So, I'm going to be talking about this randomized trial that we did in the VA. And so, I'm a health economist, and you're going to see some cost data at the very end. I'm in the School of Public Health, University of Washington, and I also do most of my work in the VA. I see a couple of VA people. Are there a lot of VA people here? Just a couple, okay. And I have nothing to disclose. So this interesting randomized trial, these are the first time we're kind of presenting the results. A couple of things I want you all to think about, since we're in the VA, we have to think about how we're really going to do this, like very pragmatic, practical ways of offering smoking cessation. And this trial was trying to figure out how to kind of take the burden off of primary care a little bit. And so, we have some interesting, some of the parts of the challenges and the benefits of doing that, and then kind of some of the costs to kind of think about how we really move forward with this a little bit. I want to thank several people, a couple people here on the research team. Scott and Spencer are here in the audience. And Jamie Hefner is the brains behind this. She's the behavioral smoking cessation person. And this is also one of the NCI smoking cessation at lung exam collaboration studies. There are seven trials looking at how to do this in lung cancer screening. And this one is funded actually by the VA, not by NCI, but it's one of the scale collaboration studies. So, a couple of things that are unique about the offering smoking cessation in the lung cancer screening context, patients, you know, smoking cessation is a hard topic, but when you talk about cancer and cancer screening, it's the C word. So, it energizes patients. They have a lot of stimulus going on. And also, you get to talk about long-term consequences. And so, you have patients at hand to talk about smoking cessation. One of the interesting things is that most of these studies are trying to recruit patients who are interested in actually enrolling in a trial. We didn't do that. So, we randomized providers, not patients, so we didn't consent any patients. But sort of the other 95% of patients who don't want to actually enroll in a trial, we need to think about them. And this is kind of the design for this study and the motivation for it. Also, our traditional behavioral change models don't really work that well in the lung cancer screening context. This pre-contemplation, contemplation, action model doesn't really work as you have patients stirred up about smoking cessation and their risks, and one day they're thinking about their screening results, and the next day they're thinking about something else, and then their screening results happen. And so, there's a lot of activity going on in that pathway that's not kind of our traditional models. And our traditional models aren't working that well anyway, because this willingness to set a quit date before you actually provide treatment is a big barrier. So, we're not getting treatment to as many patients as we probably should be getting it to. And then another thing that I spend a lot of time working on is this health certificate bias. Like, we have a, as a school public health person, there's a big risk here with lung cancer screening that patients are going to walk away from the experience thinking that I knew I was a lucky one, that there's nothing found, I'm going to not die of, you know, smoking now because I have this screening result that tells me I don't have cancer. And so, we could do a lot of good with, you know, finding early lung cancers, but if we actually kind of harm the smoking cessation rates, they could all be swamped by all the cardiac deaths and stroke deaths. So, and the goal of this trial is there are two main questions. One is can we really offset the primary care burden of this and actually do it in a different way? And if we actually do a lot of tobacco treatment medication, we're kind of proactively offering those medications, is there value to that? So what we did is we randomized primary care providers. So those, the primary care providers were the people who we consented for this study, and so 49 of them got randomized to the intervention group and 31 of them got randomized to the usual care group. And then the patients, as they showed up for lung cancer screening, either got the intervention activities or got the usual care activities. What's interesting is that we started the trial before COVID and then COVID happened and lung cancer screening rates dropped, so it was a, you know, big challenge to complete the study, but we did, so that's great. So what happened is we would pull upcoming screening appointments and we kind of go through the tobacco treatment histories, and what we really wanted to do was just mail medications to everyone and then connect them with behavioral counseling. After many battles with the IRB, what we had to do is we reviewed the notes and put a recommendation for the primary care provider so that when the results were ready, they could just click the button and have pharmacies send out medications. And then we would also send a letter to the patients, and then we'd also, you know, we used VA's quitline counselors, so the VA has a national quitline, and we contracted with those counselors to proactively call the patients after their screening results were ready, review any medications that were sent, and try and, you know, go over their screening results. They're really good at motivational interviewing and asking any questions that