**Transcript of Cyberseminar**

**VIReC Database and Methods Seminar**

**Assessing Race and Ethnicity**

**Presenter:  Maria Mor, PhD**

**May 5, 2014**

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

*Moderator:*Welcome to VIReC’s Database and Method’s Cyber Seminar entitled, “Assessing Race and Ethnicity.”  Thank you to CIDER for providing technical and promotional support for this series.  Today’s speaker is Dr. Maria Mor, and Dr. Mor is an associate director for the Biostatistics and Informatics Core at the Center for Health Equity Research and Promotion at the VA Pittsburg Healthcare System.  Questions today will be monitored during the talk and will be presented to Dr. Mor at the end of the session.  A brief evaluation questionnaire will pop up when we close the session.  If possible, please stay until the very end and take a few moments to complete it.  Now, I am very pleased to welcome today’s speaker, Dr. Maria Mor.

*Dr. Mor*Thank you.  I hope you can all hear me.  

*Moderator:*Coming through just fine.

*Dr. Mor*All right.  Thank you.  I’m going to begin today’s talk with an introduction, and then we’ll follow this with information about how race and ethnicity are collected within VA, and then more information about how the data are stored and used for research and other purposes within VA; some information about race and ethnicity data that are available for Medicare; the quality of the VA race and ethnicity data; then recommendations for using the data in the summary and where to go for more help.  Before I begin, I would like to ask the audience a question.  Have you ever used VA race and ethnicity data?  Yes or no?  

*Moderator:*Thank you, Dr. Mor.  It looks like our results are streaming in, and we’ve got a very responsive crowd today.  Thank you to our respondents.  We do appreciate you giving input.  It looks like we are split right down the middle. 

*Dr. Mor*All right.  Okay.  Yes, it does look like 51.5 percent said yes, and 48.4 percent said no.  I would agree, that looks like it’s pretty evenly divided.  Just as a brief introduction racial and ethnic disparities in health and healthcare are well-documented and persist in the US.  The causes and solutions to these disparities are not well understood, and while overall quality is improving, access is getting worse, and disparities are not changing.  Racial and ethnic disparities also exist in VHA, where financial barriers to receiving care minimized.  Again, what we’re seeing within VA is that while quality has improved, there are still significant within facilities disparities observed in clinical outcomes.  

More research is required in order to detect and understand and address these disparities in health and healthcare.  Accurate race and ethnicity data are essential to disparities research and for research on clinical factors associated with race and ethnicity.  However, within the VA, there are problems with race and ethnicity data.  In particular, these problems include incomplete data, inaccuracies in the data and the coding of inconsistent data over time.  To put the issue of examining race within the VA in context, I just want to briefly discuss the racial and ethnic distribution of veterans.  

As a whole, approximately 80 percent of all veterans are white, with the remainder 20 percent belonging to other categories.  This includes 0.6 percent American-Indian or Alaskan Native; 1.3 percent Asian; 10 or about 11 percent black; 6 percent Hispanic and 1.4 percent are some other race, including those who identify as being multiracial.  These are the overall statistics for veterans.  Use of VA healthcare does differ by race.  Asian veterans are less likely to use VA healthcare.  Black, American-Indian, and those of other race are more likely to use VA healthcare.  Within veterans who use VA healthcare, we will see that there will be a larger percentage who are black, American-Indian or some other race than what’s presented here.  

Now I’m going to talk about the collection of race and ethnicity data within VA.  Our current standards are based on the VHA Handbook, 1601A.01.  They allow for the selection for one option for ethnicity, which is Spanish, Hispanic or Latino, and multiple races may be selected from among the categories of American-Indian or Alaskan Native, Asian, black or African-American, Native Hawaiian or other Pacific Islander, white or that the race is unknown by the patient. 

Our current reporting methods include a two-question format.  Race is asked after ethnicity because in some instances, those who are of Hispanic origin may be reluctant to provide a race because they consider themselves to be Hispanic.  Often times, ethnicity is asked first; at which point, race follows.  Data are to be captured through self-report.  There are a number of race and ethnicity collection standards that are relevant to us.  The OMB Directive, Revision No. 15 sets the standards for maintaining, collecting, and presenting federal data on race and ethnicity.  These are the standard upon which our current VA handbook is based, and they were implemented in VA in the fiscal year 2003.  

