**Transcript of Cyberseminar**

**VIReC Database and Methods Cyber Seminar**

**Applying Comorbidity Measures Using VA and CMS (Medicare/Medicaid) Data**

**Presenter: James F. Burgess, Jr., PhD**

**June 2, 2014**

*This is an unedited transcript of this session. As such, it may contain omissions or errors due to sound quality or misinterpretation. For clarification or verification of any points in the transcript, please refer to the audio version posted at www.hsrd.research.va.gov/cyberseminars/catalog-archive.cfm or contact virec@va.gov*

*Moderator:*Welcome everyone to VIReC’s Database and Methods Cyber Seminar entitled Applying Comorbidity Measures using VA and CMS Medicare and Medicaid’s data. Thank you to CIDER for providing technical and promotional support for this series.

Today’s speaker is Dr. James Burgess. Dr. Burgess is a Center for Healthcare Organization and Implementation Research – sorry, otherwise known as CHOIR senior investigator and professor of Healthy Policy and Management at the Boston University School of Public where he is the director of the Health Economics Program.

In VA, his funded studies with numerous VA collaborators across more than one dozen VA centers have included work on qualitative approaches to develop and validate a measure of perceived access implementation of care management programs in primary care, mental health integration, spatiotemporal spread of second generation anti-psychotics, timely access to colonoscopy services after positive screening tests, rules of hospitalists in impacting inpatient quality in efficiency of care, composite quality measurements in VA nursing homes, evaluating cost savings for veterans receiving palliative care consults, cold hard evaluation of Medicare and VA service utilization over time, and studying the impact of information technology on nurses and patients quality of care.

As Molly indicated, questions will be monitored during the talk. And at this time, I would like to present Dr. Burgess to you. 

*Moderator 2:*Jim, are you on mute by chance? Dr. Burgess, if you are speaking, we cannot hear you so you might need to unmute your line. If your line is unmuted, you might need to dial star zero and ask the operator to moderate your line. And then just being speaking once your line is unmuted. In the meantime, I will put back up our beginning slide so that people just joining us can click the handouts if you need to. 

*Moderator:*Thanks Molly. I will always add to the attendees that at the end of the session a brief evaluation question will [Cross talk]

*Dr. James Burgess:*Sorry and thank you for covering that. Anyway, I thought I was in because you could hear me before. But now I am unmuted. So anyway, I just wanted to flip back also. My thanks to JoAnne and everybody at VIReC, especially to Denise Hynes and to Tom Weichle for their help in putting this together. So really, I am part of the VIReC National Steering Committee and also their Technical Advisory Committee. And so we agreed that I would be best to make this presentation today because I can probably answer a lot of questions on the fly as you can tell from JoAnne’s introduction. I have done a lot of different kinds of studies using this kind of data. So we will jump into the presentation now. And let me now see that I have control. Yes, I do. 

The way we will do this is I think it will best if I move through the presentation and ask you to hold questions verbally and answer the questions at the end. But you are welcome to use the question and answer and type in questions as they occur to you. And I will come back to as many of them as we can get to at the end of the presentation. 

I just want to emphasize that the main focus of this session and its objectives are to be able to name sources of comorbidity information in VA and CMS data, identify the common data elements that are used in measuring comorbidities, to recognize a lot of the important measurement issues because measurement issues are ubiquitous here. We will only do a survey of some of those. But I will talk a lot of about that. And we can delve into specific issues that you are having questions about how we use administrative data to assess comorbidities and to identify some of the common pitfalls that arise in using VA and CMS data to assess comorbidities. 

And then also this session will focus on the use of data and build on some of the previous seminars in the series. And if you have not heard those presentations before, you can go back and get those in the archives as well and listen to them. Okay, so we are not going to delve deeply into theoretical and statistical issues. Comorbidities are the theoretical statistical issues or an active issue of research. I hope lots of you are dealing with those research issues. But we will not really deal with those in this session. And we will not examine in specific particular detail specific comorbidity scales because again that would go outside of our scope for an hour today. So this is the outline of what we will do. And let me just dive into it. So we will start with an overview.

So just an overview of the idea of what a comorbidity is. And comorbidities come from some unrelated pathological or disease process that effects a patient. And this usually assumes that we have a focal condition of primary interest. So usually, when we use the term comorbidity, we are dealing with all the other things that are not our issues of key difference. And also, there is a key difference between when people are talking about comorbidities. 

They are generally not talking about health status. But they are really trying to talk about something specific that we can identify as a particular condition as opposed to a general level of health status. And comorbidities are an important component as we evaluate different kinds of issues. Of course, they are extremely important for clinical outcomes. They are very important in use to try to assess costs as mentioned. 

