**Transcript of Cyberseminar**

**VIReC’s Database and Methods Series**

**Assessing Race and Ethnicity**

**Presenter:  Maria Mor, PhD**

**May 6, 2013**

**Assessing Race and Ethnicity**

**May 6, 2013**

*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:I would like to welcome everyone to VIReC’s Database and Methods 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, Associate Director for the Biostatics and Informatics Core at the Center for Health Equity Research and Promotion in the VA Pittsburg Healthcare System. Questions will be monitored during the talk, and the Q&A portion of GoTo Webinar, and will be presented to Dr. Mor at the end of the session. A brief evaluation questionnaire will pop up when you close GoTo Webinar. We would appreciate if you would take a few moments to complete it.

I am pleased to welcome today’s speaker, Dr. Maria Mor.

Dr. Maria Mor: Thank you for joining the session on Assessing Race and Ethnicity. And, everything works until I am presenter and now I have to figure out how to get my, here we go. 

Today, I am going to start with a brief introduction of the topic. I discuss race and ethnicity in the VA data, race and ethnicity in Medicare data, the quality of VA race and ethnicity data, followed by some recommendations and where to go for more help. First of all, I do have a question to ask the audience. I would like to know, have you ever used VA race and ethnicity data in the VA?

Moderator:And, we will give everyone just a few seconds to respond here. We are at about 65% or so, so if the rest of you could click on one or the other and we will show those results up on the screen.

And, there are your results, Maria.

Dr. Maria Mor: All right. So, I see that about, almost two-thirds of you have used these data in VA before, and about, a little over a third this is new for you. All right. Am I, am I still presenter, or...

Moderator:And, Maria, I am going to give you presenter access back right here...

Dr. Maria Mor:Oh, okay.

Unidentified Female:...but, also I wanted to mention we did get a comment in that you are a little hard to hear. I am not sure if you are on a speaker phone, if you could pull that closer. If you are on a handset, if you could try speaking up just a little bit.

Dr. Maria Mor:Okay. All right. Thanks a lot. I will try speaking up louder, too. All right.

Moderator:Thank you.

Dr. Maria Mor:All right. I will start with the introduction. So, racial and ethnic disparities in health and healthcare are well documented and persistent in the U.S. The root causes and solutions to these disparities are not well understood, and while quality is improving in general, access and disparities are not improving. So, what that means is that outcomes are improving for everyone, but these differences between racial and ethnic minorities and white are not decreasing. 

Racial and ethnic disparities also exist in the VA where financial barriers to receiving care are minimized. Potential contributors do include patient factors, provider decision-making and the characteristics of the VA facilities themselves. In addition, I would like to note that because the barriers that we have to care within VA are different from what we see outside the VA. It is important that we do conduct disparity research within VA, because we do not necessarily look the same. There are some cases where disparities are reduced, and we may have different disparities than what are seen outside VA. So, in general, we do more research to detect, understand and address disparities and health and healthcare.

I am just having a little issue with advancing my screen. There we go.

Accurate race and ethnicity data are essential to disparity research and research on clinical factors associated with race and ethnicity. Within VA, we do have some problems with the race and ethnicity data that are available to us. These include incompleteness of the data – the missing data can be a very large problem for us – and accuracies in the data and inconsistencies over time. Unlike some other data sources that we have within VA, we may have multiple opportunities to assess race and ethnicity for individuals, and that can give rise to inconsistencies.

So, now I am going to turn attention to race and ethnicity in VA data. The questions that we are asked are based on the VHA Handbook 1601A.01. We ask a question on ethnicity, the allowable responses are if someone is of Spanish, Hispanic or Latino origin, or conversely, not of Spanish, Hispanic or Latino origin. And, for the race categories, individuals are able to select as many options that are applicable of the allowable option of American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Other Pacific Islander, White, Unknown by Patient. And, in addition, there is an option for the person who is collecting the data to respond if the patient declined to answer the question, to refuse the question, there is a declined option.

Our current reporting method is a two-question format. Ethnicity is asked first, followed by race. The reason why ethnicity tends to be asked first is sometimes individuals of Hispanic origin consider themselves to identify racially as being Hispanic, and if that question is not asked first, they may not answer the race question. And, our data are to be corrected through self-report.

There are a number of race and ethnicity data collection standards that are relevant to us. First off, is the OMB Directive Revision No. 15. This is the directive that sets the standards for maintaining, collecting and presenting federal data on race and ethnicity, and it was implemented in DVA in fiscal year 2003. One thing to note about those standards – let us see if I can go back up – we have a total of five different racial categories that we are to collect. We can also include other additional racial categories that may be relevant to the research that we are conducting. However, we need to make sure that they can map back into one of these five categories.

