Hemp Growing

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 .

Larvicides target mosquito larvae, representing a major advantage over adult control, in which changes in biting and resting behaviors can lead adult mosquitoes to evade control activities. In addition, microbial larvicides from bacteria Bti and Bs have different modes of action than pyrethroid insecticides; therefore, microbial larvicides do not aggravate pyrethroid resistance. Microbial larvicides are also considered safe for non-target organisms and human health. Furthermore, larval control does not conflict with but rather complements the front-line ITN and IRS malaria control programs. Larval control may now be timelier than ever, since pyrethroid resistance and outdoor malaria transmission are increasing in Africa. However, there are some potential limitations of larviciding as it is practiced today. Although there are three formulations of long-lasting larvicide available for use in different habitat types , the classification of habitats is primarily based on the longevity of the aquatic period and productivity of the habitat. The longevity of the aquatic period may be visually identified; however, the productivity of a habitat may change over time. Canopy cover in the habitat, such as grasses in the water, may affect the spread of Bti/Bs [Zhou, personal observations]. Furthermore, heavy rainfall may wash away Bti/Bs and create new habitat; therefore, additional Bti/Bs may need to be applied at an unplanned time after the rain. There are also limitations for the design. The incidence of clinical malaria is essential for the evaluation of intervention success. However, as pointed out by previous studies, crude health facility records are not always a reliable source of such information and may in fact under estimate the true clinical incidence rate. However, as long as clinical malaria was diagnosed the same way across all health care facilities,vertical farming pros and cons comparison between intervention and control groups is justified. EIR is a good measure of reduction in transmission since larval control reduces overall vector population density and EIR is measured based on vector population density.

Additional indicators, such as clinical incidence through active case surveillance, can be a more accurate estimate of incidence, and parasite prevalence through cross-sectional surveillance may be helpful. However, as per restrictions imposed by the funding policy, direct measures of human subjects are restricted. Despite very high bed net coverage, malaria incidence in many African sites is resurging after a short-time reduction when ITN and IRS scale-up was initially rolled out. This malaria resurgence is caused primarily by increases in insecticide resistance and outdoor transmission. New cost-effective methods beyond bed nets and IRS are urgently needed. Long-lasting microbial larviciding represents a promising new tool that can target both indoor and outdoor transmission and alleviate the problem of pyrethroid resistance. Comprehensive evaluation of potentially cost-effective LLML will provide critically needed data for determining whether LLML can be used as a supplemental malaria control tool to further reduce malaria incidence in Africa.Data centers are managing increasing demands in data volume and processing power. High performance connectivity between servers and storage within a rack and across multiples racks are necessary to provide sufficient data bandwidth. The type and length of the data connection depends on signaling technology and cost. Passive copper interconnects are the most viable approach of short distances up to 10∼12 meters at 10Gbps per wire pair. Fig. 1.1 shows a data center with an arrangement of racks, where the 12m shaded area shows the reach of passive copper cables. Beyond the 12m range, racks of switches need to be inserted to extend the connectivity, regenerate and repeat the data. This represents an overhead in power and cost to the data center designer. Active copper cables with embedded amplification circuitry can extend the passive copper cable reach, but are typically limited to less than 20m.

For longer lengths of exceeding a kilometer, optical fibers are the only option that offers sufficient performance but at substantial cost. Lengths of <150 meters are an intermediate distance that can be particularly suitable for multiple 10Gbps lanes within a data center to connect across a row of racks to core switches at the end of a row. This work explores an active cable approach based on a source synchronous architecture to extend the range for copper cables to >100 meters for per-pair data rate >10Gbps. Unlike 10GBASE-T signaling, the approach does not require complex symbols at a lower symbol rate across multiple signaling pairs and dissipates ∼4W per port. The proposed link uses low power and area repeaters powered through the cable that can potentially be embedded in the cable. Source synchronous links have been proposed and used in server systems for multi-lane high speed serial link applications such as connecting CPU to CPU, to memory, or to bridge chips due to their inherent tracking of correlated jitter. A source synchronous receiver can track jitter in the received data by using a clock that is forwarded from the same transmitter that sends the data. The transmitted clock undergoes almost identical noisy environment as the transmitted data, particularly with similar supply and substrate noise. As a result, data jitter is transparent to the receiver by using the received clock to re-time it. Hence, only static and slowly varying phase offset need to be corrected for using slow phase compensation loops. This approach mitigates the need for fast and power hungry CDRs that are used traditionally in embedded clock link design. In addition, the power and hardware needed for the extra clock channel is usually simpler than the circuits for data transceivers and amortized by using multiple data lanes. In practice, the correlation between the timing of the clock and the data is weakened when a delay difference between the clock and the data path is present. Excessive delaycan cause correlated jitter in the clock path and data path to add instead of subtract and thus deteriorates the jitter tolerance.

For that reason, delay mismatch between clock and data path are minimized. At the same time, uncorrelated noise need to be filtered especially for high frequencies near and above the data bandwidth. In literature, different clock forwarding techniques have been proposed to deal with the aforementioned problem. In a clean-up PLL is used in the clock path at the receiver side. The PLL has sufficient bandwidth to track correlated jitter in the data path, and cut-off high frequency jitter. Another approach uses a DLL. The all-pass characteristic provides jitter correlation between clock and data paths, and jitter does not accumulate within the DLL. Delay has to be matched carefully between the clock and data paths to avoid jitter amplification. Similar to a DLL, an MDLL is used in to multiply a lower frequency forwarded clock to one at half the data rate. As an alternative to an MDLL, an injection locked oscillator is used to filter out uncorrelated jitter. Recently, the ILO has drawn more attention in source synchronous links because of its simplicity and wider bandwidth compared to a PLL which in turn enables it to track a wide range of correlated jitter. Fig. 1.2 shows a block diagram of the proposed clock-forwarded cable-link architecture. The forwarded clock tracks the jitter of the data across a wide frequency range. At each repeater stage, the data signal is equalized, amplified,air racking and retimed by the forwarded clock before being transmitted. Since, the relative jitter between the clock and the data is reset when the signal is retimed, the data repeating distance is defined mainly by the distance that can be easily transmitted with little power cost. Clock is transmitted on a separate channel without equalization. The clock frequency is a system variable that is determined using the model presented in the next section. A CMU multiplies the clock frequency from the forwarded frequency to half the data rate in each repeater. The clock and data repeating distance are not constrained to be the same. As seen from Fig. 1.2, clock can be tapped at each data repeating stage for frequency multiplication and retiming, and amplified/buffered at each clock repeating stage. The critical challenge in a repeating a source-synchronous system is the accumulation of clock jitter. Hence, maintaining a clean clock is the focus of this thesis. Determining a fine balance in forward clock frequency is crucial in defining jitter performance of the cable link. Frequency beyond the cable bandwidth results in large attenuation of clock amplitude creating more noise and jitter accumulation along clock repeater. On the other hand, frequency well below the cable bandwidth will increase jitter accumulation time and will degrade jitter performance inside the clock multiplier.

