**Transcript of Cyberseminar**

**Spotlight on Pain Management Series**

**Racial Disparities in the Monitoring of Patients on Chronic Opioid Therapy**

**Presenter: Dr. Leslie Hausmann**

**May 7, 2013**

**Racial Disparities in the Monitoring of Patients on Chronic Opioid Therapy**

**May 7, 2013**

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

Dr. Robert Kerns:Good morning everybody; welcome to this month’s Spotlight on Pain Management webinar series. To remind you, this is a partnership between the HSR& D Center for Information Dissemination and Educational Resources, CIDER; VA’s HSR&D Pain Research Informatics Medical Comorbidities and Education, or PRIME Center based at VA Connecticut, and the National Pain Management Program Office. This is a monthly webinar series at this time each month, and is intended to provide opportunities for education related to important topics in the pain management area, with an attempt to provide presentations that have relevance to the health services research community, or broader research community, the practice, education and policy communities of VHA as well; so welcome. 

This morning it is my great pleasure to introduce Dr. Leslie Hausmann. Dr. Hausmann is a research health scientist and core investigator at the Veteran’s Affairs Pittsburgh Health Care System, Center for Health Equity Research and Promotion. She is also an assistant professor of medicine in the University of Pittsburgh, Division of General Internal Medicine. Her broad research interest is to identify, understand and reduce disparities in healthcare and health outcomes with a specific focus on understanding and reducing the role that discrimination plays in perpetuating such disparities. 

Today, she is going to share with us results of a pilot study examining racial disparities in the monitoring of patients on chronic opioid therapy. The title of Dr. Hausmann’s talk is Racial Disparities in the Monitoring of Patients on Chronic Opioid Therapy. Dr. Hausmann, thank you for joining us and please take it away. 

Dr. Hausmann:Thank you Bob, I want to thank everybody for tuning in today. As Bob said, I am going to be sharing the results of a pilot study that we recently completed here at the VA Pittsburgh Healthcare System. Before I launch into the study, I wanted to get a sense of who is on the call, so let me... I think at this point Heidi is going to put up a couple of polling questions. The first polling question, again just to get a sense of who is on the line, I would like to know what is your primary role in VA. The options are student, trainee, fellow, clinician, researcher, manager or policy maker, or other. 

Okay, it looks like the results are shown and we have four percent student trainee or fellow on the call, thirty-nine percent clinician, twenty-two percent of you are researchers, twelve percent are managers and twenty-four percent are other. I would love to know what some of these other categories are so we can be more in tune with who is on the line, but thank you all for tuning in today. 

I have a couple more questions for you, just to get a sense of what your perceptions are of the presence of disparities in pain management within our VA System. So, the next question I’d like everyone to answer is: In the national VA healthcare system, patients receive different care for pain management on the basis of their race or ethnicity. And I’d just like you to say how much you agree or disagree with that statement. The options are strongly disagree, disagree, neither agree nor disagree, agree, or strongly agree. Okay, the results are showing now and I see ten percent of you strongly disagree with that statement. Twenty-two percent disagree, thirty-two percent are undecided, neither agree nor disagree, thirty percent agree and seven percent strongly agree. 

All right, a couple more similar questions. The next one is very similar, although now I’d like to know about disparities at your personal VA facility or clinic, so again, this is the same sentence, but this time applying to your VA. In your VA facility or clinic, patients receive different care for pain management on the basis of their race or ethnicity. And just click on the level of agreement that you have with that statement. The results are now showing very similar to before. Eleven percent of you strongly disagree, thirty percent disagree, thirty-four percent neither agree nor disagree, twenty percent agree and five percent strongly agree. 

And then just to humor me, I have one last question, and I realize this only  is going to apply to the clinicians on the call, so for those of you who are in any way related or involved in caring for patients at the VA, please answer this question. The patients you treat receive different care for pain management on the basis of their race or ethnicity. Do you strongly disagree, disagree, neither agree nor disagree, agree, or strongly agree? Okay, this is the last question. We have nineteen percent strongly disagree, thirty-one percent disagree, twenty-one percent neither agree nor disagree, twenty-four percent agree and five percent strongly agree. 

I want to thank everybody for answering these questions for me. Again, I start the talk this way just because I will be talking about disparities in pain management, or at least one aspect of pain management, and I like to know how relevant people on the call feel that this issue might be to the VA nationally and to their VA more personally. I am proceeding through the slides on my screen, but they do not seem to be updating on ... oh here we go, gotcha. Let me know, Heidi if the slides do not seem to be matching what I am saying, but I think I am on the same page now. 

