Cannabis Grow

This study underscores the importance of studying in-home cannabis smoking, which occurs at a higher rate than in-home tobacco smoking; however, more research is needed. Cannabis users comprise a sizeable population, both in absolute and relative terms, and with cannabis laws becoming more liberal, the number of users is rising. A major public health goal for future studies is to identify how often in-home cannabis smoking occurs in the general public and in high-use populations. Knowing the rates in the overall population will reveal the full scope of the behavior while understanding rates in high-use groups will quantify the behavior among those who have the most to gain from intervention. Obtaining these prevalence data will inform interventions to eliminate in-home smoking and will encourage further research, including the identification of public health messages designed to prevent in-home smoking  and research on the health consequences of first hand, secondhand, and third hand cannabis grow tray smoke exposure.

As with all studies, results of the present analysis should be viewed considering the strengths and limitations of the methods used. While we included data from 107,274 men and women from 17 countries, the data were collected using anonymized web-based surveys specifically targeted toward those who use drugs through advertisements on social media and drug-scene-related media outlets. While non-probability-based sampling and self-selection bias prevent generalizable estimates of prevalence , the inferences that were presented comparing in-home tobacco and cannabis smoking by use-status are expected to have significantly less bias since it is unlikely that the primary prerequisite for selection bias  was present. In the Introduction, we referenced online information on tobacco and cannabis control policies that highlight significant variance in the type and implementation of policies across countries.

To our knowledge, no study has attempted to quantify the impact of such regulatory differences on the in-home use of cannabis and tobacco, an investigation that was beyond the scope of the present study. However, the presence and diversity of public policies toward both substances should be considered when interpreting our findings. To reduce bias related to confounding by participant-level characteristics and by country-level characteristics such as regulatory policies, our models statistically controlled for several potential confounders,vertical grow system for sale and included country as a fixed and random effect. Future investigations of how tobacco and cannabis control policies impact in-home smoking are warranted, especially studies that explore how legalized cannabis use, with and without legalized outdoor use, impacts in-home use and subsequent SHS and THS exposure. The self-report origin of our cannabis and tobacco use status and in-home smoking status is also a limitation, although the GDS is a well-respected entity among the drug-using community and is known for preserving anonymity, making it more likely to elicit accurate responses.

We were unable to study usual in-home smoking patterns  including the frequency of in-home smoking or household composition because we did not collect usable data on these important factors—future studies should capture more detailed data. As a result, our estimates likely overestimate problematic in-home smoking, although this is not expected to greatly bias results since the overestimation is likely non-differential—i.e., similar for both in-home cannabis and in-home tobacco smoking.  Transmission of severe acute respiratory syndrome coronavirus 2 , the virus responsible for causing coronavirus disease 2019 , has led to unprecedented morbidity and mortality across the U.S. . Risk factors for COVID-19-related severe illness resulting in possible hospitalization include: active or former smoking status and/or having pre-existing comorbidities or an immunocompromised status . Combustible and non-combustible tobacco users are vulnerable to clinical morbidities, including impaired pulmonary function and respiratory illnesses.Additionally, vaping nicotine, flavorings, and/or tetrahydrocannabinol  products may place individuals at increased risk for COVID-19- related symptomatology and illness due to impairment of normal pulmonary defenses to inhaled viral pathogens . Smoking, and possibly e-cigarette use, can upregulate the angiotensin-converting enzyme-2  receptor, which is the receptor for SARS-CoV-2 .

The non-linear  effect of age of first tobacco use on average HONC score can be described as a slight decrease until age 8, followed by a slight increase between 8 and 12 years, and then a sharp decrease with age. Hazardous substance use in adolescence can increase risk for substance use disorders in adulthood, which remain a major public health issue in the United States. So, it is essential to develop new tools for identifying adolescents at risk of developing hazardous substance use among co-users in order to possibly increase the impact of prevention and intervention programs. This study took a preliminary step by determining risk factors associated with hazardous use of alcohol, cannabis, and tobacco — the three of the most commonly used substances by adolescents — and employing them to build joint models for predicting risk of hazardous substance use. To our knowledge,mobile vertical rack this is the first study that attempted to jointly model hazardous use of multiple substances by adolescents specifically for the purpose of quantitative risk prediction.

