HERC Health Economics Seminar- 12 -Department of Veteran Affairs

**Department of Veterans Affairs**

**HMCS (HERC Health Economics Seminar)**

**Using Systems Modeling to Evaluate the Impact of the New York State HIV Testing Law**

**Erika Martin, PhD MPH**

**March 20, 2013**

Moderator:  We're pleased to have Erika Martin present for us today, she's an assistant professor of public administration and policy at the Rockefeller College of Public Affairs and Policy and she's also an institute fellow at the Nelson Rockefeller Institute of Government at the University of Albany, State University of New York.  So some of her current projects look at system dynamics modeling to evaluate new HIV testing law in New York State.  She's done some work identifying the potential impact of the affordable care act on AIDS Drugs Assistance Program and she's also looked at the repealed ban on federal funding for syringe exchange programs.  Dr. Martin received her BA from Brown University, her MPH in epidemiology from the University of Michigan and her PhD in health policy administration from Yale University and today she'll be talking about a systems-dynamics-based evaluation of a mandatory offer of HIV testing in New York State, so welcome Erika.

Erika Martin:  Great!  Thanks.  Well, thank you for inviting me—I think I actually invited myself, but I really like attending these seminars, so hopefully I can have something useful for you in return.  I'm excited to talk about this project, this is a project that we were doing in collaboration with the AIDS Institute at the New York State Department of Health.  So like all good projects, I really can't take credit for all of the good ideas.  I have some great colleagues, in particular, Rod MacDonald, who is a system dynamics modeler, who is on our team.  We have a lot of folks from the New York State Department of Health, who are experts in different data systems and program operations and we receive some funding from the CDC.

Today what I want to talk about is our motivation for why we did this policy evaluation and in particular why we used system dynamics modeling to do it.  I will talk about the research methods, so this is a methods-focused audience, so I'll talk a bit more than I normally would about the system dynamics modeling approach how it differs from some other modeling that you might be used to and the model that we developed, which we call HIVSIM.  I'll talk about our baseline projections of what we think the future would look like in the absence of this new HIV testing law in New York State.

I'll start by talking about what we think the future would look like without the law and then the potential effects of the new HIV testing law and then some of the key model insights that we have.

So a bit of background on the new HIV testing law is this is something that was implemented about two years ago.  The aim of this was to increase HIV testing and entry to care and treatment.  Some of the key features of the law is that it simplifies some of the HIV testing in routine care.  So this is similar to national CDC guidelines, which suggested that we should be making it easier for people to get tested and take away some of this special consent and pre-test counseling.  So now as part of the law, emergency departments for example can just post posters that explain the different consent process.

One of the major features of the law, which is actually different from other states, is that HIV testing is now required as part of routine medical care.  So as the Centers for Disease Control and Prevention recommend that HIV testing be offered in these settings, now it's something that medical doctors have to offer.

The last feature of the law is that facilities and providers that are offering HIV tests now have to arrange for follow-up appointments. So before the law, if someone was identified as being HIV positive, the provider could hand them an appointment card and say, "This is someone who you can go talk to for follow-up care," whereas it's now part of the law, they have to actually be given an appointment.

Although this law was effective in 2010, there was a statutory requirement that the Commissioner of Health had to evaluate the number of HIV tests and also the number of people who accessed care and treatment.  So clearly it was policymakers and not PhD researchers who came up with this, as there is only two years from when the law was effective until when the report was due.  So all of us who work with data know that that's not a lot of time to collect and analyze the data.  Also one of the barriers to this is that the regulations were not actually published until February of last year, so it really gave us about six months to collect the data after the regulations were posted.

The other fun thing about what the requirement was is that although the number of HIV tests and the number who accessed care and treatment seemed to be like pretty good outcome indicators, these are also the kinds of indicators that there wasn't a lot of data for.

So where we came in is that we were asked to do some system dynamics modeling as part of this evaluation study.  Some of the quantitative data—so there's a lot of different types of data systems that we could look at, we could look at surveys.  We could look at administrative data, we can look at surveillance data to think about how the law has changed different outcomes, but the problem is that they don't always address all the complexities in the system of HIV testing and care.  So in the real world a lot of the folks who would be affected by this law, will be under different health plans.  They might be switching from private insurance to Medicaid, they might not have insurance, so it's hard to get one big data system that can get everyone.

