**Transcript of Cyberseminar**

**HERC Health Economics Seminar**

**Colon Cancer Costs and Quality of Care**

**Presenter: Denise Hynes, Ph.D.**

**October 17, 2012**

**Colon Cancer Costs and Quality of Care**

**October 17, 2012**

Paul Barnett:   Great! Well, it's my great pleasure to introduce Dr. Denise Hynes. She is probably best known as director of the VIReC, the VA Information Resource Center. But she’s a productive health services researcher at the HSR&D Center of Excellence at Hines in Chicago. And she has her PhD from the School of Public Health at UNC Chapel Hill. She’s currently on the faculty at Loyola University in Chicago. Looking forward to hearing your presentation, Denise. 

Dr. Denise Hynes:   Thanks Paul. Actually, I’m currently on faculty at University of Illinois. Loyola was in my recent past. I just want to make sure that everybody can hear me. So if you’re having any trouble hearing I think we have those little hands or something, don’t we, that  we can let people, or have people, or type in to our organizers and panelists. 

Heidi:   Yeah, people can do both, so... Yeah, people can raise hands or just type in if you’re having any issues and we will help you out the best we can.

Dr. Denise Hynes:   Okay. So I’ll go ahead and get started. It's a pleasure to be able to do this today. I’m going to be talking about, what we call the VA Colon Cancer Quality and Cost study that’s been a VA study.  We’ve been – we got two papers actually in progress. When I was invited to do this I was told that work in progress was something that the audience usually liked to hear. So I thought we’d go through this a bit. I’m going to focus today on aspects shown here that really focus on the economic aspects of our study and some of the measurement issues. But first what I thought what I would is at least introduce you to this project and talk about some of the approaches, methods, and data sources used focusing on the cost aspect. And then talk a bit about some of the alternative methods that we explored for dealing with outliers and then talk a little bit about the impacts on our results. 

So let me just begin there. I just want to make sure that you kind of get a sense of what this project is. Basically in a nutshell, this is a project that VA Health Services Research and Development Service funded. It began in 2004. And you know how these projects can be; you can work on papers long after the project funding ends. And that’s what we’ve been able to do. We actually have had some subsequent supplemental projects on it. But its main focus is to examine and compare healthcare use and costs for colon cancer patients who are treated in both VA and in the Medicare setting. I’m going to focus today’s comments on the costs and the healthcare use aspects but we also looked at some issues related to treatment patterns and appropriateness of the treatment patterns. I will talk a little bit about that as we go through just to orient you to some aspects about state of the art care and colon cancer care.

Let me first acknowledge my colleagues who have been involved in this project both at the Center for Complex Chronic Care Management based at Hines, CMC3, including some colleagues at Loyola and also Northwestern, and then our clinical advisory team which includes colleagues who are involved with National Cancer Institute, surveillance, epidemiology, and end results registry programs including in Louisiana, Hawaii, and at NCI as well, and colleagues that have been in Chicago and have moved onto other places as well. 

So let me just give you a little bit of background about why looking at healthcare cost and use has been an important issue in studying colon cancer. There’s been some research focused on this. And research has taken different paths. The fact is that we haven’t had always a consistent measurement standard for looking at cancer care costs. Some studies have looked at direct medical costs and it's been estimated to average between thirty-five thousand and eighty thousand for each cancer episode. A cancer episode that can mean from the time one is diagnosed to the full course of surgical, radiation therapy, chemotherapy. It can be within the confines of a year or can extend over years depending upon the type of treatments and stage of disease one might have. Total costs of treatments for anticipated new cases based on some estimates from 2007 were on the order of eight point three billion for all the colon cancer cases treated in the United States. If you just look at Medicare treatment costs there’s been some estimates out there looking at first year Medicare treatment spending after the detection, and that’s been estimated on the order of about thirty-six thousand per case. And the estimates around care just in Medicare spending for new colorectal, that’s both colon and rectal cancer combined, are on the order of two point four billion looking forward from 2007. And then if you take another snapshot looking at treatment cost, early detection versus late stage disease, there have been different estimates. If you find a patient who is early on in treatment the estimates are about thirty thousand dollars per patient per year. And if you look at it capturing a patient who might present at a later stage of disease, estimates are at about a hundred twenty thousand per patient per year. Obviously, these are different perspectives when looking at colon cancer care costs but you see big numbers here is really the bottom line. And so a big issue within looking at colon cancer treatments and cost is to try to find ways to bring costs down and look at some strategies where we might bring in patients at an earlier phase and hopefully keep those costs down. But needless to say, to get a sense of where to target we have to have better information about what actual costs are, and over time, since treatment also changes over time. 