When we discuss the data that are available to us within VA, this is an important point to us because we have a different method for obtaining and collecting the data prior to fiscal year 2003 versus post fiscal year 2003.  In addition, the Joint Commission has also begun using the collection of patient demographic data, including race and ethnicity, as a key element of performance.  I know that for our facility in particular, it is these Joint Commission standards which have driven a desire to improve the accuracy and the completeness of the data, rather than actually VA standards.  The Affordable Care Act also has standards on elements related to disparities, including data collection standards for race, ethnicity, primary language and sex.  

The acquisition of race and ethnicity data in VHA should occur from the patient through self-report or by their proxy.  For example, a caregiver or a family member that comes in with the patient.  The information is to be completed at the time of the application for health benefits through VA Form 1010 EZ.  This form can be completed online, on paper form or by interview.  The form should be completed at the time of enrollment, but we can also obtain data on race and ethnicity at the time of a hospital admission, an outpatient visit or preregistration.  The data, again, can be obtained online, through the telephone or in person, and the information is entered through a VA facility enrollment coordinator or, for example, a registration clerk, or if the patient bypasses the registration process, sometimes they will get that information directly at the outpatient clinic through the personnel.  The data will be entered by the VA personnel into VISTA.  

Historically, and by that I mean prior to fiscal year 2003 with the new data changes, the method of ascertainment of the race and ethnicity data was uncertain and was assumed primary to be observer reported.  That is when the veteran came in, for example, the registration clerk may look at the veteran, make their own determination about the race and record that.  Then perhaps if they’re unsure, they may ask the veteran.  There was no option for reporting multiple races, and a single question captured both race and ethnicity.  The allowable responses are Hispanic White, Hispanic Black, American-Indian, black, Asian and white.  These are the data that are collected prior to fiscal year 2003.  As we’ve discussed, data are to be collected at the time of enrollment.  Many of our veterans have enrolled prior to fiscal year 2003, so that would be the original data that would be obtained from them.  The data were entered directly into VistA.  They’re contained in the race information and patient information sub files.  These data from VistA, including the demographic information from race and other demographic information, would be transmitted with each encounter to the Austin Information Technology Center and stored in the National Patient Care Database.  Medical SAS datasets are extracts of the National Patient Care Data.  If you use data that comes from Medical SAS data, the original source would be the VistA data, but they will have gone through the NPCD and then also further standardization in the Medical SAS files.  You will have a record for each encounter the patient has had with VA.  

If you use data from the CDW, it is also obtained from the underlying VistA data, it just doesn’t go through the same process in being transmitted to the CDW.  Within a clinical setting, race and ethnicity are to be obtained during preregistration if they are missing.  The data are to be collected directly from the patient or their proxy at the time of hospital admission an clinic registration.  The data are entered into VistA, again, in the race information and patient information sub files, and there’s a separate VistA field that will capture the method of data collection.  Before I continue, I would like to ask the audience another poll.  What sources of VA race and ethnicity data have you used, and if possible, please check all that apply?  One is that you’ve never used the VA race and ethnicity data.  Two is that you’ve used MedSAS files.  Three would be the CDW.  The next is the VistA or a regional warehouse or some other VA data source.  

*Moderator:*Thank you.  It looks like we’re getting lots of responses coming in, and those are still streaming in so we’ll give people some more time to submit their responses.  It looks like things have slowed down here.  I’m going to go ahead and close the poll now.  All right.  

