1 Racial/Ethnic and Gender Disparities in Health Outcomes of Persons with Spinal Cord Injury. Presenters: James Krause and Karla Reed. >> JOANN: Good afternoon everyone. And thank you for joining the webcast on racial/ethnic and gender disparities and health outcomes of persons with spinal cord injury. My name is Joann Starks with the National Center for Dissemination of Disability Research or NCDDR based at SEDL which we pronounce as "seed-el." Austin has been our home base, but until this year we were known as the Southwest Educational Development Laboratory. I hope you agree that SEDL is saving improvements. I'll be moderating today's webcast and getting your questions to our presenters. Cohosting the webcast today is Frank Martin who is also with the NCDDR. Before we move on with the introduction of today's presenters, I want to be sure to thank our partners at ILRU in Houston for helping with the technical side of the webcast. There are some materials accompanying today's webcast that can be found on both ILRU and the NCDDR websites. In addition to a description of the webcast and information about the presenters, there is a PowerPoint file for the presentation as well as a text file with the same information in format. Please remember that this information 2 is copyrighted and cannot be used without the written permission of our presenters. Contact information is provided at the end of the presentation materials. You are invited to submit questions at any time during the webcast and the presenters will try to answer them. In addition, we have set aside a question and answer period at the end of the presentation. To ask a question, you can click the submit button at the bottom of your Windows Media Player or RealPlayer screen or you can send an E-mail to webcast@ncddr.org. If you prefer, you can call in our toll free number 800-266-1832 with your question. If you should have any technical difficulties during the webcast, please call ILRU at (713)520-0232 and dial 0 for the operator to ask for assistance. This number is both voice and TTY capable. Last, I'd like to mention that we would very much appreciate your feedback by filling out a brief evaluation format the end of today's webcast. You can click on the downloads tab at the bottom right-hand side of your Windows Media Player or RealPlayer screen and you'll find a direct link to the evaluation form. A link to the evaluation is also found at the ILRU and NCDDR web pages related to the webcast. I'll remind you about this again at the end of today's presentation. Now I'd like to turn it over to Frank Martin who will introduce our guest presenters for today. Frank? >> FRANK: Thank you, Joann. The NCDDR is pleased to bring you today's webcast on the topic of disparities in spinal cord injury. We 3 are privileged to have Dr. James Krause and Karla Reed for today's topic. James Krause is Professor and Associate Dean for Clinical Reach in the College of Health Professions at the Medical University of South Carolina. Dr. Krause holds numerous positions including director of the program for movement, exercise and rehabilitative research and director for the Center for Interdisciplinary Spinal Cord Injury Research. He has served as principal investigator on several long term spinal cord injury outcomes research projects sponsored by grants from the National Institute on Disability and Rehabilitation Research, NIDRR, and the National Institutes of Health, NIH. He has served on numerous consumer advisory roles and Dr. Krause has published over 80 articles in peer-reviewed journals and has made over 100 presentations at national and international professional conferences. Karla Reed is a project coordinator at the Medical University of South Carolina. She has worked with Dr. Krause for two years as project coordinator of three federal grants. In addition, Dr. Krause has several other colleagues who have collaborated on this project including Jennifer Coker and Randy Smith. Now, I'd like to invite Dr. Krause to begin today's presentation. >> JAMES: Well, good afternoon to everyone and we're very excited to be able to make this presentation. I think you'll probably figure out very quickly that my presentation style is not to read off of slides or to over prepare. In other words, we know this research so well from having done it for such a long time that we are -- we're 4 going to go through it and cover our data and we have a lot to cover; but we're going the make it very interpretable. I'd like to thank Frank for the opportunity to do this and I think we're ready to move on to our next slide. First I would like to acknowledge that we have multiple funding sources for the current project and actually this comes from several separate projects, the majority of which are funded by the national institute for disability, rehabilitation research. We also have funding from the national institute for health and we have multiple collaborators that have contributed to the various data collection. First and foremost the Shepherd Center in Atlanta, Georgia. I was at Shepherd for 13 years and have continued to collaborate for six years since then. Craig Hospital Rancho Los Amigo Rehabilitation Center. They actually like to let you know at the beginning that we are vested in this area of research. We have contributed a great deal of time and effort and, therefore, what you will see is -- that's reflected in our research and I also would like to say that we are putting together a project with Craig Hospital with Rancho Los Amigo and we are looking for collaborate ors from traditional African American universities or organizations that are in the forefront of activities for the Hispanic population or American Indians with SCI and other disables conditions. Next slide, please. For those of you who are not very familiar with spinal cord injury, the annual ins dents puts it at 40 cases per million. That's roughly around 200,000 new cases a year depending on the specific 5 estimate, and we're pretty familiar with the immediate loss in terms of sensory function and motor function. So there is almost an immediate and permanent loss of sensation and motor function below the level of injury. SCI is associated with early mortality. It depends on the severity and it may also adversely affect function and quality of life. The majority of individuals with SCI are Caucasian, that's based on population statistics and studies of racial/ethnic minorities have actually been quite rare and the existing studies have primarily focused on African Americans rather than Hispanic and American Indians with SCI. Next slide, please. The purpose of the presentation that we have today is to describe several studies that we have conducted related to SCI outcomes. We want to highlight the comparisons as a function of race, ethnicity and gender, meaning that we are going to show comparisons between groups. It is very important to do because we want to ensure that we understand the special needs of different groups. And when we draw comparisons between the groups, it gives us a much better idea of the needs of a particular group and perhaps some of the factors which are limiting the outcomes of any particular group. And we do want to discuss the practical implications as we go and the needs for future research. Next slide, No. 5. So there are four studies that we are going to describe. We have integrated them throughout the presentation and what that means is as we come upon a new study, we will briefly describe the study, but the findings for a particular study may fall 6 into several points in the presentation because the actual presentation is broken down by the topic area. So we have four sources of data, four different studies. The first is the model spinal cord injury systems. The second is what we call the Minnesota/Georgia aging study, the third the Shepherd study and the fourth has several collaborators. The breakdown of the presentation will be by topic and we will cover essentially three topics, participation employment, subjective well-being and depression, and health. The fourth which says cross cutting we actually have integrated what is a very large scale study with multiple populations. We have now integrated that through the entire presentation. Next. We will start with participation employment and we will go right to slide 8. I do take breaks for a drink of water or diet Pepsi, so if you hear a brief pause, that's what we have. When we look at employment in spinal cord injury, racial/ethnic minorities have consistently -- with the exception of Asian Americans -- reported lower employment rates and poorer employment outcomes compared to Caucasian and I'm going to summarize data from three data sources and this will be the model systems study, a 30 year study of aging and then we'll shift the focus a little bit from employment, per se, to talking about earnings. Next. And the first study is the model systems study, and this is data from a 1999 publication we did in collaboration with various people. 