So, what I’m going to be talking about over the next hour is racial disparities in pain management and opioid monitoring specifically, but I want to start off by saying that racial disparities in pain management abound. There is an abundance of literature on this topic and a 2009 review summarized it very well. With an extensive review of the literature and they concluded that racial... there are persistent racial and ethnic disparities in acute, chronic, cancer and palliative pain care across the lifespan and treatment settings with minorities receiving lesser quality pain care than non Hispanic whites. And I put this quote up, just because it offers a backdrop for the rest of the talk in that according to the literature, it doesn’t really matter what the type of pain is that’s being treated, or the setting in which it’s being treated, there is ample documentation that minorities receive lesser quality pain care than whites. 

Now, much of the literature on disparities in pain management have focused on the use of opioids and several studies have found that opioids are prescribed less often and in lower doses for blacks than for white patients. Even after you adjust for seeing differences in severity of illness or other clinical factors that may play a role in the types of drugs that patients are prescribed for their pain. 

Now, given the risks of opioids, which can result in addiction, misuse, drug diversion overdose and death, it is debatable whether the racial difference in opioid prescribing practices actually favors whites or favors blacks. And the reason I bring this up is because resolving... This is a very valid debate. And it is one that is well beyond what I’m going to be able to resolve in the scope of the study I’m going to tell you about which instead focuses on the fact that because of all of these high risks of opioid use, all patients taking opioids should be closely monitored for treatment effectiveness and adherence. 

And, although a lot of work examining racial differences in opioid use have focused on the prescribing of opioids, very few studies have examined whether there are racial differences in how patients taking opioids are monitored once they are prescribed opioids. At the time that we began this work, we could not find anything in the published literature that looks at whether there were disparities in opioid monitoring practices. now, in the course of doing the study and before we were able to get our paper out publicizing our results, there was a study published that suggested that there may be some recommended monitoring practices that are differentially applied to blacks and whites. So, I thought it was fair to acknowledge that study here. In that study, they were looking at three practices that were designed to reduce the risk of opioid misuse. Specifically, having regular office visits, restricting early renewals of opioid prescriptions and urine drug testing. In that study, they found that two of those three practices were more likely to occur for blacks than for whites. Specifically, blacks were more likely to have regular office visits and have restricted opioid renewals, but no differences were found in urine drug testing for that population. 

Although that study offered initial evidence that our study may find some differences, it remained unclear whether the racial differences occurred more broadly in the use of recommended monitoring and treatment guidelines for patients on long-term opioid regimen. This was the first study to come out that looked at that complicated issue. That brings me to the purpose of this study, which is to examine whether racial disparities exist in recommended monitoring and treatment practices for patients on long-term opioid regimens. 

Although there are no universally accepted best practices for following patients on opioid regimens, clinical practice guidelines for use and management of opioid therapy for chronic pain has been released.  The outcomes that we try to focus on in this study were based on the recommended guidelines that were published by the American Pain Society and the American Academy of Pain Medicine, as well as guidelines that came out from the VA and the Department of Defense. I’ll be going over those outcomes in a lot more detail in just a minute. 

First, I want to give you an overview of the study. It was a retrospective cohort analysis of racial differences in the monitoring of patients in the twelve months following initiation of a long-term opioid prescription. The setting was our Pittsburgh VA healthcare system and the source of data we used was the electronic medical records that we could access through our local data warehouse. 

The study cohort included people who were age eighteen or older, who filled an opioid prescription for at least ninety consecutive days in our VA Pittsburgh pharmacy during two fiscal years. Because we were interested in... The ninety-days was an inclusion criteria because we were interested in people who were taking opioids for longer term pain management. We also wanted to exclude those who might be involved with palliative, or taking opioids for palliative care, so we excluded people who had a cancer diagnosis in the twelve months prior to their prescription, and those who died in our follow up period... in the twelve months following the first prescription. We compared our outcomes for people who had a white or black race on file. 

As I mentioned before, our study outcomes were based on recommended clinical practices for opioid management that had been released by the VA and the Department of Defense, as well as some guidelines by the American Pain Society and the American Academy of Pain Medicine. One of these recommendations was use of an opioid agreement, which is essentially a contract with the patient specifying the risks, conditions and expectations involved in beginning the opioid regimen, and also clarifying the circumstances under which opioids could be discontinued. One of our outcomes was whether each patient had an opioid treatment agreement on file. 

The assessment of pain during follow up visits is also very important for assessing treatment effectiveness, so we got us this recommendation by assessing the percentage of primary care visits for which pain intensity was documented. Just an extra comment about this outcome, with the VA having pain as the fifth vital sign, pain is actually something that should be documented at every out patient visit. However, we found early on in our... in heading up this study that was often not the case, so we knew that there would be some variability on whether or not patients had pain documented. So, we looked at whether this varies by race. 