patients have about the tobacco treatment medications, because they've talked to them many, many times, and try and do a warm handoff for more behavioral counseling. And our outcomes were, you know, assembled by monitoring the medical record, and we also had an optional survey that patients could complete at 3 and 12 months, and they got consented for the survey, but they didn't get consented for the actual intervention study. So here are the results. So we did increase the actual use of tobacco treatment medications. So our goal was to really get it to about 100 percent of the patients, so we got it to 57 percent of the patients in the intervention group. The usual care group was lower. So that was good, not our target, but we did increase medication use. We did not get a lot of extra behavioral counseling, so not a lot of warm handoffs to additional therapists or behavioral counseling folks, a little bit, but not a lot. And unfortunately, we did not actually increase smoking cessation rates, so they're about the same at 12 months between the two arms. So that was our primary outcome, and we're disappointed by that. So we're trying to understand how to do it better. So one of the main things was that we really wanted Chantix and Brenniclin to be offered a lot. There were some shortages during COVID, and the VA providers were really hesitant to offer that proactively to the patients. So that's one thing that we really want to look at in more detail. We have some survey findings, so self-efficacy and motivation to quit, which we really thought we would be targeting with the intervention, was not really different between the two groups, unfortunately. So one of the core aspects of the intervention is really talking to patients about screening and the harms and tradeoffs of screening versus quitting smoking. And we did increase knowledge. So we have this embedded question in there, kind of the tradeoffs between lung cancer screening and early deaths. And this is leading to this health certificate bias, you know, are there false assumptions about the value of lung cancer screening that patients might be interpreting that's going to lead them to not be as interested in smoking cessation. So we did have some effect there. What was really nice is that the patients really actually liked it. So they liked getting the phone calls. They participated at a pretty high rate. And the ones that did thought this was a really good idea and something that they would recommend their friends and the VA should adopt nationally. So getting it outside of the context of providers that they knew and trusted and having these behavioral counselors was a positive experience of this trial. So we looked carefully at the cost. So since we contracted with the quit line and it went on for a long time and we had to retrain counselors a lot, it was pretty expensive. So to find the patients, send them, you know, kind of put them into the tracking system, keep track of their medications or screening results, and then send their information to the counselors and the counselors to do the counseling. So it was about a $350 increase per patient in all that counseling cost. We did a sensitivity analysis. If we wouldn't contract with the VA quit line and we would do it like the time that was actually spent using different types of positions, that cost would go down, not completely go down. So it would go down to $144. And then we kind of broke out the medications costs and then the behavioral counseling. So when they did get transferred to another more counseling. You can see the medication costs are pretty low in the usual care group, but they might be a little bit higher with varenicline. Varenicline was used as much as we really want it to be used. So a couple of take-home points from this is it is possible. It is possible to offload some of the burden of smoking cessation integration outside of primary care. So I think that we should keep that in mind and think about how to do that. Patients are willing to talk and trust. Other counselors, especially people trained in smoking cessation, and they really do want to talk to patients or talk to somebody about their cancer screening results. They get these letters and usually don't get any other information about them. So they're kind of willing to answer the phone and they're curious. There are cost implications to this. So we should be, we were, I think we were a little naive on how much it was going to take to do all of the training of the counselors and keep training them and keep training them. And so that's a pretty important sustainability cost of this. And we definitely need to figure out how to really do this. We need to figure out how to offer smoking cessation and figure out how to make sure it happens. Okay. Did I make it in my time? Nine minutes, 15. Okay. Aloha, everyone. I'm Claudia Jima. I'm presenting on age-specific and racial disparities in clinical outcomes of e-cigarette and vaping-associated lung injury, EVALI, using the National Inpatient Sample Analysis. I'm a third-year chief resident at Howard University Hospital, Washington, D.C., and I have nothing to disclose. So e-cigarette use and vaping use among young people is very common today. It actually became a public health concern in 2019 when the CDC deemed it an epidemic at that time because most of the youth were presented with respiratory illness, and they were being diagnosed with EVALI at that time. EVALI is a diagnosis of exclusion, so it requires that the patient should have used e-cigarettes or vaping