**Transcript of Cyberseminar**

**VIReC Database and Methods Seminar**

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

**Presenter: Denise Hynes, PhD, MPH, RN**

**June 3, 2013**

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

**June 3, 2013**

Moderator:... entitled, Applying Comorbidity Measures Using VA and CMS (Medicare and Medicaid) Data. Thank you to Cyber for providing technical and promotional support for this series. Today’s speaker is Denise Hynes, Director of VIReC and research career scientist at the HRSR&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 Corp 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. A brief evaluation questionnaire will pop up when you close GoToWebinar. We would appreciate if you would take a few moments to complete it. I am pleased to welcome today’s speaker, Dr. Denise Hynes.

Dr. Denise Hynes:Thank you, Arika. Just testing here. Is the audio okay and slides showing up all right?

Moderator:Yes. We can hear you and just click on your slides and that will get rid of the task bar across the bottom and you will have better control over them. Perfect. Just like that. You are good to go.

Dr. Denise Hynes:Great. Thank you. I also want to acknowledge contributions from our statistician, Tom Weichle, who helped with updating some of these slides today.

We are going to try and cover a lot of materials. These are our session objectives. I would encourage you, as I am talking, to feel free to pose questions in the chat and we will try to answer questions as we can online; and for questions that we cannot address during our session today, we will direct you to specific ways to get your questions answered. But as we go along, if you can refer to a slide, even, if you have a specific question related to that, that will help us a lot.

So these are our session objectives. We hope that you will be able to name some sources of comorbidity information in both VA and CMS data. I am going to use the term CMS throughout our talk today to refer to both Medicare and Medicaid and some of the other data that are provided by Medicare. And when it is a specific, particular type of dataset, I will make sure and refer to that; but you will see that throughout.

You should be able to identify some common data elements used in measuring comorbidities, and you should be able to recognize important measurement issues and hopefully be able to identify and avoid ultimately common pitfalls in using VA and CMS data together to assess comorbidities.

I believe we have a poll coming up or shortly.

Our session, of course, builds on previous seminars assessing VA Healthcare use both Inpatient and Outpatient, Measuring Health Services Use in VA Medicare and Outpatient Pharmacy Use. If you did not have the opportunity to attend those lectures, they are archived online and you can download those at will. But our lecture today does assume that you have some understanding of VA data. Where it is very specific, I will be highlighting that with regard to comorbidity measurements.

I do want to want to make sure that you are aware that what we will not be doing in today’s lecture is discuss theoretical or statistical issues related to comorbidity accounting, and I will not be detailing specific comorbidity indices or scales. I will, however, be highlighting some citations and recent articles that use different approaches throughout the lecture today. And we have some handouts that will be available afterwards for you to refer to. So I would encourage you to take a look at those.

So this is our outline for today. We will go through these topics and try to end at the end of our session today with where you can go for more help.

So let us just go through where we are with exactly comorbidity is. Basically, so we are all on the same page with this, a comorbidity is considered a concomitant but unrelated pathological or disease process.

Now there have been some more recent publications that have emerged to challenge this approached. I will touch on some of the issues that this article by Valderas and Barbara Starfield and colleagues has raised. It has more to do with the variations around this concept related to timing, longitudinal issues and some conceptual issues that are absolutely important to take into account. But we will address it more with measurement.

Some of the issues that are really important with regard to evaluating comorbidities have to do with clinical outcomes, resource use, and workload. It could also be costs as an evaluation of resource use and quality of care. Operationalizing comorbidities is done in a number of ways. It could be a dependent variable, but it could also be a moderator, a cofounder or covariate or a predictor looking at its relationship to other measures.

Some examples of research questions requiring information on comorbidities are shown here. Comparative effectiveness studies. Outcomes research uses comorbidity assessments a lot. 

Here is one question: Is chemotherapy more effective than radiotherapy in the treatment of endometrial cancer? One might control for comorbidities in addressing that question as a covariate or cofounder, if you will.

Healthcare disparities. Do comorbidities explain race/ethnic disparities in kidney transplants? Here comorbidities are conceptualized as a predictor.

