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On May 1, 2009, Diane Orenstein and Susan Haber presented the first
of a series of events highlighting the latest releases of several
AHRQ tools. This presentation addressed the Centers
for Disease Control and Prevention (CDC) Chronic Disease Cost Calculator.
Part 1 of 2 (files split due to size of full-length
40 minutes, 18.7 MB)
May 1, 2009
Margie Shofer: Hello, I'm Margie Shofer with the Agency
for Healthcare Research and Quality, also known as AHRQ. Thank you for
joining us on this Web conference on the CDC's [Centers for Disease Control
and Prevention] Chronic Disease Cost Calculator. AHRQ partnered with
CDC to develop the calculator, which uses AHRQ's MEPS [Medical Expenditure
Panel Survey] data for some of its calculations. The calculator is a
downloadable tool that supports States in estimating State Medicaid expenditures
for six chronic diseases--congestive heart failure, heart disease, stroke,
hypertension, cancer, and diabetes--and generates estimates of the cost to
Medicaid of selected chronic diseases using customized inputs--for example,
prevalence rates and treatment costs.
This Web conference is the first of a series of events highlighting the latest
releases of several AHRQ tools. Earlier versions of these tools, including
an earlier version of the Chronic Disease Cost Calculator, were shared at two
workshops for State policymakers and data analysts, in December 2007 and January
2008. These Web conferences are a continuation of the technical assistance
opportunities that have grown out of those initial workshops. We are
also targeting this event to the Medicaid Medical Directors Learning Network
members, as this tool can be quite valuable in their chronic disease efforts. So,
if after learning more about this tool today you are interested in further
assistance in using it, please let us know.
We encourage your active participation on this Web conference, the primary
purpose of which is to help you understand how to use the tool. We will spend
some time reviewing the basics of the tool and the data underlying the calculation
and will then move on to a live demonstration of the tool.
After the presentation, we'll have an open question/answer period in
which we will address as many of your questions as possible. The question
and answer period will be moderated by Amanda Brodt and Hilary Kennedy, who
work with AHRQ on this project. If you have questions before the Q&A
period you can e-mail them to us using the question and answer box on the right
hand side of your screen. We will be taking clarifying questions in between
presentations, and will take all other questions at the end.
There are a large number of individuals on this call, so if we do not get
to your questions, please e-mail it to us at qualitytools@AHRQ.hhs.gov. We
have that e-mail address on one of the slides. We'll respond to
any questions that we receive after the Web conference.
Today's presentation will be given by Diane Orenstein and Susan Haber. Diane
Orenstein is a behavioral economist in the Division for Heart Disease and Stroke
at the Centers for Disease Control and Prevention. The focus of her programmatic
and applied research is to construct economic evaluations that describe the
cost burdens of chronic diseases and the cost effectiveness of interventions,
and to develop tools for public health practitioners and other key stakeholders
that provide innovative methodologies and guidance for policies and prevention
of chronic disease.
Susan Haber is a Senior Economist at RTI International. She has
over 20 years of experience in health policy research. Much of her work
involves studies of Medicaid and the State Children's Health Insurance
Program. Her research includes studies on the costs of chronic conditions,
and cancer treatment and screening costs in the State Medicaid program. So
with that, I'm going to turn it over to Diane and Susan.
Diane Orenstein: Thank you Margie, and good afternoon everyone
and thanks for attending the Webinar, which is an opportunity for us to share
the CDC's Chronic Disease Cost Calculator, which I'll be referring
to from now on as "the calculator." The calculator was developed
by CDC and RTI International, and input from several partners, to quantify
the economic burden of these diseases for Medicaid, which is unknown and is
likely to be quite substantial. The calculator provides States with an
interactive and easy to use tool to quantify and understand the financial impact
caused by these diseases. It's our hope that these cost estimates
would help inform State decisions on investments in chronic disease prevention
and disease management programs, and to begin a dialog about how to limit and
even reduce costs in the long run.