products within the preceding 90 days and should have imaging confirmation like infiltrates on SRA or CT of the chest, and you also have to exclude all other possible causes including infections. Most of these patients ended up being hospitalized. Some were sick enough to require mechanical ventilation, and some even died. But then we realized that there was very limited data available on the clinical outcomes of patients that were actually admitted with EVALI, and our study sought to find these differences, whether there were any differential clinical outcomes among patients admitted for EVALI based on their age and also based on their race. So we did a retrospective core study using the 2020 National Inpatient Sample Database. We identified all EVALI admissions using the ICD-10 code U07.0. Our primary outcome for this study was all-cause inpatient mortality, and our secondary outcome included invasive mechanical ventilation, length of stay in the hospital, and also the total cost of hospitalization. These were the statistical methods that were used. We just wanted to point out that a two-sided P of less than 0.05 was considered significant throughout all our analysis. So that's what we found. We found 5,105 admissions for EVALI using that ICD-10 code. Our mean age was 32 years with a confidence interval of 30.5 to 32.7. Most of our patients, like 33%, were youths between 18 to 25 years. Those accounted for 39.3% of our study population. And for the races, non-Hispanic-wise formed the majority of the study population, 79.4%. Blacks formed only 6% of the study population, and Hispanics 14.6%. For our primary outcome of inpatient mortality, the all-cause inpatient mortality was 1.8 among the study population. After we controlled for comorbidities, age, sex, and insurance status, we found that blacks were 10 times likely to die from EVALI than non-Hispanic-wise, with an adjusted odds ratio of 10.7. The confidence interval was 2.8 to 39.7, and with a P value of less than 0.001. We didn't find any statistical significant difference in all-cause inpatient mortality based on the age categories, though. So this is just a table to show you. As you can see highlighted in the red, the all-cause mortality for blacks at adjusted odds ratio was 10.67 compared to non-Hispanic-wise, and it was statistically significant. For our secondary outcomes, the first one was mechanical ventilation. So we found 10.9% of our study population had severe disease requiring mechanical ventilation support. We didn't find any significant difference in the incidence of ventilation among the age cohorts. But we also found that blacks were twice as likely to have been put on invasive mechanical ventilation compared to whites, with an adjusted odds ratio highlighted there, 2.42. For mean length of stay, we realized that increasing age was associated with a longer mean length of stay. So as you see, as you got older, you spent more time in the hospital. But we didn't find any racial differences in the mean length of stay. For total cost of hospitalization, increasing age, again, was associated with a higher hospitalization cost. Of course, it made sense because you were spending more time in the hospital as you got older. So you see the bills got higher once you were getting older. But we didn't find any statistically significant difference in the cost of hospitalization among the races. So even though it wasn't statistically significant, you can tell that blacks still had a higher cost of hospitalization compared to whites. So our study, we concluded that definitely there were some disparities in the clinical outcomes and mortality, especially among black patients that were admitted to the hospital with Evali, and then that significant healthcare interventions are needed to reduce these disparities. And actually, to just prevent vaping, and it's great to use among teenagers and young adults, especially the black patients. Of course, we do admit that further prospective studies and also a larger sample size are needed to see if this can be reproduced, and also evaluate confounding factors and determine the cause of these disparities so that we can actually intervene. Thank you very much. Thank you. This is an impressively large crowd. I looked at my watch right before we stepped up, and it was about beach 30. So I do appreciate you guys sticking around for us this afternoon. We're going to present on quantifying smoking cessation interventions and smoking-related screening in the primary care setting. I'm Dustin Norton. I'm an assistant professor of pulmonary and critical care medicine at Wake Forest University School of Medicine, and I have nothing to disclose. Hi, I'm Lauren Witek. I'm a biostatistician in the informatics and analytics department of internal medicine at Atrium Health Wake Forest Baptist, and I also have nothing to disclose. And we are presenting, it takes two of us to fill the shoes here, of John Garris. John's one of our third-year residents at Wake Forest University School of Medicine. He's actually part of our clinical scholars and informatics pathway, and this is a two-year pathway where he undergoes mentorship in a two-year ongoing longitudinal project. And this is his project. Unfortunately, he was not able to come and present today. So the objectives that we're going to try to tackle here, we want to quantify smoking-related metrics within our medical system by utilizing the EHR. We want to discuss potential differences in frequency of smoking cessation counseling based on race or payer status. We want to