**Transcript of Cyberseminar**

**VIReC Database and Methods Seminar**

**Using VA Corporate Data Warehouse for Health Services Research**

**Presenters: Polly Noel Noel, PhD, and Laurel Copeland, PhD**

**June 4, 2012**

Margaret:Welcome to VIReC’s Database and Methods Cyber Seminar entitled “Using VA Corporate Data Warehouse for Health Services Research.” Thank you, Decider, for providing technical and promotional support for this series. Today’s speakers are Polly Noel Noel, PhD and Laurel Copeland, PhD. Dr. Noel is a Associate Director, Veteran’s Evidence Based Research Dissemination and Implementation, South Texas Veteran’s Health Care system. 

Dr. Copeland is a Research Health Scientist at the Center for Applied Health Research, established in 2010 by Scott and White Health Care System and Central Texas Veteran’s Health Care System.

Questions, as Heidi mentioned, will be monitored during the talk in the Q&A portion of the GoToWebinar, and will be presented to doctors Noel and Copeland at the end of their talk. A brief evaluation questionnaire will pop when you close GoToWebinar. Please take a moment to complete it. I am pleased to welcome today’s speakers, Dr. Polly Noel and Dr. Laurel Copeland.

Dr. Polly Noel:Hello, I am Polly Noel, and I am going to be starting off the cyber seminar today. And we would like to thank everyone for joining us, and also thank for VIReC for inviting us back to make this presentation. This presentation is meant to serve as an introduction to the Corporate Data Warehouse. We are going to review data contained in the Corporate Data Warehouse. Some of the limitations in using the CDW and our own experiences in using CDW data for health services research. 

During our presentation, we are going to provide a brief overview of CDW, review possible sources in CDW data, briefly describe two evaluations of the quality of CDW data and relate our own experiences in using CDW anthropometric data in an HSR&D-funded research project. Describe a more limited experience using blood pressure data from the CDW, and also offer some recommendations to consider when using CDW data, and identify some additional resources that might be helpful. 

Before we begin, we would be interested in knowing how many members of the audience could rate their overall knowledge of the Corporate Data Warehouse.     

Margaret:The responses are coming in. We are at about sixty-seven percent. I will give it a few more seconds before I close this out. [short silence] There is your responses, and if you could just take a second to talk through them. We do need them for the recording.

Dr. Polly Noel:Oh, yes. It appears that about a third of the participants indicate they have no prior knowledge of the CDW. And with the additional third indicating slightly more knowledge about the CDW, and another third indicate more moderate to expert-level knowledge of the CDW. So, a range of participants today.  

Margaret:I am just going to go right into your second poll, if that is okay? 

Dr. Polly Noel:We were also interested in knowing from the participants, how many of you have ever used data from the CDW for research?

Margaret:The responses are coming in. We are at about sixty-six percent right now. I will give it a few more seconds before I close this out. There is your responses.

Dr. Polly Noel:Okay. About two thirds have never used CDW data, but hope to in the future. Whereas, about a third are either currently using the data for their research, or have even published papers using CDW data. Let’s see. I think we also had the whiteboard question or...?

Margaret:Yeah, but we don’t have a whiteboard so I will need you to bring your slides up so that we can see the question.

Dr. Polly Noel:Okay, okay. 

Margaret:And to Polly, as people type answers to your question in the Q&A pane, I will read them to you, okay?

Dr. Polly Noel:Okay. So, yes. Now we are wanting people to indicate if they have never used the CDW, what you want to learn about the CDW or what data you want to actually get from the CDW, some day in the future.

Margaret:And as we no longer have a whiteboard available to us in our Webinar software, if our audience could use your Q&A pane to submit your questions to us, and we will put those out over the phone line. Thank you. 

Okay, Polly, first question: Access TIU data. Second question: How do I access this data, and how do I get the necessary permissions to extract the data? How do I access data for HPDP, patient care and utilization data, pharmacy and/or lab data? It goes on and on. I think that Heidi can record this and we can get it to you. You probably want to continue with your talk. 

