Dr. Daniel Knoepflmacher (Host): Welcome to On The Mind, the official podcast of the Weill Cornell Medicine Department of Psychiatry. I'm your host, Dr. Daniel Knoepflmacher. In each episode, I speak with experts in various aspects of psychiatry, psychotherapy, research, and other important topics on the mind.

Our focus today is on youth suicide. Rates of suicidal behavior among adolescents have been rising significantly for more than two decades. National data from the CDC showed that the suicide rate among youth aged 10 to 19 years old increased by 57% from 2007 to 2016. As of 2021, suicide is the second leading cause of death among 10 to 24-year-olds, yet these overall trends are only part of the story.

A more granular look into the demographics highlights stark disparities, such as the suicide rate among Black adolescents rising at a faster pace than among white adolescents. The suicide rate among Black youth under the age of 13 is twice that of white youth of the same age. Research investigating these disparities has shown how socio-economic adversity, housing insecurity, resource scarcity, limited access to mental health care, and other factors directly influence the mental health outcomes of young people from disadvantaged backgrounds.

Our guest on the podcast today has done important research looking into the social determinants of mental health in children and adolescents, including influential work on youth suicide. Dr. Yunyu Xiao is an Assistant Professor in the Division of Health Informatics within the Weill Cornell Medicine Department of Population Health Sciences. She and her colleagues have combed through large, complex data sets to demonstrate how systemic disparities impact the risk of adolescent suicide and poorer mental health outcomes.

Today, she'll introduce us to her research findings and explain how it can be used to design novel interventions, influence more equitable social policy, and reverse the alarming trends in suicide among children and adolescents. Yunyu, thank you so much for joining me on the podcast today.

Dr. Yunyu Xiao: Thank you very much for inviting me, Daniel.

Dr. Daniel Knoepflmacher: Well, I just gave a brief introduction to your research, which we're going to be learning more about today. And I'm wondering what led you to study mental health disparities generally in youth suicide specifically? How did you get inspired to do this work?

Dr. Yunyu Xiao: Yes, I think my interest in studying mental health disparities really started by personal journeys. So, I was born in a mountainous like city called Guiyang in China, where there are a lot of exposures to poverties and look at people who have lower access to education, to healthcare. And then, I went to Hong Kong, which before I thought is financial cities, that there will be no poverty. But when I was there, I found that there are huge disparities in Hong Kong. And I was able to actually visited people from the poorest areas called Sham Shui Po. And I was shocked that where the health conditions, the environments that people are living there.

And then, I also had the opportunity to come to New York City to have my PhD. And that is another shock, because when I came here, New York City, which we all know is a financial center of the world, is also having a lot of poverty issues. So, that made me realize how deeply social determinants like poverty, like housing instabilities, like education could affect mental outcomes. And then, that effect is very central, and I think that makes me feel like it is an important role that social factors can play in terms of mental health. And youth suicide stood out because as we all know, and you just introduced that now it is the second living cause of death in the United States. And it's heartbreaking to see life lost so early. So, I just want to uncover the systematic inequalities and that leads to these tragic outcomes.

And another note is in terms of my professional training. So, I was originally trained as a political scientist and economist, which I always wanted to address the urgent issues related to the world in terms of like poverty alleviations, in terms of like how to heal the hungers. And then, I was able to go to the social work and public health school, where I was in the PhD. I got very interested in statistics and big data. And I have been taking a lot of trainings from the data science department, which then makes me feel like I'm in the real position to address the urgent issues of mental health disparities and youth suicides through this interdisciplinary trainings. And my ultimate goal is really to translate this research to an actionable policies and interventions.

Dr. Daniel Knoepflmacher: Wow. I always love the answers to that question. And in your case, just hearing about your life in different countries, in different cultures, in rural settings, urban settings, how all of that influenced this path that you're on. So, thanks for telling us about that. I want to turn to your research, and I mentioned in my introduction a few of the trends in suicide among youth. Can you give us a fuller picture of what you found studying this?