I am a health economist by training. So I am very interested in cost issues. So I am also very interested in quality of care issues. And those also are important areas where we worry about comorbidities as effecting quality and outcomes. 

And we can identify and use comorbidities in a variety of ways. We can, actually starting from the bottom, actually think of a comorbidity as a dependent variable of something that is an actual outcome that we might be interested in. But more commonly, they are used as predictors, covariant confounder variables or to moderate a relationship that we are trying to see whether a particular intervention affects a particular condition of focus. 

And here are some examples of research questions that arise. You could be using them in comparative effectiveness studies. So you are looking at chemotherapy effectiveness. And you are looking at how those comorbidities might affect the effectiveness of the chemotherapy. You can look at them in healthcare disparities. And see whether comorbidities explain race and ethnic disparities in something like kidney transplants. 

We also use them a lot when we are trying to study healthcare quality and to look at things like whether people get a flu shot or a colonoscopy. And look at whether particular types of comorbidities make people either more or less likely to get those screening tests. And then lastly, we can be looking at productivity of medical centers or individual providers. We are looking at healthcare costs. And we might want to be interested in who is providing more cost effective care. So those are all the – and these are only touching the surface of the kinds of issues where comorbidities arise. 

Okay, so we where do we find sources of comorbidity in administrative data? So one of the things that we find is that sources can arise from workload in VA or from claims in Medicare and Medicaid, issues of diagnosis and procedure codes. We also sometimes look at comorbidities as arising out of pharmacy data. So you might think that is a little strange. But it really is very important sometimes to say what is being treated with a particular medication and that is specific to a disease or condition. And that may be an important piece of comorbidity information. And then also we might use lab tests, enrollment records, or other kinds of data sources. 

And what we want to do is then move next to our poll. So we have a poll that you should be able to see over the right hand side of your screen. And now it is being moved over. Okay, now it comes up. And I am interested so that as I move through this presentation where people are in the novice, some experience, expert section. So as expected that we have lots of people. We will keep voting open for a while. 

I am getting a lot of people with some experience and not very many experts, which is helpful because again, I am going to view this at that middle level of some experience. And that is what I am getting from this. Let me see if we are getting – a few more people voting. That is probably enough. So let us end the poll. 

So again, we are having about one-third of you are novice users, almost two-thirds with some experience, and a small number who are viewing themselves as experts. So that is very helpful. I will try to discuss this whole seminar in that middle ground. But especially if some of the novices want me to come back to some item that they did not understand in the question/answer period, I would be more than happy to do that. Okay, so let us continue.

And first issue is how do we find comorbidity information in VA and CMS data? Where would we look to locate it? And as I mentioned a bit ago, primarily we get this information from diagnosis and procedure codes. This is the usual primary way that we get this. VA workload data, we find this in medical assess datasets, fee basis files, also growing ways from the corporate data warehouse, other sources, and we can find diagnosis and procedure codes that are entered through the various vista systems that feed those outside systems. And then from fee basis files that come from outside the VA that VA pays for.

Then we also can get them from Medicare claims. And there are things called the Institutional Standard Analytic Files or SAF. And then there are also non-institutional ones. And then you also, on the inpatient side, have Institutional Stay Level File that is called the MedPAR File. That would be for inpatient Medicare records.

And then from the Medicaid claims, these are obtained from the Medicaid Analytic Extract Files. And so these are all different sources. And you need to remember as you are getting information from different sources that they have slightly different data generating processes. And depending on the nature of your study, it may be important to be able to nail down what those differences are as you are pulling data from the different sources. 

It is also possible to get medication data. So as I mentioned, what somebody is being treated for with a pharmaceutical intervention. That can tell you something about comorbidity information as well, especially with things like insulin treatments, anti-diabetic treatments are indicating that someone is getting treatment for diabetes. You can get those on the VA side either from the PBM sources, the Pharmacy Benefit Management people or from the Decision Support System or DSS Systems. 

On the Medicare and Medicaid side, Medicare in recent years, the last seven or eight years, you could get data from their Medicare Part D claims where the Part D is the drug benefit in Medicare. And on the Medicaid side, there are also prescription drug claims that you can obtain. 

Then also laboratory results, laboratory results can be very important as well to indicate what people are being tested for. So that can tell you information about comorbidity. And DSS is the primary place that most people obtain for research purposes form the NDE, the National Data Extracts information at DSS.

Generally, lab results are not available though outside the VA-to-VA researchers unless you have some inventive way of getting access to private sector data. So in general that is VA obtained data. And also sometimes, you can have condition-focused program enrollment. This is primarily especially for people when we are looking at the national files for reimbursement in VA for the data systems. You can sometimes get into a registry of various kinds for things like Hepatitis C, serious mental illness, HIV, and other things. And sometimes that is helpful information that you can use.

