Hemp Growing

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.

The association between marijuana use and mental health also seems to vary by gender, although the evidence is inconsistent. We do however know that women move from initiation of cannabis use to problematic use much faster than men do. This is referred to as a telescoping effect . This may suggest differences in both reasons for use and patterns of use . In addition to rapid progression to problematic use, it appears as though marijuana has a stronger mental health impact for women than men . Women, but not men, previously diagnosed with depressive disorders and who use marijuana regularly had poorer SF-12 mental health scores compared to women who did not use marijuana . And, in a study by Lev-Ran looking at the association between mental health and quality of life in the general population, those who used marijuana had poorer mental health than those who did not, and reported experiencing lower levels of vitality and accomplishing less due to emotional problems. These differences were greater among women than men . These findings highlight the importance of considering gender in the study of motives of marijuana use and mental health, as motives might provide additional insight into what drives gender differences in the association between marijuana use and mental health outcomes. Frequency of use. Heavy marijuana use, operationalized as near daily use, has been demonstrated to be detrimental to the transition to adulthood as it has been associated with poorer educational and occupational outcomes . Compared to young adults who do not use marijuana or to those who use infrequently, heavy users are the least likely to have transitioned to an adult role by the age of 28 . Frequency of use also seems to play a role in the relationship between marijuana use in adolescence and adverse young adult outcomes,vertical growing system including depression and anxiety . However, Green and Ritter found no association between frequency of marijuana use and depression in young adult men.

Furthermore, data from the Australian National Survey of Mental Health and Well-Being indicates a positive association between marijuana use and the occurrence of affective disorders, in addition to the fact that those who used marijuana more often reported greater levels of psychological distress, greater limitations in their everyday lives due to emotional distress, and lower life satisfaction . LevRan et al. reported that for those with anxiety disorders, regular, weekly use of marijuana was associated with a decrease in mental health quality of life compared to participants who did not use. This association was not present for participants who reported less than weekly use . Daily use of marijuana in young adult women has been associated with a fivefold increase in the odds of depression and anxiety . Here too, findings highlight the importance of considering frequency of use in the study of marijuana use and mental health outcomes, as frequency of use seem to influence the relationship between marijuana use and mental health outcomes. Given that an association between marijuana use and depressive symptoms, and marijuana use and anxiety symptoms have at times been demonstrated, it is crucial to understand under which circumstances such associations are present. Motives of marijuana use may be key to do so. Better understanding the nature of these associations is especially significant for young adults given their mental health vulnerability, the rising rates of affective disorders and of marijuana use, and that individuals suffering from comorbid substance use and symptoms of depression and anxiety have a worsened clinical course and outcomes and are at higher risk of suicide, impairments, and disability . Alcohol Motives. As previously mentioned, foundational work on motives of use comes from the alcohol literature. Thus, the four most common motives discussed in the literature are borrowed from the alcohol literature. These are social motives, conformity motives, coping motives, and enhancement motives. Social motives are defined as externally generated positive reinforcement motives to obtain positive social rewards . An example of a social motive is celebration. Conformity motives are also externally generated negative reinforcement motives to avoid social censure or rejection .

An individual’s use will be driven by a conformity motive either to fit in with a group or due to peer pressure because everyone else is using. Coping motives are conceptualized as internally generated negative reinforcement motives to reduce or regulate negative emotions . As an example, an individual’s use will be driven by a coping motive if he uses because he has had a bad day, or is frustrated. Enhancement motives are internally generated positive reinforcement motives to enhance positive mood or well being . Examples of enhancement motives are enjoyment and altered perceptions. In work done by Cooper, there was a positive, significant association between enhancement, coping and social motives with quantity and frequency of drinking . There was a negative, significant association between conformity motives and quantity of drinking . Furthermore, coping, enhancement, and conformity motives were predictors of drinking problems, but social motives were not . It is likely that social motives were not predictors of drinking problems as for this given motive, drinking is occasional and only occurs in social, celebratory situations. Marijuana Motives. Although there is an overlap in motives of alcohol and marijuana use, some motives are specific to marijuana use . For the purposes of this dissertation, marijuana motives of use are: 1) motives that promote positive experiences, which are motives of celebration, altered perceptions, experimentation, enjoyment, alcohol, relative low risk, and availability; 2) motives for avoidance of negative experiences, which are motives of coping, conformity, sleep, boredom; and social anxiety; and 3) medical motives, which are motives of attention, substitution, natural remedy, pain, and nausea. Figure 2.2 details the reasons for use an individual might endorse for each of these motives. Previous research has demonstrated that motives of use are associated with differing patterns of use and risk for marijuana use problems .

Differential associations between motives of use and problematic use outcomes have been consistently documented . With regards to problematic use outcomes, enhancement, expansion, coping and social motives of marijuana use have been uniquely associated with greater frequency of marijuana use in the past 30 days . When examining whether there were differences between severity of use and motives endorsed, BonnMiller & Zvolensky demonstrated that individuals with marijuana dependence endorsed motives of expansion and enhancement more frequently than those who used marijuana only occasionally or regularly. Individuals with cannabis dependence endorsed more social motives than those who used occasionally, those who used regularly, and those who abused marijuana. Individuals with dependence to marijuana also endorsed more conformity motives than those who abused marijuana. With regards to coping motives, those with dependence endorsed more coping motives than those suffering from abuse or reporting regular, occasional use. These findings demonstrate that those with marijuana dependence are more likely to use marijuana to adjust their affective states and rely on marijuana to cope with life stressors. However, with regards to mental health as an outcome, the differential association between motives of use with both diagnoses and symptoms of depression and anxiety as outcomes has yielded inconsistent findings. For a given motive and associated outcome, findings have differed across studies. One consistency however,plant growing rack is the association of coping related motives of use with poor or worse outcomes. For instance, Green & Ritter found that individuals between the ages of 30 and 40 who endorsed coping related motives reported more symptoms of depression than those who endorsed non-coping related motives of use. With regards to anxiety symptoms, Bonn-Miller, Zvolensky & Bernstein found that anxiety sensitivity was incrementally associated with coping and conformity motives, whereas enhancement was negatively associated with it. However, Moitra, Christopher & Stein found that only coping motives, and not conformity motives, were significantly associated with negative affect. When considered as a moderator, only those who reported using to cope showed poorer mental health, increased symptoms of psychopathology, more psychosocial distress, and more life events than those who did not use . Focusing on symptoms of depression and symptoms of anxiety, which are precursors to diagnoses is not trivial. Subclinical symptoms of depression and anxiety have been associated with an increased likelihood of full blown disorders in adulthood . Most of the research reviewed has focused on clinically diagnosed depression and anxiety. It is not clear however, if these findings generalize to less severe symptoms. The generalizability is important because clinical disorders may be contraindicated with marijuana use, whereas less several symptoms may not. Gender. Gender differences have also been observed in the association between motives of marijuana use and mental health outcomes. These differences may be due to differences in motives of use endorsed as well as ensuing patterns of use. With regards to gender, expectancies for marijuana use mediated the association between coping motivated use and anxiety in women, but not men . In work done by Buckner, Zvolensky & Schmidt social anxiety was associated with marijuana related problems, coping, and conformity motives.