The guidelines also indicate that urine drug screening should be used to ensure the opioids are being taken as prescribed. There is no set recommendation for how often these drug tests take place, but we reasoned that every patient should have at least one in the twelve-month follow up period after starting an opioid regimen, so we examined whether urine drug testing for opioids was conducted, and we did this in a couple of different ways. One is, we just looked at whether they had at least one test during the twelve-month follow up period. Then for those with at least one drug test, we also looked at the number of tests that people were subjected to and whether that differed by race. 

Finally, given the complexity of managing chronic pain and the risks for developing opioid dependence, it was recommended that specialists be enlisted as needed to help manage the needs of patients on opioid therapy for chronic pain. So, we examined whether patients were referred to two types of relevant specialty clinics. One was the pain clinic and the other was whether patients were referred for substance abuse assessments. I just want to mention that although these were our outcomes, we did our best to get a comprehensive list of outcomes. We also were limited in that we were focusing on those that could be assessed using administrative data. So, we acknowledge that this is not an exhaustive list of recommendations. 

Our independent variable was patient race and this was based on administrative data. We were able to compare patients who had a black versus white race, and this was because of the demographic make up of our local VA. We had too few patients in other racial categories to examine additional groups for this analysis. We were unable to take ethnicity, which is whether or not patients identified as Hispanic or non-Hispanic. We could not take ethnicity into account because there was too much missing data on that particular field in our data warehouse. So, we had very good data on black versus white race, and that’s what we used as our independent variable. 

We controlled for almost everything we could think of that could be associated with opioids regimen, and then the monitoring of patients on an opioid regimen, so this included several demographic and clinical co-variates listed here. Just going through these included gender, marital status and age of patients, the type of pain condition. We coded using ICD-9 codes and we combined... we categorized the type of pain into four categories being limb pain, joint pain, back pain, or another kind of  pain that was not one of those three main categories. We calculated also using ICD-9 codes, the co-morbidity burden for each patient using the Charleston co-morbidity index. We coded for whether patients had a history of a mental health disorder, and also if they had a history of substance abuse disorder. We wanted to distinguish between patients who were brand new to opioid regimen, or people who may have been continuing on an opioid regimen that started before our study time frame, and to get a variable, we identified people who had an opioid prescription filled in the six months prior to the study timeframe. We identified them as continuing opioid patients and other folks were identified as new opioid prescription patients. 

We also coded for the total number of primary care visits thinking that patients may be monitored differently if they have more frequent visits with the healthcare systems. We also adjusted for the maximum pain score and the total morphine equivalent of the opioid prescriptions filled during the follow up period. 

The analysis included testing for racial differences in all of these covariants as well as our study outcomes. Outcomes that were statistically different by race were then subjected to additional progression analysis to arrive at a final adjusted model for each outcome. It included race as a predictor, and all of the other variables and interactions with race that were also significantly associated with that particular outcome. And this will probably make a lot more sense when I start going through the results and show you those final adjusted models for each of these outcomes. 

Here we have the identification flow for the study cohort. We started out with about fourteen thousand patients who met the age criteria and had filled an opioid prescription during the study time frame. Approximately twenty-six hundred of them were on a prescription for at least ninety consecutive days, so they were a chronic opioid patient. We excluded seven hundred and seventy-seven of them who had a cancer diagnosis, died within our follow up period, or had a race on file that was something other than African American or white race. And this arrives at an analytic sample of one thousand, eight hundred and ninety-nine patients. 

Here are the patient characteristics in our sample broken down by their racial group and the ones in red font are those that varied statistically by race, and you can see here that our white patients were more likely to be married than our African American patients. Our white patients were also more likely to have a diagnosis of back pain than our black patients. The comorbidity burden was lower among our white patients than our black patients with fifty-two percent of our white patients having zero co-morbidities, compared to only forty-two percent of our black patients. And then our white patients were also more likely to have a mental health disorder diagnosis than our black patients. And there were no differences on these other characteristics, including there was no difference in their history of substance abuse disorder, or whether patients were new or continuing opioid regimentation. A few more sample characteristics in all of these varied by patient race. Our white patients were significantly older than our black patients. Our black patients had significantly more primary care visits than our white patients. Our black patients had significantly higher maximum pain scores during the follow up period than our white patients, and our white patients had higher total morphine equivalents than our black patients. And just a few notes about this, I mentioned that study earlier that looked at having regular office visits and that was occurring more often for black patients. It looks like even though that was not considered one of our outcomes that looked like that trend was happening in our data as well with the black patients having more follow up visits. And also, the disparities that have been shown in the literature on the pain burden being higher among black patients than white patients, is supported here with the maximum pain score being different, and also the difference in opioid prescribing those differences that I mentioned earlier were being replicated here in our sample. But these again... we are controlling for these variable as opposed to looking at some of our outcomes. But I thought it worth pointing out that we were seeing disparities that have been shown in the broader literature. 