As you all know too well, chronic diseases are prevalent among all populations. They're
costly and, to a large extent, preventable. We developed the calculator
in response to States repeatedly asking for consistent methodology using consistent
data that all States can use to generate expenditures for selected chronic
disease for their State Medicaid spending. Given the combination of rising
costs of health care and the burden on State health budgets, this project has
focused on Medicaid because of the growing drain it puts on State budgets. As
you see, we have worked with several partners on this project, including both
Federal agencies and other States as well. We partner within divisions
of CDC as well.
The calculator is designed to be user friendly and yet flexible. We recognize
that many States may not have the capacity to conduct their own analysis of disease
burden and costs while others may have their own estimates, and we want to accommodate
both. As Susan will describe in more detail, we do provide computer codes
on the CDC's Web site for States that want to analyze costs using their
own Medicaid claims. Susan will now talk to you in detail about how we
developed these cost estimates.
Susan Haber: Thank you, Diane. Our cost estimates were
derived using three main data sources. The first is the Medical Expenditure
Panel Survey, or MEPS, which AHRQ maintains. We also use data from the
Centers for Medicare & Medicaid Services [CMS]: the Medicaid Analytic Extract
File, or MAX, which is a dataset that CMS creates from the Medicaid claims
and eligibility data that States submit to CMS. Then we have the Medicaid
Statistical Information System (MSIS).
MEPS is our main data source, and MEPS is a nationally representative survey
of the non-institutionalized population. It includes information on people's
insurance coverage, their service utilization and costs, and what diseases
they have. We supplemented our analyses with MAX data, because the MEPS
data do not include the institutionalized population and, for Medicaid, obviously,
missing the institutionalized population and missing long-term care data is
a big gap. We wanted to supplement the MAX data to capture those costs
for the Medicaid program. We use MAX data from four States to get the
estimates of the long-term care costs associated with the diseases we're
interested in here. MAX is a uniform dataset that is common across all
States created by CMS. And then finally we use the MSIS data for information
on the Medicaid population counts that we use in the calculator.
The methodology that we use to create our cost estimates is called an econometric
approach, or a regression based approach. The reason we use this approach
is to avoid the problem of double counting costs across diseases that often
arises when people try to estimate disease costs. One of the common ways
of estimating disease cost is to look at claims from medical services and look
at what the diagnosis is on that claim and then, based on the diagnosis of
a claim, decide to allocate those costs to a particular disease. The
problem is, as I'm sure many of you are aware, is that claims will often
have more than one diagnosis on them, particularly for a population with chronic
diseases. You'll often see that. So then you have the problem
of either kind of making an arbitrary decision about which disease or how much
of the costs on a particular claim to allocate to a disease, or if you attribute
the cost to both diseases on a claim, you'll have a problem of double counting
disease costs across diseases. So to avoid this problem, we used the
regression-based approach to allocate costs, based on the conditions that we
identified a person as having. And basically the regression-based approach
uses statistical methods to make that apportionment between different diseases. Once
we obtained these per-person costs associated with the particular cost using
this regression-based approach, we then combined them with estimates of prevalence
in each State derived from the MEPS data. We multiplied those two together
to generate an estimate of total State Medicaid costs.
Prior to releasing the calculator, we obtained input from a lot of State agencies. We've
really tried to make this as interactive a process as possible. Our communication
with State agencies has really gone back several years. As you can see
here, in November of 2007, we presented the calculator to the National Association
of State Medicaid Directors. As Margie mentioned earlier, we presented
the calculator at two of the AHRQ State Quality workshops. Prior to releasing
the calculator, we did Web conferences with Medicaid directors and also State
chronic disease directors. We've done a lot of presentations to
different groups over the past few years.
This has been important for two reasons. First of all, we were able
to develop a list of Frequently Asked Questions based on the input we received
from people during these various presentations and Web conferences. These
Frequently Asked Questions are on the CDC's Web site, where the calculator
lives. This list responds to some of the issues that have arisen as people
have looked at the calculator. But also these different presentations
we've done has helped us shape the modifications that we've been
making to the calculator, and as Diane will tell you in a little bit, there
is going to be a future version of the calculator.
