**Transcript of Cyberseminar**

**QUERI Implementation Research**

**September 13, 2012**

**Presenters:  Mike Bauer, Diane Cowper Ripley, Eric Litt**

Molly:And we have reached the top of the hour so I would like to introduce our presenters today. Presenting first we have Dr. Mike Bauer who is the associate director of COLMR at the VA Boston Healthcare System. We also have Diane Cowper Ripley, Dr. Diane Cowper Ripley. And she is the associate director at Rehabilitation Outcomes Research Center in Florida. She is part of the Gainesville division at the VA – I am sorry, VA Healthcare System Gainesville division. Presenting Dr. Penfold’s slides will be Eric Litt, he is part of the steering committee member for the VA GIS users group and is part of the NS/SG Veterans Healthcare System RORC. So I want to thank each of our presenters for joining us today. And at this time I would like to turn it over to you, Dr. Bauer. Are you prepared to show your screen? 

Mike Bauer:All ready to go, thank you, Molly, very much. And I would like to start by thanking both the audience and our presenters. And Rob Penfold unfortunately could not be with us due to family illness so Eric Litt, special thanks for filling in presenting his portion of the program. The program as Molly alluded to is really a coast to coast to coast collaboration between VA Boston, Group Health Institute, and the North Florida South Georgia Veterans Healthcare System. So we are very excited to be brought together on topics of using spatial analysis tools in implementation science. Had a little trouble drawing the slides here. So cyberseminar outline is what you should see on your screen. We will start with a very brief introduction from myself and then turn it over to Eric for an overview of spatial analysis with an emphasis on implementation methods. Then Diane Cowper Ripley will pick up examples of applying spatial tools to implementation issues. Then we hope to have some time for Q and A from the audience because this is, I think, very exciting new set of – relatively new set of methods to implementation science. It should spark some interesting discussion. 

We thought first to start out by a survey of who is out there. And we have a number of questions that Molly will guide us through to share with each other who we are here in the cyber room. 

Molly:Thank you, Dr. Bauer. The poll has been opened and we are receiving a lot of responses. About half of the audience has responded thus far. So the question is who do you work for: VA Research, VA Operations/program/other, Veterans Benefit Administration, or National Cemetery Administration, or other. We have had almost two-thirds of our audience vote so I will leave it open for just a few mores seconds and then we can review the results. It looks like the answers have stopped streaming in. I am going to go ahead and close the results and talk through them now. It looks like we have about 44 percent of our audience is part of VHA Research. 22 percent is VHA Operations/program/other. And about a third of our audience, 34 percent, identify themselves as other. Thank you for those responses and we are going to move onto the second poll question now. 

Please select all that apply. If you are a researcher do you do implementation or health services descriptive research? Implementation or health services intervention research? Or other? And the researchers are streaming in their answers now. We have had about 51 percent vote. So it looks like people are still writing those in. So thank you to our respondents. We will keep it open for just a few more seconds until the responses stop streaming in. it looks like out of our research audience about two-thirds have voted so I am going to go ahead and close it and share the results. So it looks like 62 percent of our audience identifies at least one of their research topics as implementation or health services descriptive research. About 70 percent also include implementation or health services intervention research, and 24 percent select other. Keeping in mind that people may have selected more than one option. So thank you again to our respondents. 

And we have one last poll question that we would like to use. Do you currently use geographic information systems known as GIS methods in your work? If so who is it for? And again you can select all that apply. The options are research, policy analysis, strategic planning, other, or do not use GIS yet. Again we have had about half our audience reply so we will give everybody a little bit more time to get in their answers. We thank you for responding to these. It does help guide the presentation and have a general idea of the knowledge level and audience participation and who is attending with us. It looks like the answers have stopped streaming in. I am going to go ahead and close up and talk through them really quickly. It looks like 29 percent use GIS methods for research, 13 policy analysis, 14 percent strategic planning, 18 percent other, and over half our audience, 57 percent, do not use GIS yet. So keep in mind that people did select more than one if they had the option or if they wanted to. So we thank you again for your responses. And now I am going to turn it back over to Mark. 