The trade-off between low frequency clock jitter accumulation in the Clock Multiplication Unit and the high frequency jitter accumulation along the clock repeaters is one of the defining aspects of optimizing the active copper link. We propose an FIR jitter filtering technique that requires little area and power cost, but drastically reduces clock jitters accumulation. We also utilize a programmable PLL/MDLL clock multiplication unit to verify and compare different clock configurations along the repeated link. The dissertation is composed of seven chapters. Chapter 2 gives the necessary overview on the most common repeater designs used nowadays and common clock multiplication topolgoies. The chapter then presents background on most commonly used CMU architectures. An overview on jitter metrics is also provided in this chapter, together with analysis of jitter on basic building blocks; an inverter and a differential pair. In Chapter 3, we propose a fast and accurate model for modeling clock forwarded repeater links. We use the model to evaluate link design space and different system parameters. In Chapter 4, system and implementation details are presented. We present a configurable and high speed clock multiplication PLL/MDLL in this chapter that is more than twice the speed of MDLLs published in literature. The experimental results are shown in Chapter 5. Chapter 6 concludes the work, list the contributions and offers some ideas for future work.Two repeater architectures are commonly used: referenceless CDR-based repeater and fully synchronized repeater. Fig. 2.1 shows a block diagram of a referenceless CDR, where phase acquisition occurs by connecting the VCO to a CDR loop where its frequency, and thus phase, is locked to the incoming data stream. The lock range is usually narrow and a frequency detector is often used to bring the VCO center frequency close to the data rate. This architecture poses the traditional trade-off between jitter filtering and the jitter tracking requirements by the CDR loop. Nevertheless, it should have wide enough bandwidth so that recovered clock can tolerate and track data jitter to minimize bit error rate due to timing wander and low-frequency noise. Jitter peaking is another system parameter that presents challenge in the design of referenceless CDR repeater. Peaking in the transfer function of the CDR due to phase margin less than 600 causes jitter amplification at the peaking frequency. Cascading multiple repeaters can cause excessive jitter accumulation at the peaking frequency which deteriorates the overall system performance and its tolerance to jitter. In addition, design of a frequency detector that covers a very wide range requires additional specialized circuits. An alternate architecture is the fully-synchronized repeater shown in Fig. 2.1. The architecture is similar except that a FIFO buffer is used to decouple the jitter filtering from the jitter tracking requirements. The CDR can thus have wide bandwidth for better tracking, and a clean oscillator/PLL is used at the output to read data from a FIFO buffer. The FIFO buffer handles any timing wander or frequency mismatch in the system. A driver, synchronized to the clean clock, transmits the data to the next repeater. This repeater is more robust but comes at the expense of more power and area due to the FIFO and clean oscillator/PLL.PLLs are commonly used to provide accurate timing signals for both transmit and receive sides of a high-speed link. In its simplest form, a PLL is a 2nd order feedback system that generates a clock signal whose output phase is aligned with respect to the phase of an input reference clock. Since phase is the integration of frequency, once the phases are aligned, both phase and frequency are locked. This alignment is achieved by comparing the phase of the output clock with the phase of the reference. Any resulting difference in phase, the phase error, feeds into a block that filters this error and generates a control signal, typically a voltage.

For Aim 1, we hypothesized that greater cigarette use would be associated with greater marijuana use. We also explored the association between past year quit attempts for the two substances without an explicit hypothesis. For Aim 2, given perceptions that cigarette smoking is more harmful and less socially acceptable than marijuana use among young people, we hypothesized young adults would have a stronger desire to quit and be more likely to have a goal of abstinence for cigarettes than marijuana. Further, given that cigarette smoking is legal federally and in more states, more readily available, and publicly used than marijuana, we expected that cousers would have lower efficacy for quitting cigarette smoking and staying quit from cigarettes than marijuana. For Aim 3, we hypothesized that the stage distributions would differ for cigarettes and marijuana with more young adults in preparation for quitting smoking than quitting marijuana. Given strong associations between cigarette smoking and marijuana use , we anticipated temptations to use and that the pros and cons of using would be associated across the two substances. Given that temptations and decisional balance are known to vary by stage of change , we included stage of change for both cigarettes and marijuana as covariates in examination of Aim 3 hypotheses.Understanding young adults’ co-use and their thoughts about use of cigarettes and marijuana will help inform whether interventions should be targeted similarly,best way to dry cannabis and possibly even simultaneously, for the two substances. Data for the present study were taken from a U.S.-based Internet survey of English-literate young adult cigarette smokers aged 18 to 25. Characteristics of the full sample and the three recruitment methods utilized have been described previously .

Advertisements that targeted young adult cigarette smokers or cigarette and marijuana users contained a hyperlink that directed potential participants to a separate website that included: 1) the study’s IRB-approved consent form with verification questions to determine understanding of the consent process; and 2) a screener for determining eligibility including English literacy. The survey assessed demographic characteristics and then cigarette and marijuana use and thoughts about use as well as alcohol use for inclusion as a covariate. Participants were required to answer all questions before they could continue to the next page of the survey, but could quit the survey at any time. Computer IP addresses were tracked with one entry allowed from a single computer to prevent duplicate entries from the same person; however, multiple entries were allowed from the same Internet connection . Over 7567 people accessed the online survey, 7260 signed online consent, and 4242 met criteria to participate . Eligibility checks excluded 494 respondents who had invalid data due to verifiably inaccurate responses, leaving 3748 valid entries , of which 1987 completed the entire 30–45 minute survey. The 972 survey completers who reported use of both cigarettes and marijuana were included in the present analyses. This study examined differences in patterns of cigarette smoking and marijuana use, quit attempts, and thoughts about use and abstinence in a national online sample of young adults who used both substances. Consistent with previous cross-sectional and longitudinal research, the frequency and severity of cigarette and marijuana use were related as were quit attempts and some cognitions related to use. Frequency of alcohol use independently predicted cigarette use frequency, consistent with prior research with young adults , yet was unrelated to the measure of nicotine dependence. Epidemiological data indicate young adult drinking and smoking are highly co-morbid with the risk of co-use of alcohol and tobacco found at any level of smoking .

