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Nothing but mental health needs and other outcomes of kids in the juvenile justice system.

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Now, when I first went to develop funding for this, of course, the funding agencies were funded mostly by NIH,

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which is National Institutes of Health in the U.S. and other federal agencies.

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And they said to me, Well, sounds like an interesting study,

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but aren't kids like yours already included in all the large scale general population studies that we already fund?

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Very good question. And I thought about it and I realised that delinquent kids are not part of school based studies.

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You know, think about how how do we draw samples when media studies many people use school based samples.

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Well, if you've got delinquent kids who are truant or incarcerated, they are not part of those school based studies.

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Another way we collect data on general populations is household based surveys.

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But again, kids like ours, kids in the juvenile justice system wouldn't be part of those samples either, very much, very often,

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because they are usually from very poor families, highly mobile, unlikely to be sampled or again detained and therefore unavailable.

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And then I found out and I discovered this by actually calling all the principal investigators of large scale studies funded by NIH in the U.S.

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I called people and I said, Tell me, do you sample from detention centres?

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And the answer was always no. And then I said, And do you retain people at follow up if they become incarcerated?

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And everyone said, Oh, no, we can't do that.

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So here we have a situation.

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And again, this is a great lesson for the trainees who are looking for holes in the literature, looking for key omissions in the literature,

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where we have a situation where a group that is probably likely to have the greatest mental health needs and the

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worst outcomes is not only not sampled but lost to follow up so that all of the studies that you see in the U.S.,

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whether they're on psychiatric disorders like the National Comorbidity Survey or on cardiac disease or cancer,

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none of those studies include incarcerated people because they're not sampled and all the incarcerated people are lost to follow up.

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It's really quite an and it was quite an amazing, amazing revelation to me.

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And it was it became the argument actually with our funding agencies for why they should fund the Northwestern Juvenile Project,

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because this is a sample that they know has great mental health needs, dire health outcomes in many ways.

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And yet this group was not part of any study that they were currently funding or had ever funded.

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Ever funded. Let me explain now a little bit about why I'm so passionate about this study,

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because I think you can tell I'm not just American, but I really am very passionate about my work.

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I'm from Chicago and we're where we are. A bit histrionic in Chicago.

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By the way, is my accent. Okay, another one.

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Can you change? You know I love British accents.

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Do you know that in the U.S., if you have an applicant with an English accent and without, there's no question who they would hire.

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Absolutely. It is so impressive. And Americans can't even differentiate a country accent.

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From what? From a posh accent. From a Australian accent.

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So we know we're we're just hopelessly ignorant about those kinds of things, but we're very impressed by the right kind of accent.

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So why did I why am I so passionate about this study?

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Two reasons. First reason is just the sheer number of kids that are processed through the juvenile justice system in the United States.

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So what we see is that it hit a peak at about 1997 and it's dropped since then.

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But we still have about 1.3 million delinquency cases disposed in a year.

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1.3 million. This is a chart.

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By the way, I'm one of these presenters I don't flash and then you don't even know what it is.

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I'm a ponderous presenter because that's how I like to see slides.

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So and if I go too fast, just tell me. But I like to focus on it, discuss the point, give you a moment.

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So I'm not pausing because I can't think of what to say, but.

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But rather because I think it will. It is absorbed better if you're slower.

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And for trainees in the audience, I think that's the best way to present is show a slide, pause, let people see it, explain it, pause.

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Go on to the next one. Just. Just for trainees here. Took me, like 20 years to figure that out.

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So, by the way, how many trainees do we have in here? Training students?

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Yeah, students. Graduates. I mean, undergrads, graduates, postdocs.

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Okay. Quite a few. Great. All right. I'll try to save you the work that it took me to learn things.

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20 years postdoc and give you some tips in the lecture.

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Good. So this shows the one day account of kids who are incarcerated in the US.

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And so once again we see that it hit a horrific high of 170,000 in the late nineties and it's dropped.

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But still on an average day, there are 61,000 kids that are incarcerated in some sort of secure facility.

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The second reason that I'm so passionate about this area is because of disproportionate incarceration of racial and ethnic minorities,

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and especially of African-Americans. We have a situation today where millions of people are incarcerated in the U.S., two point on an average day.

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2.2 million people are incarcerated. And disproportionately, they are minorities.

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So what this shows what this slide shows is the proportion of kids in corrections and the proportion of adults in prisons who are African-American.

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And the sideways arrow shows the proportion it should be, which is 13%.

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In other words, African-Americans comprise 13% of the general population in the U.S.,

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but about 40% of kids in detention and adults in jails and prisons.

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This is actually an appalling statistic.

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Also, this is from an article published in JAMA Journal of the American Medical Association about, oh, I think 12, 13 years ago.

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And they looked at a group of inner city, mostly African-American kids, and they wanted to see simply what proportion of them had been arrested.

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By age 18, they found that one in four poor African American inner city kids were arrested one or more times before age 18.

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One in four. I hope you can read this.

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This is probably one of the most appalling statistics that I ever present.

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And overseas audiences are especially appalled.

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These figures are from the United States Department of Justice,

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and they calculated the probability of being incarcerated one or more times in a state or federal prison over one's lifetime.