In women, social anxiety was related to social motives but not marijuana use related problems . However, existing work has seldom considered potential gender differences in endorsed motives for use and in the association between motives of use and symptoms of depression, symptoms of anxiety, and overall psychological distress. For this dissertation, gender is included as a moderator in Cooper’s Motivational Model of Alcohol Use as motives of use endorsed, patterns of use, and ensuing outcomes are likely to differ by gender. Thus, it is important to understand the role of gender in the association between motives of use and mental health to develop successful, gender specific prevention and intervention programs, should need be. Although work has been done to understand marijuana motives of use and associated outcomes, there are some gaps particularly relevant to a context with legal access to marijuana, that this dissertation seeks to address. There is however much that remains to be understood about the associations between motives of marijuana use and mental health outcomes in young adults who use marijuana, particularly in a context of facilitated access to marijuana. First, samples used in research thus far have mostly been identified as individuals who use marijuana for medical reasons only or as individuals who use marijuana for recreational reasons only, thus reporting an illegal behavior in this latter group. Until now, work has yet to be done that considers motives of marijuana use and associated mental health outcomes in a sample of young adults comprised of individuals who use marijuana exclusively for medical reasons, exclusively for recreational reasons or for both medical and recreational reasons, in a context with a longstanding history of legalized medical marijuana. Second, current instruments used to operationalize motives of marijuana use have been validated using college samples which are not representative of the marijuana using population at large. Furthermore, these instruments do not include motives specific to medical marijuana use when it has been demonstrated that medical and recreational marijuana use overlap significantly . There is a need for an instrument that operationalizes marijuana motives of use, to include both recreational as well as medical motives of use given the significant overlap in use . Furthermore, this instrument needs to be validated in a diverse sample of young adults who use marijuana for recreational and/or medical reasons. The sample to be used in this dissertation addresses this shortcoming. Third, in the limited literature that presents research done on motives of marijuana use and mental health outcomes, the focus is often on diagnoses of depression and/or anxiety. A better understanding of the association between motives of use and symptoms of depression and motives of use and symptoms of anxiety is a primordial precursor not only to detangling the association between marijuana use and diagnoses of depression and anxiety, but also, because symptoms are an avenue ripe for intervention. This is particularly salient for young adults as we want to be able to intervene early, should need be, to maximize the likelihood of a successful transition into adulthood. Fourth, even less is known about potential gender differences in the association between motives of marijuana use and symptoms of depression, symptoms of anxiety, and overall psychological distress.

Considering the monthly reapplication interval, this still may not be a cost-effective product for large-scale application. The new US EPA-approved long-lasting formulation, FourStar Microbial Briquets , is potentially effective for up to 6 months , and preliminary data suggest that it is effective in malaria mosquito control [GZ, unpublished data]. Field-testing is needed to determine the efficacy and cost-effectiveness of this long-lasting larvicide. The central objective of this study is to determine the effectiveness and cost-effectiveness of long-lasting microbial larviciding on the incidence of clinical malaria and the reduction of transmission intensity. The hypothesis is that adding LLML to ongoing ITN and IRS programs will lead to significant reductions in both indoor and outdoor malaria transmission and malaria incidence as well as cost savings. This paper describes a protocol for evaluating the impact of LLML in reducing malaria vector populations and clinical malaria incidence.We will conduct our study in 28 randomly selected clusters in the highland localities of Kakamega and Vihiga counties, western Kenya . A cluster typically consists of an area of approximately 4 km2 in size and comprises 400–700 households and about 2000–3000 residents. The catchment population of the study area, including intervention clusters, control clusters, and buffer zones, is estimated as 250,000 according to 2010 census data. Local residents are predominantly farmers and depend upon farming, cattle and goat herding for subsistence. Malaria transmission is seasonal, with two peaks in vector abundance reflecting the bimodal rainfall pattern: a major peak between April and June and a minor peak between October and November. Most malaria is caused by Plasmodium falciparum. The main malaria vectors in the area are An. gambiae s.s., An. arabiensis, and An. funestus s.l.. Malaria vector density was high in the early 2000s,weed growing systems decreased substantially during 2006–2008 after the first round of mass distribution of ITNs in 2006, and has gradually increased since 2008.

Pyrethrum spray collections of indoor-resting Anopheles were about 1.0 females/house/ night in 2014 compared to 0.1 females/house/night in 2007. Cross-sectional community-level surveys in May 2011 indicated that parasite prevalence averaged 11.8 % in the general population but varied between localities from 3.3 % to 25.4 %. In school children aged 6–13 years, surveys in 2012 found an average parasite prevalence of 27.2 %, which varied from 18.8 to 35.4 % among villages. Active case surveillance through bi-weekly home visits in May 2011 indicated an average annual clinical malaria incidence rate of 31.4 cases per 1000 people in the general population, varying from 28.9 to 36.2 between villages. Ownership of ITNs ranged from 78.3 to 84.2 % in 2013. There have been several attempts in the past 10 years to control malaria vectors in the study area using conventional formulations of Bti/Bs and IRS. The last community-wide mass distribution of ITNs was undertaken by the Division of Malaria Control of Kenya in 2014. Currently there is no mass distribution of ITNs or IRS and no larviciding in the proposed study area.For purposes of planning and conducting an evaluation of the intervention, we will subdivide the field area into villages , which is the smallest administrative unit in Kenya. Using villages as clusters has advantages over random sampling. First, the clinical records in health centers or hospitals in Kenya generally include the name of the village and sub-location ; therefore, clinical malaria cases can be traced back to the village level. Second, villages have been conveniently used as intervention/ control clusters in previous trials. Our field team will conduct the demographic surveys before the start of the intervention. Each team will be provided with a printed overview map and a handheld Google Nexus 7 tablet.