The consistent association between cigarette, marijuana, and alcohol use in young adults, regardless of level of dependence, supports interventions to target these multiple substances concurrently. While young adults’ cigarette and marijuana use frequency and severity were related, as were some of their thoughts about use, reported levels of interest and perceived ability with quitting were found to differ in interesting ways. Despite greater desire to quit cigarettes, greater preparation stage membership, and greater likelihood of tobacco abstinence goals, participants also reported more temptations to use tobacco, less expected success with quitting, greater perceived difficulty staying quit, and identified more pros as well as cons for using cigarettes. Very few individuals in this study were ready to quit both cigarettes and marijuana concurrently, and being motivated to quit one substance was not associated with being motivated to quit the other substance. Young adults may be more receptive to interventions for cigarettes than marijuana use, especially interventions that seek to increase self-efficacy for quitting and staying quit by providing cognitive and behavior skills to manage smoking urges. Notably, however, a sample majority reported a past year failed quit attempt for both tobacco and marijuana , and a quit attempt on one substance was associated with a 2-fold greater likelihood of a quit attempt for the other. It would seem that behaviorally, a majority of young adults are reporting recent unsuccessful efforts to quit both substances. In adults, there is mixed evidence as to whether marijuana use interferes with tobacco treatment outcomes . Data from the present study suggest that clinicians should not be deterred from supporting cigarette smoking cessation efforts for young people who use both cigarettes and marijuana. Given that many young people in the community are not ready to quit using marijuana, intervention strategies ought to include those designed to increase motivation .

It could also be important to assess young people’s perceptions of the interaction between cigarette and marijuana use to identify relapse risk and target prevention efforts accordingl. Finally, brief, motivational interventions matched to risk level such as Screening, Brief Intervention, and Referral to Treatment could be particularly helpful with young adults who may be at risk for problems associated with substance use but may not be physically dependent or willing to engage in more intensive treatment. SBIRT screens individuals with substance use and administers treatment tailored to risk: those with low risk are given a time-limited motivational interview to increase awareness of risks, while those with high risk are offered more intensive treatment. Given the frequency of cigarette and marijuana use among young adults, SBIRT screening protocols should consider substance co-use in delineating risk profiles of patients. In contrast to thoughts about abstinence, cognitions related to temptations to use and decisional balance for cigarettes and marijuana were related in our study and notable given measurement differences for the two behaviors. The smoking temptations measure was shorter and assessed three domains , while the marijuana temptation scale was longer with only one factor . Post hoc analyses demonstrated that within each substance, temptations and pros of using decreased while cons of using increased across the stages of change, consistent with work found by others across a number of health behaviors . The findings further validate the TTM constructs of temptations and decisional balance in a young adult population applied to both cigarettes and marijuana, and suggest that for both substances, interventions should target decreasing the pros and increasing the cons of using to facilitate movement toward preparation and action. Homelessness poses a major community mental health challenge,how to cure cannabis placing millions of unhoused residents at severe risk for mental health, substance use, and physical health problems each year. An estimated 326,000 to 580,000 individuals experience sheltered homelessness in the U.S. each night and 2.3 to 3.5 million individuals experience homelessness each year , with about one-third living unsheltered . These individuals are disproportionately racial/ethnic minority and many reside in locations burdened by extreme housing costs , with the number of individuals experiencing chronic homelessness—who are most likely to be unsheltered and bear the greatest mental and physical health risks—increasing 20% from 2020 to 2021 . In prior data, homelessness has been linked to numerous adverse mental health outcomes including high rates of depression, anxiety, serious mental illness, and alcohol and other substance use disorders . In addition, individuals experiencing homelessness— particularly the unsheltered or chronically unhoused—sufer heightened prevalence of chronic disease , dying an average of 20–30 years earlier than the general population with up to 10 times greater rates of all-cause mortality . Yet, despite their immense risk, we know surprisingly little about the mental health, substance use, and behavioral health treatment need of the millions of community dwelling unhoused individuals living outside of major U.S. urban centers such as New York or Los Angeles as most extant data is derived from nonresearch point-in-time counts or pre-pandemic studies with urban populations conducted at point-of-contact locations/ services versus the community locations in which they live . Accordingly, using funding from the National Institute of Mental Health and National Institute on Drug Abuse, the present community-based participatory research study investigated the scope of mental health and substance use disorders, mental health treatment need, and physical health among community-dwelling individuals experiencing homelessness—many unsheltered or chronically unhoused—in Hawai‘i. We conducted this novel mental health study in Hawai‘i because it possesses the nation’s second highest rate of homelessness yet is unique among major U.S. communities battling extreme homelessness in being predominantly rural .

However, despite its rural nature, Hawai‘i mirrors many U.S. cities with high homelessness rates in having the nation’s highest costs of living, real estate, and rental prices —rendering nearly half of Hawai‘i residents just paychecks away from homelessness . Similarly, numerous news reports and growing evidence suggest that illicit substance use and fatal drug overdoses may be rampant among unhoused individuals in Hawai‘i; consuming substantial social service, policing, and healthcare resources . Despite this, almost no empirically-focused studies have detailed the mental health or substance use challenges of unhoused individuals in Hawai‘i and relatively few have studied non-urban unhoused community populations in the U.S. . This lack of research is particularly problematic given indications that up to 40% of Hawai‘i unhoused residents may be Native Hawaiians/Pacifc Islanders ; who possess the state’s poorest economic and health outcomes due to the profound negative effects of U.S. colonization and cultural trauma on this understudied racial group . Therefore, by conducting this novel mental health investigation of unhoused individuals in a non-urban community deeply affected by homelessness , study findings may provide key insights into the potential health disparities facing other non-urban U.S. communities as they become increasingly afficted by the dual problems of rising housing costs and homelessness.Demographic variables of age, gender, education, and marital status were assessed. Depression and anxiety severity were assessed via the Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7 , which uses diagnostic cut-points of 10+to identify major depressive disorder and generalized anxiety disorder , respectively . Alcohol use disorder was screened using the Alcohol Use Disorders Identification Test-Consumption, which uses diagnostic cut-points of ≥ 4 for men and ≥ 3 for women . Lifetime and current cigarette, cannabis, prescription opioids, heroin, and methamphetamine use were assessed using items from established assessments . Opioid use disorder and methamphetamine use disorder were assessed using the Rapid Opioid Dependence Screen  and ASSIST , respectively. Mental health and substance use treatment need and treatment delay/avoidance were assessed via four commonly-used Medical Expenditure Panel Survey items . Health outcomes included general health and three key CDC-defined health indices linked to chronic disease: obesity , unhealthy sleep , and current cigarette smoking . As the first study to our knowledge to detail the mental health, substance use, and treatment needs confronting Hawai‘i’s unhoused, and often unsheltered, individuals— and one of very few community-based empirical studies of U.S. unhoused populations conducted during COVID- 19—study findings revealed exceptionally high prevalence of mental health and substance use problems in this understudied and under served community population. On average, participants evidenced high levels of COVID-19-related distress along with clinical levels of depression and anxiety as nearly 60% of participants screened positive for MDD, over half screened positive for GAD, and two thirds screened positive for AUD. Consequently, over 60% of participants reported needing past-year mental health treatment with 65% delaying/avoiding needed treatment; revealing a substantial need and unmet need for formal treatment services in this high-risk community population. Illicit substance use was pervasive in the sample with 7 in 10 participants currently using methamphetamines and one quarter currently using illicit opioids, leading approximately 80% of participants to screen positive for opioid or methamphetamine use disorders.