So I think I’ve lost control of my screen. There we go. It's just a little bit slow. Total cost of chemotherapy is another way to look at this and that’s an important episode within a cancer episode that can be very expensive. And colorectal cancer, there’s been different estimates. There’s a reference here that one might go and get some more details. But it's been shown to differ in estimates by as much as about thirty-seven thousand dollars per patient depending upon the regimen. Cancer chemotherapy has evolved over time. Drugs that were available in the 90s into the 2000s, the early 2000s, have changed over the last ten years. Some of the newer drugs as they have not gone generic yet, can be extremely expensive, and some of those regimens that include some of those newer drugs can be very expensive also with some of the new, with the class of drugs call the biological modifiers. They can be very expensive added to more traditional chemotherapy such as 5FU. 

This is also an important issue within the VA. In particular, VA treats about a hundred and seventy-five thousand cancer patients per year. That’s gone up and down depending upon the specific year that you’ve looked at that one might study. And it's increased over the years. And VA has been a focus of various specific congressionally mandated evaluations. The most recent one was conducted actually during the exact timeframe of this particular project. So you’ll see, and I’ll reference these later, some papers that have come out of that work as well. GPRA is the acronym that’s often referred to, the Government Performance and Results Act. And another dimension of studying populations, disease populations, and cancer in particular in the VA, needless to say, with resources available with National Cancer Institute, Comprehensive Care Centers that are funded outside of the VA oftentime patients may be seeking care outside VA proper. And in addition, Veterans may also be using resources outside VA because of eligibility with other health insurance programs, not the least of which is the Medicare program. So it becomes an interesting challenge to try and address questions around continuity of care, healthcare use and cost when you’re looking at a mix of institutions and providers and episodes that might be interrupted by intervals of recurrences and waxing and waning of the disease. 

So another thing to really keep in mind here before we get into looking at some of the economic aspects is understanding some characteristics about VA cancer care. And this is some summary information from the actual GPRA analysis, the congressionally mandated study. They looked at some of the characteristics of VA facilities that are treating cancer patients. In the first column that’s designated as the overall column here there are – at the time of this evaluation in 2006, there were a hundred and thirty-eight facilities in the VA. This shows the mean volume of patients seen at those facilities. And then they break out the facilities by the complexity level in terms of the kinds of intensity of resources that are provided. Do they have surgical suites? Do they provide intensive care? Do they provide various levels of severity of illness care, ICU care, etc? And there is some diversity among the VA facilities that are available and you can, in particular, see that thirty-one, thirty-two percent of them are affiliated with a comprehensive care center. And needless to say, those that are in the complexity level at the higher level where they’re providing ICU care, maybe even some trauma care, have a higher likelihood of being affiliated with a comprehensive cancer center that is affiliated with a comprehensive cancer center that is based at a university medical center. And you can also see the diversity across the facilities among those that have cancer registries. Although there is a national VA central cancer registry it depends on information from the local VA facilities. And needless to say these tend to be the ones that are providing higher volume of cancer care and also have some affiliation with the comprehensive cancer centers. And then the last category you can see those centers that have, what are called, tumor boards where they have actively engaged clinicians to discuss cases and discuss multidisciplinary treatment for cancer cases at their facilities. 

And one more introduction I want to just make sure that we’re all on the same page with where we are and understanding about patients who might have available to them the opportunity to use both the VA and services under the auspices of the Medicare program. Remember Medicare covers those under age sixty-five who might be eligible under one of the carve out programs such as end stage renal disease or if they might be eligible because of various disability issues but predominantly for those sixty-five and older. In the VA over eighty percent of elderly Veterans are eligible to use VA might also be using Medicare alone or with VA services. So they might have some level of dual use is what we’re calling it. And there’s been some evidence shown most in the distant past but some continues to come forward that there’s some coordination and quality of care challenges when people are using multiple providers in particular with VA and Medicare, and that there’s some evidence that there is some coordination of care that is lacking and, in particular, with delays in care and excessive healthcare use and costs. So this is a particular focus of our project that we wanted to capture. 