Here are the unadjusted outcomes overall, so I have not yet broken them down by race. I just wanted to show how often these things were happening in our total sample. You can see that twenty-six percent of our patients had an opioid agreement on file. And seventy... the pain was documented at an average of seventy-two percent of the primary care visits during the follow up period. Forty-nine percent of our patients had at least one drug test on file, and of the subset of people who had at least one drug test, the mean drug test on file was four, on average. This again was during the twelve-month follow up period. 

We had generally low referrals to each type of specialty clinic, with twenty-one percent of patients being referred to the pain clinic, and four percent of the patients being referred to substance abuse assessments. 

Now, here are the unadjusted outcomes by racial group, and again, those in red are those that differed significantly by black or white race. You can see there is no difference in the percentage of patients who had an opioid agreement on file. However, the pain was documented as significantly higher percentage of primary care visits for our white patients than for our black patients, with whites having their pain documented at seventy-three percent of their follow up visits, compared to only sixty-one percent of our black patient follow up visits. 

Having at least one urine drug test on file, there was a trend towards significance with black patients being more likely than whites to have at least one drug test on file, but because this was only significant at the point oh-five level, we just decided to not pursue that in the further adjusted analysis. However, on the subset of patients with at least one drug test on file, the mean number of drug tests was significantly higher for blacks than for whites, with blacks being subjected to an average of six drug tests over that twelve month period, compared to four for our white patients. 

And then we also saw differences in the unadjusted outcomes with regard to where patients were being referred for specialty care. So for our white patients we saw, they were more likely than our black patients to be referred to the pain clinic. Twenty-two percent of whites versus fifteen percent of blacks being referred to the pain clinic. However, the opposite pattern was true for the referral for substance abuse assessment with eight percent of our black patients being referred for substance abuse assessment, compared to only four percent of our white patients. 

Now I am going to get into the adjusted models. Again, these models that I’m going to be showing you always include race as a predictor, but then also, all of the variables that were significantly associated with this outcome. So, the first outcome that I’m going to go through is the percent of primary care visits where pain was documented. You can see here, that pain being documented was more likely to happen for patients who were married. It was less likely to happen for patients who had a mental health disorder, substance abuse disorder, or had a higher total morphine equivalent on file. So, these were the other variables associated with pain being documented at the visit, although after controlling for these other important variables, we still saw that patients who were black, were less likely to have pain documented. So, this was a persistent difference in this outcome, even after adjustment for these other variables. 

Here is a similar table showing the results for a number of urine drug tests. Again, this is on the subset of patients who had at least one urine drug test on file. And here we saw that the patients who were married, had a lower number of drug tests on file. Those who had a substance abuse disorder, they were new opioid patients, or had more primary care visits, were all more likely to have a higher number of urine drug tests on file. Then what we saw for this analysis was that there was a significant interaction between race and the total morphine equivalent. What we did was stratify the model and conduct it separately for white patients and for black patients, to see what the effect of total morphine equivalent was on urine drug testing for each of these sub groups. What you can see in this stratified analysis here, is that it was a significant predictor for both whites and for blacks. However the effect was much stronger for our black patients, meaning that our black patients, if they had a higher total morphine equivalent, they were subjected to a higher number of urine drug tests in the follow up period, as compared to whites. 

The next outcome is our referrals to pain clinics. Some of the other variables that were associated with referrals to pain clinics were age, and here the previous tables had shown... were continuing outcomes, so they were coefficient. Here, this is a binary outcome, so it is an odd ratio, so the interpretation is a little bit different. Anything above one means that it is a higher likelihood of having this outcome. Anything below one means it is a lower likelihood of having this outcome. So what you can see here is that higher age, had a lower likelihood of being referred to pain clinic; but you were more likely to be referred at a higher... excuse me... A diagnosis of back pain, a mental health disorder, if you were a new opioid patient, if you had a higher number of primary care visits, and if you had a higher maximum pain score. All of those things were predictors of being referred to the pain clinic. Again, adjusting for all of those other things, we still saw that blacks had a much lower odds of being referred to pain clinics as compared to whites. 

Our final outcome was referral to substance abuse assessment and the other variables associated with this outcome were age and limb pain, both of which were associated with a lower likelihood of being referred for substance abuse assessment. Some variables that were associated with a higher likelihood of being referred for substance abuse assessment were having a history of a substance abuse disorder, having more primary care visits during the follow up period, and being prescribed, or filling prescriptions for a higher total morphine equivalent during the follow up. All of those were more like... would put you at a higher likelihood of being referred for substance abuse assessment. Again, adjusting for all of these other variables, we saw that our black patients had a much higher odds than whites. They were almost twice as likely as our white patients as being referred for a substance abuse assessment. 