We obtained written informed consent and assent from interested teens and their guardians, approved by the University of California San Diego Human Research Protections Program. Adolescents were administered a 90-min telephone screening interview to ascertain family history of substance use and psychiatric diagnoses using the Family History Assessment Module screener , lifetime substance use and abuse/dependence criteria using the Customary Drinking and Drug Use Record , and history of psychiatric disorders using the Diagnostic Interview Schedule for Children . Collateral interviews were administered to a guardian, usually a parent. Exclusion criteria included history of head injury with loss of consciousness >2 min, neurological or medical problems, learning disabilities, DSM-IV psychiatric disorder other than conduct disorder, current psychotropic medication use, significant maternal drinking or drug use during pregnancy, family history of bipolar I or psychotic disorder, and left handedness. Teens meeting criteria for conduct disorder were not excluded due to high comorbidity with substance use disorders . Eligible participants were ages 15−17, and groups were demographically similar . Controls had little experience with alcohol or other drugs. AUD adolescents met DSM-IV criteria for current alcohol abuse or dependence, but had limited experience with marijuana . Only two AUD teens disclosed marijuana use in the month before scanning . MAUD adolescents met DSM-IV criteria for both current marijuana and alcohol abuse or dependence, had ≥100 lifetime experiences with marijuana and had used ≥10 days/month in the three months before scanning. One MAUD participant reported stopping marijuana use 4 months prior to scanning; however,rolling grow table the urine toxicology screen indicated recent use. Twelve other MAUD teens reported marijuana use in the week before the scan, with last use 3.3 ± 1.7 days prior to scanning.

Participants in each group had little experience with drugs other than alcohol and marijuana , had not used other drugs for 30 days prior to imaging, and had not used marijuana or alcohol for at least 48 h before scanning. Importantly, AUD and MAUD youths demonstrated similar alcohol use disorder characteristics . Both AUD and MAUD teens were primarily weekend heavy drinkers, as evidenced by an overall average 15.13 days since last drink and typical blood alcohol concentration reaching 0.107. Two AUD teens and one MAUD teen reported abstinence from alcohol in the month before scanning. AUD and MAUD teens displayed similar cigarette smoking patterns, but more MAUD teens had experiences with other drugs than AUD and control teens, although such use was limited . Although MAUD and AUD teens had higher rates of conduct disorder than control teens, severity was mild to moderate reflected by the normal range Child Behavior Checklist  externalizing scores . Substance involvement and abuse/ dependence diagnoses were assessed using the CDDR . The CDDR collects lifetime and past 3-month information on alcohol, nicotine, and other drug use, and assesses DSM-IV abuse and dependence criteria, withdrawal symptomatology, and other negative consequences associated with substance use. The CDDR also obtains information necessary to estimate typical blood alcohol concentrations reached using the Widmark method, i.e. amount consumed, duration of drinking, height, weight, and gender . Strong internal consistency, test–retest, and inter-rater reliability have been demonstrated with adolescent CDDR assessments . The Timeline Follow back obtained detailed substance use patterns for the 30 days prior to scanning. On the day of the scanning session, all participants submitted samples for Breathalyzer and urine drug toxicology analyses. Participants were asked to abstain from substance use for at least 48 h before imaging to avoid intoxication and acute withdrawal during scanning.

Imaging sessions were held Thursday evenings between 8 and 10 p.m. to maximize recovery from weekend binge drinking and maintain consistent circadian influence across subjects. According to self-report on the Timeline Follow back , the most recent alcohol use was 72 h and marijuana use was 48 h before scanning. Upon arrival for the imaging session, all participants submitted samples for Breathalyzer and urine drug toxicology for THC, ethanol, amphetamines, methamphetamines, barbiturates, benzodiazepines, cocaine, codeine, morphine, and PCP. No participant had a positive breath alcohol concentration. Due to experimenter error, toxicology screens were unavailable for one control teen, one AUD teen, and five MAUD teens. Based on available data, only MAUD participants produced toxicology screens positive for cannabinoids, and no toxicology screens were positive for any drug other than cannabinoids. Although it is possible that MAUD teens were over-reporting marijuana use, self-reported marijuana use has been an accurate predictor of verified use .Imaging data were processed and analyzed using the Analysis of Functional NeuroImages package . We first applied a motion-correction algorithm to the time series data . Second, we correlated the time series data with a set of reference vectors that represented the block design of the task and accounted for delays in hemodynamic response , while covarying for estimated motion and linear trends. Next, we transformed imaging data to standard coordinates then resampled the functional data into 3.5 mm3 voxels. Finally, we applied a spatial smoothing Gaussian filter to account for anatomic variability. After processing functional data, we examined average BOLD response to the SWM task in each group using one sample t-tests, and determined regions that showed greater response to SWM relative to simple attention , reduced response during SWM relative to rest , and greater simple attention response than SWM response.