Dr. Yunyu Xiao: Yes, of course. So, I started to understand trends in suicide among youth, especially by the different race, ethnicity, sex, sex identity, and their intersectionality groups since I was in the PhD. And from my last year of my PhD, I was able to find a national dataset called YRBS. And that is a data set that recruits naturally representative high school students.

And my question to them after that time was is there increasing race or differential race in terms of suicidal ideation, suicidal plan, and suicidal attempts across different race and ethnicity groups? And to our surprise, although we all know that white adolescents or white population in general have higher suicide deaths, what we find is that for the youth populations, that the black youth, especially their preteens, under 13 years old, was twice likely to die by suicide as their white peers. And we also found that from 1991 to 2019, Black youth suicide, especially for suicide attempts, have increased by 73%.

And beyond that, because I am coming from an Asian background, and then from what I learned from my early childhood, sexual identity or being a sexual minority youth is very stigmatized. That makes me to study another group that is sexual minority groups. And I was able to use the same data set and to study Asian American sexual minority youth. I also find that those who identify themselves as sexual minority youth also have consecutively higher, almost double the rates of all the ideations, suicide attempts, planned, and injured by attempts than their heterosexual youths.

So, what makes me feel like maybe it's not just about the identity, about race, but maybe there are some other reasons. So then, I started to use geographic information systems and then to conduct a lot of like spatial analysis. And we also find that geographically, there are higher suicide rates especially in areas where fewer mental health resources, especially rural, economically disadvantaged counties. And that has been further exacerbated during COVID. So, this makes me feel like we need to really address these disparities, especially when we are seeing the trends that are devastating. And then, we also need to see the role of the intersectionalities, because what we find is that there might be overlapping factors like race, socioeconomic status, and geographic compound risk factors. And there might be some other things underneath these identities that are driving these systematic inequalities.

Dr. Daniel Knoepflmacher: It's really important work that you've done because it's shown that it's not just a one-size-fits-all approach. There's a lot of complexity beneath the surface in these trends. And I want to know, thinking about your research, what are the innovative aspects that have contributed to improving youth mental health and preventing suicide in these populations, especially given what we already know about social determinants of health?

Dr. Yunyu Xiao: Yes. Thank you very much for asking this question, because I think what we are trying to do right now dig even deeper, moving beyond the broad categories as we just talked about, like race, sex, age, to identify more specific factors that might cause the certain individuals to engage in suicidal behaviors.

And I was also very fortunate to build up very close collaborations with expertise across the disciplines. For example, I was able to connect with Dr. John Mann, who is an expert in Psychiatry and also suicide prevention. And then, I was also able to work with Dr. Tim Brown, who is actually an economist. And then, I was neutral by mentorship with health economists and health policies by Dr. Lonnie Snowden, as well as many others who are in a multidisciplinary field. And in this case, what I found is that it is more than the identities and what we are talking about that we need to move beyond the single focus to the social determinants of health.

So, let me define social determinants of health. The social determinants of health, or SDOH, are what we call the conditions where people live, born, and educate, thrive, and hang out in your neighborhood. And by definition, it is across multidisciplines. And it has a lot of domains, including economics, including social context, including natural environment, crime, drugs, physical and health infrastructural, education, or even how stigmatized the environment are exposed to.

So, if we just focus on smaller set or single individual indicators of the social determinants of health, we won't be able to capture this multidimensional nature of social determinants of health. So, what we developed and innovated in the field is that I am able to use machine learning to create a clustering algorithm to analyze large data sets, and that allows us to identify previously hidden patterns in the suicide trends. And that is crucial for targeting interventions effectively, because then we are able to link individuals with the zip code, or the counties, or the neighborhood of where they live. And then, we're able to link them to some external factors across all the social determinants of health domains. So then, that is not individual, not small set, not cherry picked SDOH factors, we've moved beyond to identify this unbiased computed SDOH patterns from the high dimensional SDOH factors. And then, we are able to also link and to investigate the associations of these SDOH patterns to the outcomes in terms of mental health, suicide attempts, suicide ideations, suicide deaths, cognitive health, and physical health.

