**Transcript of Cyberseminar**

**VIReC Database and Methods Seminar**

**Examining Veterans’ Pharmacy Use with VA and Medicare Pharmacy Data**

**Presenter:  Kevin T. Stroupe, PhD**

**February 3, 2014**

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

Moderator:  Today’s presenter is Dr. Kevin Stroupe; he joins from Center for management of complex chronic care and the cooperative studies program coordinating center in 1999. And in addition he has an appointment as an associate professor at the Stritch School of Medicine, Loyola University, Chicago. Kevin is currently at -- affiliated with VIReC and we are now going to turn things over to you Dr. Stroupe.

Dr. Kevin Stroupe:  All right thank you. Hello everyone, thank you for joining the cyber seminar today. As has been noted in today’s session we will be exploring how to assess a Veteran’s pharmacy utilization using VA and pharmacy databases. First let’s take a poll to get a sense of the experience folks have had with VA pharmacy data. 

Moderator:  And it looks like we’re seeing just about 50/50 here; around 51% have, 47 to 48% have not. 

Dr. Kevin Stroupe:  So as it was noted it looks like there’s roughly equal amounts of people with experience with VA pharmacy data, so as part of today’s presentation we’ll be giving overviews of VA data as well as Medicare data available for -- to assess pharmacy use of Veteran’s and at the end of the presentation we’ll be referring -- giving some references that were -- where people can go to get further information and more detailed information as they want to explore VA Medicare pharmacy data further for their particular studies. 

So now let’s turn to an outlying of the objectives for the cyber seminar today. The objectives of the session first to examine how outpatient pharmacy data have been use in VA studies second will be to provide an overview of VA and Medicare pharmacy databases. The third objective then will be to describe how to find some specific types of information in the VA and Medicare pharmacy databases then we’ll present some examples of studies that have used VA pharmacy data and published articles. And then finally we’ll discuss where to go for more help about pharmacy data. 

So we’ll start by examining how VA pharmacy data have been used in published VA studies. VA pharmacy data have been used to examine trends in medication use. As an example I will present later in the session today some information from a study conducted by Elizabeth Tarlov, myself and others that recently appeared in supporting care and cancer. This study examines erythropoiesis stimulating agent use among VA patients who had cancer. 

VA pharmacy data have also been used for cohort identification. We’ll look at an example of this where colleagues used the disease modifying antirheumatic drugs, DMARDs to identify rheumatic arthritis patients. 

And then finally we’ll look at an example of -- where the leading colleagues used VA pharmacy data to examine antipsychotic use in older residents of VA community living centers. 

So next let’s turn to an overview of VA and Medicare databases. There are multiple sources of pharmacy data available for VA users. VA pharmacy data are available from local VistA systems at individual VA facilities. Additionally pharmacy data are available from multiple national data sources. VA pharmacy data are available from the pharmacy benefit management, the PBM database, VA pharmacy data are available from the decision support system, National Data Extract, Pharmacy Datasets and VA pharmacy data are available in the corporate data warehouse, the CDW. And it could be noted that VistA is the pharmacy data source for the DSF, the PBM and the data and the CDW.  Additionally pharmacy data for medications that patients receive through the Part D drug benefit are now available through VIReC. 

VIReC makes the Medicare Part D Slim File available to researchers. We’ll examine the Part D Slim File in more detail a little bit later in the session today. There are also several other important sources of pharmacy data available in the VA. There is the VSS product table which contains information specific to drugs such as the drug description. The most current version of the VSS product table can be found on the DSS internet website. 

There’s also the National Drug File which is created and maintained by the pharmacy benefit management group. For drugs approved by the Federal Drug Administration the National Drug File contains information such as the dosage, form and unit, the package size and type, the drugs trade name and the national drug code. 

The national drug file is available along with the description of the variables in the data set from the PDM’s internet website. And it should be noted that whereas the data sets that we’re described on the previous slide, the PBM, the DSS data and the CDW as well as the part D Slim File are all available at a patient level. These two files, the DSS product table and the National Drug File are not at a patient level, or a person level. 