In examining healthcare quality, one might ask, are VA patients more likely than those in Fee-for-Service Medicare to receive recommended screening tests? Here we might consider comorbidity as a covariate or a confounder.

Healthcare costs/provider productivity. One might pose the question, who provides more cost-effective care for diabetes, endocrinologists, nephrologists or general internists? In addressing this question, a researcher might want to take into account the degree to which comorbidities as disease severity exists differently in the populations that these specialists care for.

Some sources of comorbidity information in administrative data. I will tell you one of my biases: I really do not like the term administrative data, but I use it because it certainly connotes some common understanding of what we mean by data for workload. But in point of fact, these tend to be rather clinical databases, so they do not just address issues around enrollment or demographics, but in fact, they do look at discharge information, healthcare use as in the VA, and the claims data for Medicare and Medicaid for diagnosis and procedure codes. They can be used in assessing comorbidity.

Pharmacy data as well, you will see if you choose to look into this further. There are a lot of comorbidity indices now that take into account aspects of pharmacy data, whether is the medications, prescriptions, fills, and there are some indices related to using pharmacy data.

Laboratory data as well, clinical laboratory results that might indicate a particular condition for some of the comorbidity indices these can be used. 

And there are some other datasets that might be used, for example, program records might indicate some aspect of condition. Perhaps enrollment in a registry that might be a disease registry, for example, or a particular program.

Before we get too far along, we put in a poll here, and you will see the poll come up. It will ask you the question to rate your experience with using administrative data to comorbidities. We have three categories here, novice, some experience and expert. And we are asking our organizers not to vote so that we do not bias the results, but we would appreciate it if you would participate in this. It would give me a sense of how much experience you all have, if you have experience with VA data and some of the ICNI and CBC codes. Chances are you probably have some experience with comorbidity assessments. ... Just checking to make sure my audio is on.

Moderator:Yep, we can hear you. Your results are up on your screen there, Denise.

Dr. Denise Hynes:Thank you. So it looks like we have about 45 percent who rank themselves as a novice, 49 percent who indicate that they have some experience, and six percent who are experts. So I might rely on those six people or so to help us answer questions later. Just kidding. Okay, so we will go back to our slides here.

Let us dive into some of our work about just finding comorbidity information in VA and CMS data. So like I said, I am going to refer to these datasets as administrative datasets, but we all know that they reveal a whole lot more than that.

So we mentioned diagnosis and procedure codes in one of our example research questions. The VA workload data are very rich with diagnosis and procedure code data, as are Medicare claims and Medicaid claims. 

In the VA workload data, there are the Medical SAS Datasets; and for those of you who are concerned about the long-term viability of these datasets, be assured that the Medical SAS Datasets in some form or another, expect those to be around. They include information in one of our, I think our second lecture, that talked about basically inpatient events, outpatient events and also the fee-basis files also have diagnosis and procedure code data as well. These are the datasets that document care that is provided under VA auspices but perhaps outside of a VA medical facility.

Medicare claims data, and there are different claims files. Keep in mind we are talking about the claims data and not enrollment data per se. The Standard Analytic Files, Institutional, Non-Institutional, which sort of means hospital outpatients and institutional Stay Level files, MedPAR, which includes both hospitals and skilled nursing facilities. These are also rich with diagnosis and procedure codes as well as Medicare claims, and this is just one of the Medicare claims files names, the MAX files.

Medication data. Pharmacy data has a lot of information that can be used for comorbidity assessments, especially with particular types of medications that might be indicative of a particular disease. So for example, what we have here on this slide is oral hypoglycemic, or insulin, which is pretty unique to management of diabetes; and there are probably other medications similarly uniquely assigned to diseases, or at least can be used as an indicator.

VA’s Pharmacy Benefits Management Data as well as the Pharmacy National Data Systems Extract or DSS has this kind of drug information that can be used. Certainly the Medicare Part D claims, which has specific claims for medications in there; and also in Medicaid there are prescription drug claims as well.

For laboratory results, DSS National Data Extract for Laboratory Results has some information in there. Again, you may look for laboratory results, which clearly may not necessarily be uniquely tied to a particular condition, but certainly very dominantly. So for example, elevated glycohemoglobin might be used to indicate diabetes.