Okay, so for the types of diagnosis codes so currently we are still operating under ICD-9 coding. Although, and it is of note that we are in the process of converting to ICD-10 as most of you probably know that the compliance deadline for VA to convert for ICD-10 has been changed from this October 2014 to next October 2015. So that is going to be coming up soon. And I will say a little bit more about ICD-10 in a few minutes. 

But the diagnosis yields, it is important to remember especially if you are a programmer trying to pull all of this information that all these codes appear in multiple diagnosis fields. You have to be sure that you are looking carefully at your methodology to pull all the codes. And VIReC has various support documents on their website that help you with those kinds of things. And the CDC has some information on ICD codes as well. There is a link there that you can get further information on finding information on ICD-9 codes.

So then, there are also procedure codes. And there are two types of procedure codes. Even though the ICD-9 system is intended at being a diagnosis system, it also has some procedure codes built into it that primarily are used in the VA for inpatient services. 

More and more in recent years VA has been trying to use procedure codes form the CPT codes the next type in their data. But a lot of times, the data quality in VA for the ICD-9 procedure codes can be better. Again, it depends on what you are pulling them for. And the inpatient and other claims come from – on the Medicare side come from the Medicare claims.

There are also the CPT procedure codes. It stands for Current Procedural Terminology. And the specifics of CPT coding is most complete in the outpatient services in VA. And it is used for pretty much all services in the Medicaid system. And there are some more. The CPT coding system is a AMA product. So down there at the bottom is a link to the AMA website that gives you further information on those codes.

Because it is an AMA coding, originally, it was designed – the CPT coding was designed for physicians. So what people have done is come up with these so-called HCPCs codes or it is pronounced different ways by different people. But the Healthcare Common Procedure coding system codes. And these are used in billing for other services by other kinds of providers other than physicians, maybe chiropractors, social workers, and things like that. 

And so in the Medicare/Medicaid side, these are billing codes. And they are used in that way in the private sector. And if you are pulling that kind of data, you will have to get used to what you are pulling from those HCPC codes.

And this is just a little table of trying to look at the differences between diagnosis and procedure codes in the different systems, where they appear, and where you would go look for them. And obviously, if you are doing admitting diagnoses, admitting diagnoses appear for inpatients. And there is also an admitting diagnosis code and a primary diagnosis code that are potentially separate. And then there are various issues on events, outpatient events, which can be occasions of service when a patient is not present. And then some codes for those – and a visit is usually defined as when it is happening when a patient is present. 

Okay, so let me move on from there. And then focusing on the procedure codes, the procedure codes you can see appear in the different places. Note that in the VA that there are some CPT procedure codes in inpatient. But as I mentioned earlier, in general, the ICD-9 procedure codes in VA inpatient files are generally considered for most conditions more accurate. 

Okay, then we move over to the Medicare side. And on the Medicare side, these on the left hand side are the different types of Medicare files as I mentioned. The MedPAR are discharges on inpatient. There is other inpatient data. SNF is for Skilled Nursing Facility data, outpatient hospice, and home health. 

The carrier files are for individual provider files. And then you can see for the different types of files, again, it lays out different places where you can find diagnosis codes and in particular the procedure codes and where the procedure codes apply. Notice that the HCPCs codes do not apply on the MedPAR Files. They just apply on the other files where you would have to go look if you were looking for chiropractic care in Medicare.

Okay, on the Medicaid side, it looks actually pretty similar in that we have the Medicaid files are again broken out by the principal diagnosis, secondary diagnosis, and then the different types of CPT codes and HCPCs codes. And note there that the procedure coding system for that accompanies these files in the different sectors where they appear. And again, in Medicaid files, you may get also long-term care data where – and again, there is actually all sorts of other codes that appear in the long term care files, which we will not have time to really get into here in too great detail.

Okay, now I want to move over. So again, these are just areas where to look if you are trying to look for particular files. You can use these to hunt down where is the appropriate place to look for particular data. And again, as I said, most commonly, you will use the diagnosis files or the procedure code files to seek the comorbidities that you are looking for for a particular study.

However, sometimes that you are looking for additional information, and as I said, one of the places that you often go to look for additional information is from the pharmacy files. And here are the basic reasons why you might want to do that. So sometimes, the appropriate diagnostic information may not be available. That is a reason why that might happen. Another reason might be that you might have some chronic conditions that are stable that over time where you are getting pharmaceutical medications but you are not coming in for treatment. So some ideas might be hypertension or epilepsy. 

