VIReC Database and Methods Seminar- 15 -Department of Veterans Affairs

**Department of Veterans Affairs**

**VIReC Database and Methods Seminar**

**Cyberseminar 05-07-2012**

**Assessing Race and Ethnicity**

**Maria Mor, PhD**

Moderator:   Welcome to VIReC Database and Methods cyber seminar entitled, “Assessing Race and Ethnicity.”  Thank you [Decider] for providing technical and promotional support for this series.  Today’s speaker is Maria Mor, Ph.D., Associate Director Biostatistics and Informatics Core, Center for Health Equity Research and Promotion, VA Pittsburg Healthcare System.  

Dr. Mor will take questions at the end of her talk.  You can type them into the Q & A portion of GoToWebinar and we will present them to her at the end.  Also, I just want to remind you there’s a brief evaluation when you close your GoToWebinar screen after the presentation.  We would appreciate very much if you would take a few minutes to complete it.  I’m pleased to welcome today’s speaker, Dr. Maria Mor.

Maria Mor:   All right, thank you.  As you know, today, I’m presenting theVIReC seminar on assessing race and ethnicity.  Just to go over the session objectives, very quickly, we will start with an introduction; we’ll talk about the race and ethnicity data and the VA databases.  We also have race and ethnicity data available from Medicare.  We have a study that looks at the quality of the VA race and ethnicity data, followed by some recommendations.  And, finally, we have some links at the end for where you can go for more information and more help.  

So to start with the introduction; race and ethnic disparities and health and healthcare are well documented and persistent in the U.S. but the root causes and solutions to these disparities are not well understood.  In addition, while quality is improving in general, we find that access and disparities are not improving.  So what that means is within these vulnerable groups that quality is not improving at a greater rate than what we’re seeing in general.  So these disparities are staying to same, or they may actually be getting worse over time.  

Racial and ethnic disparities also exist in VHA.  It is generally presumed that because we have minimized some of the financial barriers and other issues that you have in outside healthcare systems that the disparities should not be a same issue within VA, but that’s not always the case.  Some of the potential contributors that we still have are patient factors, provider decision-making, and also characteristics of the VA facilities themselves.  More research is needed in order to detect, understand, and address these disparities that we have in health and healthcare.  In order to do so, we need to have accurate race and ethnicity data.  Otherwise, it is very difficult to conduct this type of disparity research.  

Within the VA we do have data that are available to us administratively, but we know that there are some problems with the race and ethnicity data that we have.  One factor is completeness.  We have a lot of missing data.  Another factor is accuracy and this gets at the idea of whether or not the data that we have, that collection of race and ethnicity data, are those values actually the true, underlying, self-reported race and ethnicity that we have there of veterans.  And, also, there are issues with consistency.  There are two particular issues with consistency.  We are obtaining information over time, so we half the option that if race is obtained at one point in time, it can differ from what’s obtained at another point in time.  And also, as veterans go to different facilities, they may be collecting inconsistent values for race or ethnicity at those different facilities, and both of those factors can lead to issues with consistency.  

So let’s talk about the race and ethnicity data that we have within VA.  Under current guidelines, we are collecting two pieces of separate information; one for ethnicity.  For ethnicity we are capturing whether patients are Spanish, Hispanic, or Latino.  And then we have a separate question that asks the race of the patients as they come in.  Patients are able to select from more than one race so if patients are multiracial, they can select from American Indian or Alaska native, Asian, black or African American, native Hawaiian or other Pacific Islander, white, there’s also an option for whether or not their race is unknown.  And, in addition, patients are able to decline to answer the question and that will also be recorded.  So under our current reporting method, we have these two separate fields, ethnicity and race, and the data we are collecting is to be self-reported.  

The primary data collection standard that is impacting the way in which race and ethnicity are collected within the VA come from an OMB directive from 1997.  This directive sets the standards for maintaining, collecting, and presenting federal data on race and ethnicity.  This directive was implemented in the Department of Veteran’s Affairs in 2003.  So what in means is that prior to 2003 we had different standards that impacted how the race and ethnicity data we are collected.  And we also will see that 2003 was a transition year as we moved to the current collection standards that we have.   

There are also other standards that can or will impact us, as well.  Recently, the joint commission has made collection of race and ethnicity data a performance measure.  This performance measure is not currently required for accreditation, but the language stating this does leave open that in the future that this may actually be a requirement.  And the reason that they had for [setting] this performance measure is specifically so that we are able to look at disparities in health and healthcare.  

The Affordable Care Act that’s currently under review also contains its own data collection standards this will impact race, ethnicity, primary language, and the sex.  

Within the VA, there is a VHA Health Equity Work Group that was chartered by Dr. Jesse in 2011.  The purpose of this work group was to help to advise leadership on how to coordinate these VHA components to help us to achieve equity in veteran healthcare.  This work group has made recommendations that were accepted and these recommendations were along different domains.  These domains included leadership, awareness, health and healthcare outcomes, diversity, and cultural competence of the work force, and recommendations that are specific to data, research, and evaluation; it is these recommendations that are most relevant to our topic for today.   