Mark Bauer:Thanks Molly, well, it is great to have a very diverse audience and I am particularly pleased that we have people that are both involved in GIS and not and also those in the operations and planning side as well as researchers. So welcome to all and we think there will be something there – in here for everyone. To begin with the question is why pay attention to geographic factors in implementation efforts? There are really two areas where we can think about using GIS methods. One is in pre-implementation or assessment phase. We would like to believe the VHA is a really hierarchical system but we know that that is a fiction that is held only at the highest levels of the organization perhaps on some days. But really local factors are likely to be – to play some as yet unidentified role in behavior at the VAMC level and probably below that as well. 

It is possible and we often think from our administrative perspectives that these are all somehow driven by the administrative structures that are around us and facing us in our every day life. But we are also, as we go home and leave the VA confines, recognize everything on the way home that we are really proud of our larger social spatial context. And we wonder whether those factors that are overlapping with or even independent of the VA impact on system behavior both for patients and for providers. 

Molly:Dr. Bauer, I apologize for interrupting. Can I ask you to speak up a little bit, please? 

Mark Bauer:Sure.

Molly:Thank you. 

Mark Bauer:The other arm of potential applicability for GIS implementation efforts is strategy design. So what are the social context factors that you are rolling up – an intervention out in? what is the resource availability and distribution? Diane will be giving you some very interesting examples of this. There are going to be travel and communication issues. With the VA it is very well taken interest in rural health, for instance. We have to be thinking about these positions very carefully. And even in large congested urban areas there are issues as well. Once you get implementation laid out what about issues in local context that are going to affect maintenance and sustainability? And then how does information and influence really flow? And we are interested both in attended and unattended influence flow which may or may not have an impact on our implementation goes. 

The corner of this collaboration was spawned by HSR&D IIR that we have titled The Spread of Newer Antipsychotics for Bipolar Disorder and PTSD. Part of this investigation was spurred by an observation that we made at a very sort of high level looking VISN by VISN at the rates of adoption for newer antipsychotic called aripiprazole. And it was interesting to us the VISNs appeared to split fairly nicely into two groups: those that had a fairly slow linear adoption of aripiprazole after it was released in 2004. You can see them in the dotted line. Then there was another group that came much closer to the Rogerian type S-shaped diffusion curve with a large jump between 2004, 2005 and ending up with a higher level of prescriptions by 2008. And these data are being very pressed currently in psychiatric services with Rob Penfold as the primary author. There is a series of analysis, analysis we did to try to explore this difference. But it also led us to major hypothesis of the IIR which is that both geographic factors consistent with classical diffusion theory and also organizational factors both shape the spread of second generation antipsychotics. And that once we identify the factors it will help us to segment the market and focus and to give our implementation interventions more efficiently and effectively. Because we know that most implementation strategies are extraordinarily expensive. For instance, academic detailing can cost tens of thousands of dollars per year per issue. 

More recent data from this grant and this is really fairly hot off the press over the last couple of weeks courtesy of Austin Lee and Lewis Kazis in a group that they were working with at Checker. Has identified a series of regional differences. This is a bar graph looking at the prescription of newer antipsychotics as a group for post traumatic stress disorder. I am looking at a few Veterans more than 700 thousand who received SGAs of one sort or another between 2003 and 2010. You can see the probability of getting a second generation antipsychotic varies almost 50 percent depending on whether you walk into a medical center in the south, the northeast, the midwest, or the west. And interestingly this is controlling for a whole variety of patient factors and several other organizational factors. So it appears that geography does play a role of the probability that you will get different kinds of prescriptions depending simply really on where you live. So these are intriguing diversities of behavior that we are seeking to try to unravel as part of this grant. 

By way of introduction that is about all I would like to say at this point. We can come back for some questions about these particular data sets. But I would like to turn it over to our geographer colleagues to talk – take us deeper into GIS methods and some of both the elegance and complexity that they represent. Eric why don’t you take it away? 

Diane Cowper Ripley:We have to wait until we get our [crosstalk] 

Molly:Go ahead and click show my screen then we should be all set to see your slides. 

Eric Litt:Got you. Yes. Did that. Wait, maybe not. 

Molly:Right there, there you go. 