I also want to make sure that since we’re focusing on some health economic aspects to make sure that we’re all on the same page with colon cancer treatment strategies at the time of this particular project. What I’m going to be focusing on today is some of the work that we looked at in terms of healthcare use, treatment, and cost between 1999 and 2002. Although our study went out through 2004, today’s analysis is going to focus on this particular period of time. 

Cancer, for colon cancer, it can be categorized in four different stages. Shown here are some of the degrees of treatment that might have been expected at that time. It has changed since them but you can see a progression from stage 0 and stage I where most of the focus is on surgical treatments to stage III and stage IV where you see the introduction of chemotherapy in addition to surgery and sometimes radiation therapy as well in stage IV. This is important, obviously, because as you have additional modes of therapy, surgery, radiation therapy, chemotherapy there could be multiple modes of therapy going on. And needless to say, there can be some increased costs due to increased contact with the healthcare environment, not to mention, some of the expenses of the specific therapies themselves. It's noteworthy that during stage one and two chemotherapy is really, at this time in 1999 to 2002, is really advocated in terms of clinical guidelines only within a controlled, clinical trial. And the chemotherapy was more standard treatment only in stage three and four. 

More specific to this particular study was a retrospective cohort. We focused on patients who were dually eligible for VA and Medicare benefits. So we focused on the elderly and we focused on those who were eligible in 1999 to 2001. We actually, again, this is – we started this project back in 2004 and we were actually able to get participation from National Cancer Institute SEER registries. And we were able to share information between VA and SEER registries for this project. So we actually went back to eight of the NCI SEER registries and built a finder file that combined both VA identified cases from the VA Central Cancer Registry as well as identifiable information from these eight NCI SEER registries. I can tell you it's much more challenging to do this in today’s current environment, which is why we’re continuing to do some work with these data that we have. We feel very privileged to have these data. And also, needless to say, respect the responsibility that we have to protect it. 

Let me talk about how we measured dual use in this project because this is an important exposure variable in the analyses I’ll describe. Keep in mind throughout my remarks through the remainder of our discussion today is we focus heavily on care related to colon cancer. We’ve spent a good amount of time trying to separate, if you will, general healthcare from care that’s specific to colon cancer. We relied heavily on the data sources in the VA to do this looking at colon cancer specific markers for treatment. Colectomy is a very specific treatment in colon cancer care and also chemotherapy events. And we relied on the kind of information that’s available in our VA workload data in the cancer registry data sets both VA and NCI that indicates both diagnosis, specific procedures. HCPCS and BETOS are for procedure codes. And particular kinds of events or contacts with the healthcare system that might indicate colon cancer care. 

We also looked at percentages of their care that was colon cancer specific in relation to whether it was provided in the VA and whether it was provided under Medicare auspices. And in so doing we were able to come up with three colon cancer user groups, which I’ll abbreviate as CC through the remainder of the slides and categorize them as dual, predominantly Medicare, and predominantly VA. And this is depicted on the slide here, simple ratio of the number of colon cancer related inpatient stays and outpatient events over the total in the VA and Medicare. And we basically split it up into these three categories. We really wanted to – we really found no people who had zero. So in order to really capture these three categories we wanted to make sure that we focused on the patients who we called dual users. They’re really in that middle category. And then the ones who are, who fall more predominantly they’re using the VA but there might be some small percentage of their care that they might be receiving in the other system. So we have dual users in the center and the other two are single system users but in the complimentary system, either Medicare or VA. 

So that brings us to the second focus of today and that is to tell you a bit about the approaches and data sources that we used for estimating costs. I’m going to pause at the end of the second section. I have some Q&A assistance, Margaret Browning and Tom Weichel who worked on this project with us who will help with some of the questions. So feel free while I’m talking to include some questions in our chat box and some will be answered as we move along and some will be called out when we get to the break point in the next section. 