So the recommendations that they have that relate specifically to data, research, and evaluation concern, first of all, are universal standard collection of self-reported data on race and ethnicity, primary language, rural urban residence, and sex.  So we know that for race and ethnicity, what’s really key is everybody wants to have self-reported data.  So this is what’s very important for us.  But I think that some of these other elements, such as primary language, are things that we have not been capturing within VA.  It’s also important to identify and recognize an emerging vulnerable patient population.  So we need to make sure that we have the data available to us.  That would allow us to do this kind of research.  We also want to assess and report differences stratified by vulnerable groups.  So this would include outcomes such as quality of care, patient satisfaction, and access to care.  

And then we want to harmonize whatever standards that we have for collecting our data and reporting for vulnerable compilations with standards from other outside directives such as Health and Human Services and Affordable Care Act data standards.  

So how exactly do we acquire race and ethnicity data within our VA administrative data sources?  This information on race and ethnicity should come from the patient.  The ideal standard is self-report data from the patient.  If we are unable to obtain the information from the patient, then what we would like next is our next-best option would be to have information from their proxy.  So if they have a family member that comes in with them, to obtain the information from the family member.  

When we initially obtain the information on race and ethnicity that happens at the time of enrollment.  Veterans fill out a form, VA Form 10-10 EZ, which is the application for health benefits.  This form has the initial questions that they should first answer regarding ethnicity and race.  This form can be completed online, in paper, or by interview.  Initially, I think this used to be on paper and in interview, but I think that having veterans fill out this form online is becoming more common, and so I think that we will be seeing that more and more.  

Ideally, we obtain race and ethnicity at the time of enrollment.  However, if the data are missing, we still have other opportunities to collect them and this would include at the time of hospital admission, at outpatient visits or at preregistration.  Again, the different modes of collection include online, telephone calls, or in person.  So even if we do not obtain the data at the time of enrollment, we still have other methods that we can use to obtain it.  It doesn’t necessarily have to be when the person comes in person at the facility.  

And, finally, how do we obtain the information?  We’ll have a VA facility enrollment coordinator or designee these are typically the admission or the interview clerks, or to enrollment specialist or the enrollment clerks who interact with the patients as they come into the facility and they collect this information.  If we don’t get it at the time of registration, there’s also when the patients make it to the clinic, then there’s outpatient clinic personnel who can all enter the data on race and ethnicity.  This information, they collect it and it’s entered into VistA at the local facility.   

Historically, the data on race and ethnicity were captured a little bit differently.  We don’t actually know the method or the source of the data.  We typically assume that the data was observer reported.  Which means that as the patient came up to the clerk, the clerk would look at the person and make is determination about what they thought that person’s race or ethnicity was and that they would enter it into the system.  There was no option for reporting multiple race categories and we had a single question that captured both race and ethnicity.  We were able to do so through the categories of Hispanic White and Hispanic Black.  So if someone were Hispanic and they belonged to another race, then we would not be able to capture both that they were Hispanic and their race.  In addition, we have the categories of American Indian, Black, Asian, and White.  The category of Native Hawaiian and Pacific Islander, which we now have, I believe used to be contained within Asian and was not its own separate category.  

So at the time of enrollment, this form 10-10 EZ is completed by the veteran.  The data are entered into VistA and the race and the patient information are entered into race information sub files within VistA.  And this is the first step of the data process.  Then these demographic data are transmitted, along with whatever encountered information that we have on that veteran to the Austin Information Technology Center.  And this is what moves the data from the local level to the national level where it is stored in the National Patient Care Database.  

Finally, the next step, data from the National Patient Care Database is extracted into the Medical SAS, or MedSAS Data sets that we typically use, for most of us, for research.  

Within the clinical setting, we still have opportunities to collect race and ethnicity, even if they were not obtained at the time of enrollment.  So if the data are missing, then they should be obtained at preregistration.  Race and ethnicity are to be gathered directly from the patient.  Or if the patient is unable to provide that information, from their proxy.  And we can, again, capture this information at the time of hospital admission or clinic registration.  And the data follows the same standards as before; they’re entered into VistA, into the specific sub files within VistA.  

In addition to capturing information on race and ethnicity, we do see in our National Data File, in our administrative data file, we see a separate field for the method of data collection.  I will say that there is a little bit of a disconnect between what we may have available to us in the data and what may happen in actual practice.  Within our observations at our own site here in Pittsburg, we have seen that when the clerks collect information on race and ethnicity, this field for how the data were collected, it does default to self-report and that the clerks either do not tend to change or they’re unable to change that field.  To even though, ideally, we do want self-reported data for everyone and we have that field in there, just because it says self-report does not absolutely guarantee that the data obtained were done so through self-report.  However, if you do look at the data, you will see that there are times when other data collection methods are recorded.  So we do know that that process does occur.  

So what do the act race data within the VA data sets look like?  Historically, data on race was captured in that single field, it was called “race,” and this is the field that contained both race and ethnicity.  So you had the Hispanic White, Hispanic Black, and then you’d have separate fields White and Black for those who are non-Hispanic White and non-Hispanic Black.  

As you can see from what’s listed here, is that this variable still exists in our recent data files even though this field is not really being updated and populated beyond the time that our data collection standards have changed.   But this would be your source of race information if you were looking into any data files prior to fiscal year 2003.  

