HERC Cyberseminar  Department of Veterans Affairs- 19 -

**Department of Veterans Affairs**

**HERC Health Economics Seminar**

**Medication Adherence in Chronically Ill Veterans: Copayments, Other Potential Barriers, and Health System Factors to Potentially Mitigate Cost Burdens**

**John E. Zeber, PhD, MHA**

**February 15, 2012**

Moderator:  I just wanted to welcome everybody to the February edition of the HERC Cyberseminar.  Today I'm very pleased to introduce John Zeber.  John finished in 2004 at the University of Michigan, where he got his degree in health services and worked with a lot of the VA people up there: Marcia Valenstein and Fred Blow, looking at  cost issues as they were related to medication and psychiatric issues.  So his talk today is: Medication Adherence in Chronically Ill Veterans that carries forward that thread.  He's been very productive over the past few years, now he is down in Texas, where he is a Co-director of Health Outcomes for the Scott & White Healthcare system and also at the—I think it's the San Antonio VA, John, if I'm not mistaken? 

John Zeber:  Temple, Texas.

Moderator:  I'm going to hand it over to you, John.  We look forward to it, thank you so much.

John Zeber:  Well, thank you very much, Todd.  I hope you're feeling that way fifty-five minutes from now.  I'd like to thank Todd, Heidi and Angela—and the others for helping me prepare for the session today, plus any time an audience doesn't actually have to see me in my first gray hairs is an all around plus.  I did warn Todd that he was inviting a true amature to join this seminar at least in terms of economic experience, because I tend to fit better in the sociology, behavioral health policy realms, so I welcome the opportunity to share a collection of our work over the past several years.

Upon stitching these studies together, I am pleased that they do fit pretty well together in a logical and coherent trajectory, something I'm not accused of doing very often, but your suggestions for future efforts and even potential collaboration would be appreciated.

I have presented much of this material at different times at the HSR&D meetings, so many of you have probably seen parts of it and depending on time, I may skim through some of it, but I'm happy to share reprints or more information.

Now while most of these studies reflect Veterans with serious mental illness, some target other chronic conditions, with much relevance to many patients in healthcare systems.  Since you are a semi-captured audience, it gives me a chance to share a few photos of our trips to Yellowstone and Yosemite.  

I think there's a poll question here, which is just getting some general information about who is in the audience and your primary role. 

Heidi:  We have responses that are coming in, we're at about thirty percent right now, so we'll give it a few more seconds for people to respond here.

John Zeber:  Okay.  I'll kind of talk over that here a little bit.  I'd like to first begin with a couple of our earlier projects, which I mentioned, here Marcia Valenstein, On Medication Adherence in Veterans with Schizophrenia, which then led to my dark dissertation days, where I focused on a significant policy change in VA Pharmacy Benefits.  

So by way of introduction—

Heidi:  We're actually only able to have the slides or the poll up at one time.

John Zeber:  Got you.

Heidi:  Here your poll is up.

John Zeber:  So that's half and half health services, researchers and administration, great.  We know that up to half of the patients with serious mental illness, not to mention other chronic health and mental health conditions tend to be poorly adherent.  In fact, that's at a cross-sectional view, often in the short term or even up to a year period.  

Now Marcia and her colleagues found that if you look over a three-year period, for instance, up to sixty-five to seventy percent of patients with schizophrenia are poorly adherent.  So it's safe to assume that over the long illness course, most patients will at sometime experience adherence difficulty.  Naturally these adherence problems lead to severe ramifications in terms of relapse, deteriorating symptoms, psychiatric admissions, ER visits, costs and so forth.

 A couple of years ago [Marks, Fritz and Olsen] estimated the California Medicaid system alone could save up to $106 million a year by just reducing the adherence gaps in its schizophrenia patients.

No healthcare system is really exempt from this problem.  If you see patients, adherence is going to be a problem.  We know that there are also more vulnerable patients who may suffer greater burden from a variety of potential areas we’ll discuss, this includes older patients, chronic conditions, ethnic minority. 

An entire day can be devoted to discussing how to define and measure medication adherence, plus the many different ways and lack of agreement among some, such as pharmacy claim data, patients don't report, family or provider assessment and blood level and we used a couple of these in our studies for both administrative and self-report, which we'll go over today.

I'd like to start with a couple of our favorite graphs from our earlier work with Marcia, perhaps suggesting I need to get outside more often.  This first one covers the National Population of Veterans with schizophrenia from 1999 data and what it really shows is the distribution of medication adherence as defined by the MPR or Medication Possession Ratio.  Now while my colleague, [Laurel Copeland] devoted six years of her life to the often complex coding of this, what the MPR really does is provide a percent of medications the patient should be taking.  So you're aiming for about one or one hundred percent.  

The appropriate cut point is debatable and they differ across chronic conditions.  MPR of .08 has been validated as measuring good adherence.  So on this graph, while most of the patients do tend to cluster around one or good adherence, you'll notice to the left of the arrow, the substantial proportion in there of below .08.  In fact, in this population there's 41.5 percent to be exact.  On the other extreme it's the ten percent to the far right who may be considered the over achievers of the adherence kingdom and they have MPR's of 1.5, 2 or even higher.  We found these patients to be much sicker in terms of comorbidities and they represent a very distinct group.

Laurel created this second figure which remains a highly cited paper, which shows the almost suspiciously linear relationship between adherence and the risk of psychiatric admissions.  So if you start to the far left of the worst adherers, they have MPRs around .1, nearly one-third of them are admitted during the course of a year.  As you move towards the right, this risk steadily drops to about eight percent of patients with the best adherence before shooting up again in the high MPR group.  

Now a little bit more background—we all know that there are an awful of discussions regarding rising pharmacy costs across all healthcare systems.  In the late 1990s, early 2000s, these are increasing fifteen to twenty percent per year.  So it's no surprise that many healthcare systems design a variety of cost restriction plans, benefits restrictions, formularies and other cost sharing such as copayment.  It covered a variety of different types, including putting a cap on the number of pills, dollar amounts, also recent notions regarding value benefit design and so forth.