So let me tell you just a little bit about how we built our cohort but before I get into the details I just want to pause for a moment and see if – I think this is where we were going to have a polling question to see how familiar our audience is with working with any of the cancer registry data that the VA or NCI has available. Oh good. We have a quick poll here, thank you. Have you worked with VA cancer registry or NCI SEER data? This is just a simple yes or no question. And if you could select one it’ll give us an idea. It’ll give me an idea of how familiar you are with some of the registry data and it might affect how I make my remarks today. And so far it looks like we have about – results still coming in, about thirty-eight percent have some experience working with registry, cancer registry data, and about two-thirds that do not. So thank you. Okay, I want to make sure I don’t leave the webinar by clicking on the wrong button here. So thank you for that.

So let me tell you a little bit about how we built our cohort which did, in fact, rely on registry data. In fact, that was pretty essential. And one of the reasons it was essential is because what you see in this upper right box on your screen, the 8 SEER Program Registries from California, Iowa, Georgia, Louisiana, and New Jersey, Hawaii, Western Washington, that’s the state of Washington, and metro Detroit, and the VA Central Cancer Registry. This is where we really had information about specific patients and the date of diagnosis, the stage at the time of diagnosis, and they used some standardized staging components so that we know that a stage I is consistently applied across all of these registries. This information was critical for identifying those patients who within this cohort that we called the VIReC Finder File with three point four million patients actually were cancer patients. Now this finder file are those who are known to the VA who are dually eligible to use VHA and Medicare. This has been constructed and has been updated going forward by the VA Information Resource Center as part of their responsibility for managing the VA and CMS Medicare data for research. 

And bringing this two types of data sources together it's been the VIReC finder file relies on VA workload data and some of the eligibility data available in the VA and VHA along with the VA and NCI cancer registry data to identify a cohort of stage I-IV colon cancer patients who are diagnosed between July of 1999 up through the end of calendar year 2001 who were therefore sixty-six years and older at the time of diagnosis. Even though we started with a very large number the yield was five thousand three hundred and twenty-seven patients. We selected sixty-six years or older so that we could ensure that patients in our cohort had at least some part of their first year within Medicare. So that way at sixty-six years and older we know that all patients were already exposed to Medicare. We didn’t want to miss any years or components of months of the year at the time of their diagnosis. 

And you can see in the gray box that we had several exclusions, one thousand four hundred and eighty-five. We excluded patients who had incomplete or absent healthcare utilization information. And you can see specifically how we – where we lost patients. The largest number is patients who are enrolled in one of the Medicare Advantage programs, one of the HMOs, for which we do not have any claims data available from Medicare, through VA, etc. We determined that would be such a huge gap that it wouldn’t be appropriate to focus on patients who are enrolled in the HMOs. So the final analytic cohort was three thousand eight hundred and forty-two patients across this period of time. 

The slides are just a little bit slow for moving. So let me just talk through while we move the slides forward. In particular, for these three thousand eight hundred and forty-two patients I’ll show you in just a moment a summary of how we – we’ll just move onto the next one – measuring costs. I want to just highlight a little bit some of the data sources that we used. I’ve talked about the ones that we used for defining the cohort. Twelve months from diagnosis is what we focused on for this particular analysis that I’m going to talk about today. We actually have up through four years at a minimum for patients that we followed. And we’re following some even longer to look at survival analysis. But for purposes of today’s lecture I’m going to focus on the first twelve months from diagnosis. We relied on VA – as a value – VA costs, HERC’s average cost approach, which also uses, if you will, a modified Medicare approach for costing VA. And for the Medicare components of care we used Medicare payments. We thought that these two methods together were very complementary. We included VA and Medicare acute and intermediate inpatient and outpatient healthcare use including pharmacy. Now, for purposes of measuring cost we actually excluded VA long term care and we also excluded Medicare home healthcare, hospice, and care provided just under the durable medical equipment. And the reason that we have these exclusions is because the benefit program between VA and Medicare, this is where there were some unique aspects. We wanted to focus on aspects that were somewhat complementary and consistent. And so these are the aspects that were unique in VA, the long term care, it's not covered the same way in Medicare, and similarly, home health, hospice and DME are covered a little bit differently in the VA. So we focused on inpatient and outpatient and, to the extent that we could, pharmacy care. 