Starting in fiscal 2003, the updated race and ethnic fields became available in the inpatient data files.  We have a single field, “ethnic” that captures the ethnicity of the patient.  And because patients are allowed to select more than one race, we have multiple race variables; Race 1, through Race 6 that are contained in the inpatient file that will allow the patient, then, to select more than one race.  We see similar data fields in the outpatient files, but we need to keep in mind that the outpatient data only started to get the new data collection standards starting in fiscal years 2004.  So we do have that transition in 2003 where the new data were only available in the inpatient file and starting in 2004 onward, they’re available in both the inpatient and outpatient files.  We also see that in the outpatient files we have the option to select seven different racial variables, Race 1 through Race 7.  I think that’s used later; race variables are very rarely used.  I think it’s very rare that you’ll see anything beyond Race 4 in the data. 

One other key source of race data in the VA comes from the VA Vital Status File.  The VA Vital Status File is really, I think, built to help us to identify the status of individuals, so it has death dates.  And these death dates come from multiple VA and non-VA data sources.  This is an excellent source if you are looking for death data on veterans.  This includes sources such as the Patient Treatment File, BIRLS, the Social Security Administration, and Medicare.  And it’s from the Medicare data that we’re able to obtain race.  So that’s included in the Vital Status File in addition to the information on deaths.  

If you use the Vital Status File, though, it’s important to understand how those files are structured.  We have two main files for vitals status; one is a Master File.  The Master File contains one record for each Social Security Number, date of birth, and gender combination.  What this means is that for any given Social Security Number we may have more than one record in the Master File if we have other inconsistent data that’s associated with that Social Security number.  

There’s also a Mini File.  The Mini File contains one record for each Social Security Number.  An algorithm record for each Social Security Number.  An algorithm is utilized to select the best date of birth, gender, and date of death for each Social Security Number.  The other thing about the Mini File is it does not contain as much detailed information as the Master File.  So one of the variables that we don’t have in the Mini File that we have in the Master File is the race information from Medicare.  So if you want to use the race information from Medicare, you have to use the Master File and you have then be careful about how you’re linking to that Master File because we have more than one record, potentially, for a Social Security Number or a scrambled SSN.  

There’s also data that are stored in the DSS National Data Extract.  I believe the underlying source of that data is going to be similar to what we have in the other VA data sources.  However, it may be displayed a little bit differently.  So you might actually prefer to use the other data sources rather than the DSS data.  

So let’s talk a little bit about the race and ethnicity data that’s available in Medicare.  So Medicare does provide us race information.  It is a potentially useful source for race information.  But we have to be aware that we do not have Medicare data for everyone.  The groups that we tend to have Medicare data available for are those who are age 65 and older, which is a group that we typically consider as being eligible for Medicare.  People who are disabled are also eligible for coverage under Medicare, and is also end-stage renal disease.  Once someone has been on dialysis for long enough, then they’re eligible to have their care covered by Medicare.  

What that means is that when we use the Medicare data, we do have a difference by age in terms of who has information available for them.  Approximately 95 percent of those veterans who are age 65 and older will have a record in the Medicare data.  But when we look at younger patients, those under the age of 65, only about 20 percent of them will have a record that’s available.  

To you need to think about this in terms of your research question that when you are obtaining supplemental data from another source such as Medicare doesn’t have data universally potentially for your cohort whether or not there may be any issues in differentially obtaining this information.  

The data that we have in Medicare is derived primarily from the Social Security Administration.  It is obtained at the time of an application for a Social Security card or for a replacement Social Security card.  The data are typically self-report or obtained from a family member.  I would say if an individual was going to the Social Security Administration, then typically it would be that individual providing the racial information.  I think it is commonly done nowadays that the Social Security cards are obtained at the time of birth or when children are young.  In which case, that would be a family member that would tend to provide that information.  

There are a couple of important distinctions between the current race and ethnicity data and what we have available to us in Medicare:  One is that Hispanic is a race category, it is not a separate category, which means that it makes it difficult to ascertain race and ethnicity separately.  And people are not allowed to select multiple races. 

In addition, until 1980, there were only four categories available for the Social Security Administration for collecting race.  These categories were White, Black, Other, and Unknown.  In 1980, the Other category was replaced by three separate categories.  Those categories are Asian, Asian American or Pacific Islander, Hispanic, and American Indian or Alaskan Native.  So what this means is that it can be very difficult to obtain accurate information on these categories that used to be classified as “Other” in the old data.  And the reason for that is that information that we have on most of our enrollees are limited to these original four categories.  Because, again, think about our veterans, most of them are old enough that their Social Security cards would have been obtained prior to 1980.  And, as we already mentioned, there’s only a single question that does not ask ethnicity or race separately and does not allow for multiple race reporting.  

There have been some initiatives to improve the quality of the race and ethnicity data.  These have included updates that have been targeted for for American Indians and Alaskan Natives, those were targeted by the Indian Health Service.  And, also, in1997 there was a survey of those enrollees Who are classified as Other, Unknown, or who had a Spanish suried and it requested that self-reported race and ethnicity.  It is my understanding that they were actually able to obtain information for a fair number of these others and that it was a useful addition to the data that were available.  

