VIReC  Database and Methods Cyberseminar- 13 -Department of Veterans Affairs

VDM-040212

**Department of Veterans Affairs**

**VIReC  Database and Methods Cyberseminar**

**Denise M. Hynes, PhD, MPH, RN**

**Applying Comorbidity Measures Using VA and Medicare Data**

**April 2, 2012**

Moderator:   Welcome to VIReC Database and Methods cyberseminar entitled, “Applying Comorbidity Measures Using VA and Medicare Data.”  Thank you to CIDER for providing technical and promotional support for this series.  Today’s speaker is Denise Hynes, Director of VIReC and Research Career Scientist at the HSR&D Center of Excellence here at Hines VA Hospital.  Dr. Hynes holds a joint position at the University of Illinois, Chicago, as Professor of Public Health and as Director of the Biomedical Informatics Core of the University’s Center for Clinical and Translational Sciences.  

Questions will be monitored during the talk and the Q&A portion of GoToWebinar and will be presented to Dr. Hynes following each section of her talk.  A brief evaluation questionnaire will pop up when you close GoToWebinar today.  We would appreciate it greatly if you would take a few moments to complete it.  I’m pleased to welcome today’s speaker, Dr. Denise Hynes.

Denise M. Hynes:  Thank you, Margaret.  I’m just going to pause for a moment and make sure that the slides come up.  Very good.  Okay, well thank you everybody.  It’s great to be talking to you on this Monday.  Margaret is going take us to the session objectives.  We are trying to do this—there’s three of us who are keeping this in sync today, so we’ll try to keep track of questions as we go.  We’re going to be pausing after each of the sections for questions and, hopefully we can take some questions in that way, as well as at the end.  We’ll make sure to save some time there as well.  

So what you have on the slide in front of you—for those who aren’t on GoToMeeting, but following along, we’re on slide three—Session objectives.  This is our goal for today; you should be able to name four sources of comorbidity information in the VA administrative or workload data.  Hopefully, you should be able to identify some common data elements used in measuring comorbidities, understand the important measurement issues that come up when using these kind of data for assessing comorbidities.  And then, hopefully, we’ll also advise you on avoiding some common pitfalls when you’re trying to combine both Medicare and VA together.  

Today we’re going to be focusing on using VA and Medicare data together, which is a little bit different than if you are embarking on a prospective study that is relying more on primary data.  That would be a very different focus.  So today’s talk really is relying on use of existing data sources.  It also will be building on some of the previous seminars we’ve done looking at inpatient and outpatient VA health care use, as well as Medicare health care use with VA data in the pharmacy presentation that Dr. Lee did.  So if you weren’t able to attend those, you should feel free to refresh your memory after today’s presentation.  We’ll highlight some aspects there are critical, but having access to those lectures will be most useful.  

I want to highlight what our session will not do today.  We will not be talking about theoretical or statistical issues related to accounting for comorbidities, although some of these issues will be highlighted when I talk about some of the measurement issues.  But I won’t be going into this in-depth at all.  I’m also not going to be highlighting a lot of specific details about comorbidity indices or scales.  But I will be introducing you to several, as well as providing some references for further reading.  

So let’s just start with kind of an overview about finding comorbidity information in VA and Medicare data.  I’ll break at each of these sections so that we can take questions.  

So first, just a basic definition, which we don’t want to slight.  I want to make sure we’re all on the same page with what our topic is today.  Basically, comorbidity assumes a focal condition of primary interest.  That comorbidities are generally unrelated to the focal condition.  Although they can be somewhat related; in some sort of syndromes they may be.  But we’re going to talk about the difference between comorbidities in health status in  particular.  

Comorbidities, often the terms are sometimes used in exchange with risk adjustments, case mix, they’re closely related, although they can sometimes be intended to mean something different.  Some important components in evaluating comorbidities might be clinical outcomes, resource use or resource cost in healthcare.  It also can be examined in the context of studying quality of care.  It might be looked at as a predictor variable, if you will.  I could be a covariate or a cofounder.  It could be a mediating effect.  Or it could be considered as a dependant variable.  And it really depends on what a research project poses as their research question.  

Some examples of research questions requiring information on comorbidities; we tried to just give you a snippet here of some of the types of studies that we often come across in health services research.  So some examples here—comparative effectiveness studies often try to take into account comorbidities—An example here, “Is chemotherapy more effective than radiotherapy in the treatment of endometrial cancer?”   