Qualitative research can be helpful for understanding the nuances of how people move across the system of HIV testing and care, but they generally can't generate quantitative predictions, which is what policymakers were interested in.  There were several other evaluation studies, so there were about 14 other evaluation studies, but the problem is that the empirical data were limited to a pretty short time horizon, so much less than two years and also measurable outcomes.  So there are some things that we might really care about such as the number of new HIV infections, but those aren't directly measurable, whereas you could think about that with simulation modeling.

Finally, this law was implemented in the context of a lot of different concurrent policies.  So it's not like New York legislatures woke up one day and decided that they were going to pass this law.  This is building on a long history of efforts such as the Bronx Knows Initiative and other local HIV testing initiatives.  So it's pretty hard to isolate what is the marginal impact of the law, holding everything else constant.  

Some features of system dynamics models and how it's a bit different than some other modeling that you might be used to is that these models are really set up to look at problems in complex systems.  So they could be social management, economic systems, as part of this modeling, modelers often work very closely with stakeholders and experts to come up with this system structure and incorporate data.  So in our case, we have seven people on our steering committee from the Aids Institute and these folks all have expertise in different programs and operations and they helped us understand the data sources and what the system looks like.  

Systems modeling takes a very holistic view of organizations and processes and how they interact.  In particular, thinking about system-wide results, a feature of system dynamics modeling is this dynamic component, so it uses feedback loops, to think about changes over time, so in the case of HIV modeling, as more people receive antiretroviral therapies, we would expect that there would be fewer new infections in the future because of viral suppression, so that's something that you would want to incorporate in any kind of model of HIV testing and care.

System dynamics models directly incorporate nonlinearities in the relationships among variables, so, for example, as we expand HIV testing and we are testing more and more people, and we successfully diagnose new cases, we would expect that the yield of HIV testing to decline, so as we're identifying more people, we should probably find fewer new cases for the same number of tests and this is something that can be explicitly modeled in systems models.

Finally, system dynamics modeling really focuses on these dynamic implications of policy.  So rather than just thinking of point predictions, these models are designed to look *why* and *how* these outcomes will change over time.  Why there might be some unintended consequences and why implementation may or may not lead to the intended outcomes that we expect.

So as an overview of what we did, we spent most of the time on the project developing this computer simulation model of HIV testing and care in New York State.  Every model has to have a name, so we thought that our cute name would be HIVSIM or HIVSIM.  As part of this we had a bunch of different data sources that we set into HIVSIM for parameters and also calibration.  So the New York State Department of Health helped us work with some of the surveillance systems and they gave us some special estimates that we could use.  We used some New York Medicaid claims.  Our collaborators helped us get some estimates of incidents or new infections over time.  So this is something that we can't directly measure, but the CDC has some pretty sophisticated methods that figure this out.

We used expert opinion, so there are a lot of places in the system where we don't necessarily have the data and also published literature.  After we were done with the model and felt comfortable with it.  

We ran a bunch of counterfactual analyses, so we looked at different implementation scenarios of the law and we looked at short- and long-term outcomes and what I will present today is the graphs over time which are nice and visual and easy to understand. But in our write-up in our paper we also look at changes over time across the scenarios.

This is the main stock and flow diagram of our model.  This is the very basic model structure, the actual model has over 200 equations, so this is the simplified version and before I go into explaining it, anticipating that a lot of the audience probably is not familiar with system dynamics modeling, I'm just going to walk you through the notation first.

So this is the basic notation for diagramming stock and flow models.  What you have first is that the system dynamics models, take an aggregate or a population model view of the world and the main components of the model are stocks and flows.  Stocks are represented by the boxes and these are counts of people.  So in the case of HIV, you can think of this as a prevalence, so the number of people at one point in time and the flows, which are represented by these arrows represent changes over time and the units for these are the rate of people moving from one stock to another in a given unit of time and in the case of HIV, an example of a flow would be HIV incidents or new infections or the number of deaths over time.