With information about salient combinations of individual, family and socio-demographic factors, the proposed model can be used to predict scores on quantitative measures of hazardous use for an adolescent user of alcohol, cannabis, and tobacco. A predicted score can be compared with its distribution  to assess the relative level of risk for the user. An important novelty of this work is the application of relatively new statistical and machine learning methods for analyzing multiple outcomes. To the best of our knowledge, these methods have not been used so far in the substance use literature. They are better suited for risk prediction than the classical multivariate regression because one  allows outcome-specific predictors, thereby providing flexibility for joint modeling that may lead to higher predictive accuracy; and the other  allows regularization of regression coefficients to protect against over fitting, thereby helping the model to generalize well on future unseen data. For comparison, we also fitted the classical multivariate regression model, whose results are in Supplementary Table 3.

This model identified a total of ten risk factors, all of which were also identified by MCGLM, but the latter additionally identified family history of hazardous alcohol use and had higher predictive accuracy. None of the individual models identified age of first cannabis use, age of first use of other substances, and family history of hazardous alcohol use, which were identified by MCGLM. Of these, the age of first cannabis use, an important risk factor as per the literature, was identified by MCGLM to be associated with both CPQ-A and HONC scores. Although the individual models identified guardian, parental monitoring, and parental past substance use, which were not identified by MCGLM, the effects of these variables could have been captured partially by similar variables included in the joint model, e.g., vertical grow rack parental attachment and family history variables. One of our key findings is that early age of onset of tobacco use is associated with hazardous use of all three substances. In line with previous literature, one study found that, among adolescent drinkers, past-year smokers were at a higher risk for alcohol use disorders than non-smokers .

Another study evaluating the association between e-cigarette use and the use of other substances showed that early onset of e-cigarette use was associated with increased use of alcohol and cannabis.Another key finding is that lower levels of parental attachment are associated with higher scores of hazardous cannabis and tobacco use. Lower levels of parental monitoring and attachment are also known to be associated with adolescent substance use disorders . Other findings in this study are also generally consistent with the literature. For example, increased age is associated with higher RAPI and HONC scores. Indeed, there is a higher percentage of young adults with alcohol use disorders compared to adolescents, and cigarette use tends to increase with age . The observed reduction in CPQ-A scores with an increase in maternal education is supported by literature showing lower socio-economic status is associated with hazardous substance use . The finding that increased CPQ-A and HONC scores are associated with greater early life stress is also consistent with literature demonstrating that various forms of early life stress such as physical and/or sexual abuse, negligence, parental divorce, and domestic violence tend to increase hazardous use of alcohol, cannabis, and tobacco . Our result indicating that family cigarette use is associated with higher HONC scores is consistent with other studies reporting on the association between family smoking habits and increased adolescent smoking .

More recently, a simple cumulative risk index was developed to classify which adolescents are at risk for developing persistent substance disorders in adulthood using risk factors from childhood and adolescence . Another recent study built a model for predicting quantitative risk of developing cannabis use disorder in adults based on personal risk factors using statistical and machine learning approaches . Yet another recent study  built models for predicting risk of developing substance use disorder by thirty years of age using separate sets of predictors from late childhood to 22 years of age. There is a substantial literature on exploring factors that lead to co-use of multiple substances . However, to our knowledge, modeling of hazardous use of multiple substances jointly has not been considered especially in the context of risk prediction modeling. More specifically, the key differences between these previous studies and our present work are that they  do not provide a measure of quantitative risk or score,model concurrent use rather than hazardous use of multiple substances, and  focus on a selected number of risk factors  rather than a comprehensive set of potential risk factors.

As such, there is a need to develop risk prediction models for hazardous use of multiple substances based on personal risk factors of adolescent users. This study aims to fulfill this need by developing preliminary models for jointly predicting hazardous use of alcohol, cannabis grow set up, and tobacco for adolescents who have used all three substances in their lifetime. Joint statistical modeling of multiple outcomes utilizes the correlation between them, which can lead to higher power for detecting association between risk factors and outcomes and can additionally provide insight into the shared underlying mechanisms. As our goal is statistical risk prediction rather than hypothesis testing, we consider a set of potential risk factors as suggested by the literature.However, it assumes a common set of predictors for all outcomes, which limits its applicability in our context of risk prediction. This is because if a variable is predictive of one outcome but not another, model parsimony dictates that the variable should be included only in the model for the former but not the latter. Adding unimportant variables to a model adversely affects its ability to predict accurately for new  participants that are not included in building the model. Moreover, regularization of regression coefficients in the model can protect against over fitting of the model especially when sample sizes are not large.