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They calculated it separately for males and for females.

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So what I would like to focus on is the top line, which is that one in three African Americans will be incarcerated in a state or federal prison.

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Now, this is not jail jail in the U.S. of something you call remand prisons.

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So this is prison. Prison. So one in three African-Americans will be incarcerated one or more times during their lifetime.

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One in three. I find this statistic shocking.

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That compares to one in 17 non-Hispanic white males.

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Hispanics now, you know, are the largest minority group in the United States.

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And even among Hispanics, it's one in six compared to African-Americans, where it's one in three.

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And then, of course, the rates are much, much lower among women.

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But you see the still you still see the same racial and ethnic differences where among African-American females,

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one in 18 will be incarcerated at some point in their life and compared to one in 200 non-Hispanic white females.

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So let me tell you a little bit about our study. It's actually a very straightforward study study.

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Today, I'll be focusing on psychiatric disorders, on positive outcomes and on negative outcomes.

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I'll also be suggesting some future directions for research, as well as talking about implications of our findings for public health policy.

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For me, what's kept me going over the years and in my work is that I joke my our articles don't just sit on my mother's coffee table,

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but they are used to guide national public health policy.

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And that's what's important, is that you choose work that will have relevance, that will have an impact on the field in some way.

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Although I must tell you that my mom used to complain. She would say she'd say, Honey, can't you write a book?

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The articles get so dusty on the coffee and we don't write books.

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I'm not I'm not a book kind of person. I'm like, you know, I do the perfect 4500 word manuscript, so I'm not a book kind of person.

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So she was always disappointed that I didn't have a book. So let me tell you a little bit about our study.

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It was really a very straightforward kind of investigation.

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Not a complicated design. Well, not complicated in theory, complicated in execution, as, of course, all of you who research subjects find.

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We started our study in 1995, and over about two and a half years, we drew a random sample of kids who were arrested and detained in Cook County.

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Ours is not a study of kids who perpetrate delinquent acts.

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We purposely chose kids who were in the juvenile justice system because our goal was to

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inform the juvenile justice system what they should be doing for kids in their care,

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and then also to inform the community mental health system about what they should do after these kids return.

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So we sampled kids when they entered, had just been arrested and detained at the Cook County Detention Centre and we chose their Cook County,

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by the way, as in Chicago, we chose Chicago not merely because it was convenient, but because and we thought a lot about where we should collect data.

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But when you choose a research site, you want it to be representative.

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And Chicago's so typical. It's a typical big city, typical big city problems.

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And so we felt it was a very good place to collect data because the kids would be pretty typical.

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We also investigated the legal structure and found that the criteria for detention was similar in Illinois to most states nationwide.

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So that was very advantageous.

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We also like Chicago because, as I mentioned, Hispanics are now the largest ethnic group in the U.S. and we wanted to study Hispanics.

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The problem is, of course, that if you studied Miami, you would get only Cuban-Americans.

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If you studied in New York, you would get pretty much only Puerto Rican folks.

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And if you study L.A., you would get mostly Mexican-Americans.

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Chicago's terrific because we have great diversity within our Hispanic population.

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And so that was advantageous for us as well. The last reason we chose Chicago and this is not inconsequential is that we could do it in Chicago.

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A study like this requires tremendous cooperation from everybody, from folks who work at the detention centre, the line staff,

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the chief judge of juvenile court, the chief judge of the entire circuit court, people who run Cook County jail, the prison system.

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You need tremendous cooperation from all of these places.

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And because I've been doing this kind of work for so many years, I know people and they know that I'm not a jerk.

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They know that I will not hurt them. They know that I will not go public with a press release that makes them look bad.

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They know that I will not release a press release where it starts, where the lead on TV is.

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Jails, neglect, mentally ill, women. Details at ten.

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They know I won't do that. So that we could do our study in Chicago and in other places.

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It would have been more arduous. So for all those reasons, Chicago was really a great place for us to do our study.

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We randomly selected 829 kids as they entered detention.

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We oversampled whites.

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Now, isn't that ironic?

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We have to do a report every year for our funding agencies where we have to tell them how well we're doing in recruiting minorities.

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Minorities are no problem in our when you study jails and prisons, the problem is getting enough whites.

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So we oversampled whites.

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We oversampled girls because we wanted to get enough girls to be able to look at them separately and we oversampled younger kids.

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For those of you who are statistically minded, we then adjusted all these statistics back to the true proportions of the detention centre.

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And Cook County, by the way, is very similar to most detention centres around the country,

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which is that they are mostly male, mostly racial and ethnic minorities, and especially African-Americans.

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This slide shows the demographic characteristics of our sample.

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And what you'll see is that we had about a thousand African-Americans, about 500 Hispanics,

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about 300 non-Hispanic whites, and about four of other race or ethnicity.

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We have the largest sample of delinquent girls ever collected, about 650.

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The others, about 1200 are males. The average age mean median was just under 15.

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And you'll notice we have something here called legal status.

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And that's because in the U.S., we have this unusual situation where the state legislature, you know, Colorado,

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New York, Illinois can automatically decide that some crimes are so serious that they merit transfer to adult court.