highlight a framework for an evidence-based smoking cessation intervention utilizing a provider decision tool that's built into our EHR. Smoking is the leading cause of preventable disease and death in the United States, and I highlight preventable just to emphasize that we do have an opportunity to make an impact here. Provider input matters. When used separately or in combination, smoking cessation counseling and cessation medications are associated with increased cessation rates. And the United States Preventative Services Task Force has actually recommended for us an evidence-based framework known as the five A's to help providers consistently deliver smoking cessation counseling to patients. What are those A's? Well, they're here. So we ask patients about smoking cessation at every visit. We advise all tobacco users to quit. We assess the smoker's willingness to quit. We assist their effort with treatment and or referrals. And we arrange for follow-up. And while all of these are associated with an increased likelihood of quitting, it's the last two that have the most significant impact with assisting, having a 40 percent increased odds of quitting, and arranging follow-up to follow up their progress, having a 46 percent increased odds of quitting. But unfortunately, we know from data from the National Lung Screening Trial that it's these last two that are the least frequently accomplished. And in a busy visit as we make our way down this pathway, you can imagine that it gets harder and harder to keep going as we go on. Similar things are seen with smoking-related screening. So we know that low-dose lung CT scan for lung cancer screening has a 20 percent reduction in mortality. And AAA screening has an even bigger impact, with a 35 percent reduction in mortality. Unfortunately, in real-world practice, we see significant underutilization of both of these screening mechanisms, with only 14 percent of people that qualify getting referred for low-dose CT and less than 7 percent actually getting AAA screening. So that brings us to our study. So we wanted to retrospectively quantify smoking cessation interventions and smoking-related screening in current smokers within select primary care clinics within our health system. We want to analyze socio-demographic factors that may impact smoking cessation interventions, screenings, and billing rates. And we want to utilize this retrospective analysis to develop an EHR-based intervention to improve smoking cessation counseling and screening for current smokers that we could then study prospectively. So for our study design for the cessation billing and prescriptions, we included only those who are current smokers that are 18 years old or greater, and then they had to complete a clinical encounter within the past two years at the sites that we were using. And the population size was 22,267, and I just want to make note that that's encounters. It's not individual people. We had 22,000 encounters that we included. So we did analysis of race and pair status for both of those, and then we also looked at the lung cancer screening. This is by patients. We had 1,792 patients that were current smokers aged between 50 and 80 years old, had a 20-pack year history, and had at least one lung cancer screening ordered in a two-year period was our initial criteria. And then for AAA, we also looked at current smokers, males that were greater than or equal to 65 and had at least one order for screening placed in the two-year period. And again, the AAA screening, those are individual participants or people. So for our cessation counseling, our total encounters, again, were 22,000, and this is our Table 1 breakdown, so you can see our population. We had black or African Americans were our highest population, and then commercial, Medicaid, and Medicare were all generally about the same. Of those encounters, our distinct individuals were 7,211, and you can see the breakdown between race of the individual and the encounters were similar. The insurance status, there was a little bit of a flip between the Medicare and the no insurance listed, but they were still pretty similar when you looked at distinct versus the total encounters. For our smoking-related screening, here's our populations. So for our clinics that we looked at, there was a 0.8% actual billing done for the encounters, which is quite low since you can bill up to eight times per year per person. And then you can see that with the 4.5% for the prescriptions, we are offering prescriptions for smoking cessation, but we're still not billing at the same rate that we are offering prescriptions. For our lung cancer screening, this was people that actually did have the lung cancer screening done both years, 13.2%, and then the AAA done at any point was 7.7%, which was similar to what we saw earlier in the national study that was done. So this is the proportion of provider orders by race. You can see that our cessation billing, there was a statistical significance of who we ordered for. The 3.5% is we had four encounters out of 113 with individuals who identified as Asian that had a cessation billing. And so you can see that's where kind of the skew is for that one. The medication prescription was pretty similar across the board. And then the CT lung screen order in the AAA, there was no difference. This is, again, our proportion of provider orders by payer status. There was a significant difference between both the cessation billing and the medications prescribed between the payer status. You can also see that for the CT