Then, of course, the simple fact of raising medication copayment.  We all know that results in problems for the patient respective of cutting back on their medication due to cost.  I believe that Todd Wagner in his work has worked with our former Michigan colleagues, John Piette and Michelle Heisler in a couple of papers that looked at the extent to which Veterans with diabetes cut back on their medication.  At least twenty percent of them report they did at sometime, now that sets the stage for this talk today.

A quick recap of VA pharmacy copayments in case we're not aware of them.  We have been gradually implementing this copayment plan since 1986 when Congress first declared that service connection percentage or priority status could be used to define eligibility.  The first $2 copayment was implemented in 1990, where it remained until the 1999 Millennium Bill was passed, which then led to the $7 copayment increase in 2002.  It was raised to $8 a couple of years later and I just read that it's actually $9 for some of the lower priority Veterans.

Okay.  I think we're ready for the second poll?  Here I'm just trying to get some awareness, if people are aware of what the current copayment policy within the VA or medication costs in general from their patients.  

Heidi:  Here are the results.

John Zeber:  Oh, about half and half, great.  I don't want to make too light of the situation, but this is often what patients feel when they encounter a cost for a variety of different medications.  People with chronic conditions, including multiple comorbidities, often have eight to ten or more medications a month they're dealing with and it certainly leads to anxiety.

I don't want to cover too much about this, but we know from numerous past studies across a variety of healthcare plans and copayment restrictions that they work.  There's been numerous data dating back all the way to the RAND experiment, across a variety of healthcare services, primary care specialty visits, such as psychotherapy, chiropractic, dentistry and so forth, even ER visits for urgent conditions, that when faced with higher costs, patients do cut back on all of these services and, of course, the evidence pertaining pharmacy use is also quite substantial and growing.

However, there is actually presently a few studies specifically targeting mental health conditions.  Probably the biggest precursor to my study, was the mid 1990s work of Steve Summer and colleagues at Harvard, where they examined the Medicaid cap that was put on their program, when they looked at schizophrenia patients, they observed a nearly fifty percent drop in psychotropic drugs, with a sharp rise in ER visits and admissions.

In the second paper on older patients, they found a thirty-five percent drop in fills and increased number of nursing home admits.  Once the cap was removed, things stabilized pretty quickly.  In much the same as other things, there are some findings that certain patient population groups may be more sensitive to copays or cost sharing, such as ethnic minorities and the elderly.

Now turning to my dissertation topic, which I think I have recovered from the traumatic stress to revisit now.  This is a visual representation of the study, starting with a baseline periods to define include criteria, we looked at a twenty-month period prior to the copayment increase and twenty months after, subdividing that into four ten-month periods to see if we can at least get some idea of trends prior to the policy change, a relatively simple logistic longitudinal approach, that we effectively controlled for time and numerous covariates.

We benefitted from a national population of all Veterans treated with schizophrenia in 1999, starting with about 100,000.  We excluded eighteen percent for either dying during the study period, having a hundred or more inpatient days during a ten-month period and then not having valid service connection percentage.  Even though that number of exclusions is fairly large, we did a detailed attrition analysis that revealed fairly minimal differences between those who were kept and those who were excluded.  

The data came from the VA's National Psychosis Registry developed and still maintained by [inaudible], Twick and Ann Arbor].  I was fortunate enough as a graduate student to be part of the development team on this.  It's a comprehensive database of all Veterans treated in the VA with schizophrenia or bipolar disorder.  It contains the patient demographics, utilization pharmacy and cost information.

I looked at three different groups based on the service connection percentage and you'll see that in the slides to follow, but you can really combine Groups 1 and 2, which has a distinction between non-service connection and having 0-49 percent.  We're really comparing those who did have a copayment against those who are completely exempt.  In fact, that's a natural control group of patients who never faced a copayment.  We used a longitudinal random effects model to look at each of the outcomes, which we'll describe in a second, there's one for demographics, substance abuse, illness severity, plus a new variable we've created for the Registry called "tenure", which get at how long and consistently a Veteran was using the VA services.

I thank my committee for limiting me to six outcomes.  We first looked at pharmacy utilization, total, and subdivided into medical and psychotropics.  Ideally, I probably should have sub divided the psychotropics into different sub classes, antipsychotics, mood stabilizers and others, but that can be done in the future.

We looked at both inpatient days, admits and outpatient visits as potential ramifications from lower adherence due to cost and then also total pharmacy costs from the VA's perspective.  Out of a total final sample of over 80,000, almost a perfect split with fifty percent were exempt from copayment—I won't go too much into population, but it's fairly representative nationally of Veterans with schizophrenia during that time, primarily male, older, a high percentage of minorities and they're a very sick group.

The baseline difference is that we did find a little bit of difference between the copaying exempt that they tended to be a little healthier, that's no surprise since service connection is often used as a proxy for illness severity and need.

Then just turning to the results, the total of medical bills were approximately the same scenario, so I'll just present the medical.  While the exempt patients continued to increase their utilization about fifteen to twenty percent, the brown triangle at the top, the two copayment groups at least stabilized over time after the copayment increased.

  The more interesting finding was the psychotropic fills or the exempt patients more or less stabilized following the 2002 change.  The copayment patients dropped their utilization by twenty to twenty-five percent and that's surely a significant drop there.

Then just turning to inpatient days, there was not a dramatic change, copayment patients increased their utilization about three to four percent and that reversed a decade-long trend of declining psychiatric admissions and days in the VA.

Now initially I had downplayed that small increase in the manuscript before a kindly reviewer pointed out—before rejecting the manuscript, that even a small increase in the population of this size could result in hundreds of extra admissions and percept thousands of additional inpatient days.  So that's actually triggered one of the future studies we're going to look with cost offset.  I'll just skip over the pharmacy costs as they reflect the total medication use pretty well.

So turning to the grounding of the findings in more of a sociological model, we find that I think health beliefs play a big role in this.  Because when faced with the higher cost to the patient on their medications, they were choosing to give up the psychotropics rather than the drugs for medical conditions.  Now a second point is the VA has long taken pride in caring for individuals with limited resource and treatment options. 