Obviously, you all have the best ideas about how to use the information from
this calculator, and hopefully we'll hear some of that today, people's
thoughts on how it might be used. But here are some basic ideas about
how we thought the information could be used as we in CDC were developing the
calculator. First of all, it could provide important information that
Medicaid agencies or public health departments or other State policymakers
can use to assess the value of investing in disease prevention programs and
disease management programs. It's important to know what the cost
burdens of these diseases are in order to make the argument for investing in
these programs. It can also be used to educate policymakers about the
economic cost of chronic diseases in their State, the impact on the State,
and to communicate why it's so important to address the problem of chronic
diseases. And then last, one of the goals of the calculator was to try
to stimulate communication and collaboration between State agencies that may
not always communicate as they could. In particular, chronic diseases
are a really fruitful place for communication between Medicaid programs, chronic
disease directors, health departments, State budget offices, and other policymakers.
As we've been presenting the calculator over the past couple of years,
we've heard sometimes from people that they have some estimates, either
of the prevalence of these diseases in the Medicaid program or sometimes the
costs. The question has come up, what if the State has its own estimates? One
of the things we want to emphasize is that the goal of this calculator was
not to override any estimates that the State has. It was more to provide
information for States that may not have had the resources to develop this
If you are a State that does have estimates, of either prevalence or costs
of these chronic diseases, it's possible that the estimates in the calculator
that we provide for your State may differ from estimates that you already developed
yourself. There are a number of reasons this could happen. First
of all, you may be using different data sources. You may be using a data
estimate methodology rather than the regression-based approach I used. You
might be using an approach where you tried to allocate costs based on diagnoses
on claims. And you may be using data that come from a different time
period. So, it's important to understand why things might be different
and then make a judgment on your own about (given these differences and the
possible reasons for differences,) what are the most appropriate datasets and
modeling assumptions to use given what your needs are.
I'm going to now let Diane talk a little bit about plans for future iterations
of the calculator.
Diane Orenstein: Thank you Susan. As Susan described
to you, we've had a lot of input from States--from chronic disease directors,
Medicaid directors, and health departments--about their interest in trying
to address their questions and answers. We have already begun creating
version two. Version two will have all the same six diseases that are
already in version one but they're going to also expand to asthma, depression,
and arthritis. We're also going to expand the burden cost estimates
to include not only Medicaid, as in version one, but also Medicare and other
payers. We'll again continue using the MEPS data. We're
also going to update the calculator, using recently released 2006 data so that
we'll have at least another year's worth of more current information. If
you look into and actually go into the calculator, you'll see that we
have inflated the dollars into 2007 dollars for version one, which we're
describing today. Once we do version two we'll also be able to
inflate these to 2008 dollars. What you see here also is having lost
productivity, having indirect costs. And we've actually defined that. We're
using data on absenteeism to describe indirect costs or lost productivity. The
indirect costs can be described and data can come from many sources, but our
data source will be basically absenteeism. And last, version two will
also include a forecast for these costs. We're also working with
the American Heart Association, where they are just looking at the four cardiovascular
diseases--heart disease, stroke, hypertension, and heart failure. They
are going to actually do a more comprehensive forecasting for 20 years out. We're
working closely with them. They are using the calculator and the same
methodology, so we'll have the same consistency here.
Where is the calculator available? I'm going to leave this up
for just a moment and you can see where you can log in and can actually get
access to the calculator. You can use it. You can play with it. There's
information here that if you need some technical support, where to contact.
On that note, I'm going to hand this back to Amanda, who will take some
questions and be ready for some Q&As before we move onto the actual demonstration. Thank
Question: The first question asks: are the cost estimates
limited by any age groups? Are children included?
Susan Haber: Since these conditions basically don't
occur among children or the prevalence is extremely low, the cost estimates
are based on adults. As you see in the calculator itself, you can look
at cost estimates by different age groups; that's only adult age groups. However,
when we look at what the prevalence is in the Medicaid population, we include
the total Medicaid population. So the cost estimates are based on adults
but it's within the entire Medicaid population.
Diane Orenstein: I think also to answer that question, when
we add asthma that will include a younger population.
Question: Great. That comment actually goes to another
question we received, which wants to know when version two would be ready?