So next we want to take another poll with the audience to get a sense of people’s experience with specific pharmacy databases. And note in the case of these people can select more than one because I think people have had experience with more than one type of pharmacy data. 

Moderator:  I think I forgot to set it up that way. I’m going to change it really quick. I’m sorry everyone if you could re-vote because you can do multiple at this point. I’m sorry that was my mistake on setting it up but it should be good now. Thank you. And it looks like we’re seeing about 40% have used DSS NDE pharmacy data. Around 30% the PBM pharmacy data, 36% CDW pharmacy data, about 4% the Part D Slim File and around 40% say that they have used none. 

Dr. Kevin Stroupe:  So in the session today then we’ll be providing some more information about these -- each of these types of pharmacy data that were examined in the poll so that folks that have had experience with one type of data, data source, but not the others can get a sense of what’s available and the other data sources and especially this will provide an overview for the folks who haven’t had experience at all with VA pharmacy data. So next we’ll go through each one of the pharmacy databases that we’ve mentioned already. We’ll start with the PBM database and then look at the DSS and then the CDW data. 

So on -- next we’ll describe -- we’ll start with PBM database. The PBM database and the national database with extensive information about drugs dispensed in -- by the VA. The outpatient data have been available through the PBM database since FY1999 and data for inpatient drug use are available from FY2004 and forward. Records for inpatient and out patient prescriptions or as filled by a VA pharmacy or a consolidated mail outpatient pharmacy, CMOP, are extracted monthly from each local facility VistA system and then loaded into the PDM database. These data are then housed by the PBM and are available to individuals through custom extracts. 

The PBM database includes medications dispensed through VA pharmacies, dosing instructions for each prescriptions, the national drug code identifier when applicable, cost information and provider information among other types of information available in the PBM data. We’ll discuss some of these items in a bit more detail a little bit later in the session today. 

Inpatient and out patient pharmacy records are also available from the decision support system national data extract pharmacy data sets. As with the PBM database the source of pharmacy data is the local VA facilities VistA systems. Data from the DSS national data extracts for pharmacy are available from the corporate data warehouse through custom extracts and they’re available from FY2005. 

While the decision support office which is now called the Managerial Cost Accounting Office originally created separate national data extracts for each year and for each Veterans innovated service network, each VISN, appropriate data warehouse, CDW staff now compressed those files into a single table for each of the national data extract types besides the pharmacy data there’s also the lab data and the lab results and so on. And each of the tables includes all the VISN’s and all of the years from 2005. 

For those then working with the CDW it should be noted that the CSS national data extracts are within the so called raw domains within the corporate data warehouse whereas the pharmacy data and the corporate data warehouse that will be discussed next are in the so called production domain of the corporate data warehouse.

Next then we’ll look at the pharmacy data available from the CDW, the corporate data warehouse but first a little bit of background about the corporate data warehouse and the data within itself before we get to the pharmacy data aspect. The corporate data warehouse is a national data repository comprising data from several VA clinical and administrative systems. The CDW was designed to incorporate multiple data sets throughout VA and to one single standardized database structure. The CDW relational database is comprised of data domains and within each of these domains is a set of tables with a common theme such as outpatient visits or dispensed drugs and so on. Those would be types of tables within a domain. And then within these tables are contain the individual data fields. 

The data domains in the corporate data warehouse are categorized as either production or raw. The tables in the production domains have been structured to support flexibility of querying the date whereas the raw domains reflect the structure of the data from their original source and have not been modeled, standardized or indexed. Sometimes the raw domains are for data domains that are preparation to be transitioned to production status. In addition the decision support system, national data extracts, NCDW are also in the raw domains. 

