Dr. Edward Armstrong received his Bachelor of Science degree from the University of Arizona in Tucson. He then went on to earn his doctorate of pharmacy from the University of Missouri, Kansas City. Dr. Armstrong performed his residency in clinical pharmacy at the Truman Medical Center in Kansas City, MO. Regularly published, Dr. Armstrong has more than 50 articles, book chapters, and abstracts to his credit. He is a reviewer for several prestigious publications including the Annals of Pharmacotherapy, Journal of the American Pharmaceutical Association, Pharmacotherapy, Pharmacoeconomics, and the Archives of Internal Medicine. Dr. Armstrong has given more that 100 lectures to health care providers, managed care organizations, and patient groups. It's my pleasure to present you Dr. Edward Armstrong.

Well, thank you. To talk about pharmacoeconomic issues with hormone replacement therapy is sort of an interesting topic because there's so many controversial components, and there's so many issues that go into it. I was actually sort of glad that we were able to flip-flop the lectures and have some of the discussion and questions, because we've already talked about some of those issues. Now think about a disease where we have all these different kinds of effects, some positive, some negative, and we're trying to figure out a way to look at what are the pharmacoeconomic implications-looking at some of the costs and outcome kinds of issues. It's not very simple to do that. I got interested in this topic a couple of years ago. I had some issues that came up and started looking at some of the articles that were published, and ended up looking at all these models that have been published in the literature that the different people have done. Largely, they have been done by health economists. And I wrote a paper that has been published in the Journal of Managed Care Pharmacy a couple of years ago reviewing: what do we do with hormonal replacement therapy. And sort of one of the themes in the manuscript I put together is that I'm not sure we're doing a really good job in the United States, or other nations for that matter, prioritizing who are the people that are really going to get the most benefit from the therapy, and going after-and in essence trying to identify-patients that would benefit from a drug therapy that they are not receiving.

I put in a couple of slides basically to just touch base in thinking about what are the definitions that we look at within pharmacoeconomics focusing on how some of the issues apply in looking at hormone replacement therapy. We know - in essence we're going to want to be looking at what are some of the issues that we can identify - that we can measure or estimate, how would we want to determine what those values are, compare costs and consequences, clinical endpoints, quality of life, kinds of endpoints, potentially looking at impact on the length of life. And a very important point that just came out in the previous discussion is when we look at hormone replacement therapy, we're looking at what is the balance when we look at the positive and negative issues with those therapies - when we evaluate it from a health system's perspective.

Another issue is the costs. What cost is it that we want to look at? Most pharmacoeconomic studies that are published today focus on direct costs. Costs to health care systems. When we talk about a disease, like osteoporosis, and potentially impacting that with hormone replacement therapy, there's this huge benefit to society that we would put underneath this indirect cost. They don't necessarily effect the health system per se, but there is a whole, another group of things, as far as productivity in their life and other kinds of costs, that may not be borne by the health care system but are borne by individuals or society as a whole. And lastly intangible costs - things that will make, tie into looking at quality-of-life kinds of issues. So it's complex, it's very complex.

The other thing I just put a slide in to look at, there’s different ways that we can set up for doing some kind of a pharmacoeconomic evaluation. Now cost minimization is when we say the outcomes are the same, so the clinical endpoints are the same, side effects are as the same, and then we just basically focus on cost. Well that’s not really appropriate when we look at hormone replacement therapy. So there’s no studies that are published that have looked at that, but there are studies that have been looking at the next three. I just wanted to sort of highlight those and then we’ll talk about some of those, and the way they did them.

Cost effective is probably the most common. What are the costs to use for different therapies, and relating that to some kind of nonmonetary income? Often times we will see - looking at length of life - how long do people live and what are the costs to achieve a prolongation of life?

Cost utility is very important when we look at hormone replacement therapy because that’s where it ties in the quality of those years. How do patients value those years? So there’s a phrase here looking at how long someone lives and this is in essence, what’s the quality of those years. So adding life to years, adding years to life - the two ways to look at that.