A surveillance team, comprising a field technician, a reporter, and a local guide, will visit every compound to explain the study procedures, tally inhabitants, and collect information on house characteristics. If the head of the compound agrees to participate, we will record the geographical coordinates of the main house of the compound and compound codes will be written in permanent marker on the front wall next to the door. We will record the genders and ages of all compound members on questionnaire forms using the on-site Google Nexus , which will update the database in real time together with the GPS coordinates of the surveyed compound. We will map the locations of all compounds using ArcGIS. Demographic surveillance will be done in year 1, 6–12 months prior to intervention . We will draw village boundaries based on the demographic surveys and confirm it with the field teams and the database manager. If a village is too small , we will combine the village with a neighboring village to form one cluster. Total and age- and gender-specific populations will be aggregated at the cluster level.Clinical malaria records will be collected from 8 to 12 months prior to intervention, to calculate baseline incidence rate at each cluster for cluster randomization, through to 8 to 12 months after all interventions . We will collect information on clinical malaria cases retrospectively from all government-run hospitals, health care centers, and clinics located either within the study area itself or within catchment areas overlapping the study area. We will obtain clinical data from the treatment centers through the malaria control office of Kakamega and Vihiga counties, Kenya. We will also collect patient- and treatment-related information, including age, gender, date of diagnosis, parasite species, village of patient , and prescriptions given. All personal identifiers will be excluded from this study. A clinical malaria case is defined as an individual with fever and other related symptoms such as chills, severe malaise, headache, or vomiting in the presence of a Plasmodium-positive blood smear.

The clinical malaria incidence rate is calculated as the number of clinical malaria episodes divided by the total person time at risk based on demographic surveys. We will also collect the aggregated monthly diarrhea data at each site along with clinical malaria records from local health clinics and hospitals. We will not conduct prospective passive surveillance, active home visits, or cross-sectional blood surveys. We will calculate the clinical malaria incidence rate separately for each cluster, different study period and different age group . We will include all clinical malaria cases in our study, including cases diagnosed during the four study periods : preintervention period: baseline clinical malaria records started at least 8–12 months prior to the application of long-lasting microbial larvicides till intervention, intervention period: all clinical records during the intervention period, the 8-month wash-out period, and post intervention period: clinical malaria records till 8–12 months after the last round of larvicide application.Permission to use microbial larvicides for malaria vector control has been obtained from the Pest Control Products Board of Kenya. Ethical clearance has been approved by the Scientific and Ethical Unit of the Kenya Medical Research Institute . As described, aggregated clinical data will be obtained from the treatment centers through the malaria control offices of Kakamega and Vihiga counties, Kenya. According to US Department of Health and Human Services Code of Federal Regulations 45 CFR 46.101 part 4 , these data are in the category of exempt human subjects research, which involves the study of existing data, documents, or records, with no collection of subject-level information. Informed consent will be obtained from each participant. All investigative team members in the United States, Kenya, and Australia have no financial conflict of interest with the larvicide manufacturer, Central Life Sciences.We will conduct baseline malaria vector surveillance at least 4 months prior to any application of LLMLs . We will conduct malaria vector population surveillance on a monthly basis continuously till at least 8 months after the last round of larvicide application . We will monitor both indoor- and outdoor-biting mosquito abundance using CO2-baited Centers for Disease Control light traps equipped with collection bottle rotators . The collection bottle rotator,indoor farming systems which has eight separate plastic collection bottles, will be programmed to collect active mosquitoes at 2-h intervals between 16:00–08:00. We will place two traps within each sampling compound: one inside the living room, the other outside the house 5 m away. We will conduct a total of 64 trap-nights of vector sampling per cluster per month. This will provide an estimation precision of 0.2 mosquitoes using the previously determined standard deviation. Species of collected mosquitoes will be identified and blood-feeding status will be recorded. We will test for P. falciparum sporozoite infection and blood meal source using an enzyme-linked immunosorbent assay on all specimens. For each house where the vector population was sampled, we will record the number of sleeping persons at each house on the same day as the vector survey. We will calculate sporozoite rate and EIR for each cluster. EIRs will be calculated as × × , and standardized to a monthly basis.

The trapping method will allow for comparison of indoor- and outdoor-biting mosquito abundance and determination of nightly biting activity patterns. We will calculate indoor and outdoor transmission intensities separately assuming that all mosquitoes collected from a compound had their blood meal from the same household. We will calculate EIR for the four study periods as describe above: preintervention period: baseline vector surveillance started at least 6 months prior to the application of long-lasting microbial larvicides till intervention, intervention period, the 8-month washout period, and post intervention period: vector surveillance continued till 8 months after the last round of larvicide application. To determine whether new malaria vector species are present in the study sites, we will sequence the ribosomal second internal transcribed spacer and mitochondrial CO1 gene in anopheline specimens that are not amplified by the recombinant deoxyribonucleic acid polymerase chain reaction method, and we will conduct phylogenetic analysis to determine whether the new species found by Stevenson et al. are also present in the study sites.We will conduct the intervention using a two-step approach. First, we will conduct a small-scale four-cluster trial to optimize the time, duration, and quantity of LLML application. Second, we will conduct a clusterrandomized trial to test the effectiveness and cost effectiveness of LLML. The design has two parallel arms, i.e., control and intervention, and allows for baseline survey without intervention and crossover .We will select four clusters, two in each county, for an entomological evaluation of the optimal larvicide application scheme . We will randomly select two clusters, one in each county, treated with larvicides and the other two sites will serve as controls . We will treat temporary habitats with FourStar controlled release granule formulation, which maintains effectiveness through wet and dry periods for up to 1 month. We will treat semipermanent habitats with FourStar 90-day briquettes and permanent habitats with FourStar 180-day briquettes. Application dosage will follow the recommendation of the manufacturer, Central Life Sciences: 10 lbs per acre of water surface for the granule formulation, and one briquette per 100 ft2 of water surface for the briquette formulations, regardless of water depth. We will re-treat the habitats every 4 to 5 months. On a weekly basis in the treatment and control sites, we will use aerial samplers to determine habitat pupal productivity, and use standard dippers to determine larval abundance. This will allow for determination of habitat productivity with a tolerable error of 0.5 mosquitoes, based on the standard deviation identified in previous studies. We will monitor indoor and outdoor vector abundance using 64 trapnights per cluster per month. This sample size will allow detection of a difference in average vector abundance of 0.12 mosquitoes with 80 % statistical power and 0.05 type-I error. We will use ELISA methods to determine Anopheles mosquitoes’ sporozoite infection and blood feeding host preference. We will analyze the data immediately after the small scale trial using analysis of variance with repeated measures and appropriate transformation to determine the effects of habitat larviciding on mosquito abundance and transmission intensity. We will assign fourteen clusters each in the two counties to intervention or no intervention by a block randomization method on the basis of clinical malaria incidence, vector density, and human population size per site. Year 1 will focus on preparing the study sites and working with clinics and hospitals to help them improve their routine malaria surveillance . In year 2, we will conduct preliminary surveys on all 28 sites to determine clinical malaria incidence, vector density, geographic information system coordinates of larval habitats, and human population size.

