Measuring Outpatient Pharmacy Use- 13 -Department of Veteran's Affairs

March 5, 2010

**Department of Veterans Affairs**

**Measuring Outpatient Pharmacy Use in the VA Using VA Pharmacy Data**

**March 5, 2012**

Margaret:  Welcome to VIReC’s Database and Methods Cyber Seminar entitled “Measuring Outpatient Pharmacy Use in the VA Using VA Pharmacy Data.”  Today’s speaker is Todd A. Lee, PharmD, PhD, associate professor College of Pharmacy, University of Illinois at Chicago.  Todd was at the VA for ten years and is still affiliated with Hines VA Hospital in a without-compensation capacity.  I’m pleased now to welcome today’s speaker, Dr. Todd Lee.

Todd A. Lee:  Thanks, Margaret.  It’s good to be here talking with all of you today.  As you’ve heard several times now, I’m going to be talking with you about pharmacy data and experience with using pharmacy data in the VA system.  To launch us and give me a better understanding of who’s on the cyber seminar and who’s listening, we’ve got a couple of poll questions.  The first is, have you ever used VA Pharmacy data, and if you could just give us a quick response as to whether or not you’ve got experience using VA Pharmacy data.  The second is how you would rate your overall knowledge of VA Pharmacy data.  As those results are coming in it’ll give us a better sense of how familiar you might be with respect to understanding what I’m going to be talking about with you today.  

The content is probably most applicable to those that are less familiar.  And here we see, based on responses, we’ve got about seventy percent of you that have used VA Pharmacy data in the past.  So you may be familiar with a lot of the things that I’m going to talk about today.  A third of you that responded that did not use VA Pharmacy data, this is probably very informative for you all.  It’s really targeted to be most informative for that audience, but hopefully those of you that have experience might pick up a few important points as we go through the lecture today.  

As we’re getting to the next set of output, let me just orient you to what I’m going to try and accomplish over the next fifty minutes or so.  One, I’m going to just briefly talk about how pharmacy data has been used in the past in VA studies.  I’m going to give you a high-level introduction to that because I’m really going to dive in to some of the more important elements of their use of pharmacy data later on in the lecture.  But at least I want to give you a sense of the types of studies that have used VA Pharmacy data.  We’ll talk about an overview of VA Pharmacy databases and how you might go about finding information on VA Pharmacy, then finally where to go for more help.  As you see the results here about level of knowledge, it’s quite varied from never used, twenty-two percent, all the way down to people being very familiar and very frequent users, just over ten percent of those that are on the top.  With that in mind, let’s start to move into some of the content of today’s lecture.  

You should see now the session objectives for today, and I’ve just talked about those.  Let’s jump right into the first objective and give you some examples of how outpatient pharmacy data has been used in previous VA studies.  It’s been used to measure chronic medication use, so how have veterans used medication, specifically chronic medications, over long periods of time comparing different populations.  And in this case, the focus again was on medications that would fall under chronic utilization and how that looked over a two-year period in this example in a study done by Kevin Stroupe and his colleagues.  VA Pharmacy data can also be used to measure the quality of VA care.  There are several performance measures or quality indicators that utilize pharmacy data as the measure of quality.  This study is an example that used antidepressant use as a measure of quality of care during a depressive episode.  Pharmacy data in this case is the actual quality measure and the use of pharmacy data is solely focused on whether or not quality care is being provided.  Another frequent use of pharmacy data is to look at medication adherence, or how well patients are taking their medications over time.  This example is focused on development of a new medication adherence measure, ReComp—which, again, I’ll talk about in more detail later on in the lecture—that looks at medication use over shorter periods of time and how that may compare to more traditional measures.  But again, this is a relatively frequent use of pharmacy data and seeing how patients may adhere to their medication regimens after a new diagnosis or after dispensing certain medications.  

Medication data can also be used as the primary explanatory variable in analyses to look at adverse events of medications or even the effectiveness or comparative effectiveness of medications where the drugs that we’re interested in are going to be used as our primary exposure of interest so that we can understand whether or not there is an association between the use of that medication and the outcomes that we might be interested in.  This is just an example of some work that I led that looked at the association between inhaled medications and the risk for mortality amongst veterans with chronic obstructive pulmonary disease.  I’ll use this as an example later on to really show you how we took information that’s available in some of the pharmacy databases and used it to drill down on what we thought were actual really solid patterns of medication use longitudinally.  