Of particular note for today CDW has pharmacy data in two production domains currently. The pharmacy bar code administration data and the pharmacy outpatient data. The BCMA, the bar code medication administration data includes the pharmacy data currently available in the production domains and there are other types of inpatient pharmacy data that exists in CDW and the raw domains. And it should be noted that pharmacy data within the CDW are available from fiscal year, FY2000.

Within these two pharmacy domains there are several important tables that contain data fields of interest in relation to pharmacy data. Within the pharmacy barcode medication administration domain key tables are the dispensed drug, medication log, the medication variance and the missing dose request. Within the pharmacy outpatient domain key tables are prescriptions, fill, sig and medication instructions; we’ll be discussing the sig a little bit later in the session today. The entire list of tables and variables is available in the Medidata report on the CDW share site. 

So next we’ll compare some key characteristics between the VA sources of pharmacy data. With regards to the cost data it’s important to note that cost variables in the DSS datasets include labor and overhead whereas the PBM database variables contain only the cost of the drug product itself, not the cost of the other overhead and labor involved with the dispensing. 

In addition the origin of the drug product cost differ between those -- these two sources. The PBM drug product costs consist -- contains the value in the VistA local drug file on the dispensing data whereas the DSS drug product costs are obtained from the local DSS standard table and although this DSS table is originally populated by values from the local drug file the DSS standard table is usually not updated more than once a year. It should also be noted that costs are available in the CDW. 

In addition there are differences in the dispensing detail available across these data sources. The DSS data sets don’t contain the dosing instruction but they are available in the PBS -- PBM database. The dosing instruction, the sig are also available in the CDW.

Also it should be noted that the quantity of a unit disorder reflects the number of doses dispensed, not the quantity of the drug dispensed. So for orders -- for some orders it -- it’s going to be impossible to determine the amount of the drug dispensed to a patient or the daily dose when using the DSS datasets. And all of these data sources are available to researchers through extracts although the available time periods may differ across data sources. 

Next we’ll turn to imported source of non VA pharmacy data available for VA users. Beginning in January 2006 drug coverage became available to Medicare enrollees through the Part D pharmacy benefit. While as all VA users enrollees over 65 are enrolled in Medicare Parts A and B only about a third are enrolled in Medicare Part D. Parts A and B covering the institutional and other provider costs. The Part D program operates differently than Medicare parts A and B, under Part D patients enroll in a part D plan through an insurance company and then it’s the insurance company rather than CMS, the Center for Medicare and Medicaid services that pay the prescription claims. However, CMS requires that insurance companies submit data to CMS on all the prescriptions that have been filled. CMS then makes this prescription data available to researchers through the prescription data event file. CMS also has information on the drug itself, the dispensing pharmacy, the prescriber and the insurance plan to pay for the drug. CMS has created a subset of the prescription drug event file called the Slim file which contains data elements that are commonly used by researchers and will discuss the Part D slim file further, a little later in the session today. 

Medicare Part D enrollment status is available in the Medicare enrollment files. Important variables related to Medicare Part D enrollment and the Medicare enrollment files include the number of months of part D coverage and the type of Part D plan. For example patients might be enrolled in -- have Part D coverage through a managed care plan or through prescription drug plan that’s covering just the prescription drug aspect. Medicare data are available through VIReC through the VA CMS data for research project and are provided to researchers as custom extracts. The Medicare Part D enrollment data are available from calendar year 2006 so this information then just tells -- would provide information about whether or not the patient had Medicare Part D and what type of part D coverage they had. And next we’ll look at how to get information about the actual drugs that they receive. 

The Medicare which is available in the Medicare Part D Slim File. Thirteen select variables from the Medicare prescription drug event data are available in the Part D Slim File which is available from VIReC as custom data extract beginning with calendar year 2006 data. Complete information about -- from the Part D prescription drug event and other part D data that are not available in the Part D Slim File may be requested through a special request process through VIReC. This special request process involves VIReC requesting the data from CMS on the researcher’s behalf. If this is something researchers are interested in more information about the special request process is available on VIReC’s website. 