Examining healthcare disparities, often researchers want to separate other factors from the main factor of interest, i.e., race and ethnicity.  So one might look at how comorbidities add additional information in explaining disparities in a condition of interest; here the can example is kidney transplants.  

In healthcare quality one might take into account comorbidities in the following question; “Are VA patients more liking than those in Fee For Service Medicare to receive recommended screening tests?”  And, in fact, that will be an example of what we look at towards the end of our talk today.  

And in looking at healthcare costs or provider productivity; comorbidity in the patient population is often taken into account.  This example; “Who will provide more cost-effective care for diabetes, certain kinds of sub specialists; endocrinologists, nephrologists, or general internists.”  

Some sources of comorbidity information in administrative data are shown here.  Health services use in the VA, we have what we often call the VA workload, or the VA administrative data, and we’ll talk specifically in a few minutes about what that really includes.  Claims data as one might find in Medicare and Medicaid data, specifically looking for the diagnosis codes and procedure codes.  

Pharmacy data provides information about medications, and there are some comorbidity measures and approaches that one could use using information from pharmacy data specifically for medications that might be provided for a specific condition, sort after as a marker.  

Laboratory data and results can also indicate certain criteria for conditions.  

And then there are other kinds of information, for example, specific targeted programs that might provide some additional information that might be taken into account.  For example, enrollment in particular programs such as a breast cancer support group or other kinds of programs in which there is clearly some good, valid information that would indicate the presence of a condition. 

Now before we get too far, it would be a good idea for me to kind of get a sense of who you all are.  What you have up on the screen is a quick poll—I believe we can all see the same thing.  I’d like you to rate your experience with using administrative data to capture comorbidities.  If you’ve used any kind of standard comorbidity measure, you don’t have to tell us which one.  If you’ve used, specifically, VA data or Medicare data it would be helpful to know.  If you have never used it, then you should categorize yourself as a novice.  If you’ve used a little bit, maybe on a current study, you might list some experience.  If you’ve used if on multiple studies, feel quite confident, and might even be interested in sharing code, you would be considered an expert.  

Great.  So what I’m seeing in the poll results are that it’s about 48 percent rank themselves as a novice, 39 percent are saying they have some experience, and 13 percent rank themselves as experts.  Great.  I think our objectives and content today will hopefully satisfy the broad range of participants we have today, and maybe some of you experts can help answer questions.  

So let’s move to the next section.  Slide 13 is where we are and we should be getting into information on finding comorbidity information in VA and Medicare data.  

So let me just talk a little bit about some of the basics in terms of using administrative data.  And for those of you who know me, using the term “administrative data” is not something I like to lump everything together in, but it will make our lecture today a little easier.  When I use the term administrative data I’m referring to both VA workload data and claims data, which includes a lot of information that can be more than administrative, including some clinical information.  But that’s the term I’ll use just to keep our lecture smooth.  

In particular in these data, there are diagnosis and procedure codes that are very important in pretty much any kind of comorbidity measure that’s been used.  Even if it’s something simple as a dichotomous 01 variable, these kinds of codes become very important.   In the VA workload data, we’ll be talking about the Medical SAS Datasets, and we’ll also talk about the Fee Basis Files.  And in the Medicare claims we’re going to be referring to the Institutional Standard Analytic Files, as well as the Non-Institutional Standard Analytic Files, carrier file, Part B type.  And also, the Institutional Stay File, the MedPAR data.  

There’s also other files that are available to us in the VA, including information on medications that can be obtained from VA’s pharmacy data systems, both the VA Pharmacy Benefits Management Group, as well as the Pharmacy Dataset and the Decision Support System; DSS.  Information, for example, that might be indicating specific kinds of conditions.  The example here is for medications indicating that someone is very likely a diabetic; oral hypoglycemic, insulin, et cetera.  

We also list here that in the future, Medicare Part D claims are available.  They actually are available now in the VA on a case-by-case basis.  So if you’re interested in information on Medicare Part D, which provides information on medications that Medicare covers, those claims data are also now available to us.  

Laboratory results also available in the Decision Support System, specifically laboratory results, National Data Extract, (the NDE file).  For example, you might see hemoglobin A1C value that might indicate that someone’s diabetic.  Keep in mind, laboratory results data are generally not available in Medicare data, only for some very rare situations in which reimbursement might be tied to particular laboratory results.  

Other kinds of administrative data and condition could be for program enrollment.  For example, in the MOVE! Program in the VA there actually are VA clinic stop codes, also known as DSS identifiers that can identify some specific programs.  And these might be used as a way to identify someone who’s enrolled in a condition focused program, for example. 