The Joint Commission also has standards related to the collection of the patient demographic data, such as race and ethnicity, and this also forms an element for a performance measure. 

In addition, the Affordable Care Act also has new data collection standard for race, ethnicity, primary language, and sex.

Before I started talking about the data itself, I wanted to just show you a brief overview of what does the racial compensation within VA look like, just to give you an idea of who are veterans are. Almost half a percent of the veterans are American Indian or Alaska Native, about 1% are Asian, 17% Black or African American, about 1% Native Hawaiian or Other Pacific Islander, 80% white, and less than one percent are multi-racial. Even though multi-racial does constitute a small percentage of our veterans, I think it is very important to note that for some of our minorities, they do tend to report as being multi-racial. For example, we see .6% of our American Indian or Alaskan Native – sorry, .6% of our veterans are American Indian or Alaskan Native. If we look among those who are multi-racial, our most frequently occurring category is White, American Indian, or Alaskan Native. So, even though this is a small minority within the VA, we see that a large, very significant portion of these individuals report themselves as multi-racial. So, if you are looking or even at Native Hawaiian or Other Pacific Islanders, again, we a significant portion of these individuals are multi-racial. So, if you have a specific interest in one of these other minority groups, you will have to take into consideration that they do tend to report to be multi-racial and not to discount that information.

There have been some initiatives that are interested in looking at the equality within VA, and improving the data that we have for assessing equality. One of these was in a VHA Health Equity Workgroup that was chartered in 2011 by Dr. Jesse. The goal of this workgroup was to advise leadership on how to coordinate VHA components to achieve equity in veteran healthcare. VHA did accept five recommendations, rather recommendations along five dimensions. And, if you notice here in red this included data, research and evaluation, which are particularly relevant to our topic for today. 

So, these recommendations that related to data, research and evaluation include the universal standard collection of self-reported data on race, ethnicity, primary language, rural/urban residence and sex. Also, being able to identify unrecognized and emerging vulnerable patient populations. So, it is important not only to address the vulnerable populations that we know about, but, to be able to recognize newly emergent populations. And, to assess and report differences stratified by vulnerable groups for outcome measures such as quality of care, patient satisfaction and access to care. In addition, one of the recommendations was to harmonize standards for vulnerable populations with other agencies, such as Health and Human Services and the Affordable Care Act data standards.

In addition, there is also a newly formed VHA Office of Health Equity. The mission for the VHA Office of Health Equity is championing efforts to address health disparities, cultural competency and communications, healthcare outcomes including detecting, understanding and measuring health disparities in recognized and emerging vulnerable veteran populations to achieve current and accurate data collection for all vulnerable groups. And, to achieve complete data capture of clinical process and outcome measure stratified by race/ethnicity and other characteristics associated with the risk for health disparities. So, again, race and ethnicity data and being able to assess disparities are, in fact, issues that are of great concern to VA.

So, how are race and ethnicity data acquired into our VHA data? Data should be acquired at the time of enrollment. Our patients complete a VA Form 10-10EZ, which is an Application for Health Benefits. This form can be completed online, through a paper form, or by interview. The data that are contained on that form should contain information that is patient self-report, or from their proxy. If the data are not captured at the time of enrollment with an enrollment coordinator, then they can also be attained when the veteran seeks care at the VA and we can attain it through registration, pre-registration at the outpatient clinic and at the time of a hospital admission, or an outpatient visit or pre-registration. The data that are collected are then entered into the VistA electronic files.

Historically, race and ethnicity were captured a little bit differently. The method of ascertainment was uncertain. Currently, we do keep information on the source of the data, but, previously, we did not, and the data are assumed to be observer reported. So, that means if there is a clerk asking a veteran, checking in a veteran at registration, there is that question in front of them about the veteran’s race and ethnicity. It was assumed that the clerk looked at the veteran, looked at the question and they could have answered that question based on what they observed of the veteran rather than asking directly. It may also be true that they did also ask. There was no option for multiple race reporting, and there was a single question that captured both race and ethnicity. The allowable response options were Hispanic/White, Hispanic/Black, American Indian, Black, Asian, and White. So, we only captured Hispanic ethnicity if the veteran reported themselves as being White or Black.

So, if you mentioned at enrollment, the Form 10-10EZ was filled out by the patient that contained the information on their race and ethnicity. The data were then entered into the VistA, the local VistA data file, and the race information and the patient information are contained in sub-files within VistA. The data from the local VistA were then transmitted with encounter to the Austin Information Technology Center. So, now we have the data at a national level and they are stored in a National Patient Care Database. The data were then extracted from the National Patient Care Database in order to create the Medical SAS, or the MedSAS datasets that many of us use for research. So, these files then contained standardized version in a standardized format that originally were entered into the local VistA files.

