Hello, I'm Dr. Liana Leonoff, and welcome to this session on behavioral and mental health. The learning objectives for this two-part session, and this is the overarching learning objectives for both parts, are to describe the influence of health behaviors on major causes of morbidity and mortality in the United States, to compare some major theories on health behavior change, which is the crux of helping to combat these causes of morbidity and mortality, and then in the second part, we're going to look at the prevalence of primary mental health conditions, as well as substance use and substance use disorders. We'll take a look at screening and treatment, as well as ways to prevent consequences from these conditions. So diving in, the leading causes of death in the U.S. in U.S. adults, this is the most recent data from 2020, the mortality in the U.S. 2021 report shows that heart disease is still, and has been for several decades, the number one leading cause of death, followed by cancer, and then this was during the COVID pandemic, so COVID was the third leading cause of death, and you can see the remainder of that top 10 list. Due to the shift upward into the third place of COVID, we dropped off the list influenza and pneumonia, although that certainly can be related, also what was dropped off the list, at least in this 2020 data, is the intentional self-harm, interestingly, which would be the suicide, so that came off the top 10 list. But what's really important to see is that for almost all of these top 10 leading causes of death among U.S. adults, there's a strong influence by lifestyle patterns and behavioral factors, and that's why the behavior change piece is so important to dive into and learn about. We now have the Healthy People 2030. For a long time we were following Healthy People 2020, and I'll give a little update on that, and the leading health indicators for all ages in Healthy People 2030 often are related to behavioral factors as well, and you can see the list there including calories from added sugars, drug overdoses, and on down the list. Binge drinking is another one, certainly suicides, and vaccinations are all behaviorally impacted, and so as much as we can implement science-based health behavior change techniques in our public health programs, the more successful we'll be. Here's another way to look at that list. There's binge drinking, physical activity, colorectal screening, blood pressure, cigarette smoking, employment among working-age adults, maternal deaths, and new diagnoses of diabetes. You can see the behavioral pieces, and I promised you that I would do a little bit of an update on the 2020 Healthy People, and so this was 2019 data that showed that we're not making much progress in diabetes, obesity, or binge drinking. We're certainly worse in terms of mental health and mental disorders, particularly suicide, adolescent depression, but we are making improvements in heart disease, hypertension, physical activity, substance misuse, abuse, tobacco use, and alcohol use by adolescents, and we'll take a deeper dive on that in the second part of this talk. I've made the case that so much of our mortality and morbidity is based on behaviors, and so to be successful, we need to help our populations make health behavior changes. What are the fundamentals of health behavior change? Most of us at this point realize that knowledge is necessary but insufficient to nudge behavior change. We know that there are multiple factors that influence an individual's decision to make changes, and that what's really crucial is for an individual to be able to facilitate an autonomous change, a behavior change, and of course, an increase in awareness of one's personal health behavior patterns, reflecting on that, reflecting how one's life might be different or better by making changes in one's behaviors will lead to, hopefully, to that autonomous decision to make changes, and so this takes what we call a graded system of change. It's often not going to happen overnight, and the feedback, having some evaluation on one's progress and feeling a level of self-efficacy, feeling like one is actually making progress makes a big difference to reinforce that progress, and that if one is not making progress, that we have effective ways and strategies for preventing a relapse. In other words, sometimes we might have some lapses in our behaviors, but we don't want those lapses to make us revert to our original behaviors. We want to prevent a total relapse, and so keeping those kinds of strategies on hand, which include things like having accountability partners and sources of support can make a big difference. It takes multiple levels of influence to be successful at behavior change, particularly if we're looking at populations, and as public health leaders and officers, you'll be looking at making changes within populations, so in the next couple slides, look at all those levels, and first, of course, is that intrapersonal piece, so what an individual knows, the knowledge, their attitudes, their beliefs, and their personality is going to influence their tendency towards a behavior change. Then there's the interpersonal, the influences from our family, friends, and peers are very important. Then there's the institutional influence, what are the rules, regulations, policies that can influence one's behavior in the workplace, in schools, in the community, and more specifically, broader community and environmental factors are important to an individual's social networks or groups of networks, social norms that exist in community, and of course, now there are many online virtual communities can influence behavior change, and finally, public policies at the local, state, and federal level, and policies and laws that regulate action to help with disease prevention, detection, control, and management are important. It might be policies such as what types of preventive services are covered by insurance, for example, policies about taking time off in order to get a mammogram, for example, et cetera. Let's do a little bit of a deep dive on individual risk factor screening. These kinds of health risk appraisals are common, particularly in large employer-based settings, governmental settings. They were originally used in the US Armed Forces, and now continue to be used there as well as in employee wellness programs. These are confidential risk assessments. Many of you may have already taken one of these health risk appraisals. They're used to determine unhealthy and healthy lifestyle practices, and a way to increase awareness. It usually considers both genetic, which may be non-modifiable factors, as well as modifiable factors, and does have the goal of helping to identify areas of health improvement, and is also a way to have a starting point or a baseline against which to compare during future health risk appraisals. A health risk appraisal is often a survey that is individually self-reported, but some health risk appraisals can include physiologic testing, including weight, BMI, blood pressure, heart rate, cholesterol levels, et cetera. And this can easily be accomplished as part of or prior to a behavior change regimen Again, it's really important because then we have a comparative baseline against which we can measure future changes and progress. Now, many of you are probably familiar with the Behavioral Risk Factor Surveillance System. This is a population health risk appraisal. It was initiated in 1981 by the Centers for Disease Control and Prevention. The surveys look at a number of behavioral risk factors, as well as preventive health services. It's an annual random digital telephone survey of adults 18 years of age and older. Again, it's via telephone. And some of the topics that are covered are tobacco use, physical activity, dietary choices, height, weight, mammography, bloodstool testing, proctoscopy, and health insurance status. We can use this BRFSS data to determine priority health issues in populations, identify populations at risk, help develop strategic health plans, target prevention programs, and monitor effectiveness of interventions in populations over time. We can also, of course, really focus in on prevention goals. Here we are in preventive medicine, and the BRFSS helps to monitor progress. It also helps to educate policymakers at various community levels, at state levels, and it then can support community policies that will promote health and prevent disease. There's also a youth version, the Youth Risk Behavior Surveillance System, YRBSS, and that was initiated in 1990, also by the Centers for Disease Control and Prevention, and this is administered in schools. Surveys are done in students grades 9 through 12, looking at their health-related practices. It looks at smoking, dietary choices, and participation in physical education classes. Another population survey is the National Health and Nutrition Examination Survey, the NHANES. It started in the 70s by the National Center for Health Statistics. There was NHANES 1, 2, and 3, and eventually they reverted to doing this data collection annually and releasing the data every two years. However, this was suspended during the pandemic. It resumed in 21-22, and the next data will be released in 24. It's a stratified multistage probability sample of 5,000 U.S. adults. There are also some youth fitness surveys. There are household interviews that are conducted, as well as physical assessments. Those assessments are conducted in mobile examination centers in at least 15 counties. They look at height, weight, and other body measurements, and this data is old. It's the most recent that I was able to identify, and it shows that there are some youth that are at higher rates of obesity for age 20 and over at 39.8%, and then for age 2-19 at 18.5%. In the youth fitness survey, which goes all the way back to 2012, now a decade ago, I, again, was not able to find anything more recent. There was a rate of 24.8% in the ages 12-15 who were active at a moderate or intensive level for 60 minutes or more a day. Basically, only a quarter of our youth, our teenagers, are active, and so this, of course, is a crisis that needs to be addressed. So, we've looked at some data on how we can monitor this data for our populations. Let's take a look at some models of health behavior change, these health education theories that are often asked in the boards, and they create the basis for the kinds of interventions and programs that we develop in public health. First, there's the Health Belief Model. That's one of the best-known models. It was developed by Hochbaum in 1952, and it was a part of the efforts to increase tuberculosis screenings, as well as other preventive health services, and to increase tuberculosis screenings, as well as other preventive health services, and understanding how we get people to come and be screened. It assumes that people fear disease, addresses their readiness level and their non-adherence through this balance. It looks