And lastly is cost benefit. Now we tend to not see a lot of cost benefits studies, in general, done, because they are very hard to look at. What are the outcomes, the benefits of treating a specific disease, and putting that in terms of money, putting that in terms of dollars. But there is one with HRT that I’ll look at. That was actually funded by the NIH. So we’ll see studies typically focused on these two and then there’s one cost benefits study.

Another issue that’s important that ties in to those costs is what’s the viewpoint or the perspective that should be done or used in conducting one of these specific studies, because obviously whatever viewpoint is picked, that’s going to determine what costs and what outcomes are measured. So it has a very big impact when we look at hormone replacement therapy. We can say looking at the perspective of a health system, which is what most of the studies have done, there is the one cost benefits study that came up with an estimate of both direct cost to health care systems and indirect costs impact on productivity. Or, I have not seen one but potentially, theoretically it could be done from the perspective of the patient.

Another issue that doesn’t come up for lots of studies that are done is the time horizon. Lots of things that we evaluate we’re looking at relatively short-term impacts. We’re looking at things like hormone replacement therapy, we potentially could we look at impacts on outcomes way, way far away and from a pharmacoeconomic standpoint, that can have a huge impact in calculating benefits and calculating costs. So it just makes it more complicated that much more. It’s not an issue for inflation but economists use this term about discounting. Discounting dollars that are going to be spent or gained in the future and discounting those because they don’t occur for a period of time and that’s an issue that’s very important as we look at the models evaluating hormone replacement therapy.

Also is that - I think this came up in the previous discussion in some of the questions and answers - is that when we look at HRT, there are some things that are more difficult with this. Trying to come up with pharmacoeconomic studies in comparison to a number of other diseases that we might look at. What we just alluded to, now there’s this long-term follow-up. There’s no clinical trials available that have patients enrolled for 30 years; that just doesn’t exist. There’s no databases anywhere in the world that have people that have been enrolled for 30 years to look and evaluate different issues with hormone replacement therapy - they are not there. So the way people have had to come up with the studies is develop models. There are three ways we can do studies, trials, database analysis, or do models, and that’s why all of these are looking at pulling data from different places and coming up with the best estimates possible. One of the problems with that is that their overall - keep in mind, probably some underestimate of calculating costs just because you can’t account for everything. You do your best to estimate what’s going to go into that, but knowing in real life there may be other things that we’re not able to account for. And we’ve already had discussion previously, which I’m glad about as far as some of the variation in looking at these kinds of issues. What’s the impact on heart disease? We can look at different articles and argue different points in trying to come up with what’s the best single appropriate number. It’s very difficult to say. Impacts in osteoporosis, different studies have different rates; fracture rate, same kind of thing, different studies may quote different numbers and so it’s hard to know which are the best ones, optimal ones to put into a model. And lastly potentially cerebrovascular disease, although less data there.

We had a discussion as far as endometrial cancer rates and breast cancer rates, probably the most controversial, in looking at what’s the appropriate number to put in. Most models include that. So let’s say that we have beneficial effects looking at decreasing fractures as far as length of time that patients live. We’ve got the potentially likely beneficial effects as far as coronary heart disease, and then there’s a disadvantage as far as potential impacts in breast cancers integrating those things together.

The other issue is, just to keep in mind, we’re looking at models that are going to try to overall summarize what happens within a population of women but keep in mind there’s going to be variations in symptoms, and variations within fractures, so there’s clearly limitations. The best model that we can invent is going to have some limitations.

Another one that there’s not a lot of data on, but I mention just to keep in mind, is quality of life. We are a melting pot within the United States and not all cultures value quality of life the same. So it’s unrealistic to expect everybody to evaluate some of the quality-of-life impacts the same, regardless of therapy. But that is an issue when we look at HRT. And then there’s variations within the different products and things that people have used to incorporate the different models.