Within the clinical setting, if race and ethnicity are missing, then they should be obtained during pre-registration, so the clerk should be then prompted to ask those questions of the veteran. The race and ethnicity are supposed to be gathered directly from the patient or again from the proxy. So, for example, if they come in with their spouse or a child or another caregiver, at the time of hospital admission or clinic registration. And, again, the data are entered locally into the local VistA system where they are then transferred as we described previously, into the national data files. There is a separate VistA field for the method of data collection. I will note that in at least our observations at our site, although there is a way to enter that date, it does tend to default to being self-report. So, if you look at the data that we nationally, the vast majority, like for example, 99% or so, will say that the data are self-report. I do not know that clerks in general tend to change the method of data collection, although there is an ability to do so.

All right. I have another audience poll. I would like to know what sources of VA race and ethnicity data people have used, or that you have never used it. If you have used data from the MedSAS files, Medicare, VistA, CDW, or some other regional-type warehouses, or other VA data sources.

Moderator:And, you can click on all that apply here, so you can click on more than one.

Dr. Maria Mor: All right. So, what I am seeing is that there is some variability. Again, about a third of you have never used the data, about a third have used from SAS, 12% have used the Medicare data, 43% have used VistA, CDW, or other related data sources, and then other sources other than what I have listed is about 18%.

All right. So, I am going to talk a little bit about the sources of the data in VA. In the Medical SAS datasets we have various, variable names that contain that information. The first variable name that is listed here is race. This is the original, historic race value that contained race and ethnicity in a single variable. We have that information stored in the inpatient file beginning in fiscal year 1970 through the present. And, the outpatient files, the visit files and the event files from 1997 through 1998, again, through the present. So, I will let you, caution you that even though this historic value is still contained in our inpatient and outpatient files, these are not the variable where we are updating race and ethnicity for our veterans. So, typically, you would not want to use these variables even though they are available.

Beginning in fiscal year 2003 was when we had the transition to the new collection standards for race and ethnicity. Beginning in fiscal year 2003, these new variables are contained in the inpatient files, and in fiscal year 2004 for the outpatient files. Because we now allow the veterans to select from more than one race, there are multiple race variable, Race 1 through Race 7, which contain the various options the veterans selected. There can be up to seven options, because the veterans could select from five different races, and we also have the option of Unknown and Declined to Answer. I do not believe anybody has ever selected even six or seven of these, so the face that we have six in the inpatient and seven in the outpatient does not mean that we are missing data from the inpatient. It is just a difference in how the data are stored. And, then the variable Ethnic contains the patient’s ethnicity.

For these new variables, each of these variables is a two-character field. The first character gives us information about the patient’s race or ethnicity and the second character stores the information about how the data were collected. And, as I already noted, the vast majority of these will say that they are, I guess the technical term they use here is self-identification, rather than self-report. And, if you want the specific details of what those different values are, that is contained in the VIReC documentation for the various files.

So, another source that I see is going to be more closely aligned with the data that people said that they have used is the Corporate Data Warehouse. The Corporate Data Warehouse is a national repository of data from the VistA patient file with race and ethnicity data from October, 1999 an onward. The CDW contains these data that are extracted from VistA. The data that we have on race and ethnicity and the structure of the data are going to be more closely aligned with the local VistA data, rather than the standardized data options, response options, and data structure that we see in the MedSAS files. These data contain one demographic record for each VA station a veteran has visited. It contains standard and nonstandard race values. This links back to the fact that the values that are contained across the local sites are not all standardized. There is not a huge number of them, but they are not meeting the same five different categories, racial categories that we have in the national data. They are stored in a view called PatSub.PatientRace, and there is documentation called the Best Practices Guide Race Data. I have provided a link here, this is an intranet link, so you can access this within VA, but you would not be able to access this link from outside the VA. This Best Practices Guide contains information on the CDW data, and because this structure more closely mirrors the VistA data, there are elements of this practice guide which would then also be very useful if you are using other data sources from VistA or data that are pulled from VistA and contain those same response options.

There a number of nonstandard race values in the CDW. There is a total of thirty-one nonstandard race values, but twenty-six of these can be mapped to four of the standard values that are currently being collected within VA. I have some examples of these nonstandard race values. These are text fields, so we can see that even though they are not standard, the meaning of these fields is quite clear. So, we see Amer Indian or Alaskan Native, American Indian, American Indian/Alaskan Native. These all clearly relate to our standard group of American Indian or Alaska Native. We see something similar with the Black. I would like to point out, we see that we have a combination of race and ethnicity in some of our nonstandard values. For example, Black, Non Hispanic, and Hispanic Black. These come from the older data collection standards in which race and ethnicity were captured together. But, again, the meaning of these are clear and we know that these map to our Black or African American standard that we have. We see similar patterns with White, so, just for example. So, even though these values are nonstandard, it’s fairly simple to map them to standard values. 