Eric Litt:There we go. Sorry about that. Well, good afternoon everyone. My name is Eric Litt. I am a geographer and I work down, of course, in Gainesville, Florida. And this was kind of a last minute substitution so I apologize if some of these things are kind of off the cuff. I am not exactly sure of what Rob was asking with some of these so I did – I am trying to interpret it to the best of my abilities. To start off, looking at spatial analysis tools in implementation; one of the first things that come to mind is that you are looking at site specific customization. When you are looking at local – at very specific local locations that some question has been asked. The other thing is social context when it comes to geography. Sometimes some of those geographies do not match up. So that is one of the things that we have to look at spatial analysis in order to answer some of those questions. And then the geographic units as policy boundaries just because a geographic unit goes up to a specific boundary does not mean that it does not pass that – go past that boundary. 

Some of the specific tools that we are going to be talking about are location allocation which is – it is kind of a new and upcoming tool that we are using for a lot of where questions: where do we need to put this? Where do we need to move that? Looking at also minimum set coverages which is actually a tool inside of location allocation within our GIS. Then there is also spatial temporal cluster analysis or basically hot spots where where are a lot of these things showing up? Where is this more prevalent than other places? And then also geographic weighted regressions. So to go into the site customization you are looking at specific interventions or policies that are basically tailored to local resources or circumstances. If the location does not have the ability to do A then you will not be able to offer A in that location. Some places looking at different procedures or different drug interactions or things of that nature. You are looking to find out what those differences are. And then also aggregate those differences of the needs of the patient. 

The location allocation is basically when you want to say we want to create a new facility, where is the best place to put that facility based on where our patients live? Or if you want to look at collaborations with a non-VA entity where is the best place to go for that collaboration? Also the reallocation of resources. If for instance, if you have a pod of specific drugs to combat some type of illness, if they are not distributed correctly then it could definitely put a hamper on where to – the quickness to deliver those specific drugs. Intervention for improving access. This is one of the questions that we get quite frequently is how do we improve access? Well, there are a few ways that we can do that. There is one of course, increase the number of sites, well, if two sites are amazing then let us go for three sites and make it extraordinary and things of that nature. Increase capacity of existing sites. Do we need to hire more general practitioners in order to be able to serve more patients? Of course these are all subject to budget constraints. 

So to say well, we have two locations but we need to close one of them. Which one do we close? Which is going to be – have less of an impact on the population? Do we need to move a location? If we have a mobile center, if we go to city A maybe we have to move that stop over to city B because we are going to have a bigger impact on number of patients. So the location allocation problem is basically where do we get – or it is the how much and the where. Where do we get our biggest bang for our buck in order to put a location for these facilities. What should the capacity be? If we know that if we put facility in location A we are going to need to hire this number of general practitioners, this number of nurse practitioners, to find out what the demand is going to be. We can do that with location allocation. 

This is just a basic diagram of what we are talking about. The blue square or polygon is actually a geographic unit, does not matter what kind of geographic unit it is. It could be a county state or VISN or it could even – well, it could actually also be a zip code if you wanted to. And then the boxes down at the bottom, those are all demand points. So you can see where all these demand points line up on this square that if you – we can actually determine where the best location is for every single one of those demand points and that would be one of the primary locations to suggest for a new facility. This next slide is actually kind of bringing the same concept, just bring it up to a bigger area where we are looking at four specific sites and with this – this is the output of what we see when we are doing a location allocation. You see the little blue stars as specific facilities. And then all of the green crosses with the purple lines going towards the closest – to the closest facilities. This is kind of – this is what we see when we say we want to find out the best place to put four facilities, where would that be? This is the output that we get. 

One of the big questions is well, we can not just put a location everywhere. So what is the smallest number of facilities that we can place within an area in order to maximize the amount of – the access to healthcare. And one of the examples he has is how many CBOCs would be required so that no one has to drive more than 30 minutes to reach one. That is one of the big questions that they talk to us about now. This is one of the big questions that they are asking. The other thing that we are doing is looking at broadband. We have to map out broadband so we can locate – where we can find out where there are Veterans who are – have access to that broadband for Telehealth. That is the other big push of – instead of having someone travel all the way into a CBOC to see a doctor. That they would be able to just hop on the internet and be able to basically telecommute. So this is a map that shows DSL for telerehab or what would be used for telerehab. And you see that all these dots are – as you can see in the left hand corner a centralized office that provides broadband coverage wirelessly. And the blue circles are a 12 thousand foot coverage. So you can see in the middle that there are a lot of the dots and so there is a lot of overlap and so when it comes to overlap it is what can we do in order to get rid of a lot of this overlap but yet still have a very good presence of coverage. 