Dr. Polly Noel:I think we can address some of those at the end of the presentation, or they will be covered by the presentation. Okay, we would like to start by giving an overview of the CDW. First, we want to just review the types of research that Corporate Data Warehouse is useful for. We knew that the Corporate Data Warehouse would be made available to researchers several years ago. 

It generated a lot of excitement, because the CDW contains information that had not previously been available at the national level. Specifically relevant to my group, it included data from the Vitals Package of the CPRS, such as blood pressures, pain assessments, and heights and weights needed to calculate body mass index to assess obesity status. 

This type of data is extremely useful for health services researchers, and it can be used to define patient cohorts, control for disease severity or co morbidity, and/or assess for patient outcomes. These are some of the example of the types of research questions, health services research questions, that might be addressed with data from the CDW. Such as a study that is interested in looking at quality of care, those patients in the BMI class that recommend preventive screening. Other examples are listed on the slide.

The Corporate Data Warehouse is a national repository comprised of data from several VHA clinical and administrative systems. So, it provides, in a sense, a nationwide view of all CPRS and Vista systems or data contained within those. It was originally created to provide data and tools to support management decisions, performance measurement, and research objectives. The CDW contains information dating back to fiscal year 1999. And current data is added nightly. 

This means, that unlike the medical staff datasets, the CDW files are not static, and that files may not be precisely replicable if data is re-extracted at a later date. Or if the same data is re-extracted at a later date. Also, unlike some of the other national databases such as the medical staff outpatient/inpatient databases, which are organized into sets of files separated by fiscal year and date type, the CDW is a relational database. 

Currently, the CDW includes data on consults, health factors, and vital signs data, as well as...it now contains the DSS national data extracts lab, pharmacy, data, and inpatient and outpatient encounter data.    

Our seminar today will focus primarily on the CDW’s Vital’s data. I would like to start by clarifying that by vitals, we mean data originally derived from the Vital’s Package of the DHA’s electronic medical records. These include vital signs such as blood pressure, respiration, and temperature, anthropometric measures such as height, weight, and waist circumference, as well as other measures such as pain scores. In particular, we will be discussing our own experiences in using anthropometric data in blood pressure. Dr. Copeland is going to briefly review the history of the population of the vital signs within the CDW. 

Dr. Laurel Copeland:This slide was actually prepared by Betsy Lancaster who is a VISN sixteen programmer, who has taken on a major role with the CDW. And, she did a lot of data validation work with the VISN sixteen data, when the CDW was getting started. Here you can see that the proportion of patients that are included from 2007 to 2009...keep in mind, this is a rather old type, is ultimately large compared to the all time numbers. That is to say, the inclusiveness has definitely gotten better over time. Polly?

Dr. Polly Noel:Okay. Now we would like to discuss the different type of data errors that might be present in VW Vitals data and other similar types of data. Before we go into details, it is helpful to first summarize the process by which anthropometric and blood pressure data is generated in the VA and transferred to the CDW. First, vitals data and anthropometric data are assessed by clinical staff, and hand-entered into a CPRS, which of course is the user interface for VistA, the VA’s integrated system of local information systems. 

The data are stored in VistA and transmitted via HL7 messages to the VA’s Data Health repository, from which the CDW extracts, transforms, and loads selective data into its own structured query language data fields.   In addition, many VISNs have, over time, created data marts or data warehouses that also support clinical administrative functions. So, the same data may exist at several different levels within local VistA systems, within VISN data warehouses, and also within the CDW. The CDW is updated daily, and any data values that might have been changed or written over are not maintained. 

The CDW is a continuously, regularly updated warehouse holding no stable reference files, which is unlike the VA’s medical staff data sets. Furthermore, while out-of-range values are clean from medical staff datasets, errors and values that are out of range, will be found in the Corporate Data Warehouse, so it is important to be aware of this if you use CDW data for your research. Now are going to spend a bit of time reviewing the different types of errors that might exist in CDW data. These include measurement or reporting errors, data entry errors, and data transfer or extraction errors. Although many of the examples we are going to give will be based on anthropometric data, they can also be applied to blood pressure data. 

Errors can arise during the initial measurement or reporting process in a variety of ways. Specific to anthropometric data, equipment may be incorrectly calibrated or patients may be inconsistently measured with or without shoes or clothing. The clinicians who take the measurements may round the values up or down. 