7 Next slide, slide 10. The model systems for those of how are not familiar with it, it's a federally funded number of centers of excellence in care and research in SCI. And the majority of information that we know that you see summarized from -- about SCI comes directly from the model systems and in essence these are centers that competed for research and clinical grants based again on the care they provide, the uniqueness and as well as the research component and the number of centers survey ease in five-year cycles. There are currently 14. All contribute data to the National Spinal Cord Injury Statistical Center at the University of Alabama in Birmingham. And so we are analyzing a portion of that data as related to race, ethnicity in this paper and we'll report data as follows. Next slide, please, No. 11. And what you'll see here is there is a graphic presentation of the employment rate as a function of racial/ethnic group and gender and by far the highest rate is for Caucasian as was identified in this study at this time. Again, it's a 1999 study. It's dated about ten years and the rate for Caucasians hovers around 30 percent, much less for African Americans which is actually the rate somewhat higher for African American women than African American men and it's just the opposite trend is observed for all other groups including Caucasian, Hispanic and other which is a larger category that would include American Indians and Asians. Next slide, please, No. 12. And when we look at the current data, and the data is actually presented in this next slide, and again the key points are the employment rate for Caucasian men was 8 36.3 percent and the most significant comparison is with African American men where the employment rate is only 7.3 percent. The rate for Caucasian women is somewhat lower, 28.3 percent, compared to 11.7 percent for African American women. So, again, there is sort of the reverse trend there where the outcome in terms of employment were somewhat better for African American women. The rates for Hispanics are 12.9 for men, 8.1 for women and then for other minorities, which is not a very descriptive category, but there simply weren't enough people in that database to break it down any further, but the rates for men and women are 17.6 and 12.5 percent. And on this next slide which is No. 13, we show a graph of the relationship between education and current employment rate, and we do it for various groups as a function of severity of injury. And this graph actually has nothing to do with race, ethnicity or gender per se, but I think it's very important to understand the pivotal role of education in employment. Because as we will allude to later, it is really the great equalizer as a function of severity of injury, but it's not a great equalizer in terms of race, ethnicity or gender for that matter. Simply meaning that at equal levels of education from our current evidence we don't see as good of outcomes especially for minorities as for what you see with Caucasian, but basically we have four lines one on each reflecting the severity of injury. And if you look at the bottom line, that is for people with the most severe injury, and is an accelerating function, which simply means it starts out relatively flat with very poor outcomes and it gets better as it 9 goes, but the biggest jump is between a bachelors degree and a masters degree. And it's essentially took a masters degree for the most severely injured group -- cervical 1 through cervical 4 in order to equate the outcomes to the other groups. If you look at the curve for the next severe group, which is C. 5 to C. 8 injuries, it's relatively straightforward, meaning with each advancing amount of education, there is about an equal improvement in the employment rate, but when you get to the two groups with the least severe injuries, it looks rather different. And those two groups are individuals with noncervical injuries and individuals with ASIA-D injuries and this is an index of the neurologic completeness of injuries and individuals with ASIA-D has significant motor sparing below the level of injury. So frequently they are ambulatory and they are the less -- the least severe injured of any group. And when you look at those functions, they start out better than the most severely injured group at the lowest levels of education, 11 years or just simply a high school diploma, but their employment outcomes improved rather quickly, even with an associate's degree, continue to improve with bachelors and then they really plateau -- there and is a lot of anomaly in the graph which is fairly random, but a bachelors degree across the two groups is the equivalent of a masters. So the importance of this is the more severe the injury, the more education that is required to maximize the employment rate. And we'll go on to slide 14. And this is a new series of studies. I'm going to leave it here for one second. Essentially now 10 we're moving to an entirely different study. And this is the Minnesota/Georgia study and I will describe only a small portion of it that is related to employment. Slide 15, please. The Minnesota goo aging study is a 35 year longitudinal study which means that data has been collected over a 35 year period on the same individuals. Now, we've continued to add participant samples over time. We started in Minnesota, we started with three samples. We started with one sample in 74, adding samples in 85 and (inaudible) in 1993 and then we moved on to when I moved to the Shepherd Center in Atlanta, we added samples in 1993 and 2002. I do hear a little background noise and maybe the phone can be muted on the other end just to simply avoid that. Okay, so we've had a total of 2200 participants over the years and although not displayed on the slide itself, over 4500 responses if you look at repeat times people have responded. We just have started the 35 year follow up. And all these are adult traumatic SCI of at least one year duration. So they are all 18 years minimum and we will move on to the slide 16. One of the reasons I became so interested in racial/ethnic differences in outcomes is we started the study and there were no racial/ethnic minorities in our study other than a handful of American Indian. We didn't even collect data on race ethnicity. So when we moved to the Shepherd Center, we had the opportunity to rectify that situation by over sampling racial/ethnic groups that were not Caucasian. So, in other words, we would take some Caucasians into the 11 study, but we would try to get as many racial/ethnic minorities as possible and in this chart you'll see that approximately three-quarters of our sample at the last time of measurement were Caucasian. The vast majority of the rest were African American and the others were just very limited representation of other groups. So the findings of this study will apply purely to Caucasians and African Americans. Next. I want to describe a study of earnings as opposed to simply employment rates. We had a two stage model, the first part of the model is predictors of employment status and the second part of the model was predictors of conditional earnings. And conditional earnings stated simply are earnings among those who are employed only. So it excludes all people that are not employed. And then another part of the model is combined, which looks at unconditional earnings. And when you look at unconditional earnings, then you're attributing zero earnings to people that are unemployed. And the important thing to remember with this is that if we look at predictors related to employment and we can go ahead and flip to the slide 18, and we have two types of (inaudible), attributable differences, those are factors that are not directly that we can directly manipulate as change in policy. They are gender, race, age, severity and things like this. So they fixed characteristics of the individual that cannot be changed. Now, you can retarget -- you can retarget policies based on our findings, but they are not policy factors per se, and this terminology comes from health economics. And so then we have policy 12 factors and those are the ones that can become the focus of policy changes to affect changes in outcome. So, therefore, for instance, education. Since we've already identified the importance of education to outcomes, if you change educational levels, change those patterns, you do have the hopes of actually changing the outcomes themselves. Move on to slide 19. And what this slide is is simply the relationship between the predictor variable and employment status, and since we selected these variables because they were related to employment status, it's not surprising that there are significant differences statistically as a function of these characteristics. And stated simply, that people that were more likely to be employed were men, nonAfrican American, those ages 35 to 49 were less likely than those who were younger. Those ages 50 and older were less likely than those in the youngest group. Those who had been injured 21 or more years, those who had least severe injuries based on those injuries being noncervical, in other words, thoracic or lumbar and those who are ambulatory. Also, differences of course related to education, having some education beyond high school and then substantially stronger effect for those who had a college education. Next slide, which is now slide 19. And this shows the actual conditional earnings. And from the slide you can see that there are only three factors -- I'm sorry, we are on slide 20. I'm sorry, slide 20. There are only three items that are significantly different when you only look at among people that are working. And you can go to 13 slide 21 that shows this graphically, and they are gender, men, on average, among those who worked, made $15,946 more than women. Race, nonAfrican Americans made $19,402 more than African Americans, and we did lump -- in order to keep as many people in the study as possible, that lumps Caucasians and all other groups -- nonAfrican American factor, but at the same time those numbers are small of those other participants. So it is primarily African American versus Caucasian and the Caucasian group made considerably more money and then the other one is having a college degree which you can see is the much strongest factor in conditional