We touched on these kinds of issues - risk factors, I think, are well known for those who are going to be at greater risk for developing osteoporosis. It’s important because when we are looking at building a model you’re saying are you looking at all patients or are you looking at a higher risk kind of patient, because it’s obviously easier to benefit a higher risk patient. The cost-effectiveness will be lower for a high-risk patient than a low-risk patient - just the nature of the disease; it’s easier to benefit them.

So as I alluded to, when we look at the HRT pharmacoeconomic literature it’s different than probably most other studies that we look at, because it’s largely drawn on building models and pulling information from a variety of sources to build the best estimate of, quote, - What’s truth going to be. - And again we got to do that because there’s no trials or databases available for so you can just calculate it from those. So you look at relative risk rates, or frequency rates of different occurrences, frequencies of patients dying, sort of as baseline within the population by age; and then looking at what are the implications of putting those individuals also on HRT. Another thing is how long a patient is treated. Most of the models will put into play looking to treat patients for 5 to 15 years - you’ll see some variations within those. And then most of the models that have been published today focus on conjugated estrogens, and again as we just had this discussion, as far as maybe all products are not the same. Although, most models have sort of put all of the therapies together.

I want to assess the impact of looking at the changes of different findings; we had good things or bad things. So typically what we’ll see is looking at changes of event rates as far as the complications; fracture rates; looking at the impact on side effects; looking at the impact of heart disease; potentially death from heart disease; developing breast cancer; death from breast cancer - all those kind of things, trying to build those altogether. So you’re looking at the frequency and then the impact that it’s going to have on patient’s life expectancy. Not 100 percent, but most of the models to date will include a loss of life or some increase in breast cancer. We know its controversial, but most of the models will put in some degree of loss of life with that and as well as if someone has a fracture, there is a percent chance because of complications that go from that, the patient may die prematurely.

The heart disease risk is a little more vague, and not all models have included a prolongation and life, although some have. So they’re not all the same that way, and typically the most common endpoints that you’ll see with these studies are cost per years of life extended or cost per quality adjusted life years, looking at some estimate of utilities score for a woman’s health. The other thing to keep in mind is that this is not a U.S. disease and there’s a lot of people around the world very, very interested in this whole issue and a lot of the models that have been created have come out of the United Kingdom as well as Australia and obviously the United States. So it’s certainly not limited to what we see just here.

The first study I wanted to mention, this was done by the National Institutes of Health here in the United States, so our tax dollars paid for this Clark and Schuttinga model. And here’s the reference supports over here. One of the reasons that I put it in is this is a cost-benefit study, and it’s the only one that’s published to date looking at the impact of cost as an input as well as an output. And what they did - because of the data that was available, they built a model looking at what were the risks of complications of white women that were 50 years of age based in 1988 - is when they started doing their work, what they did was their model assumed that 90 percent of high-risk, 70 percent of medium-risk, and none of the low-risk women based on bone marrow density would take hormone replacement therapy. So, big assumption, but obviously they’re focusing on the people that were going to be at higher risk of developing the complications. Then in their model they looked at the difference studies and assumed that these women would take the hormone replacement therapy for 15 years, and then calculate it based on discounting what the net present value of savings for a cost of illness. So cost of illness, meaning they looked at direct costs, costs of health care systems, and the indirect costs as far as costs to society, loss of productivity. And they did that for a theoretical cohort of 100,000 women.

So one of the things that’s important with their model is they built in the cost of doing a screening. Doing bone density screening at menopause, putting the patients on hormone replacement therapy, and then they had cost savings from reduced fractures. So they had the cost of treating those fractures, hospitalizations, nursing home visits. When they had a reduction in fractures, then there was cost savings from that, and they also put in figures for overall productivity values for the population and looked at if you prevented those fractures, then you’re allowing those individuals to be productive within society. For treatment they just said estrogen plus progestin, they didn’t specify a specific product, they just listed it as that.