Next we’re going to focus on the quality of the VA race and ethnicity data.  So our first component of quality has to do with the completeness of the data, or how much usable race do we have?  And by “usable” we mean a value that is not missing, that is not unknown, or is not declined.  So technically unknown or declined would not count as a missing value in the data field; however, those values are not useful in determining race.  When we look at the historic race data, those are the values prior to fiscal year 2003 that we see about 50 percent to 60 percent of the time, patients had usable race data that could be used.

In fiscal year 2003, which is when we started this transition to new data collection standards, it is my understanding that  because we were now to obtain self-reported race on individuals, the old race values were not supposed to carry over.  So initially, we see a dip.  We don’t quite have as much usable race as we transition to these new standards, but now we see that over time the amount of usable race that we have has increased so that now we’re looking at fiscal year 2011, we’re looking at 83 percent who have usable race.  And this represents considerable improvement over what we had in this historic data prior to implementing these new data standards.

In order to assess the quality of race data, we’re going to talk about a study that was published by Kevin Stroupe and colleagues that looked at using Medicare race and Department of Defense data for improving race quality and to assess the race quality.  The first aim really looked at how can we supplement the missing race that we have in the VA data by using these non-VA data sources?  And those sources are the Medicare and Department of Defense data.  And the second aim looked at the agreement between the VA self-reported race data and these other two data sources.  So the second aim really gets at the idea of the accuracy of the race data that we have in these data sources, while the first aim gets at the idea of completeness.

This patient cohort, with a 10 percent representative sample of VHA patients who’ve obtained services during fiscal years 2004 and 2005.  And although there’s a 10 percent sample, the sample size is still quite huge; we’re looking at about 500 to 70,000 individuals.  The Medicare race data were obtained from the Medicare Vital Status file and the Department of Defense race data were obtained through a special data sharing agreement with the VA Department of Defense Identify Repository.  This is a data sharing agreement that’s not generally available for those of us in research.  However, one thing to note about the Department of Defense data is that they do have self-reported race and ethnicity data or obtained from service members.

There are some differences in characteristics between those who had usable race and those who did not.  First, we can see that almost half of this cohort had usable race values and about a little over half did not.  There wa a slight difference in age.  Those that did not have a usable race value were slightly more likely to be of age 65 and older.  There’s also a difference by sex.  Those with usable race values were more likely to be male than those without usable race values.  I do not know about the exact particulars of this study.  I do know that in my own research we have seen this kind of a trend when we look at data overall.  But there does seem to be a confounding between gender and veteran status within the administrative data.  

And, typically, when we have restricted our cohort to veterans, we find that this difference between gender and availability of race tends to disappear.  No differences by marital status and geographic region, we do see that those from the South are more likely to have usable race and those from the West are least likely, or less likely compared to those without usable race.  These could be due to system factors in terms of divisions, in terms of the facilities that are collecting the information, or there’s actually potential that it could be due to the racial composition of the veterans that come in.  And it could be that sites with and without minorities may have different practices and different abilities to collect that information.

So now we can turn to looking at how the addition of the Medicare data enables us to improve the completeness of the data that we have.  The first thing to note is, again, when we look at our entire sample, about half of our sample, a little over half, do not have data available within the Medical SAS file.  Among those patients, approximately a little over half of them, do have a match to the Medicare data.  And among those who match up to the Medicare data, 99 percent have usable race values in the Medicare data.  By usable race values we mean that the data are not missing or unknown, they may still contain that “other” category which, depending upon how you are trying to use that data, may or may not be particularly useful for your research question.

When we look overall at how the inclusion of the Medicare data is able to improve the completeness of our data, it’s really important to look by age.  We can see overall approximately half of our cohort has VA usable race.  About a quarter of the cohort we were able to supplement with the Medicare usable race.  And about a quarter of the cohort had no race data available from either data source.  But when we look by age, the picture is very different.  Among those under the age of 65, we only have about 9 percent, almost 10 percent that we were able to supplement the race data with the Medicare usable race, which left 42 percent who do not have usable race data from either data source.  

When we look at those who are over the age of 65, we are able to obtain usable Medicare race on the majority of these so that after we supplement the race information, again about half of the cohort has VA usable race.  We’re looking at a little over half we could supplement with Medicare, with Medicare usable race, leaving only 2 percent overall who do not have usable Medicare or VA race from either of these data sources.  So that’s very different.  So over age of 65 we can almost completely ascertain race, whereas under age of 65, even with this additional data source, we’re still looking at 40 percent that are missing.

And we can look at the Department of Defense data as well.  When we do so, we are going to restrict our group to those under the age of 65.  The reason for this, I believe, the data use agreement that they had for the Department of Defense data was restricted to service members who left service 1980 onward, so this is not a useful data source for those who are older veterans.  Again, in our younger veterans we see that about half of our group is missing race from the VA data sources.  Eighty-three percent of those matchup to the Department of Defense data that we had available but, unfortunately, within the Department of Defense data a little less than half had usable race data that we could use.