The part D Slim File variables include the following information. The service date the so called product service ID, which is actually the National Drug Code, the NDC, the quantity expensed, the date supplying, the patient payment amount, the gross drug cost, the brand name, the generic name, the drug strength and the dosage form such as whether it was in a capsule, a tablet and so on.

Also extracts from the Part D Slim File from VIReC are available with the social security number or the scrambled social security number. This table shows the availability of key variables across the pharmacy data sources. The data supply, drug description and quantity are available in all four data sources. The National Drug Code is available in the PBM, the CDW and the part D Slim File, Slim File database and the National Drug Code is also available as the last 12 digits of the DSS feeder key variable in the DSS data. 

The VA drug classification system separates drugs, supplies and diagnostics into several different categories based on their characteristics. These classes are assigned by the pharmacy benefit management group. A list of the most current VA drug classes is available on the PBM internet website. The medication class is available and the PBM, the DSS and the CDW data. If researchers were to want to add the VA medication class to the part D Slim File data one approach to doing so would be to link the NDC and the part D Slim File with the NDC and the National Drug File and then obtain the VA medication class from the National Drug File. 

Next we will examine some important variables in the DSS product table and the national drug file. Important variables in the DSS product table include the IPNum, the feeder key, the short and long description and the drug class. And I’ll provide a little bit more information about each one of these and we go -- the IPNum is the DSS intermediate product number. This number is assigned sequentially by the DSS system and as such doesn’t have an intrinsic meaning itself. It’s also found in the DSS national data extract and the national drug file and can be used to link records across these databases. The feeder key variable is a 17 digit number that identifies the drug or supply to dispense. 

The first five digits contain an internal entry number, IEN which can be used to point to information and VA product, VistA product files or drug dispense. The last 12 digits contain the 12 digit version of the national drug code. 

And the 17 digit number, the 17 digit feeder key may be used to link records to the national drug files and by linking records with the -- to the national drug file additional information about a particular drug can be obtained such as the information about the formulary status. It also should be noted that more than one feeder key may be assigned to the same product. For example 65mg of aspirin tablets would have the same intermediate product number but the feeder key values would differ based on their manufacturers of bottle size and so on. Differences in manufacturer bottle size and so on could result in different national drug code.

The drug description that is obtained from the DSS product table originates from the national drug file. There are short and long versions of the drug description and the DSS table. The drug description and the DSS national data extract is limited to 30 characters which is a shortening of the 64 character VA product name which is found in the national drug file. And the VA drug classes what -- was described previously.

Some key variables in the national drug file include the VA product, the feeder key, the national drug codes and the VA class. As I just noted the VA product name is 64 character variable. The VA product name is in the PBM data and the 30 character version, the drug description is in the DSS national data extracts. And the feeder key and the VA drug class have the same that were described previously for the VA DSS product table. It should be noted in terms of the national drug code itself it’s a unique code that consists of label code, product code and a package code. The labeler code is assigned by the FDA and identifies the firm that manufacturer repackages and distributes the drug product. The product code identifies the specific strength, dosage forum and formulation and the package code identifies the package sizes. The national drug code and the national drug file uses a 12 digit format for the NDC with six digits for the labeler, four digits for the product and two digits for the package. There are also 10 digit and 11 digit versions of the NDC code in use. For example the NDC code and the NDC and the part D Slim Files are an 11 digit format.

Next we’ll explore how to find some specific information in pharmacy databases and we’ll start by looking specifically at cost data that are available in these data settings. The DSS pharmacy data sets and PBM data bases have different cost variables, which I noted previously. The PBM database contains the cost of the drug product from a supplier whereas there are multiple cost variables within the DSS national data extract pharmacy data sets. And the DSS ND pharmacy data sets when the prescription is dispensed by a consolidated mail outpatient pharmacy, a CMOP the actual cost variable includes the acquisition cost of the medication, while for pharmacy window dispensed prescriptions the actual cost variable includes the acquisition cost, the supply cost such as the supplies of the bottles and labels to prepare the prescription and the overhead costs. 