The European Medicines Agency has established initiatives for the provision of accelerated development support and evaluation procedures for COVID-19 treatments and vaccines. These initiatives generally follow the EMA Emergent Health Threats Plan published at the end of 2018 . Similar to FDA’s CTAP, EMA’s COVID-19 Pandemic Emergency Task Force aims to coordinate and enable fast regulatory action during the development, authorization, and safety monitoring of products or procedures intended for the treatment and prevention of COVID-19 . Collectively, this task force and its accessory committees are empowered to rapidly address emergency use requests . Although perhaps not as dramatic as the aspirational time reductions established by the FDA’s CTAP, the EMA’s refocusing of resources and shorter response times to accelerate the development and approval of emergency use products are nevertheless laudable. In the United Kingdom, the MHRA6 has also revised customary regulatory procedures to conform with COVID-19 emergency requirements by creating flexible regulations spanning early consultation, accelerated clinical development and review, and alternatives to facility inspection.During a public health emergency, one can envision the preferential utilization of existing indoor manufacturing capacity, at least in the near term. Processes making use of indoor cultivation and conventional purification can be scrutinized more quickly by regulatory agencies due to their familiarity, resulting in shorter time-to-clinic and time-to-deployment periods. Although many, perhaps most, process operations will be familiar to regulators, there are some peculiarities of plant-based systems that differentiate them from conventional processes and, hence, require the satisfaction of additional criteria. Meeting these criteria is in no way insurmountable,grow trays 4×4 as evidenced by the rapid planning and implementation of PMP programs for SARS-CoV-2/COVID-19 by PMP companies such as Medicago, iBio, and Kentucky Bioprocessing.

During emergency situations when speed is critical, transient expression systems are more likely to be used than stable transgenic hosts, unless GM lines were developed in advance and can be activated on the basis of demand . The vectors used for transient expression in plants are non-pathogenic in mammalian hosts and environmentally containable if applied indoors, and by now they are well known to the regulatory agencies. Accordingly, transient expression systems have been deployed rapidly for the development of COVID-19 interventions. The vaccine space has shown great innovation and the World Health Organization has maintained a database of COVID-19 vaccines in development,including current efforts involving PMPs. For example, Medicago announced the development of its VLP-based vaccine against COVID-19 in March 2020, within 20 days of receiving the virus genome sequence, and initiated a Phase I safety and immunogenicity study in July.If successful, the company expects to commence Phase II/III pivotal trials by late 2020. Medicago is also developing therapeutic antibodies for patients infected with SARS-CoV-2, and this program is currently in preclinical development. Furthermore, iBio has announced the preclinical development of two SARS-CoV-2 vaccine candidates, one VLP and one subunit vaccine.Kentucky Bioprocessing has announced the production and preclinical evaluation of a conjugate TMV-based vaccine and has requested regulatory authorization for a first in-human clinical study.These efforts required only a few months to reach these stages of development and are a testament to the rapid expression, prototyping, and production advantages offered by transient expression.The PMP vaccine candidates described above are all being developed by companies in North America. The rapid translation of PMPs from bench to clinic reflects the conformance of chemistry, manufacturing, and control procedures on one hand, and environmental safety and containment practices on the other, with existing regulatory statutes.

This legislative system has distinct advantages over the European model, by offering a more flexible platform for discovery, optimization, and manufacturing. New products are not evaluated for compliance with GM legislation as they are in the EU and the United States but are judged on their own merits. In contrast, development programs in the EU face additional hurdles even when using 8 WHO 2020. DRAFT landscape of COVID-19 candidate vaccines. Process validation in manufacturing is a necessary but resource intensive measure required for marketing authorization. Following the publication of the Guidance for Industry “Process Validation: General Principles and Practices,” and the EU’s revision of Annex 15 to Directive 2003/94/EC for medicinal products for human use and Directive 91/412/EEC for veterinary use, validation became a life-cycle process with three principal stages: process design, process qualification, and continuous process verification . During emergency situations, the regulatory agencies have authorized the concurrent validation of manufacturing processes, including design qualification , installation qualification , operational qualification , and performance qualification . Although new facility construction or repurposing/ re-qualification may not immediately help with the current pandemic, given that only existing and qualified facilities will be used in the near term, it will position the industry for the rapid scale-up of countermeasures that may be applied over the next several years. An example is the April 2020 announcement by the Bill & Melinda Gates Foundation of its intention to fund “at-risk” development of vaccine manufacturing facilities to accommodate pandemic-relevant volumes of vaccines, before knowing which vaccines will succeed in clinical trials. Manufacturing at-risk with existing facilities is also being implemented on a global scale.