Types of diagnosis codes, this is a really important aspect for those using VA data, and especially if you’re blending it with Medicare data.  The next couple slides will go over this.  But the International Classification of Diseases, 9th Revision, Clinical Modification.  I’ll say that once and refer to it as ICD-9-CM diagnosis codes.  ICD-10 is coming in, basically, the next year; I believe next fall is when it really needs to be in place in most healthcare institutions.  So you’ll see a lot of activity around this.  ICD-9-CM and ICD-10 are included as admitting codes, which might be a patient’s initial diagnosis at the time of admission.  It could be included as a primary code, a condition chiefly responsible for a patient’s visit or stay.  It could also be included as secondary code, conditions affecting services provided.  

Now we, actually, at VIReC have a lot of detailed information about how each of the VA and Medicare datasets available to us are laid out.  So that would be a good place to refer to, whether it be our first lecture or some of the research user guides that are available on our website.  So we’ll just be summarizing this kind of information today.  You’ll also see a reference at the bottom of today’s slide that provides a link to the CDC, which provides a listing of these ICD-9 codes as well.  

Another important aspect are types of procedure codes.  These are used for inpatient services VA and institutional Medicare claims data.  CPT procedure codes are used; this refers to Current Procedural Terminology codes.  In the VA it’s used for outpatient services.  

There’s also a third system known as what we often refer to as HCPCS, or “hickspicks,” Healthcare Common Procedure Coding System codes.  These are used exclusively in Medicare billing.  And there’s multiple levels for which the coding is used to build a code.  At the bottom of both of these slides, this one and the previous, we also take you to a link that provides a listing of all the codes that exist.  

A couple things to keep in mind is that these code lists, if you will; the ICD-9 procedure codes, the CPT procedure codes, and the HCPCS, these are not static by any means.  And from year to year new codes may be added and some codes may be archived, if you will.  Now that’s really important if your building a metric across many years that you refer to the appropriate years’ code list when including certain sets of codes.  

It’s really important to note, especially in the VA datasets that are available to us, which some may be used in your particular study, some you may want to consider using or adding because of the kinds of codes that might be available.  What we put together here is basically a summary of the kinds of codes that are used in the specific datasets.  So down the rows we have the components of the MedSAS datasets, the VA MedSAS datasets; inpatient main, bed section, procedure, surgery, the outpatient visit file, outpatient events, and inpatient encounters.  These are all the VA MedSAS files.  

And then across the top, the specific types of codes there are available.  So you can see in inpatient main there’s actually admitting, primary and secondary diagnosis codes available to you.  When you get down to inpatient surgery, you don’t have a secondary diagnosis code.  You’ll note, too, that different types after codes are used in different datasets.  In the inpatient procedure and surgery codes they use ICD-9 procedure codes.  But if you were to use that information, those datasets in combination with outpatient event datasets, or inpatient encounters, you’ll notice that there are no ICD-9 procedure codes.  They use CBT procedure codes.  

It’s really important to be very familiar with the dataset that you’re using.  Especially if you’re using standard code from comorbidity measures because if it calls for an ICD-9 code and all you have available are CBT procedure codes, you may have to make some decisions that would cause you, perhaps, to adapt a particular standard measure, and we’ll talk about some examples of that a little bit later.  

In the VA we also have fee basis care, which also has specific national datasets that correspond to that fee basis care.  Fee basis inpatient dataset has discharge and secondary diagnosis codes.  There’s no primary code, and when you get down to inpatient, ancillary, and outpatient, they only have a discharge diagnosis code.  Again, there are differences with the types of procedure code sets that are used, and you need to keep this in mind.  

In the Medicare data, Medicare data also has its own structure, and this summary table highlights the specific Medicare datasets.  Again, along the rows; the MedPAR file is the summary institutional file.  And then each of the standard analytic files are then listed after that; inpatient, Skilled Nursing Facility, outpatient, hospice, home health, the carrier or supplier dataset, and the Durable Medical Equipment dataset.  Most of the datasets have multiple, at least two, diagnosis codes.  MedPAR has the admitting and the secondary, whereas the outpatient datasets have primary and secondary diagnosis codes.  

Again, differences in the types of code sets that are used for procedures.  ICD-9 procedure codes are used in MedPAR, but if you are building your dataset with the standard analytic files, they also have the HCPCS codes as well as the ICD-9 procedure codes, for the most part.  Some of the datasets do not.  

Again, really important to know what your datasets contain so that when you’re searching for indicators of comorbidity, that you’re taking full advantage of the data that are available, the specific fields, and not searching for fields that basically don’t exist.  