We next compared response during SWM relative to simple attention between groups with ANOVAs, and performed pairwise comparisons between groups. We performed group comparisons on the whole brain, rather than discrete regions thought to be activated by the task, because previous studies by our group and others have suggested neural reorganization and use of alternate brain systems during working memory among individuals with AUD. To control for Type I error in group analyses, we required significant voxels to form clusters ≥1072 μl , yielding a cluster-wise α < .0167 . We utilized the Talairach Daemon and AFNI to confirm gyral labels for clusters. Previous research has suggested that neuropsychological deficits among adult marijuana users are associated with lingering effects of recent use, and that these impairments dissipate with extended abstinence . To understand whether group differences in the current study relate to recent marijuana use, we performed post-hoc regressions within the MAUD group. First, we extracted the average fit coefficient for each MAUD participant from each cluster where we observed a difference between MAUD and control or AUD teens. Next, we used regression analyses to examine whether days since last marijuana use predicted brain response within each group difference cluster. Groups did not significantly differ on any neuropsychological performance measure . SWM accuracy was 86 ± 9% in the control group, 91 ± 5% in the AUD group, and 92 ± 5% in the MAUD group, revealing a trend for MAUD to be more accurate than controls . However, one control performed at 60% accuracy,cannabis grow equipment which was >2.5 standard deviations below the mean for that group, and exclusion of this participant removed the group difference in SWM accuracy. This raised the concern that this individual impacted the fMRI group analyses. Upon further examination, we determined that this participant’s brain response was within the normal range for each significant cluster described below. Groups did not differ on simple attention accuracy or reaction time to either condition. The overall pattern of BOLD response to the SWM condition relative to simple attention was similar in all three groups. Participants showed SWM activation in several regions, including bilateral prefrontal, premotor,cingulate, and posterior parietal areas . Groups showed SWM deactivation in medial prefrontal cortex, a large posterior midline region including posterior cingulate and cuneus, and several temporal regions . Although groups demonstrated similar patterns of response localization, several significant group differences emerged. The response differences between AUD and control teens are detailed elsewhere . Briefly, AUD teens showed less SWM response than controls in the left precentral gyrus and midline precuneus/posterior cingulate, but more SWM activation than controls in bilateral posterior parietal cortex . MAUD participants evidenced altered BOLD response compared to controls in several regions: bilateral inferior frontal gyri, right superior temporal/supramarginal gyri, right middle and superior frontal gyri , and anterior cingulate . In both right inferior frontal and superior temporal regions, MAUD teens demonstrated less SWM response than controls. Moreover, while controls showed SWM activation in the right superior temporal gyrus, MAUD teens showed greater simple attention response than SWM response. In right dorsolateral prefrontal cortex, MAUD youths showed more SWM activation than controls. Both controls and MAUD evidenced SWM deactivation in the anterior cingulate; however, MAUD showed a greater intensity of deactivation than controls. MAUD also demonstrated deactivation in the left inferior frontal gyrus, where controls showed no significant activation or deactivation.

MAUD teens showed different response intensity relative to AUD teens in the right inferior frontal gyrus/insula, left precuneus, right middle temporal/supramarginal gyri, left superior temporal gyrus, and a large cluster spanning anterior cingulate and bilateral inferior frontal gyri . In the precuneus, groups showed SWM activation, yet AUD teens showed greater response than MAUD teens. Similar to controls, AUD teens showed SWM activation in right inferior frontal and middle temporal areas, while MAUD teens evidenced greater simple attention response than SWM response. In the left superior temporal gyrus, AUD showed SWM deactivation, while MAUD demonstrated no significant activation or deactivation. Finally, a group difference was observed in a large cluster spanning anterior cingulate and bilateral inferior frontal gyri. In this cluster, both AUD and MAUD showed deactivation, but MAUD showed greater intensity and spatial extent of deactivation. Days since last marijuana use did not significantly predict brain response among MAUD teens in any cluster where MAUD teens had significantly different SWM response than controls or AUD teens. A trend was found for more recent use to be associated with reduced brain response in the right middle temporal gyrus , where MAUD teens showed less SWM response than AUD teens. This study investigated the neural correlates of SWM in adolescents with comorbid marijuana and alcohol use disorders, teens with alcohol use disorders alone, and demographically similar non-abusing adolescents. The groups showed similar neuropsychological abilities, SWM task performance, and general BOLD response localization patterns. However, MAUD teens demonstrated significantly more dorsolateral prefrontal SWM activation and anterior cingulate deactivation, and significantly less right inferior frontal and superior temporal response compared to control teens. Similarly, MAUD youths also showed significantly more medial frontal deactivation as well as less right inferior frontal and bilateral temporal activation compared to AUD teens. As noted above, MAUD teens showed more SWM activation than control teens in the right dorsolateral prefrontal cortex, a brain region consistently active during working memory . A recent fMRI study of heavy cannabis using adults also demonstrated greater dorsolateral prefrontal recruitment relative to controls during SWM 6 to 36 h after last marijuana use, despite similar task performance . More intense and widespread fMRI response despite intact behavioral performance has also been observed among adult alcoholics, suggesting that while some task-related areas demonstrate deficient processing, other ancillary regions may become active to compensate, resulting in an altered functional network among alcoholics . Similarly, the MAUD teens in this study may compensate for subtle neuronal disruption with increased task-related neural recruitment in frontal regions, observed in fMRI as heightened activation. However, MAUD teens did not show the aberrant parietal response we expected given the role of parietal cortex in SWM tasks . While greater SWM task difficulty is associated with increased activity in both frontal and parietal cortices , increased dorsolateral prefrontal activation may be associated with general task difficulty, whereas greater parietal response relates to visuospatial demands . Therefore, the increase in response among MAUD teens in frontal regions, but not parietal cortex, may suggest a greater difficulty with general task demands, despite similar task performance. However, given a more difficult task, frontal regions may no longer be able to compensate, and activation may decrease in parallel with decreasing task performance. Although all three groups demonstrated SWM deactivation in the anterior cingulate, MAUD teens showed significantly more deactivation than controls and AUD-only adolescents. The anterior cingulate is highly active at “rest” , during which it is thought to monitor various environmental and internal processes .