And then a final example is using pharmacy data as a risk adjustor.  You’re probably all familiar with the concept of risk adjustment where often times we use diagnostic codes or procedures to identify important diseases or conditions that patients might have that we’re going to need to account for in any analyses that we do to make sure that there’s a balance between patient groups that we’re trying to compare.  Well, this is an example of trying to use pharmacy data as a risk adjustment tool.  When we walk through this study, we’ll see how it compares to more traditional ICD-9 or diagnostic-based measures of risk adjustment.  So I hope that’s a really quick introduction to let you all know that there are lots of ways that pharmacy data can be used and has been used in studies of veterans.  It’s probably just beginning.  You can probably think of lots more uses of pharmacy data in your own work that you’re currently doing.

Now, let me move to really orienting you to VA Pharmacy databases and the information that you will see or find in those VA Pharmacy databases.  There are a couple of key sources of pharmacy data, and we can start at the local level.  Locally we have VistA, which holds information, pharmacy information in particular.  There also may be a data warehouse within your VISN that captures pharmacy data.  These local databases can be resources for studies of medications and using medications, knowing that it’s limited to the population at your local facility or potentially within your VISN.  But lots of the information that you’ll find here is also available on the national level.  What I’m going to talk about mostly today are the national data sources that are available from a pharmacy data perspective.  These will be, having used in all of the examples that I talk about in terms of the studies that have been done using VA Pharmacy data.

There are essentially two widely recognized national data sources for pharmacy data.  The first is data from the PBM, the Pharmacy Benefits Management Group.  The second data source is pharmacy data from the DSS National Extracts, specifically the pharmacy datasets.  Those are the two primary sources of dispensing information within the VA.  So records of which medications were dispensed at medical centers or through CMOPs, consolidated mail order pharmacies.  Those will be prescription-level information, and you’ll have in those data sets information on each prescription that was dispensed within the VA healthcare system.  There are also a couple of other important or useful pharmacy data sources that you might want to keep in the back of your mind as a resource.  The first is the DSS Products Table that can be linked to the national pharmacy data extracts so that you can obtain additional information, potentially, on the medications that you’re seeing in each record of DSS data.  The other key data source is the National Drug File, which is available through the PBM Web site.  That’s a comprehensive list of drugs available in the VA data system.  Another complementary data source—actually, it’s an Excel spreadsheet—that might be important as a resource available through the PBM is the National Drug Formulary.  On that there is information about which drugs are listed on the formulary.  You can see what changes have happened to the formulary over time.  Those are also documented on the PBM Web site.

Time for another question for you all to, again, give me a sense of your level of familiarity with the two national data sources that I spoke about.  Which national data sources of VA Pharmacy have you used in the past?  For those of that have, have you used the DSS data?  Have you used the PBM data, either one of those exclusively?  Have you used both?  For those of you that are new to the national pharmacy data, neither response.  I’ll give you a few moments to respond to that poll because this will help me get a better sense of the level of familiarity amongst the majority of the audience, at least, in terms of your experience with the two national data sources, knowing that they’re available to basically everyone in the VA to use as a tool for research.  

Now, we see that about half of those that responded have not used either of the two national data sources, the DSS or the PBM data.  Twenty percent have used both; twenty percent PBM and eleven percent have only used the DSS data.  That’s very helpful.  Hopefully you will find what I show you on the next several sets of slides informative in terms of what’s available in each of those two data sources, and importantly how they contrast with each other.  What might make you choose the PBM data versus the DSS data?  

Now, I’ll walk you through the contents of what’s in the DSS National Data Extracts, pharmacy data available through the DSS.  This data became available in 2003; the data goes back to fiscal year 2002 and up to the present.  So we’ve got around ten years of pharmacy dispensing information that’s now available on veterans.  The primary source of the data is VistA, so this information’s obtained from VistA and then made available on a national level through the DSS system.  It contains information on both inpatient and outpatient prescriptions that, again, were dispensed at a VA pharmacy, either a medical center or through the CMOP, the consolidated mail outpatient pharmacies.  Important note here, this information is medications that were dispensed, not ordered, so we see which medications patients actually received through this data source.  We do not have an estimate of what actual medications were ordered through the CPRS system.  