at this balance of the degree of fear versus the barriers to action, which then, depending on if the fear is great enough and the barriers are low enough, they're going to take action. And this Health Belief Model has six constructs that influence the capacity for behavior change. There's perceived susceptibility, how susceptible we feel to contracting a disease, the perceived severity, how serious we feel a particular disease is if it's untreated and undetected, the perceived benefit of the options for treatment. Is treatment really going to be effective, and what is our perception about that? Also, perceived barriers, as I noted, then that balances that out. If the barriers are perceived very high, particularly some negative consequences to taking health action, then we're less likely to take action. On the other hand, if we have very keen cues to action, those triggers that really promote readiness to change, we are more likely to take action. Self-efficacy is a common theme throughout many of these health behavior change and health education theories. That's our confidence level to be able to take the necessary action and our confidence that we're going to succeed with that action in order to make the changes that are needed. And this is a graphic that combines all those constructs of the Health Belief Model. Clearly, the individual perceptions are important. They're also modifying factors that are important. We talked about cues to action, and then ultimately, all of these leading to a likelihood of action. Ultimately, it's this balance of perceived benefits and perceived barriers. And how do we apply this Health Belief Model? Here's some examples of how it's applied to each of those six constructs. So for the perceived susceptibility, we might be defining the population that's at risk. We can personalize that risk and really show what that actual risk is and the level of susceptibility for the particular group that we are looking to influence. For perceived severity, it's to specify the consequences of the risks and what happens when a condition is undetected and untreated. For perceived benefits, we can then identify those benefits. What are the specific health actions that can be taken? And the science that shows that those kinds of behavior changes and treatments will lead to the benefits that are expected. And on perceived barriers is to identify those barriers and then help our individuals and populations problem-solve around those barriers and obstacles. For cues to action is to get down into the how-tos. So not just knowledge, but the step-by-step how to make that change. And also just having ongoing awareness in a community about particular conditions and actions that can be taken, like mammography campaigns, for example, that promote awareness or vaccination campaigns that promote awareness are cues to action, as well as any personal experiences. So if a family member gets a particular condition or disease, that can be a cue to taking action. And lastly, for self-efficacy and ways we can increase individuals and population self-efficacy is to provide training, provide guidance, help set achievable goals and working towards those goals, offering role modeling, offering reinforcement types of not only verbal, but also actual incentives that are combined with the praise and the ability to make those changes is all part of the package of self-efficacy. Another theory is the theory of reasoned action, and it includes behavioral intention. Behavioral intention is determined by both positive and negative attitudes about a particular behavior, and it's also influenced by normative beliefs. So what does the population, what are the others around us, believe? Do they approve or disapprove of a particular behavior? And then the theory of planned behavior also includes, adds to the theory of reasoned action, and includes a perceived behavioral control. So what do others believe? What do I believe? What do others believe? And then do I really feel that I have control over the situation? And a person's going to expend a lot more effort to perform a behavior if they perceive that they have a high degree of control over the situation or over the behavior. So examples of then these constructs in this chart here, again, are that there's behavioral intention, so it's to explain the likelihood of performing a behavior and its consequences so that we can boost intention and boost perceived likelihood of performing that behavior. It's attitudes and beliefs, the belief that the behavior really is associated with the outcomes that we want. Another construct is the subjective norm, that normative belief, the belief about what others, influential others, think about a particular behavior. We've got motivation, the intention for the desired behavior that often can be also motivated by influential others. Perceived behavioral control, belief is important, and in that would be resources and supports to overcoming obstacles to the behavior, as well as perceived power. Again, that influence that we have over our performance, this perception that we have control over our performance. The social learning or cognitive theory emphasizes two constructs, the reciprocal determinism and observational learning. Reciprocal determinism is this dynamic interaction between personal, environmental, and behavioral influences. So my behavior influences my environment, my environment influences my behavior. And observational learning, so people learn through their own interactions, what's happening in the environment. They also learn by observing others. And people can shape and change their environment to better suit their needs and become successful at behavior change. So the constructs here, again, for social learning cognitive theory are reciprocal determinism. Behavior change is a product of this bidirectional interaction with one's environment. Behavioral capability, the knowledge and skill to influence a behavior. Expectations, the beliefs about a result, what result is most likely as a result of the action. Self-efficacy, that confidence in the ability to undertake and persevere and succeed at a behavior change. Observational learning or modeling, a belief that one can do it based on observing others and how others who are similar to us can do it. And seeing the consequences, if we see positive consequences and see others like us doing them, we're more likely to make a change and reinforce reinforcement. So consequences of that behavior can increase or decrease the likelihood of behavior recurring. And hopefully we have more positive consequences to reinforce that behavior. So application examples here in the next couple of slides for social learning theory include for reciprocal determinism, it's involving another person, involving relevant others to help modify the environment and increase support. For example, for healthy eating, it might be involving those family members. And so that individual who is making that commitment to make a health behavior change and eating change is influencing the family environment. And the family environment, if they make the change, are also supporting that individual. Behavioral capability, so providing the necessary skills and instruction in order to be able to be successful with the behavior change. Helping to influence expectations and really giving the data behind the likely results. You know, what are the likely consequences of taking an action? For self-efficacy, it's emphasizing the individual's strengths, encouraging them, helping them with successful small steps. If we make a small step and that's successful, more likely to take the next and the next until we achieve the ultimate goal. For observational learning and modeling, it's discussing what others have experienced and identifying role models, role models that can be emulated. It can be quite powerful. And for reinforcement, there can be both extrinsic and intrinsic patterns of reinforcement. There's a whole literature around extrinsic versus intrinsic. Bottom line is intrinsic reinforcement is probably more powerful. And that is that one gets the pleasure, gets those positive emotions, feels proud on one's own for making the change. But certainly outside incentives, rewards and praise can be useful as well. Again, there's a whole field of literature around this that has been a little bit controversial because sometimes extrinsic types of reinforcements can backfire. But we don't have a lot of time to go into that in detail. But for the purpose of the exam here, social learning theory has these components, including reinforcement, which can be extrinsic and intrinsic. Then there's a trans-theoretical model, readiness for change model, which I'm sure many of you are very familiar with. It's commonly cited in health coaching. And the thing to remember there is that with each of these stages, there are time frames. These time frames were developed for research purposes, just so we all know what one is talking about when we talk about a stage. But in real life, in reality, these time frames may not be relevant. Any individual can be stuck in any of these stages for any length of time, or they can speed right through these stages, depending on all those other factors we've been talking about that influence behavior change. So the first stage is pre-contemplation. It means that an individual has no intention to make any changes in the next six months. There's contemplation, which means they are at least thinking about making some change within the next six months. There's preparation, which is more imminent, that they have an intent in the very near term, within a month, and are starting to make some initial preparation and efforts for that change. Then there's action, where they're starting to actually make the change, but it's just been, for a short period of time, less than six months. And this is when one is vulnerable to lapses and relapse, and there needs to be ongoing reinforcement and support. And then there's maintenance, where they've been successful for more than six months and hopefully have developed a good routine and less likely to lapse or relapse. But again, that relapse prevention plan comes in handy, just in case that occurs. So some examples of then applying the trans-theoretical model here for pre-contemplation, it's discussing the general health risks. If we get too detailed in pre-contemplation stage, that can backfire. But we can mention, hey, this behavior change is generally known to have these wonderful health benefits. In the contemplation stage, we are personalizing those health risks and looking at the pros and cons, weighing risks and benefits. We can personalize it by talking about an individual patient's family history, for example. In the preparation stage, there is now a commitment to make the change. So we can be most helpful by helping the individual develop a specific action plan with step-by-step action and rewards and modifying the environment, making sure the