An over fitted model is sub-optimal for the purpose of predicting for new participants . However, regularization is not available in the classical approach. Therefore, we apply two relatively new statistical and machine learning methods, each of which addresses one of these limitations. Specifically, we utilize multivariate covariance generalized linear models  and penalized multivariate regression with a lasso penalty . These methods have not been used to model multiple outcomes in the substance use literature perhaps because the development of joint risk prediction models has not yet been considered in a formal way. Supplementary Table 1 summarizes the 18 risk factors. They include participant demographics ; general environmental factors ; their own substance use ; and family substance use . Not all participants used substances other than alcohol, outdoor cannabis grow, and tobacco, and hence for them there was no corresponding age of first use of other substances. Therefore, to include this age variable in the model, a binary indicator of lifetime use of other substances was added to the model together with its interaction with the age of first use. This way, the interaction term had a non-zero value only for the users of other We used two multivariate statistical modeling frameworks for joint modeling of the three outcome variables: MCGLM and multivariate lasso. MCGLM: It is a novel generalization of the classical multivariate regression  allowing modeling of the mean structure, variance function, and within-response covariance structure. MCGLM allows outcome-specific predictors, i.e., the predictors need not be shared across all outcomes. Its predictive accuracy was measured by root mean square error , computed using leave-one-out cross-validation .This measure differs from the ordinary RMSE and allows assessment of model performance on future unseen data more accurately by protecting against over fitting.

In the past year, 25% of EA ages 18–24 have initiated/increased cannabis use in response to negative stress and emotions – the steepest rise in use across all queried age groups; further, 43% of EA reported past year cannabis use, the highest rate observed in 35 years . While the literature supports important links between negative affect and social in- equality , as well as between negative affect and cannabis use , there is a gap in our understanding of how these processes interact with each other to contribute to EA cannabis use, as well as in our understanding of the cognitive and developmental neuroscience behind these relationships.For example, during some phases of development, social inequality may directly alter emotion and regulatory networks to increase risk. At other stages, objective and subjective social inequality may increase cannabis grow system use risk either via direct influences  or as contextual moderators of risk .

Further, effects of objective and subjective inequality may be additive or themselves interact. Future research would benefit from incorporating metrics of subjective experiences of social inequality  in addition to the widely used “objective ”metrics such as zip code, income, parent education, employment, and environmental instability  when studying relationships to EA hazardous cannabis use. In addition, differentiating the multi-component processes of emotion regulation and studying the constituent parts  may elucidate behavioral, functional, and neural correlates  that have implications for EA decision-making risk in “high-stakes ”emotional contexts like cannabis use . We are extremely far from understanding the potential neurodevelopmental and socio-emotional resilience factors in these equations . As clinicians and clinical scientists, operating without these data directly obstructs our capacity to identify essential risk/protective factors of EA socio-emotional health in a way that meaningfully helps providers prevent and intervene with EA cannabis use .

Of even greater relevance and salience, data to help elucidate meaningful neuro-developmental mechanisms of experienced subjective and objective social inequality are essential to informing and advancing better prevention and intervention approaches for underserved EA . In terms of potential future directions, noting of course that these relationships may not only be due to environment, but can be exacerbated in diathesis:stress contexts, recent studies have highlighted the importance of examining day-to-day shifts in mood and anxiety symptoms, both in terms of cannabis use risk and resilience in this age group , and also in terms of social interactions and prosocial behavior during experiences of subjective and objective social inequality and subsequent heightened stress for youth . Most important within these studies is the indication of the rapid and dynamic shifts that happen in mood and marijuana grow system use, on a day-to-day level in this age group, and the critical importance of capturing that variability within a platform such as ecological momentary assessment  and/or daily diaries, which can accurately and flexibly capture these highly dynamic emotional and subjective and objective social inequality experiences .

These approaches facilitate the capture of dynamic variance across multiple levels of the human experience,as well as the cumulative consequences of persistent and/or ongoing  experienced subjective and objective social inequality . Equally relevant to future evaluations, the study of experienced social inequality necessitates designs that combine idiographic methods to examine individual-level patterns of variability and change with more traditional nomothetic methods to examine patterns of differences be- tween groups . In this regard, Ong advocates for use of “daily process designs ”, which evaluate across a continuum of:  experienced social inequality in near real-time;  the tracking of changes in emotions, proximate to their incidents and moments of change;  preservation of temporal sequences; and  minimization of retrospection bias, in order to better capture the interactions between stressors , adaptive resources , and outcomes. Finally, in terms of future work, it is important to note that most EA do not move into full sustained cannabis use disorders. Evaluations that may be able to engage a longitudinal prospective research protocol to identify and uncover which EA are most protected from risk trajectories would be highly valuable for youth healthcare providers. Work that can help determine which neuro-developmental factors protect EA will be crucial to guide more impactful prevention and intervention programming for this age group –and would be invaluable to helping support young people thrive even amidst current and future emotional risk challenges.