*Dr. Mor*Our final results are about 40 percent of you have never used VA race and ethnicity data.  About a quarter have used the data from the Medical SAS files; 34 percent the CDW.  Another quarter have used from the VistA or regional data warehouses, and about 20 percent have used data from other VA data sources.  We’ve talked about how the data are collected and entered into VistA, and now I’m going to give a little bit more detail about the different data sources that we have to obtain those variables and how the data are stored and used.  The first source I’m going to talk about are the Medical SAS files, which it looks like a number of you have used.  The data that we have for the historic race variable, which is a single variable that contains both race and ethnicity that was captured prior to fiscal year 2003, is stored in the inpatient PTS main file from 1970 onward; from the outpatient visit file from 1997 onward and the outpatient event file from 1998 onward.  

With the new transitions on how--I guess it’s maybe not so new these days, but relatively new transition in how race and ethnicity data are collected, those variables have been stored in the inpatient file from fiscal year 2003 onward, and the outpatient visit and event files from 2004 onward.  For the inpatient files, we have the variables Race 1 through Race 6; outpatient files, variables Race 1 through Race 7, and in both the inpatient and outpatient files, a single variable ethnic captures the ethnicity data.  

Prior to fiscal year 2003, there was a single variable race, which has race and ethnicity with only one race allowed.  After fiscal year 2003, we have multiple races captured in the variables, Race 1 through Race 7.  I believe in actual practices, there are only a handful of records that go as far as using Race 4, so the fact that we have a different number of variables between outpatient and inpatient files actually is not a problem.  We have a single value for ethnicity that’s captured in ethnic.  When you use the data, it’s important to understand what’s actually stored in those variables.  For both Race 1 through 7 and ethnic variables, they have a length of two characters.  The first character contains the race or ethnicity for the individual, and the second character has the method of data collection.  It may not be uncommon that when you use these data, you may actually want to break those two characters apart and use those two pieces of information separately.  There is a common format that’s used between the race variables and ethnic for the method of data collection.  In our historic data prior to fiscal year 2003, race can contain the numeric values one through seven.  The values one through six contain the allowable race and ethnicity combinations, and the value seven or missing value would note that the race and ethnicity for that individual is unknown.  

In the data since 2003, Race 1 through Race 7 capture both the method of data collection and the race.  The first character specifies the race.  That character can take on the values there, eight, nine, A, B, C, D, and if there’s an other value, which generally would be blank, that would indicate missing.  When you do use these data, you want to make sure that you go back to the format because the first character does not map intuitively to the description.  For example, the character B would denote that the person is white and not black, when they intuitively feel that might should be the case. 

Similarly, the variable ethnic contains the ethnicity and the method of data collection, and the first character captures ethnicity.  The first character can take on the values D, H, N, U and if there’s an other value, that would denote missing.  Unlike race, though, that first character does map to the intuitive category that you would expect.  D stands for declined to answer.  H is Hispanic or Latino.  N is not Hispanic or Latino, and U is unknown.  Then for both the set of race variables and ethnic, the second character specifies the method of data collection. 

The second character can take on a blank value, if missing, for the method of data collection.  O is captured through the observer.  That is, for example, a registration clerk making that determination on their own.  P for the proxy, if the veteran came in with a caregiver.  S for self-identification, and U means that it’s unknown by patient.  I will just let you know that it is my understanding from data that we’ve observed with the clerks is that these data do default to self-identification.  The vast majority of records that you will see will be denoted as being self-identified. 

We’ve talked a little bit about what we have in the MedSAS data as far as our variables and how they’re formatted, but another issue that we have with the data is the completeness.  Unfortunately, a substantial portion of veterans do not have a usable race value in the VA Medical SAS inpatient and outpatient datasets.  For these purposes, a useable value is any value that is not missing, unknown or declined.  This is very important because unknown and declined are valid responses that can be stored, but they are not informative of the individual’s race.  Prior to the changes in fiscal year 2003, the amount of missing data or actually rather, focus on usable data varied from about 55 percent to 60 percent of the data were usable for the encounters.  Beginning in fiscal year 2003 with the new variables, the old values that had been stored were not carried over automatically to the new value so that we see initially the amount of missing data or, in this case, the amount of usable data decreased so that only about half of the data were initially usable, but over time, that has improved substantially so that if you look at more recent records, for example, utilization here in fiscal year 2012, about 85 percent of encounters have usable data. 

