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

>> I would like to welcome today's speaker, Dr. Elizabeth Tarlov, who is the Center Associate Director at the VA Information Resource Center (VIREC) and also a research health scientist at the Center for Management of Complex Chronic Care (CMC3) at Edward Hines Junior VA Hospital. I would like to turn the session over now to Dr. Elizabeth Tarlov.

>> Thank you Melissa. I'm going to dive right in here since we have a lot to cover this morning. By the end of the session, if I have done my job and technologically we are intact, you should be able to identify sources of comorbidity information in administrative data and common data elements we use to do that.  You’ll understand some of the important issues related to using administrative data to measure comorbidity and also some of the complications involved when we combine VA and Medicare data to assess comorbidities. 

This session will focus on use of VA and Medicare data to obtain information for comorbidity measurements. We are going to build on previous seminars that you’ll see there, and they are also archived in case you need to take a look at them.

>> Dr. Tarlov I’m sorry I want to interrupt for just a moment.

>> Sure.

>> This is Heidi Schleuter at CIDER.  I just want to let our audience know once again we’re having severe network issues both at Hines and at Boston today and I’m getting a lot of emails requesting a copy of the slides.  I am not able to send them because of the network issues.  I understand that this is a problem for a lot of our attendees and I apologize profusely.  Hopefully we can download them from Live Meeting shortly or we will have them available on our archive tomorrow. But right now because of the network issues we are having, I not able to send them out. Back to you, thank you. 

>> Okay.  This session we will not be able to discuss theoretical or statistical issues related to accounting for comorbidities nor we have time to examine in detail any specific comorbidity indices.  

I want to start with an overview and proceed to where and how to obtain comorbidity information in VA Medicare data and progress to discussing some of the specific measurement considerations and wind up with a case study that will illustrate some of the main concepts that I am hoping to get across to you today and finally I will touch on some additional resources that you may find useful. 

So, first with our overview it is always good to start by clarifying what it is we are talking about. The American Heritage Medical Dictionary defines comorbidity as the concomitant but unrelated pathological disease process and note here that the term infers there is a focal condition that is of primary interest. 

Comorbidities are an important component in evaluating a diverse set of outcomes and certainly clinical outcomes but also resource uses such as healthcare utilization or cost and quality of care and comorbidities can be conceptualized as any of these that you will see here if you are able to. If not, they can be conceptualized as predictors as covariates or confounders, moderators of an effect of another variable and even sometimes as a dependent variable.  

And the next slide shows some examples of research questions for which comorbidity information would be important and most of these, judging from the research question, I would guess that the comorbidities are conceptualized as covariates or cofounders.  But in the second bullet which is an example of the study of healthcare disparities, do comorbidities explained race or ethnic disparities in and kidney transplants, that would be an example of a situation where comorbidity is seen as the primary predictor.  In VA, comorbidity information is found in workflow data, in Medicare it would be claims data so in both cases the information is found in administrative data that is generated in conjunction with these healthcare services.  Also coexisting conditions can sometimes be inferred from pharmacy data, lab data and also occasionally other sources such as program enrollment records.  That concludes the overview portion.  

Before going on to the next section, we will pause for an audience poll. Please rate your experience with using administrative data to capture comorbidities, and you have three options they are Novice, Some Experience, and Expert. [Silence] So I see about 10% Expert and the remainder are evenly divided between Novice and Some Experience, now Some Experience seems to be winning out. Thank you. 

So where exactly in the the data is comorbidity information found?  Basically, diagnosis and procedure codes, medication dispensing lab results, and other miscellaneous sources.  

In terms of diagnosis and procedure codes, I mentioned VA workload data.  In particular here in the VA that would be medical SAS data sets or fee basis files.  In Medicare claims data we use the institutional and non-institutional standard analytic files or the institutional stay level or MedPar files.  I won't go into these in detail but you can refer to last month's cyber seminar slides for a review of those.

In pharmacy data, we can look for particular medications as indicators of a condition and we are going to find that in VA PBM or the DSS national data extracts, an example of medication that one might be able to use to infer a comorbid condition would be the oral hypoglycemics or insulin which would indicate diabetes.  In laboratory data, sometimes results of a particular test may be indicators of specific conditions and an example would be an elevated glyco-hemoglobin, indicating diabetes.  And laboratory data which is not available in Medicare can be found in the VA in DSS laboratory results national data extracts.  As I mentioned, occasionally there are other sources, for example, enrollment in particular programs.  One example I can think of would be enrollment in the VA MOVE weight management 

program should be an indicator, could be at used as an indicator of obesity.  In that example, the MOVE program has specific stop codes that are associated with it. One could identify individuals in the program using those stop codes.