Even EA who share experiences of inequality or similar challenges in emotion regulation may not experience similar outcomes some will experience stable or worsening problems while others will transition towards resilience and recovery. In some contexts, such as for individuals in high subjective and objective inequality contexts, differences in emotion regulation and the associated underlying neural response may be more salient predictors of outcome than in other con- texts.In the past year, 25% of EA ages 18–24 have initiated/increased cannabis use in response to negative stress and emotions – the steepest rise in use across all queried age groups; further, 43% of EA reported past year cannabis use, the highest rate observed in 35 years .While the literature supports important links between negative affect and social in- equality , as well as between negative affect and indoor cannabis grow system use , there is a gap in our understanding of how these processes interact with each other to contribute to EA cannabis use, as well as in our understanding of the cognitive and developmental neuroscience behind these relationships.

For example, during some phases of development, social inequality may directly alter emotion and regulatory networks to increase risk. At other stages, objective and subjective social inequality may increase cannabis use risk either via direct influences  or as contextual moderators of risk . Further, effects of objective and subjective inequality may be additive or themselves interact. Future research would benefit from incorporating metrics of subjective experiences of social inequality  in addition to the widely used “objective ”metrics such as zip code, income, parent education, employment, and environmental instability  when studying relationships to EA hazardous cannabis use. In addition, differentiating the multi-component processes of emotion regulation and studying the constituent parts  may elucidate behavioral, functional, and neural correlates  that have implications for EA decision-making risk in “high-stakes ”emotional contexts like cannabis use . We are extremely far from understanding the potential neurodevelopmental and socio-emotional resilience factors in these equations.

As clinicians and clinical scientists, operating without these data directly obstructs our capacity to identify essential risk/protective factors of EA socio-emotional health in a way that meaningfully helps providers prevent and intervene with EA cannabis use . Of even greater relevance and salience, data to help elucidate meaningful neurodevelopmental mechanisms of experienced subjective and objective social inequality are essential to informing and advancing better prevention and intervention approaches for underserved EA . In terms of potential future directions, noting of course that these relationships may not only be due to environment, but can be exacer- bated in diathesis:stress contexts, recent studies have highlighted the importance of examining day-to-day shifts in mood and anxiety symptoms, both in terms of cannabis grow set up use risk and resilience in this age group , and also in terms of social interactions and prosocial behavior during experiences of subjective and objective social inequality and subsequent heightened stress for youth . Most important within these studies is the indication of the rapid and dynamic shifts that happen in mood and cannabis use, on a day-to-daylevel in this age group, and the critical importance of capturing that variability within a platform such as ecological momentary assessment and/or daily diaries, which can accurately and flexibly capture these highly dynamic emotional and subjective and objective social inequality experiences .

These approaches facilitate the capture of dynamic variance across multiple levels of the human experience , as well as the cumulative consequences of persistent and/or ongoing  experienced subjective and objective social inequality . Equally relevant to future evaluations, the study of experienced social inequality necessitates designs that combine idiographic methods to examine individual-level patterns of variability and change with more traditional nomothetic methods to examine patterns of differences between groups . In this regard, Ong advocates for use of “daily process designs ”, which evaluate across a continuum of:  experienced social inequality in near real-time;  the tracking of changes in emotions, proximate to their incidents and moments of change;  preservation of temporal sequences; and  minimization of retrospection bias, in order to better capture the interactions between stressors , adaptive resources , and outcomes. Finally, in terms of future work, it is important to note that most EA do not move into full sustained cannabis use disorders. Evaluations that may be able to engage a longitudinal prospective research protocol to identify and uncover which EA are most protected from risk trajectories would be highly valuable for youth healthcare providers.

In 2014, over a one-month period, Twitter produced over 15 times as many pro-cannabis tweets as anti-cannabis ones .This trend of pro-cannabis information outweighing the anti-cannabis information online should contribute to a shift of cannabis attitudes in a more positive direction. Even so, there remains a gap between cannabis attitudes in the media and realized patient stigmas . As a result, public cannabis education efforts which emphasize the current scientific understanding and evidence for medical cannabis use are vital to meeting patient needs.Participants indicated strong support for patient access to medical cannabis in the scenario questions before viewing the educational lectures; and the support increased after viewing them. This support, though, was observed even when participants did not indicate a pro-medical cannabis stance in the absence of a personal patient scenario; participants seemed to be objectively opposed to medical cannabis grow system, but then supported it when provided with a personalized clinical need for it.