The dispensing cost variable includes the direct labor costs of dispensing for pharmacy window dispensed prescriptions and includes labor costs, supply costs and mailing costs and cost of overhead for the CMOP for prescriptions that are dispensed by the CMOPS. So the sum of the dispensing cost variable and the actual cost variable in DSS represents the total cost of filling the prescription order. The supply cost variable contains the cost of the drug and the supplies used to fill the prescription for pharmacy windows prescriptions and the acquisition cost of -- for CMOP dispensed prescriptions. But the supply cost itself is also included in the actual cost variable. So the supply cost shouldn’t be added to the actual cost variable or that would result in double counting of the cost. 

The part D Slim File contains two cost variables. The patient pay amount is the amount of the patient pays for medication and the gross drug cost is the sum of the ingredient cost, the dispensing cost and the -- any vaccine administration fees. Another issue is the national drug code for the same prescription may be different on a record in the PBM data and the DSS national data extract pharmacy data set. This can occur because the NDC’s and the PBM data and the DSS national data extract data come from different VistA files. It will also be an NDC for the same drug however; the NDC on a dispensing record may not be the NDC for the drug product it was actually dispensed to a patient. As we noted previously the NDC includes information about the manufacturer package size which may differ from the drug that was actually dispensed. This can occur if the local drug file has not been updated to reflect the currently stopped supplies at the time that the drug product was dispensed. 

Next we’ll look at some examples of how VA pharmacy data was used in some other studies. Some types of issues that have been addressed with VA pharmacy data include cohort identification to address questions such as can pharmacy data be used to identify specific groups of patients. Medication utilization, looking at questions such as does policy change impact medication use. Healthcare quality, we get questions such as are patients being prescribed medications in accordance with quality measures. Medication adherence, how much of a prescription medication are patients using and exposure to specific medication classes. So are specific drugs associated with better or worse outcomes. 

Next we’ll highlight three published studies that have used VA pharmacy data; those are the ones that we noted a little bit earlier in the session today. The first example that we’ll look at is a study by Elizabeth Tarloff, myself and some others that appeared in Supportive Care and Cancer. The objective of this study was to examine erythropoiesis stimulating agent, ESA therapy in lung and colon cancer patients who are receiving chemotherapy from 2002 to 2008. ESA’s are commonly used to treat anemia in cancer patients receiving chemotherapy; however emerging evidence from clinical trials indicate an association with ESA’s with adverse events such as vascular events, tumor progression and reduced survival. In March 2007 the Federal Drug Administration mandated a black box warning to ESA labels advising that ESA’s should only be used to raise hemoglobin levels enough to prevent drug transfusions. In this study we use pharmacy data to determine whether ESA use differed before and after this black box warning. We defined the pre period before the black box warning as January 1, 2002 to March 2007 and the post period from March 2007 through December 2009. We also looked at trends in ESA use over time.

We used the PBM database to identify ESA use and within the PBM database we identified the ESA’s based on national drug code information. It should also be noted that because ESA’s parental drug that sometimes is given in outpatient settings. We also identified ESA use in the medical datasets using CPT or HCPCS codes that indicated ESA administration. So that’s another important point too in looking at medications that there are medications that might be delivered in a particular clinic setting then you might also want to look at administration codes as well as codes for the drugs themselves. 

What we found then in this study was that for lung cancer patients the odds of ESA use increased 65% in the post period. For colon cancer patients the odds of ESA use decreased 53% of the post period. When we examined trends in ESA use over time we found that ESA use began to decline for both cancer groups, the lung and colon cancer patients before the black box warning was issued possibly due to previous dissemination of information found in the clinical trials which had noted some potential adverse events as a result of ESA use. 

Next we’ll turn to a study that appeared in Arthritis Care and Research. The objective of this study was to assess the utility of diagnostic algorithms which included prescription information to identify rheumatoid arthritis patients within VA databases. 