That gives us an idea of what’s happening overall, however, there is a difference between the amount of usable information that we have between the inpatient and outpatient files.  Historically, there have been more usable data from the inpatient files.  For example, when we look at fiscal year 2006, 78 percent of the inpatient records had usable race versus 67 percent in the outpatient files.  However, there’s been a change in how that data is transmitted and stored over time.  If we look at recent times here in fiscal year 2013, we see about 40 percent of the encounters have usable race data versus 86 percent in the outpatient file.  When we look at the inpatient ethnicity, the situation’s actually a little more extreme, 32 percent have useable ethnicity data in the inpatient file versus 92 percent in the outpatient files. 

What this means is if you are looking at an inpatient cohort; you’re using inpatient data, you’re going to have to go to the outpatient files in order to obtain race and ethnicity and not rely only on the inpatient data.  Within these data, if we look at ethnicity, we see about 90 percent of visits in fiscal year 2012 have a useable ethnicity value that’s similar to what we saw with race.  It’s a little bit higher, and perhaps that’s because ethnicity’s asked first, prior to race.  However, as we’ve noted, the completeness of the ethnicity data in the VA Medical SAS inpatient datasets is low, and the issues with the completeness appears to be systematic.  About half of all inpatient facilities have blank ethnicity data for at least 98 percent of inpatient records.  A little over a third of facilities have blank ethnicity data for all inpatient records, even though these facilities will have that data available in outpatient files.  For some reason, it’s just not being transmitted to the inpatient score.  This underscores the importance of utilizing that outpatient data with an inpatient cohort, if you’re using the Medical SAS files. 

Historically, we have used the Medical SAS files for research purposes, but now we have data available to us from the corporate data warehouse, and it’s my understanding in the future, this will be our source for all new data that we will have available to us to use.  This corporate data warehouse is the National Repository of Data from the VistA patient file, and it has race and ethnicity data collected from October of 1999 onward.  Unlike the Medical SAS data which contains one demographic record per encounter, the CDW contains one demographic record for each VA station a veteran has visited.  This should be the most recent demographic information available for that veteran at that facility, and that should be stored in the Corporate Data Warehouse.  

For the race values, there are both standard and nonstandard values stored.  The data are stored in a view called Patsub.PatientRace, and there are documents, actually a number of documents for using race and ethnicity data within CDW.  If you are using those data, I highly recommend that you look at all of those documents.  I’m not able to post the URL here for this presentation, but if you have trouble finding it, just contact VIReC and they will give you that information.  

As we mentioned, we have both standard and nonstandard race values.  However, the good news is 26 of those 31 nonstandard race values can be mapped to four different standard races.  The main issue that prevents this data from being standard is just simply the fact that they’re not typed in exactly the same way.  For example, if we look at the group for the American-Indian or Alaskan Native, we see multiple ways that it’s phrased.  They’re Indian or Alaskan Native, American-Indian, American-Indian/Alaskan Native.  All of those, obviously, will map into that one standard category.  When we look at data from those who are black or white, we will see that we still have some nonstandard values that were collected prior to fiscal year 2003 that included both race and ethnicity in the description of the race for the individual, but again, those can easily be mapped back to standard races. 

We are left with five values that cannot be mapped to standard values.  Three of these are a combination of Asian or Pacific Islander.  If you want to be able to break out those two groups separately, you will not be able to do so for those values.  If, however, you are combining them either into one group for Asian and Pacific Islanders or using an other category, you would still be able to use those three nonstandard values.  The other two values are Mexican-American and unknown.  Almost five percent of data values fall into one of these five nonstandard value categories.  Once you’ve gone through this process of looking at your data, mapping all of your individuals to standard values, the next problem that you might have or encounter is that of having multiple race values.  Within the CDW, almost two percent of patients who link to a standard race have more than one standard race.  Ideally, one would like to use the most recently recorded race for that individual, however, it is not possible to identify the most recent record for a patient.