And indeed, somebody might not come in for treatment for a long period of time. Again, also various other kinds of blood level issues that might arise. It is a whole host of them that you might be interested in. And for a lot of those, you might just continue to get your medication re-upped, but not actually come in for a visit. This also happens when people might be getting some of the physician care in the private sector. But coming through the VA mostly to get these medications.

The third area is when the conditions for which the treatment regimen might be set and time limited. So again, tuberculosis is one of the most common ones there where you get the tuberculosis treatment. You have had a tuberculosis test. I mentioned tests and that is on the next slide. But you might have the tests and you might have the pharmacy treatments. But you actually do not really come in and see a provider for a period of time. And so you might not see it in other records. 

Okay, so let us move on to some of the other important measurement considerations that we might want to think about if we are using administrative data to assess comorbidities. So what are the things we need to think about? And at this point, again, I view the introductory information. I went through it relatively quickly assuming that most people being at a mid-level of understanding will understand most of that. And I am going to slow down a little bit in this section because there are some important measurement considerations that we want to think about. And this again, is the area where I actually expect people will come back and ask me questions in question and answer.

So first of all, this is a conceptual framing issue. Are there some specific conditions of interest in your particular study? So is there, by doing research, looking at the previous literature, there are some specific conditions you are really looking for as comorbidities for a particular intervention in the area of interest that you have. And sometimes that is what is most important. Your theory should suggest what conditions you go look for.

Another thing you are looking for, sometimes you are looking for a summary measure. You just want to adjust. And one of the things we will talk about in a minute is the use of comorbidities as a risk adjuster. Sometimes you just want a summary measure that is going to risk adjust the population for other things going on. One number for a risk score that you might want for a situation.

And a third reason really, which is within a summary measure why you might want a summary measure is that you really want to do some kind of statistical model aggression or some other model. And you really want just one measure because you do not really want to eat up all your degrees of freedom with a wide variety of measures with lots of comorbidities. And indeed, remember a lot of those comorbidities may not appear in a particular patient. So if you had – if you thought about laying out five hundred comorbidities, most of your patients are only going to have one or two or maybe five or six of those. And so there will be a lot of zeros that will just tie up a lot of degrees of freedom. So a lot of times what you want to do is take those five hundred comorbidities and combine them together into some score.

And the other reason that you might want to come up with burdens of summary risk measures is thinking about how this influences what data you can use and what conditions you are able to identify. So by using summary measures, you can a lot of times count for more conditions in your measure in some way. And then in doing this though, you have to think carefully about where you are going to get it from all the different sources I just mentioned. 

Are you going to be using inpatient data and outpatient data or just want or the other? Are you going to look at particular conditions and issues just in the VA or are you combining data between VA and Medicare or Medicaid. And these are all important considerations.

And how you think about that depends on what conditions and groups you are looking to capture. So what is your population? What is your objective? What is the outcome that you are looking at? So you are looking at an outcome where you think that these measurement considerations will be very important or is something where you are just trying to make an adjustment and you really do not think those conditions or other issues are very important. It depends on all of that. It also depends on your data availability. What data do you have available and how do you look at that?

Some other issues about thinking about this information sometimes depend on whether you are using the comorbidities as a way of doing your data selection. Sometimes you have to think about whether you are including or excluding rule out diagnoses. You may have to think about how you are defining the population of interest. Maybe the comorbidities would determine whether somebody was eligible or ineligible for your study.

And when you are using rule out diagnoses, one of the concerns there is that you may want to think about how often you might want to see a rule out diagnosis. And in particular, the reasoning is that sometimes you will see a diagnosis on a record because there is a presentation by the patient about a particular issue that the physician or other provider is concerned about. And they might write down the diagnosis as a way of then noting it because they are testing for it. But then the patient may end up negative for that diagnosis or that issue. And then you want to be careful about how you do deal with that.

So ordinarily, a common way that people deal with this is that once somebody is in an inpatient care setting, a diagnosis is appearing only when it is a major factor in dealing with the inpatient care. So you would not just put a rule out diagnosis. So seeing it once on a record is generally considered sufficient on the inpatient side. But on the outpatient side, sometimes you want to see at least two records or claims at least 30 days apart before you really think that you are seeing that as the diagnosis that is really relevant. And again, this is pretty common in VA studies as well as non-VA studies.

Lastly then, you mostly should think about clinician assigned diagnosis. How you think about – and this can really get very, very complicated depending on what type of clinician issues you are interested in looking at. You may want to avoid the lab data or you may want to use the lab data. You may want to use or not use VA DSS identifiers or stop codes. You may want to use issues from physician specialty codes in the Medicare data that identify what kind of provider somebody is. You can do that on the VA side by looking at person classes, for example. 