The Serum Institute of India, the world’s largest vaccine manufacturer, is producing at-risk hundreds of millions of doses of the Oxford University COVID-19 vaccine, while the product is still undergoing clinical studies.Operation Warp Speed 13 in the United States is also an at-risk multi-agency program that aims to expand resources to deliver 300 million doses of safe and effective but “yet-to be-identified” vaccines for COVID-19 by January 2021, as part of a broader strategy to accelerate the development, manufacturing, and distribution of COVID-19 countermeasures, including vaccines, therapeutics, and diagnostics. The program had access to US$10 billion initially and can be readily expanded. As of August 2020, OWS had invested more than US$8 billion in various companies to accelerate manufacturing, clinical evaluation, and enhanced distribution channels for critical products.For example, over a period of approximately 6 months, OWS helped to accelerate development, clinical evaluation , and at-risk manufacturing of two mRNA based COVID-19 vaccines, with at least three more vaccines heading into advanced clinical development and large-scale manufacturing by September/October 2020.Once manufactured, PMP products must pass quality criteria meeting a defined specification before they reach the clinic. These criteria apply to properties such as identity, uniformity, batch-to-batch consistency, potency, purity, stability , residual DNA, absence of vector, low levels of plant metabolites such as pyridine alkaloids, and other criteria as specified in guidance documents . Host and process-related impurities in PMPs, such as residual HCP, residual vector, pyridine alkaloids from solanaceous hosts , phenolics, heavy metals , and other impurities that could introduce a health risk to consumers, have been successfully managed by upstream process controls and/or state-of-the-art purification methods and have not impeded the development of PMP products . The theoretical risk posed by non-mammalian glycans, once seen as the Achilles heel of PMPs, has not materialized in practice. Plant-derived vaccine antigens carrying plant-type glycans have not induced adverse events in clinical studies, where immune responses were directed primarily to the polypeptide portion of glycoproteins . One solution for products intended for systemic administration, where glycan differences could introduce a pharmacokinetic and/or safety risk , is the engineering of plant hosts to express glycoproteins with mammalian-compatible glycan structures . For example, ZMapp was manufactured using the transgenic N. benthamiana line ΔXT/FT, expressing RNA interference constructs to knock down the expression of the enzymes XylT and FucT responsible for plant-specific glycans,horticulture products as a chassis for transient expression of the mAbs . In addition to meeting molecular identity and physicochemical quality attributes, PMP products must also be safe for use at the doses intended and efficacious in model systems in vitro, in vivo, and ex vivo, following the guidance documents listed above. Once proven efficacious and safe in clinical studies, successful biologic candidates can be approved via a BLA in the United States and a new marketing authorization in the EU.In emergency situations, diagnostic reagents, vaccine antigens, and prophylactic and therapeutic proteins may be deployed prior to normal marketing authorization via fast-track procedures such as the FDA’s emergency use authorization .This applies to products approved for marketing in other indications that may be effective in a new emergency indication , and new products that may have preclinical data but little or no clinical safety and efficacy data. Such pathways enable controlled emergency administration of a novel product to patients simultaneously with traditional regulatory procedures required for subsequent marketing approval.

In the United States, the FDA has granted EUAs for several diagnostic devices, personal protective devices, and certain other medical devices, and continuously monitors EUAs for drugs. For example, the EUA for chloroquine and hydroxychloroquine to treat COVID-19 patients was short-lived, whereas remdesivir remains under EUA evaluation for severe COVID-19 cases. The mRNA-based SARS-CoV-2 vaccines currently undergoing Phase III clinical evaluation by Pfizer/BioNTech and Moderna/ NIAID, and other vaccines reaching advanced stages of development, are prime candidates for rapid deployment via the EUA process. No PMPs have yet been granted EUA, but plant-made antibodies and other prophylactic and therapeutic APIs may be evaluated and deployed via this route. One example of such a PMP candidate is griffithsin, a broad-spectrum antiviral lectin that could be administered as a prophylactic and/or therapeutic for viral infections, as discussed later. The FDA’s EUA is a temporary authorization subject to constant review and can be rescinded or extended at any time based on empirical results and the overall emergency environment. Similarly, the EU has granted conditional marketing authorisation to rapidly deploy drugs such as remdesivir for COVID-19 in parallel with the standard marketing approval process for the new indication.The regulations commonly known as the animal rule 17 allow for the approval of drugs and licensure of biologic products when human efficacy studies are not ethical and field trials to study the effectiveness of drugs or biologic products are not feasible. The animal rule is intended for drugs and biologics developed to reduce or prevent serious or life-threatening conditions caused by exposure to lethal or permanently disabling toxic chemical, biological, radiological, or nuclear substances. Under the animal rule, efficacy is established based on adequate and well-controlled studies in animal models of the human disease or condition of interest, and safety is evaluated under the pre-existing requirements for drugs and biologic products.As an example, the plant-derived mAb cocktail ZMapp for Ebola virus disease, manufactured by Kentucky Bioprocessing for Mapp Biopharmaceutical 18 and other partners, and deployed during the Ebola outbreak in West Africa in 2014, was evaluated only in primates infected with the Congolese variant of the virus , with no randomized controlled clinical trial before administration to infected patients under a compassionate use protocol . Although the fast-track and streamlined review and authorization procedures described above can reduce time-to-deployment and time-to-approval for new or repurposed products, current clinical studies to demonstrate safety and efficacy generally follow traditional sequential designs. Products are licensed or approved for marketing based on statistically significant performance differences compared to controls, including placebo or standards of care, typically generated in large Phase III pivotal trials. One controversial proposal, described in a draft WHO report , is to accelerate the assessment of safety and efficacy for emergency vaccines by administering the medical intervention with deliberate exposure of subjects to the threat agent in a challenge study. Although the focus of the WHO draft report was on vaccines, the concept could conceivably be extended to non-vaccine prophylactics and therapeutics. Results could be generated quickly as the proportion of treated and control subjects would be known, as would the times of infection and challenge. Challenge studies in humans, also known as controlled human infection models or controlled human infection studies , are fraught with ethical challenges but have already been used to assess vaccines for cholera, malaria, and typhoid . The dilemma for a pathogen like SARS-CoV-2 is that there is no rescue medication yet available for those who might contract the disease during the challenge, as there was for the other diseases, putting either study participants or emergency staff at risk .In the EU, the current regulatory environment is a substantial barrier to the rapid expansion of PMP resources to accelerate the approval and deployment of products and reagents at relevant scales in emergency situations.

The item read: “Marijuana is also called pot, weed, and grass. Are you planning to stop using marijuana?” . Participants were categorized as marijuana users if they indicated recent use on the staging item. All others were categorized as non-marijuana users. The following outcomes were assessed at 3, 6, and 12 months: 1) seven-day point prevalence abstinence, 2) smoking reduction, 3) presence of a quit attempt since the last assessment, and 4) stage of change for quitting smoking. Self-reported point prevalence abstinence and reduction were assessed with the item, “How many cigarettes have you smoked in the past 7 days?”. To measure point prevalence abstinence, responses were coded into abstinent in the past seven days or smoking . Reduction was calculated using baseline cigarettes per day, and coded into reduced or not reduced by at least 50% since baseline. Quit attempts were measured with, “Have you tried to quit smoking for at least 24 hours since your last Tobacco Status Project survey?” . Stage of change was measured using the Stages of Change Questionnaire , recoded into precontemplation, contemplation, preparation, or action/maintenance. Those in action/maintenance indicated that they had quit smoking. All outcomes were measured at each time point. Participants in the intervention group reported their perceptions of the intervention at treatment end by rating their agreement with 7 items. Items addressed whether the intervention was easy to understand, gave sound advice, gave participants something to think about, and helped them to be healthier, as well as whether they used the information, thought about the information,indoor grow rack and would recommend the intervention . Responses were coded as disagreement or agreement .