The 1974 book, John Ayanian asserted that one key purpose of all healthcare benefits is to more equitable distribute scarce healthcare resources.  So it seems at least within that context the 2002 policy change raises some equity issues.

Now regarding whether the copayment increase was a success, it certainly worked to cut back on higher cost medication, but as always one must consider other unintended consequences such as balancing objectives with the healthcare system's overall mission, patient needs and so forth.  Even at the time, I was wondering, couldn't there have been a more softer option than immediately tripling the out-of-pocket expenses for these vulnerable patients with schizophrenia, who have very low income?

  We must, of course, recognize that Veterans themselves are a vulnerable population and even more so in Veterans with schizophrenia and other serious conditions.  So this policy impact will obviously affect them probably more than others, and though they may be more sensitive to the same policy change or decision that other patients  may not find to be too burdensome.  I think I'll pause here for the next question.  I just like to see how often if your providers or administrators do often discuss or consider medication costs when discussing with your patients?  I'm just going to turn to one sub analysis that we did conduct on this—

Heidi:  I'm bringing up the results right now.

John Zeber:  We do have a fair percent that do at least consider and discuss this with their patients, which is good.  There's been many polls out there that indicate that providers really don't have any idea what medications cost or what the overall burden is to their patients.  

Okay.  So turning to a quick sub-analysis that we did do here.  We wanted to look at the differences by cross-ethnic groups, now if only I can get this manuscript accepted.  It is basically the same as study design and approach, except this time added a few things, such as ER visits and also extended the time period of thirty-three months pre- and post- 2002, the policy change and also subdivided that into twenty-two quarterly periods for some more of that true time series approach.  Then we looked at four different ethnic groups: White, African-American, Hispanic and then other category, and the basic finding were that all groups were restricted to their psychotropic as noted before within a fairly tight range of sixteen to twenty-two percent.  

However, the ramifications were far different, the white patients really didn't increase their inpatient days or ER visits and the ethnic minority groups did.  In particular, Hispanics increased their inpatient days by almost ten percent, which is a substantial change.  So kind of the summary and take-home message here is that the same copayment increase might have a differential burden across different patient populations.

I'd like to turn to a post doctoral fellowship project of mine.  This study is for the other side of the equation, how the policy change can be viewed from the VA's perspective, at least from an economic stand.  This is admittedly a very preliminary exploration of the financial cost and I would welcome advice and other possible ideas on how to continue this line of inquiry.  In addition to presenting this at the VA annual meeting a couple of years ago, I enjoyed sharing this, along the canals of Venice last year, and in case I wasn't feeling insecure enough, Dr. Will Manning served as the moderator for my session with John [Shu] at Harvard as the discussant, so I was shaking quite a bit at that time.

As I told Todd, although I do have an undergraduate liberal arts degree from economics way back in the Johnson administration—I'm far from being a health economist, but perhaps I can share some of these initial findings and work with others to better investigate this important topic.

As with specific copayment work in general, there's been little done in mental health to look at kind of the cost benefits effective a policy change.  The summarized studies mentioned before did find any pharmacy savings resulted from the cap were overwhelmed by inpatient or other costs by a factor of seventeen.  Almost immediately after my schizophrenia study came out, several other excellent papers were published, looking at the copayment effects in other chronically ill Veterans, including patients with diabetes and hypertension and all these papers came up with a fairly similar conclusion: Higher out-of-pocket costs led to worse adherence or potentially greater political ramifications.

Now all this accumulated evidence has led Dr. Seth Eisen to contact me and a couple of others about eighteen months ago to indicate that the VA was gathering information about all of the findings and to help evaluate the impact of the 2002 policy change.  I'm not sure what has become of that since, but it's nice to know that our concerns and information is being considered.

The basic research question has been aside from the patient ramification, what are the cost policy implications of this 2002 policy change?  Really, the cost benefit in terms of revenue and extra treatment costs from the VA's perspective?

The same pretty much study, design and population is from the prior ethnic minorities one.  We've looked at just the copayment Veterans in 1999, which is about 33,000 patients and we used the same twenty-two quarterly time points to look at the cost and savings involved.  Besides the other variables, this time we did add total cost such as inpatient and ER.  I didn't calculate ER costs directly, but used the number of emergency room visits that were in the VA and estimated those costs from the literature on Medicaid and commercial populations.  You'll see that because there is some uncertainty, I did do the sensitivity analysis to try to gauge what effect that would have.

So in pretty simple terms, we were able to say what was the balance between the additional copayment revenue, that $5 per pill and any potential savings in psychotropic bills versus higher inpatient and ER costs.  So what we really did here is try to gauge the difference between what actually was observed pre- and post- mostly post-policy period against what would be predicted to have happened should the pre- trends have continued for the past three years.  So in a sense trying to sum up the area of the blue triangle there, a fairly simple approach that's calculating cost at each of the time points and summing them up.

Now given my background in not being a program staff analyst, I used the very powerful Microsoft statistical package with all the complex coding myself I'm pleased to say and again really just focused on the post-policy utilization and cost of the copayment group.  All the dollars were adjusted to 1999 medical component of the CPI.  Again we did do some sensitivity to vary the cost estimate of the ER visits between $200 and $1200 per the literature.  Secondly, because we knew that the pharmacy savings issue is debatable and there might be some other assumptions of what happened after 2002, based on our work and the substantial literature, we know that the vast majority of the restrictions that were seen were due to this policy change, but we can't be sure that they're not unexplained factors, so I varied the percent that were attributed to the policy change between fifty and 100 percent.

Then the bottom line here is that were substantial copayment revenue increases of over fifteen million, along with some pharmacy savings cost.  However, inpatient and ER costs dominated those to the extent there was net loss of about 2.12 million to the VA or annualized around $771,000.  The sensitivity analysis showed that there was a very slight chance, like three percent, that there was either a break even or a slight gain, but it could have been a loss of up to $2.8 million as well.