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So they bypass juvenile court, they go to regular adult court.

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So because we wanted to get those kids as well, we oversampled kids that were processed in adult court.

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As an aside, by the way, you'll be very interested in this.

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As a criminologist, one would think that the offences that would result in automatic transfer to adult court would be serious interpersonal violence.

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In fact, they're often things like selling a certain amount of drugs within two blocks of a school.

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It's they're not necessarily the most serious crimes.

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And it's a highly controversial policy because virtually all kids who are transferred to adult court,

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virtually all of them are African-American, which makes sense.

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Think about it. If you're going to be sent to adult court because you sent sell drugs within a certain number of blocks of a school,

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poor people who are disproportionately African-American are more likely to live in densely

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populated areas where you could be on the street anywhere and be fairly near a school.

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So we took this random sample of kids.

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The bottom shows you the age distribution. We had a terrific participation rate.

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So many people thought we couldn't do this study. They thought that you'll never get these kids to agree.

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And in fact, and we did pay the kids.

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We paid them $15 for the interview initially and $10 for a urine sample.

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At that, we tested for drugs and almost no one said no.

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Our refusal rate from the kids was 1.8%.

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Our refusal rate from their caretakers was a little higher, 3.8%.

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And by the way, we had a special dispensation.

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If we couldn't find the caretakers, we were able to skip that step.

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But that required that's a very long process. We had special permission from that that we received from the Institutional Review Board.

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But we had terrific cooperation because for so many of these kids, nobody had ever talked to them.

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And for many of them, when we would make arrangements to pay them, they would say, You don't have to pay me.

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It's enough. You talk to me. It's enough. You listened to me.

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So these were kids aged 10 to 17, who many of whom had never really had an adult, spend time talking to them.

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So these kids are, you know, really in fairly, fairly difficult straits.

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I also want to mention the enormous social class bias in the U.S., which I think is a bit different than here we spent.

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Two and a half years collecting, amassing our subject pool two and a half years down at the detention centre.

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In two and a half years, we never had a kid from the wealthy North Shore.

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Never. The wealthy North Shore is the part of the Chicago suburbs where it's not even wealthy, wealthy people.

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It's even upper middle class enough. We never had a kid from the wealthy North Shore.

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Never. Now, I do not believe that kids who go to those high schools never use drugs, never sell drugs, never get into fights.

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But it's a completely different situation for these poor our kids,

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because when a kid from a wealthier school gets into trouble, they're the principal is more likely to try to call the parent.

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The parent is probably not working on a factory floor and can reach the phone.

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The parent is more likely to be savvy and knowledgeable and get down to the school right away even.

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And if even if the principal decides that it should result in a formal disposition,

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the parent will get down to the local lockup and try to negotiate, release.

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And if not, that parent will probably be savvy enough to have the family lawyer come down and that kid never spent a night in a lock up

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and that our sample and how impoverished they are and the lack of anyone with resources indicates to me that in the U.S.,

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the juvenile justice system especially is a system for the poor.

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It's not a system for kids with problems who have resources.

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So we sampled them at intake and then we followed them up wherever they were when their interview was due.

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So remember I mentioned that all the large scale general population studies,

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when a participant speak, when a subject becomes incarcerated, they're gone, they're just lost.

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To follow up, we did something completely different.

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We re-interviewed our subjects regardless of where they lived when their interview was due,

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if they were in detention, still underage and in detention, we went there.

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If they were in Cook County jail, we went there. If their they were in an Illinois prison downstate.

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We went there if they were at home in Cabrini-Green, a notorious housing project,

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a project my interviewers trekked up six flights of urine, urine smelling hallways to get to that participant.

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So we went wherever they were. As a consequence, our follow up rate, our loss rate is extremely low and the participation rate at all,

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the follow ups is between 84 and 97%, very high rate.

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We also implemented state of the art techniques, which is another lecture entirely because we have a complicated computerised system based on Oracle.

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So the same kind of system where you call your credit card company and they

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know that you bought a lamp for your Aunt Sadie in Manhattan two weeks ago.

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That's the kind of system we use so that we know that the last time we talked to you,

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you were living at your friend's mother and the time before you were living with your mother's friend.

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And so we keep a great deal of detailed information so that when we do lose a subject, we can literally hit the streets to find them again.

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We also had agreements with Illinois Department of Corrections, most prisons within a couple of hours,

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drive of Chicago, Cook County Jail, the detention centre to be able to go in and interview our participants.

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That took a lot of lunches. Clearly, this is not Chicago.

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No one laughed at that joke. It actually did take lunches.

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So this recaps what I just mentioned the stratified random sample over sampling certain groups and tracking and re-interviewing them.

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And we have been tracking and re-interviewing them since they were enrolled, which was between late 1995 and 1998.

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I'm going to focus a great deal on psychiatric disorders.

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So I just wanted to give you a list of the disorders that we studied.

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So there are two categories.

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If you are in, you know this, but there are two categories of disorders, what are called internalising disorders, externalising disorders.

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And so we looked at Depression Dysthymia, which is a more chronic form of depression, but sometimes milder manic episode.