It may be tempting to try to do so because there are date variables in that record that would appear to let you know which records are more recent than others.  Most of those dates have to do with the data in which the record was transferred to the CDW, not necessarily when race was recorded, and in addition, if there’s any change made to the demographic record, that date may also be updated, even if there was no change to the information for race and ethnicity.  We can’t use the most recent record.  The recommendations that have been provided for using multiple races is first if you have self-identified races, only use those self-identified races if any of them are recorded.  

Then the recommendation is if there are no self-identified races, then use all recorded races for that patient where there’s no self-identified race.  The rationale behind that is that at this point in time, there is no reason to know why one race might be preferred over another.  Until there may be further research on that, just use them all is the official recommendation.  As we had seen with the MedSAS data, there are issues with the completeness of the data that we have in the CDW.  Overall, approximately 60 percent of individuals will map to a standard race in the CDW.  However, whether or not data are available will depend on utilization.  For example, those who have not had any utilization after fiscal year 1999, we see about almost 40 percent of those will have a standard race value that could be used, compared to in fiscal year 2012, similar to what we saw in the MedSAS files.  Almost 85 percent will have a usable race value.

Charts like this, I think, are very helpful.  I think a lot of people have a lot of questions.  When they put together a cohort, they try to identify race for their individuals, and in the end, they’re left with a certain percent missing and aren’t sure whether that percent missing is reasonable and makes sense.  If you have your cohort and you understand what the utilization pattern is and when they tend to use services, you can use a chart like this to help you gauge whether or not the level of missing race that you have is reasonable for that group.  

Finally, within the CDW, we also have data on ethnicity.  Ethnicity is found in two CDW tables.  There’s the Patsub.PatientEthnicity table, and this contains the ethnicity data that are collected under the new method.  The allowable options are Hispanic or Latino or not Hispanic or Latino, and those are standardized to those two options.  However, if you do not have race available using the new methods, you will have to go back to the old method which is captured in tandem with race.  In order to obtain ethnicity under the old collection standards, you will have to go to the Patsub.PatientRace table and look at the race variables there.  Now, those variables we know do not contain standard values for the race.  If you look at the documentation here for the CDW ethnicity data, in the appendix, they will show you how to map from the nonstandard races to standard ethnicity categories.  

All of those races that we can--most of those races that we can map to ethnicity will fall within one of these four categories, they represent somebody who is Hispanic and white; white, not of Hispanic origin; Hispanic-black, and black not of Hispanic origin.  The one exception to this case, I believe, is that nonstandard racial category of Mexican-American, which is then mapped to being Hispanic or Latino, even though we cannot map a race for that category.  

More details can be found in that documentation.  As similar as what we saw with the race data, within the ethnicity about 61 percent of patients have ethnicity recorded; 88 percent of those with healthcare activity in the fiscal year 2012, is similar to but slightly better than what we saw for race.  Seventy-eight percent of those who have one standard category are self-identified, and about one percent have conflicting ethnicity categories.  Now, in order to reduce the amount of conflicting information that you have for ethnicity, you will want to use ethnicity captured through self-identification, if available.  If you have self-identified ethnicity, you will want to use that and not any other data that may be available.  Otherwise, if you don’t have self-identified ethnicity, then use the data captured through the new recording method, which the CDW will know that that’s the new method because it will be contained in this patient ethnicity sub table.  

Then only use the older collection methods, which can be captured with race in the patient race table, when no other data are available.  As an overall summary from the CDW, there are about 8.3 million unique patient records that have standard race values.  Another 2.3 million patient records that have nonstandard race values that can be mapped to standard values.  There can be multiple records per patient if the patient has visited more than one facility.  If you’re new to using the CDW data, there are sample queries in the documentation, the Best Practices Guide to Race Data, and those will show you how to map from those nonstandard race values to the standard values.  

With all of the data, when we have multiple values that are present, we want to give president and first use self-identified race and ethnicity and only use data from the older collection methods if no other data are available.  Now I’m going to talk about race and ethnicity in Medicare data.  These are data that are not collected through VA, but we do have data from Medicare that are available for us to use and link to our VA veterans. 