Engagement was measured by the number of Facebook comments an individual posted during the 90-day intervention, including comments on posts and during live counseling sessions . First, marijuana users and non-users at baseline were compared on baseline demographic and smoking characteristics. Second, differences in reported smoking outcomes between users and non-users during the follow-up period were analyzed using a series of generalized estimated equations with binary distributions and logit link functions for dichotomous variables and a multinomial distribution with a logit link function for the ordinal variable . Longitudinal analyses controlled for intervention group and adjusted for baseline stage of change , baseline average cigarettes per day, sex, alcohol use, and age participants began smoking regularly. The first two covariates were determined a priori and the latter were selected based on the observed baseline differences between marijuana users and non-marijuana users. Because all participants were smokers at baseline, longitudinal analyses only included data from the three follow-up points . Largely due to attrition, there were 493 missing data points across all three time points on the abstinence variable, 498 on the reduction variable, 489 on the quit attempts variable, and 502 on the readiness to quit variable. GEE analyses are relatively robust to missingness, and a participant’s data could still be included in the analyses if they were missing one or more time points. Third, chi-square tests for independence were used to compare marijuana users’ and non-marijuana users’ perceptions of the intervention. An independent-samples t-test was used to compare treatment engagement between marijuana users and non-marijuana users in the treatment group. Baseline participant characteristics are displayed in Table 1. Marijuana users were more likely to be male, more likely to drink alcohol, smoked fewer cigarettes per day, and began smoking cigarettes regularly at an older age than non-users. Associations between smoking variables and marijuana use at each follow-up time point are displayed in Table 2. Use of marijuana by young adult smokers was associated with a lower likelihood of reduced smoking and a lower likelihood of abstaining from smoking in the past seven days, as assessed over 12 months of follow-up.

Marijuana users and non-marijuana users did not significantly differ in likelihood of having made a quit attempt or readiness to quit smoking . Moreover, users and non-users did not significantly differ in their perceptions of the intervention or treatment engagement . This study showed longitudinal patterns of marijuana use, point-prevalence abstinence from smoking, and reduction in smoking among young adults participating in a digital smoking cessation intervention trial. Most importantly, results showed that young adult smokers who coused marijuana were less likely to reduce their cigarette smoking or to have been abstinent from smoking than were those who did not use marijuana; however, they did not differ in readiness to quit smoking or likelihood of having made a quit attempt. Although smoking marijuana in addition to cigarettes increases young adults’ likelihood of negative physical effects , smoking marijuana may make quitting cigarettes more difficult in part by perpetuating the habit of smoking. Quitting smoking requires breaking associations or cues between the behavior of smoking and other contextual factors . Young adults commonly use marijuana in conjunction with cigarettes . Thus, continuing to use marijuana may hamper cigarette smokers’ efforts to change their behavior. Indeed, results showed that marijuana users were less likely to have recently abstained from smoking or reduced their smoking over a 12-month period. On the other hand, marijuana use status was consistently unrelated to readiness to quit smoking at baseline and during the followup period. Moreover, users and non-users did not significantly differ in the likelihood of making a quit attempt over 12 months. Results are consistent with research showing that young adult marijuana users do generally view quitting smoking as important , but have less ability to follow through with a complete abstinence goal despite a desire to quit smoking . Overall, our finding that marijuana users are less likely to report recent abstinence or reduction in smoking is consistent with extant literature suggesting that marijuana users are less likely to be successful at quitting smoking .

Encouragingly, marijuana users and non-marijuana users participating in the digital smoking cessation arm of the intervention did not differ in their perceptions of the intervention or their engagement in it. This suggests that young adults who use marijuana were receptive to the content and digital platform of the smoking cessation intervention. Future intervention content could highlight the negative effects continued marijuana use may have on quitting smoking, and could serve as a resource for young adults who want to quit using one or both substances. The variables most strongly and consistently associated with smoking outcomes over time were baseline stage of change for quitting smoking and marijuana use. Both should be assessed to inform treatment efforts with young adult smokers. Strengths of this study include multiple smoking-related outcomes, a 12-month longitudinal design, and a focus on young adults . This study had a few notable limitations. First, outcomes were self-reported. Our group has previously demonstrated the reliability and validity of young adults’ online self-reported tobacco and marijuana use , as well as the accuracy and limited bias of self-reported point prevalence abstinence in the present sample . Therefore, we opted to use self-reported abstinence,indoor farming equipment which had a much higher response rate. Second, current marijuana use was categorized into use versus non-use. It is possible that the relationship between marijuana use and smoking outcomes differs by heaviness of marijuana use, which our survey item did not assess. Although past research has shown that readiness to avoid marijuana use is significantly correlated with past 30 day marijuana use , future research should include a more detailed measure of marijuana use. The measure of alcohol use was similarly nonspecific, and a more detailed measure may yield different results. Moreover, up to twice as many of the participants indicated being abstinent for 7 days at each follow-up than identified as being in action/maintenance for having quit smoking. This was especially true of participants who were not using marijuana concurrently, as reflected in the significant difference in point prevalence abstinence and reduction between marijuana users and non-marijuana users. This finding may be due to the sample including non-daily smokers, and/or the young adult age of the participants. Based on self-report, 5-10% of the sample refrained from smoking for at least one week, yet were not committing to quitting. Future research could include more nuanced measure of marijuana use and measures of smoking specific to non-daily cigarette smokers. For the first time in several decades—and concomitant with the rise in opioid use, misuse, and dependence—life expectancy has declined in the United States, and life expectancy gains have stalled in Canada. Consistent with these global estimates, in an accompanying paper for the Special Issue, Astrid Guttmann and colleagues analyzed 2002–2016 national data from the United Kingdom and Canada to identify women who likely used opioids during pregnancy and demonstrated markedly elevated mortality rates over up to 10 years of follow-up. The elevated rates were particularly striking for mortality due to avoidable causes like unintentional and intentional injuries. Using 1998–2014 data from a large sample of primary care practices in the UK, John MacLeod and colleagues show that coprescription with benzodiazepines was highly prevalent among patients receiving opioid agonist and partial agonist treatment and that coprescription was strongly associated with drug-related poisonings. This study adds to the relatively thin evidence base about the potential hazards of benzodiazepine coprescription in the setting of opioid agonist treatment. Although opioid agonist treatment should not be with held from patients concurrently taking benzodiazepines or other central nervous system depressants, these studies suggest a need for vigilance by healthcare professionals providing care for such patients to minimize the risk of overdose or death.