Another component about the DSS pharmacy data is that it’s housed at the Austin Information Technology Center, AITC.  Importantly it’s directly accessible; it’s directly accessible through the AITC.  Investigators or analysts do not need to make a data request to obtain this data.  Once you’ve got the appropriate approvals for your project, it is directly accessible for programmers and others.  Let’s contrast that or talk about the PBM as a national data source.  This goes back a little bit further, so its availability began in 2000.  It contains data from fiscal year 1999 forward, so you can make an immediate decision if you need data earlier than 2002.  You’re going to need to use the PBM data to do your study.  That’s one important difference between the two.  Its primary source is the same as the DSS data’s primary source, and that’s the data all comes from VistA.  It contains both inpatient-outpatient prescriptions, just like DSS does.  Here’s a table that contrasts the two data sources—PBM in the second column, DSS in the third column—for several key variables that you all might be interested in, the first being cost.  This is probably one of the biggest differences between the two data sources.  The PBM data contains drug supply costs, where the DSS data contains several more cost variables including the actual cost of the product, the dispensing cost of the product, and then supply and variable costs associated with that.  There is one slide further on in my talk that differentiates each of these cost components, but I’ll point you to both the VIReC Resource User Guide on pharmacy data and the HERC information on their cost variables in the pharmacy data if you are really interested in differentiating the cost variables between these two data sources for a lot more information than I could get to in today’s talk.  

The next row that we want to contrast, PBM versus DSS data, is data access.  PBM requires you make a request to them where you then fill out a form on the Web that says, “This is the kind of data that I need.  It may be based on a specific cohort of patients; it may be based on specific medication criteria.”  But you’ve got to define that for the PBM group, who will then extract that data and provide you a dataset, whereas with DSS again, as I  mentioned earlier, it’s directly accessible to programmers and investigators so that you do not have to go through this data request process but have, once you have appropriate approvals, immediate access to that information.  Both datasets contain inpatient-outpatient data, but they’re available at different time periods.  The original PBM datasets that began in fiscal year 1998 were only outpatient data; now, information on inpatient dispensings is also available beginning in fiscal year 2006 and continuing forward.  

In the DSS system both inpatient and outpatient dispensings were available beginning in fiscal year 2002.  So if you need inpatient information or you want to do a study that includes medication, exposure while a patient is in the hospital and you want to go back farther, again, DSS becomes the data source that you would need to be able to do that as long as you need to go back before fiscal year 2006.  And then the other major difference between the PBM and the DSS data is the availability of the directions for use.  In the PBM data, the directions for use, or the SIG, are available in the dataset.  You can make that part of your data request, indicating that you would like the directions for use as part of your dataset.  This information’s not available in the DSS data, so if the SIG or the directions for exactly how the medication’s been prescribed are important and necessary, then the PBM data becomes the only source that you could utilize to have that information available.  

In one of the examples of the studies on using VA Pharmacy data, I’ll show you how the directions for use were actually used.  Know immediately that this can be a very rich source of information, but it also can be a very difficult source of information to work with, as is all text information and requires lots of programming to parse out what might be important elements in that string of text.  I’ll give you an example of how this has been done, so it’s certainly possible.  But it’s something to keep in the back of your mind, whether or not it’s necessary to go through those extra steps and using that information as part of your research.  One natural question that evolves from both data sources being available is, is the same information in there?  Are they the same?  How comparable are the PBM and the DSS data within a defined cohort of patients?  Investigators at VIReC have actually made this comparison.  They use a cohort that was available to them through a CSP hernia study—it’s actually CSP 456—where there were just over 1,500 patients that were involved in that study.  They made direct comparisons between the PBM and the DSS pharmacy data, focusing only on outpatient prescriptions.  This focused on fiscal year 2002, the entire twelve-month period, and how similar both the initial dispensing and the fills and the refills were between those two data sources.  In this comparison there were more than 42,000 prescription dispensings over the course of that twelve months for these just under 1,600 patients.  The take-home message is that there was a very high direct match rate between the data sources.  That is, if you saw a prescription in the PBM data, it was very, very likely that that prescription dispensing was also seen in the DSS data.  This is very reassuring for those of us that are using or want to use national pharmacy data to know that the two data sources are going to essentially give you the same information, at least according to this analysis.  

There were some discrepancies.  Just under two percent of the prescriptions were not exact matches.  There may have been differences in some of the data elements within the two data sources; a prescription may have showed up in one data source and not in another.  So there are lots of reasons for these discrepancies.  I’ll point you to the link that’s available on this slide to give you a lot more detail on what those discrepancies might have been.  It’s available on the VIReC Web site.  

Now, there are a few things to keep in mind; I don’t want to let you walk away saying “This study definitely confirms that the two data sources are equivocal.”  That may not be the case, especially since this was limited only to outpatient dispensings.  It didn’t look at any of the inpatient dispensings, so there’s no way to draw any conclusions based on the similarity of inpatient data between the two data sources.  It’s a very selected cohort; it’s certainly not representative of the whole population.  However, it’s a pretty decent sample.  It’s definitely given me confidence in saying that the two data sources really do contain the same information in terms of prescription dispensing.  It’s not surprising, given that the source files are the same for these two data sources.  