Now, to a little bit more detail about diagnosis codes. Diagnosis codes are in the ICD-9-CM system which is a classification system. Again, I think this has been reviewed in prior cyber seminars so I won't go into detail but there are several data fields in both VA and Medicare files where these diagnoses are recorded as ICD-9-CM codes in inpatient data.  For example, there is a code for the patient’s admitting diagnosis as well as a primary and secondary diagnosis code.  And there is a lot more detail about these in the VIReC research user guide. For the medical SAS data sets. 

Procedure codes, there are three types of procedure codes that we are going to find in VA and Medicare data.  First ICD-9-CM procedure codes which are used for inpatient services in the VA and institutional inpatient Medicare claims.  CPT procedure codes which are maintained by the American Medical Association, are used for outpatient services in the VA. And finally, HCPCS or healthcare common procedure coding system codes are used in Medicare billing.  Level 1 codes the same CPT codes that I just mentioned that will capture services and procedures.  Level 2 codes are specific to Medicare and they are used to identify products, supplies and services that are not covered, for which there aren't level 1 codes. These would be services that Medicare will cover so they need to have codes for them. 

This chart summarizes diagnosis and procedure data fields and code sets used in the VA.  On the left you can see the medical SAS data set file names.  Admitting primary and secondary diagnoses are all coded using the ICD-9-CM system. You will note here that the table does not include HCPCS codes since they are not used in the VA.  And also note the outpatient visit file contains no diagnosis or procedure codes, so it is not a place you would go to ascertain comorbidities.  And finally note that inpatient procedures are recorded as ICD-9-CM codes while CPT codes are used for outpatient procedures, just to make things get more difficult for us. 

And in this slide... I thought I had progressed but I don't think I have. I seem to be frozen up now so... 

>>Which slide are you trying to access?

>>Two... OK now I’ve got it, I don’t know what happened.  Thank you.  OK so this slide shows Medicare diagnosis and procedure codes and again the Medicare files are shown in the left column.  And you want to note that HCPCS codes are used on outpatient claims, prior to 2004 ICD-9 procedure codes were also used on outpatient Medicare claims but they are no longer permitted. 

Just a note further about ascertaining comorbidities in pharmacy data. This can be of particular value in certain conditions. For example, when diagnoses and procedure information are not available, it can be a useful approach when a condition is chronic and stable and does not require an office visit... such as hypertension or epilepsy would be two possible examples.  If they don't require an office visit, an encounter is not going to be generated therefore you may not see a diagnosis code, yet looking at medications dispensed could clue you in. 

Now on to some important measurement considerations.  The first decision to be made in combordity measurement is whether the focus will be on specific conditions or on the overall burden of disease. So if it is on the overall burden, you would use one of the indices and in particular if you want to understand the effects of particular individual conditions, as on the outcome or as covariates on the predictor variable, then you will use indicators of those conditions. So the summary measure, or index provides one number so that’s an advantage, it allows for parsimony in regression models.  But the answer to this question about specific conditions verses a summary measure can influence the data that can and should be used and also conditions that are going to be sought in the data.  

Another decision that has to be made in comorbidity measurement, is what conditions you want to identify in the data.  The answer depends to a great extent on the research question or the outcome being studied as well as a study population and setting. For example, if the outcome is 

mortality, the list of important coexisting conditions could be quite different than if the outcome being studied is health services used, such as physician visits or something else. In terms of study population, certain conditions are more common in the VA and among veterans than in other populations.  Spinal cord injury, PTSD, maybe serious mental illness.  And so in veteran studies it may be very important to control for those coexisting conditions whereas in a population where prevalence may be much much lower, that may not be so important.  An additional question is what data will be used and some of that can be determined by what is available: inpatient, outpatient, or those kinds of data. 

