The increasing use of more discreet forms of vaping, particularly JUUL , may also have had an impact on social media engagement about vaping behavior, though more research is needed. Also, the associated health risks of vaping were relatively unknown during the study period, though the outbreak of EVALI in 2019 may have generated more attention and possible concern among users about potential health risks of vaping, though these conversations were not detected in this study . Importantly, most tweets that included conversations about tobacco products and behavior expressed positive sentiment. Though unclear from these preliminary results, the influence of “party culture” on college campuses, the opportunities to experiment and initiate with forms of substance abuse behavior, and the immediacy of pleasure from substance use may outweigh concerns, including those relating to long-term health risks, among college students in the United States as observed in this user sentiment . Interestingly, though marijuana tweets exhibited the highest proportion of positive tweets, they also exhibited the highest proportion of neutral tweets and the lowest proportion of tweets with negative sentiment. This finding may suggest relative homogeneity regarding marijuana attitudes, possibly as a consequence of debate regarding marijuana legalization during this time period. As the majority of all sentiment-containing tweets were positive, results from this study may suggest that outreach efforts to raise awareness about the health risks of tobacco and ATPs on college campuses may have limited resonance.
However, these preliminary data also suggest discrepancies in sentiment between tobacco products,vertical agriculture as well as differences in sentiment toward smoking across California universities. Therefore, policymakers and health promotion advocates should consider tailoring policy implementation and health communication for specific college students in California based upon evidence of latent receptivity toward anti-tobacco approaches and existing community sentiment toward smoking behaviors as detected in this study. Furthermore, future studies should more explicitly assess user reaction and sentiment to debate, communication and implementation of state-level policies that both legalize and restrict use of tobacco and smoking products, as well as how these macro policies interact with campus-specific smoke free policy perceptions for different tobacco, marijuana, and e-cigarette product categories. For example, actionable insights based on preliminary findings from this study indicate that users generally express more positive sentiment about tobacco use and smoking behavior. This may necessitate the use of campus-based health promotion and education activities that focus on reducing appeal of these products, such as restricting any form of marketing and promotion in or near campus communities. This should be coupled with broader state legislation to further restrict marketing and promotion that targets young adults and college communities. Further, perceived penalties for violating smoke- and tobacco-free campus policies may also impact compliance based on socioeconomic factors. For example, one user from UC Riverside tweeted, “other places might be more lenient, but UCs have a shitty tobacco and smoking policy and I got caught and now it’s over” [emphasis added to denote correction of misspelling].
Hence, data-driven approaches to assess receptivity and the impact enforcement has on smoking behavior should be built into smoke free program implementation iteratively. Importantly, the breakdown of smoking-related tweets between numerous college campuses as detected in this study presents challenges with respect to whether the distribution of tweet characteristics accurately reflects distributions in the underlying college populations. Nevertheless, similar work has been conducted which presents correlational evidence between characteristics of geospatially-specific social media posts and characteristics of populations in those areas . Furthermore, as over half of college students in California are between the ages of 18 and 24 , academic and demographic distributions of tobacco consumption within colleges may be the consequence of socioeconomic disparities in childhood and potential effects of these disparities on attitudes about smoking among parents, high schools, and/or neighborhoods that warrant further study . Results from our study are limited in generalizability, though complement work by others on examining the impact of tobacco free policies on US college campuses. This includes a recent study from 2020 of small colleges in Massachusetts that found that a college with a smoke-free policy had significantly more anti smoking attitude than a control campus, but did not have lower rates of smoking itself . Relatedly, a separate earlier study from 2005 that analyzed undergraduates in Texas found that campuses with preventive education programs had lower odds of smoking, whereas designated smoking areas and cessations programs were associated with higher odds of smoking . Collectively, these prior studies and our own work helps to better characterize knowledge, attitudes and behaviors of college campus communities toward smoking, as well as the smoke-free policies attempting to discourage smoking, which in turn should aid in the development of more targeted approaches to educate college-aged populations about the health harms of tobacco and also enable better implementation of anti-tobacco policies in these critical populations.
This study was exploratory in nature and collected social media messages for which latitude and longitude coordinates could be collected from the Twitter API, but this data collection methodology is limited to collecting messages from Twitter users that enabled geolocation, a specific limitation to generating a more generalizable dataset on Twitter as it is estimated that only 1% of all tweets are geocoded . Hence, the dataset used in this study after filtering for keywords was small and likely biased, limiting the generalizability of results. This method of data collection may have introduced bias in the types of tweets collected, thereby limiting the generalizability of findings as the majority of Twitter users do not geolocate their posts. Potential sampling biases for Twitter include oversampling for certain geographic areas,hydroponics flood table filtering for specific features , and the limitations of the Twitter public streaming API in lieu of other data collection approaches . Future studies should examine the use of multiple Twitter APIs to generate a more representative Twitter dataset and compliment findings with other traditional sources of data to generate findings that are more robust and generalizable, as well as use complementary Twitter and social media datasets made publicly available by other researchers. specific to identification of Twitter users and conversations associated with colleges and universities, using keyword searches, and selecting accounts affiliated with higher education should be explored in future studies. Also, inclusion criteria required tweets to be posted from college campuses, which would not have accounted for variability in smoking related tweets from off-campus housing or areas/neighborhoods at the borders of campus properties where college students may reside. Furthermore, though the study design permitted searches of the Twitter API to return different volume of tweets for different keywords, there was a smaller number of original keywords for substances containing marijuana/cannabis than those for e-cigarettes or products containing tobacco due to our purposeful filtering for tobacco and alternative tobacco product keywords . Additionally, the majority of tweets analyzed for this study were from 2015, a period prior to major public scrutiny about default privacy settings for location sharing on Twitter . Finally, this study is an ecological study and should therefore be considered hypothesis generating and not generalizable to individuals on college campuses until further studies among individuals confirm these correlational findings. Post traumatic stress disorder is an anxiety disorder that can develop following exposure to traumatic life events. Central clinical features of PTSD include a persistent, heightened experience of alarm and distress, as well as a failure of extinction processes to diminish the emotional impact of traumatic memories. Investigation of the neural mechanisms that underlie fear acquisition, consolidation, and extinction may thus enhance our understanding of the neurobiological basis of PTSD, and open opportunities for mechanism-based drug discovery and development of the next-generation pharmacotherapies for this disabling disorder. The process by which emotionally-aversive memories become consolidated is recognized to be an interaction between glucocorticoid hormones and norepinephrine, both of which are released in response to stress. The primary component of this response appears to be a noradrenergic signal that is necessary for encoding emotionally salient information.