Medicare data is a potentially useful source for veterans for those who are enrolled in Medicare.  That is approximately over 95 percent over the ages of 65 and about 20 percent of those under the ages of 65.  The main reasons why those who are under the age of 65 would be eligible for Medicare would be because they are disabled or they have end stage renal disease.  I believe once someone has been on dialysis for three months, they can become eligible then for Medicare.  The Medicare data on race and ethnicity are derived primarily from the Social Security Administration.  They are obtained at the time of an application for a Social Security number or at the time of a replacement card.  The data are usually obtained from the individual or from a family member, and there are some important distinctions from our current methods within the VA for capturing race and ethnicity.  

Hispanic is included as a race category, rather than having ethnicity captured separate from race, and there is no option to select multiple races.  Until 1980, there were only four categories captured from the SSA.  These included white, black, other and unknown.  In particular, that means that it was impossible to differentiate nonblack minorities.  In 1980, other was replaced by the categories of Asian, Asian-American or Pacific Islander, Hispanic, American-Indian or Alaskan Native.  In addition to the data that have been collected and stored within Medicare, Research Triangle Institute also created and implemented an algorithm to increase the accuracy of the race variable, especially for Hispanic and Asian individuals.  

This data element, RTI_RACE is available in the Medicare Denominator File.  They created this algorithm that uses first name, last name, preferred language and place of residence in order to improve their ability to detect those who are Hispanic or Asian.  Use of this variable improved the sensitivity of racial codes that increased from 30 percent to 77 percent for Hispanic, and it increased from 55 percent to 80 percent for Asian and Pacific Islanders.  The thing to know about this variable is this, they did not go back and collect any additional information.  This is simply a variable in which race has been imputed in order to improve the accuracy of these codes.  

The sources of the Medicare race data in VA are--there are two different sources.  If you are interested in using any of these sources, then you will want to go to VIReC and look at the methods in order to obtain access to them.  They have different methods for obtaining data access.  The last time I personally have tried to obtain access, it was easier to get access through the Vital Status File.  However, there is more information that is available from the Denominator File from Medicare.  

The VA Vital Status File contains the variable CMS_RACE.  This is the Social Security Administration race variable.  If you’re familiar with the Vital Status File, there are actually two files.  There’s a master file, and a mini file.  Race is only available in the master file.  The master file contains one record for each Social Security number, date of birth and gender combination found in VA data.  Many Social Security numbers have more than one record, and if you want to use the master file, you will want to match on all three of these linking variables.  The denominator file that can be obtained from Medicare contains two variables: the race variable, which is the underlying race data that have been collected, as well as the RTI Race Variable, which is that imputed race that uses that algorithm to try to improve the value.  

In summary, the Medicare race data quality issues are somewhat limited by the fact that we have information from most enrollees or from those who obtained their Social Security numbers prior to 1980 is limited to the original four categories.  In addition, I think there’s also issues that may impact newer veterans, as it is now the tradition that Social Security numbers are applied for at the time of birth within the hospital and not when somebody enters the workforce.  Those Social Security numbers that are applied for within the hospital may also not contain race data as well, not just limited to those categories, but they may actually have no race data to them at all. 

A Social Security application form includes the single-question format with no options for reporting multiple races.  There have been initiatives to improve the quality of the race and ethnicity data.  These have included periodic updates on American-Indians and Alaskann Natives from the Indian Health Survey and a 1997 survey of those enrollees who are classified as other or unknown or with a Spanish sur name requesting self-reported race and ethnicity data.  Both of these first two initiatives would actually change the underlying data that were collected on the individual.  Then as we’ve already discussed, there is also the RTI Race algorithm that created a new variable with an improved estimate of what the race would be that did not make any changes to the underlying data that were collected. 

I’m now going to turn my attention to looking at the quality of VA race and ethnicity data by looking at a couple of studies that have examined issues with the data.  The first of these is the use of Medicare and Department of Defense data for improving VA race data and quality.  This was a study undertaken by Kevin Stroupe and colleagues and was published in 2010.  The aims for the study were first to estimate the extent to which missing usable race data in VA MedSAS files could be reduced by using non-VA data sources.  The second aim was to evaluate the agreement between the VA self-reported race data in the MedSAS files and these external data sources.  The two aims capture two different issues related to quality: one related to completeness of the data, and the second related to the agreement between the different data sources that we’re using.  