environment is supportive of that. And then action is when we're fully structured the plan, we're starting to implement the plan, and there's frequent contact, social support, problem solving of any obstacles and barriers, etc. And then in maintenance is that ongoing reinforcement and being ready with relapse prevention types of strategies. For example, making sure that individual knows who to call or what to do if they've had an early lapse. Where can they reach for support in order to get back on track quickly before it becomes a relapse? A few years ago, I also added the FOG behavior model. This is a digital behavior change model because we've had so many digital behavior change apps. And the components of this model are motivation, ability, and triggers. And motivation is the most difficult to influence ability. We can certainly show videos and other ways, how-tos, infographics about how to do a particular behavior. So that can be relatively easy, especially nowadays. And triggers are easiest to do. I mean, that's all the bells and whistles in the various apps that we have. In fact, we can have trigger fatigue. All of this is in the science of persuasive technology, where we're interacting with computers or phones to influence what we're doing. And digital technologies, again, mostly focus on those triggers that can help us with the abilities. But when it comes down to motivation, that's where these apps often may not succeed. And that's where, over time, we've learned that we have to combine digital technologies with in-person health coaching, or at least personalized virtual or text, et cetera, coaching to really get to where the patient may get stuck, where motivation falls off, or finding their why, finding what matters to boost their motivation. Now, shifting to, okay, we're going to be doing behavior changes. We know about the theories behind behavior change and how to apply them. We in primary care and preventive practices will be consulting with patients and counseling patients on behavior changes. So what does the U.S. Preventive Services Task Force say about these kinds of counseling? I think in other parts of the board review course, you've been exposed to U.S. Preventive Services Task Force. It's a panel of independent experts in primary care and prevention. They do systematic reviews of the evidence of the effectiveness of various interventions and treatments. And so they've done reviews on the effectiveness of physician or health team counseling and what is the evidence base behind recommending that patients get this kind of counseling. And they look at topics on an irregular basis and make recommendations for future research questions. So the behavioral counseling piece can be found at their website. And what we know is that they reinforce the behavior change theories that have the two common elements of self-efficacy and social support. And this, of course, assumes that there's an individual levels of autonomy in making their behavior changes. So if you read the fine print in U.S. Preventive Services Task Force, they're saying basically, okay, there's all these behavior change theories. Make sure whatever you do that you are working with individuals and populations that are autonomous and make sure you're supporting their self-efficacy and that they have social support. Now, the categories of the recommendations for U.S. PSTF are listed here. Again, I'm sure this is covered elsewhere. And A is the strong recommendation. Their benefits substantially outweigh the harms. B is that they recommend the benefits outweigh the harms. C is there's no recommendation because the benefits and harms are closely balanced, but there's good data to show that close balance. And then there's D, which is recommending against a particular intervention or routine use of a treatment or counseling because the data shows it's ineffective or may have harms and the harms outweigh the benefits. So a summary specific to behavior change counseling and screening is on this graphic in this chart. And for tobacco use counseling, the U.S. PSTF gives it an A. For healthful diet and physical activity, the U.S. PSTF gives it a B. So it does recommend it for specific populations, namely those who are overweight, obese, or have other cardiovascular disease risk factors. It actually gives it a C, a no recommendation for populations without obesity or other cardiovascular disease risk factors. For unhealthy alcohol use counseling, they give it a B, so it's recommended. And also for depression screening and counseling around that, they give it a B recommendation. Now, these recommendations are for adults with a BMI of 30 or greater. There's a definite benefit to doing an intensive counseling. In fact, usually we're more successful with intensive counseling, which takes the whole health care team. A busy single practitioner is not going to be very successful with just a one-time counseling. But with the whole team involved, we can have successes. And in counseling youth who have not started tobacco use, there's also a B recommendation. It probably still takes the whole health care team to help with those youth. And in counseling young adults, adolescents, and even parents of young children about the risk of sun exposure for persons 6 months to 24 years who have fair skin, that's also a B recommendation. But it's a C recommendation for adults older than 24. Now, the USPSTF also gives an I recommendation for some interventions. And that means that there's insufficient evidence to recommend for or against a particular intervention. Either there's just lack of the evidence in the clinical outcomes, or there's a poor quality of the studies, or there's good quality studies but a lot of conflicting results. And so this is really important to keep in mind, which are I recommendations. And because those may change over time as more research becomes available. So right now, the I recommendations are for counseling on prevention of skin cancer in asymptomatic adults. Also screening and brief counseling for alcohol in adolescents. Also for counseling school-aged children and adolescents about the use of tobacco. Again, it's a B recommendation if they've not yet started. But if they've already started, it's an I recommendation. And then also for screening for suicide risk has an I recommendation. Then when we come down to counseling and health coaching, in order to make those behavior changes, there are three buckets that are often helpful that health coaches use. Motivational interviewing is one of those. Positive psychology is an important element there, as well as cognitive behavioral techniques. And there are many, many courses that really help practitioners learn these skills and apply them into practice. The key constructs under motivational interviewing are that it's really a discussion with the individual so that the individual can have what's called a self-negotiation. They're negotiating with themselves. They're kind of thinking out loud, and we're helping them to think out loud in order to really weigh the pros and cons of making the change. And then hopefully, autonomously, they can move forward with the change. So it's a very person-centered process. This is when we take off the expert advice hat. We may be advising them to stop smoking. But then when we go to the coaching role, we take off that expert hat, and we put on that coach hat, where it's really about the individual, what they want, where they are in life, what's important to them, what are their perceived barriers, etc. But what we can do is reflect, during the motivational interviewing process, what we observe. We can reflect any discrepancies between what the person really wants in terms of their behavioral goals, their life outcomes, and their current behavior. And we can really help them do a realistic appraisal for the pros and cons of making a change. And then we can, again, target that ambivalence to commitment. And really put that out there, making it clear that, yeah, well, you really are ambivalent. And sometimes sitting with that ambivalence can help someone get unstuck and move forward. But the fundamental goal is to facilitate that autonomous process for change. The intrapersonal behavior change constructs here that have been in these series and that I've been alluding to, this is just a nice summary here, just to have them front and center, is an individual's expectations and beliefs, their self-efficacy, their confidence that they can make change, their locus of control, their feeling that they can have some control over themselves and their environment. Their influences, social, societal, community, normative influences on their decision-making are important. And any other factors that impact their motivation to change, oftentimes it might be personal experience, a recent death of a family member who had a similar condition, might wake someone up to make the change, might be a change in life situation in general where they have more or less time to pay attention to their health behaviors, etc. All that impacts one's motivation. And we have some evidence about peer support, self-help programs that can be very helpful. Now, of course, we have all kinds of online, virtual, app-based types of support groups. But there was the old-fashioned in-person groups back in the old days, although I think there are some in-person groups that still get together. And so these are behavior change working groups where there's this peer-to-peer modeling. We see our peers, we see individuals who are similar to us, who are having struggles, and then as they're having successes, this can inspire and drive us. And our peers have a lot more credibility with us than anyone else. And so this can be quite powerful. And that's what the Alcoholics Anonymous model is based on. So one question here to test out how you're doing with what I've been presenting so far is, which single construct is key to behavior change? Is it facilitating autonomous decision to change? Is it ensuring an environment supportive of change? Is it promoting self-efficacy, addressing barriers to change, or identifying underlying beliefs and expectations? I'll give you a minute to think about it. Of course, it's a little bit of a tricky question because all of these are important and we've been talking about all of these constructs. But if we had to choose a single most important element of behavior change, I've done my best to emphasize that, it's facilitating autonomous decision to change. If someone feels coerced, if someone is doing something because of a should, they will very likely relapse and that behavior change is not sustainable. Now let's move on to community organization models just briefly because sometimes these are touched on on the exam. This is for the purpose of changing the public's health. So we're looking at populations here, making big populations and communities. Community organization and how we can influence communities is an important construct. What we want