Laboratory results are generally not avail in Medicare. There are some limited, very, very limited laboratory results around particular programs, for example, in end-stage renal disease for some very expensive medications for anemia management they may have some ranges for hemoglobin; but again, not specific to any particular condition and not generally available for other conditions. So Medicare data is not someplace you can rely on for laboratory results in general.

And then other datasets may have information, as I mentioned earlier. There might be some condition-focused program enrollment databases whether it is a disease registry or particular types of programs in the VA and elsewhere that might at least narrow the scope for conditions that you might be considering. 

Types of diagnosis codes—this is where we get into some of the nitty-gritty. And for those of you who want to get into comorbidity measurements and disease severity as well, understanding the nuances of some of the diagnosis and procedure codes is really quite critical.

ICD-9-CM diagnosis codes include information in VA databases that have an admitting code, which indicates the patient’s initial diagnosis at the time of admission. There are primary and principle codes. Sometimes we use those terms interchangeably, and this is a condition that chiefly is responsible for the visit and the admission. 

There are also secondary codes. These are conditions affecting services provided. And then there are line-item codes, which is the diagnosis supporting the service on a non-institutional claim, for example, in Medicare.

We cite at the bottom here a CDC site that has some more detail on ICD-9 codes and how they are used. It is worthwhile getting a good understanding of the data sources that you are using and how they document ICD-9 within their databases. Sometimes there are some nuances between VA, CMS and certainly other datasets, how they are used.

 ICD-9 procedures codes—these are used for inpatient services in VA. They are used for institutional inpatient Medicare claims and inpatient and other services in Medicaid claims.

The thing to keep in mind, though, when it comes to Medicare and Medicaid: it really the CPT procedure codes, and I will talk about these a little bit more, that are used for payments and what is also known as the HCPCS codes.

So these are really important to keep in mind when you are assessing comorbidity because there could be some coding issues with reliability with regard to whether CPT codes are populated and ICD-9 are populated.

Always a good idea to look at your data sources and make some initial assessments. Do not always assume that it is going to be the same from year to year; and certainly when there are changes from ICD-9 to ICD-10, that everything will be the same. Resources at the AMA are listed at the bottom on issues around billing and coding.

HCPCS—we love to pronounce our acronyms—Healthcare Common Procedure Coding System) codes: these are used in Medicare and Medicaid billing. Level 1 refers to the CPT codes, so it is the first component, to HCPCS and CPT codes for Level 1 are essentially the same thing. These are used for services and procedures.

Level 2 HCPCS is a little different from CPT codes. They are used to identify products, supplies and services that are not included in the CPT codes. So for example, ambulance services and durable medical equipment. So if ambulance service is something that you want to take into account with your own customized comorbidity measure, if you are relying on CPT codes, you will not capture it. So keep in mind that for HCPCS codes, there are both Level 1 and Level 2, and it goes beyond CPT codes. And again, more information at the bottom of this slide for this URL that you can look into.

So just to kind of give you a sense of what is in the different types of datasets, in VA we have diagnosis and procedure codes as indicated here. On the left-hand far column, we have the various VA databases: Inpatient Main, Inpatient Bedsection. These are the MedSAS datasets that are known in the VA.

And across the top, we have the different types of coding categories that are represented in that dataset. So the Principal Admitting Diagnosis Code and the Primary Diagnosis Code are sometimes used interchangeably; but you will notice that when you get down to the Outpatient Events file, only the Primary Diagnosis Code—sorry. I am trying to do a little highlighting here and want to make sure I pick the right one.

So for here in the Outpatient Event, you are only going to find Primary Diagnosis Code, but you will not find the Principal Diagnosis Code. So these are some of the nuances I alluded to when you are looking at specific datasets. 

As you go across, the Inpatient Main does not include the ICD-9 procedure codes or the CPT codes. But when you get to the Inpatient Procedure Code dataset, the MedSAS, it does have the ICD-9 procedure codes. Similarly, the Inpatient Surgery has the ICD-9 procedures.