This is not dissimilar to research on individuals changing their attitudes toward LGTBQ rights and public policy when a friend or family member comes out as gay.Currently, there is a knowledge gap among health care providers as there is no standardized curriculum for medical cannabis across nursing or medical schools in the United States . Consequently, patient demand for medical cannabis vastly outweighs the number of qualified practitioners who have been properly educated about it . Research has shown that two-thirds of medical school curriculum deans believed that their graduates were “not at all” prepared to recommend medical cannabis to patients .This is especially troubling given the patient need of and clinical evidence for cannabis as a viable alternative to opioid use . The disconnect between medical cannabis patients and their providers’ understanding of medical cannabis contributes to significant treatment issues. A recent study in Michigan found that only 21% of medical cannabis patients were comfortable with their primary care physician’s ability to incorporate medical cannabis as a treatment option .

As medical cannabis legality continues to expand throughout the United States, it is essential that further research and education efforts go beyond public education and target healthcare professionals to ensure that they can be knowledgeable and comfortable recommending medical cannabis to patients and further reduce stigmas associated with this treatment option .The strengths of this study included presenting balanced information with both the benefits and risks of cannabis and focusing on the most relevant clinical applications. There were, though, some weaknesses. The study results may not be generalizable to the national population because of its lack of variety of participants from more politically conservative areas. A large portion of the sample size comprised individuals from the state of California,where the use of medical cannabis has been legalized since 1996.Though this sample over represents California, with full access to medical and recreational marijuana grow system, given the relative populations of the states with full access to cannabis versus the states with no access, the sample does not vary from the country’s population . Further, recent research demonstrated that states’ legal status of cannabis is not a predictor of resident’s attitudes on cannabis .

The sample contained a larger percentage than the national averages of participants who identified as White or female. There was also a high dropout rate; this likely was due to the requirement of watching nearly 1.5 h of educational sessions, a large commitment with only the potential for as mall monetary reward.Additionally, researchers were unable to verify if participants viewed the educational lectures. With there being no researcher monitoring the participants, any number of external factors could have prevented the participants from finishing, and those that did claim to finish the videos could have falsified their level of completion. However, a review of the videos on the primary author’s YouTube account showed that each video had been viewed more than 125 times. This is more than the total number of participants in the study. Though this does not confirm specific participant’s viewing of the education lectures, it supports engagement from the participants as the videos were only available on the main research website and set to be viewed only to those who had the link . Likewise, the level of participant engagement with the lectures was not verified.

Here, we define “driving scenarios” as the terrain, driving route, situations encountered, and environmental conditions  represented within a simulated drive. If not designed in an evidenced-informed manner, driving scenarios may not allow for a sensitive and reliable measurement of cannabis-related effects on safe driving performance. Driving simulators also provide the ability to introduce graded challenges to test driving abilities under typical and increasingly more difficult driving situations. Therefore, in this paper we offer a framework for developing simulated driving scenarios to test for cannabis-related impairment in a controlled, repeatable, safe way, and offer a prototype driving simulation scenario as an example of this approach. This approach takes into consideration the pharmacological effects of indoor cannabis grow system, the resulting effects on sensory, motor, and cognitive abilities, and how impairments in these abilities could negatively affect driving. Scenarios can be developed to strategically target particular abilities, such as the abilities that are most likely to be influenced by cannabis.

Numerous quantitative data variables can be measured over time and results can be compared between control and experimental groups and among experimental conditions. Driving simulators often also include either a passenger seat or an operator station, thereby allowing for real-time qualitative assessments by trained driving evaluators and/or post-drive evaluations conducted using playback modes. However, the advantages of simulators can only truly be realized for this purpose if the scenarios are designed in ways that, a) target the sensory, motor, and cognitive abilities predicted to be affected by cannabis by, b) design terrain elements, environmental conditions, and events that require these abilities, c) allows meaningful, reliable, and valid outcome measurements to be extracted. For example, cannabis is known to result in reduced speed of processing. Response time can be measured using braking performance. Therefore, introducing events within the driving scenario that require drivers to react quickly, such as a pedestrian entering the roadway or a leading car suddenly braking, will allow researchers to determine whether cannabis use results in poorer driving performance as evidenced through slower braking times.