We know from our clinical colleagues self-reported heights may be entered into CPRS, instead of heights that are measured by staff or clinicians. Sometimes, heights are not re-measured over time, and that it is part of reports or in data, which suggests that the last entered data is just simply carried forward without being formally re-measured. 

In addition, data may be biased in that weight or height may be less likely to be measured for specific populations, such as morbidly obese patients, amputees, and inpatients - especially if appropriate equipment are not available. And this, we know, was a particular problem at our local facility a decade ago, in that the facility simply did not have scales that accommodated more obese patients. 

So it was a problem in obtaining accurate weight assessments of these individuals. But, I believe that has changed over time. Other errors can arise during the data entry process. During data entry, clinical staff may inadvertently transpose numbers. For example, they might enter two hundred and eight-one pounds instead of two hundred and eighteen pounds. They may accidentally key a number adjacent to the target, such as typing in eight hundred and thirty-two pounds, instead five hundred and thirty-two pounds. Or they may add or delete numbers by mistake. For example, typing in one thousand, one hundred and sixty pounds instead of one hundred and sixty pounds. 

We have also seen cases where staff may erroneously transform values. For example, entering in a height for someone who is five feet, six inches as fifty-six inches instead of sixty-six inches. Although the data fields in CPRS have filters or range checks to prevent the inadvertent entry of erroneous values, at least in some systems the ranges can be so extreme that they still leave substantial room for error. 

When we checked last year at our facility, the range checks on CPRS in our system allowed for entry of weights between zero and fifteen hundred pounds, and heights between zero and one hundred inches. And if these extreme values are entered in locally, then they will be rolled up into the Corporate Data Warehouse. 

Finally, data errors can also occur during the data transfer or extraction process. As I mentioned before, the same data can exist in several different sources at different levels within the VA, VistA systems, data warehouses, and the CDW. Variations can occur from the time data were originally entered in the CPRs, and when that same data were uploaded into CDW or a VISN data warehouse. 

Variations among these sources can occur for a variety of reasons. For example, data can be lost in transmission. Different filters can result in the inclusion of slightly different subsets of data. So, it is possible for cases or records to be in one form and not in others. But, these cases appear to be rare. But, it is also important to keep in mind that VistA systems and the CDW are constantly changing. CDW is refreshed nightly, but similar updates are not performed simultaneously by all VISN data warehouses. So there may be temporary differences between the CDW and individual VISN data warehouses. 

Other types of problems can arise during transfer and extraction of data from one source to another. Numeric data can be redefined as character data or rounded as stored with a smaller number of decimal places. And specific to the CDW, anthropometric data are stored in both text and numeric form. The text still displays as-is from the VistA extraction, while the numerical field is generated by a very conservative transformational algorithm. So, it is important to know what you are requesting and what you get. 

Errors can also arise due to miscommunication between programmers and members of the research team. A programmer may misinterpret a request or the research team may fail to specify exactly what they need. So, it is important to be clear and to check the interpretations out on both ends.  

Now I would like to briefly review two complementary projects that evaluated the quality of the CDW when the data first became available a couple of years ago. Generally, it is important to assess the quality of any novel database or data. But, the assessment of the CDW data log was complicated by the massive volume of data it contained. And, the general lack of easy access to the over one hundred separate VistA systems maintained by the VA’s new regional network. 

Since the VA no longer uses paper charts, VistA is considered to be the gold standard to check the quality of the VA’s national or regional data repository. Although both projects were able to access VistA at minimal levels, some of the primary quality assessments compared to CDW data to VISN data warehouse data, viewing the VISN warehouses as a proxy for VistA data. 

Our first evaluation was conducted by the VHA Support Services Center or VSSC by Betsy Lancaster. The VSSC monitors key indicators of quality, quantity, and cost of VA patient care, as well as compliance with mandated clinical practice guidelines. Because the usefulness of its work relies on the quality of the various data sources it uses, the VSSC undertook an evaluation of CDW when it became available. So, in particular, it compared CDW fields, data fields to one another and to overall patient utilization over time to see if they were populated as expected. Examined the data for biologically implausible values, and compared data from ten facilities. Data from both the CDW and one VISN warehouse. 