There are however, five values that are not mapped to standard values. These include three values that are a combination of Asian and Pacific Islander. Under the old, in the historic data collection method, these were all in one category. Asian also contained the Native Hawaiian and Pacific Islander group. So, if we have an older data collection, we are not going to be able to identify whether that veteran is really Asian or the Native Hawaiian Pacific Islander. If your research, you are going to end up grouping those two groups together. You may still then be able to map from these particular values. Mexican American and Unknown. So, overall, about, almost 5% of the data values fall into one of these five categories that cannot be mapped.

So, in the CDW, we have about 8.3 million unique patient records with standard race values. Another 2.3 million patient records have nonstandard race values, but they can be mapped to the standard values. About 90% of those link to patient records that were entered prior to fiscal year 2003 and those new data standards that were implemented. These patients tend to be older than those with standard race values collected, and again, this is not surprising since the data have been collected over ten years ago. The CDW data can contain multiple records per patient if the patient visited more than one facility, and then in this Best Practices Guide for Race Data you will see that there are sample queries for the CDW that will show you how to go from the nonstandard values contained in the CDW to the standardized values. So, that can be very useful when working with these data.

So, other sources of race data in the VA that are commonly used. So, I typically think of the Medicare data as being very commonly used, although it was not selected by a large percentage of you. Within VA, we have information in the VA Vital Status File. The purpose of this file is to help us find death dates from multiple VA and non-VA data sources, including Medicare. With this file, in addition to the death data from Medicare, we also have the race data from Medicare. In order to use this file, you would have to know a little bit about the file structure. For the Vital Status File, there are two different files. There is a master file that contains one record for each Social Security number, date of birth, and gender combination that is found in the VA data. So, some Social Security numbers have more than one record, if there is an inconsistency in the date of birth and the gender for that individual. 

There is also a mini record that contains one record for each Social Security number that utilizes an algorithm to identify the best date of birth, gender and date of death. It also contains fewer data elements than the master file, which also relates to race. Race is only in the master file, it is not in the mini file. Race is also contained in the Decision Support System Data Extracts. So, if you are using a DSS data, there is race data in there. The original source of the race data, again, is very similar to the data that we have in the MedSAS files, but there are some differences in how it is stored. I understand that the source of the race information is not connected to the variables the way we have in the other MedSAS files, and those who select more than one race are handled differently.

So, we need to talk a little bit about race and ethnicity in Medicare data. So, Medicare data are, they are a potentially useful source to obtain race information for veterans. Obviously, for those who utilize Medicare. Veterans who use Medicare are going to look different from those who do not. Number one, those who are aged 65 and older are far more likely to be Medicare enrollees. About 95% of those aged 65 and older have a Medicare data available. Among those who are under the age of 65, the reasons why they may be enrolled in Medicare are related to being disabled, or if they have end stage renal disease, then their care can also be covered through Medicare. So, again, those under the age of 65 are less likely to have Medicare data available. About 20% of the VA patients do, and they are not going to necessarily look like those who do not have Medicare available, because they are more likely to be disabled.

The data in Medicare are derived primarily from the Social Security Administration. They are obtained at the time of the application for the Social Security number, or a replacement card. The data are generally considered to be self-reported or from a family member, and there is some important differences between the VA data and the Medicare data. In particular, Hispanic is an option for a race category. Ethnicity is not captured separately, and there is not an option to select more than one race. Until 1980, there were only four categories allowed for the Medicare data. These included White, Black, Other, and Unknown. 

In 1980 Other was replaced by the options for Asian, an Asian American or Pacific Islander, Hispanic, American Indian or Alaskan Native. This does have important implications for the quality of the Medicare race data. Information on most enrollees, those who obtained their Social Security number prior to 1980 can be limited to the original four categories. However, initiatives have been undertaken to improve the quality of the race and ethnicity data. In particular, there have been periodic updates on American Indians and Alaskan Natives from the Indian Health Service and in 1997 there was a survey of enrollees who are classified as Other, Unknown, or with a Spanish sir name, requesting their race and ethnicity through self-report. I don’t know the exact numbers on the survey, but it is my understanding it was fairly successful and we look at our data most of our veterans did obtain their Social Security numbers prior to 1980, and we do have a lot of data that uses these new categories, rather than being classified as Other. 

So, next I am going to switch over to discussing the quality of the VA race and ethnicity data. So, the first issue that we have is with the completeness of the race data. Depending on which data you are using and how far back you are going, we do have a substantial portion of patients who do not have a useable race value, meaning a value that is not missing or unknown or declined in the VA datasets. So, among encounters that we see prior to fiscal year 2003 when the data collection standards changed, we are looking at around a little over 55% of the encounters have useable race data. Beginning with the new data collection standards those old values did not translate over. The information on race and ethnicity had to be corrected anew, so originally during that transition the amount of useable race did decrease slightly, but we can see that the data have increased in terms of completeness so that now, in fiscal year 2012, 85% of encounters have useable race data. 