So, let me define what is the patterns. So patterns, if you are thinking about, I always use an analogy of a football team, so for each pattern, sectors of the football team. So, you were able to identify youth or children who are belonging to one specific type of SDOH pattern that means that they are exposing to the similar patterns of the social determinants of health, the similar types of the neighborhoods that they are living. And then, we're able to link and use these novel metrics to quantify their health. And because we also know the racial background, sex, and age of these children, we're able to also quantify the disparities at the population levels, and it gives the policymakers a much faster way to note at which levels, at which subpopulations or groups of the children are much needed of what? Is it about poverty? Is it about crime? Is it about bias? Or is it about food or health insecurities? So, that brings a lot of innovations in the field to guide policy interventions and to guide interventions.

And another note of my innovative SDOH work is to do translational research that we're not just studying SDOH, not just about the environment, because in the field of suicide research, genetics and family history also matters. So, I'm able to collaborate with colleagues who is a genetics expert to understand the interactions of environment and genes. And that really creates an opportunity for more precision in terms of our clinical and policy interventions in the field.

Dr. Daniel Knoepflmacher: Wow. Well, thank you for describing all of that. I want to ask you something about suicide. There's tremendous stigma associated with suicide. You mentioned some of the stigma in different communities when you were just describing all of this. And that stigma makes it difficult for many to discuss this topic. As a result, I think there are many misconceptions out there and they persist. So, thinking about you as someone who's researched this, that makes you an expert on this topic. Are there specific common misconceptions that you'd like to dispel about suicide based on what you know?

Dr. Yunyu Xiao: Yes, I think based on what I've known and actually coming from an Asian and Chinese background, some of these misconceptions was also what I previously knew. But then when I studied more, I have self-corrected myself. I think one of the biggest misconceptions is that suicide is solely an individual issue. These are all related to systematic or environmental factors. But as what I have just said, our research shows that there are broader social determinants like your environment, like poverty and discrimination also play a significant role. So to prevent suicide, we really need a public health approach to start from a systematic and policy approach to intervene the environment. And that can really reduce the disparities.

I think another belief is that talking about suicide would increase the risk. But in fact, creating a safe space for an open dialogue can really encourage youth to seek help. And one more thing is that if you identify youth or children who might have a suicidal thought, it is okay and actually it is important to ask. Because asking, "Have you ever think about seriously killing yourself?" Is to build the connections. And one more recent thing that we, our group, have been discovering is about the tones and languages that clinicians or parents are communicating with the youth are incredibly important. Because if clinicians are using judgmental, stigmatizing or biased language and tones, youth are less likely to stay engaged in treatment, which will lead to worsened outcomes, hesitations to seek help, dropout, and that is more dangerous.

So, shifting to more compassionate or non-judgmental communications can make a significant difference. And we are recently working on using large language modeling to detect these kinds of tones and also to build these copilot systems that can guide clinicians to correct them.

Dr. Daniel Knoepflmacher: So, you're actually coming up with interventions based on these findings.

Dr. Yunyu Xiao: Yes, exactly. And then, to really advocate that it is not one-size-fits-all and for specific higher risk groups, we need to tailor the interventions and even to tailor the tones that we communicate with one specific individual.

Dr. Daniel Knoepflmacher: So, this gets so granular, and I think what's impressive about the work is how you've used data to verify diverging trends among different demographic groups. Can you say more about some of the key findings from what you found in the data through this research?

Dr. Yunyu Xiao: Yes, definitely. One of this research that we just published in JAMA Psychiatry actually is using this data-driven approach. We were able to find a vast data set that described over 300,000 suicide deaths from a CDC's National Violent Death Reporting Systems. So traditionally, people are just studying about the circumstances of why and what is documented by the medical examiners or legal examiners, and then to study single factors that could cause suicide.