So summing up these couple of studies, the 2000 benefit change seemed to both affect patients from the clinical perspective and also had budgetary implications from the VA's perspective.  One important point to note is the population I used here represents less than one percent of all VA patients.  So if you started adding up a couple million patients that are facing copayments in the VA, these numbers could accumulate very quickly.  I should note that this study did not include the 2006 small increase or any future plans, plus a lot more longitudinal perspective to the current day.  Plus I did not capture other economic or resource costs, such as administrative time or provider time to discuss costs with their patients, which we are hearing more and more of. 

This raises the concern about how compartmentalized budgetary decisions can be and of the "silo" approach to financial planning, even within an larged coordinated system like the VA, economic savings in one area can balloon it up in another.  I realize there are numerous limitations, including the dazzlingly imperfect pathological approach I used, but I'm hoping it provides some information for thought on where we're going from here.

I really do like this quote and I think it applies to multiple decisions that providers in healthcare systems continually make as they struggle to balance bottom line decisions with quality of care and equity issues.  

All right.  Before we go on, I think we'll move to the next poll question.  This really gets at the next study I'll be discussing and trying to understand what you think or believe are the most primary barriers to medication adherence.

We'll be turning now to an investigation of several financial and psychosocial barriers that are faced by these patients.  We know they're not just cost issues that affect medication adherence.  

Heidi:  Okay.  There you go.

John Zeber:  Okay.  Good.  That conforms well to the literature and what we were thinking when we were designing this next study that I'll be talking about.  Thank you.  Okay.  A couple of things as we transition into another realm of adherence.  I realize that much of the first twenty minutes or so could be a little depressing.  After we cover a little more bad news, I think we can end with some optimism concerning potential interventions here.  Secondly, despite the fact that I spent twenty-four years on a dissertation, using large administrative databases, I'm increasingly aware that it's not my ultimate calling or skill set in life.  I really do wish to take advantage of primary self-reported data, which I think would better answer a lot of these questions regarding patients' mind sets, helpful use and treatment decisions.

Fortunately, for several years Laurel Copeland and I were able to work with Amy Kilbourne in her large bipolar study in the VA, which did collect a very rich set of patient survey data, which I'll turn to now.  So, again, patients do face multiple barriers to adherence, yet the cumulative effect and the interactions are really not studied that often.  Most of the time you'll see in the literature that we focus on one potential barrier or intervention at a time and I think that underestimates the keenness of effect of some of these.  

Now I understand that psychosocial factors is a very broad term for the literature and sociological models presented to include illness insights, therapeutic alliance, social support, unstable living environments, health belief, access and many other dimensions, I won't really attempt to get into here.  Then you add on top of that the burden of financial barriers we just saw and you can see that patients are really running into pretty bad situations trying to reconcile all of these things with their need to follow prescribed medication plans.  

Of course, on top of these there are certain individuals that may suffer inequitable burdens of many of these barriers.  The same populations as before and we'll try to explore a little bit of that in this study.  I should note that a prior qualitative study led by Jeff Pine interviewed twenty-six patients, Veterans with schizophrenia and these patients reported 214 unique barriers to adherence.  Furthermore, there was pretty low agreement between patients' providers regarding these barriers and also disease etiology and medication benefits and so forth.  So the challenge is certainly ahead of us here.

Now, turning to a little bit more of psychosocial barriers, we know that there are many interventions that have proven successful and that includes the work of Rosencamp on using cognitive behavioral therapy to address poor insight to illness.  Another topic I was fortunate to work on—two intervention studies targeting adherence in schizophrenia, including Marcia's blister-pak for complex pharmacy regimens and there she was able to see that it did improve adherence, especially on older patients, those with cognitive limitations and those with very complex medication regimens, multiple medications.  More recently I worked with a San Antonio University colleague, Don Velligan, on her therapeutic intervention called cognitive adaptive training and this really targets the patient's chaotic lifestyle or personal environment that can limit their ability to remain adherent and this was also shown to improve adherence, at least in the short term.

Other interventions have been proven to be effective, including the one we'll get to at the end with chronic care model and patient center care.  Of course, we all know how health benefit policies can affect adherence.  

We've done quite a bit of work with Amy's data sets of several related issues, including therapeutic alliance, medication beliefs and access to care, including therapeutic alliance and its effects on suicide.  Laurel's study, looking at how patients believe medications work and adherence and some access issues with John McCarthy.  Also we also found that there's an awful lot of complementary and alternative medicine used along with the bipolar population.  I see that as another dimension of treatment preferences and medication beliefs and how that could affect adherence.

So what we wanted to do here is really explore a universe of potential barriers and try to identify which ones seem to be more important, with the ultimate goal of designing tailored interventions, to target those problems.

Amy led the CIVIC-MD study in the VA's love of acronyms, it refers to continuous improvement for Veterans in care of mood disorder.  A naturalistic population-based study group and the quality of care provided bipolar Veterans.  The surveys and primary data collected information on health behaviors, access, satisfaction with care, symptoms, quality of life and so forth, a very rich data set and she was able to collect information on 435 Veterans with bipolar disorder.  The treatment was done between 2004 and 2006 at the Pittsburgh VA and again with this study we limited it to self-reported medication adherence as defined in two different ways.  The first was the validated Morisky scale which is four items, you get intrapersonal potential barriers.  You forget to take medications or you're often careless or do you stop taking medications and you feel better or worse.  Although it can be used as a continuous variable, the standard cut point for defining poor adherence is patients that endorse more than one of those questions.  Now there are some detractors out there—this measure has been used in many chronic health studies regarding adherence.

The second measure is any report of missing a single dose in the last four days and that has been highly correlated with electronic pill caps.  

We're able to find nine different barriers in the self-reported data, starting with the three financial ones, we've looked at reported income, all these cut points for the cut and its variables defined by the literature.  Our experience using it and also looking at the distribution.  So patients reported having income below 20,000 receive a yes on that one.  The next one was the first of two items from the Cunningham survey on access problems, the ever restricted mental health treatment due to cost and then one getting at them—do they have a medication copayment or not and we relied on the patient's self-report of their service connection percentage being below fifty percent means they would have a copayment.