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Hypomania, anxiety disorders.

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Conduct disorder when they were young. Oppositional defiant disorder also.

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Which is a disorder when they're young. Antisocial personality disorder, which is a disorder that people have when they're old.

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You have to be over age 18 to have that disorder.

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And then, of course, substance use disorders, alcohol and drug.

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So let's look at some of the findings. How am I doing on time?

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It's definitely tough. Great. So this graph, this show, this bar chart shows the prevalence of disorders at the baseline interview.

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By the way, the baseline interview took about 3 hours at sometimes two.

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And we conducted that interview in a private room that the kids were that they used to have the youth meet with their lawyers.

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So it was in a private area. And we have confidence in the quality of the data collected in those interviews.

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We also use very skilled interviewers.

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We use people with a master's degree, and they also had to have had experience studying what are called high risk populations.

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So if you had only worked in a clinic with white people, we weren't interested in you.

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So we had outstanding interviewers who knew how to work with this difficult population.

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So I want to highlight here the bottom part, which is that about three quarters of the girls and by the way,

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usually my convention is to have pink for females and to have blue for boys.

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I stereotypical but if I can keep it in mind but we see that about three quarters of the girls

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and about two thirds of the boys have one or more of the disorders listed in this chart,

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one or more. And we also see that the most common disorder was substance use disorder.

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And by that, let me explain. That's not substance use, but rather it's abuse or dependence.

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To qualify for substance use disorder is actually pretty difficult.

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You have to have had a certain number of symptoms of a certain severity that interrupt your the patterns

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of your life in a certain way so that it's not merely use substance use disorders are serious illnesses.

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And so we see that about half of the boys and and just under half of the girls had one or more substance use disorders.

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And this was at the baseline interview when they were aged 10 to 17.

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This shows racial and ethnic differences among the boys.

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And so we compare the non-Hispanic whites, the lightest blue to Hispanics, the median blue to the darkest blue, which is African-American.

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And we have some, you know, kind of interesting patterns here where we see for some disorders, the highest rates are among the non-Hispanic whites.

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And we find that very depressing.

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And we believe that's because the bar is much lower for African-Americans to enter the system than for non-Hispanic whites.

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So to be a white kid and get arrested, basically, you have to be a pretty screwed up kid.

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And we have found this difference, by the way, this racial and ethnic difference in all of our studies of incarcerated populations.

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We found it in our Women in Jail study. We found it in our men in jail study.

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Typically, the folks in the system who are non-Hispanic white are in much worse shape than the African-Americans.

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Here we look at racial and racial and ethnic differences among the girls in the sample.

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Can everyone see this reasonably sorry.

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My nightmare always is. The screen is going to be that big and no one will be able to see the slides.

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So here also we see that it's for many disorders.

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It's the non-Hispanic white kids that have significantly higher rates of psychiatric disorders than the African-American kids.

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And it's interesting that that's the same for males and it's similar for females.

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This is a chart. When I present this to. Do you guys know Venn diagrams?

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Yes. Great. Okay. I present this to judges and they groan.

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But it's actually a terrific way to show the prevalence of co-morbid disorders, meaning the prevalence of people who have more than one disorder.

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You know, as you know, you're not necessarily you don't necessarily have only depression or only an addiction.

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You can have both. So what this shows is the overlap of disorders.

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This is data from the baseline interview and you can't see the numbers here.

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They're just too small. We have I have a there is an article where these are published in.

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It's now called JAMA Psychiatry. It was part of the archives, archives of General Psychiatry.

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And so we have these published, if you're interested in the numbers.

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But what I want you to focus on is the size of the overlap, where this is a very bad news chart.

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A good news chart would mean that you had less overlap because it's easier to

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treat people if they have only one disorder than if they have two or three.

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And so this is a very bad news chart because of the degree of overlap.

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So this chart shows the prevalence of comorbid disorders among males.

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And this shows the prevalence of co-morbid disorders, meaning having more than one among females.

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And what you see is that the patterns are different, but both males and females are depressingly similar in that comorbid disorders,

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meaning having more than one is almost the rule and not the exception, which does not bode well for treatment for successful treatment.

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Substance use disorders also can be co-morbid because you may have an alcohol use disorder and a marijuana use disorder or an other drug use disorder,

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you know, cocaine, heroin, whatever.

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And what we see is that especially for kids with alcohol use disorder, many of them also had another substance use disorder as well.

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This is also published. For any of you, if you want, I can send you a bibliography if you want, or even the articles if you wish.

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Some of what I'm showing is brand new and in press or even in process, and other things are published.

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So this shows the amount of comorbid substance use disorders among males.

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And this shows the overlap co-morbid of comorbid substance use disorders among females.

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I think what's interesting about this is that we've done the kind of work so often where people say,

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well, we already know that, and then you look and there's no study, so we don't know it.

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It's all based on presumption here. We know it and we can allocate the resources we need to treat these kids.

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This is a very depressing chart because the part this is a chart of IQ.

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We did a pretty rudimentary test of intelligence and we found that there were so few above average that you can't even see.

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It's like way at the top, you can hardly even see it. And what we found, and I think because of the light, you may have difficulty seeing it,

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that more than three quarters were below average, three quarters.