to do is identify problems and mobilize resources and develop and implement strategies that will move a community to make behavioral changes. There's a construct called Rothman's Typology from 1987 and it frames three intertwined models together, brings them together when we're thinking about community organization. One is called locality development. It's a wide variety of community people that need to be involved in the planning and the change making. So we can't have just one part of the community lead change. We've got to have that full buy-in. Also second construct is social planning. So it has to be some rational, deliberately planned technical process that helps make that step-by-step process towards change in the community. And then finally, social action. Social action meaning that there is, of course now we talk about equity, so that the disadvantaged segment of the population gets organized behind the change as well and that they get the support that they need. So again, that community-wide involvement and community-wide step-by-step process towards change is what's going to make that change successful. That's what the Rothman's Typology is talking about. And here's just the same three constructs in a chart format. Locality development. It's very process-oriented. It's building that group identity that, yes, we're a community. We're all a part of this and we're going to work together to make changes. There's a social planning component, and again, step-by-step, task-oriented setting goals. And outside professionals can assist. They can provide the data, the science, the rationale, the empirical evidence, and then build that into a step-by-step strategic change plan. And then again, social action is making sure to include concrete changes that minimize disparities and are all-inclusive and address all aspects of the community and everyone in the community and give everyone an equal chance towards that behavior change and that making that change. So key concepts in community organization are participation and relevance. So the community has to make their own agenda, so an outside expert can't be coming in and saying this is how you're going to fix your problem. They need resources. They need help to overcome those obstacles. There needs to be empowerment that the community members assume and expand their power, take control and lead through example and through full involvement. Critical consciousness that all the key segments and representatives within the community and all aspects or all corners of the community are engaged in a dialogue that really helps them see the root causes of a problem and then how to take community action. And so this can be anything from environmental poisons in the community and how to take action around that. It can be programs to address chronic disease by addressing walking pathways and what happens in what's available in restaurants and grocery stores. So really this construct applies in many ways that we can help advance changes within populations. And the last two items here or concepts on community organization is selecting the issue. There might be a number of problems that a community is dealing with and they're going to have to independently decide, okay, which one are we going to tackle first or which do we think is important now? And then last community competence that the members of the community, it's that self-efficacy piece feel that they have the competence, they have, they fully agree that they want to make the change. They agree on the strategies and they agree that they have the capacity and resources to implement those strategies, to achieve their goals. And then also in changing the public's health, we move from the community to organizations and we can be thinking about workplace organizations, for example, and other systems or large healthcare institutions that might need to be making changes within their purview. And what this requires is, of course, organizations are complex. We're talking about large organizations. There may be layered systems of leadership and influence and different roles of members and even cultural differences within organization. So all that needs to be taken into account. And so in order to be successful with change and within an organization, all that complexity needs to be taken into account and there may need to be interventions at several levels within the hierarchy in order to be successful. And there are two organizational change theories that can be asked on the exam and that have been talked about. One is the stage theory and the other is organizational development theory. The stage theory has these constructs. There's problem definition, it's an awareness stage, identifying what is the problem and what are potential solutions and that might involve multiple personnel. Then there's initiation of action or adoption stage. So a policy is formulated and resources are allocated to make the change. Then there's implementation. We actually make the change and we observe what happens, we observe reactions. We observe what changes occur. And then there's institutionalization of the change. So once we've tested this policy, implemented it, and we've seen some good results, then this policy can become entrenched or a part of the organization and institutionalized. In organizational development theory then, there's this recognition that there are these processes that affect worker behavior and motivation. And there's a focus on the problems that can impede an organization's functioning and involving individuals within the organizations at several levels to help with functioning and really being open to what that effective organizational functioning and organizational change will look like. It's not that, again, outside experts or public health experts come in and they have a ready-made specific change program and say, here's the answer. Certainly the many recommendations and options can be presented, but ultimately it's that autonomous decision within that organization and empowerment of the key levels and key individuals within an organization in order for this to be successful. Consultants can come into these workplace organizations and offer these options, walk through the process with the key individuals, and look for ways to make changes that then can improve productivity, improve health practices, and quality of the professional life and a well-being culture within those workplaces. Now, also in making change from a public health perspective, there's the construct of diffusion of innovation. And I love this. This is very helpful. I've used it personally in a number of programs where we think about, OK, here, we want to implement this program, but what questions do we ask about whether we should move forward and how we should move forward? And there are actually 12 constructs, which I'll get to, but here's a short definition of diffusion of innovation. This is Everett C. Rogers, 1983, who is the founder of this construct. It's the process by which innovation is communicated through certain channels over time among the members of a social system. And so here are the, in the next three slides, has the 12 constructs or the dictionary of constructs or the determinants of the speed and the extent to which an innovation is going to be taken up by or diffused within a population, within an organization. You know, what are the determinants that kind of basically make a new change really take off or not? First is relative advantage. So these are questions to be asking. Is the innovation better than what's already in place? And if it's better, we'll consider it. Compatibility, does this innovation fit with the intended audience? Does it really fit with who we are based on, it might be personalities, cultures, the way we work, the way we interact. Complexity, is the innovation easy to use? If it's complex, forget it. We probably need to go back to the drawing board and find a way to develop an innovation that's easy to use. Trialability, can we try this out and see how it works and then go back to the old ways of doing things if we decide it doesn't work very well. There's observability, are there other places that have done this kind of intervention and can we observe them? Or if we try it in our organization, in our population, is it easy to measure and observe that we're making progress and that we can measure change? Impact on social relations, what will this kind of change do to our social environment? Is there going to be an impact? Is there going to be a disruption in the way we all interact and how we're used to relating to each other in, again, a workplace or a community or population? Reversibility, what if we try it, we go past the trial period, we do it longer, and then we decide we just want to go back? Is it reversible? Can it be discontinued easily? Communicability, in other words, okay, once we decide and we're all going to be on board with an innovation, can this innovation be easily taught and easily understood by the users and communicated to others widely? And then the last four are time required. How much time does it take to implement an innovation? The risk and uncertainty level, can this be adopted with minimal risks and minimal uncertainties about what's going to happen next? How much commitment is required? Can innovation be used effectively with only a modest commitment by a person or organization? It's easier than if you need 100% commitment. And modifiability, can we take this innovation and adapt it to our particular culture, our group? Every group is a little bit different and maybe we like it, but we want to make it more our own over time. So these are very helpful, these 12 determinants of diffusion of innovation to keep in mind and questions to ask as you're coming in looking to promote public health interventions. And many of you may have seen this rate of adoption and diffusion of innovation from earliest to latest. Rogers came up with this in 1995. And when we promote an innovation and look to expand it, disseminate it, the innovators are those early risk takers and adventurers who are going to become on board right away. And that's about 2.5% in general of the group. The early adopters, which is about 13.5% are the opinion leaders that then come on board and say, yeah, this is a good idea or not a good idea. And then there's the early majority, which is 34%. And they adopted after some deliberation and say, okay, our opinion leaders, we have the evidence, we've gone through the questions, they were answered, those elements of diffusion of innovation, and this is a good idea. And then there's the late majority, 34%, who were skeptical early on, so it takes them a little more time to adopt, and they succumb a little bit to peer pressure. And then there are the late adopters, 16%, who are suspicious of new ideas and probably won't come on board no matter what. Now, as we close this whole section on behavior change, and we've been talking about various theories, it's