Okay, so that is the main results from the study. I wanted to acknowledge some limitations. This was a pilot study and as such, we were only looking at data from our own site, so we do not know if this generalizes to the VA nationally. Given the demographic make up of our facility, we could only focus on black and white differences, so we do not know if there may be other differences if you were to expand this to other racial or ethnic groups. Reliance on administrative data comes with some limitations in and of itself. One is that we acknowledge that in administrative data, there is imperfect documentation of race, which is our independent variable, and some of these other variables we have imperfect documentation. And then use of administrative data does not capture information about monitoring practices or behavioral signs of opioid misuse. It might be documented in clinical notes, but we were not able to get at that rich data that may help explain why patients were experiencing these follow up practices in different ways. 

Despite the limitations, we draw a couple of conclusions from this study. One is that some recommended opioid monitoring strategies are not widely used. Just disparity piece aside, there’s room for improvement for some of these. For example, our opioid agreement was only on file for twenty six percent of our patients, so that’s something that could be standardized and used much more frequently than it is currently being used in our study. 

Also, the mean number of primary care visits for pain documented was only seventy-two percent. Over a quarter of our patients are not getting... they come to the VA and at their visit; their pain is not being documented, so there is room for improvement there. And urine drug testing was only used in about half of our sample, so that’s another something that may be needed to be used more frequently overall. And then we also conclude that some opioid monitoring practices are being differentially applied to black and white patients. Specifically, in this study we saw differences in how often pain was being assessed, with it being assessed less often with our black patients than for our white patients. We also saw that black patients were subjected to more urine drug tests, and they were more often referred to substance abuse specialty clinics and less often referred for treatment in the pain clinic; so the involvement of specialists was very different for our black and white patients. 

The implications are that we need to increase the use of recommended opiate monitoring strategies in clinical practice overall, and we need to acknowledge and further explore some of these disparities in opioid monitoring practices. Some future directions where we might take this work is we need to really do more work to understand the reasons for the disparities that we found in this study. Again, using administrative data, all we could do here was document that these things are happening. We cannot really say why they are happening. It could be due to unmeasured patient characteristics, or things like provider bias. 

Then another thing we need more work to understand is whether the disparities in opioid monitoring impacts disparities on pain treatment effectiveness. We know from the literature that our black patients are more likely to have unmet pain needs compared to whites. It is possible that if the disparities in monitoring patients who are on opioid regimens are narrowed, that may have an impact on some of the racial disparities in pain management effectiveness; although again, this is an open question in an area for future research. 

I want to acknowledge that this work was funded by the VISN 4 Center for Health Equity Research and Promotion Competitive Pilot Research Program and I am supported by VAHSR and D Career Development Award and the views that I have expressed here are those of my own and do not represent the VA or the U.S. Government. And at this point I am happy to take questions from the audience. 

Unidentified Female:Great, thank you Leslie. We do have a few pending questions out here, but I do want to let our audience know that if you do have a question to send in, please submit it using the Q and A screen and go into Webinar. If the dashboard has collapsed against the side of your monitor, just click on that orange arrow at the upper right hand corner of your screen and feel free to type your questions into the Q and A pane there. 

Okay, we are going to start from the top here. I thought that only four percent of the population overall was referred for substance abuse assessment, so how can four percent of whites and eight percent of blacks be referred? 

Dr. Hausmann:That is a great question. This is a function of the fact that we had many more whites than blacks in our sample, so that overall average of how many people were referred was combining the blacks and the whites. So, I didn’t take it out to the hundredth decimal place, but you would see that I think if I had done that, it would have been closer... anyway, the point is that when you break out the races, you see that in that small group of African Americans, there’s a higher percentage. That kind of gets mapped when you lump everybody together. 

Unidentified Female:Okay, great, thank you. The next question: Can you remind us what total morphine equivalent is? 

Dr. Hausmann:Sure, that is a conversion that takes all of the opioid prescriptions over a given time period and converts it into a standard measurement. Not all of the opioids are morphine, so there is an algorithm that you can apply to each kind of prescription. Each kind of drug that is prescribed and the amount that is prescribed, and you can translate that into... It is basically using a common metrics for all of the different kinds of opioids, to get a sense of how much is being prescribed. So, a higher morphine equivalent means that higher overall... a higher number of opioids, or a larger prescription is being prescribed for those patients. 

Unidentified Female:Okay great, thank you. The next question: what measurement was used to evaluate pain score? 

Dr. Hausmann:That was the zero to ten scale that is used along with the vital stats from the... like if you’re looking into the electronic medical record where you document height, weight, blood pressure and all of those things, there is a place to also document pain. So, that’s where we extracted it from and it was a one to ten scale, with higher scores meaning higher pain. 