This study sought to assess whether robust algorithms could be proposed that would allow the identification of rheumatoid arthritis patients in the administrative databases without the need to rely on chart review. Using VA administrative data the study identifies patients with an ICD code for rheumatoid arthritis between October 1998 and September 30, 2009 that were treated in the Houston VAMC, VA medical center. All patients were identified as having two rheumatoid arthritis diagnostic codes, at least six months apart to meet the inclusion criteria. Pharmacy data were used in algorithms to test -- they were tested to identify rheumatoid arthritis patients. The algorithms tested were based on combinations of the presence of at least two ICD codes for rheumatoid arthritis at least six months apart. The use of the DMARDs for at least 180 days and an ICD code for rheumatoid arthritis by a rheumatologist. This study evaluated positive predictive value of various diagnostic algorithms using validation from chart reviews. The positive predictive value was defined as the ratio of the number of rheumatoid arthritis patients diagnosed by chart review to the total number tested positive for rheumatoid arthritis by the algorithm. The study obtained the DMARD prescription information from the VA PBM database. 

Adding the DMARD therapy to the algorithm increased the positive predictive value for identifying rheumatoid arthritis patients. The highest positive predictive value, 91.4% was found in a group who received DMARDs for at least 180 days and had rheumatoid arthritis codes at at least one rheumatology visit. 

So in that case medications used could be helpful to identify a specific disease cohort. And the next we’ll look at an example of a study by the leading colleagues that appeared in Medicare. The objective of this study was to assess the prevalence and risk factors for antipsychotic use in older residents of VA community living centers. Antipsychotic medications are commonly prescribed for nursing home patients despite adverse event profiles because little is known about their use. This study assessed the prevalence and risk factors for antipsychotic use in older VA nursing home or community living center residents. The study collected data on all residents age 65 years and older admitted to one of 133 VA community living centers between January 2004 and June 2005. Residents were included if they had long stay -- if they were long stay residents which were defined as a minimum admission of 90 days and had at least one drug dispensing record during that time period? Logistic regression was used to identify factors associated with antipsychotic use. 

The study -- the study then linked community living center residents, minimum data sets and medical staff data sets with all prescription dispensed during the first 90 days of their mission using VA PBM data for the pharmacy data. For each dispensed drug the study collected the start and stop date, the medication name, the medication strength, the direction for use and the amount dispensed. The study also created a pharmacy polypharmacy variable indicating use of multiple medications that they identified -- where they identified the number of unique medications per resident excluding psychiatric or dementia medications. Patients were classified as having appropriate use -- we concluded patients with either a psychiatric diagnosis where psychotic symptoms were prominent feature or the diagnosis of dementia and psychotic symptoms. The remaining patients were classified as having potentially inappropriate use. 

Overall the study found that 25.7% of Veterans used an antipsychotic drug during their community living center stay. 59.3 had documented indication for appropriate use and the remaining 40.7 had no evidence of appropriate indication for use. It should be noted that although these examples used PBM data, other data sources such as DSS and others have been used in research studies as well. 

Finally we’ll look at some sources that you can use to go to for additional help about pharmacy data. You can go to the pharmacy page on the VIReC website; this page has information on pharmacy sources and links to the VIReC resource users guide on pharmacy data. Additional resources include the HSR data list serve which you can join through the VIReC website, the list serve includes discussions among over 400 data stewards and user -- managers and users. You can post questions about pharmacy data or other topics and you can examine past messages and archived on the internet. Help is also available through the VIReC help desk. 

So finally questions?

Moderator:  We do have questions. Our first question is how frequently is the DSS pharmacy data extracted and updated?

Dr. Kevin Stroupe:  You -- could you repeat that one more time?

Moderator:  Absolutely, how frequently is the DSS pharmacy data extracted and updated?

Dr. Kevin Stroupe:  The DSS pharmacy databases or the DSS extracts have typically been produced on a -- on an annual basis. The PBM database has to be updated more frequently. For additional information about the specifics of that individuals might examine the DSS -- the VIReC research user guide on pharmacy data for more information about the various types of the PBM and the DSS data. 