good to also know about the PRECEDE-PROCEED model. And this is a framework of behavior change that pulls together the relationship of a number of theories and provides a structure for multiple theories to help you see the big picture. And the acronym stands for, I spelled it out there, PRECEDE is predisposing, reinforcing, enabling constructs in educational diagnosis and evaluation. So what are factors and ways we can diagnose a particular situation? And I'll show this in a graphic in a minute. And then PROCEED is policy, regulatory, and organizational constructs in educational and environmental development. And that's when we go into the implementation and evaluation stage and how we evaluate these interventions and their successes. So it's a lot easier to see it in a graphic in a moment. But basically, this is a model, a roadmap for various theories that can be pulled together. And it is not a single theory, but really helps you see the big picture and helps you identify which avenue to pursue and which theories upon which you can base your actions. And here it is, as promised, is the graphic, the PRECEDE-PROCEED model. And you can see on the top there are the diagnostics, that PRECEDE aspect, where we're looking at epidemiologic diagnoses, social diagnoses, behavioral diagnoses, looking at what's happening in the behaviorally and in every other way in the group that we're looking to change from a public health perspective. And then as we then start to look at identifying interventions, on the bottom, you see then there's implementation of those interventions. And then there's evaluation and testing about what's worked and what hasn't worked. So there's process evaluation, impact evaluation, and outcome evaluation. So in summary, the behavior change theories, what are some of the key constructs? Behavior is the key factor in preventing disease. We're prevention-oriented, so this is very important. We know knowledge, attitudes, beliefs, fears, and motivation are all involved, and so much more involved in decisions to make changes, that behavior change theories can help us tailor some of our interventions and be more strategic about how we approach particular individuals, organizations, and populations to move them in the direction of healthier, preventive change. That oftentimes takes multiple modalities. The change is difficult and complex, and in order to really be successful, it's not going to be a single intervention that's going to help us work. And not only multiple modalities are needed, but also that these interventions are directed at multiple levels in the society and population to really be effective. So that ends part one of this talk on behavioral and mental health, and I'll leave you here for a moment, and then we'll start up with the second segment on mental illness and substance use and substance use disorders. Thank you for your attention. Welcome back to part two of this session on behavioral mental health. In this second part, we'll cover mental illness, substance use, and substance use disorder. So we'll start with mental illness in the U.S., epidemiology, treatment, and prevention. And an important resource that I highly recommend you take a little bit of time and take a look at. It's a huge resource. There's a lot there. And throughout my presentation, I've tried to pick out some highlights that I think may be important for you to know and important on the exam. But there is so much to cover that I can't do it justice. And so I do recommend you take a look at this resource. It's called Key Substance Use and Mental Health Indicators in the United States. Results from the 2021 National Survey on Drug Use and Health. And we'll start off with the 12-month prevalence, the one-year prevalence of mental illness in adults that are meeting DSM-IV criteria. And the report shows that 22.8% of adults met criteria for any mental illness, including serious mental illness. And the age group that has the highest rates and is most affected is the 18 to 25-year-olds at 33.7%. In terms of ethnicity, those who identified with two or more races had the highest rates of mental illness at 34.9%. The single race that was with the highest rate was the American Indian Alaska Natives at 26.6%. And the lowest rate was among Asians at 16.4%. Now for serious mental illness, that's 5.5% of adults met those criteria. And again, the age group that was more impacted, highly impacted is the 18 to 25-year-olds at 11.4%. I didn't find data on gender in this report. The older report in 2016, and this has been consistent, shows that women are more affected than men at 21.7%. Also, another interesting thing to keep in mind is that overall these rates were stable during the decade from 2008 to 2018. There was a low in data collection during the COVID pandemic, and they started collecting again with the 2021 report. And not surprisingly, the rates significantly increased in that most recent report. Now, among adolescents, the report didn't provide the 12-month prevalence this time around. So a report from 2016 showed that those ages 13 to 18, 49.5% of them met criteria for any mental disorder with 22.2% having severe impairment. Again, according to DSM-IV criteria. So it's really good to keep in mind that mental illness co-occurs with substance use disorder. So where we find one, we need to look for the other. And so here are some stats around that for the 12-month prevalence. And the age group that's highly affected is the 18 to 25-year-olds. Nearly half of them have either any mental illness or a substance use disorder at 45.8%. And all adults who had either or, it was about a third, 32.5%. And you can see the rest of that breakdown. For youth ages 12 to 17, they showed data specifically related to major depressive disorder with 27.7% having a co-occurring illicit drug use with major depressive disorder. And you can see that for those without major depressive disorder, there was only a much lower rate of illicit drug use at 10.7%. Now, the questions that are oftentimes on one's minds and that can appear in the board examination are not routinely released in this national survey on drug use and health. And so there were some other surveys that have been collected around lifetime prevalence, age of onset, the breakdown of the different kinds of mental illnesses. So some of this data that I'm gonna present is a little older. These data sets are available to researchers to dig in and do their own analyses, but I'm not a researcher. So I don't do those kinds of analyses. So I'm using the most recently published information that we have from these surveys. And so one of those that provided some really good information is the 2007 National Comorbidity Survey Replication. And it shows that when we look at lifetime prevalence compared to the 12-month prevalence of mental illness among adults for any disorder, that's 57.4%. So more than half of Americans will meet criteria for a DSM-IV mental illness sometime during their lifetime. We don't have data on DSM-V criteria. And then when we look at the breakdown, you see there what is at the top of the list is anxiety disorders at 31.2%. And for adolescents, this was from a different survey from the National Institutes of Mental Health, ages 13 to 18, 46.3% of adolescents had a lifetime prevalence of mental illness in 2009, and 21.4% had met criteria for severe mental disorder. Now, they also in that report provided 12-month prevalence rates, but they also had the breakdown. So this is good to take a look at. So their data showed that for adults, 32.4% met criteria for any disorder. And then what was the highest mental disorder, highest rate, was again, anxiety disorders at 19.1%. So, and this was a 12-month prevalence from that survey in 2007 that showed that about a third of the U.S. population met criteria for DSM-IV. And for major depression specifically, women outpaced men at two to one. For adolescents, ages eight to 15, what tops the list is attention deficit disorder, attention deficit hyperactivity disorder at 8.6%. And you can see the remainder of the breakdown there. And what about age of onset? The important thing to remember here is that about half of all lifetime cases of mental illness start by the age of 14, and 75% of all cases start by the age of 24. So these make themselves known in our youth. And you can see the breakdown there of the median age of onset for the various specific disorders with anxiety and impulse control disorders tending to start younger at a median age of 11. And I also provide the ranges there. Substance misuse at 20 years and mood disorders are later at 30 years of age. Whenever we're looking at mental illness, we need to be also thinking about comorbid conditions. It's common to have more than one condition. It's common to have more than one anxiety disorder diagnosed with comorbidities of anxiety with depression, substance misuse, eating disorders, et cetera. And when we have these comorbid conditions, they then tend to have a more chronic course, poorer treatment outcomes, and well, basically a much longer duration with much more difficulty and complexity in treating these cases. Now, I'm focusing mostly in the US, but I'm gonna dedicate a couple of slides to talk about global prevalence, and important to keep in mind. And this was a meta-analysis, 1980 to 2013, that looked at data from 173 surveys across 63 countries and 29.2% of those populations combined met criteria over a lifetime for mental illness. So nearly a third. That of course brings about the global burden of disease from mental illness is very high globally. And we can measure that through DALYs, Disability Adjusted Life Years, and by using these DALYs, we can make comparisons of levels of disability. And through that analysis, what has been found is that major depression is comparable in its impact on disability to blindness and paraplegia, and schizophrenia with psychosis is comparable to quadriplegia. So definitely mental illness causes major disability. And the more recent study of global burden of disease that included 204 countries shows that there's a high level of disability and DALYs as they looked at data from 1990 to 2019. And they were also able to look at trends then over those decades. And they found that disability due to mental disorders had increased from 3.1% to 4.9%. And mental illness is in the top 10 leading causes of burden and disability worldwide. What's at the top of the list is depression with anxiety next highest, higher in women as I've noted. And I've also listed the countries where there are the highest rates and you can see the Western more developed countries having higher rates and there are lower rates in Southeast Asia, parts of Africa, et cetera. Now let's turn to suicide rates. This is data from the Centers for Disease Control and Prevention. In the timeframe from 2000 to 2018, suicides increased by 37%. They did drop during the pandemic timeframe by 5% between 2018 and 2020, and then increased again to