And so in this next slide the number of 12 locations was selected. And these are the 12 locations that actually give us the biggest bang for our buck. These are going to service the most individuals within this country. So continuing on talking about social context and networks, within the VA we have a lot of acronyms and we have a lot of verbiage VAMC and VISN. Those do not necessarily translate to the outside world. A VAMC and a VISN is going to be a little bit different than what a state is or a region, rural urban differences, counties and states, those are all things that we define within the VA. And there are different levels of aggregations associated with those just because we can go from a small unit up to a bigger unit that does not always – it does not always associate itself with a different culture. Sometimes those contexts, those social contexts are – just because it works in one area does not mean it is going to work in another area. 

Social networks are also defined constrained geographically because people interact with their local people, not necessarily one corner of the United States is going to collaborate another with the other side of the United States. It is – things happen differently in different areas. So one of the things that we talk about is space/time – looking at things in space/time. We have already hit on a few of these things of looking at where and when clusters of events happen. For instance, that new drug that we discussed earlier, the wheres and the whens. As time progresses more and more locations are going to distribute that drug. And then to see if they are randomly distributed. Is there a reason for clustering? Is it for availability? There are many possibilities. And there are many different types of distributions that you can run on these – on this raw data. This is just an example of some clusters looking at a specific area. As you can see in January, February, March, and April it is all this one concentrated location has been using said drug or said procedure. So you can definitely tell that that procedure or that drug has been adopted in that area. And so it is very easy to see that. If you start off with a small dot in January and it becomes a huge cluster in April they can see that where and when those adoptions take place. 

So then you want to know this is the why, the why is this happening and how significant is this? Who are these people that are prescribing this drug or doing this procedure? And who are the patients that are benefitting from this? Do they have enough people to do said procedure or to do a – I can not think of the word off the top of my head at this moment. But do clusters occur in specific places across multiple different interventions where you can see are interventions that are being placed on all these locations. You can see how well are they following those policies and those interventions. You can see – and those are things that we do now again across looking at mental health follow ups, TBI follow ups, who is within 30 minutes of CBOCs or primary care? We already do those things. So this just makes it a little bit easier. And then you can also locate the facilities where some of that – all those interventions and policies are not being implemented. You can target those areas for other – for more education, thank you, Dr. Ripley, I appreciate that. 

Looking at policy boundaries this is where we were talking about VAMC, VISNs, and states, these are all different types of boundaries that we have to work with and as you know they do not merge very well. There are many VISNs out there that have multiple state boundaries within them so that is something we have to be aware of. And as it says on the screen impact of state level Medicaid policies on Veterans – Veterans use VHA services. Just because the VA provides something in one specific VISN does not necessarily mean that a different government agency is going to be able to apply the same principles within a different state. And so some of the approaches that we use in order to look at this is what is known as geographically weighted regression. It is a modeling technique that we use. And then also conditional auto-regressive models basically saying that stuff closer together will tend to have more things similar. 

The geographically weighted regression is a modeling – a type of modeling where you can throw in a whole bunch of different coefficients and find out how important they are within your model. It says that models are tailoring factors to optimize implementation interventions where you might see that education might be a factor or racial ethnic values are a factor. Socioeconomic status is a factor. These are all things that you can throw within your GWR in order to find out some of the key variables that your model should be looking for. This is a map of the effect of race on coronary heart disease and with the GWR coefficients the dark blue dots are locations where there is low confidence that race actually has something to do with coronary heart disease where as where you have got the darker red or brown dots you have got a higher confidence that that is going to play a major role. 

This is a global or a geographically weight regression for tuberculosis in Houston, Texas. This is the standardized residuals. These are the standard deviations from the GWR coefficients. What this is actually saying is because there are no clusters you are showing that this is a good model. If there was clustering you are missing something. There is a key variable in your model that is not being used. So you have to go back to your GWR model and find out what are the things that you can put in there? That is the end of my section. Geography is very important that not a lot of people have been using. It is very – it is a lot easier to show a map than it might be to show a grid or a table. So there are a lot of different off the shelf tools that are out there. SatScan is a very good one. The p-median java applet, I have never personally worked with that one. The SAS macro I have – there are a lot of people that like to use the geographic weighted regression in SAS but my personal favorite is ArcGIS. That is kind of the gold standard within the VA right now. Thank you very much. 