While this may appear counter intuitive at first, two insights help to explain this result. First, blueberry farmers have already adapted to hot temperatures: pickers generally finish picking around 3:00 p.m. and avoid the hottest parts of the day. This means that I do not observe how workers would perform under temperatures above 100–105 degrees.And looking at the temperature response function in figure 1.14, it is easy to imagine due to its overall inverse-parabolic shape that there would be even larger productivity losses at such high temperatures. Second, blueberry picking is a highly dextrous job requiring workers to use their bare hands to pick only ripe berries from the bush. At cooler temperatures, berry pickers lose finger dexterity and find it uncomfortable to maintain the same levels of productivity as at warmer temperatures.Indeed, Enander and Hygge note that manual dexterity can start to be impaired at temperatures in the range of 12–15 degrees Celsius .In agriculture – as in many other industries – labor is a primary input, pay is tied to worker output, and firms cannot completely control important workplace environmental conditions like temperature. How do agricultural workers respond to changes in their piece rate wage? How does temperature affect this wage responsiveness? And what are the net effects of temperature on agricultural labor productivity? This paper addresses these questions in the context of California blueberry farmers and provides the following answers: on average, blueberry pickers’ productivity is very inelastic with respect to wages; workers seem to face binding constraints on effort at moderate to hot temperatures,commercial racks but display an elastic response to wages at cool temperatures; and both very hot and cool temperatures have negative direct effects on berry pickers’ productivity. This paper makes a meaningful contribution to the empirical understanding of how wages affect worker productivity. While the basic theoretical prediction is straightforward , previous studies have struggled to test this hypothesis directly.

Doing so is difficult since, in settings where piece rates vary over time, their variation is endogenous to worker productivity. To isolate wages’ effect on productivity, I instrument for blueberry pickers’ piece rate wage using the market price for California blueberries. I find that on average, pickers’ productivity is very inelastic with respect to piece rate wages, and I can reject even modest elasticities of up to 0.7. However, this finding hides important heterogeneity in the relationship across different temperatures. In particular, only at cool temperatures do higher wages have a statistically significant and positive effect on worker productivity. This result suggests that at most temperatures and wages, blueberry pickers face some sort of binding constraint on effort and cannot be incentivized to increase their productivity. This research raises questions for future research both about firms’ responses to changing temperatures and their choice of an optimal payment scheme. For instance, it would be helpful to analyze a different industry to see how temperature response functions differ across tasks. It would also be interesting to analyze, both theoretically and empirically, a varying wage scheme tied directly to exogenous factors such as market prices, resource abundance, and environmental conditions. With the advent of cheap, sophisticated monitoring technology, more and more industries are candidates for adopting piece rates, raising the importance for economists to deepen our understanding of the forces at work in such wage schemes. Technology adoption is an essential component of economic growth ; Foster and Rosenzweig ; Perla and Tonetti. In 2015 alone, the World Bank committed over eight billion dollars to projects encouraging people to adopt new technologies. Over the past decade, economists and policymakers have begun to recognize that social networks can facilitate technology adoption. In particular, information barriers hinder the take-up of new technologies; social networks can spread information and reduce these frictions.

Understanding the ways in which these networks impact the take-up of new technologies is relevant for policymakers across the developed and developing world. Economists face a fundamental challenge when trying to study social networks, since these networks are endogenously formed: people choose their own friends. Though there is a broad theoretical literature on social networks1 , endogenous network formation poses a significant challenge for empirical research ; Goldsmith-Pinkham and Imbens ; Jackson ; Choi et al.. In response to these difficulties, recent work in economics has relied on randomized experiments that act on or through existing social networks in field settings.Other work uses detailed data on network structures to study how information moves within existing networks.These papers represent a major development in our understanding of how information is transmitted through social networks. What they are unable to do, however, is analyze how naturally-arising changes in these networks affect economic activity. A small literature exists that attempts to address this issue by estimating the effects of plausibly exogenous shocks to existing social networks on economic outcomes. The majority of these papers in this focus on how social networks affect labor market outcomes , Edin et al. , and Beaman.Though none of these papers studies technology adoption, there is a rich literature in economics studying the diffusion and take-up of new technologies, particularly in agricultural settings.Our work is most closely related to several recent papers which study the role of social networks in agricultural technology adoption.Foster and Rosenzweig and Munshi study the network determinants of technology adoption during India’s Green Revolution. Conley and Udry study farmer learning about fertilizer use and pineapple in Ghana. Bandiera and Rasul find that family and religious communities matter for technology adoption in Mozambique.

Vasilaky and Vasilaky and Leonard randomly connect women with agricultural extension agents, and find that this dramatically improves productivity. In this paper, we are able to directly estimate the causal effects of increases in network size and composition on technology adoption in agriculture. In particular, these shocks take the form of mergers between rural congregations of the American Lutheran Church between 1959 and 1964 in the Upper Midwest of the United States. These mergers were caused by national-level church mergers, church building fires, and pastoral employment constraints, all of which were beyond the control of individual congregations. Using county-level data from the American Census of Agriculture, we employ a difference-in-differences approach to study how these mergers affected farmers’ adoption of inorganic nitrogen fertilizer – at the time, a relatively new yield-improving technology. We demonstrate that congregational mergers had an economically meaningful effect on technology adoption among farmers. The number of farms using nitrogen fertilizer increased by over 7%, and the total fertilized acreage in these counties increased by over 13%, in counties with merging congregations, relative to those without. These increases were most pronounced on the region’s major commercial crop: counties with mergers used 26% more fertilizer on corn. We perform a randomization inference test and a placebo exercise to demonstrate that our results are caused by congregational mergers and not other factors. Our results are consistent with a model where information sharing is the primary mech- anism through which social networks facilitate technology adoption. Mergers only affected use of fertilizer, a new technology, and its complements. In contrast, congregational mergers did not lead to increases in the use of existing technologies. We find no effects of mergers on durable goods with high fixed costs,greenhouse rolling benches suggesting that mergers did not ease capital constraints. The remainder of this paper is organized as follows: Section 2.2 describes the context in more detail. Section 2.3 presents a simple model of social networks and technology adoption. Section 2.4 details our data, and Section 2.5 describes our empirical strategy. Section 2.6 reports our results. Section 2.7 provides a discussion. Section 2.8 concludes. We study the effects of social networks on the adoption of a new technology in the Upper Midwest of the United States during the 1950s and 1960s: commercial fertilizer.Between 1940 and 1970, the use of commercial fertilizer increased dramatically. Figure 2.1 displays the sharp increase in usage of chemical fertilizer for corn production in the United States. Between 1940 and 1949, average annual consumption of commercial fertilizer in the United States was 13.6 million tons; between 1950 and 1959, this number rose to 22.3 million tons; and between 1960 and 1969, use had increased further to 32.4 million tons .This increase in usage had tangible results: between 1950 and 1975, agricultural productivity in the United States increased faster than ever before or since . In 1950, the average American farmer supplied the materials to feed and clothe 14 people; by 1960, he was sustaining 26 . While today, over 95 percent of corn acres are fertilized, and fertilizer is well-known to increase yields, during the 1950s and 1960s, farmers were far from being fully informed about optimal fertilizer usage and its benefits. Communication between farmers in different social circles was infrequent ; Amato and Amato ; Cotter and Jackson, but information sharing within farmers’ social networks was a major means of spreading professional knowledge.