The data for ethnicity look very similar. In fiscal year 2012 we did have 90% of visits that have a useable ethnicity value in the inpatient and outpatient datasets. So, we are slightly more likely to have the information on ethnicity compared to race, but, in general, if people answer one of those two questions they will answer both. However, there is a particular issue with the completeness of ethnicity data in the VA Medical SAS inpatient datasets. It is low, it is about 32% for fiscal year 2012, and we see for about half of inpatient facilities have very little data on ethnicity. About half of them are missing data for at least 98% of inpatient records, and then more than a third of facilities have blank data for all inpatient records. It does not mean that the facility is not collecting this information. However, it was not being translated up to the inpatient datasets. So, if you are interested in ethnicity in an inpatient data file, you are going to have to look in outpatient data files to obtain that information, unless you just happen to be at one of these facilities only where the data are contained in the inpatient data files. However, most veterans who use inpatient services, the vast majority of them use outpatient services so you expect that you would be able to find outpatient data for those veterans. And, then, just in general, looking at fiscal year 2012, about 6% of our patients were Hispanic. So, it is not as high as the number, the percent who are Black, but this is a higher number than we are seeing for the other non-African American minorities within VA.

Now I am going to discuss a study that looks specifically at the use of Medicare and Department of Defense data for improving VA race data quality. This was conducted by Kevin Stroupe and colleagues, and there are two primary aims. One was to estimate the extent to which missing useable race data and the VA Medical SAS files can be reduced by using these two non-VA data sources. The second aim was to evaluate the agreements between the VA self-reported race data in the Medical SAS files and the true data sources. So, this is very important. If you want to use data from other data sources, you want to have an idea of whether or not the data match up, and also what gave us some information about the quality of the data that we have within VA.

The patient cohort is a 10% representative sample of VA patients who obtained services during fiscal years 2004 to 2005. So, during this time period we still are seeing a substantial amount of missing race and ethnicity data and the total sample size was almost 600,000 patients. The Medicare race data were obtained from the Medicare Vital Status file and the Department of Defense race data using a VA/Department of Defense data sharing agreement, and those data do contain self-reported race and ethnicity data from service members. The data were obtained for individuals who are less than 65 years of age. I believe this due to the time in which the DoD data were available. The data just simply were not available for older veterans.

So, they looked at the difference in some of the characteristics between those who had useable race data in VA versus those who did not. Those with useable race data are slightly less likely to be 65 years of age and older. In this sample, they are more likely to be male. One thing to know is if you are doing research and you are looking at women, there are records in our administration data for people who are not veterans. They are disproportionately female. If you are doing research that is restricted to veterans, you will not see this discrepancy between those who do and do not have useable race. But, these non-veterans which are more likely to be female than our general veteran population also tend not to have the demographic information collected. So, for example, if you have employees that are seeking care at employee health, they may not ask them the demographic information, they are more liked to be female than our veteran population. So, you just need to keep that in mind if you do do research with veterans and women.

No differences by marital status. There are some geographic differences. These may be related to geography or they may simply have to do with the locations of visits and facilities that have different processes and different outcomes in terms of collecting the race and ethnicity data. We have more useable data from the south and less useable data from the west. 

Adding the Medicare data did allow for a substantial improvement in the amount of missing data. Overall about half of the veterans in the sample have VA useable race. About a quarter then have data that could be obtained from Medicare, leaving a quarter who had missing race. However, these patterns are very different by age. Among the younger veterans, only 9% had useable race for Medicare, still leaving us with over 40% who did not have race in either the VA or Medicare. Among those over the age of 65, almost all of those matched up with Medicare, leaving us with only about 2% who are missing race. So, if you are looking and the cohort is over the age of 65 Medicare is an excellent source of being able to match up your remaining patients who may be missing race and ethnicity. 

Within the Department of Defense data, we looked for those who are not elderly. So, in a younger cohort we see that we are able to match up to the Department of Defense data for more individuals than for Medicare. So, about twice as many, 19%, had useable race in the Department of Defense data, and if we use both sources we are able to increase that to 26%, leaving about a quarter of the veterans who are missing race information from all three data sources.

The second aim then actually compared the consistency of the data across all three data sources. In order to do that, they had to use, utilize categories for race that could be coded consistently across all three groups. In particular, Asian, Pacific Islander and Other were combined into one category, because that was the only way that was basically the lowest common denominator you had in terms of grouping all of those consistently across three sources. 