Now that you’ve determined what conditions you want to capture, the next issue has to do with how exactly you are going to define a comorbid condition.  So is it just an occurrence of any of these ICD-9 or procedure codes or a medication dispensing going to be indicative in your operational definition as a comordid disease.  One thing to keep in mind here is the problem of rule-out diagnoses and that is a situation where a diagnosis may not be established yet it may be entered into the record in a progress note, for example, as a rule-out diagnosis.  Let’s say the physician or provider is concerned about coronary heart disease and sends the patient for stress test and writes in rule-out coronary heart disease. In that situation, the coding rules state that the diagnosis should not be entered, yet we know that this happens so it is important to avoid this.  These are some rules of thumb.  There is a citation there that will give you more information that looks at the validity of this.  But basically in inpatient data, if you see a code, you can have a lot more reasonable confidence that the patient has that condition.  But if you're looking at outpatient data, you want to see at least two separate records with that particular code, and the record should be at least thirty days apart.  If the comorbidity does not meet those criteria, then we consider it a rule-out diagnosis and will not include it in our list of comorbidities. 

Another issue is identifying clinician-assigned diagnoses. There are some records where often a non-clinician may enter the diagnosis and those are going to be less reliable than the diagnosis entered by the medical provider.  And so ways to avoid that are first of all to identify records that are associated with high likelihood of non-clinician assigned diagnoses.  Those would be lab records, diagnostic imaging, other ancillary test events and then certain files like in Medicare, the durable medical equipment file, in the VA telephone encounters would fit that situation often.  In the VA you can identify these things for exclusion using stop codes and in Medicare you can identify them first of all by just excluding the DME file from comorbidity ascertainment, and then also the codes that you see here:  physician specialty codes, claim type codes, BETOS codes, and place of service codes. 

So some examples are shown on the slide of VA clinic stop codes used to identify clients for exclusion. For example, stop code 105 indicates x-ray, 108 indicates laboratory and then also examples of Medicare physician specialty codes that can be used to identify claims for exclusion.  For example, code number 30, which indicates diagnostic radiology. 

The measurement time period is another important issue to be addressed in figuring out how to ascertain comorbidities.  By time period I am referring to the dates you choose as a start and end date for capturing the information from your data. There are no real strict rules here. It is, answers to this are very specific to the study but it is important to be aware of the impact of differences in your decisions. So, one obvious first question relates to the length of the time period and again there is no one right answer.  The period needs to be, at a minimum, long enough to allow for health services used but not so long as to be irrelevant to the patient’s current health.  Another thing to think about is whether the definition of comorbid condition for your study is going to be limited to active diagnoses, or will it include diagnoses made sometime prior for which the patient is not currently receiving treatment and that may or may not have current health implications.  This can be difficult because there is no place in VA or Medicare administrative data to record that a condition is resolved or inactive.  One approach can to this would be to look for other evidence of active treatment, for example procedures that are associated with that particular condition or medications or other services.  Then, another issue is the relationship between the comorbidity measurement time period and the outcome measurement time period. Will the comorbidity in outcome be measured simultaneously?  Or will the comorbidity be ascertained during some prior time period?  And the answer to this question will be determined on the basis of the analytic as well as theoretical considerations of the particular study.  And finally, to what will the measurement start and end dates be anchored?  Will a particular date be the starting date for measurement and the same for every patient in the study?  Or will an event such as the diagnosis of the focal condition define the time period? For example, in a study of treatment received among patients with colon cancer, the time period could be defined as ending on or just before the cancer diagnosis date.  Or for that purpose, beginning of that time depending on the purpose of the study.  The point being that the cancer diagnosis date would be considered the anchor and comorbidity would be measured, timewise, in relation to that. 

Some special challenges that I just want to mention very briefly.  One is the difficulty of measuring functional status in administrative data.  And another is measuring severity of disease which is pretty much impossible although lab data and pharmacy data can be helpful in this.  

And then of course undiagnosed conditions, and that brings me to the point that, it may seem self-evident but when we’re using administrative data to measure comorbidities we’re only going to find those where healthcare has been delivered.  So in the case of undiagnosed conditions, obviously you’re not going to know about it.  If the patient has not sought care for a condition, no record will be generated.  It’s especially important when we’re talking about Medicare and VA data where health services are obtained outside the VA, and care for the same condition has not been provided in the VA, we will be unaware of that in the VA administration data. 

Comorbidities can be measured as ordinal, they can be summarized as a score, or they can sometimes, in a simple ordinal measure each condition gets equal weight and other situations, you want to give certain conditions more weight if they are conceptualized as having a greater impact on the outcome and then weighting can be used.  And then categorically they can be measured as categorical, meaning the indicators, simply as specific condition indicators that I mentioned earlier.