The hyperconsolidation of traumatic memories in PTSD is driven by a glucocorticoid-hormone facilitated potentiation of norepinephrine inputs to the basolateral amygdala. Recent work has revealed that this glucocorticoid action is mediated by cannabinoid type-1 receptors, a mechanism that is critical for the consolidation of aversive memories and thus implicates CB1 receptors in the etiology of PTSD. Moreover, there is an emerging body of evidence demonstrating an important role for CB1 receptor-mediated endocannabinoid signaling in the extinction of aversive memories. Augmenting levels of anandamide in the amygdala modulates short-term fear extinction, thereby resulting in long-term reduction in fear and highlighting the endocannabinoid system as a candidate system for developing novel pharmacotherapies for PTSD. CB1 receptors are the most abundant G-protein-coupled receptors in the central nervous system, and are found in high concentrations within an amygdala-hippocampal-cortico striatal circuit responsible for processing and storing fear-related memories and coordinating fear-related behaviors. Animal studies have shown that chronic stress is associated with decreased brain levels of the endocannabinoid anandamide and CB1 receptor adaptations, which in turn give rise to an anxious/depressive phenotype. However, it is not clear whether these animal findings apply to PTSD in humans. The development of a CB1 receptor selective radio tracer—[11C]OMAR22—now makes it possible for the first time to conduct a quantitative assessment of in vivo CB1 receptor availability using positron emission tomography . In the current study, we hypothesized that, relative to healthy non-trauma-exposed and trauma-exposed controls , individuals with PTSD would have increased CB1 receptor availability. In light of data from animal studies, we further predicted more pronounced CB1 receptor elevations in women than men with PTSD. A TC group free of lifetime PTSD or other psychiatric illness was recruited in order to assess the relation between trauma exposure alone and CB1 receptor availability. We also assessed peripheral levels of the endocannabinoids anandamide and 2-arachidonoylglycerol ; levels of the fatty acid ethanolamides oleoylethanolamide and palmitoylethanolamide ; and cortisol. We expected to find lower anandamide and cortisol levels in the PTSD group relative to the HC and TC groups. Finally, psychiatrically relevant biomarkers for PTSD are important yet elusive contributors towards accurate diagnosis and improved clinical care for trauma survivors. We predicted that measures of CB1 receptor availability, anandamide and cortisol would accurately categorize a majority of participants with regard to PTSD diagnostic status relative to healthy and trauma-exposed controls. Participants were recruited via public advertisements seeking individuals with non-combat trauma histories and healthy control participants with and without lifetime histories of trauma.In addition, none were receiving psychotherapy at the time of scanning. The protocol was approved by the New York University Institutional Review Board, the Yale University School of Medicine Human Investigation Committee, the Yale University Magnetic Resonance Research Center, and the Yale New Haven Hospital Radiation Safety Committee. After providing written informed consent, participants underwent a thorough medical and psychiatric evaluation that included physical examination, electrocardiogram, standard blood chemistry and hematology laboratory tests, urine analysis and toxicology, followed by a magnetic resonance imaging scan and a resting PET scan with the CB1 receptor antagonist radiotracer [11C]OMAR. Psychiatric diagnoses were made using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition – Text Revision criteria and the Structured Clinical Interview for DSM-IV , which was administered by an experienced psychiatric clinician. PTSD symptom severity was assessed using the Clinician-Administered PTSD Scale for DSM-IV and trauma history was assessed using the Traumatic Life Events Questionnaire. Only traumatic events meeting DSM-IV-TR PTSD criterion A1 for severe trauma exposure, as well as criterion A2, which confirms the emotional response to the trauma , were counted towards participants’ trauma history in this study. Additional assessments included the Hamilton Rating Scale for Anxiety, the Montgomery-Åsberg Depression Rating Scale, the Alcohol Module of the Addiction Severity Index and the Fagerström Test for Nicotine Dependence. To meet TC inclusion criteria, individuals must have been exposed to at least one potentially traumatic event that met DSM-IV-TR PTSD Criteria A1 and A2, but have no lifetime PTSD or other Axis I diagnosis. Participants with significant medical or neurological conditions, substance abuse within 12 months of the PET scan, a lifetime history of substance dependence , or history of head injury with loss of consciousness were excluded from the study.