In the pharmacy data, there are some measures that we’ll highlight just briefly that take advantage of pharmacy information.  Some of them use indicators for specific drugs.  Some of them use ICD-based measures.  This can be used when diagnosis information is not available.  You might use pharmacy data for identifying stable conditions that are not occasioning a provider visit.  For example, hypertension or epilepsy, things that go on for a long time, some sort of chronic condition.  So on every visit it might not be indicated as the reason for the visit, but if you search for a particular medication that a patient has, it would indicate, very likely, that that person has that condition.  

And then conditions for which the treatment regimen is set and time limited, for example, tuberculosis.  You might not necessarily see a clinic visit that indicates tuberculosis, but you might see a specific medication that indicates such.  

Now I’ve gone over a lot of detail about specific datasets and the kind of information to look for and the kind of code sets to become familiar with.  This is really going to be your bread and butter when you’re working with these kind of data for measuring comorbidity.  To I want to just stop here and see if we have any questions.  I actually not seeing any questions in my list here.  

Question:  Denise, there are a couple of questions out there. 

Denise M. Hynes:  Okay. 

Question:  They’re very technical and beyond what I can read. 

Denise M. Hynes:  Okay. 

Question:  If you open up your questions pane. 

Denise M. Hynes:  Yeah, it’s very short.  So, for example, one question is, “Can you clarify dx-prime versus dx1 in Ttf?  Dx1 is admitting diagnosis, where dx-prime is the major diagnosis assigned at discharge.”  Well what I highlighted here were the fields that we have in this particular set of data.  I’m not going to get into the specific datasets.  I know we have covered some of this in our previous lecture on the VA inpatient and the VA outpatient datasets.  But you can set in the slides that—I believe it’s 19 and 20 where we’ve highlighted the diagnosis and procedure codes.  So there is an admitting diagnosis code, a primary diagnosis code, and a secondary diagnosis code.  In the inpatients’ datasets.  

I’m just having a hard time because the questions, there’s only one line at a time, Heidi?

Question:  If you actually grab the word “questions,” you can pull that pane out and enlarge the whole pane.  So you grab the word “questions,” just drag it over to the left, and it separates it; undocks it.  And you can grab the right corner and drag it out to make it bigger. 

Denise M. Hynes:  Yeah, it won’t drag. 

Question:  Oh, shoot. 

Denise M. Hynes:  I’ll bet Margaret has another question. 

Moderator:  I have one question, and to the audience, we will collect questions that are not answered during the seminar and get answers for you from Dr. Hynes or the VIReC helpdesk and send it to Heidi.  I have one question here, which is a question about, “What is the VA Inpatient Encounter File?” 

Denise M. Hynes:  What is it?  The IE, Inpatient Encounter File; that provides information on events that occur generally concomitant with an inpatient admission, so someone might be having an outpatient visit during the time that they’re having an inpatient event.  And increasingly consultations are being included in that dataset, so that’s what the IE dataset is. 

Moderator:  Okay, thank you.  

Denise M. Hynes:  Okay. 

Moderator:  That’s the only question. 

Denise M. Hynes:  Okay.  I figured out how to undock it, Heidi.  So for those of you who are asking very technical questions that have to do with printing and things, we’ll make sure that you know how to do that.  One question that I see jumping out here are HCPCS codes; other than CPT codes, not used at all in VA?  That’s correct.  HCPCS codes are only in the Medicare claims data, they are not included in the VA datasets.  

So let’s move on to issues around measurement.  And if we have time, we’ll come back to some of these other questions as well.  So some issues to consider as you’re jumping into measuring comorbidities versus measuring comorbidity burden or considering a summary risk measure.  First decision is usually, is a specific individual condition what you’re interested?  Or are you interested in the overall burden of disease?  So for example, is your outcome measure something that you are looking at that’s related to mortality outcomes?  Are you interested in something that’s more related to health service use?  

Conditions that are more prevalent in VA than in the general population to be identified such as spinal cord injury, post-traumatic stress disorder or serious mental illness might be something to consider.  Another aspect might be whether your comorbidity index will dictate that the conditions specifically be identified, and we’ll talk about that in just a little bit.  

You also want to think about whether it’s necessary to use inpatient data or outpatient data.  In some conditions, and this is where it really gets to what you as the researcher, or the research team know about the particular condition under study.  If you are interested in a condition that is treated primarily in the inpatient setting, you may not necessarily need to look at outpatient data.  If you’re interested in something that happens over time, a chronic condition, for example, you may want to consider both inpatient and outpatient data as well.  