earnings of $36,000 essentially. Next. Now we'll go on to slide 22. And these are the unconditional earnings. And the unconditional earnings, again, because this factor is different than employment rate by virtue of if you have 20 people from one group who are working and the other group that is 10 people working or 10 percent versus 20 percent, then the group that only has 10 percent working, all those other people that are not working are getting zeros allocated so their earnings are going to be substantially lower on average. And what you'll see in this slide, and you can actually go to slide 23 and see it graphically, that men have unconditional earnings of just over 12,000 more than women. NonAfrican Americans just over 16,000 more than African American. Those with noncervical injuries, 5,000 more than those with cervical, those who are ambulatory, 8800 higher than mon ambulatory. Just under $10,000 higher earning for those with some education beyond high school compared to those who do not have more 14 than high school or less, and those with a college degree, $37,800 difference. One thing I would point out although we're looking at race ethnicity, when we went through these slides, you may or may not have noticed it, but the point is when it comes to severity of injury, it's significantly related to employment such that those with more severe injuries either by injury level or ambulatory status were less likely to work. And when you look at unconditional earnings, their unconditional earnings are lower, but among those who were working, we observed no differences. So I think that's something that we can all feel good about. If you can get people working that have more severe injuries, their earning power seems to be (inaudible) to those with (inaudible). Now we'll go to slide 24. We did another study. The methodology was essentially the same, only this study used the 30 year data, the one I just described used our 25 year date A. this gave us a larger sample, basically twice the number of people from 615 to now just less than 1300. And we had a greater diversity of policy variables in the second study. So we will go on to the next slide. And again I'm going to gloss over this slide relatively quickly because it basically is the same finding as the 25 year and that's the same groups are at an advantage when it comes to becoming gainfully employed. The same sort of advantage, men, Caucasians, those who are younger, less severely injured and those with more education all were advantaged in terms of becoming employed. 15 Next slide. Now we again look at the earnings levels between groups and I will refer you to slide 27 which is actually the graph of this while I go over the numbers. So when you look, again, you'll e see a similar set of variables with conditional earnings. Again, among those who are employed, men made an average of 11,000 more than women. Caucasians about 12,600 more than minorities. And those with a college degree, about 21,800 more than those who had education of high school diploma or less. Now, those numbers are less than the other study, and the reason for that is we included a number of other variables, some of which were important and some weren't. So we also looked at work status and gin you are I, whether they returned to the same job after the injury, whether they returned to a different job in the same company, the total number of jobs and none of those factors were significant. So the earnings of people regardless of their status on those variables was essentially the same. They were not statistically different, but when we looked at total years worked since SCI for each year worked, there is an 850-dollar difference more earnings. The percentage of time since SCI working, factor of $1,300. I will apologize, I do not remember quite how we scaled that, but again, the greater portion of the time working after SCI, the higher the conditional earnings. Being employed by either the government or private company were both significantly -- earnings were significantly higher than if the person -- the comparison group there was family business or self-employed. And I think the reason for that simply is that's a real mixed bag. People report that they are self-employed 16 can be a lot of things, but their earnings aren't quite as good. One of the important facets of this study that I want to point out, we controlled for number of hours worked per week so the a tribute Al differences you see, those differences mean after you account for all these other factors, including differences in the number of hours, working, severity of injury, years of education, you still see substantial differences in earnings -- at a disadvantage in terms of earnings. And we will go to the next slide, No. 28. And this is the unconditional part -- unconditional earnings, and again since we have a lot of other -- since we factored in employment status, you'll see a lot more variables significant. It's not really that important, but you can look at it in slide 29 graphically. The point being again the same variables that were significant in the previous study -- gender, race, age, severity of injury, ambulatory status, education, all those variables are significant again and at the same time (inaudible) job has an advantage. If returned to the same job you were more likely to be working. The number of jobs, total years working, all the other indicators, percentage of time working skins SCI are associated with higher earnings. Next slide, No. 30. So I want to summarize this area before I move on. This will summarize our area of employment. So I actually would at this time, while I do close this and make this summary, I think we'll ask if anybody has any questions. Go ahead and submit them while I summarize and if they come in in time, we will take a question or two and then 17 we'll move on. So again the employment rates clearly are lower among certain racial/ethnic groups and primarily African Americans with the poorest outcomes. Men typically are more likely to be employed than women, but this reverse trend was observed for African American. It's not a very powerful relationship, in other words, the differences weren't great, but it's a difference in pattern. Education is key in both return to employment and traditional earnings. So, in other words, if you are more educated, it's more likely you'll be able to work. If you work, you're more likely to make substantially higher amount of money. And just to touch again the disparities (inaudible) between men and women and between Caucasians and African Americans are actually accentuated by differences in earnings. So, in other words, if you're a woman or you are from one of those minority groups on average, the likelihood of return to work is less and among those who return to work, they make on average less money. And the other is that multiple policy variables were related to earnings, policy variables are again very important because those are the things that become the focus of (inaudible). We'll go to slide 31 and I will ask our moderators if we have any questions. >>I have a question, Dr. Krause. And these studies that you are getting data from, the 25 and 30 year studies, are these continuing on? Are these studies that you will be continuing to follow over time 18 or are these studies completed with their data at those year levels? >> JAMES: What it is, is a 25 year study means 25 longitudinal. It has been 25 years since we collected the first data -- or I shouldn't say we, the University of Minnesota collected the first data and so every four to five years we do a follow-up. So we've completed the 25 year. We have completed 30 year, and we currently are funded by NIDRR of the Department of Education to conduct a 35 year study. And I think we have about maybe 350 returns with a goal of getting up to about 1,000. Every alternate time we do the study, we add a new sample. So we did it in 74. The next follow up was 1985. It was 11 years. We added a sample. It's been four to five years every year since then. We did not add a sample in 89: We added in 93. We did not add in 98. We did in 2002 and in the current cycle we are not adding a sample, but when we hopefully have the opportunity to again expand on the study in another couple of years, we will do so again. It's ongoing. We're always adding new people, but we're also always maintaining as many former respondents as possible. >> FRANK: This is Frank Martin. I have a data question and then I have a procedural question. In terms of the data question, you mention several -- what I think could be characterized as quality indicators of employment, earnings, competitive outcomes, but one that wants mentioned, it could be that this data point is collected is whether or not medical insurance is provided by the employer. I wonder if you see that as a possible quality indicator and in this 19 case would that be a possible disincentive if that's influencing outcomes or if that's just something -- what are nor thoughts about that? >> JAMES: I certainly think it is a quality indicator. We have not focused on that in our research, but one of the things that you see is that -- especially in the area of employment and spinal cord injury, the majority of research comes from the model system. And the model systems national dataset is rather limited. It's not a focused study on employment. So the variables that tend to be studied over and over again are the same. So that's why we looked at earnings when we looked outside of the model systems that is not a model systems study. So we have looked at things like the incentives and actually in the Minnesota/Georgia study, we did not put all