescent cigarette smoking , and on the other hand, AYAs’ personal smoking experience decreased their perceived risks and increased their perceived benefits of cigarette use over time ; however, less is known for the longitudinal relationship between perceptions and AYA cannabis use. In addition, we did not collect data on potential confounders which may have affected participants’ perceived risks and benefits of cannabis use. Longitudinal and more comprehensive data are needed to better understand how the perceptions impact initiation and continued use of cannabis among AYAs. Findings from our small school-based sample in California may not generalize to other young populations or geographic locations that have different demographic characteristics and cannabis-related policies. Self-reported data might be subject to recall and social desirability biases. The small sample size did not allow us to examine perceived risks and benefits in a more comprehensive set of user categories . Future research should examine perceived risks and benefits among these groups of cannabis users to elucidate an association between cannabis-related perceptions and use patterns. In conclusion, this study indicated that AYAs’ perceptions of risks and benefits differ by cannabis product and use status, with greater perceived risks and benefits for combustible cannabis and blunts than for vaporized and edible cannabis. Prevention efforts should take into account perceptions of both risks and benefits and tailor educational messages to specific products to prevent all forms of AYA cannabis use. The coronavirus disease 2019 pandemic has had wide-ranging impacts on society, particularly among vulnerable populations. People who misuse substances may be particularly susceptible to social isolation and other pandemic-related hardships.

A recent U.S. report found that 13% of adults started or increased substance use to cope with COVID-19. The stress of the pandemic,cannabis growing systems combined with the social isolation resulting from essential public health strategies to contain transmission, may contribute to worsening mental health and/or increased substance misuse. Prior work has identified links between social isolation and these outcomes. Increased negative emotions due to the pandemic are also likely, which could increase coping-related substance use motives that precipitate use. Other than alcohol and tobacco, cannabis is the most commonly consumed drug in the U.S., with prevalence typically highest among emerging adults , who may be especially impacted by social isolation. Given that smoking is a primary method of cannabis consumption, individuals who consume cannabis may also have higher risk for respiratory and pulmonary complications of COVID-19 infection. To date, studies examining smoking and COVID-19 have focused on tobacco rather than cannabis. It remains crucial to examine the association between substance use and other related behaviors among broad samples of cannabis-using emerging adults during this pandemic, with most research to date focusing solely on college students or other age groups. For example, in a survey of college students, both binge drinking and illicit drug use declined after COVID- 19 onset. Another study of university students in Russia and Belarus found that one-fifth to one-third reported pandemic related increases in tobacco, alcohol, cannabis, and other drugs. Among Canadian adolescents , recent 3 week prevalence of binge drinking, cannabis use, and vaping were lower compared to the 3 weeks prior to the pandemic, with increases in mean alcohol and cannabis use days. Importantly, findings for pandemic-related changes in substance use may differ among higher risk samples engaging in regular substance use or misuse.

Therefore, to contribute to the nascent literature on COVID-19 among vulnerable substance-using populations of youth, we examined self-reported perceptions of changes in cannabis and alcohol use and other psychosocial outcomes, among emerging adults who regularly use cannabis. We collected data as part of an ongoing cannabis intervention study initiated just before the COVID- 19 pandemic hit the U.S. Given the limited prior literature, we had no a priori hypotheses and rather sought to provide a descriptive exploration to inform future research and prevention services. Note that we examine perceptions of behaviors before/ during the pandemic and do not examine outcomes relative to the pilot randomized controlled trial as follow-ups are ongoing.The present data were collected within an ongoing pilot RCT of an online cannabis intervention for emerging adults; all procedures occurred online. We recruited participants using social media ads that led to an online consent and screening survey to determine RCT eligibility 3+ times per week. Advertisements included photos and headlines, such as: “Use weed? Participate in a research study, earn $$$. See if you’re eligible, click here!” and participants were recruited regardless of their intentions to change cannabis use. The RCT involves group-based intervention and control conditions conducted separately by age and residence ; eligibility criteria were the same regardless of state residence. Recruitment procedures paralleled prior work and took place in two waves. Wave 1 was recruited in February 2020 prior to full emergence of COVID-19 in the U.S. Wave 2 recruitment occurred in May 2020 . This study was approved by our institutional review board and we received a standard Certificate of Confidentiality from the National Institutes of Health.As the pilot RCT is ongoing, we cannot examine outcomes. Currently, we focus on data related to COVID-19 only; nonetheless, we provide a brief description of the study conditions for context. Participants in each wave were randomized to either an 8 week intervention or control group, separated by age group. The 8 week intervention occurred in secret private groups on Facebook, moderated by health coaches who posted content for 56 days straight .

Content addressed cannabis use directly as well as upstream motives for cannabis use and prevention of related consequences using a motivational interviewing style where participants and coaches interacted. Consistent with MI, participants were informed that any changes they might consider making to cannabis use or other health behaviors would be completely up to them. The control group was parallel in length and format, except coaches posted entertaining social media content unrelated to substance use or mental health.COVID-19 items were designed to assess the prevalence of COVID-19 and perceived impacts. We modified available items to assess whether participants experienced COVID-19 symptoms , contacted a health care provider due to symptoms, and if they engaged in pandemic-related quarantine or isolation. We developed items to assess the following: COVID-19 hospitalization, known infections in participants’ households and social networks, changes in employment status, lost childcare and school closures, and dates of quarantine/social isolation . Among those reporting isolation, we asked about their cannabis use during isolation compared to their usual use of cannabis in the 3 months before the pandemic affected their geographic area . We asked participants about their emotions and behaviors in the 30 days before the pandemic came to their area relative to the past 30 days . Emotions assessed included feeling: lonely, stressed, anxious, depressed, hopeful, and happy. Among those who endorsed each of the following in the past year, we queried changes in: cannabis smoking, vaporizing, dabbing, and eating; using cannabidiol , drinking alcohol, smoking tobacco, vaping nicotine, and exercising. We assessed changes in eating and social activities. Participants rated the degree to which they agreed or disagreed that COVID-19 had impacted their lives in positive and negative ways . We asked an open-ended question, “please describe the ways that the coronavirus pandemic has or has not affected your life,”flood table which is presented in the qualitative analysis below.Participants completed an online Timeline Follow Back assessing past 30 day cannabis and alcohol use days. Items regarding past 30 day cannabis use methods, medical cannabis certification, sources of cannabis acquisition, hours high per day, and time to first use upon waking were adapted from prior work. When answering questions about cannabis, participants were prompted to respond about products containing THC and to exclude reporting on “CBD-only” products.We provide quantitative data in the form of means, standard deviations , and proportions of participants. After using chi-squared and t-tests for preliminary examination to conclude that Wave 1 and Wave 2 participants did not substantially differ on demographics and cannabis use indicators , we pooled data from the two cohorts for this descriptive paper since each group completed measures close in time . We used chi-square analyses to examine relationships between perceived changes in cannabis consumption and negative emotions. We conducted content analysis of responses to the single qualitative item. The first author reviewed ~50% of responses and noted emerging themes for a code book of potential response categories, then incorporated the last author’s feedback. The first author trained the second and third authors in the coding scheme. The two coders independently coded 10 responses, then resolved discrepancies and clarified code definitions with the first author. Next, they coded 15 responses and met with the first author to resolve discrepancies and refine code definitions prior to coding the remaining responses. The first author reviewed this coding and resolved discrepancies, which, out of 291 codes applied , occurred on 70 occasions . Codes were enumerated to assess the prevalence of themes in participants’ responses.

Participants’ agreement with the statement “The coronavirus pandemic has impacted my life in positive ways” was as follows: 6.4% strongly agreed, 29.8% agreed, 22.7% were neutral, 21.3% disagreed, and 19.9% strongly disagreed. Their ratings for a parallel statement focused on negative impacts were: 30.5% strongly agree, 46.1% agree, 18.4% neutral, 3.6% disagree, and 1.4% strongly disagree. Table 5 provides exemplar quotes from openended responses about the impact of the pandemic on participants’ lives. Overall, themes reflecting negative impacts were most prevalent, although positive aspects were mentioned. Negative impacts on employment and finances , social isolation , and stress or negative emotions, including worsening mental health were most frequently mentioned. Perhaps of interest given the developmental age of the sample, uncertainty about the future and lost opportunities or milestones came up less frequently. Among positive themes, employment and finances were mentioned most frequently with some participants having increased income due to stimulus checks and federal unemployment benefits during the initial pandemic response. Very few spontaneously mentioned changes in cannabis use as a positive or negative impact of the pandemic, although they had already reported on this in the quantitative survey.We have provided a unique snapshot of the perceived impact of the COVID-19 pandemic on the lives of emerging adults across the USA who regularly use cannabis. A few months into the events of the pandemic unfolding in the USA, many of these emerging adults were experiencing significant changes, including ongoing social isolation, increased loneliness, anxiety, and depression, lost wages or jobs, and/or changes in school or residence. Many participants felt that their use of cannabis increased during the pandemic, particularly when socially isolated , with rates similar to those reported previously. Descriptively, there were more participants reporting perceived increases in cannabis use than there were reporting increased alcohol or tobacco/nicotine use, which was consistent across cannabis consumption methods. It is possible that the minority of the sample who felt their cannabis consumption decreased had limited access to cannabis during the pandemic; however, nearly all participants reported accessing cannabis in the prior month . Perhaps most concerning are the third to half of the sample who felt they increased their cannabis consumption due to the pandemic, given that greater frequency of consumption is correlated with greater severity of cannabis use disorder , which has a mean age of onset around 21 years and is associated with greater risk for depression and anxiety disorders. However, we did not assess the diagnosis of CUD, which should be included in future research to characterize the severity of cannabis use. Nonetheless, the clinical features of the sample and the large proportions reporting increased depressive feelings raise alarm given the association between mood disorders and escalation of cannabis use disorder severity. Although we could not examine causal effects in these one-time COVID-19 survey questions, because the majority of participants reported changing their cannabis use in the wake of the pandemic, it seems clear that COVID-19 has far-reaching impacts on other areas of public health beyond disease transmission. This concern is underscored by the finding that pandemic-related increases in negative emotional states coincided with reports of increased cannabis smoking in particular.

In addition to choosing attributes based on significance, we considered the importance of attributes for policy implications. For this reason, we kept the densities of bike lanes and bike racks even though they were not statistically significant in the final model. The major limitations of this study stem from the nature of the bike sharaing activity data that is used. The time period observed is the first full month of JUMP operations in San Francisco, which is likely to include travel behavior of early adopters and novelty rides that do not reflect more regular patterns that may have emerged among JUMP users since its launch. In addition, by comparing JUMP and GoBike trips during this time period, we observe the interdependent effects of both the dockless model and the electric pedal-assist bicycles on JUMP travel behavior compared to that of GoBike, which used non-electric bicycles with a stationbased model during the study period. While differences in travel behavior related to elevation may be more directly linked to the e-bikes in the JUMP system, most other trip attributes examined may be influenced by a number of variants in the operation and/or ridership across the two bike sharing systems. The lack of user data linked to the trips we observe constrains our ability to account for socio-demographic differences across the riders of the two systems. We use census data to differentiate bike sharing trip destinations by the socio-demographic makeup of the surrounding census tracts, though we cannot directly draw conclusions about the sociodemographic characteristics of riders, nor of the actual points of interest visited during each trip. In addition, we used suggested bike routes from the Google Directions API to estimate trip distances, durations, and elevation gain in the absence of trajectory data. However,trimming tray we chose not to incorporate bike path availability along these suggested routes in the DCA model due to a concern that the results would overestimate the use of bike routes. Lastly, there is a degree of endogeneity in our DCA results for GoBike, as the destination choices of GoBike users are completely constrained to the station locations of the GoBike system.