In their findings was the present value after the discounting of savings in cost of illness, indirect and direct costs over that 40-years time period, was $5.1 million. So that’s what they estimated if you can get the, what we talked about, as far as the compliance, there’s 90 and 70 percent of those higher- and mid-risk women there would be actually a net reduction of, I guess you’d say a cost savings, a reduction in cost to society. And then they estimated, if you were to apply that at the time there were 1.9 million women in 1988 that met their parameters as far as being 55, white women, that obviously blows up significantly. So you’re estimating a net, total net savings of $27 million to be expected if you had only 50 percent of the women screened and then treated based upon whether they were high risk or medium risk for fractures. So the unique feature with this study is that they looked at productivity, and in direct costs not looking just at direct costs, that’s why there’s a significant cost savings that they estimate.

Now you’ll see these authors published a lot - Tosteson and Weinstein in particularly, they published a number of articles evaluating hormone replacement therapy. And I picked as a reference down here if you’re interested, and probably the one that many people quote the most, but they developed a very complicated model - an extremely complex model. That’s the one today I would emphasize here, and that’s sort of how I got interested in this, was looking at all of the things they took into account. But they basically looked at epidemiologic data, they looked at a lot of the clinical trials and economic data, and built this very complex framework looking to see how long women lived and integrating all of these different factors that we’ve talked about. That’s why a lot of people reference them because they put this very, very thorough model together. They set it up as a Markov model looking at different transitions and the probabilities within each transition. It’s very complex, you couldn’t do this kind of a model without a very complicated computer program. One of the limitations with our model is that they did assume 100 percent compliance and we know that’s just not real. That’s not anywhere close to real, but that’s the way, when they built all of the different factors in sort of saying people really took these things, what would happen. for them.

As far as cost and effectiveness issues that they looked at, obviously they’ve put in what were the costs of the drugs themselves. They would put in what were the costs of nursing home visits if, say, the patient did have a fracture, the percent of them they would have to go into a nursing home, how long, all the money that was wrapped up into that. Also, they did include an increase in breast cancer. So they put in what was the cost to treat those individual patients. When hip fractures would occur, what were the costs to treat those. Their effectiveness endpoints is they looked at what were to be their predicted change in life expectancy looking at an adverse effect on breast cancer, a positive effect on heart disease, and a positive effect as far as reducing the number of fractures. And they also tied in looking at utility scores to then calculate that change in life for the quality of life that women would have with those different states. So looking at the morbidity issues, because of the hip fracture, at symptom relief, the base motor symptoms are obviously going to improve and then we can’t have the adverse effects, the bleeding and other problems from HRT itself. So they took all of these things together, so it’s a very complicated model.

As far as their treatment groups, they have all these different tables in their manuscripts, but they looked at some women if you treated them for 10 years or of the women if you treated them for 15 years. And looked if you had women who had a hysterectomy or those that had a uterus present. So, estrogen .65, conjugated estrogens was there, estrogen progestin 5 to 10 mg on the first to 13th days is what they built into the model. That was based largely on most of the clinical studies that have been published in the late ’70s and early ’80s. Overall, after they’ve put all of those things into their model and started crunching all the numbers - what they found when they say both strategies just looking at women with the uterus and those without a uterus whether or not the progestin was added.

So they found that both strategies extended life. The length of years that these women would live was predicted to be increased by putting them on hormone replacement therapy and the single group that had the largest increase were those individuals who had a uterus in place that were treated for the full 15 years. So they have the greatest increase in length of life. So most treatments had a net increase in life expectancy - now keep in mind, for this study they did not look at indirect costs, no attempt to look at societal kinds of issues. They focused on what were the costs to the health care system, so that the cost of the drugs and the monitoring that went through in caring for those patients was larger than the cost savings as far as reducing hospitalizations and reducing the nursing home visits, that kind of thing, which is what we would expect. Vaccinations are the classic that are known that you may actually save money from what they actually cost. For most other diseases we spent a net increase in cost to treat to those diseases - to get the clinical outcome. So this is not a surprise at all in that regard. That’s true for most everything.

What their ranges were was cost of life per saved went from $15,000 to $25,000 roughly, which when you compare to other things like hypertension we actually spend more than that typically to treat mild hypertensive patients to get the benefits of those diseases.