Molly:Thank you Eric and I think we are ready to pull up Dr. Ripley’s slides. Thank you. 

Diane Cowper Ripley:When we were at the HSR&D meeting last July Mark and Rob and I were involved with a workshop on GIS and health services research. So after we got back from the meeting I got an email from Mark asking if I wanted to do a cyberseminar on using spatial analysis tools in implementation science. And I said, “OK, what do you want me to do?” And he said, “Why don’t you just give some examples of some work that you’ve been doing?” So we went back and forth a little bit and we finally came up with two examples that I am going to talk about today. So first was from a rapid response project called access to acute stroke care and the DHA. PIs on this project were Dr. Glenn Graham and Dr. Charlie Jia [Huanguang Jia]. And the second example I am going to give to you is the expansion of Telehealth which is something that the Office of Rural Health has asked us to look at. And when I say us I need to acknowledge my partners in crime who are Eric Litt and Lauren Wilson. So we are a unit and we work together on many of these things. 

In these two examples I would actually consider these pre-implementation where we are defining gaps and trying to come up with some solutions. I will go to the first example. We know it from a number of guidelines that the quicker you get to thrombolytic therapy for people who are candidates for that the better the outcomes are after a stroke. There is lots of literature that says that the timely and effective GPA administration can help a lot with the patient survival and also improve functional outcomes and also the quality of life for Veterans who suffer a stroke. Many people have seen the commercials on TV that say time loss is brain loss and this is what we are talking about. So this is a best practice. The sooner you can get people in for tPA the better. So Dr. Graham wanted to get a big picture acute stroke care within the VHA. And he casts a fairly broad net looking at both ischemic and hemorrhagic stroke. And he wanted to have us do a travel time analysis to acute stroke care with the idea that shorter travel time would be better – would have better patient outcomes. 

The 60 minute to stroke care was a benchmark that we used in the study. He was also interested in looking at where VA may partner with non-VA entities to reduce overall travel time to get optimal acute stroke care. The methods that we used were we identified patients with acute stroke by zip code between fiscal year 06 and fiscal year 10. As I said we cast a very broad net and used ICD-9 431- 437. We aggregated the patients’ locations up to the zip code level. So that was the first thing that we did to get our patient cohorts. Then we needed to identify the VHA facilities that have specialized stroke also identify the locations of the non-VA stroke centers. And then draw our travel times around all VHA and non-VA stroke care. So from VA’s central office, the neurology office courtesy of Dr. Graham, he gave us 113 VHA facilities that could deliver three different levels of stroke care. There are our primary stroke care who are 24/7/365 days a year will provide and have the capacity to administer tPA for acute stroke care. He located – he gave us 28 VHA locations that had limited hour stroke care meaning they could administer tPA but they were only in business from nine to five Monday through Friday. The third were the 55 VHA locations that were classified as supporting stroke facilities that have limited resources for stroke care. Also from Dr. Graham we received a list of 1,074 non-VA stroke care centers across the country. 

So here is what the VHA levels of stroke care look within United States. And you can see that these are not distributed equally. There are fewer facilities out west and there are more in the east and a lot of that has to do with composition of the population and enrollees in different areas of the country. So Dr. Graham asked us to conduct some scenarios. The first thing that he wanted to know was what does the current stroke coverage look like and how many of those who had had a stroke were within an hour of acute care facilities that could administer tPA? So that was what I call our baseline. 

Our scenario one was to upgrade one limited hour facility saying that the VA could upgrade one additional facility to a 24/7 stroke center, which one should that be? Our scenario two which I call the maximum VA capacity was upgrading all limited hour facilities to primary stroke centers. I call that the maximum VA capacity because the supporting stroke centers probably do not have the resources to jump from a supporting center up to a full coverage stroke center. So that is scenario two. Scenario three was saying that the VA could not upgrade any of its own facilities so where are the top three non-VA stroke centers that VA could potentially partner with? And scenario four was the maximum VA plus the maximum non-VA capacity in the VISN. The example that I am going to use is for VISN seven but this can be done in all VISNs and the scenarios will be different and the recommendations will be different based on area. So we had 742 patients with acute stroke in 06 through 10. 