We begin with a visual analysis of the geographical and temporal distribution of demand for each bike sharing system. Figures 4.a – d. display heat maps of bike sharing activity during February 2018 by time of day. Areas in which the departures constitute the majority of activity are shaded green, while areas in which arrivals constitute the majority of activity are shaded red. Thirty-two percent of JUMP trips, 33% of GoBike non-member trips, and 43% of GoBike member trips took place during the AM period . Both JUMP and GoBike exhibit concentrated AM demand destined for dense employment centers along Market Street and in the South of Market and Financial District areas just South-East and North-West of Market Street, respectively. These neighborhoods are home to many large office buildings housing numerous corporate headquarters and branch offices. The intensity of trip arrivals around the Civic Center could represent multi-modal trips, as bike sharing users may choose to transfer to the Bay Area Rapid Transit line at this most North-Western access point. There is a clear difference in the trip origins of JUMP and GoBike in the AM period, where we see a concentration of GoBike trips departing from the CalTrain and Embarcadero BART stations, while JUMP trip departures were spread out in neighborhoods outside of the CBD. In the PM period , we observed both systems servicing riders originating in the CBD, but the destinations of JUMP trips were again spread out in neighborhoods farther away from the CBD, while GoBike trip destinations were concentrated at the Caltrain and Embarcadero BART stations.The distribution of bike sharing trip distance and duration for each of the two systems exemplifies the behavior observed in the visual analysis. We assess the trip characteristics of GoBike members and non-members separately, noting that 95% of GoBike trips were made by members. JUMP trips tended to be longer in distance and duration than GoBike trips . The average JUMP trip was about a third longer in distance and about twice as long in duration as the average GoBike member trip. While this may be a result of the newness of JUMP in February 2018, the similarity implies that JUMP tended to be used for longer, potentially more recreational trips, which are more similar to GoBike non-member trips.

Indeed, 7% and 8% of JUMP and GoBike non-member trips, respectively, are longer than one hour in duration, compared to less than a third of a percent of GoBike member trips. Unlike GoBike members who pay on an annual basis and are incentivized to make the most out of their membership regardless of trip length, JUMP users pay per trip and thus may prefer to make longer, less frequent trips. Next, we present the results of the destination choice model estimation to better understand the influence of different factors on bike sharing users’ destination choices in the GoBike and JUMP systems . The final log-likelihood values for the destination choice models for GoBike and JUMP trips were -1,402 and -1,713, respectively. The R squared values for the models were 0.26 for GoBike and 0.27 for JUMP, while the R bar squared values for each were 0.23 and 0.24, respectively. Across both systems, increase in estimated trip distance and elevation gain were both strong negative factors in bike sharing users’ destination choices. The estimated total elevation gain was by far the most negative coefficient in the GoBike model, indicating that destinations that involved climbing in elevation were very unpreferable to GoBike users. The coefficient for estimated distance in the JUMP model was more negative than that of estimated total elevation gain. The range of estimated trip distances for the destination choice set was inherently larger for JUMP than for GoBike, since JUMP users were not entirely restricted by the service area of the system. Seven percent of JUMP trips in our dataset were completed outside of the service area. JUMP users were fined for ending a trip outside of the service area, but they were not prohibited from doing so. The large positive coefficient for the JUMP service area indicator reflected this incentive. Factors indicating the level of activity at a destination were significant and positive across models. In addition, the density of the resident population and the ASC for low-density residential census tracts were both significant, negative coefficients in the JUMP model, suggesting an affinity of JUMP users to travel to lower-density destinations. Conversely, the model results support our findings from visual spatial analysis that GoBike users were largely bike sharing to work, as the activity level parameters were the two most positive coefficients, and the employment center ASC was positive and significant.

The age and income characteristics of destinations were mostly insignificant in the model. JUMP destination choices were significantly negatively influenced by the fraction of residents over the age of 55 in a destination census tract. The median income of destinations is not a significant factor in the destination choice models for either system, with coefficient estimates close to zero in both models. While bike rack density is unsurprisingly an insignificant factor in the GoBike model, it is a significant positive factor in the destination choices of JUMP users. Since JUMP users were instructed to lock the bikes to public racks,weed trimming tray this finding has two possible implications for the destination choices of dockless bike sharing users: 1) JUMP users may prefer destinations with a higher availability of public bike racks with which to easily end their trips, and/or 2) the spatial distribution of public bike racks is well suited to the preferred destinations of dockless bike sharing users. On a related note, the density of GoBike stations in a destination tract was a significant positive factor in the GoBike model. Again, the location of docking stations may attract users, and/or they are well-placed to serve the destination preferences of GoBike users. The insignificance of bike lane density in both destination choice models may be an artifact of our choice to model this factor as an alternative attribute rather than a trip attribute. Bike lane density along suggested destination routes or even the cumulative bike lane density across each of the census tracts along the destination route may provide a more explanatory variable with which to assess the bike sharing user sensitivity to bike lane availability. The composite suitability maps reveal the geospatial distribution of the “bike ability” for users of JUMP and GoBike in San Francisco. In particular, residential neighborhoods in the Northwest and along the Northeast of the city provide opportunity for expansion for both systems to improve equity based on physiological and economic factors. The distribution of the population over 55 and elevation in these neighborhoods appear to be the main constraints in these areas, while considerable job density and available bike facilities provide opportunities. Though e-bike sharing has potential to overcome physiological barriers for older residents in these areas, considerable social barriers may exist since JUMP is only accessible through a smartphone application. Additional social constraints, which are not visualized in the bike ability maps, may stem from language barriers or broader cultural differences across the city. Finally, introduction of temporal variables would aid in assessing the opportunities and challenges for equitable bike sharing based on the time of day. Bike ability may vary across time periods with different levels of congestion, or across hours of daylight versus darkness. Shared micromobility service models are growing across the U.S. including: docked, dockless, and e-bike sharing models.

Our research analyzes the trip making behavior of JUMP dockless ebike sharing and GoBike docked bike sharing users in the first month of the JUMP pilot program. Travel behavior and destination choice analyses reveal that the two systems appear to complement one another: GoBike trips tended to be short, flat commute trips, mostly connecting to/from major public transit transfer stations while JUMP trips were longer, more spatially distributed and more heavily servicing lower-density neighborhoods. The average JUMP trip was about a third longer in distance and about twice as long in duration than the average GoBike trip. In addition, JUMP trips underwent about three times the elevation gain per trip, on average, compared to GoBike trips. Our findings suggest that the assistance provided by e-bikes in addition to the flexibility afforded by the dockless model are serving mobility demand outside the dense urban core of the city, where docked models are not available. Furthermore, we found that the destination choices of docked bike sharing users are positively influenced by the density of stations, and bike rack density was a significant positive factor for JUMP users. The location of facilities necessary to use either the docked or dockless system may attract users and/or be well-placed to the destination preferences of users. While the sensitivity of destination choices to factors influencing equity, such as older age are slight, our bike ability analysis reveals that the composite effect of constraints and opportunities that impact bike sharing demand can have adverse effects in neighborhoods otherwise ripe for bike sharing expansion. Additional research is needed to more closely link the characteristics of shared micromobility users with differences in travel behavior across business models and service areas. This study focuses on San Francisco, a city with unique topographic, sociodemographic, and cultural features which have distinct effects on travel behavior that may not be generalizable to other locations. As policies and guidelines for shared micromobility are being piloted and refined, similar data sources to those used in this study complemented with user surveys can be used to monitor the emerging trends in ridership across multiple shared modes. Research into the multi-modal trip making and trip chaining using shared micromobility is needed to further the understanding of the potential positive impacts of electric and dockless models on overall mobility and accessibility across trip purposes. Finally, time series analysis of travel behavior before, during, and after the implementation of innovative policies would provide invaluable insights to help hone public interventions strategies that effectively bolster mobility while promoting sustainability and equity within the broader transportation system. The unexpected outbreak of e-cigarette or vaping-associated lung injury was reported nationwide beginning in September 2019, causing more than 2800 hospitalizations and 60 deaths.The specific biological mechanisms of EVALI, as well as the chemical causes, are still under investigation.Evidence shows that EVALI is associated with vaping tetrahydrocannabinol containing e-liquid cartridges that were obtained on the black market. 

The evidence for this is mixed, however . Whereas medical users report that alleviation of acute symptoms of these disorders was a primary motivation for permit applications , a systematic review by Walsh et al. reported that this was not consistently observed across credible studies. Of course, marijuana use itself may constitute a risk factor for suicide apart from alleviating symptoms related to depression and anxiety, at least among some populations and for some levels of suicidality . A meta-analysis by Darvishi, Farhadi, Haghtalab, and Poorolajal supports the strong consensus that alcohol use disorder “significantly increases the risk of suicidal ideation, suicide attempt, and completed suicide.” With respect to medical marijuana, of course, the theoretical prediction depends on whether marijuana is used in addition to or instead of alcohol. If marijuana and alcohol use are combined, one might expect no change in suicide risk or even an increase in suicide following legalization. If marijuana replaces alcohol, on the other hand, one might expect a decrease in suicide risk following legalization. Self-reports by medical users in California and Canada indicate that a substantial proportion substitute marijuana for alcohol and other drugs. Since the instruments included items about patients’ potentially criminal behaviors, self-reports are potentially biased. With a few exceptions, opportunity pathways have received less attention in the suicide literature. Noting that suicide risk is highest when the victim is alone in the absence of guardians who would otherwise intervene , Chew and McCleary use the routine activity theory to explain why risk is relatively lower on weekend days, when other household members are more likely to be present, and relatively higher on Mondays, when other household members are likely to be out of the home at school or work. By analogy,vertical grow system if access to medical marijuana obviates the need to leave home, one might expect a lower suicide risk following legalization.

If medical users substitute marijuana for alcohol, moreover, legalization may result in less time spent in licensed alcohol establishments. Anderson, Hansen, and Rees use this argument to explain their finding of a reduction in motor vehicle fatalities following legalization. Firearms access is a relevant opportunity pathway. The positive correlation between firearms access and suicide risk has been demonstrated for U.S. metropolitan areas and counties . These correlations are subject to the ecological fallacy, however. At a disaggregated level, compared with matched controls who live in non-gun households, individuals who live The 1993 Brady Handgun Violence Prevention Act prohibits the purchase of guns by individuals who are addicted to controlled substances. Though used for legal medical purposes, marijuana remains a controlled substance under U.S. law.1 Since California medical marijuana users were not allowed to purchase firearms in 1997, and since California firearms regulations are relatively strict, we expect a reduction in suicide risk following legalization. In sum, in 1996, California legalized marijuana use for medical purposes. Implementation was abrupt and uniform, presenting a natural experiment that we take advantage of in order to estimate the causal effect of a medical marijuana initiative on suicide risk. In the current study, we aggregate total, gun, and non-gun suicides by state for the years 1970–2004. Using a Synthetic Control Group quasi experimental design , we construct a control unit for California from time series of the 41 states that did not legalize marijuana during the time frame. We interpret post-intervention differences for California and its synthetic control time series as the effects of the medical marijuana law on suicide. Significance of the effects is assessed with permutation tests. In 1996, California voters passed an initiative Proposition 215, which legalized marijuana use for medical purposes. Because the Proposition was implemented in an abrupt and uniform manner, legalization presented a “natural experiment.”