The patient cohort was a 10 percent representative sample of all VA patients who obtained services during fiscal years 2004 to 2005 and contained nearly 600,000 individuals.  Medicare race data were obtained from the Medicare Vital Status File, and the DoD race data were obtained using a data sharing agreement between VA and DoD.  An important thing to note about these data is that because of the time at which they were available, they’re only being used for those under the age of 65 years.  We don’t have data that we can use from DoD that’s going to help us identify those over the age of 65.  However, we know that the Medicare data can be used for those individuals.  The data that we do have, though, is self-reported race and ethnicity data that were obtained from service members. 

They did examine if there were differences in characteristics between those who did and did not have usable race values available.  There are a couple of things to note.  First, males were more likely to have usable race.  Females were more likely to be in the group that did not have usable race.  This is a 10 percent representative sample.  In data that I’ve looked at, I’ve seen a similar result when I look at all records.  However, we do have--within our VA data, we have a number of records that are for non-veterans, who tend not to have demographic information collected on them, and they’re much more likely to be female.  When I’ve restricted my samples to those who are veterans, I find that this difference in the ascertainment by sex usually is diminished or goes away.

In addition, there were differences by geographic region.  Those from the south are more likely to have usable race data, and those from the west were less likely to have usable race data.  Oh my.  Okay.  For some reason, this chart did not come through.  I will just try to walk you through what it’s supposed to be showing you or maybe it shows up on your screen and not on mine.  

Overall, for the entire cohort, about half of the cohort had a VA usable race.  This is what we would expect, given that the cohort’s from about fiscal year 2004 and 2005.  Overall, as we said, half had usable race from VA; 25 percent had usable race data that we could obtain from Medicare, leaving 25 percent who did not have usable race from either data source.  For those under the age of 65, the amount of data that we had that was usable from Medicare was significantly reduced.  Only 10 percent had usable Medicare race that we could supplement, leaving 40 percent of the cohort that had no data available either from VA or Medicare.  Whereas for those who are over the ages of 65, almost everyone had usable race data from Medicare.  About 98 percent of the sample had usable data from VA or Medicare using those two sources combined. 

` the data landscape is changing, knowing where to find your data and how to access it and what you have to go through in order to access data is more important.  They also have documentation on some of the VA datasets, especially on the MedSAS data files.  In addition, other data sources like the CDW, et cetera, will have links out *[inaudible 51:45]* or those other data sources available as well.  

The HSRData listserv is a good source of information to answer more specific questions on the data.  You can join at VIReC and if you are interested in looking for answers to specific questions, I would first recommend that you search in the past messages on the archive because your question may have already been answered.  You may be able to find that immediately.  Otherwise, you can also go to the VIReC helpdesk to either have our question answered or to be directed in the appropriate direction to get the answers you require.  

If you pull down the slide, you will be able to see that there are a number of references that you may choose to look at, but obviously, we won’t go through these in the presentation.  Now, I think we have some time for questions.  

*Moderator:*Thank you very much, Dr. Mor.  Sorry about that technical hiccup.  When we uploaded the slides, some of the conversions didn’t come through.  We’re going to get back--all right.  Sorry.  Computer’s running quite slow.  Melissa, if you want to go through the questions. 

*Moderator:*Okay.  Thank you, Dr. Mor.  A couple of questions have come in.  The first question here, if a patient has multiple races entered and the dates they were collected are missing, what should we do? 

*Dr. Mor*I assume then if you have--I just want to put an upcoming seminar slide up.  If you have dates that are missing, then I presume that means you’re getting the data from the CDW or some other related data source, and that’s really where you want to look to see if you have self-identified race for those individuals.  That’s where you’re going to give that preference because, unfortunately, you just cannot tell what is the most recent data.  You can get clues as to what may be the more recent data based on if there’s a method of ascertainment for the ethnicity data or the location of the data, and also, if you have data that have nonstandard values, those should’ve been collected under the older data collection standards.  Those are clues that you can use to help you identify which data may be more recent and which you may want to use.