But from our approach, we use a statistical technique or data-driven approach called latent class analysis to identify these subgroups in this cohort that are experiencing shared circumstances in their lives that were meaningfully associated with their circumstances or suicide risks.

And this analysis revealed that people can be actually categorized into one of the five groups based on the live events associated with substance abuse, mental, physical, health, crisis, relationship problems. And what stood out from this data-driven approach is that instead of the mental health as a problem, we find that there are around 32% of these suicide deaths were linked specifically to struggles with physical illnesses.

So, this could represent potentially individuals who were grappling with physical manifestations of mental health problems like depressions, in addition to experiencing emotional challenges due to conditions like severe disabilities, chronic pains. But these people maybe, and this particular group is the largest of those five high-risk clusters we identified who we're generally actually not seeing any mental health specialists prior to their suicide deaths, and they were far less likely than the other groups to be taking these medications. And I find that this is really important findings because it can encourage that interventions by training primary care physicians to better recognize when their patients may need additional treatment for mental health conditions or suicide preventions when they are suffering or just complaining about their physical health. So, it is a means for delivering more holistic care for the population. And if you are not using this data-driven approach, we would always have the misconceptions that suicide is just a mental illnesses issues. So, I think this is a very interesting and important finding.

Dr. Daniel Knoepflmacher: Yeah. It relates actually to a topic we're going to have on a future episode about integrated behavioral health care being embedded in primary care settings where you might catch on screen for some of these individuals who might otherwise be lost to the system as you've discovered through this research. A lot of this, and you mentioned this earlier, touches on structural factors in our society and these underlie mental health disparities. Can you describe in more detail your findings about how social determinants impact youth mental health specifically?

Dr. Yunyu Xiao: Yes, definitely. I'd like to draw our attention to one of the papers that we published in JAMA Pediatrics, and that is a study that we used a cohort of over 10,000 of children. And we used this machine learning approach to identify the underlying patterns of this multidimensional social determinants of health, and then to see how that affects the their mental health and also their suicide risk. And we were able to link each of these children to almost to over 86 factors of the neighborhood level geocoded variables across seven domains of SDOH, or social determinants of health, like bias, like education, physical housing infrastructure; natural environment, like climate change; socioeconomic status, social context, and crime and drugs, And using this approach, we were able to identify four types of these patterns.

So, the pattern one is the traditionally more affluent children, that they are less exposed to this poor SDOH. The pattern two, well, they are not very poor or they are more educated and living in a better place, these environments they are living in have a high stigma. For high stigma, what I mean is that they have a survey that you ask people to answer if they are experiencing some implicit bias towards their sex, towards their immigrant status, towards their race factors.And then, for the pattern two of this SDOH, they are living in the places whereas everything else sounds good, but it's very stigmatized.

And then, the third group is what we traditionally call the higher socioeconimic deprivation groups. That means that they are living in this place with high income disparities and very high growth rents out of their pocket, poverties and unemployment are very high, and the families are living with the public assistance a lot.

And the final group is also related to the places where they have higher problems in terms of their education. That means that they have very low and fewer early education centers, fewer high school graduations, and high school poverties. But what stood out is that these places have a lot of crimes and drug sales.

And to our surprise that for suicides particularly, beyond that the problems that are associated with socioeconomic factors, we find that for children who are living in this high stigma environment, when they are growing up, the trajectories of suicidal ideation and suicide attempts are significantly higher than the other groups.

And for mental health in general, like their anxiety, their depressions, aggressive behavior, social problems, children living in this high socioeconomic deprived environment are consistently and significantly having this problems than all the other groups. Of course, this is also related to their physical health and cognitive health. And what we found is that if children are living in this affluent pattern one group, that they will have much better cognitive health, like crystallized intelligence, fluid intelligence, or composite score in terms of their cognitive behaviors and reasonings as well. And if they are living in this high crime group, what we found is adversely affecting their physical health, which is, as I just mentioned, that is correlated with suicide deaths. That means that they have more sleep problems, and then they have more chances of being obese. And that is all associated with the suicide risk factors among youth.