that data up there, but I think that -- clearly disincentives are factor in people returning to work, but I think what we need to move toward, frank, is looking more at job retention, job satisfaction -- excuse me -- earnings. We need to look conceptually at different ways. And it was beyond the scope of this presentation to outline some of our conceptual ideas in that area. One thing that I did find very interesting in one of the studies is when we looked at the barriers people reported, among those were unemployed. We asked why are you unemployed? Checklist. A lot of people put down financial and medical disincentives. Loss of benefits and money, but when we looked four or five years later, those factors really didn't relate to whether somebody went back to work. The only 20 factors that we found that related to not going back to work were actually the health of the people. People that had recurring pressure ulcers, things like that, so people had those barriers. They were important to them, but they found ways around them. I think if you look at the system, the VR S. system, I think it's flawed in that it encourages people to go back to work. It doesn't encourage them to retain it. You can go on a trial work period and keep all your money for so long. After that, they start cutting into it. So just even from a behavioral perspective the cases are closed based on somebody going to work. That doesn't reflect poorly when individuals lose their jobs. That case is already closed. So that's why I think we see with spinal cord injury about a two to one ratio of people who are in the employable age range. For every two that go back to work and stay at work, there is one that does not stay at work. >> FRANK: Okay. From a procedural standpoint -- first off, I applaud your work that you mention at the Shepherd Center in terms of recognizing the under representation of minorities and the real need to increase those numbers and increase involvement. I'm wondering if you can share any of your experiences in terms of challenges of facilitators to that recruitment process special for individuals who you'd like to stay in the study year after year. >> JAMES: Are you talking about research participants? >> FRANK: Yes, I am. >> JAMES: Well, we've been able to retain the people. It's a 21 mazing what a small inducement -- you used to give people ten dollars and you used to get a lot of response. And now I think 50 doesn't really do it but then again our questionnaires keep get longer. So we're not quite sure what's at play there. Sorry we had to close the door. We were getting a copying machine behind us making noise. But we've been able to retain the participants. I think once they know you're vested in the research, we contact them regularly in terms of the studies. We try hard to give them a stipend. We do call them back if they say they are going to participate and haven't sent it in. And I think that the bottom line is they come to realize that we really do have an interest in this area and that we're not just another survey. >> FRANK: Good point. >>Let me go ahead and break in and say that we have now completed our first 40 minutes. So we still have 50 minutes to go. I just wanted to let you know that, Jim, that that's where we are in our timing. We can run over though if we're not we can run late. >> JAMES: Trust me, we can wrap up to the minute. Slide 32. This is our next topic area and this is subject fief well-being. We will go to the next slide which is 33. And the definition of subjective well-being -- and this is the definition that I'm giving to it. Others may differ on this, one come pontes of quality of life and it reflects the individuals subjective appraisal of his or her life rather than the actual activity pattern. So, in other words, someone -- for instance, their staigs with finances versus how many 22 money they make, and the reason we look at subjective well-being is two people may be in objectively very similar circumstances. One might be very satisfied, one might be very dissatisfied. So this was a primary consideration in terms of quality of life. And we will move on to slide 34. And in this slide we have seven subjective well-being scales that we've developed over the years in the Minnesota/Georgia study. And they relate to -- in fact, you'll see nine here on the slide. The first two are just overall summary scores of satisfaction and problem areas that the other Steven scales are what are primarily important. They were derived from a factor analysis of 50 items, 20 satisfaction items, 30 problems items. And what a factor analysis is for people that aren't familiar with it, in essence, it lumps similar items. So, for instance, if someone says I have problems with money. I have problems with finances. I'm satisfied with finances. Those items tend to get lumped use ag statistical technique and then when we're done with it we look at that time items that go together and we give a name to that. what does that seem to reflect? And these seven scales are what we feel under lies subjective well-being of people with spinal cord injury. And I will name them and we can actually go to slide 35 and graphically look at the results as a portion of race, ethnicity, minority participants are lumped together. And what I mean by that is those are racial/ethnic groups that are underrepresented. Primarily African American, but some Hispanic and some American Indians. And the seven scales are engagement, which are people that 23 are satisfied with their activity levels. Negative affect and higher scores are actually worse with negative affect. Health problems stated -- means exactly what it says. And again, higher scores are worse. Finances, career opportunities -- and that reflects employment, but also opportunities to learn and to improve one's life. Living circumstances, home life, family situation, interpersonal relations and sexual relationships the seventh scale. And what we find, if you look at the graph, you'll see that the largest differences between the Caucasians and nonCaucasians are in the area of the finances and career opportunities. It's actually two of only -- only three of the scales were statistically different at all. The other being living circumstances which favors Caucasians a little, but not a lot. So the important point I think here as we go to the next slide which is No. 36, and I'll finish my thought before discussing this slide, but the important point that those scales suggest are that there are discrepancies or disparities in the areas that reflect traditional opportunity in society. And that those carry over into the area of spinal cord injury and that shouldn't surprise anybody based on what we just saw with employment. Now, what I have on slide 36 is these are individual items. These are some of the items that make up the scales. And I picked out three that had the largest discrepancy between Caucasian and minority based on the F. statistic which is an index of statistical significance and analysis of variance and if ire not familiar with that I probably can't explain it reasonably well today. I'd have to 24 have somebody who is more technically sophisticated myself to get into the details of it. The larger the F. ratio, in general, the larger the difference or the less likely that the observed difference is due to chance. So, again, employment, finances, job opportunities, those are the areas that are different. So then you look at items favoring minorities. And I put these up there because even though the differences are not large, satisfaction with sex life and satisfaction with physical appearance were actually higher among minority participants. And I don't want people to fall into the belief of saying necessarily everything favors Caucasians or one group rather than another. You really need to do the research and look at what areas are different and the direction of those differences. Next slide, please. And this is a very busy slide. I only put it up here to show in green the number of life areas in which there are no significant differences between Caucasian and nonCaucasian participants. And statistical significance is largely determined by two things: One is the actual differences between have between the groups, the size of the difference and the other is the size of the sample, how many people you enroll, and the larger the number of people you enroll, then the smaller the difference that is required to be statistically significant. And what that in essence means is because our samples are large, they are sensitive to very small differences between -- very small discrepancies between the two groups and it would identify those as statistically significant, yet when you 25 look at the items in green, social life, general health, life opportunities, accomplishments, activity level, how I spend my time, availability of health care which frankly is surprising, and control over life, the participants are not reporting differential satisfaction or differential problems that is our satisfaction items. They are not reporting differential satisfaction, meaning that on those items there are no racial/ethnic differences. So again we don't want to fall in the trap of thinking that everything is different, but we want to hone in on those areas that are important. And what we found so far is the areas that are important are those having to do with opportunity, career opportunity and the like. We will go to slide 38, and again this is a very similar slide to the one two slides ago, only these are the problem items. And nonCaucasians are reporting the largest differences with Caucasians having problems with their transportation, having an adequate