Now the nine psychosocial barriers, including the next Cunningham item: Do you have trouble accessing or getting an appointment with a mental health specialist when needed?  Therapeutic alliance as defined by the healthcare climate questionnaire, a ten-item instrument designed by Yvette Rudman, specifically for bipolar population and it really measures different aspects of the patient's comfort within their environment.  The cut point here of twenty-five really does indicate a pretty negative of their patient-provider relationship.

Now turning to medication perspectives, we used a scale developed by Meredith, which is a four-item question, getting at their beliefs that medications work or not and the cut point of seven from our prior work does indicate a pretty poor belief in medication.

Our substance abuse variable was a standard for binge drinking, five or more drinks any time in the past month.  You had a proxy for social support, also associated with adherence for patients who lived alone, then finally there was a distance barrier for patients who had to travel over fifty miles to get to their VA appointment.  Unfortunately, we did not have information on the side effects that's been, as you noted, as a primary area to adherence, it just wasn't available at the time.  If you're looking at this study.  Another [inaudible] has come out recently on Veterans with epilepsy, it's probably no surprise that the two drugs that have the highest medication resistant ratios are the ones that have the lowest side effect profile and then the three drugs that patients fail to take most have the worst side effect profile.  So no surprise there.  We could not look at that here.

Despite the large sample size, we are concerned with a little bit of power limitations, so we first examine the bivariate association with each barrier adherence and then combined with its conceptual significance, entered five of them into the final model.  With the logistic regression predicted, the likelihood of poor adherence across both of the outcomes and we controlled for some relevant comorbidities, including education, homelessness and affective symptoms as defined by the internal state survey, recent episodes of mania, depression or anxiety.

Due to the population, which is primarily African-American in Pittsburgh, there were three different ethnic groups: White, African-American and other.  We ran two different models, one using the total number of barriers up to nine as the primary predictor and second, entering the five most important ones that stood out to be a separate model.

Now I'll go over these results fairly quickly.  Almost half of the patients reported poor adherence for the Morisky, while a substantial number missed some doses in the past four days, the number of barriers was 2.8, many of them experienced four, five or even more.  There was an ethnic difference on those barriers.  African-Americans had an average of 3.3 versus 2.6 for white patients.  Looking at the bivariate associations, although the financial ones were by far the most prevalent, they were the least associated with adherence, which I have to admit kind of surprised me.  Instead it was five of the six psychosocial barriers noted here in green that had the most strong relationship with adherence and they were entered into the final model.

We also observed some ethnic differences across at least three of these barriers, minorities reporting lower income, poor access to specialists and binge drinking.  Surprisingly, African-Americans indicated a better medication belief than the other patients in the study.

Turning now to the multivariate results, for the first model, looking at the number of barriers, there's an odds ratio of 1.29 per barrier, which means that if you had three barriers instead of two, your likelihood of having poor adherence would increase by about thirty percent.  Having current affective symptoms doubles that likelihood and the 'other race' was an important factor as well.  The 'no miss' days was not significant in terms of an outcome here.  

Now turning to what I thought was the more interesting model, 2, which looked at the coefficients separately for these barriers, the most important ones that stood out, these three were significant insights to medications or beliefs in medications, in shrinking and limited access to a specialist.  Once again effective symptoms also heard in here.  We did some work with some interaction models, but once again power became an issue.  We had some limited affects, showing perhaps that ethnic minorities might achieve benefit more from a therapeutic alliance, but I think a lot more needs to be done in that area.

So again patients do experience numerous barriers of both the number and the type associated with adherence problems.  Some of the more significant ones include the medication belief, substance abuse and access, but also having current affective symptoms doesn't help the situation.  Some ethnic differences were observed, but we need a lot more work in this area and then I'm surprised thatfinancial barriers were not as significant in this population, but there could be some collinear issues with the definition of the financial barriers or we relied on self-reports for that service connection or simply perhaps for bipolar patients, there are other more serious barriers to adherence as some of the audience members pointed out here.  But I really think the results do support designing tailored interventions to improve adherence,  For instance, using blister-paks for older patients with many medications, adaptive training for a chaotic lifestyle or perhaps even re-visiting the VA's copayment policy, that doesn't seem likely, since it really literally takes an act of Congress to change copayments in the VA, but that's at least a possibility for the future.  Due to time, I have to change gears a little bit here.

Moderator:  [inaudible]?

John Zeber:  Sure.  

Moderator:  You have a couple of questions that have come up about the alliance measure that you're using, the core alliance—if I'm interpreting it correctly.  I think it's back to about Slide 32, 33, if you could just talk a little bit about what is the measure used for core alliance?

John Zeber:  That's a ten-item instrument developed for a bipolar patient, which really more or less gets at how comfortable he feels receiving treatment in their environment.  Some of the questions get at: "Did you feel comfortable raising your concerns about medications?  Do you feel like they listen to you regarding taking your treatment preference into account?  Do they coordinate your care amongst different providers," and so forth?  And these ten items are added up for each element of 1 to 6 [Likert] Scale to get a total score of 60.  In our previous study we found a mean of thirty-nine, which indicates a fairly core average perspective of the patient-provider relationship.  So we used a cut point of below twenty-five to identify patients that say they have truly poor alliance.

Moderator:  So with that ten-item scale, is it pretty easy to complete?

John Zeber:  It is, the six-item Likert scale that they fill out doesn't take very long.

Moderator:  One of the people wants to know if it's publicly available?  

John Zeber:  It is.  I'd be glad to provide the citation for that, Ludman 2004 or '05, I believe.

Moderator:  Okay.  Thanks, John.

John Zeber:  Sure.  No problem.  We're going to turn to a non-VA study that I was working on with Mike Parchman in San Antonio.  Mike is soon to take over the director of the McCall Center Group Health.  While in San Antonio, he used is non-VA community-practice-based research network and the main goal of this study was to try to see if we can facilitate the small primary care practices to use elements of the chronic care models to improve the care that's delivered and outcomes to patients with chronic illnesses, such as diabetes.  So it's really provider level and an organizational intervention.  It involves intensive practice with the patient to improve communication, improve the therapeutic alliance and actually encourage activated patients involved in their own care.

We have a couple of studies here, I'll probably just skim over a few of the highlights, but I'll be glad to share reprints or other information.  I'll stop for the final poll question, Heidi.