lung screen order. The AAA, there was no difference. Again, the AAA was pretty small compared to the other three, so it is harder to see differences among that. So as you can see, smoking cessation counseling and smoking-related screening in select primary care clinics in our health system was unfortunately quite low. Provider efforts are clearly not being appropriately captured through billing, as we showed between the discrepancy in the rate of medication prescription and how often we actually billed for the counseling for those prescriptions. Significant differences were found in the rate of smoking cessation intervention and smoking-related screening based on socio-demographic factors of race and insurance status. And that brings us to our future directions and how we hope to improve our clinical care in the future. So this is the next part of our study that hopefully we'll be able to present here next year, but we've developed a clinical decision tool built into our EHR. We used Epic, as you can see. And this is a way that can walk the physician, either in the room or after the encounter, through those evidence-based 5 As. So it takes them through asking the patient, assessing their willingness to quit, advising them to stop, assisting them, and helps with the evidence-based mechanisms in which to do that with direct links to prescription recommendations. Lung cancer screening, AAA screening, are built into this as well. And then, most importantly, arranging follow-ups, seeing them back and assessing how their progress is going. This then links to an order set that will build smoking cessation instructions into the after-visit summary. It will include the diagnosis of tobacco dependency in the medical records system. It will give smoking cessation medication options so that it's easier for the provider to then just click buttons rather than think through the process. The screenings are there, the option to refer. And then, most importantly, for the health system, not necessarily for the patient, is providing the opportunity to bill within all of this. It will directly import the bill, making it easier for the provider. And as we know, things that are easier are done more frequently. So hopefully we'll see impact in our delivery of smoking cessation counseling and aid over the next year. Thank you all very much. Thank you. I'm going to talk about the National Youth Tobacco Survey and the report of data of the years 2021 and 2022. My name is Jose de Jesus Mendez Castro. I'm a research assistant at Durrington Medical Associates at Houston, Texas, and I'm a medical student from Universidad Popular Autonoma del Estado de Puebla in Mexico. Me, neither the rest of the co-authors have nothing to disclose for this presentation. First, the objectives for this session is to describe the prevalence of cannabis and tobacco consumption among the youth all over across the U.S. and to determine the association between the categorical variables and with the consumption of these substances. I would like also to thank to all of the co-authors in this presentation and all their affiliations which are listed around here, and especially to Dr. Joseph Baron, which is the corresponding author for this presentation, and my mentor. Well, the National Youth Tobacco Survey, it's an initiative conducted by the CDC, which aims to collect data of tobacco consumption, and most recently, of cannabis among middle and high school students across the U.S. It was first performed in 1999, and since 2011, it's became into an annual format. This project was really important for me because ever since I was in middle school, I could see that many of my classmates were consuming tobacco or even cannabis, even outside of the school, and they could even reach to consume two packages of cigarettes a day. So that was, that's why it has always been concerning to me. And one day, when I was talking to some of my partners at the clinic, they showed up with this survey that I didn't know, and I have always wondered how many of these students are the ones that are consuming these substances. And when I read through the data of the survey, I went through all of this, I realized that no one has ever talked about cannabis consumption, despite it has been asked for a couple of years already. So that's why I thought it was really important to mention this. Also, it's important to mention that due to the COVID-19 pandemics, this survey transitioned into an online format survey. That's why it could not be compared with previous data years. So, well, here we have the participation rate for both of the years, which is not that encouraging for the percentages of school, looking at more than 500 schools across the U.S. were eligible for the survey. But in 2021, only 54.9% were the ones who were willing to participate. This percentage increased to 2022, which shows that 59.4 of the schools were willing to participate. But the students answer, participation rate was a little bit higher. That's why I consider that it's a little relevant. In 2021, more than 25,000 surveys were delivered, from which 81.2% were fully answered. And in 2022, more than 37,000 were delivered, from which 76.1 were answered. Okay, so for the analysis of this study, we included all the students which reported either tobacco or cannabis usage. And we used frequencies and percentages to summarize data and chi score test to determine the association between the categorical variables. Well, as part of the results, we could see that 20,413 students in 2021 and 28,291 students in 2022 