Comparing the VA self-reported data and the Medicare data showed that there is large agreement among those who are White. Those who are White, 99% chance of being coded White in Medicare, 96 % chance of being coded Black, if you are Black in the VA data. However, when you look at the other minorities, the agreement is not as good. More than half of North American Natives veterans are coded as being White, only about a third are coded as North American Native in the Medicare data. About half of those who are Asian, Pacific Islander or Other are coded as White in the Medicare data and almost half are coded as Asian Pacific Islander or Other in the Medicare data. In addition, if you are interested in Hispanic ethnicity, what was found is about 25% of those who self-reported as Hispanic in VA are coded as Hispanic in Medicare. The remaining 75% are coded based on their race, with most being coded as White. So, that means we can’t really use the Medicare data to help us to identify those who are Hispanic, because many of them are selecting a racial category rather than Hispanic ethnicity.

We see a somewhat similar pattern comparing the VA data to the Department of Defense data. In particular, the agreement for Whites, again, is good, 93%, for Black, 95%. We again see about half of North American Natives are coded as White. About 40% are coded as American Indian in the Department of Defense data, and for those who self-report as Asian, Pacific Islander or Other in the VA race data, we see almost a quarter of them are coded as White in the Department of Defense date, but nearly two-thirds are coded as belonging to this Asian, Pacific Islander or Other category in the Department of Defense data.

So, the conclusions from the study were that supplementing VA with Medicare and DoD data can substantially improve the race data completeness that we have for our veterans. However, more study is needed to understand the poor rates of agreement between the VA data sources and the external data sources in identifying non-African American minority individuals.

And, lastly, I just wanted to briefly mention a quality improvement initiative that was undertaken  here in Pittsburgh. It has a slightly different feel to it. This was a funded collaboration between the Center for Health Equity Research and Promotion and the Veterans Engineering Resource Center. We wanted to understand a little bit more about why the data are missing, and the particular data I am going to show you here was based on a survey that we did with 173 patients. These were patients with missing or declined race, so we though these were people who are most likely to be uncomfortable with providing information on race. They were seen at the VA Pittsburgh Healthcare System, either at one of our primary sites, or at one of the cbox [PH], and we surveyed them by telephone regarding their comfort with being asked to provide race and ethnicity, address and telephone, and insurance when coming to the VA. 

The hypothesis is that veterans are uncomfortable with answering the questions about race and ethnicity; clerks are therefore uncomfortable about asking those questions. So, we wanted to assess amongst this group that we thought might have a higher level of discomfort. What did that actually look compared to what other data elements that are collected at the same time? And, also we had a hypothesis if there is sensitivity in terms of answering this question, would they prefer to provide that information to a clerk, or to a more neutral party, in this case a computerized kiosk that have been used extensively at our primary site in Pittsburgh. So, we thought if they had discomfort they might rather enter that information through the computer rather than in person.

For our results, we found that actually, even among this group that we thought would have greater discomfort, they were completely comfortable for the most part in answering questions about race and ethnicity. In particular, the percentage who are completely comfortable answering this question, the race and ethnicity, is virtually identical to their comfort in answering questions about insurance with over 90% of these veterans that we surveyed being completely or somewhat comfortable in providing information on race and ethnicity. 

When we looked at the smaller group of veterans that we interviewed, 48 of them, who utilized our primary site that has the kiosk in place. Again, we thought maybe they would be more comfortable entering this information at the kiosk. What we found is that most veterans do not prefer to use a kiosk. Almost half prefer to go to a clerk, and almost half have no preference between a clerk and the kiosk. But, actually among those who prefer to use the kiosk, they are actually less likely to prefer to use the kiosk for the race and ethnicity data.

So, in conclusion, what we saw was that veterans are, in general, comfortable asking these questions and they are comfortable asking these questions in person and they do not need a more neutral mechanism in order to do so.

So, what are the general recommendations? Probably the most readily available source of data that we can obtain to help augment the VA data that we have on race is from Medicare. The data can be obtained from the VA Vital Status file. In addition, if you go through the application process, we have other data that are available from VIReC and that would include race and ethnicity as well. If you use the VA Vital Status file you want to be sure that you match on the date of birth and gender, in addition to the scrambled SSN. Use of the Medicare data can substantially help reduce the problem of the missing data in the VA studies that use administrative data. If you are augmenting with external data sources, we do know that we can accurately define the Black and African American group similarly between the data sources. However, we cannot say the same for those who are of other minority races. And, so, in that instance, we would suggest that you just simply categorize them as Other, because it is going to be hard to come to up with finer categories that you can classify appropriately using other data sources.

So, as we saw for the VA North American Natives or Hispanics, and also for the Native Hawaiian Pacific Islander group they are frequently misclassified in the Medicare data or in the case of Hispanics they are classified by race rather than ethnicity. However, the Medicare White and African American categories both have high predictive values for VA race. So, we cannot use the Medicare data to help identify ethnicity, and you can also consider other supplementary data sources, such as the Department of Defense and other surveys. And, I did see that there do seem to be a considerable number of you that are utilizing other VA data.