Dr. Daniel Knoepflmacher: So, I want to touch on one of the things you said there, the stigma piece. How is that measured in this research? How did you identify stigma and pull that out from the data?

Dr. Yunyu Xiao: Yes, that is a very important factor. So, stigma, as I mentioned, there are two types, actually. One is the structural bias and stigma variables, which I just shared. So, the variables are previously validated and modeled and factors that extracted from a survey. And this survey means that they are asking people from the different states, across all the 50 states, about their experiences of stigma domains across these variables like implicit biases for sexism, for racism, for sexual orientation, and immigrants. And for this statistical analysis, we did a model-based factor score that centered around the mean of zero. And then, everyone would have the normal distributions. And then, in this case, the higher these indicators values are, the higher their exposures to the structural stigma, like the environment that they're living in, that people have these biases towards this sexism, racisms, immigration status, and also sexual orientations. So, these factors that characterize the stigmas across the environments that they are living in.

And another type of the stigma, which I touched base in terms of like the communications, like the stigmatizing languages of the people who when they are communicating or when they are talking to one of the clinicians that they are describing them. For example, there are factors that are particularly using dismissive languages, they quote, they like people or youth who are having suicidal ideation or suicide attempts as attempters, as ideators, calling them, like, they are labeling these languages. Or oftentimes, and I actually talked to one of our colleagues here, that they also are heavily involved in describing and sometimes questioning the accuracies, credibilities of the patient describing their experiences. For example, maybe the clinicians would say that these children just claim that they cannot cope. And finally, there are scare quotes, like in terms of in the clinical notes, we can, and we actually built a model to extract the clinical notes, that we find there are a lot of quotation marks to describe dismissive  or mock the patient's symptoms.

For example, they would say, "Patient 'needs help.'" And that can be really if a patient, because we now have the open notes and can really discourage if you see these negatively toned languages. So for our research right now, we really want to highlight that there is a need for more advanced way of computationally detect and then mitigate this type of stigma in real time.

Dr. Daniel Knoepflmacher: Got it. Well, that takes technology to do that. And technology, which of course is all over the news is an important focus within mental health. There's controversy about the influence of social media on adolescent development, and there are concerns about how AI is going to impact our society, yet there are also important roles that technology plays in research, certainly in your research, and in the delivery of effective mental health interventions.

So, I was wondering if you could share what your research first has shown about social media and youth, which is a very hot topic, and also tell us how technology has really been crucial to your work.

Dr. Yunyu Xiao: Yes, thank you for this question. I think this is a very, very timely question as we just know that the discussion of this deep seek actually understanding like how the technologies of languages and how they can really affect people's usages as well.

So, going back to another hot topic that we discussed last week in the news about TikTok as well. In our lab, actually, we have been investigating the associations and what we actually innovate is about what are the causal in like relationship between social media and you suicide as well. And what we find is actually social media have a different domain. So different kinds of measurements.

Social media is not just about one platform, but it is across like social media platforms like Facebook, TikTok, Instagram, and use right now are also using different screens such as like video games. And also they are texting, they are doing FaceTime, video chatting, and then also watching TV. So from our research and we use a data-driven approach to understand the trajectories of using social media.

And we divide it into social media usages into two categories. One is the time that they spent on the different social media platforms. And the other is that we used validated skills of understanding the addictic like behaviors across social media platforms, across video games, and then across like how their feels like they cannot.

Get rid of the social media or video game or mobile phones. So the three categories, and we find that it is not just about the time that is spent, but it's about the addictive behaviors and exposures also to some cyberbullying behaviors that are more strongly related to suicide risks. And we also find, and it is a causal effect, like the associations between time spent on the different, like, mobile phones or social medias to the suicide attempt among youth is not just, like, linear factors, it's also about, like, how much that you spent and some of the spending on the time on the social media factors are differentially associated with boys and versus girls as well.