Coprescription of alprazolam may warrant particularly heightened scrutiny, however, given that it is the short-acting benzodiazepine most frequently involved in drug overdose deaths.The elevated mortality risks facing people with opioid use disorders are attributable to a complex web of interrelated structural and psychological causes. The concept of the “risk environment” may be useful to reference here, given its focus on the interplay between various structural factors that increase vulnerability to morbidity and mortality. The study by Zehang Li and colleagues provides an example of the use of spatiotemporal data to characterize one aspect of the risk environment. Applying a Bayesian space–time model to emergency medical services dispatch data on suspected heroin-related overdose incidents from Cincinnati in 2015–2019, the investigators identified significant spatial heterogeneity in the distribution of these calls, with strong associations with features of the built environment and temporal spikes corresponding to local media reporting. Analyzing 2005–2016 claims data, Yu-Jung Wei and colleagues identified more than 200,000 adults with new claims related to opioid use disorder or overdose. They found that, by the end of the study period, nearly one-half had filled no opioid prescriptions in the 12 months prior to an incident opioid use disorder diagnosis or overdose. Among those who had filled opioid prescriptions, nearly three-quarters were prescribed a mean daily dose lower than the threshold needed to trigger most risk stratification algorithms. Also noteworthy is the analysis of 2015–2016 data from the US National Survey on Drug Use and Health by Joel Hudgins and colleagues. These authors found that approximately 1 in 20 adolescents and young adults reported either past-year opioid use disorder or past-year non-medical use of prescription opioids and that three-quarters of those reporting non-medical use of prescription opioids had obtained them from outside the healthcare system. These estimates are generally consistent with trends identified in similar, previously published analyses of NSDUH data. Thus, although opioid prescribing patterns undoubtedly played a significant role in how opioid use disorders came to be so highly prevalent and asymmetrically distributed in the US, a public health response that focuses solely on prescribing behavior is likely to be ineffective in reducing the number of fatal and nonfatal opioid overdoses.For people with existing opioid use disorders, opioid agonist treatment is known to reduce mortality. Monica Malta and colleagues add to this evidence base with a systematic review showing a wide range of health and prosocial benefits of opioid agonist treatment for people with opioid use disorders who are incarcerated or have recently been released. Opioid agonist treatment may have important collateral health effects as well. Analyzing data from a 3-country cohort of people who inject drugs, Charles Marks and colleagues found that people who inject drugs and who receive opioid agonist treatment are approximately half as likely to assist others in initiating injection drug use. They then developed a deterministic, dynamic transmission model of initiation into injection drug use, ongoing drug use, and cessation of drug use.

A greater presence of marijuana co-marketing in neighborhoods with a higher proportion of school-age youth and lower median household income raises concerns about how industry marketing tactics may contribute to disparities in LCC use. The study results also suggest that $1 buys significantly more cigarillos in California school neighborhoods with lower median household income. Policies to establish minimum pack sizes and prices could reduce the widespread availability of cheap cigar products and address disparities in disadvantaged areas. After Boston’s 2012 cigar regulation, the mean price for a grape-flavored cigar was $1.35 higher than in comparison communities. The industry circumvented sales restrictions in some cities by marketing even larger packs of cigarillos at the same low price, and the industry’s tipping point on supersized cigarillo packs for less than $1 is not yet known. The retail availability of 5- and 6-packs of LCCs for less than $1 observed near California schools underscores policy recommendations to establish minimum prices for multi-packs .A novel measure of marijuana co-marketing and a representative sample of retailers near schools are strengths of the current study. A limitation is that the study assessed the presence of marijuana co-marketing, but not the quantity. The protocol likely underestimates the prevalence of marijuana co-marketing near schools because we lacked a comprehensive list of LCC brands and flavor varieties. Indeed, state and local tobacco control policy research and enforcement would be greatly enhanced by access to a comprehensive list of tobacco products from the US Food and Drug Administration, including product name, category, identification number and flavor. Both a routinely updated list and product repository would be useful for tobacco control research,bud drying rack particularly for further identifying how packaging and product design reference marijuana use.

This first assessment of marijuana co-marketing focused on brand and flavor names because of their appeal to youth. However, the narrow focus is a limitation that also likely underestimates the prevalence of marijuana co-marketing. Other elements of packaging and product design should be considered in future assessments. Examples are pack imagery that refers to blunt making, such as the zipper on Splitarillos, as well as re-sealable packaging for cigarillos and blunt wraps, which is convenient for tobacco users who want to store marijuana. Coding for brands that are perforated to facilitate blunt making and marketing that refers to “EZ roll” should also be considered. Future research could assess marijuana co-marketing across a larger scope of tobacco/nicotine products. The same devices can be used for vaping both nicotine and marijuana. Advertising for vaping products also features compatibility with “herbs” and otherwise associates nicotine with words or images that refer to marijuana . Conducted before California legalized recreational marijuana use, the current study represents a baseline for understanding how retail marketing responds to a policy environment where restrictions on marijuana and tobacco are changing, albeit in opposite directions.20 The prevalence of marijuana co-marketing near schools makes it imperative to understand how tobacco marketing capitalizes on the appeal of marijuana to youth and other priority populations. How marijuana co-marketing contributes to dual and concurrent use of marijuana and tobacco warrants study, particularly for youth and young adults. In previous research, the prevalence of adult marijuana use in 50 California cities was positively correlated with the retail availability of blunts.

Whether this is correlated with blunt use by adolescents is not yet known. Consumer perception studies are necessary to assess whether marijuana co-marketing increases the appeal of cigar smoking or contributes to false beliefs about product ingredients. Research is also needed to understand how the tobacco industry exploits opportunities for marijuana co-marketing in response to policies that restrict sales of flavored tobacco products and to policies that legalize recreational marijuana use. Such assessments are essential to understand young people’s use patterns and to inform current policy concerns about how expanding retail environments for recreational marijuana will impact tobacco marketing and use.Patients with obstructive sleep apnea experience apneic and hypopneic events that, when untreated, have detrimental cardiovascular and neurocognitive consequences. Under normal conditions, blood pressure and heart rate decrease during non–rapid eye movement sleep and increase commensurately upon waking. This is attributed to a decrease in sympathetic nervous system activation and a subsequent increase in cardiac vagal tone during sleep . The transient episodes of hypoxemia and hypercapnia caused by apneas or hypopneas, as well as arousals, result in an increase in cardiac output and heart rate that leads to sympathetically induced peripheral vasoconstriction that causes a marked increase in blood pressure. The result of this chronic sympathetic excitation and inflammation does not resolve upon waking, and over time, together with the loss of the normal nocturnal blood pressure dip, it can lead to pathophysiologic changes such as impaired vascular function and stiffness . This impairment in the untreated patient with moderate to severe OSA has been found to increase the risk of both acute coronary syndrome and sudden cardiac death . The increased sympathetic nervous activity, inflammation, and oxidative stress seen in OSA can lead to hypertension.