Another issue to consider is what conditions or condition groups to capture.  And this really depends on your population of study.  If you’re looking at a general population or a specific disease population such as a cancer population, but you want to take into account other comorbidities that that particular chronic disease population also has.  You also might want to take into account, what’s the purpose of your undertaking for measuring comorbidity?  Is it for case mix adjustments, or is it your primary outcome measure?  Also, exactly what is your outcome measure?  There are some comorbidity indices that were developed and validated in association with specific outcome measures, and may be a better choice in some situations.  If you’re looking at costs, you might narrow your choice that’s if you’re looking at mortality issues.  

Another dimension is, frankly, the availability of data, especially if you’re taking into account more clinically relevant information such as laboratory tests or certain medications, and that may only be available, to a large extent, in a managed care system like the VA, not so available in the Medicare setting.  

We definitely have some references on this that are included in our additional information that we would refer you to.  And should think about this in terms of—especially the contrast between VA and Medicare data, and we’ve got that highlighted on our slide as well.  

Another aspect is what conditions to capture?  You need to think, again, about what the condition is of interest, and then think about what conditions you want to particularly narrow it by.  For example, you may want to exclude rule-out diagnoses.  Sometimes codes might be in the datasets that do not necessarily meet the inclusion criteria that you want to include in your datasets.  So rule-out diagnoses; operational definition for this is any diagnosis that does not meet the following criteria.  And this was defined in an article by colleagues at NCI, and the reference is listed on our slide here.  Condition that appears at least once on a record claim for inpatient care or a condition that appears on at least two records for outpatient care, at least 30 days apart.  So if you have an occurrence of one diagnosis code that does not meet these criteria, you might consider them a rule-out diagnosis.   And this is just an example of one group’s definition of how they define that.  

Another aspect is too think about some things that we’ve learned specifically in using VA datasets with regard to the data that would be considered, and we actually have a typo on this slide, so not to be confused with the next one.  Both of these slides highlight how to identify nonclinician assigned diagnoses, and you might want to consider this when you’re developing your algorithm for identifying specific conditions.  So, for example, there are specific data recorded in the VA datasets, like laboratory data, that might come with a diagnosis code, these are not assigned by a clinician.  It might be assigned be some sort of default process that the lab uses, or by default what comes in as the clinic diagnosis code, but it does not have any more additional value, in fact, sometimes might be contradictory of some other information.  So we discourage folks from using lab diagnostic imaging, other ancillary tests, DME prosthetics, or telephone encounters for inclusion criteria, if you will.  Because these are non-clinician assigned diagnoses.  VA stop codes are similarly—they’re just identifiers in decision support system to identify records and they don’t have any particular clinical meaning.  So we discourage folks from using the clinic stop codes in DSS identifiers.  

In Medicare claims, there’s also often diagnosis codes in the DME, the physician specialty codes, claim type, BETOS codes, place of service codes.  You might want to use these with caution because they are not clinically assigned.  

And to just kind of get down into it a little bit more, for example, some examples of a VA clinic stop code that might be used to identify claims for exclusion would be x-ray lab, and these are just the specific stop codes that we pulled up here to highlight for you.  And indicators of Medicare sufficient specialty codes, diagnostic radiology, mammography screening center, diagnostic labs, these are the specific codes here.  So the point for pulling these up for you is so that you understand that in a record that is identified as diagnostic lab 72, if there is a diagnosis code imbedded within that record, you probably don’t want to use it for identifying somebody’s specific diagnoses.  So I hope that was clear.  

Some other important considerations to consider is in what condition the capture is time period.  You should think about, very carefully, what are relevant start and end times for capturing diagnosis information.  If you’re looking for a year before when a diagnosis really occurs, you might want to go back a lot farther.  If you’re really looking for historical information, you may want to go back even farther.  If you’re looking for something that is an active diagnosis, you may want to use a narrower time window so that you’re not just picking up some information that might linger for some period of time, but might not be the primary reason currently active.  

There’s really no right answer here.  It really requires becoming familiar with some of the datasets.  There are some situations in which for some reason it might be included as a code, but may not necessarily be something that’s active.  And this is where, I guess, the art of science kind of comes into play here.   You may want to be using administrative data that includes diagnosis codes, together with some other indication that it’s actively being treated.  