*Moderator:*Okay.  Thank you.  Next question, could you please clarify is there only one record per patient per station, patient SID, in the patient race table in the CDW? 

*Dr. Mor*Yes, it’s my understanding there’s only one record per patient, per station in the CDW data.  That patient, though, may have self-reported to be multiracial, so they may have multiple races available to them.  There should only be the most recent demographic information available per patient, per station. 

*Moderator:*If the most recently collected raise is patient refused to answer or unknown, can I use the second recently collected race value?

*Dr. Mor*Yes, and that’s the case where I think it’s key to differentiate between usable race and the fact that there’s a value there.  If you don’t have usable self-reported race, yes, go back and look at what other data you have available for that individual.  

*Moderator:*A question about access, can non-VA state agencies obtain race and ethnicity data? 

*Dr. Mor*I think that’s going to be a question actually that you should answer.  I mean I’m sure there’s methods to obtain data, but I’m not sure how to obtain it outside of VA.  

*Moderator:*Okay.  

*Dr. Mor*Yeah, I definitely think that’s a question for VIReC.  

*Moderator:*Melissa, are you able to see the questions that come in from the caption company as well? 

*Moderator:*I don’t think so.  

*Moderator:*They should be up in the QA box.  Perhaps you already got to them.  Please clarify is there only one record per patient per station.  Did you get to that one? 

*Dr. Mor*Yes.

*Moderator:*Okay.  Maybe we’ve gone through them all. 

*Moderator:*It looks like I’ve gotten through most of it.

*Moderator:*Excellent.

*Dr. Mor*All right.  Except for that one question about non-VA access to the data, and there I’d recommend that the individual go through VIReC to find out what their particular situation is.  

*Moderator:*Great.  For our attendees, I am going to put up our feedback real quick.  At this time, Dr. Mor, feel free to give some concluding comments, and also, you can X out of the meeting but stay on the phone if you’d like.  

*Dr. Mor*Okay.

*Moderator:*Yeah, if you have any concluding comments, feel free to give those now.

*Dr. Mor*I’m not sure that I have concluding comments other than the fact that I think these particular data elements may be a little bit more confusing to use based on the fact that we have two different methods of data collection: those prior to fiscal year 2003; those post fiscal year 2003, and that we also have two different primary sources that are used for the researchers, which are the MedSAS files and the CDW data, which just has completely different ways of storing the data and completely different methods for accessing the data.  In addition, we have this issue with a lot of missing data, and people can report as having multiple races.  I think it makes this particular topic a lot more confusing than perhaps some of the other data elements that might be more straightforward than the VA data.  You just have to kind of work through what you have; what you’re trying to get, and you’re going to have to go to multiple sources and look over time in order to try to obtain the most information that you can and then also see if you can obtain data from outside sources, if necessary.

I think it’s a little bit more complicated.  I think everybody feels it shouldn’t be, but it is.  

*Moderator:*Thank you so much.  Melissa, do you have anything you need to wrap up with?

*Moderator:*I just wanted to thank you again, Dr. Mor, for taking the time to develop and present today’s session and remind everyone, any additional questions regarding the topic they can please forward to the VIReC helpdesk at virec@va.gov.  Our next session is scheduled for Monday, June 2 at 1:00 p.m., Eastern, entitled Applying Comorbidity Measures Using VA and Medicare Data and will be presented by Dr. Burgess.  We hope that you can join us. 

*Moderator:*Excellent.  Well, I want to echo your thanks to Dr. Mor, and, of course, thanks to Melissa and to our participants for joining us today.  As you can see, I do have the feedback survey up on your screen.  Please take just a moment to fill out these quick questions.  It is your responses that help guide where we go with the program and what we have presented.  I will leave this up so feel free to take your time.  That does conclude today’s HSR&D Cyberseminar.  

*[End of Audio]*

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