Religion was an important driver of farmers’ social connections ; Azzi and Ehrenberg ; Swierenga ; Cotter and Jackson. The Upper Midwest had a high rate of religious adherence: according the Association of Religion Data Archives, in 1952, 64%, 62%, and 58% of the population of Minnesota, North Dakota, and South Dakota, respectively, were religious. We focus on these three states, because they contained large Lutheran populations: 51%, 48%, and 33% of religious Minnesotans, North Dakotans, and South Dakotans belonged to a Lutheran church. Figure 2.2 demonstrates the prevalence of religion in the United States in the 1950s, as well as the concentration of Lutheranism in Minnesota, North Dakota, and South Dakota. In the 1950s and 1960s, national Lutheran church bodies underwent significant institutional consolidation. At an April 1960 meeting in Minneapolis, Minnesota, three of the largest national Lutheran church bodies – the American Lutheran Church , the United Evangelical Lutheran Church , and the Evangelical Lutheran Church – voted to merge and form The American Lutheran Church . This merger officially took effect on January 1, 1961. A similar merger between the United Lutheran Church in America, the Finnish Evangelical Lutheran Church of America, the American Evangelical Lutheran Church, and the Augustana Evangelical Lutheran Church created the Lutheran Church in America in 1962. In 1963, the Lutheran Free Church , composed largely of congregations that originally opted out of the 1960 TALC merger on theological grounds, decided to join TALC as well, extending the scope of this major Lutheran branch .Figure 2.3 depicts the major mergers between Lutheran church bodies in the United States since the 1950s. For historical context, we focus primarily on TALC for two reasons. First, congregations of TALC were geographically clustered in the upper midwest whereas congregations of the LCA were more disperse throughout the country. Second, we have access to yearbooks from TALC detailing congregational-level statistics throughout the 1960s. National-level mergers, arranged by the constituent churches’ theological and institutional leadership, had far-reaching impacts. The TALC merger was reported in local newspapers across the Upper Midwest ; Dugan ; Press . National mergers forced local congregations to adopt new constitutions, bringing them into alignment with the newly-formed national church . Prior to the mergers, many towns had congregations from multiple church branches. As a result of the merger, these congregations suddenly found themselves in the same national denomination. This frequently led to mergers between local congregations that were previously impossible ; United Lutheran Church Laurel . These mergers brought previously socially disparate groups of people into contact with one another. Each of the merging national-level church bodies were linked to a different ethnic group: the ALC had German roots, the ELC had a Norwegian background, and the UELC was historically Danish. Especially in the early parts of the twentieth century, this often meant that congregations across the street from one another were holding services in different languages. Some congregations were even conducting multiple services, each in a different language ; Murray County .Cross-branch mergers between local congregations were large shocks to churchgoers’ social networks, since the congregants were not likely to have interacted frequently prior to the merger. In addition to the local mergers that were precipitated by national church changes, a number of congregational mergers resulted from other plausibly random events. Several congregations initiated mergers after natural disasters destroyed congregation buildings ; St. Mark’s Lutheran Church. Other congregations merged due to difficulties hiring full-time clergy.

Motives of marijuana use to promote positive experiences For motives of marijuana use to promote positive experiences, none of the motives were directly, significantly associated with any of our outcomes of interest. This finding is consistent with the hypothesis as well as with what has previously been documented in the literature. Social motives, as well as motives of enhancement and expansion, which can also be conceptualized as motives of use to promote positive experiences have not previously been found to be associated with psychological distress . Furthermore, in a study by Brodbeck et al. , no differences were found between young adults who use marijuana for social motives and young adults who do not use marijuana with regards to psychological distress. Although no indirect associations between motives of marijuana use and psychological symptoms were found, there was a direct, significant association between the motive of celebration and past 90 days use. The association between the motive of celebration and use, but its lack of association with problematic outcomes has previously been documented in the alcohol literature and the marijuana literature . This would therefore imply that some motives of marijuana use are associated with increases in use but are not associated with mental health outcomes. Tying back to the underlying assumptions driving this work, when marijuana use is motivated by a desire to celebrate, the use behavior it gives rise to is not associated with mental health outcomes. This suggests that, in this sample, there may not be any mental health consequences resulting from celebratory driven use. Other motives of use,hydroponic shelf system namely those to avoid negative experiences, are more relevant to the associations between motives of marijuana use and mental health.

Motives for avoidance of negative experiences Results from the multiple linear regressions indicate that only the coping motive of use is significantly associated with symptoms of depression, symptoms of anxiety, and overall psychological distress. The association is such as that the more use is driven by coping, the more severe the symptoms of depression, symptoms of anxiety and psychological distress. This finding replicates what has previously been documented in the literature. Previous work has, in fact, demonstrated that a coping motive of marijuana use predicted anxious arousal and anhedonic symptoms of depression in a sample of young adults , as well as internalizing and externalizing symptoms in a sample of high school students , and was negatively associated with mental health functioning, whereas mental health functioning decreased with an increase in coping motives, in a sample of middle age individuals who use marijuana for medical purposes . The significant, direct, association between coping motives of marijuana use and symptoms of depression, symptoms of anxiety, and overall psychological distress fits with the concept of avoidance coping which includes both cognitive and behavioral strategies and is “oriented towards denying, minimizing, or otherwise avoiding dealing directly with stressful demands” . In other words, avoidance coping can be summed as behaviors one engages in to avoid dealing with a stressor. Although avoidance strategies may seem desirable because they engender reductions in stress and prevent paralyzing anxiety , avoidance coping is maladaptive and is not associated with desirable long-term outcomes. Avoidance coping has been associated with lower likelihood of remission in depressed patients and increased distress among other outcomes . The coping motive of use has also previously been associated with increased past thirty days use and progression to problematic cannabis use .