And, where you can go for more help is VIReC through the webpage. You can get access to information on the data sources, how to access the data, documentation on some of the VA datasets with especially good documentation on the Medical SAS datasets. The information on the HSRData Listserv is there, how to join the Listserv. It contains more than 500 members who can answer and respond to questions. And, the first thing that you want to do if you are using Listserv for the first time, is go to the archive – it is available on the Internet – and check to see if somebody has already answered the question that you have. And, then you can contact the VIReC Help Desk directly. You can talk to the staff to see if they can answer your question or direct you, and you can contact them by email or by telephone. And, there a number of very small references here. So, if you get the handouts you will be able to see that at a font that you actually view.

So, I am going to open it up now at this point to questions.

Moderator:Thank you, Maria. We do have a few questions. Here is the first. If race was entered prior to the change for separating ethnicity from race, is it changed when the vet comes in for service?

Dr. Maria Mor:Yes. So, if say the last time they had the race and ethnicity question answered was back before fiscal year 2003, then those new race and ethnicity data variables would be blank and when the veteran comes in, the clerk should be asking them those questions. Now, they do not always ask the questions, but they should be asking those questions and the data should be newly ascertained based on the new data collection standards.

Moderator:Great. Thank you for that. Do you know the percentage of Unknowns and race and ethnicity from MedSAS files for both inpatient and outpatient. This is, 10B [PH] says...

Dr. Maria Mor:I...

Moderator:I am sorry. Go ahead.

Dr. Maria Mor: Sorry. Oh, no, no, you can finish the question. Sorry, I thought it was over.

Moderator:This at 10B [PH] says they got 40% unknown from 1998 to 2012, and they are asking if this is correct.

Dr. Maria Mor:So, that could be correct. So, I guess I – good thing I listed to the whole question. I thought it was a question about which, what number actually categorized as Unknown, which actually tends to be fairly low. The number that are Missing could be higher. But, as you saw, if you are going back as far as 1998, then it would not be surprising that you are missing data on about 40% of your veterans. Now, what I do not know is if you are going all the way up to fiscal year ’12, where you are starting to see about an 85% who have useable race value, depending on how your cohort is defined and all of that. You might expect something higher that you would have known in 60%, but, I cannot say that that 40% Unknown, that could be perfectly reasonable.

Moderator:Okay. On slide 30, is declining to answer considered a useable race value? In a recent query of MedSAS data for a cohort, I found 58% did not indicate a race value other than Not Disclosed. Is there a better source of race data?

Dr. Maria Mor:Can you repeat the very beginning of that question? 

Moderator:Sure. On slide 30.

Dr. Maria Mor:Oh, slide 30, okay. That is the part. Okay. Let me go to slide 30.

Moderator:Okay.

Dr. Maria Mor:Okay.

Moderator:Is Declined to Answer considered a useable race value?

Dr. Maria Mor:No. Declined is not considered a useable race value. So, in most of our values, and I do not have this right in front of me, but, I think most of our values that are missing are purely missing. I think the number that are explicitly coded as Missing, I mean as Unknown or Declined is low. But, that would not be considered useable, even though it is going to prompt the clerk not to ask for the information again, because they have a response from the patient. It does not help us understand what their race values are.

Moderator:And, they have a follow-up question to that. Do you have a suggestion for better places to get race from MedSAS? Should I be looking at the Vital Status files?

Dr. Maria Mor:So, if you are just simply trying to obtain more information, the Vital Status files, again, it is going to depend on your patient population. If you are looking at a population that is older, then you are going to have very good luck going to the Vital Status files and getting the Medicare data. If you are looking predominantly a younger population, that is not going to help you as much. Now, you could then try to get the data from the Department of Defense. I have never done that, but I understand from when I gave the presentation last year that it is possible to go through that process to obtain the data.

Moderator:Okay. Do you have information about Medicare’s system for ensuring that the registration process more consistently asks participants to identify their race.

Dr. Maria Mor:You mean in terms of the Medicare process?

Moderator:That is what they are asking.

Dr. Maria Mor:That, I really do not know about. I presume that what that really is, sometimes I think when I have reapplied for my Social Security card when I got married, I know you fill out a form and I think all it is, is you have that form and they are presuming that the person that is asking for the card, or somebody in their immediate family is the one that is completing that form. And, probably, the person who is getting the form has to physically go in, if you are going into the office to get the card. But, I do not think there is, you know, a special process. It just happens to be something that is collected on the form at the time.

Moderator:Okay. Thank you. Is the Vital Status algorithm for determining best race available?