There might be some reasons that we might see some differences between the two data sources.  There’s some level of data cleaning that goes on by the PBM group before they make their extracts available that doesn’t happen on the DSS side.  There might be reasons why we might expect slight differences.  But on the whole, I would say that the recommendation to use one data source versus the other is not driven by the differences in terms of the dispensing data that’s available in those two data sources.  It’s really those other factors that I talked about a few slides ago in terms of what makes DSS data different from PBM data in terms of the SIG or the need to go back further in time, or the need for a direct access by the investigative groups.  Those are the things I think you should think about when making a choice between the two data sources.  It will be important moving forward to make these same comparisons in the inpatient data and maybe in a more nationally representative cohort because there have been anecdotal examples that the match rate’s not as high.  Looking at specific prescriptions, there may be differences between the two data sources.  It’s definitely worth further investigation between the two data sources.  

I talked about these two files as other pharmacy data sources, one being the DSS products table.  It’s available on the DSS Web site.  It contains several key variables that are linkable to the DSS pharmacy data extracts.  It contains things like the feeder key.  It may give you a longer description than that available on the DSS pharmacy extracts.  So if you need more information about the product that was dispensed, you might be able to find it here.  It’s going to contain information on the VA drug class.  You can use this as a resource to link back to the DSS pharmacy data.  The other file that, again, is available through the PBM on their Web site is the National Drug File.  It contains the VA product description; it contains the National Drug File NDC, its class information.  Both of these files can be used to obtain additional information that’s not available in the pharmacy data extracts themselves.  

Let’s talk a little bit more specifically about some of the data elements that are available in these two different pharmacy databases.  Key variables that you might be interested in as related to pharmacy or medication use are the days’ supply variable.  So for a given prescription, how long would we expect that prescription to last?  How many days of medication did a patient receive when that prescription was dispensed?  That’s the days’ supply variable.  It’s available in both the DSS and PBM data.  It’s important when we’re thinking about medication adherence or measuring the use of the medication, whether or not a patient was using the medication at the time an event may have occurred.  The days’ supply variable becomes important in those calculations.  

Another important variable for medications is the drug prescription, what drug was actually dispensed.  It contains information on the name of the drug; it will contain information on the strength of the drug.  Was it twenty milligrams?  Was it forty milligrams?  This becomes another important component of any pharmacy-related research.  If you need to identify specific medications, you might have to do it through this drug description variable.  If you’re not interested, for example, in just a class of medications but very specifically want to find a specific type of drug, you’re going to have to rely on this drug description variable in searching within that drug description for the drug that you’re interested in.  

Quantity can be an important variable available in both datasets.  This tells you how many of the medication were dispensed.  So for oral medications, for example, it’ll tell you how may tablets or capsules were dispensed.  If you see that an individual received a thirty day supply and it’s a once daily product, you would expect that thirty tablets or capsules were dispensed with that medication.  The quantity field can be used to understand how much of a given product a person might have used over the course of a longitudinal period, if I wanted to quantify the cumulative exposure to a given prescription drug and compare that to the cumulative exposure for another prescription drug.  

The NDC is available through both datasets.  You may actually have to link it back to the DSS drug file that I talked about previously to get the NDC number.  But it is an available resource.  I’m going to, in a couple of slides, talk about some important differences between the two data sources in terms of NDC and why NDC may be different in DSS versus PBM, and what information you can rest assured is going to be the same about a given NDC code.  An NDC is the national drug code, which is uniquely assigned to each medication that’s available in the United States or approved by the FDA.  

The final key variable is medication class, and this is a variable that describes the therapeutic class that a given medication may fall into.  For example, antihypertensives, there are several antihypertensive classes, things like beta blockers, ACE inhibitors, and diuretics.  Within diuretics there are thiazide diuretics and other categories of diuretics.  All of this can be identified with a medication class variable.  So if you’re interested in working on a specific class of medications, your initial step may be to reduce the pharmacy dataset by only identifying that class of medication.  That can be done with the medication class variable.  Now, you can find these categories on the PBM Web site if you want to know what VA class beta blockers are in.  This information is available, again, through the PBM’s Web site in an Excel file.  Know that even if you limit your dataset through medication classes, if you’re interested in specific drugs you want to make sure that your drugs are falling into that class or examples where drugs might fall into an unexpected class.  And so you may want to use a combination of both class and drug description to ensure that you’re identifying the drugs that you’re interested in.  

What about cost variables?  I mentioned briefly several slides ago that the cost variables are different between these two data sources.  PBM only has a single cost variable, and that’s the cost of the drug product from the supplier, whereas DSS has information on the dispensing cost or how much labor cost for the pharmacist was required when dispensing the prescription, and if it was through a CMOP for mailing the prescription.  It includes the supply cost, so not only the cost of the drug product but what was used.  Again, preparing the prescription might include the vials or the bottles that contain the prescription including label costs.  