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So these kids do not have a lot of resources. This is from a recent article.

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And here we're looking at changes in disorders over time.

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And we look here at the baseline interview,

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the first interview we had and then what we call here the time one interview which occurred about three years later,

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and then the time to interview, which occurred about five years later.

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And there's a lot of very interesting patterns here.

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The blue dotted lines are males, the solid pink lines are females.

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So what we see here is that the girls, for example, with major mood, they start much higher than the boys, but they really plummet.

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And so there's still differences five years later, but the differences are far less.

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And we see something that that's very similar with anxiety disorders,

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where initially their rates are so much higher than the boys and then the girls plummet.

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And this is something that we'll see again and again here. And I want to state now what what the overall finding is,

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which is that the girls get better do better than boys, which is not something that we would have anticipated.

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We see similar patterns with substance use disorders and disruptive behaviour disorders.

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Disruptive behaviour disorders means conduct disorders or ADHD or when they're older,

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antisocial personality disorder, they're are they all have to do with behaviour.

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And so what we see here is that the girls just that the girls rates just plummet compared to the boys

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they're not different at the baseline dropped quite a bit three years later and dropped even more later,

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five years later. And so and we see that this is the pattern with African-American females, Hispanic females, non-Hispanic white females.

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So it's a very interesting finding because we had anticipated that the girls would do much worse than the boys.

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We just thought that because there have been a number of articles written with tiny samples talking impressionistic about,

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you know, the girls did so badly, they got pregnant, they did this, this happened.

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They had pimps that abuse them.

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They were addicted. And so we thought that our the girls in our sample were just going to be in dreadful shape.

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And in fact, they did better than the boys. More Venn diagrams, and this one looks at co-morbid disorder over time.

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This is a very interesting chart because what you'll see is that the level of comorbid disorders, meaning overlapping disorders,

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goes down over time because look how big the circles are at baseline and they're

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smaller at three years later and they're even a little bit smaller five years later.

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And so what happens is that disorders are going down, but co-morbid disorders are also dropping.

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That's the case for the boys. And that's also the case for the girls.

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I like to show this chart because it contradicts what we expect of delinquent kids.

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So this shows the prevalence rates of substance use disorders.

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From the baseline study up through the 12 year follow up interview.

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And remember that the information we collected wasn't from records.

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It was from face to face standardised interviews with well-trained interviewers that were the project staff.

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What I find there's a number of things I find here fascinating.

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But most interesting is that look at the rate of other drug disorders, which would be cocaine, heroin.

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In other words, a drug that's not alcohol, not marijuana. Look at it.

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It's like nothing. And we always think of these kids as being cocaine addicts and heroin addicts, and they're not.

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The rate is just so low and it starts low and it stays low.

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They don't it's it's I find this chart fascinating.

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And we also see that the most prevalent disorder, substance use disorder, rather, is not alcohol, but it's marijuana.

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For our kids,

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they told us that it was much easier for them to buy marijuana than it was for them to buy alcohol because for alcohol they were underage.

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And so that was a problem. But marijuana, that was easy.

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So this is a very interesting chart. This chart shows males.

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This chart shows females and we see the same kind of pattern where the most prevalent disorder at baseline is marijuana and that that drops greatly,

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which is very good news that it dropped so much.

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But again, what's most shocking is other drug.

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It's so uncommon at all of the interviews.

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It's, you know, 5%, 6% for it's nothing, absolutely nothing,

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completely contradicting what one's expectations would be about kids in the juvenile justice system.

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This is a chart looking at racial and ethnic differences, and this is males.

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This is a also a very depressing chart, given what we know about disproportionate incarceration of African-Americans,

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given that African-Americans are disproportionately incarcerated, especially for drug crimes.

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You would expect that African-Americans would have much higher prevalence rates of cocaine use disorders, heroin use disorders.

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You know, the really bad drugs, that's what you would anticipate. But look at the rate for African-Americans, the dark blue line.

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Look at that rate and compare that rate to that of the non-Hispanic white males, which is about 20% at baseline.

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And then it's still about 15% at the 12 year follow up interview.

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So think about that,

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that all of these African-Americans are incarcerated for drug crimes and yet they do not abuse the most serious types of subcategories of drugs.

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So that's the chart for the males. And we see that it's the same thing for females.

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Now with females, the rates among the girls drop a great deal because I mentioned that girls tend to do better than boys pretty much in every way.

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That's not a very good chart, huh? All right.

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So what this looks at is life time, substance use disorders,

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meaning did what proportion of these kids had a substance use disorder at some point in their lifetime?

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At the 12 year follow up interview. So at the 12 year follow up interview, they would have been, you know, late twenties, early thirties.

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And it's astonishing. I found this shows the top part shows males, the bottom part shows females.

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And what we see is that the prevalence rates for most of these subgroups, the lifetime rates are simply so high.

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Let's take a look, for example, at the first part, which is any substance use disorder, alcohol or any drugs.

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And what we see is that it's about 90% of the white females.

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It's about close to 90% of the non-Hispanic white males.

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Just incredible rate. And so we see that addictions are one of the most serious problems that these kids have.