So in that cohort that’s under the age of 65, when we supplement with Department of Defense data, we are able to obtain supplemental race information for almost 20 percent of our cohort, leaving about 30 percent overall that are missing data from both data sources.  When we add in the data from Medicare, we are able to improve that so that overall we’re looking at about 25 percent who are missing race.  Again, this is [still] looking different from what we have in the older cohort.  From the older cohort we can get down to a missing data rate of about 2 to 21⁄2 percent from just VA and Medicare.  Within the younger cohort, using VA, Medicare, and Department of Defense, we’re still looking at about a 25 percent rate for those that are missing from all three sources.

For the second aim of this particular study, they looked at the consistency of how the data were coded across all three of these data sources.  In order to do that, there were some rules that were required in order to define what is considered as being consistently classified.  All three data sources had a White category, so that could be consistently coded.  All three had a Black category, whether it was called Black or African American that was still consistently coded across all three.  It’s similar with the North American Natives, the terminology may be a little bit different whether it’s American Indian or Alaskan Native, or North American Native, but that underlying category existed in all three data sources.

When we come to the other minority groups, these are classified ultimately as Asian, Pacific Islander, or Other.  And we see within the VA categories that would include Asian, and the category of Native Hawaiian or other Pacific Islander.  From Medicare that would be Asian and Other.  From the Department of Defense, it would be Asian or Pacific Islander, or Other.  And so all of those categories from those different sources would be combined into one overall category for the purposes of determining consistency.

We can compare how those data codes look from VA race compared to Medicare race.  And when we do so, we can see the agreement is very good for Whites.  Those who are classified as White in the VA race data, 99 percent of them are classified as White in the Medicare data.  For those who are classified as Black in the VA data, 96 percent are coded as Black in the Medicare data.  However, when we start to look at the other minority groups, we see that a different pattern emerges.  

For Those who are North American Native, only about a third, a little over a third, 36 percent of those are coded as North American Native in the Medicare data.  And we see that 55 percent of them are coded as White.  So what this means is for North American Natives they’re actually more likely to be coded as White in the Medicare data, instead of as a North American Native.  We see a similar, but not as extreme pattern for those who are Asian, Pacific Islander, or Other.  They’re equally likely to be coded White or as Asian, Pacific Islander, or Other in the Medicare data; 47 percent for both of those categories.

We can look at the overall agreement between these two data sources.  The Kappa Statistic provides us an overall measure of agreement.  A Kappa level equal to one would be perfect agreement.  We see that the agreement for Whites and for the categories of Black and African American are quite high.  The agreement between these two data sources is very good.  However, when we look at the other minority groups; the North American Natives, or the Asian, Pacific Islander, or Other, the level of agreement has dropped considerably.  Also, the sensitivity of those particular fields in Medicare is not very high.  So if we relied upon those fields, we would be missing a lot of patients who are North American Native or Asian, Pacific Islander, or Other.  However, we see the specificity is high.  What does that mean?  It means that they’re actually coded as belonging to one of those minority groups within the Medicare data, then there’s an extremely high chance they’re also coded as belonging to one of those minority groups in the VA data.

And, finally, in looking at the Medicare data, we have to keep in mind that we had a separate category for Hispanic.  And this category can make it difficult for us to [tease] out ethnicity and race separately.  So when we look at those who, within the VA, are self-reported as Hispanic, we want to see what racial categories they have selected within Medicare, we see that we cannot — they don’t always choose ethnicity or always choose race.  About a quarter of those Hispanics are coded as Hispanic in the Medicare data.  Sixty-four percent are coded as White, 5 percent Black, and 6 percent as Asian, Pacific Islander or Other.  Keep in mind, given the old data collection standards for the Medicare data, Hispanics would have been one of the categories included with other.  So what does this mean?  It means that we can’t really assess ethnicity from the Medicare data and we cannot assess race if an individual has selected Hispanic as their racial category in these data.

Within the Department of Defense data, we can also compare the VA data and see how they match up.  One general note that I’ll make about the Department of Defense data, I’m not familiar with these data, but it does appear that the Other category in those data was used more extensively than what we saw with our Medicare data.  When we look at Whites and Blacks, again, we see very high agreement; 93 percent of those who are White and 95 percent of those who are Black within the VA are coded the same way in the Department of Defense data.  When we look at the North American Natives, we see a similar pattern as what we saw with the Medicare data.  Patients who are North American Native in the VA racial data are more likely to be coded at White in the Department of Defense data and we see about 40 percent of them are coded as American Indian in the Department of Defense data.  When we look at the Asian, Pacific Islander or Other category, it’s not quite as extreme.  Again, this may be due to more accurate coding, or it may be due to a more extensive use of the Other category.  But we see about 27 percent, that’s still a substantial portion, of those veterans are still coded as White within the DOD data, but 65 percent of them have an agreement that they are coded as belonging to the Asian, Pacific Islander or Other group between these two data sources.

We can again look at the overall descriptions of the agreement and we see a similar pattern.  Our agreement, our Kappa’s, are high for the categories of White and Black or African American.  And for our other non-black minorities the Kappa’s are very low.