To estimate the causal impact of legalization on suicide, annual time series of total, gun, and non-gun suicides were analyzed by comparing California with an estimated counterfactual state in a Synthetic Control Group design. The synthetic control time series for California were constructed as a weighted combination of 41 states that did not legalize medical marijuana during the time frame. Post-intervention differences between California and its constructed control time series were interpreted as the causal effect of the medical marijuana law on suicide. The statistical significance of these effects was assessed with permutation tests. Findings reveal that rates of total suicide and gun suicide dropped significantly in the aftermath of Proposition 215. Findings also reveal, however, that legalization’s impact on non-gun suicides is considerably smaller, and arguably no different than what would be expected to occur by chance. Confidence in these findings is underscored by the methodological approach undertaken in the study. A strength of the Synthetic Control Group Design is that it allows us to examine the net effect of medical marijuana legalization on suicide. Despite the strengths of this design, important limitations remain, many of which present opportunities for future directions in research. Because we examine suicide trends over eight post-intervention years, we are fairly confident that the effects are permanent. Because our time series end in 2005, on the other hand, it is difficult to generalize our theoretical result to subsequent years. We are limited by the fact that medical marijuana laws began to proliferate across the U.S. after 2005, threatening to contaminate the “donor pool” of untreated states. In virtually all the states that legalized medical marijuana after 2005, moreover, reforms were not implemented abruptly or uniformly, making confident causal interpretations more difficult. Another limitation that presents a future direction relates to the mechanisms that may account for the findings of the study. What are the mechanisms responsible for the sharp decline in total, but especially gun, suicides following medical marijuana legalization in California?

We proposed mechanisms related to the substitution of marijuana for alcohol and other related substances; marijuana use itself, which may reduce actual motivation for suicide; the inability of medical marijuana patients to purchase firearms; and changes in the culture of recreational substance use, leading to fewer unsupervised opportunities to commit suicide in the home. Each of these pathways should be tested, although many will require additional data collection. For example, one likely fruitful research direction would be to collect annual data on alcohol consumption in California and assess whether it is a plausible mechanism by which medical marijuana legalization could cause a reduction in gun suicides. Beyond adjudicating these various pathways, testing mechanisms could yield insight into why we do not find the expected reduction in non-gun suicides following legalization. Unfortunately, we do not have the data to test these mechanisms, yet it will be essential for future researchers to do so. In the U.S., use of prescription pain relievers , also known as prescription opioids and opioid pain relievers, has been increasing dramatically. Worldwide, prescriptions of PPRs have almost tripled since 1990, and the U.S. is a factor in this rise, as it has the highest percapita consumption of PPRs in the past ten years . This increase has become dangerous, as opioid use carries risks that include addiction, sedation, respiratory depression, overdose and death . Between 1999 and 2010, deaths attributed to PPRs rose five times among women and 3.5 times among men . Of all prescription drug OD deaths in the U.S. in 2013, 71.3% involved PPRs . PPRs and marijuana are biologically linked; like PPRs, marijuana induces analgesia, acts on some of the same brain regions, and partly exerts its effects via opioid receptors . This connection is especially relevant due to the changing legal status of marijuana. As of August 2016, 24 states and Washington D.C. had legalized medical marijuana. Between 2007 and 2012, the number of past month marijuana users rose from 5.8 to 7.3% 2013, and between 2001 and 2013, past year adult marijuana use increased from 4.1 to 9.5% in the U.S. . Further, legalization of medical marijuana has been associated with increased odds of marijuana use among adults ,cannabis grow equipment though no consistent association has been determined among youth/young adults . Distinct theories attempt to explain how medical marijuana legalization affects use of substances other than marijuana. The relationship between different substances can be impacted by 1) change in cost of a substance, 2) policy alterations that influence availability of a substance, 3) shifts in legal consequences of using a substance, and/or 4) the psychoactive/pharmacological effects of a substance . More U.S. states are legalizing medical marijuana , and marijuana shares some psychoactive/pharmacological effects with PPRs. The substitution theory postulates that there is a substitution effect, whereby an increase in marijuana use coincides with a decrease in the use of other substances – in this case, PPRs . There are logical reasons why individuals would opt to use marijuana instead of PPRs.

With the new legal status of medical marijuana, individuals can access it through medical dispensaries and enjoy a lower legal risk if they live in a state where it is legalized. Individuals also report switching to marijuana for pain control because when compared to prescription drugs, marijuana has fewer side effects and withdrawal symptoms . Studies supporting the substitution effect have demonstrated that either increases in the use of marijuana or the legalization of medical marijuana is associated with reductions in opioid use, hospitalizations for opioid dependence/abuse, PPR ODs, and opioid OD mortality . In contrast to the substitution effect, there may be a complementary effect, where an increase in marijuana use is associated with an increase in the use of PPRs . In support of this theory, researchers using National Survey on Drug Use and Health data found a positive association between marijuana and increased use of PPRs . In another study, researchers focused on individuals who were prescribed long-term opioid therapy and found that those who also used medical marijuana presented with greater risk of misusing prescription opioids. Additionally, a prospective cohort study using the National Epidemiologic Survey of Alcohol and Related Conditions data determined that use of marijuana was associated with a greater risk of using non-medical prescription opioids three years later . However, in these studies, researchers did not analyze how co-use of other substances would impact the direction and/or strength of the relationship between marijuana and opioids/PPRs. To determine if there is either a substitution or a complementary effect between marijuana use and PPR use, co-use with other substances needs to be studied. Additionally, there is a strong positive association between nicotine use and PPR use. When compared to non-smokers, tobacco smokers experience more intense and longer lasting chronic pain, as well as a higher frequency of PPR use . Studies have demonstrated an interaction between nicotine and opioids that is associated with an increase in the total consumption of the two substances and contributes to other effects of the drugs . The relationship between the use of these two substances has a basis in the biological connection between them, as the endogenous opioid system is an underlying mechanism for several behavioral outcomes related to nicotine . Like marijuana, nicotine is involved in anti-nociception via endogenous opioid system mediation, suggesting that nicotine is used for the self medication of pain ; and in fact, nicotine heightens the anti-nociceptive effects of both opioids and marijuana . Several studies have documented common use patterns among tobacco, marijuana, and opioids/PPRs . For example, a prospective study of NESARC data demonstrated that early-onset of smoking cigarettes increased the odds of beginning opioid use and that frequency of both cigarette and marijuana use increased the odds of beginning opioid use, re-initiating opioid use after previously stopping, and continuing opioid use among current users . Thus, the three substances share anti-nociceptive actions mediated by the endogenous opioid system, and evidence indicates that marijuana and nicotine use predict opioid use among adults. From 2003 to 2012, NSDUH data revealed a significant increase in the co-use of marijuana and tobacco . Further, smoking tobacco is significantly associated with cannabis dependence . Given the national trend toward marijuana legalization, co-use is likely to increase. Cigarette smokers and marijuana users are a crucial population to study, as nicotine and marijuana share mechanisms of action with each other and with opioids, and use of each substance has been shown to be associated with use of opioids/PPRs . However, whether there is an association between prevalence of marijuana and PPR use among current smokers has not been determined.

Residues were common in the legal cannabis supply — a 2017 investigation found that 93% of 44 samples collected from 15 cannabis retailers in California contained pesticide residues . Some studies of data from the unregulated period suggest a relationship between cannabis consumption and exposure to heavy metals , while others demonstrate that potentially harmful microorganisms may colonize cannabis flowers . A 2017 study raised concerns that in immuno compromised patients, use of cannabis contaminated with pathogens may directly affect the respiratory system, especially when cannabis products are inhaled . The currently prevailing statutes governing cannabis testing are contained in Senate Bill 94, the Medicinal and Adult-Use Cannabis Regulation and Safety Act of 2017 — which brought together all of California’s previous cannabis legislation, including Proposition 64, the Adult Use of Marijuana Act of 2016 . Since MAUCRSA, state agencies have propagated regulations for both medical use and adult use . MAUCRSA amends various sections of the California Business and Professions Code, Health and Safety Code, Food and Agricultural Code, Revenue and Taxation Code and Water Code, and introduces a new statewide structure for the governance of the cannabis industry — as well as a system by which the state may collect licensing and enforcement fees and penalties from cannabis businesses. A significant portion of MAUCRSA is comprised of testing rules that aim to certify cannabis safety . These rules, however, may increase the production cost and therefore the retail price of tested cannabis, thereby reducing demand for legal cannabis in California. Thus it is important to understand the costs of cannabis testing relative to the value of generating a safer product. We first review maximum allowable tolerance levels — that is,cannabis grow supplies the amount of contaminants permitted in a sample — under the state’s cannabis testing regulations and compare them with tolerance levels for other food and agricultural products in produced in California.

We then briefly compare testing regimes and rejection rates in other states where medical and recreational use is permitted. Finally, we use primary data from California’s major cannabis testing laboratories and from several cannabis testing equipment manufacturers, as well as a variety of expert opinions, to estimate the cost per pound of testing under the state’s framework for the cannabis business . We conclude by discussing implications of this research and potential regulatory changes.Since July 1, 2018, all cannabis products have been required to pass several tests before they can be sold legally in California. The specific test for each batch of cannabis depends on product type. Types include dried flowers , edibles , vape-pen cartridges containing cannabis oil and a wide variety of other processed cannabis goods, including tinctures, topicals and cannabis in crystallized, wax or solid hashish form. In order to enter the market legally, all these products must be tested for cannabinoids and a large variety of contaminants. Table 1 shows the substances measured in each test , provides a description of each test and specifies the products to which the test applies and the criteria for passing the test. Most tests, such as those for potency, presence of foreign materials, pesticides, heavy metals, mycotoxins, microbial impurities and terpenoids, apply to all batches. Moisture tests, however, apply only to flowers and solid or semi-solid products — while tests for solvents or processing chemicals apply only to processed or “manufactured” products. That is, the specifics of each test depend on which cannabis product is tested. Independent, licensed testing laboratories are responsible for receiving samples for testing from licensed distributors. The laboratories then conduct a full set of analyses, following the criteria established by MAUCRSA and specified by regulations. Laboratories must deliver to distributors a certificate of analysis indicating the results of each analytical test. A batch must pass all required tests before it can be released to retailers. Table 2 shows a list of residual solvents and processing chemicals, with the maximum permitted tolerance levels for legal cannabis. Tests evaluate two groups of solvents and processing chemicals , with a very low tolerance established for those in category I. Table 3 shows tolerance levels for pesticide residues and heavy metals.