Comorbidity time period and outcome measurement time period are pretty important.  Are they simultaneous, or do you want to know about comorbidities that were measured prior to the time of a diagnosis of interest, for example?  And you also need to, of course, again, depending upon your research questions and how your cohort was built, for example, is this a particular date over a particular time?  Was the anchor around a patient’s diagnosis, or a particular event a patient has had?  Or is this a specific calendar year that you need to search for?  Again, it depends on your research questions.  

Some other important measurement consideration that require some challenges have to do with measuring functional status, measuring severity of disease, and some  undiagnosed conditions.  We’ll talk just briefly about this.  Functional status, really, there’s some measures that incorporate some aspects of this, but really, you’re kind of out on your own when you’re looking at functional status and you’re kind of at the mercy of what datasets bring to bear on this.  In the VA, we do have a functional status database for a limited period of time; we’ve talked about it in previous lectures.  But similar information is not available in the Medicare side, and it’s measured at a particular point of time.  Again, depending upon the type of data you might be collecting in terms of primary data, you might be able to bring some of that information together with administrative data. 

Measuring severity of diseases is very tricky; there are no standard bits of information in any of the datasets that we describe that really get at severity of disease.  When you’re looking at some of the other more clinical datasets, that we’re not going to get into today, such as some registries, there may be some measures about severity of disease but, for the most part, severity of disease is not captured in the work load datasets that we’ve listed out today.   And then, of course, undiagnosed conditions, what we’re not seeing in the datasets is really, frankly, unmeasured.  

An important aspect to keep in mind when looking at issues around comorbidity measurement is that it’s extremely tied with, you know, whether people are using health care.  So it’s worth emphasizing here that we have sort of an inherent ascertainment bias when we’re looking at comorbidity measurement.  Without a healthcare encounter, there’s no record generated and, therefore; there’s no diagnosis recorded.  And in some situations, I suppose that that could be moot, but it could be, also, important.  Health services obtained outside the VA will generate procedure and diagnosis codes that may not be available in the VA.  For example, if you’re not including Medicaid data in your study and someone is going to a Medicaid provider, you’re going to miss all that information.  And it might be that the person really does have diabetes, but they’re seeing a non-VA provider for that particular condition. 

More frequent users of care will have more opportunities for diagnoses made and recorded.  And the opposite is also true.  People who do not use health care very frequently will have fewer events recorded.  So if your comorbidity measures rely on the frequency of an occurrence, that’s something that you have to really keep in mind.  

Depending upon what type of dataset you’re using, or measure that you’re using, you may want to think about how to actually construct your metrics.  In many situations comorbidity score is summarized.  There also can be simple counts of conditions found.  Weighting can adjust for relative differences; sometimes there’s a score, which could be a sum of condition weights.  And there’s also a simple categorical approach which can be a simple dummy variable, a 01 indicator for the presence or absence of each condition.   We include a reference here that’s a nice summary on methodology, design, and analytic techniques to address measurements of comorbid disease.  We encourage you to take a look at that.  

Some commonly used comorbidity measures; ambulatory care groups, Charlson, there’s a couple of adaptations to that Charlson Index, we’ll talk about that in a little bit more detail.  Dr. Elixhauser has developed an approach that indicates numbers of conditions.  Hierarchical condition categories and diagnostic cost groups, we’ll talk about that in a little bit.  Functional comorbidity index, Rx risk, which relies on medication data, and there are many others that you may want to become familiar with, but we’re not going to highlight in today’s details. 

Now I’m just going to briefly go through these.  I want to make sure that we get to an example today.  Charlson Comorbidity Index, it was developed originally to predict mortality.  That’s something important to keep in mind, what you’re outcome measure is and how you’re going to be using these indices.  Others may be more relevant, depending upon what your outcome measure is.  It looks at 19 chronic conditions, each has a weight, and it creates a sum of weights.  It’s also been adapted by Richard Deyo and Mary Romano.  These measures are very useful and have been used widely in VA and non-VA research.  

The next slide talks about the hierarchical condition categories and diagnostic cost groups.  It basically is a broader range of conditions.  It was developed to predict costs.  It includes about 15,000 ICD-9 diagnosis codes, and it basically condenses those into about 185 categories, or buckets, which represents what’s considered a group of homogeneous conditions.  And then these categories are further grouped according to how it’s affecting an organ system, specifically, and they’re arranged hierarchically.  Patients who fall into more than one of these condition categories within the same HCC are assigned to one of the highest predicted resource use.  So these are very useful when you’re looking at costs, and it’s also been used in looking at resource use as an outcome measure.  And we, again, have several references on this for you to get into the weeds on this and we encourage you to do so.  