So this slide summarizes the results of their evaluation, as to whether or not the data fields were populated as expected, over time. As a reference point, between 2004 and 2007, the number of unique patients in the VA grew from 4.8 million to 5.2 million. The most highly populated data fields included blood pressure and the pain assessments. Specific to blood pressure, the number of records increased from about 3.6 million to 35.3 million. In comparison, weights were recorded far less frequently and increased only from 14.7 million to 15.5 million weights recorded each year between 2004 and 2007. 

There were approximately forty percent fewer heights recorded than weights. There were even fewer recorded assessments of weight circumference. We believe this pattern reflected general, clinical practice during the time period. As research in non-VA and VA studies have also found that heights tend to be recorded less often than weights, in reaching clinical practice. Another interesting pattern that we observed is that of all the measures, only height decreased over time. 

Although we can’t be certain, we also believe that this may have reflected clinical practice as opposed to missing data records, in that once a height has been entered on a patient, clinical support staff may feel less of a need to re-measure. Perhaps because of the assumption that adult height is relatively stable. And that as the population of veterans increased over time, the height measurements simply do not keep up with that. At least during that time period. 

In addition, the VSSC found that fewer than one percent of the 15.5 million weights and the 6.3 million heights recorded in fiscal year 2007, were outside the biologically implausible range. And this is not actually shown on this slide. But once again, the VSSC in their evaluation, showed that biologically implausible weights outside say, higher than seven hundred pounds and less than seventy-five pounds, and heights outside the range of forty-eight inches to eighty-four inches were reported in the CDW. 

The VSSC evaluation also found what appeared to be an obvious transmission error. When they compared CDW and VISN data warehouse where ten facilities and one VISN, and examined the total number of monthly records over a three year period, they found that the number of records in the two sources varied slightly by less than one percent, due to a difference in refresh rates. 

Beginning in mid 2006 and coinciding with the new CDW extraction method, the CDW began showing consistently fewer cases than the VISN warehouses. But, a significant anomaly occurred in November 2007, resulting in nearly five thousand fewer records in the CDW due to a transition failure. This problem and other issues identified by Betsy Lancaster that worked in VISN 6, but it does show that there is that potential for transmission errors to occur.  

Members of our own team also conducted a second evaluation of CDW data that we obtained for an HSR&D funded study. Data required for this study included heights and weights to define a cohort obese primary care patients. In 2002, to track obesity care they received through 2006, and the impact of the practices on BMI and other outcomes. 

At the time we were funded, the CDW was not yet available, so we designed the study to first obtain data from VistA systems or data warehouses from six different VISNs, indicating that we would try to use CDW data, if and when it become available. We were, of course, hopeful that we would be able to use the CDW data, because the difficulty in defining multiple IRBs, to access data in six different VISNs. We were thrilled when we received indication that the CDW was ready to start releasing data for research, and we applied for it. It may have been one of the first to obtain CDW for research purposes. 

Because it was this novel data source, we want to first check out the quality of the data, in regards to the height and weight data we wanted to use for our study. First we identified the number of patients matched on SSN, who have had the same number of measurements, or no recorded measurements in both the CDW and one of the four VISN data warehouses, over a three-year period by data type. This data presents the results for the weight data, and indicates that concordance generally range from about 97.5 percent to 99.5 percent. The only exception being a concordance of only 62.3 percent for one of the VISNs in fiscal year 2002. 

Height data not shown showed a similar pattern with very good concordance for all the VISNs in all years, except for this one VISN in fiscal year 2002. Further examination of our data indicated that there was no height and weight data from this particular VISN for five of the facilities in fiscal year 2002. And we were not able to obtain clarification from the programmers who performed the instruction for us as to whether or not the data were actually missing from the data warehouse, or some had been inadvertently been omitted during creation of the data file for the study. 

Also, we examined the concordance from our weight and height values. We don’t have a separate slide for this. We were interested in looking at the values that were recorded at the same day and time, at the same facility, for the same individual matched on SSN. And looking at that concordance for the values...the ones that were available in the CDW versus the VISN data warehouses. And, the concordance for these were excellent, ranging from 99.7 percent or higher. 