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This is one of, I think, the most fascinating charts. And again, this is not use it's addiction.

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Yes. What was to classify as a substance use disorder again.

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But it wasn't just you know, it's not use it's called a substance use disorder.

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But in fact, the it's it's hard to get that diagnosis because it means you have to have a

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certain number of symptoms over a certain period of time of a certain severity.

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And so DSM would do a much better job of explaining it than I did.

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And the diagnoses, by the way. They're not made in an ad hoc way, so they're not that the interview just talks to the kid and says,

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Oh, yeah, you have trouble with school and do you smoke marijuana?

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It's not it is not casual. It is.

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Our interviews are what are called structured psychiatric interviews, where we use assessments that have been proven to be reliable and valid.

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We then have our own validity check in our own study.

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So we used the diagnostic interview schedule for children at the baseline, and then we changed the assessments as the kids aged.

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Because you can't use the same questions with a ten year old that you would use with a 20 year old.

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And we use things like the like the skid, like what's called the CD, CDI.

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I can give you more information if you're interested.

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But the important part here is that these disorders are diagnosed in a way that is consensually understood,

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established, therefore have construct validity and are assessed in a systematic way.

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And so what I find frightening here is the really the horrific rate we have of lifetime substance use disorders up to the late twenties,

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early thirties. This is a complicated chart.

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I have I have four of them. And I couldn't figure out whether I should show them or not.

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But I find them very, very interesting and I realise that Oxford has small screens, so it was probably not a good choice.

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But this slide shows among kids who had a substance use disorder or not at the baseline,

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what proportion of them had a substance use disorder five years later?

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And what's interesting about this is that we always think of kids in terms of progression.

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You know, they start with alcohol, then they progressed to marijuana, then they progressed to cocaine, then they progressed to heroin.

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And so we were really interested in seeing, in fact, was that true?

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Which we could do is show that the rows here represent the kids, but the prevalence of kids at Baseline who had no disorders, that's the bottom line.

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Alcohol disorders, that's the second marijuana use disorders.

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That's the third alcohol and marijuana use disorders.

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That's the fourth and then other at the fifth.

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And then what the bars show is what proportion of them had a disorder at the follow up.

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And so what we see, we see something in the expected direction, which is that among those kids who had no substance use disorder at baseline,

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about three quarters of them still did not have a substance use disorder at the follow up.

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In other words, they did not develop one.

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And in contrast, what we see, if we look at the top line among kids who had an other drug use disorder, meaning cocaine,

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heroin at the follow up, five years later, about 18% of them still had one of those substance use disorders.

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So it's a very interesting kind of chart,

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and I think I'm going to skip over the others because I think this room is too small for me to do these justice.

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And given it's late afternoon and we have alcohol to look forward to, I don't want everyone to fall asleep.

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I can say that because I'm an alcohol researcher, I a user.

393
00:48:26,980 --> 00:48:29,799
So let's look at positive outcomes. How are we on time, by the way?

394
00:48:29,800 --> 00:48:34,990
And I'm in a very good job of this as long as we're out of this room by ten to just 20 minutes from now.

395
00:48:34,990 --> 00:48:44,260
Seven at a time. Okay. No question. Okay, great. So we looked at positive outcomes, which we could do because we had a sample of kids.

396
00:48:44,680 --> 00:48:48,190
And so first, I'm going to show you positive outcomes five years later.

397
00:48:48,190 --> 00:48:55,300
And we looked at educational attainment, employment assistance from criminal activity, non-violent personal relationships.

398
00:48:55,780 --> 00:49:04,940
Residential stability. Psychological well-being. Just re incarceration five years later.

399
00:49:04,960 --> 00:49:12,220
Very depressing. 93% of African-American males were re incarcerated one or more times.

400
00:49:12,820 --> 00:49:16,750
The lowest rate was among non-Hispanic white males, about 40%.

401
00:49:23,740 --> 00:49:29,410
We also looked at this. We just set a date May two, 2012.

402
00:49:30,130 --> 00:49:35,920
And again, the incarceration rate is astonishing given how young these kids were when we sampled them.

403
00:49:36,220 --> 00:49:44,560
So nearly all of the African-American males were incarcerated one or more times by May 2012.

404
00:49:50,470 --> 00:49:54,430
Outcomes. We see that they don't do terrific.

405
00:49:55,780 --> 00:49:58,930
They don't psychological well-being.

406
00:49:59,110 --> 00:50:01,960
We have we have by the way, we have very low bars on this.

407
00:50:02,380 --> 00:50:13,480
So that criminal assistance we originally said we defined assistance as not having been arrested or any crime.

408
00:50:14,140 --> 00:50:19,140
And then we had to drop it to any violent crime because everybody fell out of the sample.

409
00:50:22,210 --> 00:50:32,560
Looking at all of those criteria that I mentioned, we found that only 9% of the sample were doing well on all those criteria.

410
00:50:32,800 --> 00:50:43,030
9%. This is information on positive outcomes 12 years after detention.

411
00:50:44,200 --> 00:50:51,670
And again, we'll be working on a paper on this. These data are probably best viewed in the context of that paper.