nearly the peak in 2021. And that rate is 14.1 per 100,000. We actually had the largest one-year increase in suicides between 2020 and 2021 at 4%. And the highest race ethnic group that has been impacted are non-Hispanic American Indian and Alaska natives at 28.1 per 100,000. Highest among males at 22.8 compared to females at 5.7 per 100,000. And the reason I look at that is males comprise about half the population, but yet they represent about 80% of the suicides. The highest age group for suicides is the oldest old, 85 and older at 22.4 per 100,000. Firearms are the most common method of use and I'll show a slide in a little bit that makes comparisons with males and females. What about youth? The suicide rates among those age 10 to 24 increased by 52.2% between 2000 and 2021. It's the second leading cause of death for this age group. And even though it's lower than other suicide rates in other groups, it's still a prominent cause of death in this age group. And non-Hispanic American Indian and Alaska natives are impacted the greatest yet again at 36.3 per 100,000. In terms of suicide attempts, the survey showed that high school students report, 9% of them report suicide attempts. Highest at 2.7% among 18 to 25 year olds. This is in the NSDUH report, the National Survey on Drug Use and Health. Higher among girls, higher among non-Hispanic American Indians and Alaska natives. Also higher among lesbians, gays and bisexuals. Physician suicides are, as you're I'm sure well aware, are very common. Physician suicides are nearly double that of the general population. And they particularly are common among female physicians. So male physicians have about 40% of suicide rates compared to the general population. And female physicians have 130% the rate of suicide compared to the general population. And when we look at suicide rates, we can also assume that these rates are underestimates because there are a number of other death reports that may actually be suicides. So injury deaths, poisoning deaths that are not reported as suicides may actually be suicides. Motor vehicle accidents, firearm deaths, drug overdoses, et cetera. So keeping that in mind, suicide clearly is a huge issue. And as I promised, here's a nice chart from 2014 that compares methods of suicide that really highlights that males tend to use firearms and females tend to use poisoning. Now, as physicians and managing medical populations, we need to keep in mind mental illness and particularly depressive disorder in medical populations. The primary care populations have rates of about 5% to 10% according to this older study, but we don't have too many frequent studies like this. So very fortunate to have this to really bring out that then specific subgroups of our patients in medical care have higher rates. So coronary heart disease rates are 15 to 23% according to this study. And in diabetes, the rates of depression are 12 to 18%. And the other thing that we also need to keep in mind is that in medical settings, we have a difficult time identifying depression and anxiety, but particularly major depressive disorder. So in this study, they showed that it could be up to half of major depressive disorder was identified accurately. So that means half was inaccurately identified. We tend to use measures such as the PHQ-9, the Physician Health Questionnaire-9 in the Beck Depression Inventory. But these are very difficult to tease out what symptoms are attributed to depression versus what symptoms are attributed to the underlying medical condition in these medical populations. So there's this high symptom concordance between depression and chronic pain and medical illness. And so there can be some high false positive rates. They can be two to three times the actual compared to community samples and three to four times the actual compared to chronic pain, in chronic pain and depression and diabetes populations. Now, why is that? Well, the sensitivity is pretty high on these screeners that we use in medical care settings and in primary care settings, but the sensitivity is high, but the specificity is quite low. And again, we have this difficult time teasing out those symptoms. Even so, and in a busy practice, doing this two-question screener can be helpful at least to identify and do that early screening. And then we have to do a good job of further delving into symptoms and teasing out and making the diagnosis. So the two-question screener that was actually recommended by the US Preventive Services Task Force is during the past month, have you been bothered by feeling down, depressed or hopeless? And during the past month, have you been bothered by little interest or pleasure in doing things? Now, why is it so important to identify depression, mental illness, and anxiety in our medical care settings? Well, besides the fact that this is a major cause of disability, poor quality of life, risk for suicide, et cetera, it also complicates medical treatment and medical treatment outcomes. So social adjustment, rehabilitation, adherence to treatment is decreased among medical populations that also have depression. And relapse, higher medication use, higher use of the healthcare system, lower ability to get back to work, greater treatment dropout rates, and less adherence to healthy behaviors are all tied to major depression. So those depressed are more likely to have sedentary behaviors, smoking, and poor eating habits. And the same is true for anxiety disorders. Again, those who have anxiety disorders tend to have lower treatment adherence and have worse health outcomes from their medical problems because of this interaction with their anxiety disorder. They tend to also be accessing the healthcare system more with greater health complaints, greater medication use, greater utilization of the system, and more pain and disability complaints. Now, specifically to major depressive disorder and the most recent report, what are the rates and are we able to offer treatment to these populations? In the youth ages 12 to 17, 20.1% had a major depressive disorder with or without severe impairment, 14.7% with severe impairment, and in adults, it's 8.3% had major depressive disorder with 5.7% having major depressive disorder with severe impairment. And again, there you go. The age group 18 to 25 has the highest rate at 18.6%. Then what about treatment? This is what's so disconcerting. So among ages 12 to 17, 40.6% of major depressive episode with or without severe impairment got treatment. But then those with severe impairment didn't get any significant increase in being identified and treated. So only 44.2% of those with major depressive episode with severe impairment received treatment. So not much greater than those who didn't have severe impairment. And the same is true among adults, 61% with or without severe impairment got treated and only 64.8% with severe impairment got treated. Something to pay attention to for sure. And this is back to that old data set, the National Comorbidity Survey replication that shows some really interesting data that I like to keep in here. Just to remind everybody that in general, these kinds of patients are seen in medical settings. So where did people who got treated, where did they get their treatment? They got their treatment through their general medical provider first and foremost in this survey at 22.8%. And then you can see the breakdown. They also were able to see non-psychiatrists at 16%, psychiatrists at 12.3%, and other services. The other interesting thing is that the median number of treatment visits, if they're being treated by a mental health specialist, is much higher than if they're being treated by a general medical provider at 7.4 versus general medical provider at 1.7. So they're probably not getting as effective treatment. If they're only coming in once or twice, they're probably just getting a prescription. For example, if they have a depressive disorder, maybe they're getting a prescription for a serotonin reuptake inhibitor, and off they go. Whereas in a mental health setting, they're probably getting much closer follow-up. Maybe they're getting some additional psychological treatment in addition to drug treatment. Of course, now with COVID and major changes in the ability to do virtual visits, telehealth is increasingly being used, and that is making a big difference to increase treatment patterns. And hopefully, the next study that comes out on these treatment rates overall will be better because of the accessibility through telehealth. And in youth, though it is a little bit different picture, youth tend to get their treatment services through mental health specialty settings at the top of the list, and that's 18.3%. And then in educational settings, and only about 3.8% are getting their treatment through general medical settings. Also, you can see that child welfare, juvenile settings also offer some of these services. Now, why is there so much undertreatment of mental illness? And of course, there's stigma, there's attitudes that the symptoms will go away, lack of information about where to get treatment, and of course, cost and coverage, and lack of parity in mental health coverage compared to other medical care coverage. And in the 2017 National Survey on Drug Use and Health, they broke this down into much more detail, and I'll let you take a look at this on your own. But the thing to remember is the top of the list is they basically said they couldn't afford this kind of mental health care and treatment. And when we're talking about mental illness treatment, we need to revert back to thinking about efficacy and effectiveness. And I think you've been exposed to this elsewhere in this review course. Efficacy is what we are able to identify as benefits from a treatment that are observed in clinical trials. And if the treatment is superior or statistically superior to the control, then we can say that a particular drug is effective. And unfortunately, though, that's not representative of the real world. So effectiveness is the benefits that we see in the real world. And so we often have a gap between efficacy, what are in the research studies, and what happens in real life. And why is that? Why is this efficacy-effectiveness gap? It's for multiple reasons. One thing is that in the studies, they're able to pick and choose their subjects. They often exclude subjects who have comorbid conditions, so it's a more homogenous population. And they can have more strict protocols with highly trained personnel offering these treatment protocols. There's a lot more frequent and structured monitoring of the subjects in the study. And oftentimes, these are funded studies, so the care is being offered free of charge. So it's a much more ideal setting in those circumstances compared to real life. And so we have that as an issue affecting our treatment effectiveness, as well as that just the treatments themselves, both drug treatments and psychological treatments, are not as effective as we would like