So what was the conclusion of this study?  The first is that using the other data sources, Medicare and Department of Defense, gave us the opportunity to substantially improve the amount of complete racial data that was available for veterans.  However, is that more study is needed to understand the poor rates of agreement that we had for these non-black minorities.  I do think that this issue is not an issue that pertains only to the VA data or the data sources that we had looked at today, but I think in general, there can be issues in identifying these minority groups in these administrative types of databases.

And, finally, I just wanted to turn attention to a study that we have undertaken, it’s actually a quality improvement initiative, here in Pittsburgh.  This is a T21 funded collaboration between the Center for Health Equity Research and Promotion and the Veteran’s Engineering Resource Center.  The focus of this study was to identify measures that have undertaken by our facility that have improved the collection of race and ethnicity data, as well as to understand the process by which the information is collected and see if there are ways to improve that process as well, or to understand it better.

We’re going to focus on two particular elements that were obtained as part of this initiative.  We did conduct a survey of 173 patients throughout the VA Pittsburgh Healthcare System.  These were patients who were seen at one of our main Pittsburgh campuses or at one of the associated [CVOCs].  We specifically targeted patients who had missing or declined race.  The reason for this is that we are looking at that group that we’re not able to obtain the racial information on and we wanted to find out about what was happening with this particular group.  

Two questions  that we asked of them as part of a telephone interview was their comfort with being asked to provide race and ethnicity, address and telephone, and insurance  when coming to the VA.  One assumption about why the data on race and ethnicity tend not to be collected has to do with the clerks saying that the patients are not comfortable in answering those questions and, therefore, the clerks are not comfortable in asking those questions.  And so we wanted to see what it was like among this group that we knew was likely to have more of an issue with answering those questions.  And we also wanted to ask about the preference for providing that kind of demographic information to a clerk or a computer kiosk.  

Within Pittsburgh, we have been using computer kiosks for a number of years.  We now have new kiosks that are out that do allow patients to change their race and ethnicity data and to verify it.  And so among those patients who go to one of our main campuses that have been using the kiosk, we wanted to know whether this more neutral format, rather than talking to a clerk, would assist in collecting the information.  In brief, what we’ve found was that patients, even among this group that didn’t have the race and ethnicity data in the database, were actually  just as comfortable providing information on race and ethnicity as they were in providing information on insurance, which is a field that has to be collected when you are going into a healthcare setting, they have to collect that information on insurance. 

We also found that contrary to what we thought, there was no preference for using the kiosk to obtain this sensitive information.  If anything, among those who had a preference, they were less likely to want to enter their race and ethnicity data at a kiosk compared to you say something neutral like insurance or address that they actually had more of a preference to enter the information at the kiosk.

So, finally, what are the recommendations what are the recommendations?  When you want to obtain supplemental information  for race and ethnicity, the best source that we have available to us within the VA is going to be the Medicare data in the Vital Status File.  When you  use the Medicare data and the Vital Status File, you’ll want to be sure that you match on the date of birth, the gender, in addition to your scrambled SNN, so that you are going to be able to obtain the best match to the record you are looking for.  The use of the Medicare data will help in reducing the amount of missing data that you have for your cohorts.  Again, especially if you’re looking at a cohort of those over the age of 65.  

But there are some issues with the accuracy of the data from Medicare.  And so in order to have the most accurate data, you may want to consider dichotomizing your race classifications so you have, Black and African American, and Others, in VA studies.  The reason for this is that we know that there are non-black minorities who tend to be frequently misclassified as white; it makes it difficult for us to ascertain that data from the Medicare data.  And, also, that the Medicare White and African American categories had a high agreement with the VA data, so those seem to be very accurate.

Also, we found that the Medicare data cannot really be used to identify Hispanics with a great degree of accuracy or completeness.  And that you can also consider supplementary data sources.  The example that we had today was from the Department of Defense, I don’t think that this information are generally available, but I there may be instances where you could set up a Data Use Agreement to obtain that information.  And also, depending upon the cohort that you are studying, there may be special surveys or special repositories of data that you may be able to use to supplement your data.

Finally, where can you go for more help?  The VIReC webpage has information on VA data sources.  In particular, I know at least if you use the Internet link, there is a link right off the front page that’s specific to race and ethnicity data.  The HSR and D Listserv; you can search the archives to find past messages on race or ethnicity, you can post messages or questions that you have generally  if you do so, you do want to search the archive first to make sure that information has not already been addressed.  If neither one of those two sources are helpful for you, you can also contact the VIReC help desk directly, either through e-mail, or for phone, and someone should be able to help you.

In addition, at the end of the presentation, we have a number of references, as you can see, they’re probably quite small on your screen, but if you have that presentation, you can go through those and see if there are sources  that are useful for you.

Moderator:  So, Maria, thank you very much; that was excellent.  I think we have a couple of questions here; some more may be typed in now as people have, you know, you’ve finished and they have time to formulate their questions.  Let me just read you one comment, actually.  The person writing this in doesn’t know if you’re familiar, but they’re wondering if the VA has a common training program for registration staff — the people who interact with the veteran, the preregistration — whether or not if you had such a program that could make a difference in the accuracy of information and it could be that some facilities have a more complete training program than others.  But, I don’t know whether you could speak to Pittsburgh at all, do you know if...?