412
00:50:52,120 --> 00:50:56,590
But what we see is that they do not do very well.

413
00:50:57,430 --> 00:51:03,330
And this is 12 years later. The girls, by the way, do better than the males.

414
00:51:07,620 --> 00:51:14,700
And also the non-Hispanic whites do much better than the African-Americans, even though, as you recall,

415
00:51:15,120 --> 00:51:20,700
the non-Hispanic white kids had much higher rates of substance use disorders like cocaine, heroin, etc.

416
00:51:31,490 --> 00:51:34,910
You can't read this chart in the back. You can probably barely read this chart.

417
00:51:35,120 --> 00:51:41,750
Yeah, this chart lists all of our deaths since the study started.

418
00:51:43,010 --> 00:51:47,780
Eight out of 829 kids, 122 have died.

419
00:51:48,410 --> 00:51:55,430
And this chart lists in tiny print because we kept having to reduce the font as more and more kids died.

420
00:51:57,740 --> 00:52:02,630
This was the cause of death the age, gender and race and ethnicity.

421
00:52:02,930 --> 00:52:10,310
Interestingly, most studies report deaths in one sentence in a method section.

422
00:52:11,060 --> 00:52:20,060
We had so many deaths after six years that we published an entire paper just on our death rate in the medical journal Paediatrics.

423
00:52:21,230 --> 00:52:26,390
And our second paper, again on Death, will be published in Paediatrics in July.

424
00:52:27,080 --> 00:52:32,239
Papers on Death Rates. Who would think you could write papers on death rates among 1800 kids?

425
00:52:32,240 --> 00:52:39,450
Who would think you would have the sample size? But we did have the sample size.

426
00:52:39,780 --> 00:52:52,890
So this is one of the charts that'll be in the paper and paediatrics and it shows the causes of death among males and females in our sample.

427
00:52:53,760 --> 00:53:03,420
And so what we see is that close to 90% of the boys died from homicide with a firearm, but 90%.

428
00:53:13,370 --> 00:53:20,750
This compares our sample to the general population and we adjusted the general population for demographic differences.

429
00:53:21,350 --> 00:53:23,300
So we weren't comparing apples and oranges.

430
00:53:24,080 --> 00:53:33,860
And so what we see is that our rate of homicide among males is close to three times higher than that in the general population.

431
00:53:34,010 --> 00:53:37,850
Chicago is a dangerous city, so we have a high homicide rate. But even so.

432
00:53:43,000 --> 00:53:55,629
And we see a similar situation for girls. Homicide in the general population is 14% and homicide in our sample was 28% with a

433
00:53:55,630 --> 00:54:01,480
firearm and 13% of other one girl was knifed to death by her boyfriend in the kitchen.

434
00:54:01,480 --> 00:54:06,620
Another girl was run over by her boyfriend because she went out with someone else.

435
00:54:06,640 --> 00:54:15,810
It's very common. I just clarify. So so those examples would come under the other because I was thinking your other homicide, other homicide.

436
00:54:15,910 --> 00:54:19,930
So the other is death through illness, cancer, whatever.

437
00:54:19,940 --> 00:54:27,540
Yes, yes. Okay. Yes. Which we had very few. So in your delinquent male population, almost no threat of what you might call sort of no normal illness?

438
00:54:27,550 --> 00:54:32,620
No, no, it doesn't happen. They don't live long enough to die from illness.

439
00:54:32,620 --> 00:54:38,260
No. So we have we also looked at risk factors because ironically,

440
00:54:38,890 --> 00:54:43,450
there were so many deaths since our last paper that we could actually look at risk factors.

441
00:54:43,930 --> 00:54:50,500
And we found that three variables assessed in adolescence predicted early violent death.

442
00:54:51,460 --> 00:54:55,840
And that was alcohol use disorder, by the way, not drug use disorder.

443
00:54:55,960 --> 00:54:59,080
And that makes sense because drugs usually make you pretty calm.

444
00:54:59,140 --> 00:55:02,260
Most drugs, alcohol doesn't make you such a nice person.

445
00:55:03,130 --> 00:55:14,350
So alcohol use disorder was a significant predictor of early violent death, as was being involved in the drug economy.

446
00:55:14,710 --> 00:55:21,280
And these are factors measured in adolescence, but they were still risk factors for people in their late twenties.

447
00:55:22,720 --> 00:55:26,320
And then the last significant risk factor was gang membership.

448
00:55:26,980 --> 00:55:34,240
And again, gang membership early on predicting early predicting violent death when you're in your late twenties.

449
00:55:37,210 --> 00:55:46,090
What I'd like to do now is just talk a little bit about future directions for research and then open it up to questions.

450
00:55:48,190 --> 00:55:54,190
One area that I'm very interested in are the consequences of incarceration.

451
00:55:56,080 --> 00:56:01,480
Many studies examined recidivism and among people who have been incarcerated,

452
00:56:02,320 --> 00:56:08,770
and then many people actually examine health among currently incarcerated populations.

453
00:56:09,280 --> 00:56:19,239
But no study that I've ever found examines how patterns of incarceration affect health and being a professor in a medical school.