them to be. Certainly, they're more effective than placebo. And combining psychological and drug treatments makes them even more effective. But still, overall, the treatments that we have available to us and that we can offer to patients, they help, but they're not as effective as we would love to see them be. And some data show that the drug treatments are about 15% to 20% superior to placebo. Sometimes what complicates things is there might be inadequate dosing or inadequate duration of the treatment. That's common. For a while there, we were thinking, well, perhaps these kinds of medications, psychotropic medications, are less effective than other kinds of medications in healthcare that we use. But this study from 2012 was basically saying they're probably not less efficacious. Oftentimes, the treatments that we offer in medicine are not as effective as we'd like them to be. So the overall response rate is about 50% to 60% from pharmacological treatments for mental illness. And then psychological therapies are comparable. And again, as I mentioned, combining them increases efficacy. So cognitive therapy in meta-analysis has efficacy about 45% to 47%. And behavioral modification, behavioral therapies have efficacy of about 56% to 58%. Now, here we are in preventive medicine. What about prevention of mental illness? Well, there's not a lot written about this. So that's why I'm citing this Institute of Medicine report from 1994, which shows and talked about the risk factors that increase susceptibility to mental illness. Some of them are fairly static, such as gender, family history, background of chronic illness. Some are more dynamic and perhaps more changeable, like education, social support, substance use and misuse, unemployment, which relates to low socioeconomic status, violence, and trauma. We may be able to impact those factors in order to help prevent mental illness. And so then the protective factors are to help our populations get the education they need, get the employment they need so that they can have a higher socioeconomic status, and get them the social support, help them with the coping repertoire, particularly if they are living in circumstances where there's a potential for trauma, and help them really access care as needed. And another thing to remember is that we still have few insights about the illogic factors of mental illness. We're making progress over the years, we certainly have some insights. We know there's a combination of genetics and other environmental exposures, perhaps even infectious exposure, stress, but it's pretty complicated. That biopsychosocial model of mental illness is pretty complicated. So then from a prevention public health perspective, what do we do? It's a challenge. I'm very passionate about positive psychology and resilience and well-being. This is the area that I work in. And there is new research, more increasing research about the preventive types of interventions in mindfulness practices, in resilience building practices that can actually have changes in the brain and also show promise in epigenetics so that future generations do better and have lower mental illness despite exposure to trauma. We live in a society where social media is widely used, and so the question of the impact of social media and mental illness, social media, particularly depression and anxiety, has come up. We certainly need more studies. I'm citing here, this is just a cross-sectional study, so not the very best type of study, but it does suggest that there is a higher odds ratio if you're in the highest quartile of social media use compared to the lowest quartile of social media use at 2.74, and this was in adults ages 19 to 32. Again, we have limited studies, and the studies are mixed. Some show that, yes, absolutely, social media is impacting levels of depression. Others say not so much. And I think if you tease it out a little bit, what's coming to the fore is the type of social media use. So if you're using social media in a way where you're hypervigilant of your likes, hypervigilant of what your friends and others are doing and have this fear of missing out or comparison, sort of an upward comparison, that's going to affect your mental health. And so we need to be educating our populations that perhaps social media may be useful for making some connections and staying connected, but then really making a point of having authentic, positive, in-person connections offline. That will make a much bigger impact on preventing mental illness and boosting positive emotions and boosting resilience. So here's a question for you to consider. Which is the greatest barrier to improving outcomes of mental illness? Is it inadequate dosing and duration of medications, lack of studies and guidelines about long-term treatment, lack of studies and guidelines about effective treatment for specific populations, lack of insights about etiologic factors, lack of highly effective treatment interventions? So I'll give you a moment. So it's a bit of a tricky question because all of these are correct. All of these are barriers to improving outcomes for mental illness treatment. But which is the greatest? I tried to make a point that the treatments that we have available to us, they're effective but not highly effective. So it's E, lack of highly effective treatment interventions. At about 50 to 50, effectiveness is not as good as we would like it to be. But it's what we have, and hopefully research, particularly as I was mentioning, into prevention, building resilience, building supports and building care mechanisms that can react fairly quickly, that people can access, and that when treatment isn't effective, they can either increase duration, change dosing, change medications, offer some different kind of psychological treatments, and be very proactive in treating these patients will make our outcomes for mental illness much better. So now let's switch gears to substance use in the U.S. epidemiology and public programs. And what this is the National Survey on Drug Use and Health. And I put in here, now that we're in this segment on drug use, what this survey is about. It's an annual survey that's released every couple of years because it takes some while to process the data. It's usually about 70,000 individuals that are interviewed, they're ages 12 and older, they used to all be in person, and now they've done some multi-mode interviews, what's called multi-mode, which includes web and virtual visits. They query about illicit drugs, alcohol and tobacco, they query about people's diagnoses, as well as access to treatment, and there's some information on mental health, which we mentioned in the mental illness section. Key to remember is that they took a break because of the COVID pandemic, and when they restarted, they did these shift to the multi-mode interviews. And so they are officially saying that estimates based on this multi-mode data collection is not really comparable to prior years. What are they coming up with for substance use in the past month? Well, 57.8% of those 12 and older reported use of tobacco, alcohol, or an illicit drug. 47% drank alcohol, and this includes even anything more than just a sip. 19.5% used tobacco products, 14.3% used an illicit drink, 14.3% used an illicit drug, and these estimates are not mutually exclusive. Now, specific to substance use disorder, what percentage of those age 12 and older met criteria for substance use disorder? That was 16.5%, and that's meeting at least two or more of the 11 criteria, except for there are substance use disorders with hallucinogens and inhalants that it's two or more of 10 criteria because the withdrawal criteria is not relevant to those substance use disorders. The race ethnic group that's highly impacted at 27.6% is the American Indian Alaska Natives. The age group that's highly impacted is the 18 to 25-year-olds at 25.6%. And then in this age group, 12 and older, what is the most common substance use disorder? And you can see that's alcohol use disorder at 63.7%. And then I show you the rest of the breakdown there. What about tobacco use? Well, in the past month, those 12 and older, 22% said that they'd use tobacco, and that includes smokeless tobacco, cigars, pipes, or nicotine vaping. Of these users, 11.9% used the smokeless tobacco. And in youth 12 to 17, it's 6.7% tobacco use. And this is older data there on that last bullet point, but important to highlight that current smokers in youth ages 12 to 17 are also then more likely to use illicit drugs at 53.9% compared to 6.1%. So huge difference. So once these kids are smoking, they're more amenable to trying other drugs. Now, what is the illicit drug use rate in the past year? That's 21.9% in adults. And for youth 12 to 17, it's 14.1%. And what are those drugs? Well, here's the breakdown. What's at the top of the list? And these percentages, remember, it's of those who then use drugs, what percent are using these various drugs. So of those who say they use illicit drugs in the past year, marijuana was at the top at 85.8%. And you can see the rest of the breakdown. Then alcohol use and alcohol use disorder, 47.5% of the respondents 12 and older were current drinkers. So that's any use over the past month. And 21.5% of the current users were binge drinkers. So that's where you're having five or more drinks at least once in the past month for men and four or more drinks at least once in the past month for women. Highest again in that 18 to 25-year-old group at 29.2%. And then 5.8% of the current users were heavy drinkers. And that's defined as five or more binge episodes in the past month. Underage drinking, that's ages 12 to 20 at 15.1%. And then what is the rate of alcohol use disorder? We've been using talking about alcohol use. Now what about meeting the criteria for a disorder for 12 and older? That's 10.6%. Opioid is a huge crisis in this country. So let's take a look at that opioid misuse and use disorder for the past year. Those 12 and older misused opioids at a rate of 3.3%. 3.3% said that they had misused opioids. 