I've identified so many potential barriers to adherence and a lot of them in the literature kind of puts the onus back on the patient, so I'm wondering what we think are the extent to which healthcare systems or individual providers can actually help to improve adherence and that's kind of a leading question, since I'll get into some of our work that targets that identical issue.

Heidi:  Responses are still coming in, so I'm going to give it a couple more seconds here.

John Zeber:  All right.  I'm just going to briefly start talking about this study, Mike was PI in both a pilot study of five clinics and then a larger NIH-funded study of forty clinics, which was a randomized trial.  Again, the goal was to facilitate delivery of diabetes care, try to improve clinical outcomes.  

Actually it was a pretty positive response, optimistic, that's good, I like that.  Why I think this is so important to use the chronic care model as a lever to improve diabetes care is we know that the chronic care model has been shown to improve the structure and process as part of care delivery.  Very little has been done to target clinical outcome, so this is one study that tries to address that question to a certain extent.  We know that education efforts directly targeting outcomes of providers are less successful and that was kind of the background goal of what Mike and his team were trying to accomplish here.  I apologize to Mike and Ed Wagner for trying to synthesize fifteen years of their work in one slide, but this is really kind of the unofficial conceptual model, trying to facilitate, providing them a toolkit of strategies they could use if they choose, such as using a Diabetes Registry to five-minute informal huddles every morning, all the way up to implementing an EMR, which very few clinics could afford to do.

Then this would eventually lead to better chronic care delivery and patient-centered medical home care.  There's a lot of overlap between chronic care models and patient-centered medical models.  So some of these outcomes will be improved directly, but others we think are going to improve indirectly through activation and better medication adherence.

So the ABC study—which the title is derived from the three intermediate outcomes for diabetes care, relied on five clinics and 157 patients.  There is also provider information that was the main focus, so the first couple of papers we got out of this was looking at provider perceptions and how they delivered chronic care and the first paper showed that we could actually reduce cardiovascular risks in clinics that actually provided more chronic care model associated care and actually cut the attributable risk of coronary artery disease by up to a third in this first paper.

Then in the second one that just came out recently, we wanted to look at patient engagement and how that could interact with the chronic care model in improving medication adherence and their immediate clinical outcome.  While we found that better activation didn't necessarily directly improve A1C levels, for instance, it did improve medication adherence and, therefore, that improved clinical outcome.  So the structural equation model was used in this study.

Now based on the 2 from another John Piette article that found that patients who trusted their providers more had fewer cost related problems.  I believe Todd may have been on that paper as well used a similar structural equation modeling approach in the same two variables from the last study, which I'll talk about more in a second, to see if the chronic care model delivery could actually mitigate cost-related adherence problems.  So the variables we used here, the two of them were from patient engagement and self-activation scale by Lorig, three simple questions, which really got at their perceptions of how activated the patients felt in the clinical encounter.  Do you prepare a list of questions to ask in advance, do you feel able to clarify treatment goals and so forth.

The second one got at how the patients perceived their doctor and their collaborative style, that was the Kaplan scale of patient-centeredness. That was also three questions which got at the very similar things to the Ludman scale.  Did they take your treatment preferences into account?  Do you feel listened to and so forth?

Then the final questions that we used were the cost-related medication ones developed by Piette and we used medication adherence per the Morisky as the primary outcome, but also cost burden as an intermediary outcome and basically the findings were that there was no surprise, the direct relationship between cost burden and adherence, but more importantly that patient-centeredness was associated with self-activation, which then improved medication adherence.  

Then we found that the self-activation part at least potentially or somewhat mitigated the cost issue.  So we're writing up that paper now, the coefficients are not incredibly strong, so I don't want to focus too much on that.  There are actually some small benefits, but I think this leads to the fact that the therapeutic alliance—at least as defined by these elements: communication, patient-centeredness and so forth can improve clinical outcome.

Here is just a visual scale of the thing—I'll be glad to share it with you.  Everything is very statistically significant, but the coefficients weren't that strong, so I don't want to focus too much on that, but at least provide some introductory work from the pilot study here.

Quickly turning to the full study, they now have patient surveys on 2400 individuals, along with provider information, assessments and so forth.  Our first paper on adherence was just accepted recently, which looked at how the chronic care model could mitigate hopefully again cost barriers.  This was a little bit of a simple study design we just looked at a nested random effects model on 1,800 patients that did report a chronic health condition and what we found that—again it was not extraordinary strong effects, but patients' perceptions of their chronic care delivery were associated with lower cost-related problems.

Also an interesting finding to me was the sub analysis which showed the patients who reported having either good or fair adherence, so kind of in the middle, not at the extremes of excellent or poor, those are the ones that benefitted most from the chronic care model element.  So maybe those patients that are continually struggling with adherence tend to be the ones that are more reachable by the chronic care model and I think that's an optimistic finding.

Then for the last couple of minutes, I just want to go over this last study where we're looking at in a little more depth, the [inaudible] analysis.  I think some better models, which make the results a little more interpretable, I presented this at Academy Health last year and we were pleased that it was awarded the best abstract for the chronic care implementation theme.  I think it really struck a chord of how in using chronic care delivery in patient-centered medical homes can improve outcome.

Just turning real quickly again to the measures, we use the John Pate's five items.  I'm not sure which one of us gets credit for the acronym CRAB, but we summed up his five measures to create an overall cost-related adherence burden score.  How patients perceived their chronic care delivery was from the validated PACIC item, which is the twenty-item instrument, very similar to the Ludman approach, I believe, and it gets at their perceptions of the primary care treatment.

Some of the questions include: Were you given treatment choices to think about?  Were you satisfied that your care was coordinated, and so forth.  We averaged these across the patients, so each patient got a score between 0 and 5, which is the Likert scale mean there.  Then when you used a regression model controlling for patient nesting within clinics and demographics, see how that predicted the CRAB or cost-related burden.

We only used the first about 1,400 patients, but we are going to re-run the models at close to 2,400 and what we find—skipping to the major findings were that the CRAB mean of 1.5 indicates that patients on average endorsed at least one and a half of the five cost restrictions and I'll tell about those real quickly, those include: Did you ever have to postpone a fill?  Did you ever skip doses to stretch out your medications, and so forth?