System dynamics often use a plumbing analogy and talk about bathtubs, so you can think of the stock or the number of people who are infected with HIV as being a big bathtub.  So you have inflows, which would be the new infections coming in or the incidents and the outflows would be mortality.  

Anyone who is familiar with HIV has probably seen a slide from the CDC and what we see in the orange line is that the prevalence or the number of current living cases and that is going up over time in a pretty linear fashion.  The yellow line which is the triangles is the number of AIDS cases and the blue line is the number of deaths.  As you see, even though the number of AIDS cases or the incidents is pretty steady over time, we see that the prevalence is going up.  An assistant dynamists would say that the reason for this is because the inflow is bigger than the outflow.  It's not saying anything different than how an epidemiologist would explain this, but it's using slightly different terminology.

So going back to the same basic stock and flow diagram, which is very difficult to read—so this is the zoomed-in version.  What you have is four different stages of disease from stage zero or acute infection all the way to stage three or AIDS and people progress from the acute stage to the late stage, which is stage three due to disease progression.  So we assign them average amount of time in each of these boxes and people flow from left to right as they age and as they progress with their disease.

So then what we have going up and down—you'll see in the top row that that's for people who are unaware of their HIV infection, so they're not yet diagnosed.  The second row is for people who are aware, but not yet in care, so these are people who are diagnosed, but not yet linked to care and the third and fourth row is for people who are engaged in care and then the last row is for people who were in care, but now they're transitioning in and out of care because their care has collapsed.

So what we have is that there's a slower disease progression for people who are in care and the idea is that if people are on treatment, they're on antiretrovirals that their disease progression has slowed down.  

People move from top to bottom, so moving from unaware, to diagnosis, to being engaged in care, and your disease progression depends on your level of engagement in care.  So we have that in the model.

Then one of the things that I started off saying that's important to system dynamics models is the notion of dynamic process.  So this is the idea that this is an infectious disease, so people who are living with HIV-AIDS should be able to transmit infection.  What makes this really difficult is that where you are in the system affects your likelihood and the frequency at which you'll transmit new infections.  

So, as I said that modelers would normally think that you would transmit an infection because you have a contact with someone who is uninfected, there's some sort of encounter and the likelihood is that you will transmit depends on the number of contacts you have, the mixing patterns in a population, whether or not you're using a barrier method and, for example, your viral load in the case of HIV.

The problem with doing that with this model is that those data are very, very difficult to find.  So in particular there are published cohort studies, which have tried to figure out the infectivity or the likelihood that you would transmit an infection in a given encounter, but most of those studies are done with heterosexual couples in Sub Saharan Africa, which is not at all relevant to New York State. 

So what we did is we took some basic insights from modeling literature on HIV to come up with four main findings.  So first we found that people who are unaware—so these are people who are in the top row, they're not yet diagnosed, they transmit about 3.5 times more infections than diagnosed individuals.  This is partly due to risk behaviors, so they're engaged in more unprotected encounters and it's also because they have a higher viral load, so they're more infectious.

We found out just from local state data that three-quarters of New Yorkers who are engaged in care—so these are the people in that third row of the model, have a transmission rate of zero, so they have complete viral suppression, so even if they have an encounter with someone, they're not likely to transmit the infection.

We found—based on literature—that about a quarter of new infections are attributable to acutely infected individuals, so these are the people in the first column and we assume that people in stages one through three have identical transmission rates.

So what this means for our model is that we assigned much higher transmission rates for people who are unaware, so these are the people on the top row and much higher transmission rates for people who are in the acute disease stage on the left column.  We also assign very low transmission rates for people who are engaged in care and have achieved viral suppression, so this is for people in this third row.  

As the goal of the model was to talk about HIV testing, we also have an HIV testing structure that fits onto the model and in this we realized that in our model we thought that people can get tested through either background testing or else incremental testing.  So background testing is all of the high risk testing that's currently happening, so this includes initiatives like Bronx Knows, this includes the mobile testing vans, this would include testing in STD clinics.  Incremental testing is testing that would happen as part of routine care, which is part of the new law.  