The maximum permitted tolerance levels for pesticide residues are particularly tight when compared with tolerance levels for other agricultural products in California. For many pesticides, the maximum residual level is zero, meaning that very stringent tests are required and that no trace of the chemical may be found. Among pesticides with allowable limits above zero, the tolerance levels for inhalable products are particularly low. In some cases, tolerance levels for inhalable products are one-four-hundredth the levels for other products. To help interpret the cannabis tolerances, it is helpful to consider them in the context of food safety testing. The top row of table 4 shows, based on more than 7,000 samples, the percentage of California food products in which, from 2015 to 2017, any pesticide residues were detected . These percentages were above 60%. The second row of table 4 shows that, despite the high share of food products in which some pesticide residue was detectable, only 1.51% of samples in 2016 contained pesticide residue above tolerance levels set by the U.S. Environmental Protection Agency — and only 0.45% exceeded those levels in 2017. The bottom panels of table 4 show that, of the 7,000 samples tested, more than 12% of 2017 samples would have been above California’s product tolerance limits for inhalable cannabis. More than 3% of the 2017 samples would have exceeded even the less stringent tolerance levels established for other cannabis products. As shown in table 4, similar results apply to the samples for the other two years.In California’s licensed, legal cannabis channel, all products must be held by a licensed distributor while they are tested in an independent, licensed laboratory. Licensed testing laboratories do not publish their prices and the costs of testing services are not generally available. Testing prices depend on the number of samples to be tested, the type of product tested and the specifics of the contract between the distributor and the laboratory, among other factors. We collected detailed data to construct in-depth estimates of the capital, fixed and variable costs required to run a licensed testing laboratory in California. This information included the costs of equipment, facilities, maintenance, supplies, technical and non-technical labor, taxes and other inputs. We gathered data from established cannabis testing companies , new cannabis testing companies, laboratories that test other agricultural products, and other industry sources, including advisors of the cannabis industry and cannabis retailers.

We collected prices for testing equipment, supplies, chemical reagents and other cannabis testing inputs by contacting the sales representatives of large equipment supply companies . We considered the costs of sampling and transportation to and from test facilities, adjusting those costs estimates according to the geographical configuration of testing laboratories and distributors across the state. Finally,grow lights for cannabis we used data from the California Department of Pesticide Regulation and some assumptions based on experience in other states to estimate the share of cannabis that fails testing and therefore the lost inventory due to failed tests. To make these cost calculations we accounted for inventory that first fails testing, but then is remediated. In addition, to understand the opportunity cost of cannabis used in the tests or lost in the process, we use data from wholesale prices and a survey of retail cannabis prices conducted by the University of California Agricultural Issues Center . Based on this information, we developed a cost per unit of cannabis tested for representative labs of three different sizes to approximate the distribution of costs in the industry. For simplicity, we assumed that testing labs of different sizes use the same inputs, but in different proportions, to provide testing services. We assume economies of scale with higher share of capital costs per unit of output for the smaller labs. We used information reported by the Bureau of Cannabis Control in the first half of 2018 to compile a list of cannabis licensed testing laboratories and distributors in California .We used information on the geographic location of testing labs relative to cannabis production and consumption to assess the cost of transporting samples from distributors to testing labs. In March 2019, there were 49 active testing licensees and 1,213 licensed distributors. Both testing licensees and distributors are located in many areas across the state, but they are concentrated in traditional cannabis production areas in the North Coast region of California and in large population centers. Table 5 shows capacities, annualized capital costs, and other annual expenses for three size categories of testing labs: small, medium and large. The size categories are based on the number of samples analyzed annually and were chosen to represent typical firms, based on our discussions with the industry. We assume about 25% of labs are small, 25% are large and the remaining half are in the medium category. By regulation, these labs test only cannabis. The annualized cost of specific testing equipment and other general laboratory equipment is a significant share of total annual costs. The cost of equipment and installation is about $1.5 million for a small lab, about $2.4 million for a medium lab and about $3.8 for a large lab. These costs are expressed as annual flows in table 5. To account for the annual cost of investment in equipment we use a discount rate of 7.5% per year that reflects the combined effects of depreciation and interest over a 10-year horizon, using the standard equivalent annual cost formula, typically used in budgeting studies: Annual Cost = K/−10 where K is the invested capital for each of the three testing labor sizes.

These annualized costs of the invested capital for each size of testing lab operations are shown in the top row of table 5. Our survey and discussions with laboratories provide the rest of the estimated costs. Equipment maintenance costs, rent, utilities and labor also are large cost categories. Each of these costs is less than proportional to the number of samples tested and thus contributes to economies of scale. This cost of consumable supplies is calculated on a per sample basis and thus is proportional to the number of samples tested. Finally, the return to risk and profit is estimated as 15% of the sum of the foregoing expenditures. Our estimated total annual costs are about $1.6 million for small labs, $3.3 million for medium labs and $7.0 million for large labs. The scale advantage of larger testing labs is reflected in the testing cost per sample: $324 for large labs, compared with $562 for medium labs and $750 for small labs. These cost differences arise from economies in scale in the use of laboratory space, equipment and labor. Each large testing lab processes about 10 times the number of samples as a small lab but has annualized operating costs only about five times those of a typical small testing lab. That means that small-scale labs tend to specialize in servicing more remote cultivators or manufacturers that have products handled by smaller and more remote distributors located at a cost-prohibitive distance from large labs. We used data on the annual testing capacities of small, medium and large labs and our assumption about the number labs of each size to calculate the share of testing done by labs of each size category. We expect that small labs will test about 6% of all legal cannabis in the state by volume, medium-sized labs will test about 33% of legal cannabis, and large labs will test 61% of legal cannabis. Using these shares, the weighted average cost per sample tested is about $428. Let us now turn from the cost per batch tested to the cost per pound of cannabis marketed. The per pound costs of laboratory testing depends on the number of pounds tested in each test. Therefore, we must consider batch size. Regulations have set a maximum batch size of 50 pounds of cannabis flowers . We expect that the batch size will differ within this constraint depending on the product type and origin and size of the cultivator and manufacturer and explore implications of batch size differences. Using the weighted average cost per sample of $428, the testing cost for a small batch of 5 pounds is $85.60, while for the largest-allowed batch size of 50 pounds, the cost is just $8.56 per pound. Next, we turn to several costs not included in the cost of testing a sample in the lab .

Through adenosine antagonism, caffeine enhances neural activity by blocking the inhibitory affects of adenosine activation . In addition, by inhibiting adenosine binding to receptors on smooth muscles cells, caffeine reduces the ability of blood vessels to dilate and causes an overall reduction in baseline cerebral blood flow . All of these factors can lead to BOLD signal changes. Previous work by our group assessing caffeine’s effect on resting-state BOLD fluctuations has shown that it reduces both the stationary correlation and amplitude of the fluctuations in the motor cortex . While it is difficult to determine the underlying physiological mechanisms behind this effect, recent studies suggests that it may stem primarily from changes in neural activity coherence. For example, preliminary work by our group with magnet oencephalography found similar reductions in the correlation of MEG power fluctuations in the motor cortex, which do not have the same vascular confounds that are present in the BOLD fMRI signal . In addition, caffeine has been shown to impair motor learning compared to a placebo . Since it has been shown that the strength of resting-state functional connectivity is related to memory performance , these findings suggest that the caffeine-induced reduction in BOLD correlation may represent underlying neural changes. In this study, 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, vertical grow system albeit less frequently. A consistent decrease in correlation could be caused by an overall change in the vascular system induced by caffeine.

However, it is unlikely that a shift in the state of the vascular system would give rise to an increase in the non-stationarity of the correlations, when viewed on a time scale of tens of seconds. Instead, a caffeine induced increase in the temporal variability of the correlations would tend to support the existence of greater temporal variability in the coherence of the underlying neural fluctuations. The data used in this study were collected for a previous experiment examining the effects of caffeine on resting-state BOLD connectivity as assessed with stationary correlation measures . Participants were instructed to refrain from ingesting caffeine for at least 12 hours prior to being scanned. The estimated daily caffeine usage for each subject based on self-reports of coffee, tea, and soda consumption is presented in Table 3.1. The assumed caffeine contents for an 8-oz cup of coffee, an 8-oz cup of tea, and a 12-oz soda were 100 mg, 40 mg, and 20 mg respectively . Each subject participated in two imaging sessions: a caffeine session and a control session, in that order. The two imaging sessions were separated by at least 6 weeks.The caffeine session consisted of a pre-dose and a post-dose imaging section, each lasting around 45 minutes. Upon completion of the pre-dose section, participants ingested a 200 mg caffeine pill and then rested for approximately 30 minutes outside of the magnet before starting the post-dose section. The first resting-state scan of the post-dose section began approximately 45 minutes after the caffeine pill was ingested to achieve approximately 99% absorption of caffeine from the gastrointestinal tract . Control sessions used the same protocol, but without the administration of caffeine between sections, similar to the protocol used in . Subjects were not given a placebo during the control session. However, for convenience, we will still refer to the two scan sections as the “pre-dose” and “post-dose” sections.

Each scan section included a high-resolution anatomical scan, a bilateral finger tapping block design, and two five-minute resting-state BOLD scans. Bilateral finger tapping was self- paced and the block design run consisted of 20s rest followed by 5 cycles of 30s tapping and 30s resting. Subjects were instructed to tap while a flashing checkerboard was displayed and then to rest during the display of a control image, consisting of a white square situated in the middle of a gray background. During resting state scans, the control image was displayed for the entirety of the scan and subjects were asked to maintain attention on the white square.Images from each scan section were co-registered using AFNI software . In addition, the anatomical volume from each post-dose section was aligned to the anatomical volume of its respective pre-dose section, and the rotation and shift matrix used for this alignment was then applied to the post-dose functional images. The outer two slices of the functional data were discarded to minimize partial volume effects associated with the rotation of the post-dose data, and the first 10s of each functional run were not included. In addition, voxels from the edge of the brain were not included in the analysis in order to minimize the effects of motion.The second echo data from the finger tapping scans were used to generate BOLD activation maps of the motor cortex. This was accomplished using a general linear model approach for the analysis of ASL data . The stimulus-related regressor was produced by the convolution of the square wave stimulus pattern with a gamma density function . Constant and linear trends were included in the GLM as nuisance regressors. In addition, the data were pre-whitened using an autoregressive model of order 1 . The statistical maps were based on the square root of the F-statistic, which is equal to the t-statistic in the case of one nuisance term .