from middle and high school students were the ones who participated into this study. And the gender distribution, it was equally distributed. First, here we have all the percentages in which we can see the percentage of consumption among all the students. In 2021, it's alarming to see that almost one out of four students across the U.S. consume any of these substances or both at the same time. These percentages, we can see here that they also increased to 2022, saying that almost three out of ten students were the ones who reported the usage. Okay, and if we divide all the students that were the ones who consumed these substances by gender, first we can see that girls and young ladies were the ones who were more likely to consume simultaneously cannabis and tobacco and exclusively cannabis. We can see here that 11.8 of the total of the female population were the ones consumed in 2021. And in 2022, it went up by 3%. And well, we could just see an increase of 1% for exclusive cannabis consumption. And according to boys and young men, we can see here that in this gender, it was more prevalent the exclusive tobacco consumption rather than in females. And we can see here also that tobacco was mostly consumed rather than simultaneous use and than exclusive cannabis use in 2021. But in 2022, we can see that simultaneous consumption was higher than exclusive tobacco. Okay, here we have a graphic that shows the distribution of consumption and how it has increased according to numbers. Here we can see how they have been increasing. And around when all the students turned 14, they start consuming more. So that means that mostly the students, when they reach high school, they start consuming more and more of these substances, reaching a peak at 17 years of age, and then going a little bit down. Okay, and what's alarming here, it's the percentage of the consumption of these substances. We can see here that, well, the distribution of the consumption, the most prevalent percentage of consumption are the nine-year-olds, which in 2021, it reached 50% of all the population. Despite being few, it's really alarming that literally the half of nine-year-olds are consuming these substances. And in 2022, this percentage went a little down to 42%. After that, we can see how the distribution starts going up, same as the quantity, also the percentages. But we can see here that in comparing 2021 to 2022, that 2022 starts going a lot up. But for cannabis, we can see here that it's equally distributed, as well as tobacco. Okay, so we also wanted to see which were the grades more likely to be involved to consumption of these substances. And we can see that the students which reported mostly Bs and mostly Fs were the ones who reported a higher incidence of simultaneous consumption and exclusive cannabis consumption. What's also concerning here is that the ones who had mostly Fs increased from 23.4% to 33.5%. So that's an increase in 10%. And for cannabis exclusive use, it increased to 4.5, sorry, to 9.1, so we doubled the percentage of the incidence. And according to exclusive cannabis, sorry, exclusive tobacco consumption, we can see that the grades reported were the ones with mostly As and mostly Ds. But the percentages didn't go up that much. Okay, in this analysis, we also wanted to include some social and behavioral factors. So I was looking that in the survey, they asked two questions from the patient health questionnaire 9, the screening for depression, and two questions out of the generalized anxiety disorder questionnaire in the DCM-5. So those were the options given, as same as in these questionnaires are. And here we can see the results of them in which the ones who reported depression symptoms nearly every day increased, well, a lot. In 2021, it was from 11.6, and it turned into 21.6 for simultaneous consumption. That's really alarming because just in one year, we could see that it doubled almost the consumption. Other than that, the rest of the percentages are not that alarming. But still here, it would be remarkable that more than 3 out of 10 were the ones who consumed these substances. And in 2022, it turned into more than 4 out of 10. Okay, and for anxiety symptoms nearly every day, we can see that the percentages in both of the years didn't increase that much, but still the numbers are alarming. You know, more than 500 students across the U.S. are consuming these substances simultaneously. And in 2022, 932 were the ones who reported it. So, well, to conclude this presentation, our study shows a significant prevalence of the consumption of these substances among many groups across all over the U.S. And it shows a possible correlation from the usage of these substances with many demographical factors and emotional instability. And why is this important for us? Because this, as we have seen in previous sessions and in previous presentations here, this is a preventable cause of many health issues and death in the future. So, we can see that as earlier, as they start, it can be also more difficult for them to quit the consumption of these substances. So, we as clinicians should address this topic, especially during the consult. That is our primary weapon against this topic. And we must discourage the usage of these substances as much as we can. I would like to finish the presentation with this phrase that I liked really much from Desiderius Erasmus, which says, prevention is better than cure. Thank you all.

prevalence

youth

tobacco consumption

cannabis consumption

National Youth Tobacco Survey

2021

2022