Unidentified Female:The next question: Did you look at abnormal urine drug screens as a variable for rechecking urine screens and morphine equivalent? 

Dr. Hausmann:We... that is a wonderful question. That was something that we wanted to do, but when we started opening that can of worms, we found it was really hard to link specific outcomes of tests to specific tests that were ordered. So we were... it was a case where we had a lot of data, but couldn’t code it in a way where we could figure out how to do that analysis. So, we do not have information on whether patients had an abnormal drug screen or whether the tests were confirming or validating a positive or negative screen. That was something we were not able to do. 

Unidentified Female:The next question: Did you look at racial differences in the results of the urine drug tests? 

Dr. Hausmann:Again, that was related to the previous question. We did not do a whole lot with the results of the drug tests because the data were in a state that we could not figure out how to code it effectively to do those kind of comparisons. So we were not able to look at that. 

Unidentified Female:Has this study been published? 

Dr. Hausmann:It has. It just came out in... published in the Journal of Pain and it... I am trying to think... I mean it was fairly recent, it was earlier this year. I would be happy to... I should have put the actual citation up here. I do not know, Heidi, if there is a way to give the group that after the call, but I would be happy to share that. 

Unidentified Female:I will be sending the archive notice out to everyone tomorrow, so if you do want to send me that, I can include that with that e-mail that will be going out to everyone tomorrow. 

Dr. Hausmann:Great, yes... so I will do that... and there was also a commentary on that article, so I’ll send both the citation for the article itself as well as the commentary piece. 

Unidentified Female:Perfect and I will make sure to get that out to everyone. The next question here: Was there a difference in the percentages with the urine screen that was positive for unsubscribed opioids?

Dr. Hausmann:Again, we were not able to look at the results of the urine drug screens, so we were not clear on that one. 

Unidentified Female:I think this is just somebody trying to clarify, so your response indicates that the dominant mode of care that you are describing is for whites, so it’s blacks have such a relatively small sample. 

Dr. Hausmann:The total... yes... I’m going to scroll... I don’t know, are my slides still showing? 

Unidentified Female:Yes they are. 

Dr. Hausmann:Okay, so on this slide here you can see that there were around sixteen hundred whites and two hundred and fifty blacks. So, when you look at the overall outcomes, it is going to be skewed more towards those sixteen hundred whites, because the two hundred and fifty blacks get averaged in. but then, when you look at them by race, that’s where you see these differences. Hopefully, that’s what they were asking in that question. if not, you can feel free to e-mail me and offline if I haven’t quite answered your question. 

Unidentified Female:The next question here: On slide eleven, you have fiscal year 2007 through 2008, but in the flow chart of slide sixteen, you have fiscal year 2008 through 2009. is one baseline, or do they refer to the same fiscal years? 

Dr. Hausmann:They refer to the same fiscal years, so fiscal year 2007... oh... I see what they are saying... I may have referred to it and honestly... I don’t remember which one is right, but the... going off of here, it’s actually fiscal years 2008 and nine, and then the follow up period would be fiscal year 2010; so thank you for catching that. 

Unidentified Female:Could you control for geographic variation and how about pain clinic availability in each geographic location? 

Dr. Hausmann:This study was conducted just at a single VA, and we didn’t have... that was in Pittsburgh... we did not have access to... or we did not look into where patients were coming from because this was a single site study and so we did not control for any sort of variation in region. As far as pain clinic availability, what we looked at... again, they were all being referred to the same pain clinics because these were VA Pittsburgh patients, and we had one pain specialty clinic here. 

We looked at referrals to pain clinic. So, we were able to see from the data warehouse, we could extract whether patients had been referred, meaning a provider put into the patient’s file, that they need to be seen at the pain clinic. We do not have follow up data on whether the patients actually followed through on those visits because we felt that was something... We were interested in trying to document outcomes that were more within the providers source of control and we felt that the provider can recommend that a patient get seen at a clinic, but the provider can’t make the patient follow through on the referral. So, the outcomes for both referrals for pain clinic and referrals for substance abuse assessment, were just were they ever referred. So, the referring physician thought that those specialists needed to be involved, but whether that patient ever went back... and I think that the person’s question about did we control for access to pain clinics... all patients had access to the VA Pittsburgh’s pain clinic. I don’t know how easy it is to get an appointment there, but they were all being referred to the same pain clinic. 

Unidentified Female:How did you operationalize whether pain was addressed at a primary care visit? 

Dr. Hausmann:I think this is getting at the outcome of what pain documented, and as I mentioned in the response to a previous question, the way we operationalize that was using whether or not the pain scale had been completed for that patient at a visit, and we got that from the vital statistics associated with each visit. 

Unidentified Female:Was PTSD singled out with respect to milligrams morphine equivalence per day? And they also send in a follow up, and by race? 