Diane Orenstein: Our hope, and certainly RTI is working furiously
already on this…I hope I'm comfortable, Susan, in saying a year?
Susan Haber: I think so.
Diane Orenstein: I don't think any longer than a year. The
structure is already in process now. The analysis--everything is going
Question: That's good news and I'm sure people
are eagerly awaiting version two. We have another question asking whether
the calculator could be used to identify cases of excess cost?
Susan Haber: Yeah. I don't think you could use
a calculator in exactly that way, but you'll see that. What we
do is provide you with what the average cost of a person with this condition
is in your State and the prevalence of the condition in your State. We
also provide information on national averages, so you could compare those to
the national average to see whether you are high or low. That's
the best information, the closest information we have to what is excess. Obviously,
there are so many factors that drive the State variation in costs for these
different diseases: the cost of medical care in your State, practice patterns. Prevalence
is going to be driven by the population characteristics. There's
a lot that goes into it. Even in making the judgment of how you stand
next to a national average, you certainly want to think about all of those,
deciding whether your costs are excessive or not.
Question: Any possibility for use to gauge the effects and
cost of obesity?
Susan Haber: The tool as it's set up now, it doesn't
really look at a risk factor like obesity. Diane, I'll let you
respond also, but from my perspective I would say the answer is no, we're
not really looking at the costs associated with risk factors such as obesity.
Diane Orenstein: I would agree with Susan.
Question: Great. What
we're going to do is we're going to answer one more question that's
come in. We seem to have several in the queue that we think will be appropriately
addressed with the demo. Then we'll go back to those questions
after the demonstration has been completed. But the question of clarification
before that is, just how many States are currently doing this work?
Diane Orenstein: I can answer that from about a month ago. What
we've done is basically looked at how many hits we've had and how
many downloads we've had. It wouldn't be fair to say how
many States. We've had over 400 hits and over 70 downloads, so
that's more than 50 States. I think it's being used very
broadly, but we have not conducted any type of evaluation to find out who's
using it other than the feedback that we've been given when we are having
any kind of interactions with the States. So that doesn't break
down whether it's a State health department or Medicaid department. We
don't know that difference at this point.
Amanda Brodt: Great. Given the level of questions we've
had, Susan, I think we have people who are eager to see the tool. Can
you do the demo?
Susan Haber: Excellent. Great. I'm going
to give you all a view of my desktop, where the calculator is residing. Now
that I brought up the desktop sharing, the captioning panel has disappeared
from the screen. If there's someone who still wants to use captioning,
there's a toolbar in the lower right-hand of the screen. Just click
on the icon to the furthest to the right of the toolbar. If you want
to increase the size of the captioning panel, drag your mouse across the top
edge of the panel until you get a double-sided arrow and click and drag upwards
to increase the size of the captioning.
If you were to download the cost calculator, this is what you would see. At
the start, this is our welcome page to the cost calculator. I'm
going to click on where it says begin here.
You come to a page that has some introductory information on the calculator,
just a general description of what's in the calculator and how we're
defining some of our terms. It just gives you an overview of the calculator. There's
also much more detailed documentation of the calculator that's available
for download from the CDC Web site and a more detailed user's guide as
well. But as you'll see as I'm walking through it, this is
a pretty easy tool to use and pretty intuitive as you get through it.
To move on from the introduction page, click continue. I get
our main switchboard. This is where you have choices of various things
you can do to input information. This is where you identify what State
you want to calculate costs for, what diseases, what subpopulations, things
like that. Or if you've already input information, you can click
on the bar that says show cost base, current input. If you hadn't
entered information before, and want to start over again, you can clear the
inputs. You can go back to the calculator introduction. You can
also save inputs to what's called an MCC file--like an Excel file. If
you saved inputs from there in the past and you want the use them again, you
can load them by clicking that button. If you want to leave the calculator,
you can click on the button that says exit calculator.
Now I'm going to go ahead and input information. Today, we're
going to use Kansas as our sample State. So basically, we have all 50
States and the District of Columbia listed here. So we just click on
whatever State it is that you want to calculate the cost for. So I'm
going to click on Kansas. You always have the option at any point of
going back to the previous screen by clicking on the back button or you can
return to that main menu, which is the screen we were at just before.