Work done on coping and marijuana use in adolescents has demonstrated higher levels of depressive symptoms and greater lifetime and past 12 months marijuana use as well as increases in negative mood for those who engaged in avoidant coping through marijuana use . The conformity motive was negatively associated to past 90 days marijuana use, which was in turn negatively associated with symptoms of depression, generating positive indirect effect for the motive of conformity on symptoms of depression through past 90 days use. It was expected that the conformity motive of use would be associated with use given that use is a common behavior in our sample and that it is the least endorsed motive by the participants in the sample, or to be positively associated with marijuana use as the desire to conform would engender use. Previous work done on motives of marijuana use that included the conformity motive found conformity to be positively associated with use , not associated with use , or to be a negative predictor of use . Clearly, there is no consensus on the association between motive of conformity and marijuana use, let alone its relationship with mental health outcomes. It is possible that this finding is a Type I error, as there is no logical or theoretical way to explain it. Gender was found to moderate the associations between the motive of social anxiety with symptoms of depression and overall psychological distress. For both outcomes, the effect is worse for women compared to men. The more women endorse social anxiety as a motive for marijuana use, the worse of their mental health is as it pertains to symptoms of depression and overall psychological distress. Endorsing the social anxiety motive of use seems to have no effect on the mental health of men with regards to symptoms of depression and overall psychological distress.

This is contrary to what has thus far been documented in the literature. As previously discussed, for men, the social anxiety marijuana motive of use is akin to a social avoidance coping motive compared to a more social/celebratory motive for women . Social anxiety motive of use has therefore been tied to greater severity of problematic marijuana use in men but not women . Thus, it was expected that the association between social anxiety motive of marijuana use and symptoms of depression or psychological distress would be worse for men compared to women. Surprisingly, there was no finding of significant gender differences in the associations between motives of marijuana use and symptoms of depression. As illustrated in Figure 4.25, using the coping motive as an example, plotting the trends for men and women reveals an interaction effect where the effect of the coping motive of use on symptoms of depression appears to be worse for men than women. However, the lack of a significant interaction term in this association is likely due to insufficient power resulting from the small sample size. Medical use motives Interestingly, given the make-up of our sample, none of the medical motives of use were significantly directly associated with any of the mental health outcomes of interest. It is plausible that this is the case because using as a natural remedy, or using to combat nausea can be conceptualized as a form of coping. In a study of individuals who use marijuana for medical reasons, where no medical motives of marijuana use were specified, coping was significantly associated with greater health functioning but poorer mental health functioning . Furthermore, there was no finding that mediation or gender effect for the coping motive of cannabis drying racks commercial and associated outcomes, only direct effects indicating that the association is strong and not gender dependent. The marijuana motive of use for pain was positively associated with past 90 days use, which was in turn negatively associated with symptoms of depression, thus generating a negative indirect effect. There is some evidence that marijuana use might be beneficial for pain . It is therefore plausible that an individual might be driven to use for pain relief purposes and that, in turn, relief from pain might be associated with alleviated symptoms of depression. The association between the marijuana use motive of attention to daily number of hits is positive and the association between daily number of hits and symptoms of depression is negative, thus generating a negative indirect effect between the attention motive of marijuana use and symptoms of depression through daily number of hits. Work done as it pertains to attention and marijuana use has typically investigated whether marijuana use negatively affects attention. Yet, in work done by Gruber et al. , medical marijuana patients demonstrated some improvements on measures of executive functioning post consumption of cannabinoids but not post tetrahydrocannabinols consumption. This points to potentially beneficial effects of CBD but not THC consumption for attention. This effect is hypothesized to occur as CBD use could lessen symptoms of sleep disturbance, symptoms of depression, and impulsivity, thus resulting in improved cognitive functioning . Therefore, in our sample, use might be motivated by a desire to improve attention with the expectation that use will help alleviate distracting factors such as pain, and in turn, help alleviate symptoms of depression. This is however contradicted by other studies that have demonstrated impairments in attention and concentration post THC administration .

Surprisingly, there was a small, negative, significant association between past 90 days marijuana use and symptoms of depression, and daily number of marijuana hits and symptoms of depression. However, the magnitude of the effect is somewhat negligible, being almost zero. Furthermore, this finding is contrary to previous work in the literature exploring the associations between marijuana use and depression as regular use of marijuana has previously been associated with an increased risk of depression and anxiety . Although user group, as a control variable, was not significantly associated with depressive symptomatology for either mediators, it is plausible to speculate that given the medical nature of use reported by participants in the sample, it could account for this association. If in fact use alleviates the burden of a medical condition, then one could report feeling less depressed. The findings discussed above have implications for both the literature and prevention/intervention strategies. Although not representative of the young adult population at large, this sample differs in its composition than those most currently published in the literature. This is a sample of young adults that use marijuana very heavily, both with regards to past 90 days use and to daily number of marijuana hits. On average, this sample reported using marijuana 69 out of 90 days. Participants also reported a daily average of 23 hits. This is a significant departure compared to other samples considered to be composed of heavy users where, for example, participants reported using marijuana approximately 6 days per week but with an average of 4 hits per day . This sample also distinguishes itself from others in the literature as it is composed of young adults who use marijuana solely for medical reasons, young adults who use marijuana solely for recreational reasons, and young adults who report using marijuana for both medical and recreational reasons. This sample, although non-random, does provide us with a wide range of individuals who use marijuana for different reasons in a context of legalized medical marijuana. Work on marijuana use has predominantly been conducted in settings where marijuana use is not legal and although such behavior is illegal for about half of our sample, it is a legal behavior for the other half. Although the data come from a convenience sample, they provide preliminary evidence regarding the associations between motives of marijuana use and mental health outcomes.This sample also differs from most with regards to sociodemographic characteristics. For instance, most of the other samples in the literature on motives of marijuana use and associated outcomes are under 21 years of age. This is relevant as it has been hypothesized that individuals can mature out of drug use whereas marijuana use declines as adult responsibilities increase . Furthermore, this sample is not composed primarily of Whites as has been the case to date in the literature, nor is it solely composed of undergraduate students. Only about half of the individuals in this sample report some form of college level education. This latter point is especially relevant when we consider that marijuana use is associated with limited academic achievement . However, not unlike college samples, individuals in our sample primarily report using marijuana for enhancement purposes , in addition to health/medical motives. When examining the indicators that compose each motive and while considering our definition and conceptualization of motives of use, it could be argued that some of the factors generated by the confirmatory factor analyses do not completely fit with some of the conceptualization of motives of use found in the literature.