So an important aspect when you are trying to ascertain comorbidity. Make sure that you use the full complement of VA MedSAS datasets. If your comorbidity assessment tool is relying on ICD-9 procedures codes and you are only using the Inpatient Main, your comorbidity measure is going to look really low. So that is always something to gauge when you are doing comorbidity measures. If you feel like your measures are too low or too high, it is always a good idea to go back and revisit which datasets did you include and are you confident that your code actually captured the specific fields that correspond to each dataset.

Fee basis diagnosis and procedure codes—again, a little bit different and something that you really need to be aware of if you are applying standardized comorbidity indices. Know your datasets. Know which types of codes are actually included in them.

So for the Inpatient fee basis datasets, it includes Discharge Diagnosis Codes, Secondary Diagnosis Codes and ICD-9 Procedures Codes; but it does not include CPT Procedure Codes. You would have to rely on the Inpatient Ancillary file, and of course, this is only available since 2009; whereas with outpatients the only diagnosis and procedure codes that are included are discharge diagnosis codes and then jump over to CPT codes. So again, you have to be very specific what you are searching for, depending on what datasets you are including in your analysis.

Medicare diagnosis and procedure codes are listed here. Down this left-hand side are the well-known Medicare claims datasets, MedPAR, Inpatient, Skilled Nursing Facility, Outpatient, Hospice, Home Health, Carrier and Durable Medical Equipment. Carrier, for those of you who may have missed the previous lecture, corresponds to physician services, some suppliers, and some outpatient care that is not provided in either outpatient or in institutional settings. 

Again, some nuances here: diagnosis and procedure codes are predominant, but depending upon which dataset you might be using, for example, MedPAR is often a dataset that is a summary-level file that researchers will use rather than get into all of the statistical—sorry, the standard analytic files that are below the MedPAR dataset. 

And if you are using MedPAR, it does not have HCPCS procedure codes. So that is something that you may want to think carefully about. You would have to rely on ICD-9 procedure codes, secondary diagnosis codes and admitting codes. If you really want to get into comorbidity measures that rely on the HCPCS or the Level 1, which is CPT code, you may want to consider the standard analytic files.

Okay, and then for Medicaid datasets, again this is the MAX file. It includes information on Other Services and Patients and Long Term Care are the three dataset types; and the types of diagnosis and procedure codes include HCPCS codes and others serve as an inpatient. But when you get to long term care, HCPCS codes are not included. So again, repeating myself a little bit, know your datasets, know what diagnosis and procedure codes are included in the specific dataset that you are utilizing so that when you build your algorithm you tap the right variable or table, if you will, and include that in your algorithm.

So let us talk a little bit about pharmacy data. Potential value in using pharmacy data is that there are some medications that might not have ICD based measures in them, but some of the information in them may be helpful. So you might want to use pharmacy data, for example, when diagnosis information is not available in other sources. 

Perhaps you are using some sort of registry for a specific condition but it does not include any information about any comorbid condition. It is not a workload dataset. So for example, if you are using a cancer registry, it might not have any information about comorbidity, but if you marry that with a pharmacy dataset, pharmacy utilization might be somewhat indicative of the existence of comorbidities, especially if you pick the right medication. This is where, really, the art of research comes in play, if you will.

You might find stable chronic conditions that are not occasioning a provider visit. For example, hypertension and epilepsy might be something that you can pick up from pharmacy data, the medications that are specific to that. Or conditions for which the treatment regimen is set and time-limited, for example, tuberculosis.

Okay. Let us go on to some of the important measurement considerations. And these are some aspects that you will find in some of the more recent summary articles, the one that I highlighted earlier, that challenges our thinking about how we measure comorbidity or comorbidity burden and really taking into account aspects of your particular research study. 

So one of the questions you may want to ask yourself is, are the comorbidity measures specific conditions of interest to you? A lot of studies take into account cardiovascular risk factors, but you may not particularly be interested in it for whatever question you may be studying; or it may be very important relative to some other conditions. Perhaps in your cohort you have already excluded cases that have had certain conditions, so even trying to measure it will not be appropriate. That is probably a more obvious one, but you need to think carefully about your inclusion and exclusion criteria for the cohort that you will be applying the comorbidity measure to, and that in itself may narrow the scope.