So this area of understanding clinician assigned diagnoses can get very complicated. Again, I am just giving you a sense for the kinds of things you want to think about. Do you want to use imaging or not? Do you want to use telephone encounters or not? Do you want to use claim types to determine whether you use a piece of data or not. All of this is going to require you to understand very well the data generating processes from outpatients who get these codes and how you think about it.

This, again, is impossible to cover in a short seminar today. In general, let us talk about what we can do with some examples. So an example you might use VA Clinic Stop or DSS Identifier Codes to identify particular claims for exclusion. So you might use x-ray, which is a 105 or laboratory, which is 108 where you might exclude or include or explicitly include or exclude telephone codes like the 103, 147, 178, and there are other telephone codes.

On the Medicare side, you may or may not, again, want to be looking at particular exclusion claims based on diagnostic radiology, mammogram screening, clinical lab issues where there was a billing for clinical labs, any of those kinds of things. Remember that in the Medicare data, you generally will not have the outcomes of the labs. But you can have billing to see the labs were done. And sometimes that is all you want to know.

Okay, then the next period is the issue about measurement time periods. Let us think about what are the data periods that you want to figure out. What is the start and end date for capturing the appropriate diagnosis codes for your particular study? It is really important to note here that there are never right answers to this. It is just a question of what is your study aims, what are you trying to look at? Thinking about whether you are looking at a long enough period to see the kind of health service use that you are looking for. But once you go out long periods, you can get all sorts of extraneous data and information because people are just – it is really a whole new condition. 

So remember I said earlier that it is very important that usually you are focusing your study on some particular area of treatment. And then you are using the comorbidities to look at other things going on. But remember that the relevance of those other things to the intervention and the study is going to vary quite a bit. And so you have to think about that. You also think about conditions like diabetes that once people have it they tend to persist versus things that are more acute. And so they occur at one point in time. And then once they are dealt with, they do not come back. Maybe more like tuberculosis. You get tuberculosis. You get tested. Find the tuberculosis positive. Get it treated. Then it is really not relevant after that.

And we think about the anchoring dates of the – anchor start/end dates. You want to think about if you want to use the same anchoring dates for every patient or do you want to vary it. And then think about the events that you are studying. Think about whether you want to think about when they start and end. And all of these things are going to be decisions that your research team is going to have to make. And these are generally – this particular slide in the measurement time period can take a lot of time to really do well to make sure you are doing the right thing. 

Again, I think sometimes err on the side of too much data sometimes here when they go and look at time periods that are too long just because they can. And then they get so much data that it because perhaps irrelevant to what they are really trying to look at. So some care here is usually warranted to try to get a study that finds what you really want to focus on.

So here are some other special challenges. We are going to look at measuring functional status, measuring severity of disease, and looking at undiagnosed conditions. So I have touched briefly on some of these already. But here is some more detail on each of them.

So comorbidity measurements, again, we want to tie things to healthcare use. It is truism, but a lot of times, researchers forget this. Unless you have an encounter, so unless there is some opportunity to collect information, there is no data generated, no diagnosis recorded. So remember that does not mean that somebody does not have a diagnosis. It just means that it was not recorded. So you have to always be careful to remember that encounters are an opportunity to get information. But again, they may not be complete. So remember somebody does not necessarily do a complete head to toe work up on every patient that walks in the door. So you can have diagnoses that are present, but not recorded as well. Again, the clinician is not really supposed to do that if they are not really treating or dealing with that condition at the visit.

So the other issue is that it is important to note that you might think well okay, if somebody has a condition and has a diagnosis that it is going to be in the case and place in the data for VA as well as non-VA data like Medicare and Medicaid. That is not really always true. Remember that the VA data does collect its own encounters through fee basis care when people are treated outside the VA and reimbursed by the VA that these situations vary a lot in the local issues for individual patients. You have to keep an eye on that.

And more frequent users, again, are going to have more opportunities for diagnoses to made and recorded. Again, another truism, but keep remembering that people with a lot of diagnoses also may just be people who are coming in for services a lot. 

Okay, so let us move on to strategies for dealing with comorbidity measurements. And there are three different kinds here we can think about ordinal data, rating data, or categorical data. And so first, this is a summary slide that tells us all the different kinds of issues. And I am going to get into some details of some of these on follow up slides. So remember that this is where we get into what is sometimes called the alphabet soup of doing comorbidity measurements.

So the ACGs are looking at a particular way of measuring cost risk. The HCC or DCGs are another ways of doing that. Those are two different ways both using diagnosis codes to try to come up with generally summary scores. Another way to do things is you look at particular conditions that are known to be creating greater risk of mortality and the Charlson Index does that. And there are some different methodologies for doing Charlson Indices by Deyo and that is one version and then also a Romano adaptation to that. 

