In our multivariate analysis that included RASS and CAM-ICU findings, age did not continue to be a predictor of WS . However, the RASS and delirium findings, when added to the model, significantly increased the model fit. That is, we found that they are both related to WS. From a conceptual and clinical perspective, it could be important for providers to recognized agitation/restlessness and delirium when caring for ICU patients being weaned from opioids and/or benzodiazepines.The final model also showed that cumulative opioid dose amounts prior to weaning were associated with development of WS, although the number of days that patients received opioids was protective. In our study, as expected, days on opioids and cumulative opioid dose were strongly correlated . The nature and the strength of the relationship between these two variables could be the reason behind our findings: in the multivariate regression analysis, while holding the cumulative opioid dose constant, the “days on opioid” variable showed a slightly protective odds ratio for WS . That is, given the same cumulative dose of opioid, patients with longer duration on opioid had lower odds of developing WS.Another explanation for this finding is that there are several factors that can cause differences in opioid tolerance, the precursor to WS, at the opioid receptor level.In addition, genetic differences in opioid receptor synthesis and variable opioid receptor affinity, the difference in type of opioid administered, and the use of continuous versus intermittent administration may be influential factors . Use of multi-modal analgesia may help to counteract development of WS through reduction of opioid amounts administered to the patient . However,cannabis grow tray further research is warranted on time versus amount differences in opioids and their risk for WS. This study has several strengths. The assessment of WS was done using a prospective approach two times a day for 72 hours or more. Furthermore, in the absence of an instrument validated to measure opioid and benzodiazepine WS in ICU adults, we developed a checklist using several reliable sources: the DSM-5 criteria, International Classification of Diseases, 10th Edition criteria WS, and symptoms identified in previous adult WS studies.
Therefore, the checklist had content validity. Furthermore, given that our study was exploratory in nature, we were able to conduct several analyses by constructing various models between patient- and clinical-related factors and the probable presence of WS. Our study has notable limitations. Consistent with other WS studies in ICU , our sample was small. In addition, the TICU did not have a protocol for daily sedation interruption or a pain management protocol. Therefore, there was a large degree of variability in the opioid and/or benzodiazepine weaning process between patients; this could have influenced differences in WS development. In addition, we were unable to evaluate some symptoms on the checklist in patients with RASS –3 to –5 such as hallucinations, delusions, illusions, dysphoria, nausea, insomnia, and delirium. Also, the intensity of the probable WS sign and symptoms was not evaluated. Our checklist has not yet undergone a formal validation process and reliability testing. Interrater or intrarater reliability was not possible because only one person performed all measures and the occurrence of WS was not constant between measurements. Finally, the signs and symptoms on our checklist are not specific for WS; thus, we could not rule out other conditions associated with these signs or symptoms. Further research on the psychometric characteristics of our checklist is warranted.Social media is now a common source of health-related information. This includes user-generated conversations about a variety of topics, with an emerging field focused on better understanding tobacco and alternative and emerging tobacco and electronic nicotine delivery system related knowledge, attitudes, and behaviors. User generated social media conversations can be assessed to better understand how health behaviors are changing closer to real-time.This approach introduces certain advantages over traditional survey methodology including faster identification of emerging trends. However, methods to appropriately code social media content for specific health-related topics remain underdeveloped, particularly in the context of characterizing transitions in behaviors that change over time. Twitter is a micro blogging social networking platform that allows users to tweet 280-character messages, which can then be retweeted, favorited, and shared across a network of online users. Users can form online communities by interacting with other users who share similar beliefs, interests, and opinions about topics.This includes users who initiate, use, and transition between different tobacco and ATP and ENDS products.