The second stock and flow diagram relates to the main diagram is that the people in the top row of the main stock and flow diagram, so this people who are uninfected and also all of the people who are infected, but unaware of their infection, these are the people who are eligible for testing.  The people in the bottom row—so these are people who are diagnosed, but not getting care or else they're in care, these are people who have previously tested positive and we're not thinking about testing them as part of our model.

So mapping back to the second diagram, the top two boxes—so not recently tested and recently tested, these are the people who are eligible for testing.  This bottom box of people who are diagnosed these are the population that previously tested positive.

So what happens is that people can either be tested as part of background testing or else incremental testing.  So in our case we have these two boxes for not recently tested or recently tested and this refers to these offers as part of routine care or incremental testing.  So people who are not recently tested have not recently been tested in a doctor's office and people who are recently tested have been.

One of the questions that we were posed by the Department of Health was to look at different scenarios of the frequency of testing offers.  So we have a special variable in the model where we can put in a time delay between tests offered which can help us think about the frequency of testing.  

As far as how you get to this box as being diagnosed, anybody can be diagnosed as part of background testing, so even if you just had a test as part of routine care, you can still get diagnosed in the next time period as part of background testing.  However, only the people who are eligible for a test in routine care can also be diagnosed through incremental testing.  So this valve is only open to people who are not recently tested as part of routing care and who are eligible for a new test.

Going back to the dynamic part of the model, some people might have been recently tested, but then they subsequently become infected, so this is definitely part of the model, but people who are previously uninfected might later become infected.

So if you fell asleep, this is the main thing that you should know, we have this model of HIV testing and care, what it does is it aggregates all these different individual trajectories at the population level, so specifically we're thinking about the disease progression and different stages and levels of engagement in care.

A model incorporates a lot of dynamic feedback, so most importantly, existing cases generate new infection, so over time there will continue to be new infections, but how infectious you are changes depending on your disease stage and how engaged you are in care and whether you are in treatment.  Similarly, people who are in treatment have better health outcomes.  Then there's also a lot of nonlinear feedback, so if you continue to test the whole entire population, over time you'll have a lower yield from new tests.

The fun part of the model is actually getting to run some policy scenarios, so we came up with these based on consultation with our working group.  The first scenario is no law, so what would happen if New York never passed this new law?  You can also think about it as our base case.  We looked at three different levels of implementation.  We looked at perfect implementation, which would be that everyone gets offered a test by their doctor and everyone who's offered a test, accepts it.  Perfect implementation will never happen in real life, but this is just a nice scenario to look at with the maximal benefit of the law and we also had a high and low implementation scenario.

We looked at three different levels of the frequency of repeat testing in routine care.  We thought about what would happen if everybody who's in care—so everybody in the third row has perfect viral suppression and we looked at a range of implementation times.  In the best case scenario, you could think that maybe the law was implemented in a year and a half and we varied this all the way up to five years.

Just to be clear, all of these scenarios represent implementation of incremental testing in routine care settings.  So in all of the scenarios, we continue to assume that New Yorkers are also getting diagnosis as part of background testing.  

We had about ten different outcome variables.  We looked at the change in the number of HIV tests in the routine care, differences in new infections, a bunch of outcomes related to new diagnoses.  We looked at newly diagnosed cases linked to care and the number of people currently in care.  We looked at prevalence of people living with diagnosed infection as well as people who are living with HIV, both diagnosed and undiagnosed.  Also the fraction of cases who are undiagnosed.

You'll notice that two of these items are starred, so those are the two items that were required as part of the evaluation for the governor's office, but you also notice that there were a lot of other interesting things that our group cared about.  A couple of these are things that can't be measured, so new infections in the fraction of cases who are undiagnosed, these can't be directly measured with empirical data.

What we expect to happen in the absence of the law is that in our base case we found that there would be a continuing decline in the number of new infections per year and a continuing decline in the number of new diagnoses and also the fraction of undiagnosed cases.

At the same time, even though those other outcomes were going down, which seems like a good thing, we found that there would be a slight increase in the number of people living with diagnosed HIV infection and the diagnosed cases currently in care.  So even though the things in the first bullet are going to decline over time, we think that there will be a slight increase in prevalence in people needing treatment.