We can also look at Functional Comorbidity Indices where we are looking more at function. The RxRisk is a diagnosis based one that is based on medications. So we can look more on medications and look at that. And Elixhauser Measures, there have been a number of different ways of looking at these measures. And I want to point you particularly toward the 2005 Medicare data that comes out of the Quan Study that lies and Charlson and Elixhauser measures that I think are very useful to look at.

Again, this is an overview slide not intending to go intending to go into the details of any of these measures. But the next few slides, we give you a little bit more about that. 

So let me first talk about the Charlson comorbidity approach. So it was originally developed to predict mortality. But we found it is also very useful to look at comorbidity for other purposes like cost and other things. It comes out of looking at 19 chronic conditions where each one has a wait that is based on looking at measures that people have used to focus in on what the risk scores might be for individual conditions and that you sum up these weights to get a score. And it was adapted by different people. And so you can get slightly different measures of Charlson Indices. Whenever you use the Charlson Index, you should note where you are getting the data from, whether it is from a particular study and a particular approach because those do differ by a little bit.

Like I said, I was going to come back to the ICD-9/ICD-10. So the Quan Group has the medical care data from their study from 2005. I said if you are especially interested in what is going to happen in the future when we move for ICD-10, one of the things that has happened, of course, is we have been using ICD-10 in places around the world including up in Canada. And the nice thing about this study is for our use and looking forward is that they were able to find that in comparing ICD-9 to ICD-10 that there were similar results for Charlson and Elixhauser methods using the Quan version from ICD-9 or ICD-10.

So one of the things we are looking for when go inside ICD-10, we are hoping that that does not really mean that we have to completely redo everything we were doing from before. But we are hoping that that will work in a fairly useful way. Well again, that is going to be a work in progress that happens. As I mentioned, the ICD-10 is presently – we are putting it off for a year until 2015. So that will not be happening right away. But it will be happening really soon.

Let me now move on to HCCs and looking at hierarchical methods for looking at different ICD-9 codes into groupings that uses the diagnostic cross group method for trying to look at things. So this is a method that is specifically appropriated for people looking at cost. So usually, this is used when people are trying to look at a cost method. It looks at 15 thousand of the ICD-9 diagnosis codes. 

And then it breaks them down into 185 buckets of homogenous conditions. And done them hierarchically to try to look at cost foci in terms of single organ systems and other things. And patients will fall into different buckets based on the highest resource groups. That is the hierarchical part of it. So you identify people across multiple conditions. But then we use a hierarchical structure that differentiates people from different levels. And then you can also get a risk score that could be calculated that gives a risk score that we can then use for a single measure that we can use for other uses.

So then, the RxRisk Score is a pharmacy-based measure. That is a chronic disease measure with 45 chronic disease categories. And this is looking at medications. And when we look at medications, you can look at the Kevin Sloan study there for medical care 2003 if you want more information on the RxRisk Score. And sometimes that is a very useful way if we are looking at medications, but no other measures that we can use to look at risk score comorbidities.

So now, we will move on to the issue about looking at combining VA and CMS data or Medicare data. And a current issue that we are worried about in trying to do that is to try to focus on differing incentives because we can have different information from VA or CMS. And those issues can arise from different time periods where sometimes people use VA. Then they may stop using VA and using Medicare and going back and forth. And so multiple codes to be useful to look at.

And a really important issue there is to then think about importance of issues for complete data. And here a number of other issues from the Byrne, et al. group that they looked at for different dual eligible issues from different risk scores from combing VA scores versus Medicare scores or versus looking at them individually. So this is, again, an important study that will help you to try to understand what you might get depending on what kind of risk scores you would look at for different data.

And so here is the question that the Medicare data basically looks at only about 80 percent of individuals total illness burden. And VA data tends to capture about one-third of the total illness burden. And then we have these concerns about the different issues. So that is a summary from that you can look at.

Now we are going to move on to a case study because we have about 15 minutes left. We will try to move on to that. And we will look at this French study that looks at a comparison of complication rates in veterans for cataract surgery. And where they have both VHA data, VA data, and Medicare data. And we will use this example as an example of particular study. And again, this is medical care data from 2012 you can look at for this particular example for some more detail. So I am just going to summarize that a little bit about some of the issues that arise from this study. 

So the background of this study is that we are looking at the rate of surgical complications that might happen for our particular situation. And the objective of this study was to compare rates of secondary surgery after cataract extraction for VA and Medicare patients that might – and we are going to look at the comorbidity indicators for their predictor variables that might occur from different both from VA and from Medicare. And these are elderly patients. So they are over 65. So they are going to be Medicare eligible as well as VA eligible. And we are looking at that in calendar year 2007.