You should also consider whether summary measures are useful or not to you. Some summary measures will provide one number, a score that may simplify your analysis, and it may allow for parsimony in statistical regression models. But you may prefer a series of dichotomous measures because for whatever reason you want to describe in more detail some aspects about particular conditions rather than a summary measure.

The other thing you should think about is that how you measure comorbidities can influence the data that you are going to use and how you summarize it, and the specific conditions that you want to identify. Again, that gets back to do you use the summary measure? Do you use the dichotomous measure for a series or how you include that in your analysis.

Also, what conditions or condition groups do you want to capture? Of course, this depends again on your population. You may want to take some pause to think about if  you are setting a younger population versus an older population, certain conditions may be more important. What are your objectives for your study? Do you need to take into account and adjust for case-mix? If that is important in your study, you will want to carefully consider how you capture comorbidity. You may want more than just a summary measure. You may need to take into account very specific conditions, and some conditions more specific to others. You may need an indicator for one particular type of condition or perhaps a summary measure for others.

Also, it depends on outcome. Are you looking at mortality? Are you looking at specific post-acute events like post-stroke rehabilitation? Are you looking at costs? And there is definitely literature around better choices of comorbidity indices and measures depending upon what the outcome is that you want to measure. We are not going to go over all that in detail in this lecture, but you should know that there are some distinctions between what might be better choices. For example, looking at economic outcomes as opposed to survival outcomes.

Of course, you also need to consider what data sources you have available, and that certainly will have a big impact on how you construct your comorbidity algorithm. You have to decide whether you are able to use some of the existing algorithms that are out there in the published literature or whether you will have to construct your own or invent your own that the rest of us can potentially use later. Innovation is always good and we make discoveries all the time. 

There are two articles here. I believe we have citations for these a little bit later that will be useful for you.

It is also important, and I mentioned this earlier, to think about not only what conditions to capture, but whether to exclude, rule out diagnoses, and I think our next slide is what ones to include. I have to say in my research team we tend to the inclusions first and then the exclusions, but both of these aspects are important to consider. So rule out diagnoses. 

The operation definition here is: any diagnosis that does not meet the following criteria a diagnosis that appears at least once on a record/claim for inpatient care or appears on at least two records/claims for outpatient care with visit/ claim dates that are at least 30 days apart. We refer you to an article by Cary Klabunde, Joe Warren and colleagues in Medical Care from 2006.

And so the point here is to try and exclude those conditions that really have codes in the claims that are really more for just ruling it out as opposed to the fact that the diagnosis persists over time. And so excluding rule-out diagnoses is an important consideration.

There is also consideration for identifying those that would be considered clinician-assigned diagnoses. And again, depending upon your research questions, you may need to refine this more or less. If you want diagnoses that are clinician-assigned, you would probably want to avoid clinical laboratory diagnosis or procedure codes or diagnostic imaging such as radiology and x-ray or ancillary test services, DME/prosthetics, telephone encounters and some of the other examples here. You definitely want to use those that are indicating a service so that it is something that is assigned for an inpatient event or an outpatient event, whether it is Medicare or Medicaid.

There may be some situations where you want to capture certain outpatient kinds of events, and that is again a nuance of your own research questions. But these are the recommendations we have for those that are clinician-assigned.

For identifying non-clinician-assigned diagnoses, here are some examples: a VA Clinic Stop code used to identify claims for exclusion, x-ray is – the codes are listed here. And some examples of Medicare Provider Specialty codes that are can be used to identify claims for exclusion. So these are ones that you would want to exclude because the codes are assigned by non-clinicians. They just tend to be assigned by someone who does not have clinical diagnostic capabilities.

Another important consideration when you are measuring comorbidities is the time period. Again, specific to your research project, you may want to take into account active diagnoses. You may want to think about the timing of certain events related between the comorbidity measure, if you will, and the outcome measure. Maybe you are only interested in comorbidity around a one-year or a six-month period so perhaps you do not need to go back five years and take into account an entire history of five years. You take a snapshot. 