Maria Mor:  I can speak to Pittsburgh.

Moderator:  Okay.

Maria Mor:  As far as I know, there is not a unified system.  I know that the staff here has been very involved in trying to create their own training and have worked extensively with the staff that we have here.  And they have also gone out to the [CVOCs] in order to help improve the training of those individuals as well.  In those conversations I have never gotten the feeling that they are implement some sort of a national standard, but rather than they’ve been working very hard and very diligently on their own in trying to improve this issue.  So I don’t know for sure that there isn’t a national standard but my guess is that probably much of what happens is based on the local institution.

Moderator:  Okay, thank you.  Here is another question and I’m actually going to provide you with some updated information.  The question is:  “Will the race information from the Department of Defense be available in the Vital Status File?”  I was the one who passed on to Maria information about this study and about Kevin Stroupe’s study using the DOD data, and going through VAIDR, VADOD — I forget what that acronym is.

Maria Mor:  I think it’s ID Respsitory.  Yeah.

Moderator:  Oh, thank you.  Anyway, I had asked the researchers about whether or not that route was generally available to researchers.  I understood that it was not and now I stand corrected.  Researchers can contact VAIDR and think about getting DOD data.  I would suggest that anybody interested in pursuing that, contact the VIReC help desk and we’ll get you some more information about it.  So I’m sorry, Maria, that I passed on the wrong information to you, but it’s good to have that corrected now.  And do contact us, VIReC, if you want to find out more information about working with DOD data.  

Here is another; Maria, I’m reading these, so it kind of — it’s hard — oh, okay.  “Does VIReC” — this will really be our question — “have plans to develop a database containing best race ethnicity derived from multiple sources similar to how it has been provided for the best date-of-death?”  That is something that, Maria, I will answer but I’ll answer by saying I’m going to get a VIReC official answer and provide it [Decider] to everybody.  Obviously, the Vital Status File does have race in it.  I do not know myself whether or not there was a process of going through best race data.  But I can find that out from the people who were involved with it and get an answer out to this audience.

Is there any data on race ethnicity of non-responders.

Maria Mor:  I’m not quite sure what’s meant by that question.  I do know that there are other surveys available.  For example, and I’m sure all of the terminology has changed but I’m thinking that OQP has the SHEP survey that has self-reported data.  And so it is possible that there are other VA data sources that contain information from non-responders.  But in general, I think that if we have administrative data that’s available, it will be in there, in the database that we have.

Moderator:  Here is another question.  “At some point will VA clarify the concept of primary language?”  This person starts to write that it could mean various things.  But the comment isn’t finished.  “Do you know anything about what you are meaning by “primary language” in this new equity effort?”

Maria Mor:  So I think that, you know, what’s happening is that I think that there is a lot more interest in really trying to identify some of the disparities and reasons for disparities.  I think that in terms of what the VA uses to define primary language, my suspicion is that we’re going to follow agencies and other guidelines and that that terminology and those questions and definitions will be given to us.  So I cannot tell you for sure that I know that, again, when we come back to say something like the SHEP survey, that there is a language in there about what is the primary language that is spoken at home?  But, again, primary language could be multiple things; it could be the first language somebody learned, it could be the language they predominantly speak, the language they predominantly speak at home.  And I think all of those are important issues and they will have to be addressed before, you know, collection of that type of information is achieved.

Moderator:  Thank you.  Next question:  “How do you deal with lack of consistency of race data in the Med SAS Files for individual patients over time?”  You should answer in terms of your own approach.  I think, you know, people have different approaches.

Maria Mor:  I think people do have different approaches.  So one approach that we have taken is often times you have some sort of — you can have some kind of an [index] event or you can have, again, a primary site.  So, again, that inconsistency can happen over time, or it can happen between sites.  And so, typically, our data is defined in this way, in which case we would take the race, the most recent race that is on or immediately prior to this [index] event at the primary site for that individual.  And that’s how we would generally solve that delimma.

Moderator:  Okay.  Maria, here’s a question I don’t quite understand.  “Can you explain the scrambled SSN link?  Would this value be the same across all groups?  They’re totally different, race and the algorithm for scrambled SSN.”  

Maria Mor:  Right.  So, again, it depends on what the question means.  So when we are looking at a scrambled SSN, we know for a given individual we have the scrambled SSN, you know, across all of our data sources, and I don’t know if that’s the question that they are getting at.  So even when we obtain data from outside data sources, we still have that same scrambled SNN that is put in there.  I’m not sure if this is also maybe perhaps an issue with linking to the Vital Status File where we do have some discrepancies in other fields such as the gender or the date of birth that might be associated with a scrambled SSN.

Moderator:  Right.  I would also add that often you certainly try to merge data using a scrambled SSN but, in some cases, you need a real SNN in order to merge different databases.  The Medicare Data available from VIReC, for people interested in combining Medicare and VA data for race, is distributed with scrambled SSN.  So that, you know, which are uniform across all databases that use them.

Next question:  “I was wondering if there has been discussion about the new race ethnicity categories from the 2010 census, namely that Hispanics don’t identify as white or black and consider Hispanic/Latino as a race.  Also, has there been any discussion about matching race prevalence rates with larger EPI-studies like the NHIS as a means of capturing how inaccurate our race estimates are?”  I can start over with that, Maria, one sentence at a time.