Dr. Hausmann:Could you repeat that first part for me? 

Unidentified Female:Was PTSD controlled for in terms of milligram morphine equivalence per day? 

Dr. Hausmann:We did not control for PTSD by itself. That would have been something that if they had a history of PTSD, it would have been captured in the mental health disorder code that we used. So, people who had any mental health disorder, PTSD was one of the codes that fell under that category. So, we adjusted for mental health disorder overall. We didn’t look at interactions between mental health disorder and total morphine equivalent, which I think is more of what the question is getting at, but we controlled for each of them separately, so control for mental health disorder and we controlled separately for the total morphine equivalent. We looked at different mental health disorder categories, to see if we could get a more fine-grained analysis, and when we only had two hundred and fifty African American patients, it is hard to break things down into a more fine-grained category, so we could not look at that diagnosis all by itself. 

Unidentified Female:Were the races of the providers available and was there any correlation between provider and patient race? 

Dr. Hausmann:That would have been one of those questions I would have loved to have been able to answer. We were unable to because patients see a number... although each patient is assigned to a primary care provider, at any given time that assignment can change and we looked in to whether we could link patients to specific providers and it just got to be too overwhelming. So we were not able to look at... link patients to a single provider, and that meant we couldn’t look at any of the demographic characteristics of the providers who were caring for these patients. So no, we were unable to do that. 

Unidentified Female:On the in light of field, et al, PTSD is a significant factor in milligrams morphine equivalent per day. They were just following up there. 

Dr. Hausmann:Thank you. 

Unidentified Female:The next question here: were there any differences by race in which opioids were prescribed? 

Dr. Hausmann:Yes, that can be seen here in this line here. Again, as I clarified earlier, total morphine equivalent is another way of quantifying the magnitude of opioids that were prescribed over the course of a year... the year being the follow up period... and you can see this number being so much higher than this number. Whites were being prescribed higher doses and so the morphine equivalent takes into account a couple of things. It takes into account how frequently, how many prescriptions are being prescribed, but then also how many... the length of the prescription, how many prescriptions... we looked at this in several different ways. We looked at like the daily morphine equivalent, the total morphine equivalent. It did not really matter how we adjusted for that variable, so we opted to just do the total morphine equivalent in the published paper because it didn’t really matter how we liked that variable. But, to get at the question that was just asked, we had a higher morphine equivalent for whites than for blacks, meaning that they were being prescribed a lot more opioids than our black patients were. 

Unidentified Female:But the question is which drugs specifically were prescribed... for example Vicadin versus Methadone. 

Dr. Hausmann:Oh... we did not break out specific drug categories like that, so unfortunately I’m unable to say if there were certain drugs that were being prescribed versus others and whether there were race differences in those. 

Unidentified Female:Will you be conducting further studies in this area to determine why these disparities exist? 

Dr. Hausmann:That is a great question and I hope to do so. As of right now, this was the beginning study and we are still formulating what our next step is going to be. We would like to do more research to try to figure out... to understand what... the future direction slide really is the work that I think is very important as follow up and these two things are the things that I would love to know more about. The reasons for these disparities and as well like do we... I mean the whole reason of focusing on disparities as a discipline is because we want to make sure everybody from any group is getting the highest quality care. If the disparities in opioid monitoring are having an effect on the pain treatment effectiveness, then there is a need to reduce those disparities in how patients are being monitored. But if how they are monitored has no correlation with the treatment effectiveness, then our efforts to reduce disparities in pain management may be more suited to be directed elsewhere. Right now, this study being a pilot study and we are in the beginning stages of this line of research, there is just a lot of different ways we could go with it, and these two are the two things that I’ve identified as being very important next steps. 

Unidentified Female:Who created the sequel queries to get your data? 

Dr. Hausmann:That would be Shasha Gao, she is a statistician here at the center for health equity research and promotion, and she did all of the data pulls and the data analyses for us. 

Unidentified Female:Do the total numbers of whites and blacks in these samples influence the conclusions of the study in that so few blacks are available to be selected for the study? Did you know in advance that there would be so few blacks? 

Dr. Hausmann:Well, I am going to answer this in a couple ways. First, I want to say that we know in Pittsburgh that the percentage of blacks in our population is about ten to twelve percent. So, we knew that there would be about ten to twelve percent of blacks in whatever cohorts were able to analyze for this. The two hundred and fifty, out of about eighteen hundred, that is right in line with those percentages, so it is what we expected. To get the greatest number possible, we looked at two years instead of just a single year to maximize our numbers. We did what we could to get as many patients of both races in the study cohort, but being a single... I guess my first part is we have done everything we could to get as many people as we could, and the percentage of blacks is representative of the percentage of blacks in our VA. I trust these numbers for our VA. I would not go applying them to the VA nationally because I know our demographic make up is much different from the VA nationally, so people should generalize with caution. But I think that the results, although they are based on a relatively small sample of blacks, are still pretty robust differences of controlling for lots and lots of things and I think they warrant further study in a national population in trying to look into these reasons for why they are happening. We do acknowledge that the sample size is limited. 