You have to think about the implications that may have on whatever outcome measure you are ascertaining. If you want to know that somebody has a history of cancer that was treated five years ago but perhaps they are not treated in the last several years, you may need to go back farther than just one year. So again, it depends on what your research question is.

Another aspect is to think about what your anchor is going to be. If your outcome measure is more time-dependent, then you may want to take into account a temporal relationship to your comorbidity assessment. If your anchor is an event, you are looking at a prescription for a medication as your outcome measure, you may be more interested in the date of a particular comorbidity around that particular event. You also may be looking for some sentinel events and you may need to look at comorbidities around that sentinel event that is specific to a patient as opposed to some implementation of a national policy which they have a static anchor for all patients, but you need to look at it at an individual level for patients around that particular calendar date. 

So again, it depends on your research questions.

Some special with comorbidity assessment. There are some comorbidity measures out there that take into account some aspects of functional status. Again, you have to be careful about what data sources do you have available to you to actually ascertain functional status. Many of our datasets that we are highlighting here in VA, Medicare and VA datasets do not have functional status in them and so you have to think about what functional status measures can you actually ascertain.

Severity of disease is also very tricky. It is one thing to know that a comorbidity exists by virtue of specific codes existing. But for the most part, they will not indicate how severe that condition is. You will only know that it has been coded for a certainly number of years or at a particular point in time. 

And then, of course, undiagnosed conditions are clearly not accounted for in these datasets. We are only capturing conditions that are ruled out or conditions that are physician-assigned and non-clinician-assigned; and so if they are not diagnosed yet, there could be some underlying gap.

Another really important aspect to keep in mind is that we are talking about relying on VA and Medicare healthcare use, healthcare workload, and administrative data. By definition, patients are coming to the healthcare system. So people who have more encounters with the healthcare system are more likely to have more ICD-9 codes, CPT codes, et cetera. So you need to think carefully about the fact that patients who have less healthcare are going to have fewer opportunities for comorbidities to be assessed and therefore fewer codes to search. So you might need to think carefully with your statistical experts what bias that could potentially introduce into whatever code word you are looking at.

We mentioned a little bit how you measure comorbidity can affect your analysis. Certainly, there are some really good approaches that include not only summary measures built into some of the indices that are available, but whether you are using an ordinal scale, whether you are doing any kind of weighting if one condition is more important in your analysis than others, or some sort of categorical grouping. There is a really good article that talks about some of these analytic approaches in the Journal of Gerontology Analytics Biological Sciences in 2007 listed here by Rebecca Silliman and colleagues.

Although we do not endorse any particular comorbidity measure, we just want to put up some of the most commonly used ones that we have come across. I am not sure how recently we have updated this, but there have certainly been lots of uses of the Charlson Inversion within a lot of the VA work and the Rx-Risk is one that is based more on the pharmacy data. Certainly some of these others are well used with looking at both mortality and cost outcomes. You should definitely take a look at some of these and take a look at some of the articles we have cited in this presentation today.

The Charlson Comorbidity Index is, like I said, one that is used a lot in VA. It includes 19 chronic conditions. Each has a weight and it does have a score that you can basically sum and it has been extended by others both within the VA and outside the VA. It has been around since the ‘80s. So it has been well honed within the VA.

The HCC/DCG Method was really developed to predict costs and it uses basically a reclassification of the ICD-9 codes into from 15,000 ICD-9 codes categories to basically 185 homogenous conditions. And they are pretty well validated as far as predicting costs. It may not be the best approach if you are looking at mortality. 

Pharmacy data. I mentioned the Rx-Risk, also known as the Chronic Disease Score. Kevin Sloan and colleagues developed an approach looking at it within the VA. I refer you to this reference in 2003 in Medical Care. It includes 45 chronic disease categories identified through the prescription data in VA.

Some things to take into account. Combining VA and CMS data to measure comorbidities. The biggest pitfall is not using both data sources. There are definitely different incentives to record complete information. Dates-of-service issues may be very important. Definitely if you are going to start getting into comorbidity measures, I would take those three slides that we have with the tables for the different datasets, what is included, and stick them on your dashboard, electronic or otherwise, to remind yourself what codes to search for in the datasets.