Our analyses also focused on the twelve months after diagnosis. Descriptive analyses looked at the inpatient and outpatient costs. We focus intensively on our user group, which I’ll refer to as the dual, predominantly Medicare and predominantly VA for their costs. Actually adjusted for predisposing, enabling, and contextual factors in our multiple regression analysis, and I’ll talk a little bit about the generalized linear models that we used. And we – in this particular analysis, adjusted costs to 2004 dollars. We used data in all of our analyses. 

Some descriptive information that I shall share. So I will – you’ll see a consistent number here, three thousand eight hundred and forty-two. And just to give you an idea of what the distribution looks like across the entire cohort, and then I’ll talk a little bit about some of the splits across our main exposure variable. You can see that the majority of our patients were kind of between the sixty-six to eighty-five-year old group. We do have five percent of our population that were in that higher category, predominantly male, as one would expect in a VA population, fifteen point five percent African American, and a good proportion of subjects who were married but a large minority who are not married. And then we still have that unknown category that we left – analytically, we left in the unknown. A pretty good distribution across the stages of disease, the largest percentage was in stage II. Now remember, this is stage at diagnosis. It's a little bit surprising to see that there’s nineteen percent of patients who are actually diagnosed in a very advanced stage of disease at stage IV. And that really bodes, if you will, poorly for both survival and cost aspects, which you’ll see revealed later. But that was a little bit disturbing that we see patients being diagnosed at such a late stage. Comorbidity score – in the VA population we have with the eighteen point five and the four point five with some substantial comorbidities at the time that they’re diagnosed. So this is another dimension that has to be managed when treating the colon cancer as well. And in this cohort you’ll see that patients during this period of time about a third of them actually received chemotherapy and then predominantly a colectomy, which is expected. You’ll recall when I showed the standard of care from stage two on higher colectomy is particularly the treatment of choice and even some of the early stages surgical therapy is expected, and chemotherapy is not. 

Colon cancer care, twelve month costs, and I’ve broken it out by user group so you can sort of see how this sort of reveals itself. First column, predominantly Medicare – billed use is in the center and predominantly VA on the right hand side. So across the top bar we have total colon cancer costs in the predominantly Medicare is where we have about fourteen hundred patients, average cost and standard deviation are shown here, not surprisingly when looking at healthcare costs, and this is, again, specifically for treatment of colon cancer, approximately thirty-nine thousand over the year. Standard deviation is pretty high at thirty-one thousand six hundred. In the dual use, costs are somewhat higher, forty-four thousand two hundred and sixty-four, again, with pretty high standard deviation, about five hundred and ten people in this group. And in predominantly VA we have one thousand nine hundred and fifteen. Costs are thirty-six thousand one hundred and forty-six, again, with pretty high standard deviation. And you can see that the driver of cost in terms of inpatient and outpatient, the larger proportion of costs tends to be inpatient. Now keep in mind although people are predominantly Medicare they can still have some of their care occurring in the VA and similarly with predominantly VA while they might be receiving fifteen percent or higher of their care in the VA, they still might be receiving some care under Medicare. That’s why you’re seeing, for example, in the predominantly VA group, there are eighty-three patients who had some of their care for inpatient in Medicare averaging eleven thousand one hundred and ninety-nine dollars during the course of that year. I should note that all of these differences across the user groups were statistically significant. Of course, these are unadjusted. 

And just to kind of give you another perspective, so we just saw the healthcare costs. Now look at the healthcare use during this period of time, again, colon cancer specific broken out in the same way, same column orientation. Inpatient admissions, one point five for the, if you will, the single system users predominantly VA and predominantly Medicare were precisely the same. The higher volume of use in patients was among the dual use at one point nine. You see a little more spread when you get to the outpatient care and, yes, it's a very high number when you start looking at outpatient visits per patient, thirty-five point seven in the predominantly Medicare and somewhat lower when you start looking at the dual users and predominantly VA. And that reveals some of the contributors to the differences in cost as well. 