Unidentified Female:Remind us again, where ICD-9 codes were used to differentiate between patients presenting with overall generalized pain? For example, headache, and those, which may be closer to your target population, for example back pain? 

Dr. Hausmann:Yeah, we had four categories. I went through this quickly, so we had four categories of pain and we drew our... I mean I consulted with others who have wrestled with trying to categorize pain into analyzable categories, because there are so many pain related ICD-9 codes, so these categories were drawn from previous studies that others have done where they’ve categorized pain into different categories. About a quarter of our sample had limb pain; about half of our sample had joint pain. These are not mutually exclusive because people can have more than one kind of pain. Half the sample had back pain, and then about a quarter of the sample had another kind of pain; I believe headache was one of the diagnoses that we had to lump in that other pain category. We have several... in paring those types of pain categories down; we started with lots and lots of categories, and then had to keep narrowing down until we had an analyzable number, so the other pain category kind of catches everything that didn’t fall into limb, joints or back. 

Unidentified Female:Did any clinicians have anything to do with creation of the queries and determination of the appropriateness of which captured data points to use from the data dictionary?

Dr. Hausmann:Yeah... we had two clinicians on the study team. One is... I will just go back to this first slide here... Shasha Gao was the person who did the data pull for us, she’s the statistician I mentioned earlier. Edward Lee is very active in opioid management clinic here at our VA; and Kent Kwoh is a rheumatologist who treats patients with rheumatological pain and has done a lot of work with knee and hip osteoarthritis as well. So Ed and Kent were my go to people for helping me to make sure that the data that we were pulling was comprehensive and appropriate for the study question. 

Unidentified Male:This is Bob Kearns, can you hear me? 

Unidentified Female:We can hear you Bob. 

Unidentified Male:I apologize; I want to use my privilege... I am not able to stay on the call longer. I wanted to thank Leslie Hausmann in particular for her wonderful presentation and generous offer to speak with us today. I truly think this study is very important. I am glad that it is now in print and combined with work that others in VA are doing, notably Diana Burgess and Kelly Allen, among others that are interested in racial differences and disparities in pain and pain management. I think this work stands to be quite important and should be used in a way to help inform our efforts in VA to improve pain care for all. I also want to thank... so, thank you Leslie, for a great presentation. Thank you also to Heidi and CIDR for their support of this webinar series. And thanks to every body else for participating today in the call and for so many great, clarifying questions. I am unfortunately going to have to get off the call today. If there are questions that come up, Heidi, that you might thing... or Leslie, that you think I might be best able to answer, please send them to me via e-mail and I will try to answer them. So, thank you everybody. 

Unidentified Female:Thank you Bob. And we actually just have one final pending question here and we will be able to wrap things up here, so Bob won’t be missing too much of this. The last question I have here for you Leslie: was there any stratification of pain with respect to chronicity, chronic non-cancer pain, or are you presuming chronic pain on the basis of chronic opioid therapy? 

Dr. Hausmann:That is a great question. We were unable to... basically, we were inferring the use of... we were inferring chronic pain by the fact that people were taking opioids for at least a three-month period and we excluded those who had cancer, so we knew it was not cancer related pain. We also excluded people who died within the follow up period, which is also another way to get at people who are maybe taking opioids for palliative care. However, we were unable to figure out a way to look at the chronicity of pain, and if others on the call have ways of quantifying that, I am sure I, and other researchers who are interested in this topic, would love to hear more about it. So, feel free to contact me if you have advice for how to quantify that for research purposes. 

Unidentified Female:Okay, that is all of the pending questions that we have here. Leslie, I want to echo Bob, and I want to thank you very much for presenting today. You did a fantastic job, the audience obviously really responded very well. A lot of great questions coming in on your session today. We really appreciate the time you put into presenting for us today. To our audience, thank you for joining us today. As you leave today’s session, you will be prompted with a feedback survey, if you could take a few moments to fill that out, we would appreciate it. And I just want to let you know that we do have our next session in this series scheduled for June 4, at 11 a.m. Eastern, and Drew Hummer will be presenting The Veteran Experience of Chronic Pain from Musculoskeletal Injuries: Lessons from the War-Related Illness and Injury Study Center experience and we will be getting registration information out to everybody on that very shortly. I want to thank everyone for joining us for today’s spotlight on pain management cyber seminar, and we hope to see you at a future session. Thank you. 

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