There is a really good article by Margaret Byrne, Laura Petersen and colleagues from the Houston group looking at the effect of using information from only one system for dually eligible health care users. I will not go into this in a lot of details in the interest of time today, but you should take a look at this article. It is a really good lesson and a cautionary tale about what you will miss if you do not use both data sources for our VA patients who are dually eligible. They have found that on average a given patient who uses both VA and Medicare services that more diagnoses were recorded in Medicare than in the VA. They did find some redundancy but not all the time. The Medicare data alone accounted for approximately 80 percent of individuals’ total illness burdens. So you would really be missing a lot if you do not capture that.

I am going to pause here before we get into our case study. It is an article by French and colleagues that we will make available, but I wanted to pause and see if we had any questions. Arika?

Moderator:We do have a couple of questions. The first one: will researchers be able to obtain Medicare Part D claims to be used in the identification of comorbidities?

Dr. Denise Hynes:Sure. Thank you. Definitely. For those of you who have been for other lectures, VIReC happens to also make the VA Medicare available on request. The Medicare Part D data are becoming available this summer, specifically 2006 to 2010 Part D Slim File will be available later this summer. These are data that are just arriving at VIReC as we speak for processing. They have been available outside of the VA for a little bit of time in special projects, but they will be available on request for VA researchers.

Moderator:Thank you for that, Denise. How about one more. What will be the impacts of the change from ICD-9 to ICD-10 codes in the identification of comorbidities?

Dr. Denise Hynes:Well, that is a good question. VA is definitely preparing for this transition, as is every healthcare institution and insurer and provider in the country. Basically, researchers and analysts will need to become familiar with both classification systems. There are definitely some differences. There is some mapping software that exists to cross list between ICD-9 codes and ICD-10. There have been a couple very recent articles coming out about this. I am sure some entrepreneurs will have some software to help us with this transition as well, but you should know that there is some open source totally out on the Web crosswalks available for this, and these are definitely things that will have to be considered as we move forward with assessing comorbidity that relies on ICD-9 in the past.

Moderator:Thank you, [overlapping voice].

Dr. Denise Hynes:So I want to move on and—thanks, Arika—and we will take on some more questions as we go forward. I wanted to highlight a recent paper that was published by Dustin French and colleagues. It was published last year in Medical Care and focused on complication rates in veterans receiving cataract surgery. We thought this was a good example of how comorbidity assessments were used as a control variable. It is probably one of many studies that we could cite, but since this is the more recent one, we thought we would walk you through this.

This study questioned whether something intrinsic to a healthcare delivery system can have an effect on the rate of surgical complications; and they compared the rates of secondary surgeries after cataract extraction for patients having surgery provided through VA or Medicare, so they used both VA and Medicare. Comorbidity indicators were used as a predictor variable. They looked at veterans who were at least 65 years and older who received outpatient cataract surgery during calendar year 2007.

They looked at Medicare, VA and fee-basis data and they looked at outpatient data.

The measurement period for comorbidity was specifically calendar year 2007, so they anchored it around dates. 

They looked at clinically relevant ocular comorbidities identified using ICD-9 codes. So they looked at some very specific comorbidities. I am not going to try to pronounce these here, but they looked at some specific ones and I do not have the specific ICD-9 codes. But certainly, these are available in the article.

They also looked at medically relevant comorbidities, again identified using ICD-9 codes, based on a modified Charlton Comorbidity Index, and that was published in one of their early articles from ’87 that Mary Charlson published. They looked at the standard set here that I will not read, but you can see from that particular manuscript how they applied it.

What they found in their particular study—again, this is looking at cataract surgery, secondary surgeries. They found that comorbidities were prevalent in both groups. Patients in the Medicare cataract surgery group had a greater proportion of cancer, chronic respiratory disease, prostatic hyperplasia and congestive heart failure. They felt that a limitation of their study was the exclusion criteria were applied from 2005 forward, which they felt could miss diagnoses that existed before that time and perhaps were never updated in the electronic record. But this was a limitation that they felt they would work with in this particular study.