For consistency with our prior study , active voxels were defined using a method based on activation mapping as a percentage of local excitation . In summary, the √ F maps were separated into left and right hemispheric regions. The highest value in each region was identified and then every voxel was converted to a percentage of the peak statistical value for the region ×100. Active voxels were required to exceed an AMPLE value of 45% and a √ F value of 2 . The final activation maps were defined from the intersection of voxels active in both pre-dose and post-dose scan sections. Regions of interest were then defined for the left and right motor cortices from these activation maps. Thus, the same ROIs were used in the comparison of pre-dose and post-dose functional connectivity within an imaging session. Nuisance terms were removed from the BOLD resting-state data through linear regression. Nuisance regressors included constant and linear trends, 6 motion parameters obtained during image co-registration, physiological noise contributions , low frequency variations in cardiac and respiratory rate , and a version of the global signal that we will call the “regional” signal term. The regional signal term was calculated as the mean signal from the anterior portion of the brain in order to minimize bias that can occur when all the voxels in the brain are used to define a global mean signal as a nuisance regressor . Data were then temporally low-pass filtered using a finite impulse response function with a cutoff frequency of 0.08 Hz. This cutoff frequency was chosen for consistency with previous functional connectivity studies . To quantify functional connectivity strength, we extracted average BOLD signals from the left and right motor cortices. A stationary measure of inter-hemispheric functional connectivity was calculated as the correlation coefficient between the right and left motor BOLD signals computed over the entire length of each resting run. To assess temporal variations in inter-hemispheric motor cortex connectivity,vertical grow rack system we applied a sliding window over the length of each resting run and calculated the correlation between the left and right motor BOLD signals within each window. Correlation variability was quantified as the standard deviation of the sliding window correlation time series. To assess the affect of window length on correlation variability, we varied the window length from 10 seconds to 100 seconds. We found that significant caffeine induced increases in variability occurred for window lengths of 31 seconds and less. For all subjects, metrics were averaged across the two resting runs in each scan section. Two-tailed paired t-tests were performed between the pre-dose and post-dose results to assess caffeine-induced changes. Stationary measures of functional connectivity are shown for each subject before and after caffeine ingestion in Figure 3.1a, where the solid line represents equality between the two states. Consistent with our previous study , we find that caffeine significantly reduces inter-hemispheric BOLD connectivity in the motor cortex = 3.2, p = 0.012. Figure 3.1b shows the pre-dose and post- dose functional connectivity measures obtained in the control session for each subject.

There were no significant changes in these metrics = -1.2, p = 0.25. Windowed BOLD signal correlations between the left and right motor cortices are shown as a function of time for three representative subjects in Figure 3.2. While correlation varies with time in both the pre-dose and post-dose scan sections, temporal variability generally appears larger in the caffeinated state. However, extended time periods of strong correlation still exist in the post-dose measures. The scatter plots in Figure 3.1c and 3.1d show correlation variability using sliding window lengths of 30s and 20s for each subject during the caffeine and control sessions, respectively. Caffeine ingestion significantly increased variability | ≥ 2.5, p ≤ 0.04, while the control session data do not display significant changes in variability between scan sections for either window length | ≤ 0.55, p ≥ 0.6. To show that the caffeine-induced increase in correlation variability is not an artifact of using a specific window length, caffeine-induced changes in correlation variability are shown for different window lengths in Figure 3.3, which plots paired t-statistics between the post- and pre-dose scan sections versus window length. T-statistics are shown for both the caffeine session and control session . In this case a positive t-statistic indicates that variability is larger in the post-dose state, and data points above the top dashed line represent significant post-dose increases. Significant caffeine-induced increases in correlation variability are present in the caffeine session for window lengths of 31 seconds and shorter. Longer windows tend to smooth out correlation variations, making it more difficult to detect the effects of caffeine on correlation variability. In contrast, the control data do not show significant | < 0.39, p > 0.7 changes in correlation variability between the pre- and post dose scan sections for any window length. If the regional signal term is not included as a nuisance regressor, we find that correlation variability for the caffeine session data is significantly greater in the caffeinated state for window lengths of 27 seconds and shorter. BOLD time courses from the left and right motor cortices are shown before and after caffeine ingestion for a representative subject in the top panel of Figure 3.4. To visualize temporal variability in cross magnitude and phase, we created time-frequency plots of the windowed cross power spectra, which are shown below the BOLD time courses in Figure 3.4. These were created by computing the cross power spectrum between the left and right motor cortex BOLD signals for each 30- second sliding window period and displaying the resulting spectrum as a column in the time-frequency plot. For visualization purposes, we “increased” frequency resolution by zero-padding to 4 times the window length. In the plot, the color scale represents magnitude in units of normalized log-spectrum log2 , where σx and σy are the standard deviations computed over the entire length of the two BOLD time courses.Arrows are used to represent phase, with each arrow pointing to a position along the unit circle given by its phase angle. A 90◦ phase would have an arrow pointing up, a 180◦ phase would have an arrow pointing to the left, and so on. The frequency axis is restricted to frequencies less than the 0.08 Hz cut-off frequency that was used in the processing of the data. The plots in the bottom row of Figure 3.4 show sliding window time courses for correlation , cosine of the average phase cos , and average cross power magnitude M0XY . The power spectra in Figure 3.4 show that periods of low joint BOLD signal power correspond in time with reductions in correlation, shown in the plots below. This relationship is also captured by the M0XY time courses , which appear to track the correlation time courses fairly well, particularly in the pre-dose state. In addition, non-zero phase differences between the two signals also correspond with decreases in the correlation time series shown in the plots below, especially in the post-dose state.

The estimates from Table 3.7 are not statistically different from those of Table 3.2, but they are more precisely estimated in the restricted sample. The magnitude of the effects is largest for weekend and nighttime accidents, again suggesting that increased medical marijuana availability for older adults leads to positive externalities as they treat marijuana and alcohol as substitutes. Overall, access to medical marijuana significantly decreases alcohol and opioid abuse for older adults. The results suggest that, for adults aged 45-64, any costs of increased marijuana use may well be outweighed by a reduction in the substantial health costs associated with heavy consumption of these other substances. This is consistent with surveys of medical marijuana patients, who often report that they use medical marijuana to quit or decrease their use of alcohol and prescription pain medications . However, there is a policy trade off, as growth in the legal market significantly increases traffic fatalities caused by young drivers aged 15-24. These findings do not contradict those of past research, but they highlight the importance of considering the mechanisms by which medical marijuana liberalization generates spillovers to young recreational users. Individuals whose cannabis consumption decision is affected by law passage may have a different likelihood of joint use of marijuana and alcohol or opioids compared to an individual whose consumption changes with increased marijuana availability. By estimating the effects of growth in the legal market and not initial MML enactment alone, I show that spillovers to youths generate additional negative externalities as younger users treat marijuana and alcohol as complements. However, youths and adults differ significantly in their demand relationships for alcohol and cannabis. This is consistent with some past work showing cross-price elasticity estimates between alcohol and marijuana differ depending on the population studied. For instance, Farrelly et al. find higher alcohol prices significantly lower marijuana use among youths aged 12-20, but have no effect on cannabis use for adults aged 21-30.

Additionally, Williams and Mahmoudi find that the complementary relationship between alcohol and cannabis is strongest for those who use both substances simultaneously ,vertical grow rack system and poly substance use is much more common about adolescents and young adults. From the 2013 National Survey of Drug Use and Health, the prevalence of past-month use of both alcohol and marijuana peaks at age 21, when about 20% of individuals report past-month use of both substances. Participation in the use of both alcohol and cannabis declines slowly until sharply dropping to about 5% for individuals aged 35-49 and 3% for individuals aged 50-64 . Simultaneous use of alcohol and marijuana shows a similar pattern. About 11% of 18-20 years-old report using marijuana and alcohol together in the past month, while only 2% of 35-64 years-old report joint use . While this study’s use of aggregate data limits identification of the mechanisms driving these differences, below I outline a few insights from behavioral economics that might explain these findings and will be explored in future study. Evidence from the neuroscience literature suggests that differences between adolescents and adults in risk-taking behavior can be attributed to age differences in the stage of brain development. Development of the brain’s prefrontal cortex between ages 12-25 is associated with lower impulse control, increased sensation-seeking, and limited resistance to peer pressure , which may make adolescents and young adults more likely to jointly consume marijuana with other addictive substances or to drive under the influence of drugs and alcohol. Differences in brain development between adolescents and adults do not necessarily indicate that youths behave irrationally. In fact, experimental research has largely found that youths and adults are similar in their awareness of potential consequences and in their perceptions of the likelihood of facing those consequences. By age 15, individuals have logical reasoning comparable to that of adults in perceiving risk and estimating their vulnerability to it . A number of economic studies have similarly shown that youth substance use responds to price and perceived risk , which is consistent with some degree of rational decision-making.

This suggests that adolescents and adults are similarly able to assess the expected costs and benefits of addictive substance use within a rational framework. However, there is evidence that youths differ from adults in how they value the outcomes of their decisions. When presented with risky situations that have both potential rewards and costs, adolescents are more sensitive than adults to variation in rewards but less sensitive to variation in costs . In experimental studies, the presence of peers significantly increases risk-taking among teenagers, moderately among college-age individuals, and not at all among older adults .Differences in how youths and adults value consequences reflect differences in preferences. Traditionally, economists have dismissed preference-based explanations of human behavior since differences in preferences “explain everything and therefore nothing” . However, this is true only if there is no empirical evidence available to place structure on a model of preference heterogeneity. In the case of risky behavior, adolescence is shown to be a period when less value is placed on self-assessed potential negative consequences compared to the potential gains from experimentation, novelty-seeking, and social acceptance. Models of identity-formation , experimentation , and peer acceptance offer methods for incorporating these preferences in an economic framework. The utility derived from the consumption of alcohol, marijuana, and opioids is likely driven by some latent demand for intoxication. Motivational models of substance use have categorized these demand drivers along two dimensions, positive vs. negative affect regulation and intrinsic vs. extrinsic reasons for use . Crossing these two dimensions results in four classes of motives : positive/intrinsic , positive/extrinsic , negative/intrinsic , negative/extrinsic . A large body of literature has shown clear age patterns in self-reported reasons for using alcohol and cannabis.

Most adolescent users report use related to social motives, some for enhancement reasons, and very few for coping motives . However, youths tend to “age out” of these positive and extrinsic motives for substance use. Individuals who continue alcohol and marijuana use through their mid-20s are significantly less likely to report reasons for use related to having fun with friends, fitting in, and increasing the effects of other drugs; instead, adults are significantly more likely to report use of both alcohol and marijuana to relax, of alcohol to sleep, and of marijuana to decrease the effects of other drugs . Differences in these latent demand properties have been shown to generate heterogeneous substitution behavior. A recent study of college-aged individuals shows that most students use alcohol and cannabis for social reasons, and they tend to treat these substances as complements. However, those students who report using alcohol or cannabis to relieve negative affect are significantly more likely to treat them as substitutes . Increased marijuana availability for youths who consume to enhance positive mood or social interactions may thus have the unintended consequence of increasing alcohol use; for older individuals who consume alcohol to alleviate negative mood, access to marijuana may offer a substitute manner of coping. Potential economic models to incorporate affect in the utility-maximization process include Hermalin and Isen and Loewenstein . Adults and youths also differ in where they access and use alcohol and cannabis. Compared to older individuals, youths under the age of 21 are less likely to consume alcohol at bars or restaurants, and are more likely to drink in others’ homes, parties, outdoors, and in vehicles. They are also significantly less likely to purchase either alcohol or cannabis. Rather, for underage youths , opportunities for alcohol and cannabis access frequently occur in shared social settings. Increased supply of marijuana may make co-use more likely due to shared availability in the social markets where youth consumption occurs. In contrast, adults over age twenty-one have legal access to alcohol consumption in public places such as bars or restaurants. For adults who drink socially at bars and restaurants,grow rack with lights increased marijuana availability may keep drinkers away from these establishments and reduce their social consumption of alcohol.9 Even if alcohol consumption is not reduced, a shift of alcohol use from public places to an individual’s own home could generate significant reductions in alcohol-related traffic accidents. The role of environment may help reconcile differences between this paper’s findings and studies of the minimum legal drinking age that show marijuana and alcohol are substitutes.10 Being of MLDA decreases the total cost of consuming alcohol, but increases the cost of jointly consuming alcohol and cannabis . Figure 3.5 provides evidence in support of this hypothesis, as the prevalence of past-month use of both marijuana and alcohol peaks at age 21, but there is a sharp drop in the share of concurrent users reporting simultaneous consumption. Bernheim and Rangel present an economic model of addiction that explicitly incorporates the role of environment in determining an individual’s consumption decision. Briefly, their model describes an individual who chooses an environment or “lifestyle” activity , and some allocation of resources between an addictive substance and a non-addictive good. The decision-maker enters each period in a “cold” mode and rationally chooses her lifestyle activity. Along with her history of use, this decision determines the probability of encountering cues that trigger a “hot” mode. If triggered, consumption of the addictive good occurs regard- less of whether it is optimal; if not triggered, the decision-maker rationally chooses whether or not to use.