Then there are the actual costs of the product.  That’s the drug product cost, all of the supplies rolled together, and then indirects and overhead added into that.  Depending on the research question that you have in mind in terms of cost, this can drive you to one dataset versus the other.  I’ll point you to the HERC information or the VIReC RUG for additional information on a lot more details on these cost variables that are available in the two datasets.  One of the frequent questions that has come up over and over again is why is the NDC for the exact same prescription different in the PBM than the DSS record?  Again, this is one of the reasons that we saw a discrepancy in the hernia study comparing the DSS and pharmacy data.  The NDCs were not always consistent for the same dispensing for a given prescription.  

Well, the first bullet you see here is that the NDCs essentially come from a different source.  They don’t come from a different source initially.  Both datasets rely on the NDCs from the VistA system and the local drug file.  However, the PBM data will draw this on the data from the local drug file on the day the product was dispensed, whereas generally the DSS data are pointed to a subsequent file that’s initially generated from the local drug file that may not be updated as often as the local drug file.  Actually, I think it may be updated only annually or semi-annually.  And so this is one reason the NDC numbers can be different for the same dispensing between the two data sources.  Again, not that the local drug file is different, but the next layer of files that are created from that that DSS data draws the NDCs from may not be updated as frequently as the data that the PBM draws the NDCs from.  

Now, there can also be differences within the local drug file on how frequently it’s updated.  If you see an NDC in the PBM data, it may not be necessarily the NDC for the actual drug that was dispensed on that day.  If the local drug file has not been updated to say there was a change in the generic manufacturer that’s now supplying our beta blocker, you’re not going to get the accurate NDC.  How important is that?  In general it’s not going to be that influential, except maybe on cost.  And cost would be the big place where there might be some differences.  NDCs contain information on the drug, the manufacturer, the strength, and then the size of the package that the product actually comes from.  So if you get a 1,000-count bottle of ibuprofen and you use that one day, but then the next time it’s a 500-count bottle, there’s going to be two different NDCs.  If the local drug file has not been updated to say, “They’ve changed from getting this in bottles of a thousand to bottles of five hundred or five thousand,” then you’re not going to get the exact match on NDC.  But that’s probably not that important because the drug is going to be the right drug; it’s going to be ibuprofen.  The strength is going to be the right strength.  It’s just going to be whether or not the overall package size available in the pharmacy itself, not the package size that goes to the patient, whether it’s thirty tablets or sixty tablets.  But the package size that’s being used to dispense the drugs in the pharmacy might be different.  If we’re just interested in “Did this person get ibuprofen?” the difference in NDC is not going to cause a problem at all.  But if we’re interested in “Is there a difference in cost?” one could imagine scenarios where there might be differences in cost between one manufacturer and another manufacturer, although when we move to generics they’re going to be roughly similar.  So this may not be that influential on cost depending on whether or not you’re using generic or brand prescriptions.  In the big picture, this slight discrepancy in NDCs has relatively no influence if I just need to identify the type of drug that a patient got and maybe has some influence or should be something that you keep in the back of your mind as a limitation if you’re thinking about measuring pharmacy costs for patients.  

Let’s move now from an overview of the data sources into several examples of how pharmacy data was used in VA studies and exactly how these investigators used pharmacy data in answering their research questions.  I’ve got some on the examples that I showed you on chronic medication use and on guidelines adherence.  One could imagine other scenarios for using pharmacy data.  One could be cohort identification; maybe I’m just interested in looking for people who are using a certain type of medication, and then I need to use pharmacy data to be able to identify that group of patients.  Pharmacy data’s been used to answer medication utilization questions.  Are there changes in medication use patterns longitudinally?  It can be used to look at natural experiments, policy changes that might influence medication utilization.

Healthcare quality, medication adherence - both are examples of how pharmacy data might be used in a research setting.  Exposure to specific medications or medication classes in thinking about comparative effectiveness research or comparative safety research, we often use existing data sources to compare medication exposures or different groups of medication exposures amongst populations to see if the benefits or harms are different across those two groups.  You can look at combinations about patient and pharmacy data to identify events.  For example, in identifying acute exacerbations of COPD it’s often a combination.  It’s not just that one data source might be sufficient.  So if a patient has an outpatient visit that has a diagnosis of COPD, we don’t know if that’s for an acute problem or for just a regular outpatient visit.  However, if that’s coupled with a medication dispensing for oral steroids or an antibiotic, we’ve got some reasonable assurance that there’s been an acute exacerbation that might have precipitated that visit.  There have been algorithms developed for combining outpatient and pharmacy data to identify these acute events.  