For many scenario elements, a range of difficulties should be included, since it may be that some measures are only sensitive to cannabis-related effects if the difficulty level is sufficiently high. This graded-difficulty process further highlights the advantages of customized simulation scenarios in that it allows researchers to safely test challenging situations and analyze performance patterns across different difficulty levels within individual participants and across participants. Factors such as route of administration and dosage of THC ,cannabis grow set up should be considered when developing driving simulation scenarios and experimental protocols. For example, the length of the driving scenario should target a time window within which impairment would most likely occur . Impairment of any magnitude in drivers’ sensory, cognitive, or motor abilities can potentially lead to unsafe driving . Michon’s hierarchical model classifies driving behaviors into three distinct levels of performance, namely the “operational level” , “tactical level” , and “strategic level”. Cannabinoids can affect all hierarchical levels of driving behaviours.

For instance, cannabinoids affect various parts of the central nervous system, including the basal ganglia , the hippocampus , and the neocortex. These effects can result in reduced abilities in the domains of visual acuity, coordination, reaction time, concentration, tracking of moving objects, divided attention, sustained attention, critical tracking, working memory, and decision-making ability . As such, simulated driving scenarios should incorporate elements that target each driving behavioural level . For instance, operational performance elements should be implemented to assess drivers with impaired sensory processing and motor coordination abilities, while strategic performance elements may be useful to assess higher-level cognitive impairments such as problems navigating to the desired destination. In order to present graded difficulty levels, increased sensory, motor, or cognitive loads can be introduced, such as by including, for example, low visibility environmental conditions , unexpected events , or multitasking requirements. Below we highlight several representative cannabis-related effects and corresponding scenario elements and driving measures that can be used to help quantify and characterize cannabis-related driving impairments. These effects and associated scenario elements are summarized in Table 1, although the examples listed are not mutually exclusive or exhaustive.

For example, smoked cannabis and use of concentrates among adolescents have been associated with a higher risk of continued use relative to other modes , and smoked cannabis may carry the greatest risks due to exposure to carbon monoxide  and other harmful by-products of combustion . Yet, little is known about the prevalence or correlates of different modes of cannabis administration during or before pregnancy. Previous research indicates that smoking is the most common mode of cannabis administration during pregnancy, followed by use of edibles and vaping . However, data on mode of cannabis administration in the preconception period is limited  and studies have not described sociodemographic characteristics associated with specific modes of cannabis administration. Further, the prevalence of some modes may have changed in recent years due to the perception of health risk  and the availability of new cannabis products. To address this knowledge gap, our study examined correlates of different modes of preconception cannabis use among pregnant patients in Northern California who self-reported preconception cannabis use in 2020 and 2021, during which time cannabis grow tray was legal for recreational and medical use.

We focused on racial/ethnic differences and associations with neighborhood deprivation, to examine use patterns among potentially under-served populations. Younger patients were more likely to smoke , smoke blunts, use dabs/wax, and they had a greater number of administration modes, while older patients were more likely to report using edibles/ oral and lotion/topicals . Smoking  was most common among Hispanic and Black patients and least common among Asian patients, while smoking blunts was most common among Black patients and least common among non-Hispanic White patients. Edibles/oral were most common among Asian patients and least common among Hispanic and Black patients. Use of lotion/topicals was least common among Black and Asian patients. Finally, Hispanic patients were the most likely and Black patients were the least likely to report more than one mode of administration. Examination and surveillance of cannabis use practices and modes of administration before pregnancy is an essential aspect of prevention. Using data from a large healthcare delivery system with routine preconception cannabis use screening, we found substantial variation in preconception use and co-use of different cannabis administration modes among patients who self-reported use.

While smoking was the most commonly endorsed mode, more than half of individuals with preconception cannabis use reported using edibles, more than a quarter reported vaping, 12% reported using high potency concentrates , and 10% reported using lotions/topicals. The prevalence of different modes of cannabis in our sample was similar to findings on modes of past-year cannabis administration in a nationally representative US sample of adult women  with a slightly lower prevalence of smoking and higher prevalence of edible use in the current study. The proliferation of cannabis administration options in the context of recreational legalization may entail evolving yet rarely studied risks to individuals who are pregnant or contemplating pregnancy . In our sample of patients who reported preconception cannabis use in Northern California where cannabis is legal for medical and recreational use, vertical grow system more than a quarter reported using daily and use of more than one mode was common. Notably, daily cannabis users were more likely to report each mode of administration and had a greater number of modes of administration than less frequent users, with the exception that daily users were less likely to use edibles.