Another thing obviously is hormone replacement therapy had a significant impact on quality of life, particularly treating the vasomotor symptoms. And when you looked across the different categories, the patients, the cost for quality adjusted life year was lowest in symptomatic women. Meaning that women that are symptomatic, they have vasomotor symptoms, it’s easiest to benefit them. So the cost is going to be the lowest for those individuals. So they all benefited, but symptomatic women, the cost to do that was lower because it’s easier.

If you’re interested in wanting to look at the pharmacoeconomic studies, this would actually be the single source that I would recommend to you if you would like to pull it. It was published in Pharmacoeconomics and it’s actually a two-part series. The first paper emphasizes really looking at vasomotor symptoms and the pharmacoeconomic issues tied into that. The second paper is the one that primarily focuses on the issues with osteoporosis, but it’s a very nice paper - Whittington and Faulds wrote it. Basically they talked about some of the issues that go into what is the cost to treat hip fractures, made a nice point about variations by country. One of the reasons that other countries raise this a lot is we are an expensive health care system. It costs more to have a hip fracture in the United States than basically every other country. And so other countries with said, - Gee, I’m not sure I want to claim the savings like the United States claims, because it costs more to be in the hospital over there then it does in Spain or U.K. or wherever. - They pointed out very limited cost benefits data because it’s just the one study that we’ve already briefly reviewed. They made the point that most of the models, because so many focus on the Weinstein model, did not include impacts and compliance. That’s clearly a critically important issue when we’re using hormone replacement therapy. And then they made the point that hormone replacement therapy is most cost effective in women who have had a hysterectomy, women that are high-risk for osteoporotic fractures. And in those women that take it for a longer duration of time, they get more benefit because they don’t have the immediate drop off, as it was brought up in the question and answer, when they would stop taking the HRT.

When you look at the different studies it probably makes no sense, but those studies that look at cardiovascular risk, in looking at benefit as far as decreasing coronary artery disease and other complications, then it obviously makes sense that those studies are going to show a higher - they are going to be more cost-effective, because it’s going to get more benefit for the money that’s spent in caring for those patients.

Screening and treating high-risk patients is typically listed as being more cost effective than universal treatment. And overall in the figures that the quote is - that is for the screening and high-risk group - it’s around $12,000 per life-year saved. And the universal treatment that they came up with is more in the $68,000 per life-year saved. Again, still comparable to what we used in looking at other disease states like hypertension.

And then lastly we talked about the utility scores, looking at quality of life, emphasizing where the quality of different states the patient may be in, emphasizes the importance of menopausal symptoms and therefore women that have symptoms. It’s going to be easier to benefit them. So it improves the cost-effectiveness ratio.

Another issue that’s really not been talked about a whole lot, but it sort of makes sense, is the drug and monitoring costs are going to have an impact on cost-effectiveness because were talking about taking therapy for longer periods of time. And there’s not been a lot of analysis looking at a range of different therapies and what impact they may have. Again, with cardiovascular we talked about significant quality of life lost if a patient does have a hip fracture - how debilitating that can be and the problems that women can have for a long time after that. And again lastly, the cost for quality adjusted life-year with HRT overall is comparable to that when we look at a number of other disease areas.

So trying to summarize some of those kinds of things, we look at some of the conclusions of the pharmacoeconomic literature. Hormone replacement therapy has a beneficial net economic and clinical impact in the prevention of fractures. There’s not a lot of argument that goes into that. When you look at the models, long-term therapy, which is typically defined as people taking the therapy for more than 10 years, is typically more cost effective than if they were to take it for a shorter period of time. Quality of life is significantly improved in women that do receive hormone replacement therapy, and cost for quality-adjusted life-year is going to be lower in those individuals, when looking at those quality-of-life issues rather than just looking at how long women live.