So this study in particular, we were looking at the clinically relevant comorbidities and looking in particular trying to look at things like glaucoma, ocular hypertension, and other things. So that this particular study that is the clinical area that they are looking at for this particular example. 

And then they are also breaking out Charlson Index comorbidities. And these are some of the major common Charlson issues of things like AMI, myocardial infarction, congestive heart failure, heart failure patients, vascular disease, respiratory disease, diabetes, cancer, liver disease, renal failure, and hyperplasia. So this is again some of the major issues that are built in with the Charlson Index. And this particular study used that approach to try to look at mortality risks for that particular population.

So again, here, they looked at the comorbidities of prevalence for both groups of patients who are Medicare cataract surgery group. And then look at them. And the exclusion criteria here are looking for 2005 forward. And that is, again, the particular approach that they did.

So then, we will move to where do we go for help and move toward the end. And then we will have about ten minutes left that you can ask questions in particular areas that you want to look at some more.

So again, to really understand any of this, again, this is just really an introduction to really get into more depth to studies that you might want to do. We, again, strongly recommend that you go to the VIReC website and look in more detail about the information on the VA data sources and access to data sources. And of course, there are some very specific areas of the different areas of VA data, all different kinds, things like the medical care data from SAS datasets, from DSS datasets, from the Corporate Data Warehouse, and from CMS data that they get through VIReC is the access point to do matching data to Medicare and Medicaid. 

And there are tutorials that the VIReC has step-by-step approaches to measure these things in terms of looking at how you can construct comorbidity indices both methods for getting a single value that you can use for many, many studies. Or looking at targeted studies that were something that you were looking at that is clinically relevant to your particular population that you want to look at. 

Also, we always recommend the HSRData Listserv that is used. There is currently about 650 people in the data stewards, managers, and people who are able to look at questions that you might have. Also, one of the things that I am willing to do in my assistance with the VIReC Technical Advisory Committee, if you want to ask me some specific question. I will also help VIReC either direct you toward things where the VIReC helpdesk can help you or answer any specific questions that you might have.

So we are toward questions. And so you can start to, if you want to go back, and one of the things I can do, especially if you want to dive back into particular slides that we went through. I can go back to particular slides as you would do that. 

A few other things as we are going to head towards the end of this presentation today is that the next presentation is coming up on July 14 where they are going to present information on improving mortality ascertainment using the VA Vital Status Database. So that will be the next one coming up. And so that is where we are from present.

So we have gone through each of these. And we will now turn it over to – again, we saw in the initial presentation that there were a number of people who had presentations. So we have now, by my watch, about nine more minutes to answer some questions that we can go back and dive deeper into any particular issues people want to raise. So I currently do not see any.

*Moderator:*We do have a couple in there actually. Thank you so much doctor. I just want to let our attendees know that a lot of people joined after the top of the hour. So to submit your question or comment, just type it into the question and answer box in the lower right hand corner of your screen. Type it into the lower box and press the speech bubble. And we will get to it in the order that it was received. And JoAnne, I believe you have access to the questions. Would you like to moderate?

*JoAnne Stevens:*I do and thank you Molly. Yes, the first question is – actually Jim, I can answer this if that is okay.

*Dr. James Burgess:*Okay, now I see them up here. Okay, go ahead JoAnne.

*JoAnne Stevens:*I am not affiliated with the VA. Is the Comorbidity Index Tutorial publically available? And the answer is no. It is behind the intranet firewall.

*Dr. James Burgess:*That is correct. Yeah, so one of the things that happens on the cyber seminars is lots of people are people both inside and outside the VA. And we use these cyber seminars. So it is very important to note that people who are not affiliated with the VA, especially to get at some of the details would have to work with somebody within the VA. Sometimes, there are people within the system that will work with people outside the VA. And that is always possible to do to work as a collaborator. And there are a lot of ways to do that. 

So that is something I would encourage people to do. Okay, I do now see where I am supposed to be over here. I think I can work myself to the next one there. So let me read the second question here. Can we discuss the summary files that already have the Chronic Condition Warehouse? And then it says and/or the RDDC. So I am not sure I know what the RDDC is. But we do not have the source ICD-9 data. So if someone is interested in the – let me see. And then it is asking – yes, I am not sure what RDDC is. So JoAnne or somebody from VIReC, do you know what they are asking there, a little missing on this one particularly what the question actually is.

*JoAnne Stevens:*No, I do not Jim. However, I would suggest that the requester perhaps they could tag on to their question and let us know that so we can get to that information.