Based on studies in other populations, edibles are perceived to be less harmful than other modalities and they may be the modality of choice for those who use cannabis on specific occasions  and for reasons of discreetness . However, additional research is needed to better understand the higher prevalence of edibles among individuals who use cannabis less frequently. The high prevalence of blunt smoking among individuals with preconception cannabis use warrants special attention as tobacco is known to adversely affect maternal and fetal health . Co-use of cannabis and tobacco is associated with greater risk for cannabis use disorders and ongoing long-term cannabis use . National data indicate that rates of blunt smoking are increasing over time among reproductive-aged women, and continued surveillance of blunt use in this population is important . In addition, in clinical settings these individuals could potentially be targeted for additional assistance in quitting cannabis use during pregnancy.

Given the many toxicant components found in cannabis smokers, it is not surprising that cannabis smoking notably alters the oral microbial ecology. Importantly,long-term repeated oral inoculation of A. meyeri, which mimicked cannabis exposure-increased oral A. meyeri in humans, resulted in the development of CNS abnormalities.Recent studies have found correlations between Actinomyces and Alzheimer’s disease. For example, brains from patients with Alzheimer’s disease have been reported to have strikingly large bacterial loads compared to controls . Actinobacteria, a phylum of Actinomyces,were exclusively detected in the post mortem brain samples from patients with Alzheimer’s disease compared with those of normal brains . Actinobacteria were also found enriched in the gutmicrobiota of patients with Alzheimer’s disease . Another study using 16S rDNA sequencing in the brain cell lysates further found Actinomycetales, Prevotella, Treponema,cannabis grow system and Veillonella were exclusively present in the brain of patients with Alzheimer’s disease .

In a previous study, oral microbiome and resting-state functional magnetic resonance imaging  scans were conducted in cannabis smokers; the enrichment of Actinomyces in the oral microbiome was positively correlated with brain resting-state functional networks which are significantly perturbed with Alzheimer’s disease.Neuropathological hallmarks of Alzheimer’s disease include loss of neurons, progressive impairments in synaptic function, and deposition of amyloid plaques within the neuropil. Although mice do not readily develop amyloid plaques, our results show Ab 42 deposition was increased in the brain from A. meyeri-treated mice compared with controls, suggesting oral microbiome-induced neuronal responses that have relevance to Alzheimer’s disease neuropathology.Previous studies have suggested that bacteria in the oral cavity were initially taken up by tissue macrophages which may facilitate CNS infection . In the current study, A. meyeri treatment resulted in increased myeloid cell migration and phagocytosis in vitro and elevated macrophage infiltration into the mouse brain in vivo, compared with those of N. elongata treatment.

The cytokines that differed in cannabis users and non-users and in A. meyeri-treated mice and control mice are related to monocyte/macrophage functions. The TNF super family cytokine promoted a compromised blood-brain barrier, and monocytes migrated across the BBB into the brain in response to MCP-1 . Although it is not clear if macrophage infiltration results in CNS abnormalities in the setting of disease-associated immune perturbations, macrophage infiltration into the brain has been demonstrated in the pathogenesis of several diseases.In the current study, A. meyeri administration increased plasma levels of MIP-1a in some mice. However, cannabis smoking altered oral microbiome notlimited to A. meyeri; thus, marijuana grow system the decreased plasma levels of MIP-1a in cannabis users may stem from myeloid cell activation by other bacteria or by reduced total bacterial translocation due to cannabis reduced barrier permeability . In general, bacterial stimulation reduces phagocytosis and promotes proinflammatory cytokine production by myeloid cells. Unexpectedly, A. meyeridid not affect phagocytosis and did not induce prion flammatory cytokines but did increase myeloid cell infiltration and amyloid production in the brain.