Finally, using CDW data only, we examined the extent to which height and weight data contained improbable variation in repeated measurements, recorded on the same day or within the same year, for the same individual. We looked at measurements recorded during three years for individuals in all VISNs for which we had data. Since we found similar findings across years, this data shows data for fiscal year 2006 only. 

And this also only reports the height data. So, over thirty-three thousand occurrences, in which heights of individual patients in which individual patients had two or more heights recorded on the same day, the majority were equivalent. Another five percent were different by one inch or less, which might be explained by variations in measuring patients with or without their shoes on. But the remaining seven percent, fell into more improbable ranges of variation, as much as ten or more inches. 

We also found improbable variation among individuals with two or more heights recorded in the same year. The majority of heights were equivalent, but over five percent of individuals, these last three categories, had same year variation that fell into improbable ranges. Once again, differences of up to ten inches or more. Although one can expect more variation in weight, we also found improbable levels of variation in height. 

We found this in about eleven percent of over one hundred thousand occurrences in which individuals had two or more weights recorded on the same day. It is not shown in this slide. And in about seven percent of thirty-three thousand individuals who had two or more weights recorded in the same year. So, this would have been weights that varied as much as a thousand pounds within a single day or over the course of a year, which is highly, highly improbable. 

So, to summarize our evaluations of the quality anthropometric data in the CDW, indicated that weights are more likely to be recorded than heights. And those were more likely to be recorded in waist circumference, reflecting in all likelihood, clinical practice at the time. Although, it is expected that recent initiatives in performance measures targeting screening for obesity, has increased these over time. Also, that some of the anomalies due to transition errors can occur. But at least the ones that have been previously identified have been corrected, as a result of the administrative work of the VSSC. 

We also concluded that the concordance between the number and values of heights and weights stored in the CDW and VISN warehouses was generally excellent. And that none of the biological implausible values were noted, as well as biological improbable variation in repeated measurements. 

So, we feel that the CDW appears to reliably reflect heights and weights recorded in the VistA and VISN level resources. But, data errors are present in both data sources and researchers need to be mindful of this when using this data. But in spite of these errors, CDW is a viable and indicated previously. 

And now, I would like to briefly summarize the results of a case study derived from a funded HSR&D study examining obesity care practices in the Veteran’s Health Administration. The goals of the project were to describe variations in the provision of obesity care practices, and examine system provider and patient-level factors that predict these variations. We also wanted to examine the impact of obesity care on BMI and other important clinical outcomes. And also to identify longitudal patterns of BMI trends over time. 

Both access to the CDW heights and weights greatly facilitated our ability to complete the project. We knew we faced challenges, because our preliminary quality evaluation suggested that the data contained some errors. But, we wanted to be able to guarantee, to the best of our ability, to distinguish between variation that was due to error as opposed to true variation. For example, weights are inherently variable over time. 

Minor weight fluctuations can occur day to day, even within the same day, for some individuals. Moderate weight losses and gains can occur due to changes in energy balance over time, and more dramatic weight or gain can occur due to disease, surgery, or traumatic injury. Even with heights in adults, heights can change due to injury, disease, or the aging process. We knew one of our challenges would be to distinguish between changes in height and weight that were due to error, as opposed to those that were real, and find a reasonable way to eliminate or reduce the impact of these errors in our data. 

Our original plan for the study was to identify cohort of obese primary care patients in fiscal year 2002, and track BMI and other outcomes through fiscal year 2006. Because we already suspected from our own pilot work, that heights were recorded less frequently than weight, we knew that for any given year, a patient might not have a height recorded. 

So, we originally planned to use the last recorded weight in fiscal year 2002 and the last recorded height in 2002 or the first available in fiscal year 2003 through 2006, to identify our cohort. We had also planned to use a filtering scheme to remove biologically impossible values for the adults of veteran population. 