We're going go ahead and look at data for Kansas. So I'll
click save and continue to move to the next screen. This is
where you select what diseases you want to calculate costs for. We have
our six diseases listed here that are in the calculator. You can click
on any subset of these diseases that you want to calculate costs for. Or
if you want to calculate all six, just click on the button that says select
all diseases. If there's any disease you don't want
to calculate on, just unclick it and it goes away. We'll move
ahead and calculate cost for all six diseases in the calculator. So again
I'll click save and continue to move to the next screen.
Okay. This is the first screen where you really are starting to input
information. As you'll see, throughout this, we always provide
default values for your State, for whatever State you're calculating
costs for. On the other hand, if you have information that you would
rather use instead of our default information, then you're always free
to do that. I'll show you how to do that.
So the first piece of information is: what's the total number of Medicaid
beneficiaries in your State? We have taken data from the 2004 MSIS. That's
the most current data available from CMS. If you have more up-to-date
numbers on the size of your Medicaid population, you can certainly enter that
information. You would just unclick that check mark there where it says use
value from MSIS, unclick the checkmark, and you can go ahead and enter
your own number. Let's say that Kansas' Medicaid population
has gone up to 450,000. You can enter that there if you wanted.
The other thing is that we have these question marks. Those are help
or information boxes that we provided. This gives a little more information
about the data that we're using for the number of Medicaid beneficiaries
in a State. So for the purposes of my demonstration, I'm going
to go back and use the default value for Kansas, which was the number for the
2004 MSIS data. Then you'll see a choice of, in addition to calculating
costs for the total population, you can do it either by sex or by age group.
So if you wanted to do it by sex, you need to provide what the breakdown of
the Medicaid population is between males and females. Just enter the
percentages there. If you want to enter your own number rather than use
the default that we got from MSIS, you can enter your own number. You'll
see that we only require you to enter one number, because basically we're
going to force the male and the female percentages to sum to 100% because we
need that for our calculation. Alternatively, if you wanted to calculate
it by age group, you could do that. Here, we provide the breakdown of
the Medicaid population by age group, again using the MSIS data. Again,
you can enter whatever numbers you wanted to enter there, but they'll
be forced to sum to 100%.
Why don't we go ahead and use an example where we calculate it in total
and age group and use the MSIS default data. I'm going to click save
and continue. Now this screen asks for prevalence on each of the
conditions--by age group, in this case, because we're doing it by age
group. You'll see here we're also going to do it by total
as well as by age group. The information on the prevalence by age group
and the distribution by the Medicaid population by age group is going to be
used to calculate the overall prevalence as well. As I mentioned before,
going back to one of the questions we got, you'll see here that because
the prevalence of these conditions is so low among children we're looking
at prevalence and costs among the adult population, and we use 18–44,
45–64, and 65 and over as age groups.
Again, here we're using the default estimates of the prevalence in Kansas
that we got from the MEPS data. If you want to enter your own values,
you can uncheck the box and enter whatever numbers you think are more accurate
for your State, perhaps based on some analyses that you've done. At
the top here, the overall prevalence in the Medicaid population in Kansas is
estimated to be 5.8%. That's based on the prevalence in these three
adult age groups. It basically assumes a prevalence of zero among children.
These are the numbers for heart disease, and you would see a comparable screen
for congestive heart failure. Here is one for hypertension, stroke, diabetes,
and cancer. They all work in exactly the same way. You either accept
the default numbers that we're providing for you or you provide your
own numbers of prevalence in your own populations. You can go ahead and
do that yourself. You can accept the default numbers for some diseases
and not others. It's completely flexible in that way.
Okay. I'm going to go ahead using our default numbers here and
I'm going to go back to heart disease. Then we move ahead and save
and continue. This is the screen where you enter in the information
on the average costs associated with this disease per person in your Medicaid
program. Similar to the prevalence screen, there's a separate tab
for each of the diseases. In this case, since we're doing it by
age group, there's a place where you would enter the average costs per
person associated with heart disease for each of the three age groups. We
provide default numbers, or you can enter your own. There's an
overall population average based on the cost per person and the prevalence
by age of each of these diseases.