When we adjusted for the other covariates, let me show you sort of what was revealed. Here you have the results from our generalized, linear model estimated expense rate ratios for the thirty-eight, forty-two population shown here are the regression results, the reference category, ninety-five percent confidence interval around expense rate ratios. And if you’re not familiar with this, obviously, very similar to looking at odds ratios just that our outcome variable is cost for twelve months. Again, colon cancer specific costs, and you can see that older patients have significantly higher costs reflected in the ERR of one point two four. African Americans had slightly higher costs compared to non-African Americans. It was statistically significant as reflected in the confidence intervals. And similarly when you look at stage of diagnosis patients who presented at a later stage such as stage IV shown here, two point two seven expense rate ratio, significantly higher cost were experienced when they presented at a later stage. And you can see the gradation as the stage increased. Comorbidity scores similar trend, those with a higher comorbidity score at presentation had higher cost during the course of the year. Chemotherapy did not contribute to higher cost in this population. However, those having surgery did have higher cost. Of course, in this population recall that eighty-nine percent of them actually had surgery. So we have a small percentage only eleven percent in that reference group. But it was statistically significant. And then our main exposure variable user group you can see that those who were in the, if you will, single system categories predominantly Medicare or predominantly VA had significantly lower cost compared to those who were in the dual user group. And just to point out, I did not show all of our covariates, our adjustors. We also adjusted for the contextual factors shown here. But just to sort of  like put this in perspective, if you just sort of look at this in terms of percentages, patients who are in the single system Medicare colon cancer user group had fifteen percent lower cost compared to the dual users, whereas the single system VA colon cancer users had twelve percent lower cost.

So let me just pause here and see if our assistants on the question and answers have any questions that we should put out to our audience. Margaret or Tom...?

Margaret:   Denise, this is Margaret, Tom and I have been responding. It seems to be a pretty good system. We have a couple more that we’re just beginning to respond to, which we can go ahead with or we can give them to you. But why don’t we just type in a response, Denise, and we’re submitting all the questions, all the responses to everybody. 

Dr. Denise Hynes:   Okay, we’ll pause again at the end of the next section and maybe Tom and Margaret you can pick out a question even if you’ve answered it and share the question and the response with our audience today. So let me just talk a little bit about some of the ways that we tried to do, if you will, sensitivity analysis considering some alternative methods for dealing with outliers in influential cases. There we go. For those of you who like to work in econometrics and some of these modeling techniques, you know that when you’re looking at costs we’re usually dealing with distributions that can be pretty skewed. And GLM models that we used in our study can accommodate most of this but if you don’t have the specified, specify the right functions you can make some big mistakes. If you misspecify the variance function, you can lose precision and if you have issues with large log scale errors in your variance or error distribution on a log scale with heavy tails, you can lose efficiency. 

Let me just pause here for a moment and I want to just bring you back to the slide that I showed just the distribution in this table format. I added here the range so you can see how – why the range is for our cost. You already saw that the standard deviation for cost was fairly large. And I’ve seen much – I’ve seen others. But I mean let me just pose a question, those of you who have worked with cost data before I think we have a polling question out there. How would you rate this kind of distribution? Do you see it as having low, medium, high in terms of skew? You can see the ranges here in the third level for total cancer, total colon cancer costs for the predominantly Medicare we have average cost with thirty-nine thousand one hundred and thirty-six with a range from forty-two to four hundred and five, eight hundred and ninety-two. And similarly we have in the dual use category costs that go up to six hundred and seventy-nine, four hundred and seventy-one within one year just for colon cancer specific costs. And then in the last category costs go as high in at least one subject and up to five hundred and fifty-three, one hundred and fifteen. I’m not sure if...

Heidi:   Denise, you want me to go ahead and put the poll up? 

Dr. Denise Hynes:   Sure, thank you. How would you rate the skewness of this cost data?  Would you worry about it? Would you move on? Would you just go with your GLM model?  Rank it as high, medium, or low. I don’t want to bias you but we decided to explore it a bit, obviously, because our range, we were concerned. And what you don’t know yet is what I’ll show on the next slide sort of how this distribution kind of panned out. You can see the two extremes and there are definitely ways that we can deal with this. So fifty-two percent of you thought this was high in terms of skew and then forty-two percent medium, and a small percentage thought it was low. So let’s see kind of where this works with our analysis. Thank you, returning to my screen. 