job, making money, lack of job skills, lack of opportunities to learn and lack of income. So, again, there is this large area that relates to the ability to work and make money and having those sort of opportunities which the nonCaucasians self-reporting to be more problematic. The only item out of these 30 which the nonCaucasians reported fewer problems with making friends and that's actually pretty consistent with the two areas in satisfaction. Next slide. We're going to shift again to a very different study. And so I need to spend a little time describing the study and I'm going to have one drink of diet Pepsi before we start. They are 26 not giving me any royalties by the way for saying the product. This is a study of gender, race and aging. We are going to propose to do a follow-up within this grant mechanism that I alluded to at the beginning of the study. And this is the only study like this. We're very blessed to have the best of collaborators that anyone can have, and that is, again, the Shepherd Center in Atlanta, Rancho Los Amigo hospital in Downey, California, Craig Hospital in Colorado and Santa Clara Valley Medical Center also participated in this. We have not followed up with Santa Clara simply because the population is primarily Asian Americans and we just simply have not been able to find enough folks within that group to draw meaningful analyses, and so they I guess are the -- among the racial/ethnic groups, the one that we've had the most difficult time finding enough people to participate to draw meaningful data to address their problems. I wish we certainly could because we would want to know about the uniqueness of the situation they face. And that's why we do this research. Each one of these hospitals has contributed largely a certain demographic that's within their region. So, for instance, the Shepherd Center largely contributed an African American population. Rancho a Hispanic Latino population, Craig Hospital, American Indian, largely Navajo, but also Sioux and Santa Clara, Asian Americans. The way we proceeded is we took as many of these diverse groups as we could from each institution and then if we had the opportunity within these groups to select, we did try to get as many women as 27 possible. We actually -- 40 percent of our sample were women compared to what I guess would be about 20 percent in the general SCI population. So we were fairly successful in doing it except with American Indians, we simply could not find enough, but yet women are reflected in our analyses for that group. Next slide, No. 40. And this is simply a breakdown of the racial/ethnic diversity of our sample. And as can you see visually from this, the size of the groups were relatively equal except for Asian Americans which I believe was 7 percent. It looks a little bigger in the slide, but it's not. And the other groups were relatively equal in terms of statistical analyses, they were I would say equally meaningful size groups although I do not have the exact numbers in front of me. They ranged between 20 and 25 percent of the overall group again with the other I believe 7 percent being Asian American. Pf. No. 41. We once again have a very busy slide here, but I want to draw your attention to the most important aspects of the slide and that is the primary differences, just as the differences were with the previous study, Minnesota/Georgia study, when you look across these diverse samples, the largest differences in terms of statistical significance between groups were in those same two areas, finances and career opportunity. So once again, now we've expand I had yont African Americans to other groups and we find these same sorts of disadvantages and you can go to slide 42 and we'll look in detail at the financial -- the finances and when you look at that you'll see the 28 Caucasians report the highest level of subjective well-being and really I guess followed by the American Indian sample, somewhat less for African American and then Hispanic probably about the same level as African American. The groups other than Caucasian reported actually quite similar responses to each other, but different from Caucasians. If we look at slide 43, this is a breakdown of the scale in terms of career opportunities. And when you look at the career opportunities, what I find interesting in this, two things that are striking in this graph and about the differences, once again, Caucasians report that the best career opportunities, but women actually reported somewhat better or more career opportunities well-being than men for every group except African Americans. And if you look at American Indian and Hispanic groups, theirs are actually somewhat lower than African Americans in terms of well-being with career opportunities. You might also notice that the relationship between men and women is exactly the opposite of what we showed with employment earlier, but this is an anomaly that happens when you use smaller samples. So even though we had about 120 participants from each racial/ethnic group, we still had -- that's still a relatively small sample. And I actually looked at the employment status of these groups after the fact. And after observing the trend and ironically that simply does reflect the fact that within these groups for employment status, women tended to do better than men for over I group except African Americans. 29 I would still hold to the general trend that men do better in terms of employment than women overall. There is a reverse trend for African Americans as we reported previously. So with regards to this particular study, I think I would ignore the gender differences on this particular scale because we did observe the different pattern in employment and just noted racial/ethnic group differences and again I think that the satisfaction or well-being model for the American Indians and Hispanics was actually a bit lower than even for the African Americans. Next slide, 44. So the summary again, largest differences in the area of employment, finances and career opportunities. Not all significant differences favor Caucasian. Many areas of life and problems that are unrelated to race/ethnicity, then when we flipped over to the health study, we observed and actually that should say comprehensive study of the multiple groups, we observed the same basic pattern with African Americans fairing no worse and sometimes better on outcomes than the other racial/ethnic groups. And lastly, and I think the primary conclusion to be drawn from that is again that the areas of dissatisfaction are consistent with opportunities and disparities on the general population. Next slide. These are the references for this section. I'm not going to pause here and go right to slide 46 and go to a related topic which is depression. Depression is in essence a form of subjective well-being, but it's a form of when people start experiencing very low subjective 30 well-being overall, and it can develop into a syndrome which is very compromising to quality of life and can be related to health problems or even suicide. No. 47, I'm going to explain another totally new study or different study. And this is the Shepherd health study. This is the last study we will go over. This is a study we did in 1997 and 1998. And it's in there because we surveyed close to 1400 people at that time, and we actually are performing a current ten year longitudinal follow up. We have about 750 of the same people that have responded to date and I don't think we'll get a lot more than that, maybe 800. Mortality of course is an issue and has reduced that Sam considerably. The inclusion criteria is the same as the Minnesota/Georgia study. They are adults with traumatic onset SCI, at least one year duration, just over a quarter of the population were minority -- of our sample was minority and the majority of that group were again African American. Go to 48 and you'll see on this slide again that just reflects the numbers that I just had reported that again a practices matily three-quarters African American and again limited representation of racial/ethnic groups. Now we go on to No. 49 and I'll take one drink before we start. Our study design is what we call a mediational model, and our hypothesis is basically that African Americans would have a greater likelihood of a depressive disorder than Caucasians. This would disappear after accounting for socioeconomic status. And we were looking at two indicators, education and income. And we can go ahead 31 to slide 50 and put up the graphic. The requirements to demonstrate mediation must have race, must be significantly related to the mediators and the depressive diagnosis. So, again, you have a relationship between race and depress sieve symptoms where one group is higher than another group on depressive symptoms, then you introduce a relationship that's there between the race and the mediators which are in this case education and income. Those mediators must also have a relationship with the depressive diagnosis and when you account for those mediators, the relationship between race and the diagnosis must disappear if you're going to demonstrate mediation. So on slide 50, graphically you can see that we expected aging to be related to depressive systems, but we weren't looking for any mediators with that, but we were hypothesizing that we would observe a relationship between gender and race ethnicity and depressive symptoms, but as you account for years of education, that would become less prominent, that relationship would, and after you also introduced income so socioeconomic status, that the depressive symptoms -- the differences in symptoms would disappear. And in essence what