So this seems a bit counterintuitive, but the reason for the discrepancy is we think that there are some system delays, so in our retroviral therapy it's great because it has survival benefits, it has transmission benefits, but it means that people are staying in the system for a long time, that's why, even though the number of new infections should decline, we would expect to see a slightly higher prevalence over time.  

As far as the reason why we anticipate a continuing decline in these measures is because New York is currently doing a pretty good job with HIV prevention and we expect this to continue.  

So what would happen if the law were actually implemented?  First, we found that there would be continued reductions in annual new infections in the fraction of undiagnosed cases, above and beyond what we would expect in the absence of the law.  

We found that there would be an initial surge and then a decline in the number of newly diagnosed cases per year and a steady decline in the number of newly diagnosed AIDS cases per year.  

So the explanation for this is that we'll be rapidly identifying people who are unaware, so these are the people in the top row, so we'll be pulling them down to the second row of people being diagnosed and we anticipate that you would see a steady decline in the number of newly diagnosed AIDS cases because we would be identifying people earlier in their infection before they progressed to late stage.

So what these graphs actually look like—so this is a graph of new infections over time.  So on the Y axis what we have is people per year and what you see in the X axis is looking from 2006 through 2020 and the law is being implemented in 2010.  There's four different lines, so the blue line is what would happen in the no law scenario, so this is the base case what we expect would happen if the law had not been implemented.  Then we have three different lines for the different implementation scenarios.  So all of these assume annual testing and we have the red line, which is the lowest line is what we expect under perfect implementation, the green line is what you would expect under high implementation and the gray line is what you would expect under low implementation.

So focus less on the axial point prediction, but instead on the modeled behavior and how the lines would change.  As you see, although the expected decline in new infections over time the decline is even larger under perfect implementation.  So the law would lead to improvements in this area.

A similar story appears when we look at newly diagnosed AIDS cases, so we anticipate a slight decline over time and we expect an even larger decline under the law.  We anticipate a similar story that the law would reduce the fraction of undiagnosed cases, although you'll note that this still never approaches zero.

This graph looks a little bit different, so this one looks at the number of newly diagnosed cases.  So these are not new infections, but these are people that are new to the system and the surveillance department.  Whereas we see that over time we had anticipated a steady decline in the number of newly diagnosed cases, we anticipate a bit of a surge and then a rapid decline under perfect implementation and the explanation for this is simply that we're identifying these people and pulling them into the system. 

So if the law is implemented as designed, we anticipate that even though there will be the surge in the number of people who are being diagnosed, we didn't anticipate a very large surge in the number of people being newly linked to care.  We anticipated minimal changes in the number of people who are living with diagnosed infection and the number of people in care and the number of new infections in the fraction of undiagnosed cases do not approach zero.  So these outcomes get better, but they don't get to zero.

Some of the explanations for this is that we see a declining trend in people who are newly linked to care and there's also some system delays between when people are diagnosed and when they actually get linked to care.

Although the outcome should get better, there will never be zero new infections because there are still going to be people in the system who can transmit new infections.

What these graphs look like—this is the number of newly diagnosed cases who are newly linked to care, so there is a bit of a surge similar to the last graph where we looked at newly diagnosed cases but there isn't a large absolute increase in the number of people who would expect to be newly linked to care and getting treatment.  

This is a graph of people who are living with diagnosed HIV infections, so this is prevalence and although there are numerical differences in these lines, overall there really isn't much of a difference in the HIV prevalence even under perfect implementation of the law, so this line is not budging much.  

When we're comparing outcomes—so if we're looking across the scenarios that vary the level of implementation, scenarios that vary the frequency of testing, we find that varying the frequency of testing from annual testing down to one-time testing that there really are not a lot of differences in these outcomes.  So one outcome that changed a lot is the number of tests performed per year, so obviously there are a lot more tests if people are getting tested annually, but things like the number of new infections, the number of new diagnoses don't really change.  However, we did see some changes when we moved from low implementations to perfect implementations.  In particular, increasing the amount of implementation, improves the number of new infections, newly diagnosed cases, newly diagnosed cases with concurrent AIDS and also the fraction of undiagnosed cases.  So we did see differences in these areas across the implementation scenario.