The prevalence of hypertension in moderate to severe OSA ranges between 13% and 60%, and OSA is considered the most common cause of secondary hypertension . Arrhythmias can be common in patients with OSA, and the prevalence of atrial fibrillation is higher in these patients than in patients without OSA. In fact, severe sleep disordered breathing is associated with twofold to fourfold higher odds of having complex arrhythmias. In addition, untreated OSA has been associated with higher rates of failure to maintain sinus rhythm after cardioversion or ablation therapy . Inflammation, atrial fibrillation, and atherosclerosis are all associated with OSA and overlap with risk factors for cerebrovascular disease. OSA may be frequently diagnosed after stroke, and it can be difficult to determine whether the condition is causal or resultant. Evidence suggests that OSA is associated with an increased risk of stroke in elderly patients, and untreated OSA after stroke increases mortality risk during 10-year follow-up . Another disease state affected by sleep apnea is heart failure. Both OSA and central sleep apnea are common in patients with acute and chronic systolic and diastolic heart failure. Untreated OSA in this patient population has been associated with an increased risk of death. However, screening for sleep disordered breathing can be difficult because patients with OSA and heart failure often do not report excessive daytime sleepiness. This absent symptom raises challenges in diagnosis and treatment adherence for OSA . Untreated OSA can affect many cognitive domains, including learning, memory, attention, and executive functioning. Data suggest that OSA is linked with cognitive impairment and may advance cognitive decline or dementia . In addition, intermittent hypoxemia and sleep fragmentation have been linked to structural changes in the brain that may be responsible for cognitive impairment . Given the increased prevalence of obesity and the common nature of diagnoses such as hypertension, coronary artery disease, atrial fibrillation, heart failure, and neurocognitive impairment, healthcare providers should be cognizant of the hazards of untreated OSA .Positive airway pressure therapy is highly efficacious in treating OSA, but its effectiveness relies on adherence. There is a dose–response relationship between continuous PAP usage and clinical outcomes in OSA, although the optimal adherence threshold may vary depending on the clinical outcome of interest . Consequently, recognition of barriers to use and interventions to augment adherence are pivotal to the successful management of patients with OSA. Studies have explored potential modifiable and non-modifiable predictors of PAP adherence with inconsistent results . Most adherent patients have higher baseline daytime somnolence, possibly worse OSA severity based on the apnea– hypopnea index , higher self-efficacy ,4×8 tray grow and confidence for troubleshooting as well as greater social support, including bed partner engagement. Patients who have challenges with PAP adherence tend to have lower socioeconomic status, type D personality, high expectations in treatment outcome, claustrophobia, and small nasal passages. Patient age, sex, marital status, and amount of anxiety and depression have not been shown to consistently predict PAP adherence . Therefore, an individualized patient centered approach is recommended to optimize PAP adherence in OSA. Interrogation of PAP tracking systems can reveal patterns and the duration of PAP use and may help identify potential modifiable targets to improve adherence, such as mask leak . High residual AHI can point toward suboptimally treated obstructive events or the emergence of central events. Prompt and early troubleshooting of any side effects is important, as the pattern of PAP usage is established early and has been shown to predict long-term use.

Attention to mask fitting is essential, with otolaryngologic evaluation helpful in patients with narrow nasal passages or nasal congestion that may be amenable to surgery . Given the psychological influences on PAP adherence, educational and behavioral interventions aim to address patient perceptions and promote self efficacy. Motivational counseling by psychologists during appointments with follow-up phone calls has been shown to increase adherence by 99 min/night compared with control subjects . In a meta-analysis, behavioral therapy improved average PAP usage by 1.44 h/night and increased the number of nights with >4 hours usage from 28% to 47%, although the quality of evidence was low . In addition, studies exploring educational and behavioral strategies are limited by heterogeneity, often combining various modalities and thus making generalizations difficult. Increasingly, technological innovations are being used to improve PAP management and adherence. Cloud-based platforms and wireless capabilities offer real-time monitoring and active patient engagement. In a retrospective analysis of two cloud based databases, patients who actively engaged in real-time feedback through a website connected to their PAP devices had 87% compliance compared with 70% compliance in the usual-care group , as defined by the U.S. Medicare criteria for compliance . These technologies are also being incorporated in telemedicine. In a recent trial on telemedicine education and telemonitoring on CPAP adherence, patients randomized to receive web-based education and automated message feedback through telemonitoring had a Medicare adherence rate of 73% compared with 55% in the usual-care group . Modern PAP devices are including features in an attempt to improve comfort and adherence, including ramp, automatically adjusting pressures , expiratory pressure relief technologies, lighter interfaces such as nasal pillows, and heated humidification . Although none of these have been shown to consistently improve adherence, these technological advancements reflect ongoing efforts to personalize OSA management.Chronic insomnia, characterized by difficulties falling asleep, staying asleep, or early morning awakenings, is the most prevalent sleep disorder in the United States, affecting an estimated 10–15% of Americans. Symptoms occur despite adequate opportunity to sleep and are associated with daytime impairment, including impaired attention and cognition, increased risk of industrial and motor vehicle accidents, reduced work productivity, and increased healthcare costs . Insomnia is also a risk factor for multiple chronic health conditions, including cardiovascular disease , mood disorders, and pain conditions . Despite the widespread public health impact, insomnia remains both under recognized and under treated. Nearly two-thirds of patients with insomnia are unaware of available treatment options, and ~40% self medicate with alcohol and unproven over-the-counter sleep aids . Evaluation of insomnia should include a comprehensive sleep history, including sleep/wake routines , daily behaviors that impact sleep , and screening for comorbid sleep disorders . Given the high prevalence of concurrent mood disorders and other comorbidities, a thorough medical and psychiatric history is also warranted. Prior treatments for insomnia should also be reviewed. Use of a sleep diary to capture the patient’s habitual sleep patterns, including differences between weekday and weekend sleep routines, is very helpful. Comorbid depression and anxiety as well as pain syndromes are distinct but overlapping entities and should be treated concurrently . Current guidelines recommend multi-component cognitive behavioral therapy for insomnia as first-line treatment for chronic insomnia in adults . Data suggest that compared with pharmacotherapy, the effects of CBT-I are similar but more durable and have a better safety profile . CBT-I is also effective for insomnia in adults with comorbid moodand medical conditions, including conditions in which sleep medication may be contraindicated. The key components of CBT-I include sleep hygiene, stimulus control, sleep restriction, cognitive restructuring, and relaxation techniques .