Finally, you might think of using pharmacy data as an indicator of disease. Because patients are on a specific type of medication, we may able to determine whether or not they have a specific type of chronic condition.  Now, this varies based on the specificity of the medication itself.  If a given medication can be used for lots of different things, we don’t have a great deal of assurance that it’s for one problem versus the other.  But if a medication is very specific to a given chronic condition, that information becomes very useful when we’re trying to identify chronic conditions amongst a group of patients.  These are just examples of ways that pharmacy data can and have been utilized within the VA system.

Now, let’s move into some of those specific examples.  Here is how VA Pharmacy data was used as a measure of chronic medication use.  This was work done by Kevin Stroupe and his colleagues where they wanted to understand whether or not an increase in copayment for medications within the VA system resulted in differential use of chronic medications.  Here’s essentially the study timeline.  Between February of 2000 and February of 2001 they used it as a baseline period to characterize their cohort.  But then in February of 2002 there was a policy change.  That’s when copayments for prescription medications were increased for a select set of veterans.  The investigators in this study wanted to measure the influence of this copayment change on chronic medication use.  Between February of 2001 and February of 2002 they used pharmacy data to measure chronic medication use during this one-year period.  They then used the same criteria for defining chronic medication use and measured it again within their cohort after the change in copayment from February 2002 to February 2003.

 To get into some of the details on how they used pharmacy data, they turned all of the dispensing information into the VA into thirty-day equivalents.  If, for example, the most common days’ supply that you see with any medication within the VA, especially chronic medication, you’re going to either see a thirty-day supply or a ninety-day supply.  What they wanted to be able to do was make comparisons on thirty-day equivalents.  They used those days’ supply information to generate thirty-day equivalents over the twelve-month period.  So if you’ve got a ninety-day supply, that’s three thirty-day equivalents.  

I hope it’s straightforward that days’ supply was a key component of this analysis.  They needed the days’ supply data in the dataset to be able to calculate these thirty-day equivalents.  The focus was on chronic medications.  The investigators went through several steps to identify what fell into a chronic medication and one of those steps including getting rid of any medication a patient received that was not dispensed for at least a thirty-day supply.  As you can imagine, this gets rid of lots of antibiotics and short-term pain medication because these may only be dispensed for fourteen days or seven days or twenty-one days.  It wasn’t felt that the copayment may be influential on these medications.  So they wanted to use this criterion of “You’ve got to have received a medication with a thirty-day or greater supply for that medication to enter into this analysis.”  

If at least one dispensing had a thirty-day supply, they rounded up for any subsequent dispensings for that medication that may have been less than a thirty-day supply and assigned a thirty-day equivalent to that.  They then summed up all of the thirty-day equivalents in the pre-period and in the post-period and made comparisons between three specific groups.  They made comparisons between those with copayments for no drugs, which is the top group, which had 55.4 thirty-day equivalents over that one-year period before the copayment.  Here’s a comparison group, the people that paid copayments for some of their medications.  They had slightly fewer thirty-day equivalents before.  And then copayments for all drugs, they had the least amount of thirty-day equivalents, 34 thirty-day equivalents over that twelve-month period.  These were then compared to what happened after the copayment.  

For each of the groups the difference is highlighted in the column.  You can see that there was an increase in thirty-day equivalents after the copayment was increased for those that didn’t pay copayments, copayments for none of their prescriptions.  They saw an increase of 2.5 thirty-day equivalents.  For the other two groups there was essentially no change or a slight decrease in the number of thirty-day equivalents.  When you make a comparison, a difference in the differences, you can see that there’s a marked difference between what happens in terms of the number of chronic medications dispensed after the copayment between those that paid copayment for some of their medications and those that paid copayments for all of their medications relative to those that didn’t pay copayments.  So there may have been an impact on the medications that were dispensed following the copayment in that those that had to pay some additional cost received fewer medications after that copayment increase.  They did all of this work by relying on the ability to identify chronic medications and then using days’ supply information to generate thirty-day equivalents.

The next example we’ll walk through is measuring quality of care.  In this example, it’s measuring quality of depression care against a cohort of veterans with diabetes than then have a major depressive disorder.  This was published in *Psychiatric Services* in 2008.  One of the guidelines for appropriate management of patients with a new major depressive episode is whether or not they have a sufficient supply of antidepressants following the initiation of that.  The quality measure here focuses solely on how much antidepressant medication was received by a patient following this initial identification of a new major depressive episode.  