that means is we are saying we think that any differences we see between women and racial/ethnic minorities and depressive symptoms will directly relate to years of education and income. And as we've already demonstrated with the employment study, there are already well established differences as a function of gender and race ethnicity in employment earnings and the like. So slide 51. This is our actual 32 data. The first thing you'll notice is what's called an odds ratio. And an odds ratio stated simply is what are the odds of a depressive disorder in this instance or an outcome between two or more groups. And the way that it's done statistically just to explain it in its most basic form is that one group is set to one. And then it becomes a ratio after that. So in this case, Caucasian males although it's not put up on the table is essentially one. You establish them as one. That is the comparison group and then any other group could have an odds higher or lower for the likelihood of an outcome which in this case is clinically significant symptoms. And clinically significant symptoms is based on a score of six or higher on our depression measure and clinically significant symptoms is not a full blown disorder, but clinically significant systematology of depression is related to poor outcomes. So it is a very important diagnoses and we actually did this with clinically significant symptoms and a depressive -- probable major depressive disorder. We could not include both here in, so this is the example that we have. When you look at the slide, you'll also see that minority males have a 1.85 times the likelihood of clinically significant symptoms than Caucasian males and minority females have 3.37 times the odds of having that symptomatology. So the key point here is race is indeed related in this study to the depressive diagnosis. Next slide. Now we are on slide 52. So now if you look, we've introduced years of education in the slide below, and as one might 33 expect, the group that is -- that has more than 16 years of education is set to one in here. You just see two dashes, but it's essentially is the base group. It's equivalent to one and the odds of the depressive disorder are listed for varying levels of education, and that's not the key point of this slide, but just to summarize it, the odds of a depressive disorder go up the lesser the education level. And when this is fact toured into the equation in terms of the relationship between race and gender, and the odds of a depressive disorder, you still see statistically significant greater odds of a depressive disorder for both minority males and minority females compared to Caucasian males. So the point of this is we've introduced years of education. We've factored that out statistically, and even when you account for different levels of education, there still is a relationship between race, ethnicity and a depressive diagnosis. So before we go to the next slide, I would ask people to think what would you expect we will do next? And here is the answer -- slide 53. We're going to introduce income levels and just as what might be expected, if you look at the bottom of the table, those with 75,000 or higher we established them as one and compared the odds of the depressive disorder of the other groups. Nobody is less than one. That means that the highest income group did indeed have the lowest likelihood of -- least likelihood of a depress I have disorder particularly in comparison to those who made the least amount of money. The key points in terms of what we're talking about with race ethnicity, if you look in gold -- I believe it's in gold on your 34 table -- minority males, 1.30 now times the likelihood of a depressive disorder than Caucasian males. That's no longer statistically significant, meaning we don't know that it's just not a related chance. We're not confident that it's anything other than chance. So we dismissed that. The only significant relationship is between minority women reporting 2.64 times greater odds of depressive disorder than Caucasian males. So what that means is after we've introduced income, race is no longer associated with a depressive disorder for men, but it still is for women. So accounting for education and income, there is still something with the minority women in this study that puts them at a greater odds of a depressive disorder. I would also cite even though what you see here is only race, years of education and income, we indeed did control for other factors in the study like injury severity, age and all those things. It would be too busy to put them in the graph. Next slide. And this is another slide -- No. 54. This is another analysis of depression. This time using the multigroup study that we talked about earlier. We'll go to the next slide. And this graphically depicts the level of depression in terms of number of items endorsed as a function of race ethnicity and, again, you'll see the Caucasians, generally the lowest rate, but you can see the Caucasian and African American men were not different. The American Indian men had the highest level overall, along with Hispanic women and the relationship was such that the three racial/ethnic groups other than Caucasians tended to report some or similar 35 outcomes, but once again, African Americans reported equal or better outcomes than the American Indians or Hispanics you'll also notice the flip flop between men and women this time between the American Indian group and to they are groups. Where the American Indian women reported lower levels of depressive symptoms than men, however, that again -- I need to stress that was a small sample. And because it was a small sample of people, I believe it was 20 or 22 American Indian women, I'm not very confident in that finding: The sample with men was strong, so I think we can state with confidence that the American Indian men in the study had a much greater likelihood of depressive symptoms, but I don't know that we can say much about the women in the study within the American Indian group just based on sample size. Slide 56. So I'm going to go through the summary now. From the Shepherd health study, race appears to be related to a depressive disorder. Socioeconomic factors mediate this relationship for minority men, such that the relationship is no longer significant when introducing or accounting for socioeconomic status. Income appears more important than education in reducing the disparities in the depressive disorders, and there was no mediational relationship for women. So we need to look beyond education, beyond income to explain the higher level of depressive symptom. And again this was almost exclusively African American. So I would not draw conclusions from any study other than the large comprehensive collaborative study to other groups. However, within the comprehensive collaborative study, women did have higher depression scores overall. That is consistent 36 with the general literature, the exception being American Indians with -- where the sample size was small for that group. I don't think we can draw did conclusions. Next slide. And here are the references, and I would say at this point we're going to move on to No. 58 health outcomes. If people have questions about subjective well-being or depression, why don't you turn them in at this time, and I'm going to have another drink. Still diet Pepsi. >> JOANN: To give you a time check we're at 3:10 right now we're doing well with our time line. And please do send in any questions. Again, the address is webcast@ncddr.org or you can feel free to call our 800 number had is 800-266-1832 if you do have any questions. >> JAMES: Okay, if there are no questions, we'll move on. And this is now the area of health outcomes. And I'm going to again refer to the comprehensive multigroup study, the collaborative study, and we'll move to slide 59. This is probably the most interesting of the studies in several ways, and you know the sample sizes are big enough where we think we really have credibility with study. They are small enough where there are very interesting questions raised where we just aren't quite sure why we're finding the things we are. What I have in this slide is a comparison of the groups by both race, ethnicity and gender. And there are six outcomes that are related to self-reported health. And these are items from the behavioral risk factor surveillance systems by the centers for disease control. And when we look at these, we 37 find significant differences only on two items, self-rated health and satisfaction with health. You might go down to slide 60 and see this graphically. When you look at it -- and this is the self-rated health item. You'll see that Caucasians have just a very slight edge in self-reported health and then is followed by African Americans and American Indians, and the Hispanic group slightly lower. When you look at a self-report that does not pull out much difference. And we will go town to slide 61. Now, pressure ulcers, and this is where it gets to be pretty interesting. So in order to identify differences in a more objective outcome, in other words, something that could be measured or evaluated outside of an individual's self-report, we looked at the odds of a pressure ulcer as a function of race, ethnicity and we actually controlled for other bio graphic factors, again, age since injury and severity and all these other factors. And then we looked at variables to staying in bed. What do you expect when you get a pressure ulcer, what is the recommendation? And it is bed rest. And yet that's easier said than done for a number of reasons, and we look at potential barriers to that and then we