1.9% among the youth at ages 12 to 17. And then what is the rate of the opioid use disorder? That's 2%. So 2% of individuals 12 and older qualify for a disorder. And the highest racial group here is American Indian Alaska Natives again at 4.4%. This high misuse of opioids and opioid use disorder has led to high rates of deaths, so drug and opioid overdose deaths. What's happening? This is according to the Centers for Disease Control and Prevention. The drug overdose death rates increased by 31% from 2019 at a rate of 21.6 per 100,000 to in 2020 to 28.3% per 100,000. So basically it was a 31% increase between 2019 and 2020. And at that rate was in 2020 28.3% I'm sorry, 28.3 per 100,000, not percent, meaning it quintupled since 1999. And 75% of the drug overdose deaths in 2020 involved opioids. And a high percentage, you can see, was actually synthetic opioids. So overall, actually since 1999, a thing to remember is opioid overdose deaths, including fentanyl, increased eight fold, a huge increase, quite a crisis. So then how do we address this opioid crisis? Well, we need more prescriber, patient, and family education on the risks, improved access to alternative chronic pain treatments, easy disposal of the unused medications, monitoring of opioid prescribing and overdoses, distribution of naloxone as a rescue medication, access to opioid agonist and antagonist medications to support recovery from opioid use disorder. And there are other new treatments that are being researched. And I've mentioned some here in terms of wearable devices, etc. It's just so you are aware that these exist. I don't know that we can make any recommendations around these just yet. What about co-occurring mental illness and substance use disorder? We mentioned this earlier to highlight it here again. The co-occurring condition of major depressive episodes and substance use disorder is very common. So 3.7% of adolescents age 12 to 17 had both a major depressive disorder and a substance use disorder in the past year. And 2.9% of them had that with severe impairment of the major depressive disorder plus the substance use disorder. And 2.5% of adults had both a serious mental illness and a substance use disorder in the past year. So what about treatment for substance use disorder? Only about 6.3% received any substance use treatment in the past year, those ages 12 and older, with an illicit drug or alcohol disorder. So this is quite a crisis. Why is this? What are the reasons for not getting treatment? This was the breakdown just in this most recent report. Top of the list is they just weren't ready to stop at 36.7%. And then near the top is just not having health coverage at 24.9%. And you can see the rest of the distribution there. Drug impairment and driving is a huge issue. So we'll spend a few slides here talking about that. Alcohol is the most common drug found in the blood of drivers involved in crashes, and marijuana is the second most common. Those age 21 to 25, 15% of them admit to driving under the influence of alcohol or drugs, and 7.7% ages 16 to 20. Men more likely to drive under the influence. And drugs were detected in a growing share of drivers responsible for fatal crashes, according to the Governor's Highway Safety Association. So 44% of drivers killed in crashes in 2016 had tested afterward for having drugs in the system. That's up 28% from the decade earlier. And 20% of drug positive drivers in 2016 tested positive for opioids, up from 17%. So 17% to 20% in that year for that study. And then for marijuana, that was up by 35% in the previous decade. So they were comparing 2006 to 2016. So for opioids, it went up from 17% to 20%. And for marijuana, it went all the way up to by 35% to 40, from 35% to 41%. Apologies. So basically nearly half of all fatally injured drivers, though, are not tested for drugs. So these are all underestimates anyway. And that's the other sad thing to remember. So what can we do particularly for alcohol misuse? What are our prevention goals in public health and prevention? Well, we certainly want to reduce consumption and binge drinking. We want to reduce violence and property damage associated with that. We want to reduce underage consumption. And we want to certainly decrease risk taking while under the influence of alcohol, driving, boating, aviation, diving. And of course, all of this impacts productivity, sick leave in the workplaces. So there's so many consequences that if we can intervene and prevent this kind of misuse, it would make a huge difference in our population's morbidity and mortality. Specific to alcohol and motor vehicle accidents, this is from crash stats at the Department of Transportation. Motorcycle riders involved in fatal crashes had the highest percentage of alcohol impairment compared to other motor vehicle types. 43% of motorcycle riders who died in a single vehicle crash were alcohol impaired. And this is more common at night. And 24% of drivers of passenger cars involved in fatal crashes were alcohol impaired. And this is older study, but very important to keep in mind, which is what were those alcohol levels? So this 2005 study showed us that 39% of all fatal motor vehicle crashes in the US involved blood alcohol levels of 0.01 grams per deciliter or above. And in fatal crashes in 2007, 27% of that motorcycle riders had blood alcohol levels of 0.08 grams per deciliter or higher compared to 23% drivers of light trucks, 23% passenger car drivers, and 1% large truck drivers. And a systemic review of the literature shows that the odds ratio of a fatal injury is 1.74 for every 0.02% increase in blood alcohol concentration. So at 0.08, the odds ratio is 13. So then what do we do about this? What are some strategies to reduce alcohol related problems? Well, we need controls on media advertising, alcohol pricing and taxation, community-based interventions, licensing revocation, sobriety checkpoints, minimum legal drinking age, penalties for serving minors and penalties for drinking and driving. The media is a huge culprit because the media does portray these positive messages with drinking. These messages are ubiquitous in various events, also in musical events, sporting events, etc. And again, there's this positive depiction where drinking is problem-free, it makes you popular, it makes you be rich, have a higher socioeconomic status. And some research is inconclusive around this, but it's suggesting that as we get exposed to this kind of media, we have a high recall of this kind of advertising and that this increased exposure to these advertisements can increase alcohol use in adolescents. Certainly the surveys and this one study showed that these adolescents had a higher expectation that they would drink in the future years. So we need some counter-advertising strategies. We need increased public awareness about this, warning labels on packaging, but that's not enough. So we also need programs in schools and we need to get kids connected in schools with healthier activities so they're not drawn to alcohol and drug use. Having them do positive activities, physical activities, sports that get them involved with their peers and get them socially connected, that feels meaningful, is a real important intervention for these teens and youth. Certainly some random screening in the schools can perhaps identify some issues and from a public health perspective, which regions have greater problems and which regions might need greater interventions. And partnering between public health schools and providers of rehab programs are important to offer the best programs. And speaking of those programs, the research has mainly been around the Surgeon General's warning of packaging and certainly increased awareness helps and particularly awareness around pregnancy and alcohol, for example, has been shown to work. But some of the earlier school programs, there was a program called Drug Abuse Resistance Education Program, were not very effective. In 2009, they retooled some of those programs, put some newer programs in that are a little bit more effective. These programs are constantly evolving and changing. So if you are looking for which ones are effective, then I recommend checking out that National Registry of Evidence-Based Programs and Practices on the SAMHSA website. And then specifically, I mentioned this minimum drinking age is interesting from a public health perspective to know about this. Then in the 70s, we reduced the minimum drinking age from 21 to 18 to match the voting age. And we had high incidence of motor vehicle accidents, high morbidity and mortality. And so in 84, under pressure from Congress to withhold national highway funds, the minimum drinking age was returned to 21. And the resultant impact was there was a 15 to 20% decrease in alcohol-related accidents in drivers under the age of 21. So we also need some impaired driving policies that can be a deterrent in terms of then perhaps arresting individuals who are not complying with these policies. Many states have reduced the legal blood alcohol level from 0.1 to 0.08. And we know the probability of accidents triples at that blood alcohol level of 0.08. And the probability of failed crash doubles for every 0.02 blood alcohol level above zero. And so we were looking to have 0.08 blood alcohol level as a standard for the country. However, in 99, the efforts to make this a national standard were defeated in Congress by the lobbyists, unfortunately. So what we have available to us is, again, licensing revocation, punitive fines, mandated rehab programs. And there is evidence that this kind of, these community policies and license revocations can be a deterrent to driving under the influence. So to wrap up, mental illness is a prominent and disabling condition in the U.S. and worldwide. Mental illness is increasing in the U.S., particularly since the pandemic and perhaps because of the pandemic. And there are many reasons for that, including social isolation. Mental illness is underdiagnosed and it's difficult to diagnose in medical settings where there are medically ill patients with these concomitant symptoms. It's certainly undertreated, despite a range of treatments that are more effective than placebo, not as effective as we'd like them to be, but certainly better than placebo. And we need to be increasing the range of mental illness treatments. And we are now looking more towards prevention in terms of resilience building, building what's called post-traumatic growth, which we didn't get a chance to talk about. That's part of my area, but it's basically learning skills to stay emotionally well, despite the face of adversity. And we're certainly in this country now really exposed to war overseas, mass shootings here in this country, the threat of climate change, et cetera. So our entire population is under siege with these kinds of traumas, what we call mini-traumas, that can increase mental illness. And so having these kinds of resilience programs and well-being programs can potentially make a difference. Of course, we need more research on that. And then substance use not only persists, but has increased. It's a public health crisis. The opioid epidemic is a crisis, and we are in dire straits to help these individuals get into treatment. You could see that the treatment rates are very low. So we have a long ways to go in this area of mental illness and substance use and substance use disorder treatment. So I'm hopeful that this review will help you on your board examination. I wish you luck on your board examination and in your careers. And thank you for your attention. Take care, everybody.

behavioral health

mental health

health behaviors

morbidity

mortality

health behavior change

substance use disorders

Health Belief Model

Theory of Reasoned Action

Social Learning Theory

Healthy People 2030

mental illness prevalence

opioid misuse

prevention strategies

public health strategies