With growth in the legal medical marijuana market, the likelihood of encountering triggers for marijuana consumption increases for both adults and adolescents. However, an important distinction between adolescents and adults is that adolescents face a more limited choice set for environment or lifestyle. Youths also likely face more uncertainty in the probability of entering the hot mode with respect to multiple addictive substances. For example, an adult wanting to engage socially may choose a restaurant environment where he knows the probability of encountering an alcohol trigger is high, but the probability of encountering a cannabis trigger is low. Alternatively, he could choose a cannabis club environment where he knows the probability of encountering a cannabis trigger is high, but the probability of encountering an alcohol trigger is minimal. An adolescent, on the other hand, will often face more uncertainty about the probability of encountering one or more cues, and the “exposure” environments available to youths will often have high probability of triggering hot modes for multiple substances. Overall, access to medical marijuana significantly decreases alcohol and opioid abuse for older adults. This is an especially important finding given new research showing that the mortality reversal among white non-Hispanics aged 45-54 in the last decade is largely attributable to increased deaths from drug and alcohol poisoning and chronic liver diseases . Availability of medical marijuana as a substitute for these more lethal substances thus has the potential to lower mortality and morbidity from these causes. However, there is a policy trade-off, as youths treat marijuana and alcohol as complements. For adolescents and young adults, increased cannabis consumption due to growth in legal marijuana markets has additional negative consequences by increasing alcohol-related poisonings and fatal traffic accidents. For optimal regulation considerations, there needs to be some understanding of how we should trade off these effects. In Table 3.9, I calculate back-of-the-envelope estimates under a number of assumptions to quantify these health consequences. The numbers of lives saved and lost due to medical marijuana market expansion after the Ogden Memo are calculated by comparing actual mortality counts post-Ogden to those predicted under a counter-factual regime in which no registration rate growth had occurred. The counter-factual predictions are determined using results from Chapter 2 and the coefficient estimates from the regressions used to produce Tables 3.2 and 3.4. Overall, Table 3.9 shows that there are substantial benefits to expanding legal marijuana access for older adults, and regulations should be focused on limiting access to adolescents and young adults. These results imply that legal marijuana markets should be segmented, and costs should be increased for young adults and decreased for older adults. One such policy would be to differentially price marijuana by age, subsidizing the use of medical marijuana for older individuals while taxing younger adults. Since differential unit pricing may generate arbitrage opportunities, an optimal policy could consist of two-part pricing, in which medical marijuana purchases are subject to higher taxes but higher registration fees are subsidized for older adults.

The purpose of the model is to provide theoretical predictions of how supplier responses to the changes in federal enforcement should differ depending on state MML regulations. I can then link these predictions to newly collected empirical evidence on the size of the legal market. The model also provides intuition for the regulatory variables used in the empirical analysis and clarifies the key assumptions upon which my approach relies. As the illegal market for marijuana is not directly observed, modeling the interaction between the legal and illegal markets is helpful in motivating the appropriate empirical framework to estimate how legalizing medical marijuana affects recreational consumption. The model describes the market for marijuana in an MML state as composed of consumers and suppliers who respond to changes in the probability of being prosecuted by state or federal enforcement. Suppliers operating in the legal medical marijuana market also face capacity constraints that vary by how strictly the state regulates producers. Each state’s market is assumed to operate in isolation.7 Aggregate demand D = βd is assumed to be a downward sloping function of potency-adjusted price p. Enforcement by the state and federal government against marijuana users serves as an aggregate demand shifter, with ∂β/∂es ≤ 0, ∂β/∂ef ≤ 0. Users’ expected probability of arrest, penalties, and distaste for illegal activity will all be captured through β. It is worth noting that this setup does not rule out various forms of addiction or habit formation. Both rational and irrational models of consumer behavior imply price and risk responsiveness, but the models differ in the degree of responsiveness to these costs. With enactment of an MML, users who register as medical marijuana patients face new perceived risks from state and federal enforcement. The perceived federal risk of enforcement facing a registered user could exceed that facing an illegal user if, for example,grow racks individuals believed the federal government would use state medical marijuana registries to target individuals for prosecution.

As users can always face the same risks as during the pre-MML period by choosing not to register, I assume aggregate demand in the MML period is no less than demand under prohibition. The magnitude by which demand shifts will depend on the enforcement risk facing a registered compared to an unregistered user, and on how users weight the relative risks of state and federal enforcement. Most MML states have mandatory medical marijuana registration programs: laws requiring medical marijuana users to register in order to receive protection from state arrest.An individual who wants medical marijuana must first obtain a physician’s certification that the individual has a medical condition which could benefit from the use of marijuana. The patient then must submit an application to the state authority, along with a registration fee. If the application is approved, the patient receives documentation providing access to dispensaries and protection from state prosecution. By definition, all other consumption is illegal. The number of registered medical marijuana patients provides an observable measure of the size of the legal market. A limitation faced by previous research has been that state records of registered patient counts are not readily available and are not maintained similarly across states. To overcome this limitation, I collected monthly data from a number of sources, including contact with state officials, state department websites, news articles, and academic papers. Since my outcome variables are annual state-level prevalence measures, I linearly interpolate missing end-of-year registration rates using the nearest available months of registered patient counts. The final measure of market size is the annual registration rate, calculated as the percent of adults registered as medical marijuana patients at the end of December in a given year. I include the voluntary registration data available from California after 2005, but due to lack of data, Maine and Washington are excluded.

From this data, the medical marijuana market in 2013 is estimated to consist of about 1,139,098 legal medical marijuana patients,and industry reports estimate annual retail sales of legal marijuana in 2013 at $1.43 billion .While the legal market size is dwarfed by the estimated $25-$40 billion size of the illicit market , growth in the legal marijuana market over the past decade is twice that of the illicit market. The estimate of the number of registered patients in 2013 represents a more than 300% increase in the number of medical marijuana users from 2007 compared to only a 150% increase in the number of total adult past-month users . The legal market for marijuana is thus rapidly expanding. To study how changes in the legal market affect the illegal market, it is important to first understand the factors driving legal market growth. To isolate the supply-side drivers of growth in legal medical marijuana markets, the ideal policy variation would be such that production costs were exogenously shifted in some MML states but unchanged in others. Based on the model outlined in Section 2.2.2, the Ogden and Cole Memos approximated this ideal by shifting production costs differentially more in MML states with lax supply restrictions compared to those states with strict supply restrictions. Figure 2.1 connects the model’s propositions to the data by documenting registration rate trend breaks at the Ogden and Cole Memos for a sub-sample of states that exemplify the different state production restrictions. Hawaii and New Mexico serve as examples of more restrictive production states; Colorado and Montana are representative of MML states with looser supply restrictions. For all four states, few patients register during the first few years following MML passage. The initial slow growth in medical marijuana take-up suggests that, conditional on federal enforcement remaining high, reduced state risk following MML enactment had little effect on the size of the legal market.

The empirical evidence from Figure 2.1 is also consistent with the model’s prediction that the Ogden and Cole Memos had far greater effect in states with loose production limits. In Hawaii, where suppliers had strict limits and could only serve a single patient, the Ogden Memo had very little effect on registration rate trends. Correspondingly, there is also little break in trend following the Cole Memo. In New Mexico, which allowed state licensed dispensaries that had higher production limits but faced heavy regulation, the Ogden and Cole Memos also seem to have had small effects. In contrast, the states with lax production limits saw dramatic changes in registration rates with the Ogden and Cole Memos.16 After the Ogden Memo, Colorado experienced the “Green Rush,” a proliferation of dispensaries that arose following decreased fears of federal intervention.17 Alongside the expansion of unregulated dispensaries in Colorado, patient registration rates spiked . Similarly, in Montana, where caregivers were permitted to produce for an unlimited number of patients and receive compensation for their services, the number of caregivers providing for 20 or more patients increased seven-fold within one year following the Ogden Memo. This was accompanied by a nearly six-fold increase in registered patients . In line with the propositions from the model, the Cole Memo reversed this trend.The decline in registration rates in Montana is particularly dramatic because, concurrent with the Cole Memo, Montana’s legislature passed Senate Bill 423 which effectively dismantled the medical marijuana supply industry by establishing caregiver patient limits and preventing caregivers from receiving compensation.To examine this relationship for all states, I exploit the model’s predicted trend reversal between the Ogden period and the Cole period in the relationship between a state’s laxness of medical marijuana supply regulation and registration rates. Intuitively, in states with loose medical marijuana production limits, the Ogden Memo decreased marginal costs over a broader range of production , inducing producer entry and supply increases by existing producers. Column reports regression results that allow federal enforcement changes to also affect MML states with strict restrictions on supply. Consistent with the model predictions,grow table both federal policies had far larger effects on registration rate trends in states where marijuana suppliers were relatively unrestricted. Between the Ogden and Cole Memos, states with strict production limits on average saw an additional 0.2% of the adult population register as medical marijuana patients , which is statistically significant but an order of magnitude smaller than the increases seen in MML states with loose supply regulations . There is no effect of the Cole Memo in strictly regulated MML states, consistent with evidence that federal enforcement related to the Cole Memo targeted large-scale production. Limiting the sample to only those states with MMLs by 2012 in columns and yields coefficients of similar magnitude but with slightly larger standard errors. Unlike initial MML enactment, the federal memos substantially altered the size of the legal market, with larger effects in states with looser supply regulations. The differential response of registration rates to the federal government’s policies in states with lax compared to strict producer restrictions suggests that patient registration rates are driven primarily by supply-side shifters. Therefore, to estimate the causal effect of changes in medical marijuana supply on recreational consumption, my empirical strategy uses the timing of the federal memos and differences in initial MML supply restrictions as instruments.