It is possible that A. meyeri maybe a new exposure to mice which induces the immune responses and CNS effect. However,there is no evidence on the causal link between a new bacterial exposure in the oral cavity and neuropathology in mice. Thus, we believe that A. meyeri is a unique oral bacterium that is linked to CNS function.We have tested novel object recognition in C57/B6 mice after 6-month exposure to A. meyeri, but did not find significant memory deficits.The reasons for the null finding are as follows: 1) more than 6-month exposure is necessary to see memory changes, 2) the nature of wild type C57/B6 mice, and 3) the age of mice might play an important role with our mice being too young to detect any changes. Todate, there were few to no published studies measuring effects of a specific oral microbial dysbiosis pathobiont on behavior in wildty pemice. In 2018, the study of P. gingivalis found that this pathobiont induces memory impairment in 13-month-old mice and not 2-month-oldmice suggesting an age-related effect, but without enough age cross sections to determine when susceptibility occurred. Thus, we have refined our future strategy to analyze other neurological defects or pathological signs  and started to conduct studies that use mice at different ages and include memory-related longitudinal measures, such as the Novel Object Recognition  task that focuses on the hippocampus and prefrontal cortex memory functions, the Novel Tactile Recognition task that focuses on the hippocampus and parietal cortex memory functions, and finally the Water radial arm maze that focuses on spatial memory and cognitive flexibility.

Our findings could serve as baseline data for future ad campaigns. According to Snapchat’s policy, ads that “promote cigarettes , cigars, vaping products, tobacco, nicotine, or related products of any kind” are prohibited.  Leveraging Snapchat’s platform features can help monitor and amplify the reach of health education campaigns. For instance, incorporating Snapcodes  in health messages can link members of the audience to additional evidence-based resources related to cessation. As indicated by past work, use of geofencing to deliver tailored messages to adolescents and young adults in specific geographic locations could improve the impact of the messages.  More generally, ad targeting features are available on most social media platforms suggesting that communication strategists could deploy similar messaging across platforms and evaluate exposure and engagement by target population . This may be crucial while considering hard-to-reach populations or those who may be priority populations for tobacco or cannabis use prevention.

Government and advocacy organizations may turn to Snapchat for targeted reach for their tobacco and cannabis-related ads cannabis grow equipment. Using a public dataset, the present study demonstrated how a communication strategist could collect and analyze ad metrics to inform future efforts. For example, a campaign may find that highlighting health consequences of poly-use of cannabis and tobacco  may outperform a campaign that highlights legal consequences. Future research should explore determining factors shaping ad performance metrics on the adoption of sponsored social media health education campaigns. Findings may not generalize to other social media platforms or other time periods. This study could not determine if each ad was viewed in its entirety or viewed passively. This study was unable to determine whether all tobacco or cannabis-related ads were captured in the library or perform significance testing between themes and other categories of acteristics of ads sponsored on Snapchat and other platforms during 2019, which limits the interpretability of the findings. However, a prior study suggests that a million views or impressions is considered large on social media platforms.

Cannabis legalization is rapidly spreading throughout the United States . In 2010, approximately 27% of Americans lived in states with legal recreational and medical cannabis or medical cannabis only . By 2019, this figure had increased to 58% . In this rapidly changing legal environment, cannabis use has shifted. According to the National Survey on Drug Use and Health ,weed grow table for adults, past-month cannabis use increased significantly between 2002 and 2016 among 18-to-25-year-olds  and adults 26 years of age and older. Conflicting data on cannabis legalization’s impact on public health has led to a quarrelsome debate regarding the relationship between cannabis use and traffic safety. Driving simulation data suggest that cannabis use impairs driving ability . However, national Fatal Accident Reporting System  data has produced conflicting results on the effects of cannabis use on traffic safety. While one analysis of 2006-2008 FARS data found no relationship between testing positive for cannabis and traffic fatalities , an analysis of 2007 data did find a relationship .

A third analysis  found a significant positive relationship between testing positive for cannabis and the severity of the injuries from crashes . Research on the effects of cannabis legalization on traffic safety are similarly complex. Although two analyses of FARS data from the 1990 s and 2000 s found fewer traffic fatalities in medical cannabis states , another analysis  found no association between medical cannabis legalization and testing positive for THC. The only exception was in states with medical cannabis dispensaries; those states showed an increase in cannabis-positive drivers . Analyses in two recreational cannabis states, Colorado and Washington, suggest an association between recreational legalization and increases in self-reported driving under the influence of cannabis, the number of drivers testing positive for THC , and cannabis-related traffic deaths . Similarly, insurance claims data showed 3% more collisions over time in states that legalized recreational cannabis than in neighboring control states . Several reasons exist for variable findings. In addition, tests for cannabis impairment are limited in terms of their ability to account for frequency or dosage of use, both of which affect impairment while driving . Given the limitations of other data sources on DUIC, several studies have examined self-report data . For example, Fink et al.  combined multiple national data sources to examine changes in the prevalence of self-reported DUIC between 1991 and 1992 and 2012 to 2013.