*Dr. James Burgess:*Yeah, so again, in the Medicare claims data, again, I will probably add something as a general statement that somebody who may not have a lot of experience with the VA data is one of the things that happens is the VIReC, as it states, as we done and gone back into the past to look at people who are patients who have touched the VA, used the VA system and then matched their records to Medicare data. The effort in doing this has been pretty comprehensive. 

So a lot of times then if you have somebody who is let us say has maybe some attachment to the VA system and has had some use in the VA system. But then you are looking at really the extensive Medicare claims data for those patients. VIReC has a pretty extensive piece of information about those people. And so if you are very knowledgeable about the Medicare data, maybe very knowledgeable about that and just you are trying to find the right people who have touched the VA with certain conditions, you can actually do that kind of work even if it is really focused on issues that are happening outside of the VA. 

Those kinds of studies can easily be done. But then again, as mentioned from the first person asked, in order to get access to that data, you have to be within the VA system in some way to get that. 

Okay, let us keep going. There are three more here. So now that I have seen the right place to go, I can see the third question. So is there a way for people with non-VA email addresses to join the listserv. So this is again a question back to JoAnne. If people want to get access to the information from the listserv, how can they do that from their non-VA email addresses? So why do you not answer that question. 

*JoAnne Stevens:*Certainly Jim, thank you so much. Actually, the only way to access the listserv is behind the VA firewall. And you must have a va.gov email address. So as Jim mentioned earlier, the methods to perhaps to hook up with another researcher. Or there are other ways to do that. Feel free to write the VIReC helpdesk. And we will let you know what those options are.

*Dr. James Burgess:*Okay, and so the next question. The next question is what are your thoughts on the Saleem Comorbidity Index and thank you? So that goes into the technical areas of asking. One of the things we said we were not going to present in this particular cyber seminar today. But let me say a couple words about different people have done many, many different doing comorbidity indices out there. So there are quite a lot of people who have done many different kinds. And the Saleem study is out there. You can look up the – do a look for some of the published research on that comorbidity index.

What are my thoughts on it? So let me just say something about – and part of the reason – the way I approached it the way I did in presenting this and the way we and VIReC together put this together. It is actually really for the most part very important to note that most people really – which measure you use tends to be far less important than the fact that you are using a comorbidity measure to do whatever risk adjustment you are trying to do or whatever else you are trying to measure. So I am a big fan of saying when you are going in and doing this, getting overly caught up in choosing which measure you are going to use is not always the most important to do. So that is what I would say about that.

Okay, so let me see. I have lost the question.

*JoAnne Stevens:*Jim, I have a suggestion. We are two minutes to the end of the hour. We either can continue or we can allow the attendees to complete the evaluation.

*Dr. James Burgess:*Let us go on to the evaluation. Again, let me just end with a couple of comments. If people want to, I am happy to – if someone has a particular comment that they would like to follow up on by sending me email directly, I am happy to help with it. Also, some of the VIReC, their helpdesk also is willing to answer questions. And we are willing to follow up with other questions thinking through.

But I think that is right. With now one-minute left, why do you not go ahead and take over to do the evaluation to end the session. And then we will be done for today. I just want to thank everybody for their patience to go through this. And we hope that this has been useful in helping you see the complexity and how to approach doing these kinds of studies.

*JoAnne Stevens:*So once again, t hank you to Dr. Burgess for taking the time to develop and present today’s session. And also as he indicated, please email the VIReC helpdesk, virect@va.gov. And we will be happy to pass those questions along and post responses to those questions.

As also indicated, our next session is scheduled for July 14. Please note that the scheduling for this session has now changed. It is only for the second Monday in July instead of the first Monday. But again, it will be from 1:00 to 2:00 p.m. Eastern. And we hope that you can join us at that time. So thank you everyone. Please complete your evaluations. It does help us to develop our programs. Thanks everyone.

*Moderator 2:*Thank you so much too JoAnne from VIReC and also Dr. Burgess. For our attendees, I just want to let you know that I will be leaving this evaluation up on the screen for quite a while. So feel free to take your time. Also, we did have a question come in asking where you can find the recording and the Power Point. As I mentioned, you will receive a follow up email two days from now with a hyperlink leading to that recording where the PDF is also available. Or you can email cyberseminar@va.gov. And I can email you a copy. Or also, you have the reminder email from this morning, which has a link leading directly to it. So there are many resources for it. And I would like to echo JoAnne’s thanks for everybody joining us today. Once again, I am going to leave this survey up so take your time. But this does conclude today’s HSRD Cyber Seminar. Thank you.

*Dr. James Burgess:*Okay, and thank you ever much for your attention today. And we hope you enjoy your day and hope this was useful to you in your research.

*JoAnne Stevens:*Thanks everyone.

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