Here, you see several commonly used comorbidity measures that can be used when using administrative data.    

And just one example, Charlson comorbidity index was developed in 1980s originally, to predict mortality and it now has 19 chronic conditions although originally was 17. Each of the conditions is given a weight and then the final score is the sum of the weight.  The original Charlson has now been extended and adapted by Deyo, who -- originally the Charlson was not constructed using administrative data, Deyo extended it for the use of administrative data and then Romano made further adaptations.

[silence]

We are on the slide that says HCC / DCG method.  HCC is hierarchical condition categories.  These were developed to predict costs.  They take into account a much wider range of diagnosis codes -- 15,000 that are put into 185 buckets of homogeneous conditions.  Then categories of conditions are arranged hierarchically and those buckets are grouped with like cost, call diagnostic cost groups.  This was developed by Medicare originally. But is commonly used including here, in the VA. 

And very quickly, in pharmacy data, an example is a VA-based version of a summary measure called the RxRisk. There is a citation there that can learn more about it but it includes 45 chronic condition categories that are identified through prescription data.

Combining VA and CMS data to measure comorbidities.  First pitfall that I want to mention, is to simply not use both data sources when they are available.  Keep in mind that there are differing incentives to record complete information in the two systems.  Medicare claims are generated for billing purposes and there are particular billing rules around them.  And in general, the more codes, the more services, means the more reimbursements the provider gets and so there is certainly an incentive to record all the services.  That same incentive does not exist in the VA.  And so, more recently, there are some incentives that are not related to billing so much from the provider point of view but in terms of productivity measurement and quality measurement that may offer some incentives.  Dates of service issues also can impact the measurement time period. In inpatient data for example, in both VA and Medicare, exact diagnosis dates can’t be captured.  And so in terms of how you're going to ascertain comorbidities, rules need to be established for what date you’re going to use when you’re determining how far you’re going to look back.  For example, let’s say you’re going to measure comorbidities over a six-month period. What if the beginning of that six-month period falls in the middle of an inpatient stay?  Are you going to exclude all the diagnosis and procedure codes from that inpatient stay?  Or are you going to go a little bit further back so that you can capture all of them from the time of the admission?  

Another issue related to that, in Medicare, some services are billed just periodically. For example, home health where a bill is submitted just once every 60 days and again, the exact date on which the service was provided is usually not known.  And then of course, another complication in using the two data sources is simply the multiple types of codes that are used in VA and Medicare. 

I'm going to, in the interest of time, I'm going to skip by a couple of slides here that just provide an example of the importance of using complete data. You can take a look at this yourself when the presentation is archived.  Or even now, in the PDF.  Essentially, it is a study by Byrne and colleagues in which they looked at this and you may find that very useful but the take-home message is that a lot can be missed when other sources of healthcare data are not taken into account.

I’m going to proceed now to a case study and this is a published stud in the Annals of Internal Medicine in 2009 by Louise Walter and colleagues. In San Francisco. And to set up the context a little bit, for those who can't see, I want to state the name of the article.  “Impact of Age and Comorbidity on Colorectal Cancer Screening Among Older Veterans”. In terms of background, older adults, who are unlikely to live five years or have significant comorbidities that would preclude treatment are unlikely to benefit from the colorectal cancer screenings.  The objective of this study was to determine whether colorectal cancer screening is targeted to healthy older patients and is avoided in older patients with severe comorbidity.  So, how appropriate is the targeting?  And note first of all, in this study, that comorbidity is seen as a predictor variable. It is the variable or construct of primary interest.  The sample for this study was VA patients 70 years and older.  

I keep having a problem with advancing the slides. 

Comorbidity data for this study was obtained from both VA and Medicare files, inpatient and outpatient data.  In terms of the measurement of comorbidity, they used a measurement period of 12 months, anchored to the beginning of the outcome observation period which was January 1, 2001.  And they used the Deyo adaptation of the Charlson comorbidity index which again was developed to predict mortality, not this specific outcome. That is comprised of 19 chronic conditions that are taken into account.

What the investigators did was take the score, which ranges from 0 to 8 and categorized it. So a zero Charlson score is no significant comorbidity, score of one through three was characterized as average comorbidity, and a score of four or greater was categorized as severe comorbidity.  In addition, they used an additional measure of comorbid condition or more specifically, functional status to provide a little bit -- remember that I mentioned you can't get a sense of severity of the conditions in administrative data.  So what they did was they looked for enrollment in the VA home-based primary care at the start of the study period, indicating the individual was homebound and they used that as an 

additional indicator of health status. 