Now the average patient perception of their chronic care delivery was 3 and that reflects their average view was I think chronic care was delivered some of the time, so not incredibly enthusiastic about the chronic care model delivered in their clinic.

Turning to the findings, we found that CRAB was inversely associated with the total PACIC score, not incredibly high odds ratio, but, again, if you can improve some of the time to much of the time or all of the time, perhaps we can actually improve adherence quite a bit.  

Then we turn to the subscales and there are five subscales scores in the PACIC and the three that were significant were patient activation again, being able to problem solve with your provider and the practice design and these other issues imply that you can maybe improve adherence by up to twenty-five to thirty percent per point of the perception of the PACIC.

Here is just a visual presentation of that, a little surprised here coordination was not significant, but again three of the five subscales were fairly influential.

And, finally, just going over the discussion point, this one the patient care they perceive at least as being more consistent with the chronic care model.  They did have lower cost-related adherence burden and this leads to the goal of trying to develop more engaged and activated patients in their own clinical decision making and I think that's a definite possibility.  In fact, this really aligns well with the current VA movement toward the patient center medical home or PACIC.  I don't think there's anything surprising here and it's well within our capability to do this and this is something that providers can do to increase medication adherence by bringing it up at least in the context of the clinical appointment.  

Final thoughts here, we know that there are many interventions that do work.  Unfortunately, the literature suggests they are often not cost effective and, of course, that's a major barrier for implementing these in small clinics or even large healthcare organizations, but because they do work and especially again the patients center medical home goal I think there is certainly room and we can be hopeful that these interventions will work.  Because I still have a couple years before retirement, my next steps, which I would appreciate feedback on or any other ideas, I will be presenting an abstract at the upcoming July HSR&D meeting, looking at longitudinal adherence instability, so not just a cross-sectional look of being for adherence.  I would like to work with Mike to work on some sub group analysis for the chronic care model effects, such as within ethnic groups or older patients, for instance.

I just submitted a merit review grant to look at improving medication adherence in Veterans with schizophrenia, using cell phone reminders and electronic pillboxes, that seems to have some potential as Don Velligan's pilot study in a community clinic had seemed to work pretty well.  Jackie Pugh is leading another current VA study, called The Learn and Relate Project, which looks at how clinical microsystems interact and work together and actually learn from each other in providing patient-centered care.  That's an over simplification, but we are going to look at some adherence data and how that relates to the interaction.

Then, finally, from our non-VA path, Laurel and I are working with Scott & White Healthcare, we have access to the Health Maintenance Organization Research Network for Commercial Groups for possible dual system use in medication decisions and adherence.

So I'll end with a clichéd image that at the end of the day we know that medication adherence problems are going to be with us and they probably can't be completely eliminated, but I do think there is some room for a variety of interventions that can at least make some strides there.  So despite that rambling nature, I hope we have a few minutes for questions here and my [cats] tend to get much bigger responses than I do, but I'll be glad to try to answer a few more specific questions you have and turn it back over to Todd and Heidi.

Moderator:  One of the person is interested in the cell phones and electronic pillbox pilot studies are there references for that?

John Zeber:  Dawn Velligan would be the lead author on that one.  She did a very small pilot study of the electronic pillboxes, but not the cell phones, that's why we're hoping to extend it.  It has been shown in other populations, not schizophrenia, that these do work.  If people contact me, I'll be glad to pull some from my grant proposal.

Moderator:  Are there other current IT tools that are freely available that you would recommend to assist patients with medication compliance or adherence?

John Zeber:  That's a great question.  I believe that there are some studies coming out of the VA in Southern California, where they used kiosks for patients to input—much like a serving monkey approach that was confidential—that provided them an avenue to report daily barriers they were experiencing that may affect their taking their medications as prescribed.  This information was relayed directly immediately back to the clinic for potential interventions right away.  I haven't reviewed the literature too much on other information, but I'll be glad to look into that more.

Moderator:  Okay.  Do you have any information on provider panels, disease management-only versus spread out over a range of clinical providers.  I'm not quite sure I fully understand that question, but does it make sense to you?

John Zeber:  Well, probably since these community clinics were very small, one to two providers each, so maybe they were looking at within, for instance a VA on how to get at data and provider panels and their adherence levels.  I'm not sure about that, but KLN menu might have some of those data and I can just turn to my expert, Laurel Copeland for more information there, if they want to contact us offline.

Moderator:  Sounds good.  Do you have any information on people's use of fee bases, when these changes happen with medication adherence?  I know that fee bases have been going up a lot and that's concerning managers.

John Zeber:  Actually I think that lies a little outside of my expertise area, maybe you would have some information there?

Moderator:  I'm just curious if you'd looked at it, but we haven't looked at it and so I was just curious if there is any information on how these people are using fee bases and how changes affect fee bases use.

John Zeber:  Meaning non-VA pharmacies?

Moderator:  Yeah.  Non-VA pharmacy or non-VA ER use that is paid for by VA?

John Zeber:  I haven't looked at the ER question, but again with our connection to this commercial HMORN research network, which includes Kaiser Group Health and other large organizations, we do want to explore dual pharmacy use across systems, so if they're not receiving care, including pharmacy use from the VA, where are they going?  I think that's a very important next direction.

Moderator:  We do have another question that's just come in.  "So there are side effects to be expected, what do you do when you have them?  There's concerns about addiction dependence on the medication, it doesn't appear that these were part of your study.  Many of these appear to be based on information that you could be provided by the provider pharmacist, could you comment on that?"

John Zeber:  Well, certainly side effects are—as noted here—one of the major barriers to taking medications regularly, not necessarily starting with the medications or interfering with their health at least, but it's just a burden that most patients don't want to deal with.  There's been some work, of course, in using—switching to medications that have lower side effect profiles and that's where our epilepsy study kind of framed it in a comparative effectiveness conceptual model, that if certain medications all worked fairly well—at least they're efficacious to a certain extent—similarly, perhaps we should consider going to—in those patients anyway that have side effect issues, switching to ones that have a better profile.  So, for instance, of course the typical newer antipsychotics as opposed to the older ones, but also within drug classes there are certain medications that have lower profiles and that's definitely a clinical thing to work on.