In pharmacy data, there’s the Rx risk, or chronic disease score.  It includes 45 chronic disease categories that are identified through prescription data.  Kevin Sloan developed these along with colleagues; there’s a reference here that we highlight.  It’s really prescription based, and it takes into account specific information about the medication and the chronic condition that it’s being used to treat.  

Combining VA and CMS data to measure comorbidities; we just want to highlight a couple important aspects about areas to be concerned about.  The biggest mistake that people make is not using both data sources.  And we’ll take you through a little bit of an example where it’s been documented how much you can miss if you don’t use both data sources together?  Hopefully, you know that within the VA, Medicare data and Medicaid data are available for use in your studies at no cost, generally, except for a few special datasets.  But these are available to be used in VA so that there really is no reason not to use them.  That’s not to say they’re not complex, but we can do a lot of coaching on that.  

There are differing incentives to record complete information, but it would be a shame if you missed events that might be indicative of comorbidities that are occurring outside the VA.  And this is especially important because most people who do come to the VA for their healthcare are also using some kind of care outside the VA.  

It is important to keep in mind, however, that there are some differences with the datasets that are used and we highlighted the different types of codes that you have to be mindful of.  There’s a study that Margaret Byrne, and colleagues, from the Houston Center of Excellence put together in 2006 that’s still relevant and highlights the benefit of using these two types of data together.  What they tried to do in their study is show what happens, basically, when you look at the burden of illness, if you use all diagnoses, or separately.  And they calculated risk scores using VA-only, Medicare-only, and both VA and Medicare data.  And the bottom line is in this study is that there was, on average for a given patient who used both VA and Medicare services, more diagnoses were recorded in Medicare than in the VA system.  And they found that the ratio of relative risk, based on an individual’s total predicted cost, that is based on diagnosis, over the average observed costs of the Medicare population, was about 2.4.  So the bottom line here is that if you are using one data set alone, you will be missing a lot.  

I want to pause here and just see if there’s any brief questions before we go on to the case study, which shouldn’t take us too long.  There’s a couple of questions in here about linking VA to Medicare data; I’m going to refer you to our previous lecture.  And we have identifiers in both the VA and the Medicare data; you would have to link it by a particular, if you will, person identifier.  It does not necessarily have to be a Social Security Number, it can be a scrambled Social Security Number, and that information is available.  So you can link VA and Medicare data at a particular person level.  

To make sure that a patient is actively using Medicare or VA, there’s actually enrollment files for the Medicare and, of course, by definition, if a person has got a record in VA or Medicare data, you can be sure that they’re actively using.  And in some research projects, researchers have come up with different rules that are relevant to their particular research questions, depending, again, upon whether you’re looking at a longer period of time, you may be interested in a patient actively using VA or actively using Medicare for particular window of time that’s built around their particular condition.  So that’s kind of a hard question to ask without kind of giving you back a question.  

Another question is, “Is the only important to combine VA and Medicare data when you include patients over 65 years old?”  Yes and no, again depending upon the population.  Remember that people who have contributed to Medicare over the years can be eligible for disability.  This is particularly relevant for people who go on disability earlier than 65, again, it depends on the condition of interest.  Certainly, there are carve-out populations such as end-stage renal disease, they become eligible for Medicare 90 days after dialysis commences.  Certainly, it’s important in 65 and over for certain, but there are some populations under 65 that you might want to be aware of.  We’ll hold the other questions so we can get through our case study and, hopefully, we can address these at the end, or individually after our lecture.  

So, hopefully this is an article that we’ve provided as a handout.  This is a study that was published in *Annals of Internal Medicine* in 2009, Louise Walter and colleagues looking at the impact of age and comorbidity on colorectal cancer screening among older veterans.  So, again, this is a Medicare population.  They looked at both Medicare and VA data in their study.  They also specifically focused on those adults who were unlikely to live five years.  In other words, that have significant comorbidities that would preclude treatments and they’re unlikely to benefit for colorectal screening.  And they tried to determine whether colorectal cancer screening is targeted to healthy older adults, and is avoided in older patients with severe comorbidity.  They used comorbidity as a predictor variable and to be certain that they included older adults, they focused on those 70 years and older.  

So they looked at both VA and Medicare data.  They included inpatients and outpatient datasets.  And they looked at a measurement period of 12 months.  In their study their anchor was the start of the outcome observation, which was a calendar year date.  They specifically used the Deyo adaptation of the Charlson Comorbidity Index because it was developed to predict mortality and they wanted to look at the 19 chronic conditions, which were weighted for strength of association with mortality.  So they looked specifically at this is how the comorbidity score breaks out; zero is no significant comorbidity; one to three, average comorbidity; and greater than or equal to four, severe comorbidity.  Now you can break out the one to three’s; in this specific study they collapse them.  