So the results of the study, first of all, you see the distribution of the scores, 36% were in the best health category, 52% had a score of 1 to 3 so they’re of average health and 12% in the worst health category.  And they presented adjusted accumulative incident screening rate and in the best health category, the incidence was 47.1, in the average health category, it was 45.9 or 46 and in the worst health category, the screening rate was 41. So you can see that what they found is that there were not very large differences between the rates of screening.  They were in the direction that one would hope and so the study concluded that screening is not as appropriately targeted as it ought to be.  And the authors acknowledged a limitation of the study which is that the comorbidity index can't account for all factors that may impact the likelihood of screening, for example, functional status. Although again, they took the additional step of identifying people who were homebound.

That concludes the substantive portion of my presentation. For additional resources, you can go to the VIReC website and the URL is there.  You can find information on VA and Medicare data sources and how to access the data.  And a lot of other information.  Particularly, a specific detailed documentation on some VA data sets including the medical SAS data sets and the DSS clinical national data extracts. 

We always like to remind people also of the HSR data listserv which can be a tremendous resource. If you go to the VIREC website you will find information on how to join the listserv if you are not currently a subscriber. It is a discussion among more than 500 individuals, researchers, data stewards, data managers and you can also access archives for past discussions.  There is a search function there which can be very useful. So that’s a place where you can pose your own specific question and people are often usually very helpful.  And then finally, the VIREC help desk, you can email us or call us.  Email address is virec@va.gov and our phone number is there on the slide.

>> Thank you very much, Dr. Tarlov. Before we begin our Q&A portion, this is Molly Kessner from CIDER and I just want to make a quick announcement. I am putting up our evaluation feedback form at this time. It will take a second to load but please take a moment to answer these few questions. It helps us to provide changes to our program that reflect your comments. So please, and thank you for filling that out in advance and also, for those of you that join the meeting late, please use the Q&A function of Live Meeting to type in your question. You can activate this by going to the upper left-hand corner of your screen and clicking on the Q&A tab and type in your question or comment in to the top box and press Ask.  And for those of you listening in on the VANTS line and are unable to type in your question using Live Meeting, please email your question or comment to cyberseminar@va.gov and I will read your question aloud over the line. I will turn it over to you at this time, Melissa. 

>> Molly are you seeing any questions at this time? 

>> I have not seen any regarding the content just yet. We will give it another minute and see if anybody types in.

Due to the connection issues that we are experiencing today, I do apologize for that.  As with all of our sessions, we are going to be recording today's session and making the archive available online on the internet for later viewing.  Anyone wanting to access today's session's archive can go to the HSR&D website and click on cyber seminar catalog and you can find a copy of today’s session and all the past sessions and videos there is also a version of the slides. And the next Database and Methods session will be on February 7 at 1pm on Assessing Race and Ethnicity.

And Melissa, there is a question now.  

>> Okay. Dr. Tarlov, the first question we received is the following: “are any of the comorbidity indices available through the VA?” 

>>  OK. Let's see here. If the questioner is asking about, essentially code for ascertaining these conditions in the data, I am not aware of any formal set of codes that have been publicly made available.  This is something, an example of something that could be very useful to go to HSR data listserv for and people are often willing to share the code they have used in studies.  I can also refer the questioner to, for the Charlson and adapted Charlson, there is code that has been posted on the National Cancer Institute website that is a version of the adapted Romano that has been further adapted for cancer studies.  I think I may have just recently seen that there may also be something on the AHRQ website.  And then, I believe that there is actually, since my memory is bad, we can all consult a recent VIReC data issues brief. I think we put an announcement in there from the VSFC which indicated that DCG scores are now available at AITC (Austin Information Technology Center). I don’t remember what those files are called. They are constructed by the VSFC and again, in a recent VIREC data issues brief, you can find a little bit more information about what the files are called or where to find it. 

>> Dr. Tarlov, I did receive a clarification from the person that asked the question and they are asking, “is the programming for the Charlson or RxRisk available?” 