What they did is they identified a cohort of patients that had diabetes and then had a new episode of measured depressive disorder.  If you look in the depression performance guidelines, there are clear descriptions on how to make sure this is a new depressive episode.  You have to look back a certain period of time to make sure there’s nothing occurring previously.  In order to be considered guideline consistent these investigators needed to see at least three months of antidepressant medications within the six months following their initial diagnosis.  So immediately we know that days’ supply is going to be an important component of this measure in that we need to see at least ninety days of a given antidepressant medication over a 180-day period in order for an individual to be considered guideline consistent.  

They evaluated actually two outcomes, whether or not a patient received any antidepressant and then whether or not guideline-consistent antidepressant use was seen amongst patients with new major depressive episodes.  Let’s again walk through exactly how they use pharmacy data in this analysis.  First, they went to the inpatient or outpatient data to identify diagnostic codes.  Here they identified when depression occurred and from there they found out whether or not a patient had received an antidepressant prescription following that depression date.  

Now, they’ve got to merge this depression date with their pharmacy data.  And they’ve probably identified antidepressants through either VA drug classes or by looking specifically for the drug prescription, restricting it only to antidepressants.  They’ve narrowed their pharmacy data by focusing only on antidepressants through probably a combination of these two mechanisms.  “Let’s look at VA class, and then let’s look at drug descriptions to make sure we’ve got all of the antidepressants we would be expecting.”  They’re going to then look in this 180-day follow-up period that’s shown on the screen in green, how much antidepressant prescriptions the patient received in terms of days’ supply over this 180-day period.

There’s an important additional way pharmacy data was used in this analysis.  Again, this is a new onset depression date, so there’s a criterion in the guideline that says you have to look backward 120 days from the initial antidepressant prescription to make sure that there were no other antidepressants used during that period.  They’ve got to make sure there is this window of time in which there were no other antidepressant dispensings.  Then during follow-up, they figure out whether or not an individual’s gotten any antidepressants.  The presence of one of these will qualify for that outcome, and then how much.  What are the total days of therapy?  They’re going to sum up for all antidepressants that a patient received during this 180-day period.  They’re going to sum up the days’ supply to figure out how much antidepressant was received.  This was done by using probably a combination of medication class and products’ names to identify their antidepressants.

So what did they find?  They found in this specific analysis that a little over half received an antidepressant during follow-up, so just fifty-one percent of the cohort received an antidepressant.  If you look at the whole cohort, only thirty-one percent received guideline-consistent antidepressant care where they had received at least ninety days of medication during that 180-day follow-up period.  They did some regression analyses that found that age was associated with a decreased likelihood of receiving an antidepressant, any antidepressant during follow-up, in that if you saw a mental health provider or had a mental health specialty visit there was a marked increase in the likelihood of receiving an antidepressant for that major depressive episode.  If you look at guideline-consistent care, the only significant factor in terms of age here is those over seventy-five were much less likely to have received guideline-consistent antidepressant care and the same increased association between having seen a mental health specialist and having at least ninety days of antidepressant therapy during that follow-up period.

The next example is focused on medication adherence.  This study by Chris Bryson and his colleagues in Seattle looked at development of a new medication adherence algorithm.  Many of the medication adherence algorithms we use look at long periods of time, like a year or two years.  Their goal was to look at shorter periods of time and developing what may be more responsive medication algorithms in terms of a disassociation with short-term outcomes we might be interested in.  They used data from a large study, ACQUIP, to compare three measures of medication adherence.  

The first is MEDSUM, which is basically medication possession ratio or MPR, if you’ve heard of that term before.  It’s the total number of days’ supply divided by the total period.  The second is MEDOUT that looks at the number of gaps in medication, so how many holes that you have between when you thought a medication should end and when the next one should be dispensed, then their ReComp algorithm.  They used these three outcomes to look at the association between the adherence to simvastatin and LDL cholesterol, antihypertensives and blood pressure, and then beta blockers and heart rate.  

Here’s just an example of walking through exactly how the algorithm for ReComp is calculated.  They do it for both ninety days and thirty days.  This initial person had three dispensings.  So in the first thirty-day block their ReComp measure is zero; the second thirty-day block it’s one because they have a thirty-day supply during that thirty-day period.  The third block is one.  And if it’s done over a ninety-day period, you can see that they have two thirty-day supplies for sixty-seven percent or sixty of the ninety days in which the person had a medication.  They do the same thing for the second ninety-day block.  Example “b” shows you that their algorithm actually carries over extra prescriptions.  So in this, the second thirty-day block, there are two thirty-day prescriptions.  In the next thirty-day block it will actually carry forward one of those extra prescriptions [inaudible].  I would point you to this paper if you want additional details, but essentially what they did is then compared their ReComp measure to both the MEDSUM or the MPR measure and the medication gaps measure, which in this case tells us what percent of the time, one minus their gaps.  So what percent of the time did they not have a medication gap?  The actual mean values for the LDL cohort were .47 for ReComp, .45 for their MPR value, and then .75 for one minus the gap measure.  You can see differences here between the measures when we move to those with blood pressure measure and those with a heart rate measure.  