looked at barriers to seeing a physician to treat a pressure ulcer. So we'll go to slide 62. Slide 62 shows the odds of a current pressure ulcer and this time we actually did put the one in the table, but the Hispanic group had the lowest odds of a current pressure ulcer of any of the four racial/ethnic groups. And when you look at 38 statistical significance, both African American and American Indian cohorts reported significantly higher odds of a pressure ulcer, 4.5 men, 9 for American Indians -- excuse me -- African Americans and 4.54 for American Indians. So highly significant findings after controlling for other factors. So race was significant at a high level, statistically significant, statistical significance means unlikely to occur by chance. The difference were large enough that they were meaningful differences. They were clinically important. And the important point again that the Hispanics had the lowest odds in the current pressure ulcer. And we're go to slide 63. Now, this is the odds of a pressure ulcer within the last year. We again have set it to one for Hispanics who had the least -- they were the least likely to report at least one pressure ulcer in the last year, and this time only American Indians reported a greater odds of pressure ulcer within the last year. And that is somewhat less than four -- 3.65 greater odds. So again compared with Hispanics, the American Indians reported a greater likelihood of a pressure ulcer and had we run the analyses and set any of the other groups to one, I'm sure they would have been higher than those groups. I'm 95 percent sure I'd have to run I guess the statistics to say 100 percent sure, but the differences are fairly substantial there. Okay, so what we have here is that a percentage of people reporting barriers to staying in bed to heal a pressure ulcer, and three items were statistically significant and two were not. And the two that were not different between groups, I'll say those first just 39 to get them out of the way -- needing to go to work was not statistically different between any group, and placing a strain on relationship with a spouse or significant other, meaning no racial/ethnic differences in those -- between groups at all. Needing somebody to stay with you, that actually was highest among Caucasians meaning they reported that to be most problematic. When they get a sore, finding somebody to stay with them is the greatest barrier to being able to stay in bed. So that's a significant concern. It was the least prominent concern among African Americans -- excuse me, let me restate that. African Americans were the least likely of any group to endorse that item. Caucasians were the most likely. Then you look at not having enough money to pay for extra attendant help, and Hispanics had by far degree the lowest percentage of endorsement. Compared to 59.4 for African Americans. 50 pho .50 for Caucasians and 59 for American Indian. And then we look at boredom, and Hispanics again had the lowest percentage of boredom as far as staying in bed if they had a pressure ulcer. African Americans the second lowest, then followed by American Indians at 58.2 percent and Caucasians 65.4 percent. I don't know what to make of these findings. Personally, I think they are intriguing because of what they suggest because it's a different pattern to something that's fundamentally required when something bad happens to one's health. And everybody really needs somebody to stay there -- to stay with them. They need some sort of 40 help. They need -- they need to be able to tend to that sore. And I think that the lowest rates with -- the low rates that you'll see of Hispanics endorsing those items might suggest something about some type of social support system that is not otherwise reflected in here. I thought it was interesting that boredom and money -- paying people -- were relatively high among Caucasians and it would appear just from the overall pattern that maybe Caucasians rely more on paid help rather than others providing that support. But I think this is really a strong basis for a much more detailed study into these factors. And we will move on to No. 65. So slide 65 is the percentage reporting barriers to seeing a physician to heal a pressure ulcer. And not having enough money or insurance. And what is interesting here is this item is significant not having transportation is significant. The items that are not different between groups are not trusting a physician, not having good doctor you can see close to where you live. So people aren't saying between the groups different racial/ethnic groups, they are not saying I don't trust a doctor, I don't have one close, but what the differences are is I can't pay for one or I can't get to one. And when you look at these groups, Caucasians have the lowest percentage of endorsement, which would again reinforce the same overall pattern. They have a better access to care it would seem based on those two items. Now, if you look at Hispanics, they are next lowest in both of those groups, and again, I think that brings up an interesting point. 41 Because something is going right with the Hispanics group of finding ways to circumvent or finding enough money or insurance would not be a group that one would think would be advantaged? Terms of access to care, not having transportation. Again, I think we need to look at something in this (inaudible) system because is there something there that we can identify and become a pattern that could be used to help all people with spinal cord injury, and the other things we see there again, the American Indians and the African Americans both relatively high on items of not having enough money or insurance and not having transportation. I would also note that again people -- we drew these samples from different places. The African Americans were primarily from Shepherd, large urban. American Indians were through clinics put on by Craig Hospital. They actually go to outreach to reservation in rather rural areas and there may be something affecting the findings within that system. And Hispanic was largely from Los Angeles, rancho loss amigo. So I think these findings raise more questions than answers but they do raise some interesting cultural and maybe questions based on social support. Go to the next slide. So, again, the disparities were not that great for self-reported evaluations of health as they were for pressure ulcers. So again the self-report differences reflect the nature of the outcomes investigated that they were ratings rather than secondary conditions or hospitalization. I think the research clearly needs to look more at things like hospitalizations, how often a 42 problem occurs rather than simply looking at the ratings and the general data. Then at least for pressure ulcers, Hispanics reported better outcomes in terms of both the prevalence of getting an ulcer and fewer barriers to treatment among those that got pressure ulcers. Do you have these resources -- so Hispanics were lower in both the prevalence of the pressure ulcers and then also in terms of the barriers to treatment among those that had them. and clearly the barriers did vary by race ethnicity. It is a topic for future research and once again they varied by -- in many senses by ability to have access to care and economic opportunity. Next slide, 67. These are simply the references for this study -- these studies. These are all articles that we've done over time? Each of these sections the PowerPoint will be available after the presentation. Slide 68, future research and we feel like there must be greater involvement of institutions that have access to serve large minority populations that have been traditionally underrepresented in SCI research. This still has not bench of anything with American Indians and Hispanics. More with African Americans, still not enough to say that we know as much about the problems faced by anybody from one of these underserved populations. Need to utilize population-based participants in order to avoid selective participation and bias. I think this is a huge problem in spinal cord injury research. We have model spinal cord injury systems and there are established systems of care that see patients and draw 43 them in clinically. So our knowledge is largely based on people that have either been self-selected through their finding a large center to get treated, or through the policies of any particular set of centers and what they require in terms of insurance and such. Yet, we need to understand differences in health behaviors and health disparities and health practices and their relationship is health outcomes, particularly among diverse racial/ethnic groups. This also tends to get not as prominent of attention as it needs and we could simply go on focusing on SCI specific health issues such as pressure ulcers, but we also must begin to investigate disparities and prevalence of chronic diseases and the contributing health behaviors among minorities with disabling conditions. And whether they parallel the general population. And this point fits with the point about the model systems. We really need population-based research where a given area such as a particular state where every case is surveyed. Every case in the state, every person that has an injury is enrolled. We also must look at chronic diseases, and the reason I say chronic diseases is, for instance, African Americans in the general population are much higher risk for certain chronic diseases. Well, what happens after accord injury? People focus on their SCI needs. They focus on prevention of pressure ulcers and infection. And it's almost an after thought that they still might