>> So that is essentially the question that I answered in the first part.  Again, it has been posted for everyone to use. One thing I should mention, is that there isn't one program that is going to work for everyone. Partly because people are using different data sometimes you're including Medicare and sometimes you're not so of course they have to be adapted for that.  But again, HSR data is a good place to go.  In terms of RxRisk, there is an article that was cited on one of those slides and I believe the first author on the article is a VA investigator and I would certainly encourage the questioner to contact that person. 

>> Great.  Thank you.  There are a couple of other questions. “When you are discussing physician diagnosed comorbidities are you suggesting to use labs, x-rays, etc. only if you want high sensitivity?  Is it better to stick with diagnostic and procedure codes?” 

>> My suggestion there is -- and maybe I shouldn't call it a suggestion. Maybe I should say what I am suggesting is that people be aware that if they used records from lab services, x-ray services etc., it cannot be assured that those diagnoses are actually provider, physician -- that they are formal diagnoses that have been assigned by the individual who is responsible for the patient's healthcare.  So for example in Medicare, on the claim, a diagnosis has to be entered and often that diagnosis is entered by the particular provider say of the durable medical equipment or the lab service, rather than a clinician.  So you want to be aware of that.  I am not sure that including those records will increase sensitivity at all.  And I think the downside outweighs any possible increase in sensitivity.  And is it better to stick with diagnostic and procedure codes? I would say yes.

>> OK.  Another question: “would you recommend using the ‘problem list’ in CPRS as a source of comorbidity?”

>> Well. Absolutely. The assumption that we have been going on in this presentation is the problem list is not available.  And it is not available in national data. So that is a problem.  But if you have access to the problem list, if you have a study sample that is of a size where it is possible to view, feasible essentially, to look at individual problem lists it can be useful.  I don't personally have experience with that. 

>> Another final question I have for right now: “how would you go about comparing the pros and cons for different methods using VA data?” 

>> OK.  I know this is kind of frustrating to hear. But the truth really is that it is very specific to the study.  So a big difference between the Charlson and the Alex Howser, the Alex Howser takes into account I think it’s 30 diagnoses so it is a broader set of diagnoses whereas the adaptive Charlson is now 19 I think.  But a big difference between them also is that the Charlson is weighted whereas the Alex Howser is not. And weighting is used when you want to take into account the fact that certain conditions may have a larger impact than others.  Let's say an example is, you are looking at a surgical outcome and you want to control for differences in coexisting illness.  So, you may be collecting information about a range of conditions.  Let's say coronary heart disease and osteoarthritis.  Well clearly, you would expect that coronary heart disease will have, is likely to have larger effects on the outcome than is osteoarthritis.  So that’s an example of a case where you want to do some weighting.  But also, I highly recommend the reader to look at the published, original published papers of Alex Howser and Charlson in addition to the Charlson paper, the Deyo and Romano papers as well.

>> An additional question came in. “I was wondering if there is a mental health comorbidity index.  Could the Charlson index be adapted for mental health research?” 

>> That is a good question. And I am not aware of a mental health comorbidity index. Let me see if that is true. Something is ringing a distant bell.  I have seen some recently published papers that were looking at the validity of some comorbidity measures in populations of patients with chronic serious mental illness.  So these were not indexes that were developed specifically for that population but the validity of them was tested in the populations.  So that may be very useful.  And, could the Charlson index be adapted?  Certainly.  You can add any condition.  Once you do that, you do not have, you are then out on your own without any information as to its validity.  Another thing that can be done, if you want to take mental health conditions into, oh for mental health research.  Another thing that can be done, is that in addition to an index, you can use specific indicators of other conditions that you think might be important to take into account in your particular study that are not included in say the Charlson or Alex Howser or whatever you are using. 

>> Great, thank you.  I believe those are all of the questions for now.

>> OK well if anybody thinks of additional questions, of course they are welcome to email the VIReC help desk and we will do our best. 

>> This is Molly Kessner again from CIDER. Thank you so much, Dr. Tarlov, for your great presentation and thank you Melissa for your assistance.  As I've mentioned previously, the session was recorded and we will be posting the archived video in the next 24 hopefully hours and you can access it by going to the HSR&D website, click on cyber seminar catalog.  And also we'll have our next VIReC Database and Methods session coming up on February 7 at 1 PM.  And the topic is Assessing Race and Ethnicity and the handouts will be up on our archive website within the next hour. Once again, you can go to the HSR&D website click on cyber seminar catalog you can access the slides from today's presentation.  We apologize for the technical difficulties the network was experiencing.  Thank you all and that concludes today's session.