Now, they then compared those to relatively short-term outcomes, not heart attacks or longer term outcomes that we’ve seen medication adherence compared with but rather short-term outcomes in terms of what’s its impact on cholesterol changes.  You can see that for each of these measures ReComp has a higher R-squared for both the thirty-day measure and the ninety-day measure when comparing these adherence measures for simvastatin to LDL cholesterol.  The same is true for blood pressure.  Then for heart rare, at least the thirty-day measures have an R-squared value, whereas the ninety-day measures are more similar between the two traditional measures of medication adherence and the ReComp measure.  Just know that this is an available resource for investigators if they’re interested in measuring relatively short-term medication adherence.  Again, I’ll point you to Dr. Bryson’s medical care paper in 2007 for additional information.  

Here’s an example of how we measured medication exposure amongst a cohort of patients with COPD when we’re looking at the association between specific medication use and overall mortality.  It’s a case control study in COPD patients.  We identified all cause and respiratory-specific mortality and cardiovascular-related mortality, and wanted to really identify the association between specific medications and patterns of medication use for these events.  We used pharmacy data in lots of ways, whether or not a medication was ever used in the prior 180 days.  We created medication algorithms to determine a regimen and then whether or not somebody might be an active user.  That is, does their days’ supply extend to the date in which they’re having an event?  

What I really want to talk to you about with respect to this example is how we used the dispensing directions.  Here’s an example of some dispensing directions for respiratory medications that our cohort received.  I highlighted one here in a red box that’s an inhaled corticosteroid that says a patient got this drug fluticasone, the strength 220 micrograms per spray.  The fact that it’s a thirteen gram inhaler will help us understand exactly how many actuations are available.  They received one.  But the thing I also want to point you to is that the richness of information that’s available in the SIG field, or the directions for use field.  Here it tells us that the patient was prescribed two puffs BID or two puffs twice daily.  Now, if you’re going to use this information, you’re going to probably need to bring in somebody who knows something about pharmacy data and pharmacy information because you’ll be able to see very quickly that if you have to parse information out it can get relatively complex to do that.  Here in line seven we’ve got “inhale two puffs by mouth four times a day.”  And here in the next line, line eight, we almost have exactly the same information.  But it’s written in shorthand.  This SW will translate to “shake well” and “inhale four puffs by mouth,” QID is four times daily; UD is as directed.  So you’ve got to have somebody with expertise or understanding of how this information will then translate into how much medication a person is going to be receiving.  We use this information, VA product in the SIG, to determine what specific medication a person received, the strength and the number of actuations in that product, the SIG to determine how many doses per day they received, and their total dosing frequency.  We used that information and calculated inhaled corticosteroid equivalence.  But essentially we had to break out the drug, the strength, and the size of the container to figure out how many doses of fluticasone they’re going to get and what the strength of that is to be able to convert it to an overall strength of a medication.

The final example is risk adjustment.  As I talked about earlier, you can use pharmacy data to identify chronic diseases, especially if there’s a high degree of specificity for that medication for that disease.  These investigators compared RxRisk-V, which was a specific pharmacy risk adjustor, to other existing risk adjustors, specifically the hierarchical conditions category HCC, the ACGs, and then the traditional RxRisk.  What they found is that RxRisk-V performed relatively similar in predicting prospective costs in the next twelve-month period to more traditional measures like the HCCs and the ACGs.  So when we move both to the unadjusted and the adjusted R-squared, pharmacy data performed similarly to these other more traditional risk adjustors and may serve as another mechanism for risk adjustment.  

We’re right at the top of the hour.  I apologize for not getting to your questions, but I’m going to point you to some resources on where to go for more help.  The VIReC Web site contains a great amount of information on pharmacy data, and specifically the RUG will contain a lot more details in terms of the information that I talked about today.  Then I would also point you to the listserv.  There is an ability to search the archives and ask questions.  There are just lots of good information on pharmacy data available on this listserv for people who have probably come into the same questions and tried to answer them previous to yourselves.  You may be able to find lots of answers to your questions on this listserv.  I’d encourage you to look there for additional pharmacy-related information if you’ve got any questions.  Then finally, you can always contact the VIReC help desk for your questions.  Thanks, everybody.

**End of Recording**

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