So it is not just like one size fits all when we're talking about social media is really bad, but it's about the addictive behaviors that children are spending and then they cannot get rid of the social media too. So that really informed us to think about, like, beyond just thinking about time, beyond just lock the phones actually parents or clinicians have crucial roles to observe, like, whether children or youth are addicted.

And then, so that's parental monitoring is very important. So, another thing that we are finding using the technologies is that now we are really in a good position to use big data. That means that we can leverage like electronic health records data with social media data.

And this social media data, such as Twitter, that they have like geolocations. And in one of our research as well, we also find that if we are extracting the suicide risk signals from the social media posts and the social media posts is linked to geographic levels, we can actually build the models that can precisely have identified the suicide risk at a particular location.

Thank you So that can really help us to build the precise early warning systems through the social media.

Dr. Daniel Knoepflmacher (Host): Wow, I didn't realize that was one outcome of the work that you're doing. I want to, to, Turn to something you said earlier, in thinking about, all of the health inequities that fuel mental illness and suicide rates among adolescents. There's always the question about what to do about it.

And specifically, you just mentioned one thing right there, but I'm wondering on, a public policy level. how can your research findings be used to advocate for change?

Dr. Yunyu Xiao: Yeah, thank you. I think a lot of my work has been Building these data-driven models to understand the associations or causes from social determinants of health and youth suicide and disparities. And I always also ask myself about, so what and why? Throughout my career, and from when I was starting my career, I was also very fortunate to be mentored by a very knowledgeable health policy researcher, Lonnie Snowden, at UC Berkeley, and then he also educated me a lot about discrimination, racism, and bias that can be magnified, the associations between SDOH and youth suicide.

And we have been talking a lot about how to reduce and what is the actionable approach for us to change the public policies. And we built models that now can also use this clustering algorithms to identify and quantify policies and co occurring policies that we can leverage to address youth suicide.

So let me illustrate that. Before policymakers have a struggle about in certain areas, These that might have like a, been a hotspot of suicide. What we can do? Should we do like poverty interventions? Should we do more investment in food or housing securities? So it's always a struggle. But from our research, as I just mentioned, there are differential effects and differential associations between what type of SDOH, like poverty oriented, like crime oriented, or bias SDOH.

And for that, policy makers can use these tools to We more quickly identified what it is the targeted resources to which communities most in need. And we can also identify this racial differences even within these communities. So, from our research, We really wanted to turn these findings into actionable policy advices so that we can advise and we can identify and build tools to facilitate and inform policy makers what kind of resources should be invested in a certain environment, and that is at the community levels.

At another study, we also identified some very important resources at school. So schools based policy mental health programs can really be life saving, especially when, like, the children or adolescents cannot really get access to the home to care at home or within their neighborhood. And finally, our findings right now find that although telehealth actually wanted to bridge the gaps of the health access, mental health access, it is actually broadening some of these disparities because the higher youth are more concentrated in white, higher income, or urban areas.

So that gives us also a clear policy interventions that we should also do more investment into like broadband accesses in terms of for medicating broad use that we need to be more flexible in terms of this reimbursement. So in general, I how can we turn this research findings into actions is just not one sentence, is that we need to really build comprehensive models from a comprehensive and multidimensional approach that we, we need to build. From the beginning, use these data-driven tools to identify where and who and which subgroups are really needed.

And then we should not only just focusing on the hospitals, but to reduce these disparities, we need to fully address the like with, and working with policymakers to identify this systematic SDOH factors that could be used to reduce through these policies and interventions to bridge the gap between the SDOH and the clinical strategies.

And I think one more thing is about, I think right now, we are also very fortunate to build in these technologies and the leverages of these telepsychiatrists but we need to really try to enhance these abilities to bridge the gap through more investment into like the access and also for example like the health literacies to use them as well.

Dr. Daniel Knoepflmacher (Host): So that's on the policy level. What about clinical interventions, social interventions down on the ground? What can be done to directly address the risk factors that you've identified?