So again, for more details, I would refer you to their article. They do have specific details about the ICD-9 codes that they used, some excellent tables comparing the groups and also a breakdown for the Medicare, the VHA and the fee-basis groups. They also include both unadjusted and adjusted rates and break it down by the particular CPT codes. So it definitely has some good details to improve any kind of replicability that you might require for borrowing any of the documented CPT codes that they used.

Let us focus now on where to go for more help and we are closing in on the last five minutes, so I want to make sure that we have a couple minutes for some questions. 

I definitely refer you to the VIReC website. We have information there. I do not know if it is working today. We had the same experience that Heidi alluded to with all kinds of cleanup and maintenance going on this weekend, so you might want to wait until tomorrow to go to the VIReC website. 

Information on VA data sources, how to access data. There is also documentation on the most commonly used VA datasets including the corporate data warehouse that is sort of right in the middle, CDW, not so commonly used yet, but we are starting to include documentation about it. That is so that as more people are using it you will know what to expect.

In the future, we are developing a tutorial to provide some step-by-step guidance on the construction of a comorbidity index and that will be on our website hopefully before the end of the year.

We also have the HSRData Listserv. The Listserv keeps growing. We have more than 650 data stewards, managers and users, a lot more interactive discussion and the past messages are archived and are available to search.

There is always the VIReC Help Desk. If you prefer to call us or send us an email and get a specific question answered, this is where you can contact us.

Arika, how are we with any questions that folks might want to ask?

Moderator:We do have a few questions. Here is one. What are the potential negative effects if non-clinician-assigned diagnoses and rule-out diagnoses are not excluded?

Dr. Denise Hynes:Thank you. Probably the biggest concern would be that by not removing the non-clinician records you would reduce the likelihood the clinician-assigned diagnosis codes are used. And then not excluding the rule-out diagnoses will increase the potential that a patient might be misclassified as having a condition that was really assigned as a rule-out diagnosis. So the rule-out is really to just allow some identification of somebody who might be at risk for something. But a rule-out diagnosis does not mean that they have that condition. So you would potentially misclassify them as having the condition.

Moderator:Thank you for that. Here is another. What are some examples of anchor dates or events that can be used to define the measurement time period?

Dr. Denise Hynes:Okay. So for example in cancer studies, the date of a cancer diagnosis, that would obviously be specific to a particular patient. In policy studies, when a particular guideline was implemented. For example, we did some work with anemia management and cancer. There was a particular policy guideline, a Coverage Decision Memo that Medicare came out with in a particular time in 2007 and we could use that as an anchor date around which to look at comorbidities. In end-stage renal disease, the date relative to the start of dialysis. You sort of pick your particular condition, so an anchor date or an event could be important in your research question. Any others, Arika?

Moderator:Just a few. Here is another. Are the fee-basis files in MedSAS or do these need to be loaded from AITC?

Dr. Denise Hynes:Thank you. So the fee-basis files are at Austin. They are at the Austin Information Technology Center. But it is a separate SAS file. So the fee-basis file is not included in the inpatient or outpatient MedSAS. So you would need to go to the AITC and get the particular code for fee-basis dataset to get that segments of data. It is a specific, unique dataset separate from other MedSAS datasets.

Moderator:Thank you for that. How about one last one since we are nearing the top of the hour. Is there a way to identify present un-admission codes?

Dr. Denise Hynes:Present un-admissions. Well, certainly in the VA there are admitting diagnoses. But that would be separate from the principal or primary diagnosis. So you could use the admitting diagnosis in VA. I am not aware that that is available in the Medicare data, though. Medicare relies on, for inpatient in particular, it is all based on what happened to the persons during the admissions. I am not sure if anybody else wants to add to that. But presence on admissions really would only be applicable, I believe, for the VA datasets for the admitting diagnosis.

Moderator:Thank you, Denise. We are nearing the top of the hour. Our next session is scheduled for Monday, July 22, from one to two p.m. Eastern and it is entitled, “Improving Mortality Ascertainment,” presented by Elizabeth Tarlov. Thank you to everyone.

Dr. Denise Hynes:Thank you

*[File ended mid-sentence]*

Page 1 of 14

Page 13 of 14