454
00:56:19,240 --> 00:56:28,240
I am interested in health and so think about all of the variables we have age of incarceration, the number of incarcerations and releases,

455
00:56:28,840 --> 00:56:37,030
length of incarceration, the amount of time you spend in the community and between incarcerations.

456
00:56:37,690 --> 00:56:42,100
Terms of release your experiences with probation, parole.

457
00:56:42,430 --> 00:56:49,810
We have all these variables and we have no information how those experiences affect health in adulthood.

458
00:56:52,300 --> 00:57:02,650
The second question I'm really interested in is re-entry and what variables improve the likelihood of successful re-entry because these kids,

459
00:57:02,830 --> 00:57:08,920
they re-enter all the time. The initial length of stay for detained kids is only a couple of weeks.

460
00:57:10,270 --> 00:57:19,150
So what we need to do is figure out how to improve short term outcomes as well as improved long term outcomes,

461
00:57:19,330 --> 00:57:23,560
and especially for minorities, because they're the ones who are having the most difficult time.

462
00:57:26,330 --> 00:57:37,489
When people ask what I want to do in the future, I would like to get funding to study the offspring of our participants because

463
00:57:37,490 --> 00:57:42,830
most of them now have kids and we have compiled a database on their offspring.

464
00:57:43,100 --> 00:57:50,750
And it's an opportunity for us to look at the intergenerational transmission of alcohol by alcohol abuse, drug abuse and violence.

465
00:57:53,180 --> 00:57:59,780
In closing, because I do want to leave some time for questions. Unfortunately, I have five more pages of notes, but which breaks my heart.

466
00:57:59,780 --> 00:58:04,400
But I will skip it because my whole public policy section.

467
00:58:04,610 --> 00:58:11,360
But I just want to say that it's we have had health care reform.

468
00:58:12,320 --> 00:58:23,900
The it's unclear whether health care reform will help people like our participants because they may just fall through the cracks.

469
00:58:23,930 --> 00:58:27,530
Many states have not increased Medicaid. You may be aware of.

470
00:58:29,120 --> 00:58:32,570
And so it may not health care reform may not help these kids.

471
00:58:32,930 --> 00:58:37,160
But even if we even if they do have insurance.

472
00:58:37,430 --> 00:58:43,640
The problem is, I don't know if the community is ready for subjects like ours because they have so many problems.

473
00:58:44,870 --> 00:58:48,830
Most have been abused or neglected. They have addictions.

474
00:58:49,190 --> 00:58:57,470
They have comorbid psychiatric disorders. They have so many risk factors that it's actually difficult to disentangle them.

475
00:58:58,070 --> 00:59:06,020
So the question I always pose is whether the community health system is ready for participants like ours.

476
00:59:07,160 --> 00:59:10,880
Questions? Depressing topic. It is very depressing.

477
00:59:11,030 --> 00:59:13,140
See, my title was not misrepresentative.

478
00:59:13,190 --> 00:59:20,510
It's very good process, Rachel, but I actually find it an uplifting topic because we're doing something about it.

479
00:59:20,870 --> 00:59:27,140
What is depressing are all of the general population studies that cost the NIH millions of dollars,

480
00:59:27,410 --> 00:59:32,540
and they never included any people that have systematically had the worst outcomes.

481
00:59:32,810 --> 00:59:39,440
That's what I find depressing. This is not depressing. We're doing something about it and it will be used to advance public policy.

482
00:59:41,510 --> 00:59:44,510
I had a methodology methodological question.

483
00:59:45,470 --> 00:59:57,680
I was curious about your decision to use face to face interviews as opposed to something like anonymous self-report questionnaires.

484
00:59:58,490 --> 01:00:04,790
And if you thought it had an impact on how forthcoming the participants were about

485
01:00:04,790 --> 01:00:11,010
potentially stigmatised issues such as substance abuse or mental health problems.

486
01:00:11,720 --> 01:00:13,040
I think that's a great question.

487
01:00:14,900 --> 01:00:21,380
The we thought long and hard about our decision and it would have been a lot cheaper to do it in the way you suggested.

488
01:00:21,590 --> 01:00:23,660
It would not have been effective for many reasons.

489
01:00:24,020 --> 01:00:33,020
One, they would have refused the questionnaire to these kids, as you recall, from the IQ side, would not have been able to fill out a questionnaire.

490
01:00:33,500 --> 01:00:37,970
They these kids live in impoverished neighbourhoods with terrible schools.

491
01:00:38,210 --> 01:00:45,290
They do not have the verbal skills to be able to complete a questionnaire, even with the interviews.

492
01:00:45,500 --> 01:00:49,880
Sometimes we have to explain a question so that they even understand it.

493
01:00:50,060 --> 01:01:00,350
So that's one part of it. The other part is that these kids seem to have no difficulty talking about their problems.

494
01:01:00,560 --> 01:01:05,930
We made it very clear to them who we were. We were not part of the judicial process.

495
01:01:06,410 --> 01:01:14,970
We had letters attesting to that fact. I do do not believe that it at all influenced it.

496
01:01:14,990 --> 01:01:20,200
And I think we would have had no sample or had very unreliable data had we done it.