Dr. Yunyu Xiao: Yes, I'm glad that you also mentioned about like social interventions because in my earlier work, I studied social network influences in suicidal ideation and suicide attempts among youth. And I was able to use a nationally representative data that have over like 10, 000s of these children who were born and we extracted their suicidal trajectories from the high school and tracked them until their younger adolescence, adulthood, and then when they are in the middle age.

And I was able to identify different types of social networks, like their social network structure, their social network functions. And who are they interacting with, like the families, parents, their friends, or school, or neighborhoods. And to my surprise as well, even though that throughout the journey of like growing up, you have been interacting with different people and your social networks are growing, it is actually The parental closeness that are very important and serves as a protective factors to make this group of children always being the lower risk of suicide.

So that gives a very clear intervention point at the social level that we need to really start with the families and especially for preteens youth that we need to help the parents recognize. the early warning signs, and then also build an open communication with their children.

Dr. Daniel Knoepflmacher: we've touched on so many different complexities and, findings from the work that you've done. I'm curious if you could tell us about what's ahead for you, what's your future research going to look like?

Dr. Yunyu Xiao: Yes, I am very excited actually about my future research that I think I am going to definitely continue working and expand our work using the longitudinal studies, especially right now we are linking the social determinants of health metrics that we created. We're using the same algorithms to apply to linking the social determinants of health patterns.

To this large electronic health records across the country, and I was able to build connections with other countries as well, and then helping them to build their social determinants of health patterns. So we can track how social determinants of health influence youth suicide over time, real time, and also not only in the U.

1. But also in other countries, so we can observe different cultures as moderators as well. I'm also working on the, as I mentioned, the interplay between biological factors like the genetics, microbiomes, and the social determinants of health in shaping youth suicide trajectory. And for that, I think we can now leverage a lot of like clinical interventions at the biological levels to identify early warning signs.

And finally, I'm also working on this project. To use the large language model is to identify the tones or stigmatizing languages. And that is very important because it can not only build this real time, systems at the hospital levels, but we can also experiment. And then we expand that to different conversational therapies, to different, like a crisis line interventions that can really identify what the people seeking help need, and then to show compassionate voices.

And these compassionate voices are very important to do the interventions. The crisis, which we all know that is very important for you, suicide prevention, because most of them, like over 70 percent died from the first attempt. So if we can identify how can we communicate, what can we help, what language we can use, that is crucial.

And I want to conclude with one thing, is that I think collaboration is key, and I feel like throughout my work, I was very fortunate to build interdisciplinary teams with the experts from the different fields to really develop this process. Scalable interventions. And whenever I wanted to seek help from like health policy, economics, health services, psychiatrists, I was able to identify a group or a person that can work with.

So I think that really makes it a real difference in the communities. So for that, I think preventing youth suicide is a collaborative work. And I think throughout the clinicians, from the social factor, from the societies, policymakers, we all can make a difference.

Dr. Daniel Knoepflmacher: Wow. It's amazing how much you have accomplished and as you just described are going to accomplish you knew it was. It's really wonderful to have you here today to be able to speak about your work on this episode of On the Mind. You really highlighted some of the important research you've done and I want to emphasize how much it's really deepened our understanding of the societal factors that underlie youth mental health and suicidal behaviors.

going to be exciting watching how your ongoing work really will help us provide more effective care to address all of these long standing mental health inequities and hopefully reverse this burgeoning problem of youth suicide. So thank you for doing this important work and for talking with me today.

Dr. Yunyu Xiao: Thank you for inviting me.

Dr. Daniel Knoepflmacher: it was wonderful having you on, Yunyu, and I want to also thank all who listened to this episode of On the Mind, the official podcast of the Weill Cornell Medicine Department of Psychiatry. Our podcast is available on many major audio streaming platforms, including Spotify, apple Podcasts, YouTube, and iHeartRadio. If you like what you heard today, subscribe so you can stay up to date with all of our latest episodes. and please give us a rating.

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