be susceptible to chronic conditions. They still have to watch their cholesterol and blood pressure and all those things. And I'm knot not sure that there is much attention to that. We had some research that suggested people do tend to focus on 44 their SCI needs through the relative exclusion of their general health needs. We didn't feel it was on firm enough ground to present it or that we might have time to get into that detail. Slide 69 -- ongoing collaborations. If you represent a historically black African American college or university, Hispanic serving institution of higher education, American Indian tribal organization or university or any other institution of higher education whose minority enrollment is at least 50 percent and are interested in additional webcasts developing disparities research, please contact us. We are hoping to work with Frank Martin in doing future webcasts and again we're actually applying for our grant mechanism where we can bring in a lot of collaborators and provide both capacity building and technical sis tans so we can get the information out to people who need it most, link them up with the systems of care that can help them to become more competitive in this research. And lastly, I will just put on slide 70, which is our contact information and for more information on future dissemination, please contact Jennifer Coker at Cokerj@musc.edu. We have a research site for our team, we have a lot of content to add to it, but I think if we try to do the job that we are working in the process of putting out information to both consumers and professional audiences. Lastly, my E-mail address is up there, and that brings us to our very last slide, and that is thank you very much. So what I would like to do is actually ask Frank to page back to slide 70 so that now that we've 45 thanked everybody that we can keep the contact information up there for anybody that wants to write it down. We have three minutes to take any questions if there are any, and if not, I would like to sincerely thank everybody who has hung in here with us for an hour and a half listening to me ramble on about a lot of different things with our research. >> JOANN: Thank you very much, Dr. Krause. It was a very interesting presentation. You got so much data from so many different sources, it really is almost mind-boggling how much you've got there. And I really appreciate your interpretation of all those data. Frank, do you have any questions that you've received? I have not received any over here? >> FRANK: I have a couple of questions and comments that we might want to chat about since we didn't have an opportunity to pause on dissatisfaction study data. And then the last comments you made regarding the press you are ulcers. First, regarding dissatisfaction data, and I'm referring in part to slide 37. I think -- and these are more comments than necessarily questions. I think one of the things that's compelling here is that you were talking about the test and how this is a measure between groups. I think another part of that equation is the within group differences, and so for studies for some of those lines where you had nonsignificant findings, I'm thinking or I'm wondering if you think there is a possibility that what we have is greater within group variation and that also might have some implications for providers and 46 others who are trying to get a better understanding of subjective well-being issues for ethnic minorities? >> JAMES: Well, I would actually say -- and I think that's a wonderful point and a wonderful question because there is always -- and I won't give you many absolutes and you'll hear me talk about anything, but one absolute I think is that there is always more variation within a group between groups and I was educated at the University of Minnesota, there was a large individual difference emphasis. So when we get to presentations of disparity, we're talking about generalities and differences. So there is always going to be more variation within a group than between groups. So, in other words, the groups are more alike than different, what are the differences at a group level, but certainly in the area of employment, for instance, even among African American males, there are a lot of very, very successful African American males with spinal cord injury. We're looking for the trends, not the variability within that group. The point is well taken, and usually it's the variability within a group that gives you a better indication of how to help people in that group. So you need to know about the people that don't do well, and you need to know about the people that do very well. So you can find out what the differences are. Those differences are what will give you the indicators that will help you to plan your intervention. Thank you for bringing that up. It's a point extremely well taken. >> FRANK: Great. Another question I had, for example, slide 48 47 or 49 where you were talking about models and you put in several variables. You put in race and then income -- I think income was the last -- oh, education and then income. One of the things I'm wondering -- and if you don't have this now it's fine -- but I think some people might be interested in knowing our square for this model, in other words, how much variance was explained by this model and how much was sort of left remaining? >> JAMES: No, I don't have that. And typically you can do it with logistic regression, but usually when you use logistic, because you have the odds ratio as a measure of -- >> FRANK: That's your primary concern. >> JAMES: It's not as typical to look at an R. squared, even though I do recall we probably reported it. Typically -- well, I'm trying to buy a little time here to see if we can actually find it. I'm afraid that we probably won't be able to, but my guess is -- no, my guess is I don't want to make a guess. Just because it was a fairly strong relationship. Usually your R. squared without comes that are subjective are somewhat larger than those without. So again, usually when your outcomes are subjective in nature, your R. squared are bigger. Usually when you use nonbiographic and injury characteristics, your R. squared are much bigger. So again race is not -- race ethnicity and gender typically are not going to be very large in terms of R. squared. And looking at my publications to find this one -- >> FRANK: I know that in many studies the contribution of race to 48 the variance explained is typically very small. So in yours where you are add additional factors, it seemed to be very important and relevant. It seems that that would be increased. So it would be interesting just to know how much increase we get by adding these other factors that seem to influence the diagnosis of clinically significant symptoms. >> JAMES: It would be, and again I'll just say -- I've looked, but I just don't have it. >> FRANK: I do have one final question or comment and this is about Hispanics. I'm wondering in the data if Hispanic -- if people who are Hispanic also can indicate a racial group since Hispanic isn't a racial group, it's an ethnicity. >> JAMES: I don't remember what the break down was but frankly it's Mexican in this study. Largely a southern California group and you're right with ethnicity there are differences. It would be white Hispanic, black Hispanic, but typically because this is a group tay we identified from rancho, southern California, I am confident in saying it is nearly exclusively Mexican, but I would say probably 80 percent at a minimum. We did get some participants from other areas or Mexican/South American/central American I should say, but it's a point well taken. And the whole concept of race is becoming a little more difficult to measure. And a lot of people are multiracial now. So our challenge in the future is to really look at different indicators of ethnicity, a little more detailed and look at how that translates into cultural differences because, you know, America is a very diverse 49 place. >> FRANK: Absolutely. >> JOANN: Thank you very much. We've run over just a bit, but we should wrap it up now. You should go ahead and send your questions in still to us at webcast@ncddr.org or send your questions directly to Dr. Krause. And I want to thank you, Dr. Jim Krause and your associate Karla Reed for making the presentation today and thank you to everyone that's participated this afternoon. I do want to encourage everyone to fill out our brief evaluation form. This will be helpful to us at the NCDDR as well as to our presenters. I will just take a couple of minutes so please do it right now before we sign off. You can click on the downloads tab at the bottom right-hand side of your Windows Media Player or RealPlayer screen and there is a direct link to the evaluation form. Can you also get to this link from the ILRU and NCDDR web pages related to the webcast. I wanted to thank the National Institute on Disability and Rehabilitation Research, NIDRR, that provided funding for the webcast, and especially again to thank the staff at ILRU because without their efforts the webcast could not have taken place. An audio file and a transcript of the webcast will be available on the ILRU website archive page in a couple of days at www.ilru.org and a few days after that will be available on the NCDDR webcast pages. Once again, thank you on behalf of my cohost Frank Martin and the 50 rest of the NCDDR staff. We'd like to encourage you to visit our website at www.ncddr.org. For more information and to view our additional archived webcasts on various topics of knowledge translation, including disability, diversity and research outreach. Thanks very much. Goodbye.