The main threat to identification is that registration rates and illegal use may be jointly determined by unobservables affecting demand. Time-varying state covariates included that potentially affect recreational marijuana use can be categorized as: demographics influencing recreational marijuana use, economic characteristics, and substance-related policies influencing marijuana consumption. A full listing of covariates is provided in Table 2.3. For all specifications, to account for heteroskedasticity and serial correlation, robust standard errors are clustered at the state level . To account for potential violation of the parallel trends assumption, specifications including state-specific linear trends are also presented as robustness checks in section 2.6. Even after controlling for state and year fixed effects and state-year covariates, identification of β is challenging due to concerns of endogeneity between recreational marijuana use and registration rates. While fixed effects will account for issues of cross sectional endogeneity, β could still be biased due to some omitted state-level time-varying variable that affects both registered users and illicit users. For instance, β will be biased upward if changes in local perceptions regarding the health risks of marijuana use led to changes in both medical and recreational use. To account for potential endogeneity, I instrument for registration rates by way of two-stage-least squares using equation 2.6 as the first-stage specification. The instrumental variable estimates are valid as long as the exclusion restriction is satisfied. In the context of equations 2.6 and 2.7, this occurs if E[εjtZjt|uj , vt , Xjt] = 0, where Zjt is the vector of instruments including the interaction of MML supply restrictions with trend breaks based on the exogenous timing of the Ogden and Cole Memos. As state and year fixed effects are included, the identification is not threatened by level differences between states or by national trends in marijuana consumption . However, the exclusion restriction will be violated if changes in federal enforcement following the Ogden and Cole Memos had differential effects on demand in states with loose compared to strict production restrictions through any channel other than supply. Evidence validating the exclusion restriction is presented in section 2.6. The measures of marijuana consumption come from the National Survey of Drug Use and Health . The NSDUH is an annual survey funded by the Substance Abuse and Mental Health Services Administration of the US population over twelve years of age. The public-use NSDUH provides estimates of the prevalence of past-month use and past-year initiation of marijuana, available separately for age groups of 12-17, 18-25, and 26 years of age and older. Representative state-level data broken down by two-year averages is available from -. Table 2.3 provides summary statistics for the marijuana use measures and state level covariates used in the NSDUH analysis. Comparing mean differences, MML states with a positive number of registered medical marijuana patients have higher levels of past-month cannabis users for all age groups than states without MMLs. However, MML states on average are more likely to have decriminalized marijuana,have higher cigarette taxes, and consist of populations that are on average younger and more male. As such characteristics are potentially correlated with prevalence of recreational cannabis use, all regressions control for the state covariates listed in Table 2.3.

The resulting heterogenous mixture was concentrated under reduced pressure into a crude solid. The solid was dissolved in a mixture of water and methylene chloride.The combined organic layer was dried with sodium sulfate and solvent removed in vacuo. The resulting solid was dissolved in minimum amount of hot methylene chloride/hexanes mixture and allowed to cool slowly to room temperature to afford off white crystals . The spectroscopic data of the product agreed with the reported literature.Using the procedure of Zhang,60% NaH was portion wise added to a stirred solution of 3-cyanoindole in tetrahydrofuran cooled in an ice bath and then the reaction was slowly warmed to room temperature. After stirring at room temperature for 30 min, phenylsulfonyl chloride was added drop wise. The reaction was stirred for 24 h at room temperature. The resulting heterogenous mixture was concentrated under reduced pressure into a crude solid. The solid was dissolved in a mixture of water and methylene chloride. The aqueous layer was separated and extracted three times with methylene chloride. The combined organic layer was dried with sodium sulfate and solvent removed in vacuo. The resulting solid was dissolved in minimum amount of hot methylene chloride/hexanes mixture and allowed to cool slowly to room temperature to afford off white crystals . The spectroscopic data of the product agreed with the reported literature.

To a solution of crotyl alcohol in dimethylformamide was added a solution of 1 M LiHMDS and stirred at room temperature for 30 min. N-Phenylsulfonyl 3- formylindole was added and the reaction was stirred at 60 °C for 30 min. The reaction was quenched with saturated ammonium chloride aqueous solution and diluted with ethyl acetate . The organic layer was washed twice with water ,botanicare trays once with brine , and dried with sodium sulfate. The organic solution was concentrated under reduced pressure and the crude material was purified by flash column chromatography to afford a white solid.To a solution of alcohol in dimethylformamide was added a solution of 1 M LiHMDS and stirred at room temperature for 30 min. The N phenylsulfonyl 3-substituted indole was added and the reaction was stirred at 60 °C for 30 min. The reaction was quenched with saturated ammonium chloride aqueous solution and diluted with ethyl acetate . The organic layer was washed twice with water , once with brine , and dried with sodium sulfate. The organic solution was concentrated under reduced pressure and the crude material was purified by flash column chromatography to afford the desired product.As of 2015, twenty-four U.S. jurisdictions have medical marijuana laws , which provide legal protection for individuals who use marijuana for medical purposes, physicians who recommend marijuana to patients with certain medical conditions, and growers and distributors who supply these patients. Past studies have exploited state-time variation in MML enactment to estimate the effects of liberalization on recreational marijuana use,1 traffic fatalities , obesity , suicides , and crime . However, interventions may not attain full steady-state effectiveness immediately upon implementation , and estimation based on the timing of MML passage will likely not capture the full effects of these policies as medical marijuana markets evolve.

While MML enactment alone may signal a shift in governmental acceptance of the drug, effects on marijuana availability and price will depend on the specific regulations established by MML policy and the duration of exposure to the more liberal regime. Changes in social access, perceived community approval, and spillover effects to illegal use and other public health outcomes may well vary according to the extent to which legal users and suppliers actively participate in the medical marijuana program. If recreational and medical marijuana consumption decisions are made based on similar consumer optimization problems,2 both medical marijuana participation and use in the general population will follow similar patterns. Understanding the factors that drive changes in medical marijuana participation can thus offer insight into the mechanisms by which MMLs generate spillovers to recreational use and other health outcomes. This is the first paper to investigate the determinants and dynamics of medical marijuana participation by legal users using newly collected data on medical marijuana patient registration rates. While some recent research has examined data on registered patients, these studies have either been descriptive , cross-sectional , or limited to one state . By collecting data on registered patient counts from both administrative and non-administrative sources, this paper presents the most comprehensive state-level monthly panel dataset to date on medical marijuana participation to date. Data was obtained from 1999-2014 for the sixteen states3 that required individuals to register as medical marijuana patients in order to receive the legal protections afforded by the MML. The data show that there have been dramatic changes in medical marijuana participation over the last two decades. Registered patient counts were relatively low until 2009, when they sharply increased. The number of registered patients continued to climb until mid-2011, when they leveled before resuming an upward trend a few years later. These trend breaks in medical marijuana patient registration rates coincide with the timing of federal enforcement policy changes that have been widely ignored by past research.

While federal law has strictly prohibited the use and distribution of marijuana since 1937 regardless of state policy, a federal statement of non-enforcement in MML states was released in October 2009 ; in June 2011, another federal statement was issued to clarify that this did not apply to large-scale producers . To understand the factors responsible for driving these changes in medical marijuana take-up, I first outline a conceptual framework whereby individuals apply to register with the medical marijuana program if the expected utility from registering exceeds that of not registering. Costs to patients include transaction costs associated with registration fees and finding a doctor to provide a recommendation, as well as perceived risk from state and federal enforcement. Benefits include access to legitimate sources of marijuana , which will vary depending on the production limits established by the state’s specific MML regulation. The federal memos are predicted to affect medical marijuana participation through changing the perceived risk associated with federal enforcement for both registered patients and state-legal producers. The empirical results confirm the descriptive evidence showing that the federal memos significantly affected medical marijuana take-up. Controlling for state-level demographic and economic variables or time-invariant state characteristics dampens the magnitude of these effects slightly, but they remain large and statistically significant. However, the federal memos did not affect all MML states equally. Interacting the federal memo changes with state-specific supply-side regulations shows that the magnitude of their effects was significantly larger in states that imposed relatively lax restrictions on legal producers. These findings imply that medical marijuana participation is primarily driven by the expected benefits associated with access to legal supply. Additional robustness checks support that the extent of medical marijuana participation is highly responsive to supply-side changes in the legal market. This paper builds on recent work recognizing that heterogeneity in the specifics of state MML regulations may generate heterogeneous effects , and it contributes to a broader economic literature showing that the effects of regulatory changes depend largely on the specifics of their design, implementation, and enforcement.4 Section 1.2 details the history of marijuana regulation in the U.S. and provides background on modern MMLs and changes in federal enforcement policy. Section 1.3 outlines a conceptual framework to suggest the factors determining medical marijuana participation. Section 1.4 presents the data, empirical framework, and results for the determinants of medical marijuana participation. Finally, section 1.5 places these results in the context of the existing literature on MMLs, and section 1.6 concludes. The first federal regulation of marijuana was introduced with the Marijuana Tax Act of 1937. The Marijuana Tax Act did not criminalize the possession or use of marijuana, as this was a potential violation of the Tenth Amendment’s limitation on federal power,flood table but instead made it illegal to grow or distribute marijuana unless the grower obtained a federal stamp. Since stamps were largely unavailable and there was no application process, the Act effectively served as federal prohibition . Marijuana use remained limited until the mid-1960s, when the baby boom generation reached adolescence. Thicker drug markets associated with this larger youth cohort resulted in a significant increase in illicit drug use among college and high school students .

In 1970, Congress responded by passing the Controlled Substances Act , which repealed the Marijuana Tax Act5 but classified marijuana as a Schedule I substance with high potential for abuse and no accepted medical value.6 The CSA criminalized the manufacture, distribution, and possession of marijuana for both recreational and medicinal purposes, and it provided the system of federal penalties and enforcement that remains in place today. Despite federal prohibition, marijuana use continued to rise in the United States. By the early 1970s, eight million people were using marijuana regularly, at least half a million people were consuming it daily, and 421,000 people were arrested for marijuana offenses annually . In 1972, the National Commission on Marijuana and Drug Abuse, which had been created as part of the CSA, released a comprehensive report based on surveys of health experts and law enforcement officials. The report recommended the removal of criminal penalties for marijuana possession and advocated further scientific research on the substance’s potential medicinal value. President Nixon rejected the Commission’s recommendations, but the 1972 report helped trigger a push toward liberalization policies . In 1972, the National Organization for the Reform of Marijuana Laws filed the first petition to reschedule marijuana. In 1975, the federal government established the Individual Patient Investigational New Drug program, which enabled participating physicians to prescribe marijuana to enrolled patients. The federal program was designed to accept patients with serious illnesses and directly provide them with marijuana through the National Institute on Drug Abuse. While the IND ostensibly established a legal channel by which patients could obtain marijuana, the application process was complicated and burdensome, and only six patients were accepted into the program during its first decade of operation . Still, this signaled a movement toward federal acceptance of marijuana’s medicinal value, and many states began to adopt their own legislation allowing the use of cannabis for medical purposes under specified conditions . Figure 1.1 graphs the number of proposed state-level medical marijuana initiatives from 1972-1995. Statutes are classified as therapeutic research programs , rescheduling provisions, or physician prescription laws. While these laws demonstrated increasing state recognition of marijuana’s therapeutic value, they had little practical significance . The federal approval process for state TRPs was complicated and costly. In the few states that obtained the necessary federal permissions, enrollment was highly restrictive and largely dependent on receiving marijuana from the federal government. In theory, physician prescription laws and rescheduling provisions allow physicians to legally prescribe marijuana for medicinal purposes outside of a TRP.However, since the federal CSA classification of marijuana as Schedule I supersedes any state CSA, physicians who prescribe marijuana outside of an officially recognized state TRP risk facing federal sanctions. Additionally, even should a patient obtain a physician’s prescription, these statutes did not establish any legitimate supply channel for patients to obtain marijuana. By 1984, the wave of state medical marijuana initiatives had quickly come to an end. This shift occurred in large part due to the spread of the crack-cocaine epidemic and increased federal emphasis on drug policy enforcement, seizures, and interdictions under the Reagan and Bush Administrations . It became increasingly unlikely that NORML’s 1972 petition would result in federal rescheduling of marijuana, and the government’s IND program was suspended in 1991 and discontinued one year later. State policy mirrored the federal stance, and by 1990 a number of the existing decriminalization and medical cannabis statutes expired or were repealed.The discovery of naturally occurring cannabinoid receptors in the human brain in the early 1990s led to a resurgence of medical interest in the potential therapeutic value of marijuana .There was increasing evidence that smoked marijuana offered significant benefits for patients suffering from symptoms of cancer and HIV/AIDS, and in 1995 the Journal of the American Medical Association ran a commentary supporting the use of marijuana for medicinal purposes and calling for increased research. In 1996, with the passage of Proposition 215, California became the first state to establish an effective medical marijuana law that removed criminal penalties for the use, possession, and cultivation of medical marijuana by qualifying patients and their primary caregivers.