The clinical and economic benefit as far as when you look at the studies is: Who are the people it most likely will benefit? Who are the ones - if we can attract and convince them to try to stay on - that are going to be the individuals that have symptoms? Those people we can clearly benefit. The studies show those that have undergone hysterectomy, it’s easier to benefit them. The cost-effectiveness ratio is higher in those that are going to have the highest risk of fractures - makes sense. Those are the ones that we’re going to benefit the most. And lastly, what cardiovascular risk factors and potential benefits are there. Those individuals at higher risk also would benefit more from hormone replacement therapy.

Key point, though, is compliance. Is it the cost effectiveness that’s directly related to compliance rate? When the patients don’t take it, it’s not cost effective, it’s not working, it’s not going to work.

Now if you’re interested, this is Torgerson and Reid. They’re from the United Kingdom; they’re health economists. They’ve written a couple of papers I put in references here where they basically have gone through and critiqued some of the models that have been published. And then basically they’d go through and raise an issue and then sort of comment and talk about some of those kinds of issues that, going to the model, potentially look at ways to make them better. That’s where we are at right now. So it’s a very thorough critique. So if you’re interested in that, I really recommend you pull these two papers - especially the one that was published in ’99. They make a very strong argument for the first two cases here, that hormone replacement therapy is very cost effective to treat the vasomotor symptoms. And I think we would all acknowledge that they make a very strong argument there. They also make a very strong argument for hormone replacement therapy in asymptomatic women who are very high risk based on bone marrow density. There’s a very strong cost-effectiveness data in looking at those two groups. Some of the issues that they raise when they look at some of the different models is variations in hormone replacement therapy product costs - and most of the models to date have not taken into account some of that variation, so it just may change the end numbers somewhat. They also talked about the way some of the models looked at developing and evaluating hospital costs. And that they typically would look at average bed days. And we know people get discharged quicker. The beginning days are going to be more expensive than later days, typically, and so is average bed day the most appropriate figure to use? - maybe not, but I’m not sure people typically have a better figure to use in place of it. They also had an interesting discussion looking at menopausal symptom relief vs quality-of-life benefits from prevention of fractures themselves.

And also then the quality of life lost that occurs if a woman does develop breast cancer. And it sort of was saying that he thought some of the models were extending the benefit, quality-of-life benefit, too long and potentially looking at some of the menopausal symptom benefits. Obviously, we know it’s the controversy with breast cancer. Most models include that, but there is the controversy to know what number is the best one to use. Also there’s discussion we know about the cardiovascular endpoints and the issue about women that are healthy, that are more motivated to take care of themselves, the ones more likely to take the drugs and looking at the impact that they may have on cardiovascular endpoints or developing different diseases. And lastly he talked about - he really made an argument that more research is needed in using prophylactic hormone replacement therapy in older women, because that has not been evaluated to the same degree as looking at younger women.

The last study I wanted to touch on was fairly recently published in the American Journal of Managed Care. This was conducted over in Lovelace, in Albuquerque, and what they did was look at the hormone replacement therapy compliance rates, and it’s sort of an interesting study. I haven’t seen anyone else that has conducted anything like that. But they wanted to know are there differences in the short-term benefits and overall health care costs in looking at patients who are taking hormone replacement therapy and relating that to those women who were compliant vs those women that were not compliant with the therapy.

As far as their methods go, this is a database study, based on the Lovelace claims data, looking at the medical and pharmacy claims. And they set their parameters to look at women that were continuously enrolled within Lovelace. They were at least 40 years of age at the trial onset. They went into pharmacy claims and looked at the first prescription when they got the hormone replacement therapy. They did a twist, and I’m not sure if it would work in a lot of places, but they looked based on surname in the database - and they put in a code as far as did it appear to be a Hispanic surname vs a non-Hispanic white surname. It was dichotomous; you didn’t have other options. In their methods they talk about referencing another study that was validated doing this. One of the other variables that they came up with was looking at, in essence, an estimate of ethnicity for patients. Obviously there’s going to be some holes in that.