Obviously, one of the things you'll notice here, which probably won't
surprise you given that you all are so familiar with Medicaid programs, is
that the per-person cost for individuals 65 and older is actually lower than
the cost of the younger age groups. That's of course because essentially
everybody over the age of 65 in Medicaid is dually eligible for Medicare, and
Medicare is picking up a good chunk of the costs for these dually eligible
beneficiaries. If you take a look at something like diabetes, for example,
where there's a large outpatient prescription drug component, you see
a different pattern. These data, remember, are from prior to implementation
of Medicare Part D. This was during the time that Medicaid programs were
still paying the outpatient prescription drug costs for dual eligibles. The
next version of the calculator is going to use data from 2006, so we'll
be able to more accurately reflect the current reality for dual eligibles since
Medicare is paying most of the outpatient prescription drug costs for them. We
have the costs for stroke. For stroke, you see higher costs than the
dual eligible populations because of the long-term care component of their
expenditures. That wasn't a good example. I'm sorry.
I'm going to move onto the next screen. This is basically the
final screen of the calculator. This is where you see the results of
the calculation. You'll see it for Kansas. We provide an
estimate for the average of the United States overall. So overall, in
the Medicaid population of the United States, the prevalence of heart disease,
for example, was 6.5%. That translated into 3.6 million people with heart
disease in the Medicaid program. The cost per beneficiary per disease
is $1,320, and that translated into over $4.7 billion in Medicaid spending.
Then we show the numbers for Kansas. So we have the overall population
numbers for Kansas. Slightly lower prevalence of heart disease than the
U.S. average, 5.8% rather than 6.5%. There are about 21,000 Medicaid
beneficiaries, we estimate, in Kansas with heart disease. The average
cost per person with heart disease in Kansas' Medicaid program is about
$1,300. So it's quite similar to the national average. For
Kansas that translates into $27 million in Medicaid spending each year.
Then we break it down for each of the age groups. You can see, not
surprisingly, the prevalence rises with age. Despite the fact that the
per-person costs are lower for the 65 and over population, the total Medicaid
spending is substantially highest for that group because of the higher prevalence
of heart disease for that age group (among the elderly than among the younger
We have the same type of output page for congestive heart failure. We
don't provide estimates on the prevalence in the cost for beneficiary
broken out by age group for congestive heart failure. That's because
the number of observations in the MEPS data of people who had congestive heart
failure isn't large enough to estimate by age group. So we just
don't have large enough samples to do that. You'll see insufficient
data to estimate.
Here is hypertension. It's a huge cost because of the very high
prevalence of the disease. You see almost two-thirds of people age 65
and over in Kansas' Medicaid population were estimated to have hypertension,
and that translates into about $74 million in expenditures in Kansas alone,
and over $16 billion in the United States' Medicaid program overall.
Here's a similar page for stroke. There's diabetes. Cancer. Then
there's a summary page for all the selected diseases, which doesn't
provide the information on the prevalence and the per-person costs. It's
just the total estimated costs in the Medicaid population. So you can
just get a nice summary snapshot by looking at that screen.
Then on this page or on any of these pages on this calculated cost screen,
you can print the output if you want to. You can save the outputs to
a file you could import into Excel and do some calculations on it if you want
to. As I mentioned in the very beginning, you can save the inputs into
an MCC file and those inputs--the prevalence, the size of the Medicaid population--can
be used and saved in future analyses. You can always be back. That's
pretty much the end of the calculator.
I hope it's apparent that it's quite simple to use. It's
pretty intuitive. We provide lots of information on the screens, but
as I said, you can always go to the documentation on the CDC Web site for a
more detailed user's guide and also a more detailed technical explanation
of what went into the calculation.
If you want to get out of the calculator, you have to go back to the main
menu. Click on return to main and then you would click on this
button here that says exit calculator.
But for now, I'm going to exit my desktop demonstration and we'll
go back to the main presentation.
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