The receiver comprises a continuous time linear equalizer with 6 dB of equalization, and half rate slicers. 2-4 data deserializer and BERT are also needed to measure BER and characterize data quality. Quadrature outputs of the CMU are applied to a phase interpolator to set the clock phases for timing recovery at receiver. Absolute Jitter is measured at each clock repeater output using the real-time oscilloscope TDS6154C TIE measurement. Different clock forwarding configurations are examined. Namely, all-reference, all-PLL, MDLL with FIR, and configurable PLL/MDLL clock forwarding. The results are depicted in Fig. 5.5. The plot shows absolute jitter accumulation from one clock repeater to the next across the link. As obvious from the plot, reference clock forwarding and PLL clock forwarding results in 16.5ps and 6.6ps of RMS jitter at the link output, respectively. Those are excessive amounts of jitter that cannot be tracked by a practical receiver at the end of the link. Although all-PLL forwarding shows less jitter accumulation than all-reference forwarding, yet the clock exhibits excessive jitter peaking due to cascaded stages of PLLs and high ring oscillator jitter. The 4th repeater in the all-PLL forwarding also has excessive jitter transfer peaking due to process variation. The other lines in the graph show the benefit of CMU configurability and FIR technique in lowering the link jitter. The FIR filtering technique with MDLL divided output results in 2.2ps RMS jitter. The configurable PLL/MDLL yields 2.7ps RMS jitter. Note that with an FIR a PLL setting is not needed since the input noise is kept low. The settings for the FIR coefficients and the CMU input mux are overlaid in the figure. As discussed in section 3.5, all-MDLL and mostly-MDLL setting is needed early on the link to favor forwarding of the clean clock. Later on the link, PLL or semi-PLL setting is used to benefit from PLL filtering. Also repeater 4 CMU shows excessive jitter in PLL mode and is bypassed altogether from clock forwarding by setting .

While absolute jitter is a crucial metric for asynchronous data reception by a CDR module at the end of the cable,drying room uncorrelated jitter is important for synchronously clocked systems to determine tracking between the data and clock paths. Fig. 5.6 portrays the untracked jitter accumulation across the link for a delay of 1 forward clock period. For an All-PLL system, the untracked jitter is 3.4ps rms at the output of the under-damped 4th repeater, and 2.8ps rms at the last repeater. The jitter tracking for the FIR filtered technique outperforms the all-PLL design. It shows 31% reduction in untracked jitter with respect to the all-PLL at the 4th repeater, and 18% reduction at the last repeater. The result suggests the link can be extended even further with good jitter tracking capability. The plot also shows the allreference forwarding has excessive untracked jitter across one period that renders it unusable for clock forwarding. Data path characterization is performed at the last 2 repeaters where clock jitter is the maximum. Fig. 5.7 shows the bathtub curves for the link at 12Gbps. At the Tx output, the bathtub plot is obtained by sweeping the clean source clock phase with respect to the data. The Tx eye is completely closed for all-reference and all-PLL clocking forwarding. For the configuration using the MDLL with FIR filtering, 0.55UI open eye is measured at the Tx output. At the receiver input the eye opening is 0.27UI at BER of 10−12 for a PRBS31 pattern. Data eye diagrams are shown in Fig. 5.8. The eye diagram is completely closed in case of all-reference forwarding whether it’s triggered by an asynchronous clean clock as in Fig. 5.8, or triggered by the same forward clock as in Fig. 5.8. In case of allPLL forwarding, eye is completely closed with asynchronous triggering in Fig. 5.8, which suggests inoperability of an asynchronous CDR at the Rx side. However, all-PLL clock forwarding shows good tracking with data, Fig. 5.8, and 4.9ps RMS jitter. On the other hand, MDLL and FIR filtering outperforms all-PLL clock forwarding whether it is triggered asynchronously or synchronously. In Fig. 5.8 and , the eyes show 4.4ps and 3.9 RMS jitter at the end of the link with asynchronous and synchronous triggering, respectively.

The pattern used is PRBS-31 pattern. Table 5.2 summarizes the link performance. The CMU section consumes a total of 9.6mW. The transmitter driver and pre-driver dissipate the majority of power at 24.4mA due to the voltage swing and the pre-emphasis. The receiver front end consumes 1.2mA. The clock driver consumes 5mA. Total chip power is 48mW including high-speed pattern generators and error detectors. Reliable reception is demonstrated at the end of a 115m CAT7 cable at 12Gbps. Total repeater chip area is 1mm2 . With such a small area and power, it is feasible to embed such a repeater within a cable for extending the reach of copper cables. The chip micrograph in Fig. 5.9 shows the design with each building block marked individually. This work demonstrates the potential use of source synchronous repeaters as means of extending multi-Gbps cable links for distances exceeding 100 meters. A 115 meter CAT7 cable is used to demonstrate reliable data transmission at 12Gbps. Longer reach can potentially be achieved since the total jitter at the end of the cable is only 4.4ps RMS. The work describes and validates an area-efficient phase filtering technique using a phase-based FIR to filter jitter across cascaded repeaters. To achieve both the clock multiplication and the phase delay of the FIR, an MDLL is used that can produce a 6GHz clock output. While challenges such as mechanical attachment, robustness to strain, tracking of environmental variations, etc., clearly exist for a repeater based copper link that embeds the repeater within the cable, this work indicates that the power and area requirements for the electronic circuits is feasible. The work also provides a fast semi-analytical method for modeling linear time variant noise accumulation across clock forwarded repeaters. The analysis provides accurate correlation with transient noise analysis and measured accumulated jitter for low and moderate levels of jitter accumulation. Because the analysis is based on a linear noise analysis, it tends to overestimate jitter when noise level increases and perturbs the DC operating point of the repeater. The work also demonstrates the fastest multiplying delay locked loop implemented in literature.

We were able to push the MDLL operation up to 6GHz by accurately placing the select aperture with respect to reference and VCO pulses. This MDLL exists at the core of the our clocking and filtering scheme. Its inputs can be configured to select the lowest of the input clock and the VCO clock for clock multiplication. Thus, it prevents jitter accumulation along the link. Outputs are also mixed to provide an effective FIR phase filter to filter the output clock. In the implemented prototype, a dedicated clock channel is used to forward the clock. In future work, the dedicated channel can be eliminated altogether and clock forwarding occurs on the common mode of the data channels or the power lines. Along that same line, a referenceless CDR could also be a viable alternative that needs to be investigated for repeater based copper cables. Finally,pruning cannabis to complete the experiment, multiple data channels could be added and the effect of cross talk on the entire link should be examined. Evaluating the health effects of social policies is critical to researchers, funders, and decision-makers seeking to promote healthful, evidence-based programs. Study designs such as differences-in-differences and panel fixed effects , which exploit variation in the timing and location of policy changes, have the potential to reveal causal inferences. Changes in health outcomes that are tied to the jurisdictions and times at which a particular policy is adopted can be used to isolate the causal effect of the policy . The amount of empirical health research on social policies using these methods has increased rapidly and yielded influential findings in recent years in epidemiology and other fields . One major concern with study designs that leverage variation in the timing and location of policy changes is that cooccurrence of policies can render it difficult to separately identify the causal effects of each policy. Isolating individual policy effects is crucial for delivering to decision makers evidence on whether to adopt a policy. Yet multiple related policies are often adopted or implemented in the same jurisdiction simultaneously or in quick succession, rendering it difficult to isolate the effect of 1 policy from the other. For example, a government that moves to overhaul its social safety net is likely to change multiple welfare-related policies in a single wave of legislative changes . Consequently, bundles of related policies, selected to address a particular set of health or social priorities and thus with similar potential health effects, are adopted concurrently, creating co-occurring policies. Co-occurring policies confound one another. Thus, if the co-occurring policies are relevant to the health outcome of interest, failing to account for co-occurring policies can severely bias estimated effects of specific social policies.

For example, if an effective policy A and an ineffective policy B are routinely adopted as a set, and their true effects are unknown, when researchers analyze effects of policy B without accounting for policy A, findings are likely to spuriously indicate that policy B is effective. Yet if jurisdictions typically adopt both policies together, adjustment for policy A to isolate the effect of policy B can lead to imprecise or unstable estimates and bias resulting from data sparsity . In extreme cases, estimates may be severely biased, undefined, or rely entirely on extrapolation because there is no independent variation in the policy of interest . Strong confounding and consequent data sparsity arising from co-occurring policies can be conceptualized as lack of common support in the data, also known as a violation of the “positivity assumption” . Lack of positivity implies that some confounder strata do not have variation in the exposure; for example, because places and times with the confounding policy always adopt the policy of primary interest . A rich literature exists on the problem of positivity and the use of propensity scores to assess and address it . However, several aspects of the policy co-occurrence problem make it important to consider separately from positivity issues that arise with other exposures. First, due to the nature of policy making , the levels of co-occurrence among policy variables may be far greater than those typically observed in non-policy studies . For example, governments adopt similar policies at similar times in part because they are responding to the desires and values of their constituents. Second, the most relevant analytic solutions may be distinct. For example, analytic solutions such as data-adaptive parameters that rely on large sample sizes may not be feasible for policy studies that are typically based on a small, fixed set of jurisdictions. Meanwhile, stronger theories or substantive knowledge about the mechanisms by which a particular social policy operates could guide analyses leveraging mediating variables for causal effect estimation . For example, how education policy affects educational attainment may be better understood than how educational attainment affects health. Furthermore, if some policies are always adopted together as a set, the most policy-relevant approach may be to modify the exposure definition to encompass both policies and then evaluate their combined effect, as opposed to attempting to disentangle their individual effects. Thus, the policy co-occurrence problem presents unique challenges and potential analytic solutions beyond typical confounding. Characterizing the extent and impact of policy cooccurrence is a crucial step for the development of rigorous evidence on social policy effects. Yet, to our knowledge, no epidemiologic research has directly addressed this issue. Authors of applied studies of social policies in fields including epidemiology, economics, and political science have acknowledged the issue by critiquing existing policy studies or, in some cases, applying solutions . Similar methodological challenges have arisen in environmental epidemiology when studying correlated and multi-pollutant exposures, but the emphasis of this research has been on identifying analytic solutions appropriate for pollutant measures, rather than on quantifying the extent of the problem .