So we took a look at a couple different approaches and Tom Weichel, one of our statisticians, and Ramone Durazzo were key in helping us do this. We looked at two approaches. One, adjusting for outliers and, two, trying to look at ways to look at influential observations. And basically the four categories that we considered are shown here. Box plot analysis, which really looks at really sort of the quartile ranges and making some decisions based on where the cases fell along the quartile range. Winsorization, which looks at replacing or limiting extreme values based on specific criteria and we used, I believe, two approaches that I’ll show you here. And we also looked at influential observations, and one is Cook’s distance, which measures the aggregate change in your regression by looking at steps of omission by taking out some of the cases and looking at the impact on your overall model. And then something called DF BETAS, which focuses on the impact on each regressor in your model. 

So let me just show you, this is kind of a fully packed slide here. And I think I might even be able to use one of these little tools here. So if you focus on – I don’t want to obscure the information here but in this first column that we have shown here, and if you look down from here, this shows the actual distribution that we were dealing with what we’re referring to as the full cohort, the thirty-eight fifty-two, our mean cost across the entire cohort for colon cancer care is thirty-eight three hundred and twenty-seven. And you can see some of these outliers that we had here out at – I mentioned about six hundred and fifty thousand and then some that spread all the way down here. These dots don’t necessarily represent one case but a cluster of cases. And then in this next category shown here, this is a box plot where we excluded some based on the interquartile range and you can see precisely where we cut off the cases based on the quartile range at this point. So the range here for cost then got cut off at about a hundred seventy-five thousand per year. The next one is a Winsorization technique that narrowed the range even further, and so on, with stricter criteria with a second alternative for Winsorization. And you can see how each of these methods actually snipped particular cases off the cohort that we actually analyzed. And you’ll see in a moment the impact of these different approaches on our actual results. 

So what we did to look at or explore this particular, these approaches – I’m going to just erase my drawing here so we don’t get confused – we looked at two of our key cost drivers. So you recall in our regression model, stage of disease was a particularly influential driver on our outcome measure of cost. So we looked at how these different approaches of either adjusting for outliers or adjusting for influential cases actually affected the expense rate ratios for these particular variables. So you can see the black circle here is our basic model, our GLM model for the full sample. And you can see how the regressors were affected according to each of the methods. And if you can sort of focus on how within each of these domains the results actually hung together pretty well. They weren’t too far apart but there were some differences at least with stage II. You come out here to stage IV and you can see that we start to see some progressively significant differences as you look at both the techniques that adjusted for outliers as well as the techniques that looked at influential cases compared to our baseline shown in the black circle here. Makes sense conceptually, in stage IV we had some more variation in the treatment that is considered standard, radiation, surgical care, chemotherapy, and also higher costs for these patients. So one consideration is to look at both the range of costs but also the particular actual values of the costs. Also, similarly, we looked at it for colectomy, which was another cost driver, and we did not see as much variation as we saw, for example, with stage IV but you can see that there is for box plot, which is shown here in the red, that was different from some of the others. 

So bottom line here is that when we looked at exploring, if you will, sensitivity analysis, trying to consider influential cases and outliers, frankly, we were not impressed by having to consider, if you will, losing a large portion of our sample. I’m taking you back to this slide here with every technique that you – when you’re looking at influential cases and outliers you have to make some decisions whether you want to use your full sample as a tradeoff for precision that you might gain versus the loss of cases that you might want to examine. Ultimately, we decided to stick with our full sample because we felt that the distribution was not as impressive as we were concerned about. It’ll be interesting to see if you have a different opinion when we get to our Q&A. 

So we felt that the cases shown here in our analysis, we looked at some different issues with lots of different data sets. The cases reported from NCI SEER, we should note, might not represent all the VA cases, reported outside of the VA. This is just, one, there are eight of thirteen SEERs that we included and even if we included all of the SEERs it only represents eleven percent of the national population.  Coverage in the VA and Medicare differs somewhat. We tried to focus on comparable care that is inpatient and outpatient care. It's also noteworthy, we did not really address selection bias in this particular analysis. We kind of struggled with the three level exposure variable. That is our user group. And it's not really easily amenable to propensity score matching. We’ve actually explored some aspects but we really haven’t included that in this particular analysis. 

I want to make sure that we have a few minutes for questions and I just want to jump to our conclusions here. So in particular I just want to kind of close with our summary statements about looking at some of these aspects with regard to adjusting for outliers and influential cases. And we really found that these cases were similar using the different approaches. So we relied on our base case preserving our full cohort. But the results message is that costs were lower for single system users compared to dual users. We also found, of course, that costs were higher among patients who were African American, had more comorbidities, were older, or had more advanced stage of disease. And of course, we did not, in this particular analysis, look at the course of treatment, quality of care. We have other analyses that are looking at some of those aspects, and needless to say, that really needs to be examined in the context of some of these issues as well.