Our data showed that of over one million primary care patients served in fiscal year 2002 by the six VISNs targeted in our study, only eighty percent had both the height and weight recorded in fiscal year 2002. Searching forward, we were able to find heights for an additional 8.5 percent of the patients. We were unable, however, to calculate BMIs for over eleven percent of the population of primary care patients, because they simply did not have sufficient height or weight data recorded, thus limiting the size of our cohort. 

Furthermore, even though we knew we had trimmed the biologically implausible values, we knew that improbable variation remained. We might not have worried about it, assuming that these errors are random infecting only a small percent of records, but when we used a single height and weight to calculate BMI, we kept getting extreme BMI values. BMIs greater than a hundred. So, after a bit of trial and error, we decided on a strategy that we felt would best minimize the impact of these extreme outliers that were still in our data. 

To identify our cohort, we were really not that interested in establishing the precise point prevalence of obesity, we just wanted to identify individuals who are most likely obese any time in fiscal year 2002. So, for our weights we divided the fiscal years into quarters, and calculated the median weight, if available, for each quarter. For height, we used the modal height among all heights available between fiscal year 2002 and 2006.    

There were over nine thousand patients who had two or more modal heights. And so, we ended up averaging the heights for these individuals if the difference between the modes was three inches or less. We deleted over seventeen hundred cases of individuals with modal heights that differed by three inches or more.  Our baseline BMI was then calculated using the median weights for fiscal year 2002, and modal height for fiscal years 2002 and 2006. And then to track BMI, we calculated the median weight so the average weight for each twenty quarters across the five-year study period. 

For our tracking the BMIs across the five-year study period, we also used the same middle height used to calculate baseline BMI. We know this may have actually introduced some error for older patients, because studies clearly indicate that the average man would have lost 5 centimeters from their maximum height, and the average woman will lose approximately 6.2 centimeters and this process accelerates with age. 

And that such processes can be aggravated by conditions such as osteoporosis. However, we felt this approach was necessary given the outliers in the data. And, we simply noted that as a limitation in our research. And that our analysis was conducted assuming stable height of over time, when we calculated the BMI trend. 

So this review kind of indicated some of the modifications that we had to make to our study, based upon the limitations of the data. And now, Dr. Copeland is going to review her experiences with blood pressure data and some of the strategies and limitations that might occur with that kind of data. 

Dr. Laurel Copeland:Hi, this is Laurel Copeland. Go ahead with the next slide, please, Polly. I looked for publications that use CDW data prior to the presentation, and came up with several examples from across our VISNs, but primarily on the west coast. I think I found publications out of Seattle and L.A. and VISN nineteen, and then Medford had quite a few. But I think the rest of us need to get cracking. 

In terms of publications that focus specifically on blood pressure, I didn’t find one on CDW data, but [Inaudible name 00:46:04] and her colleagues that that VA Ann Arbor have published quite a bit off of the Vista data, which of course, is similar data. And they used these guidelines for valid values for blood pressure. That is, 75 to 250 close up for systolic, and 25 to 180 in close up for diastolic values. They did mention that they usually use the last value in a period. Next slide, thanks.

For my study on late life patients with Schizophrenia, which started with a cohort in 2002, I requested CDW data on their blood pressure values and I got 2.5 million records for about 39 thousand patients. 

When I took a look at the data, I found that there were 14 thousand records that didn’t have valid numeric data or both diastolic and systolic pressures. About .7 percent had extreme non-missing values. Primarily as 0s, actually. And then there were very few high values exceeding that suggested guideline of 250. So, I actually had only a 119 records that showed a systolic value over 250, and 13 records of diastolic pressures over 180. So, mostly the problem appears to be missing data. Next slide, thanks.

For this study, I had a cohort of about fifty-two thousand patients who were treated for Schizophrenia in fiscal year 2002 who were aged at least fifty-two years old in that year. Among them, they were many who had already been diagnosed and treated for Diabetes. I excluded them for this particular study, because I was interested in looking at monitoring of patients who are at risk for dis-regulation of their glucose, but weren’t already being treated or had not already been identified. 

So, I limited my study to thirty-nine thousand, two hundred and twenty-six persons, and then I got their data from CDW as I mentioned. That 2.5 million records, and then I looked at another interesting variable that I think you should all pay attention to, and that is where were the blood pressures taken?  