We were also looking at what would happen if we varied the time to implementation?  We found that there really weren't a lot of substantial changes in outcomes if we improved the implementation time, so we might find people sooner, but in ten years the graphs were very similar.  The idea is that we would be identifying people sooner, but the number of people who are unaware is relatively small, so diagnosing them a few years earlier doesn't end up having a large impact on the number of new infections for the future.

As another scenario, we thought about what would happen if everybody who is currently engaged in care, the people in that third row, had perfect viral suppression.  So if it happened, then they would not be likely to transmit any new infections.  We found that this scenario had pretty similar improvements in the number of new infections when we compared it to the perfect implementation of the law.  Not surprisingly, the largest impact on the number of new infections is from an add-a-dose policy if we have both perfect viral suppression among people in care and perfect implementation of the law.  

This is the graph of new infections and the blue line is the same blue line as before, this is the base case, what would happen under no law.  The red line is what would happen under perfect implementation, so this is the same line as before.  The gray line is what would happen if there were no expanded testing, but everyone in care had perfect viral load suppression and the green line is the additive effect of these two  policies and you see that the best outcome is under the two policies, but similar to before new infections still does not approach zero.

So the limitations of our analysis are pretty similar to limitations of all model-based analysis, so first all models are wrong, they're imperfect representations of reality.  You just hope that you got the best—an adequate representation.  We tried to address this by having biweekly meetings with our steering committee.  We floated our stock and flow diagram with folks outside of our committee and outside the State Department of Health and I can talk to you to tears about all of the texts that we have and whether we could replicate our historical data.

Some parameters—there were no empirical data.  So, for example, there are no empirical data on infectiousness in the different stages of engagement and care or for different types of populations, but this is a case where we can actually use the modeling to generate some of these items and then check them with experts to make sure that they are realistic.

Finally, the true level of implementation is unknown, so going into this, the law was being rolled out as we were doing this evaluation study and it's unknown how much implementation is actually occurring.  This has actually strengthened a feature of what we were trying to do is that our clients wanted to know what could happen under these different kinds of implementation scenarios and where should we be targeting our money.

The major insights and things that we learned is that through our modeling we thought that it was really important to continue to invest resources in programs that provide HIV medical care, improve retention in care and encourage reductions in risky behaviors.

The evidence for this is that even under the baseline projections of what would happen under no law, we project an increase in the number of cases in care and this has to do with the system delays and there is an overall increase in the number of living cases despite these declining infection rates over time.

Our sensitivity analysis on what would happen under viral load suppression highlights the potential gains that we might have from both reducing infections among individuals in care and implementing the testing law.  This is actually pretty consistent with CDC's interest in prevention with positive programs.

The second main insight that we had is that temporary increases in new diagnoses under the law will be offset by an anticipated decline in the number of new infections and new diagnoses under baseline projections.  So although we expect to see a relative increase in the number of diagnosed cases that are newly linked to care, we didn't anticipate a huge surge, if you look at the absolute values.

Even without the law, New York State is doing a pretty good job of reducing new infections through its HIV prevention efforts.  So we think that this number will go down even in the absence of the law.

Our third insight is that it's important to continue to use this broad policy approach with a bunch of different HIV prevention interventions in addition to the law.  So the law is not going to solve the HIV epidemic and even under perfect implementation and even under annual repeat testing, there will continue to be new infections over time.

Our modeling suggested that one-time testing and routine care in addition to all the great stuff that New York is doing in terms of testing in high risk settings is the most efficient use of resources, so we didn't see a lot of differences in outcomes when we compared to that annual repeat testing and the one-time testing scenarios, but we did see large differences in outcomes when we varied the level of implementation.  So given limited resources, we thought that the state could maximize its return on investment by working with hospitals and providers to provide testing once to all patients rather than setting up systems where they could figure out how often they should offer the test again.