Maria Mor:  Well, I think I caught what was there.  So, you know, I personally would not  be involved in changing the data collection methods that we have.  So I cannot say anything about that.  What I will say is that when you look at the data, it is true that there are Hispanics who identify as being Hispanic and they do not identify with a separate racial category.  And you will see this in their existing data that they will specify that they’re Hispanic, but they will leave the racial category as unknown  or they will not answer this question.  So you do see that.  And that’s one way you can get at the information  with our new data construct that we have.  With this historic data, they would have had to select Hispanic White or Hispanic Black.  

The second question had to do with comparing to the census.  I personally have not done that.  I know that we have talked about that a little bit in the past.  Again, you’re going to have some issues there that I think are the racial composition of our veterans is not the same as the general population.  And I don’t know how the racial composition of veterans who actually utilize VA services compares to veterans in general.  I do know that there is information from the census on veteran status and I don’t know how specific they are that would allow you to identify veterans who actually use the VA so that you would have a comparable group that you could really look at.  We have, you know, upon occasion, looked globally at some of these data and without being able to really drill down to the appropriate population, that comparison was not useful for us.

Moderator:  Okay.  Next question:  “Are there any plans to potentially see if state Medicaid race ethnicity data could possibly be used as piloting in one or two Western states to see if there is opportunity to get those usable rates up?”

Maria Mor:  So, again, this is not a question that I know about and I also don’t know to what extent.  You know, when we’re saying Medicare, it was my understanding that the data that we have is CMS, is combined Medicare/Medicaid data.  I don’t know how much Medicaid data is available to us already within the VA as any other...

Moderator:  Okay, Maria, the study that was done at VIReC that you reported on was Medicare data.  VIReC now has Medicaid data available for VA researchers.

Maria Mor:  Okay.

Moderator:  I think I personally have worked on studies with Medicare data, never Medicaid and I don’t think we have a lot of experience, well, I mean, I speak for myself but I don’t think a lot of people have used the Medicaid data to date.  But it is available and what it does for race, you know, we may find out. 

Okay, another question — we’re getting close to the top of the hour, Maria, do you have another few minutes for a few more questions?

Maria Mor:  Yeah, if there’s anything I can answer.

Moderator:  Okay.  And Heidi, is that okay?

Heidi:  Yep, we’re good for a few more minutes, thank you.

Moderator:  Okay, just about two minutes.  The race data field in the VHA Vital Status File is as follows:  “Race identified in the 2005 and 2006 and subsequent years’ data from the Medicare Vital Status File for the SSN...”  Oh, sorry.  Sorry, I’m reading this and I didn’t realize — Dr. Heinz has typed this in to provide information to the audience, so let me read it again.  “The race data field in the VHA Vital Status File is as follows,” and this is quote, “race identified in the 2005 and 2006 and subsequent years’ data from the Medicare Vital Status file for the scrambled SSN.  So that would suggest it is not — there is not an algorithm being used for best race data value, but it’s coming directly from those two years and subsequent years.”  So that’s in answer to the question previously about, “does the Vital Status File have a best race the way it provides best death dates?”

Okay, one last question and then we’ll stop.  “I think patients also have difficulty answering questions when they are of mixed races, i.e., Latino father and white mother.  Is there going to be a recommendation for clerks to identify, or something in writing on EZ 10-10 Form?”  Well, it would seem to me you’re invited to indicate more than one race?

Maria Mor:  Yeah, the way the form is set up now, they can specify that in terms of whether the instructions are clear, that may be another issue.  One thing that I will add is that when, you know, our folks have gone and observed the data collection process with the clerks.  The clerks don’t actually have the questions in front of them.  When you fill out the 10-10 EZ, you have those questions in front of you.  When the clerks get the information from the patient, all they have are those response categories, and so there may be some difficulties in asking those questions.  So, really, the 10-10 EZ is set up better than, I think, the clerks are, at least for us locally.  But the forms are set up so that that information could be collected as long as the instructions are clear to the patient.

Moderator:  Do you think clerks in your Pittsburgh clinics know that there’s more than one race that can be indicated?

Maria Mor:  Oh, I know, because I know it’s in there.

Moderator:  Okay.

Maria Mor:  But my suspicion is that it’s not so much an issue of recording the data as I think it’s more a question of they’re (and this probably adds to their discomfort to asking the questions)  if they’re not entirely sure how to ask the question, and if they had that kind of instruction or question in front of them, that might also ease the process as well.  And I’m not even sure if they make it clear to, you know, veterans when they ask a question, that they can select more than one category.  I can’t speak to that.

Moderator:  Okay.  We definitely have gone beyond the hour.  I want to thank Dr. Mor very much for a lovely presentation.  I want to let the audience know that the next VIReC Database and Methods Seminar will be on Monday, June 4, and that seminar will be on the Corporate Data Warehouse, in particular, the Vital Sign data.  So thank you all for joining us today.  Thank you Dr. Mor.  Thank you [Decider] and please fill out the questionnaire when you log out.  Thank you.  Bye.

**[End of Recording]**