So as you know, when a patient is an inpatient, their in an acute state and much more likely to have a relatively higher glucose and blood pressure values or if they are [Inaudible word 00:48:34] very low blood pressure values. But at any rate, they are not your normal outpatient values. You need to pay attention and always request your location variables when you ask for blood pressure data from CDW. 

I usually found that seventy percent of the readings were for inpatient encounters, so of course, we are checking those patients all the time. Thirty percent of readings were from outpatient encounters and six percent had no location listed. Alright, in terms of how many people, about three quarters are outpatients and about one quarter were inpatients. Of course, there was some overlap. Those were the ones at the valid locations. On to the next slide.  

I wanted to look at whether hypertension was a predictor of survival in black and white patients with Schizophrenia. The sample site is a little bit smaller because of missing data on race. You are probably aware that race is not always recorded on databases. It happens that Schizophrenia patients are frequently inpatients, so the level of missing data was not so bad. I have thirty-four thousand persons in this analysis. 

I had a little regression model to look at the association of blood pressure with mortality or survival. Well, it isn’t related. The answer is, it depends on what you look at. In general, I looked at average measures and found they were protective as an inverse association with mortality. The maximum were positively associated with relative odds of death, and the minimum were inversely associated with relative odds of death.  

This is obviously not a study I published, it is something I did for this presentation to illustrate that it really matters which value you choose to look at. So, when you are setting up your space, think it through. What are the measures I am going to look at? What am I going to do with these millions of records of blood pressure, and what will it mean at the end of the day? Go ahead with the next slide, please.

Number one, I will just go quickly through some recommendations and let you have some questions at the last few minutes of this presentation.

Margaret:Laurel, this is Margaret. There are lots and lots of questions, and people can look at the slides for the recommendations. So maybe you can quickly go over them, and then I will give you a few of the questions and audience, the remaining questions will be gathered, will be answered, and will be sent out to everybody. So, go ahead, Laurel.

Dr. Laurel Copeland:Okay, thanks. Polly, could you skip a couple of slides. Go up to “How to Acquire CDW Data.” The way you do this is, you start with Dart – the Data Access Request Tracker. Many people want to know, “Can’t I just access the data myself?” The answer is no, you cannot. You need to have the CDW programmers extract the data for you. We recently learned on HSI data listers...go ahead and skip up a little bit, a few more slides...that the National Data Extract CSS files, the thing that gives us cost data and pharmacy data that is not from PBM, are going to be fully to CDW as of this coming October. So pay attention to where you get your data and how do you get it.   

I am going to skip ahead to the end of these slides. These are mostly URLs, slides you can go to, to get more information about what is in the CDW and how you get it. How you get help if you have questions. You really need to join the HSIS data list serve. So, you can go ahead and skip through these. I am ready for your questions.

Margaret:Okay, Laurel. Thank you very much. There are lots of questions. Let me pick some that you can address before the top of the hour and the remaining will be answered and sent to the audience. First question, are all vital signs manually entered into VistA CPRS, and then extracted to CDW, or are any of the measures...for example, blood pressure...collected from the devices through VistA CPRS to CDW?

Dr. Laurel Copeland:I don’t know. That would be a question for people familiar with clinical operations. I am not a clinician. 

Margaret:Okay.

Dr. Polly Noel:With respect to anthropometric measures, the heights and weights, I have never heard of any being...their entry being automated, the way say lab values are. With regards to blood pressures, I guess it is possible, but I have never heard of any reports of that.  

Margaret:Okay. Here is another question. How does the CDW data differ from information contained in VireC’s MED SAS Data Sets?

Dr. Laurel Copeland:It is very similar. As I mentioned, if you consider the NDEs from DSS to be part of the Medical Test Data Sets, which many of us do since they are just sitting there for our taking at Austin, then it is actually the same data. If you wanted to do a validation study, I suppose you could do that, but you have to keep in mind that the source is the VistA System, which is where we are getting the CDW copied out of. And also, keep in mind the fact that the Medical Test Data Sets are static, so they are closed in the middle of October. And they don’t change thereafter unless they come out of the revised Dataset, and then in that case it is a different path.  