Now in their study they actually considered an additional measure and that is whether the patient was home bound.  They were able to measure that specifically because there’s basically an enrollment database for VA home-based primary care that began in 2001.  So they included this measure as an additional dimension, not specifically in the comorbidity measure, but as a separate measure.   

The next slide summarized their results and when they looked at the Charlson Deyo score, their population broke out as 36 percent who were, basically, in the best health, and 12 percent in the worth health.  And when they adjusted it, the cumulative incidence of screening, they found that adjustment really did make a difference.  It didn’t account for the all of the differences.  Forty-seven percent of patients were in the best health, whereas 40.5 were in the worst health.  That found that, basically, comorbidity index can’t account for all the factors that might impact the likelihood of screening, such as functional status.  

What I’d like to highlight before we close today is where you can go for more help, and then we’ll see if there’s any further questions.  

Number one, I want to make sure that you go to the VIReC website where we provide information on VA data sources, as well as copies of the previous lectures.  These are also available on the archive cyberseminars as well.  But on the VIREC website you’ll see information on the data sources, including documentation, and documentation can be as detailed as the research user guide.  In some cases we have historical code sets that might have been used in the past.  We also have some basic breakouts of frequencies for common variables on all these datasets.  More information on the datasets that have been around longer, needless to say.  

We also have the HSRData Listserv.  Its be very active lately with questions.  I believe we are over 650 members/participants.  You can pose a question; you’ll be amazed and impressed at how helpful your colleagues can be.  It includes data stewards, people just like you, programmers, users, people who know other people, and you can also get to past messages and it’s searchable on the Internet, and there have been discussions about measurement issues.  

If you don’t find what you’re looking for on either the website or the listserv, we absolutely encourage you to call our VIReC Helpdesk, either by e-mail or by phone.  We try to get back to you pretty promptly and generally one of our staff in VIReC can help you, but we also can help you find resources outside of our own staff and experts in specific areas as well.  So don’t hesitate to contact VIReC directly.  

I think that concludes our formal slide set and, I don’t know if we have time for anymore questions, Heidi.  I’m going to let you tell us or whether we want to take the last two minutes to do our poll? 

Question:  Well, the feedback survey comes up when people leave the session, so we don’t have to worry about bringing that up. 

Denise M. Hynes:  Okay. 

Question:  We did receive a few other questions in here.  I think I’m start out where you left off here. 

Denise M. Hynes:  Okay. 

Question:  “Comment on using veteran priority groups as measure of functional status or disease severity.” 

Denise M. Hynes:  Yeah, I am not familiar with anyone using the priority groups other than exactly what it is.  I’ve seen papers where researchers will call out specific priority groups.  Remember, that’s only information on the VA side.  But I’ve never seen it combined with any other measures.  So if you were going to use the priority groups, veteran priority groups are indicative of the classification of benefits a veteran is eligible for.  But it doesn’t necessarily identify anything specific about severity of illness.  It combines both income and conditions for which a veteran might be eligible.  I could absolutely see using it and have seen folks use it, but not necessarily called out as a comorbidity measure. 

Question:  Okay. 

Denise M. Hynes:  I’m seeing some of the other questions in here; I’m going to pick the easy ones in the interest of time. 

Question:  Actually, I would suggest going to the last question, because it’s a question about one of your slides; that should be a fast response. 

Denise M. Hynes:  “The total of the adjusted cumulative incidence of screening is more than 100 percent; why is that?”  To be honest, I’d have to go back to the manuscript and see if we made a typo, or if there’s some other reason.  I would absolutely encourage you to take a look at their paper.  That is the paper by Louise Walter and colleagues. 

Question:  Okay, we are at the top of the hour.  Denise, did you want to take any more questions on the call, or should we handle them offline? 

Denise M. Hynes:  I think that we should handle them offline, as long as we know who asked them. 

Question:  We do know who asked each of the questions, so that’s no problem to get back to everyone. 

Denise M. Hynes:  Good. 

Question:  Okay. 

 Moderator:  And I would like to say please take a minute to answer the evaluation as you close down GoToWebinar.  And our next session will be on Monday, May 7th.  It is entitled “Assessing Race and Ethnicity.”  Thank you Dr. Hynes, thank you CIDER, thank you audience; enjoy your day.  

**[End of Recording]**

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