Moderator:  Yes.  I guess there's a whole line of research that Bob Rosenheck looked at in some of his trials, trying to figure out if the second generation so marketed as such versus the older generation, offer any benefits.  I think some of his most recent research suggests that it's not really offering any—it's not cost effective.

John Zeber:  Exactly.  In fact one of the earlier Valenstein papers did look at patients who switched from older drugs to newer ones and the adherence rates didn't change that much.  Whether or not that's an issue that involves patient illness severity or other concerns, there's no guarantee that switching to a lower profile drug will work, but it's certainly one strategy that can be discussed in the clinical encounter.

Moderator:  I have to clarify—two questions ago there was a question that asked about the provider panels and the asker sort of clarified their question.  "Are provider panels of diabetes only patients versus diabetes patients cohorts across the different providers—is there a difference in these types of interventions, looking at how the patients are paneled?"

John Zeber:  That I don't know, I can certainly refer them to Michael Parchman and his team that have done some extensive work in this area.  Certain clinics have established, for instance, patient registries, so that the providers can focus just on the needs of these very complex patients and we're turning to looking at exploring outcomes of those patients in clinics that do use that panel approach.

Moderator:  Sounds great.  That's all the questions I have right now.  Oh, one more.  "How would you comment on MPR as a measure of medication adherence to these pharmacy refills?  Any explanation for excess adherence found in the MPR?"

John Zeber:  Well, the second part of that is much easier to answer.  These patients are very unstable, so when I was doing Marcia's medication adherence blister-pak study, I went through the charts to identify patients and reasons for the adherence problems and we see that these patients who have high MPRs were getting refills one after the other.  They either changed medications, went to a higher dose or lost their fill and therefore doubled up, so there are many reasons why that extreme group has over achievers.  They're really not better adherers, but they're just very unstable patients.  Now as far as its utility as the measure, it's certainly easy to use administrative data, and therefore is used frequently in studies.  It has its limitations because you really can't over—for instance, a year period identify whether it's consistent use, you know, they're regularly taking it or do they skip a month and then double up?  There are many limitations to it, but it is very convenient and again we did validate it in that second earlier slide for an outcome measure—psychiatric admission.  It is being used frequently in many chronic health conditions.     

Moderator:  There is another question that's come in that asks me if I could ask about electronic medication event monitoring systems for meds?

John Zeber:  Yes.  That will be the second arm of the proposal I just submitted besides the cell phone or monitors way of automatically recording when the patient opens it, the capsule, in a sense can send information immediately through the Web to the provider.  Some of them are very sophisticated, where they can also prompt the patient, how are they feeling at the exact moment?  Do they not want to take them?  If they didn't take them, why did they open it and decide not to?  Did they flush it and so forth?  So it's an incredibly handy tool.  Unfortunately, many of them are very expensive.  At the time that Don Velligan was doing a study seven or eight years ago, they were almost a thousand a person for these very expensive devices and that is not feasible to roll out to the population of 100,000 Veterans with schizophrenia, but from a research perspective, it does offer quite a few added dimensions to understanding why patients aren't taking the pill.

Moderator:  I guess one of the questions has to do with as you get to the purchasing of these things and OI&T limitations and so forth and the person who asked the question actually just chimed in and said, "I have an order that was placed twelve months ago through VA funds that is being hung up and that comes from Switzerland.  I expect a lot of these things are going to get held up with OI&T regulations and I wasn't sure if you had any comments about that.  How can you maintain innovation and adhere to regulation?"

John Zeber:  I hear you, that's one concern I do have is a good portion of the budget that I proposed does involve the system to provide the reminders or the devices themselves.  I do have several contacts in the States that provide these services, so you don't have to go to Switzerland, for instance, and I'll be glad to share what I've found by doing a quick Internet search on different companies.

Moderator:  Switzerland might be fun, though, right?

John Zeber:  It would be.  I worked on Marcia's grant with a company from Australia.  She, unfortunately, was too cheap to send me there for a site visit, but it turned out to be a wonderful project there.  It was much cheaper than these electronic devices, though.  

Moderator:  Yeah.  It's not related to psychiatric medications, but there's also a Web site called [addmopolis], where the guy has been working to develop systems so that he can see in real time when people take a puff of their inhaler, so it's similar ideas in trying to figure out and be innovative about how to collect real time data on medication adherence and use.

John Zeber:  I agree, the potential for getting more clinical information about areas that are experiencing at the time, not just in the clinical encounter once every three to six months offers tremendous benefits to enriching the administrative or other self-report data.

Moderator:  The person who just asked that question about Switzerland—she sent me her e-mail, so I will forward that to you, John.

John Zeber:  Please.  I'll be glad to see what I can do there.

Moderator:  I think that's all the questions.  We're actually two minutes over, so I really appreciate everybody's patience in hanging on with us.  We had a tremendous participation show up today, so I thank you so much.  This was a wonderful overview of all your work and I'm very thrilled that we're going to have the national meeting this year, so as John mentioned, he's going to be presenting in July—we don't know where yet, but I'll look forward to seeing you there.

John Zeber:  Thanks everybody.  Feel free to contact me if you do have any other questions or thoughts.  

Moderator:  Sounds great.  Thank you, Heidi, for all your help.

Heidi:  Oh, you're welcome.  Thank you to both of you and for our attendants who are just about to sign off, just a reminder that when you log off, a feedback survey will pop up on your screen, if you could take a few moments to fill that out, we would really appreciate getting the feedback on today's session.  We definitely use your feedback in our current and upcoming sessions.  Our next session in this series is scheduled for March 21st and Jason Hockenberry will be presenting Cost of Re-admission in the VHA—Implications for Reimbursement policies.  Thank you to both Todd and John for all of your preparation and time putting into today's session.  We appreciate everything that you guys did for today's session.  Thank you to our attendees for joining us today and we hope to see you at our next session.  Thank you.

**[End of Recording]**