Margaret:Okay. Could you briefly explain what health factors are?

Dr. Laurel Copeland:Polly, what are health factors?Dr. Polly Noel:I am actually not that familiar with that data source. I know, let’s see...one of our...when they sent out that query, I know some have reported that they used the health factor data for one of their publications. Do you remember who that was?

Dr. Laurel Copeland:No, I would have to look through archives. You know the archives of the HSIS data list are a wealth of information. Their postings on CDW about health factors might capture that answer for you. I am more familiar with the mental health scores which are a little part of health factors. I do have a list of the mental health scores. It is a very long list. Gosh, I don’t know...more than thirty different scores that are currently recorded in CDW.

Margaret:Okay. Is the process for data generation used for Med SAS in CDW Data Sets identical in terms of how CPRS, VistA data, gets transformed, unloaded into a national data set when the same constructs are assessed and appear in both data sets?

Dr. Laurel Copeland:No, the Medical SAS Data Sets go to a regulation set of processing at the DSS units at the facilities, and then they get sent to Austin, and there is more programming there that is standardized. So it is a little bit different. 

Margaret:Okay. More of this question. If one uses CDW for cohort identification based on ICD9 CM Data, could he or she expect to obtain the same cohort, as if it were obtained from the MED SAS Data?

Dr. Laurel Copeland:No, there would probably be small differences because of the static nature of the Medical SAS Data Sets versus the dynamic nature of CDW. Also, even in Medical SAS Data Sets, you have updated records. You may not be aware of that. There are records that get corrected and you have to look at the...there is a sequence number for records. 

So that when things look very similar but there is a small change, it could be an update. So you may not be looking at the correct ICD9 code in the first place. In terms of how they plays out in VistA, in CDW, I would probably guess but don’t know. It is a little more change-able.

Margaret:Okay. How can I access the Data Dictionary to see what types of data are available?

Dr. Laurel Copeland:Good question. Go ahead and look at your slides and choose some of those URLs and you will find that there are a list of variables in there. Some are a little hard to follow. We are working on some manuals to help with that, but that is what we have right now.

Margaret:Okay. Can you comment on message for queries on patient cohorts? Are you able to use the SQL in clause to capture a larger cohort based on, say, SSN?

Dr. Laurel Copeland:Yes. There is essentially, no limit on the complexity of the SQL language. The programmers are very adept. You won’t probably have control yourself, but I guess that is between you and your programmers at CDW. 

Margaret:Okay.Moving right along. We have just a few minutes. What is the nature and source of the CDW pharmacy data? Does it come from DSS or PBM? 

Dr. Laurel Copeland:It comes from DSS.

Margaret:Okay. Is there any process to ensure data integrity? For example, pain scores are zero to ten. Yet sometimes, another number is entered. This is not helpful in evaluation of pain scores.

Dr. Laurel Copeland:As far as we understand it, there is no cleaning of out-of-range data. 

Margaret:Okay.Let’s see here. Are you screening for implausible values within the initial SQL query or some other filtering after the query?

Dr. Laurel Copeland:Typically, you have to do that after the query. I prefer to take the text values and then to use them, texting query language to pull out the numeric values, jettisoning the characters that are in there, so that I get more records that are valid. At the same time, I also have to apply cleaning rules or maxima, minima. 

Margaret:Okay. How do I fill in form 9957? Where do I find the functional task codes, web servers Unix accounts?

Dr. Laurel Copeland:Okay. There is a website with a link and you can get that from VIReC and look for accessing data, and you can get to a website for researchers. I am assuming you are a researcher, and then you would get to choose either real SSN information and complete the instructions there. If you are still stuck after that, you should write and ask help from VIReC. 

Margaret:Thank you, Laurel. We have reached the top of hour. I want to thank both Doctors Noel and Copeland for the time and effort they put in to presenting. Our next session is scheduled for Monday, July 9th, which a week later than usual due to the July 4th Holiday. It is entitled, “Measuring Laboratory Use and Results Using VA Decision Support System National Extract Data.” Thank you all and we will get answers to the remaining questions disseminated for you. Good bye.

Unidentified Female:Thank you. We will see you next time.

[End of Audio]   

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