One of the things that we were asked to do with the model was to think about what would be going forward some useful indicators of whether the law is working or not?  What we found is through our modeling is that the most useful indicators that the state can use going forward for evaluation is the number of newly diagnosed cases and the number of HIV and AIDS cases per year.  So these are pretty simple outcomes, they're easy for the surveillance team to do over time, but what makes these good indicators in this case is that even under different levels of implementation, we'll see a big difference in these outcomes.  

Other indicators that we looked at such as the prevalence and the number of people living with HIV, these cross-sectional counts are not changing significantly over time.  Our concern with using new infections or the fraction of cases who are undiagnosed, so these are indicators that did change over time that they can't be measured directly and some of our indicators which were fractions, so in particular the fraction of newly diagnosed cases of concurrent AIDS ended up being very difficult to interpret across the scenarios because of the change in denominators, so it made it hard to interpret trends.  So that's all I had and I guess we can open it up to the Q and A and I apologize for all the technical difficulties.

Moderator:  There are a couple of questions in the queue.  The first question asks: "What did the modelers assume about the proportion of people who visit doctors during the year and the frequency of visits?"

Erika Martin:  The proportion who visited doctors during the year and the frequency of visits—so we actually had that.  We pulled some data from the Kaiser Family Foundation Web site for the number of visits per year and the number of unique patients seen by providers.  I'd have to go back to our textual documentation to remember that, and then we made some assumptions.  I think we assumed that maybe 75% of people saw a doctor within the year.

Moderator:  The second question asks: "Was the model used to produce estimates of the costs of the new law?"

Erika Martin:  We went back and forth on this.  We did not end up doing that in part because we just didn't have time to add it.  Going forward, we're thinking that that might be a useful thing to do.  So what we can do with the model is add some co-flows, so we can assign average costs to the different boxes and use that to generate cost estimates, but we have not yet done that.

Moderator:  It would be interesting to see if there were any cost savings.  You're expecting to see fewer infections in the future, so you could see that that might be some cost savings, even though there might be some short-term increases in getting people into care?  Are other states thinking about adopting that similar type of HIV testing laws?

Erika Martin:  I think California is currently thinking about this and so they contacted us, to ask some questions about the model and the evaluation report.

Moderator:  Okay.  Great.  There aren't any other questions in the queue right now, so if anyone has any more questions if you could please type them in to your Q and A panel.  This is one of the questions from me: Do you think the affordable care act since the number of people with healthcare insurance and potentially will get more people into care that this will affect your model?  You'll have potentially a bigger pool of people who will be then tested for HIV?

Erika Martin:  It may or it may not—what's going to be different with it, is that it's going to shift costs across the system.  So the U.S. Preventative Task Force just came out with some draft recommendations that HIV testing should be given an A recommendation, which means that rather than the Health Department having to pick up all the expenses, that health insurance plans would have to reimburse those costs.  So what we might see is not so much that the health insurance law—the Affordable Care Act will actually increase the number of people getting tested, but it might shift costs across the system.

Moderator:  We have a new question that asks: "What software do you use for system dynamics analysis?"

Erika Martin:  Zensim.  If you send me an e-mail, I can put you in touch with my colleague, who can tell you all about it.  It's Z-E-N-S-I-M. 

Moderator:  Thank you.  We don't have any other questions at this time.  Heidi, does everyone know how to get a copy of the slides?

Heidi:  Yes.  In the reminder e-mail that was sent out to everyone this morning, there is a direct link to the slides.  That's going to be the fastest way for you to get it or I will also send everyone an archive notice tomorrow when we have this recording posted for your viewing and there will be a link in there that you can use to get to the handouts, so you can get it either one of those two ways.  It looks like we do not have any other questions coming in at this time, if one does come to you at some point in the future, feel free to contact Erika.  I'm sure she would be happy to answer any questions you have on this.  Erika, I really do want to thank you for taking the time to put this together and present for us today.  We really, really do appreciate it.  For the audience, thank you very much for joining us today.  As you leave the session today, you will be prompted with a feedback survey, if you could take a few moments to fill that out, we really do read through your feedback suggestions and do make a lot of changes by what we hear from you, so please do take a few moments to fill that out and I hope everyone can join us at a future HSR&D cyber-seminar.  Thank you everyone for joining us today.

**[End of Recording]**

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