Then they looked at health-care utilization data for 18 months after that initial estrogen prescription. They looked at what outpatient visits the patients had, hospitalization rates, prescriptions, overall prescriptions as well as the hormone replacement therapy prescriptions. They calculated medication possession ratio as sort of an estimate of compliance. And we know there’s all kinds of equations that are available that different people use. And basically what they did was look at total days supply for all of the prescriptions within that 18 months and then divide it by the 18-month window, to come with an estimate of compliance. They made the assumption, which I think you’re sort of forced to do, that people are getting the prescriptions refilled, that they are taking them probably, I don’t know how else you could be more accurate in that. And for the 21-day cycles they assumed that patients were going to be 100 percent compliant with those.

Then they came up with some guidelines for compliance. Low compliance was defined as less than 20 percent, high compliance was greater than 80 percent of the medication possession ratio. Probably reasonable numbers. So there is obviously a number of people that are going to be in the middle there, that don’t fit either of those two parameters. Then they looked at what was the cost data for their outpatient visits, hospitalizations, and prescriptions. What I thought was interesting was that they set up a logistic regression. And looking at the logistic regression is the dichotomous dependent variable - looking to see high compliance vs low compliance and see what other factors went along with predicting who was going to be compliant with their hormone replacement therapy.

So the results: a little over 1100 women were in their sample. Out of that, 269 met their categorization being a low complier - a medication possession ratio of 20 percent or lower. You had 427 patients that met their criteria for being a high complier with an MPR of 80 percent or higher. Overall they looked at their high compliers, who were younger in age and they were more likely to be non-Hispanic white, as far as looking at that ethnicity variable. They also found that the high compliers took the three highest doses of estrogen as far as the prescriptions that were filled. Then when they saw people in the low compliers, the biggest drop off was right away at months two and three, so it was not months later, basically right after their first prescription.

So when they extended out and looked at months four to six, only 32 percent of low compliers had prescriptions filled compared to the high compliers - 98 percent of those individuals had prescriptions filled, so a very significant difference looking at the compliance rates. Low compliers had significantly more emergency department visits and emergency department costs but they did not have a difference in hospitalizations, total health-care costs were not different with low compliers, and OB/GYN costs were higher within the high compliers.

And I think we would anticipate cost of the estrogen/progestin therapy was higher in the high compliers because they were taking those drugs, therefore their costs were going to be higher. The HRT cost overall was about $180 - was their overall average. Another interesting feature is that the high compliers of HRT also had more refills of non-HRT prescriptions, they were more willing in essence to comply with other therapies as well.

Then they set up logistic regression into looking at, what would be characteristic features. The people who were compliers tended to be younger, 55 years of age was the cut off. They had OB/GYN visits - that went along with being a complier with HRT therapy, and they also had a higher use of non-HRT medication costs because they were taking those therapies. Turning it around the other way, the low compliers in the regression were older, over 55 years of age, they have that Hispanic ethnicity code, and those individuals had an increase in emergency department visits.

Some discussion that goes into the Hurley study is obviously - it was a database study so there is no way you can identify what were the potential reasons for noncompliance and why the number of people dropped off right after the first couple of months. There is the question as far as why were they taking HRT. Potentially, there are other indications that they were not able to separate out. And they raised the issue that HRT compliance may be associated with a positive health maintenance attitude as far as identifying women that are more interested in taking care of themselves. And that was what the association was in looking at their compliance rates.

And lastly, they maybe had an inadequate sample to look at the cost differences, because the overall total health care costs did not differ between the two groups, and that potentially may have been an issue of power - of just not having enough patients.

So, overall, looking at the pharmacoeconomic issues with hormone replacement therapy, I think there is a net clinical and economic benefit when we look at and evaluate hormone replacement therapy. We look at the broad range of women - the ones who benefit the most from a cost outcome standpoint are those that have menopausal symptoms, those that have undergone a hysterectomy, and those that have the greatest risk of fractures as far as the ones that are the most likely to benefit from therapy. And lastly, with the Hurley study, what’s the overall compliance with HRT is not associated with an increase in total health care costs. There’s not a penalty, in essence, that goes along with looking at individuals who are in compliance with their HRT.

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