Dr. Maria Mor:Oh, sorry. The Vital Status algorithm is really, is designed to help you find the best date of birth, gender combination, along with the date of death. So, the main focus for the Vital Status is determining essentially vital status, whether or not the individual is living or not. And, so that is where the focus of that algorithm is. The only race data I believe that is contained in that file is the Medicare race and that is a single value that is going to come from the most recent Medicare data that are available. However, I will say that I believe that Medicare does, what we talked about today, was the self-reported or family-reported data that are collected from Medicare. It is my understanding that they do have an additional variable that they have gone through an algorithmic process to sort of improve upon that race data, and I think that may be available if you go through the process of obtaining the Medicare data from VIReC and not from the Vital Status File. But, I believe that there has been another Medicare data file that also contains that variable as well. I know it exists and I am pretty sure that we have used it from the data we have obtained from VIReC. And, you would have to look up and see what their algorithm is for that.

Moderator:Okay, great. Thank you. Can you provide more detail about differences between using the CDW patient race file versus the Vital Status file? How do the two compare and which is preferable?

Dr. Maria Mor:Okay. So, I am going to give you a caveat that I have never used the CDW data. I have used some of the local data before; usually when I view these data other people have obtained them for me. But, the sources of those data are very different. The Vital Status file is the Medicare data. The CDW data is going to contain the data that came from the local VistA. This is going to be similar, but not a standardized version of what we have in the MedSAS files. So, they are coming from completely different sources. The CDW data are coming from the VistA data that is collected through VA. The Vital Status file is coming through the race data that is collected through Medicare. It is also my understanding that the CDW data is going to be a little bit different from the data we see in the MedSAS files, because the CDW data should have your most recent, up-to-date race and ethnicity data that is available within VistA, whereas the MedSAS files are going to contain all those values. You are going to have that whole trail of values over time. So, if they are going to different facilities or changing their race and ethnicity values, you will be able to see that trail in the MedSAS files.

Moderator:Okay. Thank you so much.

Dr. Maria Mor:So, they are different sources. I do not know one is preferred over the other. They are just different.

Moderator:Are you aware of other review or major studies on health disparities in VA besides Saha 2008?

Dr. Maria Mor:Just in terms of health disparities? That is, I am a statistician, so I am really sort of focused on the data. But, if somebody is interested, if you send that question on, I know other people in our group that would be able to answer that question and get them other resources.

Moderator:Thank you. Do you have a standardized race ethnicity data, say for FY ’06 that can be accessed at least for comparison purposes? My concern is that each researcher can come up with their own set of complete data.

Dr. Maria Mor:So, do I personally have? There is really nothing that I would, I certainly do not have anything that I would, you know, kind of get out or disseminate. 

Moderator:Or, do you...

Dr. Maria Mor:So, I have at times sort of created race data files that contain as of that point in time, my best guess for race. Now, the way I approach it may be different from the way somebody else does. So, for example, what I tend to do is I will take the most recent value that I have. I have been informed in our conversations that we have had with clerks and others higher up in the process, that when the data are changed that should represent an improvement in the data. So, the idea being that what was there before was incorrect, and so the new value should be a correction on the old value. So, in general, I do tend to use the most recent value that is available and is relevant in terms of the cohort that I am creating. If I creating a cohort, say back in 2006, I am not going to go and look at the data from 2012 if I am not following them through to 2012. I do not know if that answered the question.

Moderator:Great. Thank you. Yeah. In Aim 2 on pages 37 and 38, how are Hispanics categorized?

Dr. Maria Mor:All right. So, for Aim 2, so in terms of the categorization here, I guess this is slide 37, it is not numbered, there is no space for the number. This is purely looking at race, so Hispanics are not categorized here. So, really where Hispanics come into play is within VA, we have both the race and ethnicity. So, if somebody self-reported as being Hispanic then most likely also self-reported a race. And, so that race value they self-reported is what is going to show up here for slides 37 and 38, and it is really only in slide 39 where we are going to look at the ethnicity of that patient in terms of what they did in this particular study, and compare that to Medicare.

Moderator:Great. Thank you so much, Maria. And, if there are any other questions, please be sure to contact the VIReC Help Desk at VIReC@va.gov. Our next session is scheduled for Monday, June 3, from 1 to 2 PM Eastern, and is entitled “Applying Comorbidity Measures Using VA and Medicare Data” presented by Dr. Denise Hynes.

Thank you to everyone.

Moderator:Thank you everyone for joining us today. We hope to see you at our next session, and just to reminder, when you leave today’s session you will be prompted with that feedback survey. If you could take just a few moments to fill that out, we definitely do read through all of your feedback. Thank you everyone for joining us today and we hope to see you at a future VIReC Database and Methods Seminar. Thank you.

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