So I just wanted to kind of close so you don’t have – so we have a conclusion on our results and on some of our methods. And let’s see if we have any questions. While we switch to Margaret and Tom I also want to make sure that you know I actually included some references at the end. I don’t expect you to be able to read all that’s here but these are some of the citations both for the project and for some of the methods that we used in this particular study, both the main results and some of the sensitivity analysis as well. So Tom or Margaret are there any questions worth highlighting for the whole audience here? 

Margaret:   Well Denise, we’ve been trying to keep up with them. There were a couple questions about the relationship between Medicare covered care and VA care and fee basis,    questions like does Medicare reimburse the VA for care that VA supplies to Medicare covered patients? And then how does fee basis fit into that? And we’ve given them some answers. And Paul Barnett, I believe, responded to one of those questions. 

Dr. Denise Hynes:   So generally speaking, and I’m sure this is similar to what you’ve said on your reply, is Medicare and VA are very separate. While care might be coordinated, costs generally are not. So for care that’s provided under Medicare auspices, those are paid separately under the Medicare program. And VA does not pay for Medicare covered care. They don’t reimburse for that. Similarly, Medicare does not pay for care provided in the VA.  I hope that helps. 

Margaret:   Okay. We have a question here but we need to let the audience know that there is not an answer at the moment. How do you explain the estimated expense differences between African Americans and non-African Americans? 

Dr. Denise Hynes:   Yeah, one thing that – there’s a couple things that we can look at. We could look at some interaction terms, for example, one thing that we’ve considered is, is it something confounded by perhaps African Americans might be presenting later. So it might not be so much that is the nature of race but it could be the nature of some other factors that could be confounded with that. We started to explore some of that. I don’t have the definitive answers that I can share with you today. I do see a question here and I can also see the answer. So if this is something that Tom wants to expound on we can. Wondering if you could talk a bit more on what made you use GLM with GAMA? Specifically, what made you decide on GAMA? I expect this is Tom’s answer. We decided on using GLM with GAMA based on the results of a modified part test. I failed to mention that, determining the family distribution. Tom, do you want to add anything to that? 

Tom:   Yeah, first we decided to use GLM because in terms of modeling cost outcome, of course, we’re highly in the skewed, in the skewness of it, and using ordinary least squares may not be the most appropriate for that. So GLMs could account for that skewness based on the choice of the family distribution. So like you said Denise, we decided to use the part test to examine the relationship between the mean and the variance. And we found that the GAMA was closest to the result that we found from our part test. 

Dr. Denise Hynes:   Thank you Tom. 

Paul:   So Denise ,this is Paul, if I might just interject that the HERC econometrics class covers exactly this, which distribution should you use and also which link function should you choose when you have a cost regression? It's actually the second lecture on cost regressions in the HERC econometric course which is on the HSR&D cyber seminar site. So if you’re interested in how to do this we have some examples and actually I think the last time we gave the lecture we did a desktop demonstration on how to do this...

Dr. Denise Hynes:   Excellent! Thanks Paul. It looks like we’re at the top of the hour so to be respectful of people’s time I’ll turn it over to my hosts and I thank you for the opportunity to talk to you all today. 

Heidi:   Thank you so much Denise for taking the time to put this together and presenting for us. We do very much appreciate it. Thank you to our audience for joining us for today’s session. We will not be having a HERC monthly session in November or December because the schedule would fall so close to the holidays. We do have the cost effective analysis course going on right now. And our next session in that series is next Wednesday at 2:00 pm, same time. And the presentation will be medical decision making and decision analysis. And you should have all received registration information for that earlier today in your email. We hope you’re able to join us for that. Thank you for joining us today. 

Paul:   Heidi, if I might just say to let people know what this is about. If you’re trying to model the long term costs and outcomes of an intervention, say, beyond the end of a trial or something that is you can’t observe directly this model building is what this lecture’s about, would be helpful to you. 

Heidi:   Fantastic! We hope everyone can join us for that session. Thanks Paul. 

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