Moderator:  Great, thank you for that. Why should I apply to use PBM if I already have access to DSS data?

Dr. Kevin Stroupe:  Well what’s noted earlier there could be some types of information that might be available in the PBM data. For example one big difference between the PBM data and the DSS data are that the PBM data contain the sig or the dosing instruction and if that happened to be a particular interest for a given study then someone might need to consult the PBM data for that. However, for other -- that would be a big example where one might need to go to the PBM data. If for -- in many cases though you are finding the same type of drug information and so if some of those particular aspects such as the sig needed for an individual study then someone might if you have the DSS pharmacy data you might not necessarily need to access the PBM pharmacy data as well. 

Moderator:  Great thank you for that Dr. Stroupe. Here’s another question, is there a way to get the dose received when drugs are received via drip and large volume intravenous administration?

Dr. Kevin Stroupe:  Is there a way to receive -- could you repeat that one more time please?

Moderator:  Certainly, is there a way to get the dose received when drugs are received via drip in large volume intravenous administration?

Dr. Kevin Stroupe:  The IV drug information are available through the -- within the databases. The -- they’re -- so that might be -- that might be possible to get that information. To find the -- to get that -- to determine if that’s exactly the type of information someone is looking for you know, it might be useful to consult with -- to follow-up with the VIReC help desk to confirm that so that can be examined further. But IV information is -- IV information is available, but that might have to be explored further to make sure that that’s sort of exactly the information that someone is looking for and to -- so that might be potentially available but could be something to be explored a bit further.

Moderator:  Okay great, thank you Dr. Stroupe. Here is another question, is there a crosswalk to RX norm codes?

Dr. Kevin Stroupe:  That -- that might -- that will be something that we might have to have the VIReC help desk might have to follow up on that and get back to the requester about that; I don’t know the answer to that right off hand.

Moderator:  Okay sounds good and I have a comment from one of our attendees, Thomas Weichle he says, please mention that pharmacy cost data is also available in the non-VA care formerly CBases outpatient pharmacy file. Thank you for that Thomas Weichle. 

Dr. Kevin Stroupe:  Thank you.

Moderator:  A couple of more questions so that we have time for the attendees to complete the evaluation. Here’s another, what is meant by a custom extract if we submitted the SSNs for a cohort of enrolled research subjects? Could we access the data for only those individuals? Should I repeat that for you?

Dr. Kevin Stroupe:  If a cohort has been submitted to VIReC or to the CDW process, a specific cohort of individuals then an extract of those individuals would be created and that would be the data that they would have access to, through the custom extracts the difference would be whereas data might have previously been individuals could download that directly from the databases through the custom extract process then the research or the analyst isn’t accessing the data directly and they’re only getting the data from that extract when that’s -- the extract is being created for them and they would only have access to information about the patients that were -- the cohort that was submitted for that extract to be created.

Moderator:  Great and let’s take one last question. Is there help/examples for VA proposal submissions using pharmacy data? I’ve worked with the data as a walk but would now like to submit as a PI on a new project?

Dr. Kevin Stroupe:  Well at the end of the -- following these criteria which you’ll have access to later there are some references to studies that have been made -- that have made use of VA data that are in the published literature. One approach might be for an individual to look at study -- published studies where use the VA pharmacy data and then to contact the investigators involved to find out if they would be willing to share proposal information with them. 

Moderator:  Sounds great; thank you so much. And thank you so much for presenting today Dr. Stroupe.

Dr. Kevin Stroupe:  Well thank you.

Moderator:  Any remaining questions please contact the VIReC help desk at VIReC@VA.gov. Our next session is scheduled for Monday, March 3 from 1:00 to 2:00, is entitled using Capri and VistA web and will be presented by Dr. Linda Williams. We hope that you can join us. Have a great rest of the afternoon everyone.   

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