In fact, Twitter has specifically become a platform for sharing information about electronic cigarettes a nicotine delivery device commercially available only in the past decade.Evidencing growing popularity of vaping behavior, studies have shown that online searches for electronic cigarettes have increased. However, increased uptake of different types of e-cigarettes , particularly among youth and young adults, has not been without controversy. Ongoing concerns about the long-term health impact of nicotine consumption, e-cigarette-related adverse events and mixed evidence about the efficacy of ATPs as cessation devices, continues to generate public health and patient safety concerns. These concerns are accentuated when trying to assess the interaction of use behavior between traditional combustible tobacco products and ENDS. Understanding the pathways of transition of tobacco and ATP use—including what products users initiate on, why they switch between products, and unique health harms related to dual-use —is still a relatively underdeveloped area of study. Hence, the objective of this study was to examine Twitter user conversations to characterize users’ conversations in relation to transition of use associated with ENDS, with a focus on developing an inductive coding approach specific to characterizing transition of use knowledge, attitudes, and behaviors.To identify themes in our full corpus of tweets, we used an unsupervised machine learning approach called the Biterm Topic Model designed to detect patterns in data and summarize the entire corpus of tweets into distinct highly correlated categories. BTM is used to sort short text into highly prevalent themes without the need for predetermined coding or training and has been previously used for exploration of key public health topics. For each topic, BTM generates the top 20 words that represent the topic cluster. These topics were then reviewed and selected to identify clusters of Twitter conversations relevant to vaping and transition of use. Using BTM, we are able to identify “signal” topics based on the BTM output and eliminate irrelevant topics. BTM topics were first generated after applying keyword filters and were included for further analysis if they were pertinent to vape and vaping behavior, topics were excluded if they contained irrelevant topics or appeared to correlate with non-user generated conversations.We then extracted all the posts from the select vaping BTM topics and manually coded the content of tweets in these topics to ensure relevance to user-generated tobacco and ENDS use behavior. Posts were excluded as signals if they were: news related and not organically user-generated content; not written in English; and retweets, the tweets that were retweeted counted as only one tweet. However, all tweets, replies, and tweets containing photos or videos were included to assess additional contextual information in addition to content analysis of text of tweet. Transition of use was classified as switching from one tobacco or ATP/ENDS product to another.Tweets and any associated URLs/hyperlinks were aggregated into a table and imported into Atlas.ti qualitative software for content analysis. A first iterative, inductive analysis of the data was conducted to identify thematic areas and classify tweets into codes with code descriptions. Tweets were read for identification of thematic areas in the dataset,vertical grow system for sale then coded based on thematic areas of interest. Codes and coding descriptions were developed and modified iteratively throughout the coding process. A second analysis of the dataset was undertaken to expand the codebook to include subcodes. Subcodes and subcode descriptions were created and modified iteratively during a second round of data coding. Once a coding scheme was developed, the data were coded, extracted, and reviewed to assess the validity of the coding scheme by a second coder . Te final coding scheme and distribution of codes is presented in Fig. 1 and Table 1.Data was collected from the Twitter public API stream and included publicly available tweets that were filtered for posts with geolocation/geotagged information.
As the study did not involve human subjects, involved no interactions with online users, and only used publicly available data that was further de-identified for research purposes, ethics, and IRB approval was not required and twitter users were not consented into this study. Any user identifiable information was removed from the study results. This study explored user-generated conversations occurring on Twitter in relation to tobacco and ATP/ ENDS use, with a specific focus on transition of use between these highly addictive products. We observed that this subset of Twitter users actively tweeted about their experience using tobacco and ATPs/ENDS, representing powerful information about this behavior that is influenced by a changing landscape of new and emerging nicotine products. Te majority of tweets reviewed related to tobacco and ATP/ENDS use and behavior characteristics, including users asking about tobacco/ATP/ENDS products, how to quit, observations of tobacco/ATP/ENDS use behavior, opinions about products and vaping , sharing knowledge about tobacco/ ATP/ENDS products, and specific characteristics of use Close to half of all conversations discussed transition of use behavior, including users actively discussed the types of tobacco/ATP/ENDS products used and switched between, as well as provided reasons for product use change. A wide variety of tobacco/ATP/ ENDS products were mentioned, including combustible tobacco products , chewing tobacco, different types of e-cigarettes and cannabis smoking products. Transition was observed between different products and within specific product classes , with some users self-reporting poly tobacco and poly-substance behavior . Users expressed various sentiment about different products including how products could act as substitutes for others, what products made them feel better, attempts to quit use of one product by switching to another, and issues related to cost and access. Some users stated that cannabis vaping products helped them with cessation of nicotine addiction. Based on these preliminary results, Twitter appears to enable robust conversation and sharing of information related to tobacco and ATP/ENDS use and can act as a digital forum for smokers and vapers to accumulate knowledge, share experiences, and actually lead to potential behavior change associated with nicotine use and addiction.Since early March of 2020, the COVID-19 pandemic and the mitigation strategies put in place have had dramatic impacts on mental health and well being around the world . The public health response to COVID-19, such as social isolation and quarantines, resulted in several unintended consequences that increased the risk of anxiety and depression, as well as substance misuse and overdoses . The rise in substance misuse is particularly alarming, given that prior to the pandemic, in 2019, 60% of the United States population aged 12 and older used substances, including tobacco, alcohol, and illicit drugs, and an estimated 20 million had a substance use disorder . In addition to SUDs, one in five U.S. adults had a mental illness, which often co-occurs with SUDs . The prevalence of mental health symptoms continued to increase throughout the pandemic while disruptions in health care services resulted in unmet mental health care needs for many . At the same time, many individuals may have been reluctant to seek treatment for SUDs due to stay-at-home orders and worries about contracting COVID-19 . The increased rate of substance misuse raises several concerns regarding its impact on people who use drugs and overwhelmed health care systems. A rapid and drastic rise in drug overdoses and overdose-related deaths led to an emergency health advisory by the Centers for Disease Control and Prevention . Moreover, people with SUDs have been at increased risk of COVID-19 infection and poorer health outcomes . In turn, COVID-19 may increase the risk of overdose in PWUD . Furthermore, the pandemic exacerbated long-standing social and health disparities among under served and vulnerable populations. Not surprisingly, pre-existing disparities in mental health conditions and substance misuse have only widened during the pandemic . Among those who may be disproportionately affected by the pandemic are people living with HIV , a socially vulnerable population over represented in U.S. minorities . Indeed, COVID-19 infections correlate with social vulnerability and with county-level HIV prevalence .