And here is baseline. This is the scenario one where 136 are 18.3 percent of patients who had an acute stroke were within an hour of a VA full stroke center. So we did a location allocation analysis and discovered which one of the limited hours facilities should be upgraded. There were two in this network to choose from. The one that was chosen upped the number who were covered within – who were within 60 minute travel time to 273. And that percentage on the top is an error because it is more like 36. because it almost doubles. So sorry about that. That is an error. The next scenario we ran was the maximum capacity which upgraded both limited hours facilities to full stroke centers. That covered almost half of patients who had had a stroke then were within 60 minutes of acute care.

Moving along this is the scenario where none of the VA facilities could be upgraded but VA could partner with three. When we did that the percentages, that raised of percentage coverage to 42 percent. This is the maximum capacity in the VISN and again these percentages are off. I think when we –

Eric Litt:87 percent.

Diane Cowper Ripley:87 percent, yes. Sorry about that. Here we go. Here are the correct numbers. You can still – baseline 18.3, and these numbers are wrong. 

Mark Bauer:Yes, these numbers are wrong. 

Diane Cowper Ripley:The implications are facilities and patients are not distributed the same in all VISNs. GIS can help identify access gaps in specialty care. Can assist in build or buy decisions, can show current maximum capacity in VISNs and also as we are moving, if we want to move to 100 percent coverage can assist in where new telespecialty CBOCs or supporting stroke facilities would be most beneficial. Our second example I am going to go really quickly through was an example that the Office of Rural Health asked us to look at which was broadband coverage because they were interested in the expansion of telehealth in VHA, VHA to CBOC, VAMC to patient home, CBOC to patient home. And the objective is to move closer to where rural Veterans reside. We assembled the data and this was from the National Broadband Map. We downloaded the data. We merged the state files together and then mapped the coverage and then identified the number of enrollees outside of coverage. 

So this is what the National Broadband Map looks nationally. Here is a broadband coverage map for VISN one. The colored dots represent the number of enrollees in the – by zip code in the VISN. None of these spots are individual Veterans. They are all aggregated to the zip code level. Here shows VISN three and the broadband coverage and you will see this is a well covered network. This is my VISN, VISN eight, and you see there are gaps of broadband coverage and some of those easily explained by the Okefenokee Swamp up in Georgia and Lake Okeechobee in south Florida. Here is 23. Here is 21. So the implications are that you may have a great telemedicine program that you have piloted and it works out really well at the local level but if you want to roll out that program you need to see if the roll out is even feasible. So GIF can help with understanding the spatial capacity for new technology. And in addition to the broadband we have also downloaded and we can map wireless coverage as well. So if you want to know any more about what we do this is us, this is the geospatial outcomes division at the RORC as well as the Veterans Rural Health Resource Center Eastern Region in Gainesville.

I want to make a plug for the VA GIS users group. There is such an entity. If you would like more information about them I have given you the website. Also this is very exciting, for the first time the VA is participating in National GIS Day which is November fourth. I am sorry, November 14th. So join us. Any questions? 

Molly:Thank you very much to all of our presenters. Yes we do have several pending questions from the audience. I am going to go ahead and check in. Mark, would you like us to start with the questions from the audience? 

Mark Bauer:sure. 

Molly:Sounds good. So a couple of these are directed to Eric. So Eric when you mentioned aggregate difference

in patient needs what exactly did you mean? 

Eric Litt:Well, when we say aggregate – aggregation to patient needs is when you are looking at specific – it could be something as specific as looking at influenza where you see hot spots of influenza. As Dr. Ripley was talking – when we were looking at stroke there is something called the stroke belt where you see a higher prevalence of stroke within older populations. So to look at needs of one location to another they could be localized, they could be on a regional scale. Does that answer your question? 

Molly:Thank you for that response. [laughter] If you did not answer it adequately the person who submitted it can always ask for further clarification. Next question: this one was also for you, Eric. How is GWR different from a normal multi-level model that simply includes geographical barriers as predictors? 

Eric Litt:Well, that – it is kind of they are very similar. GWR is just – is the modeling tool. That is what it is called within our GIS. It is more based on spatial location has a higher prevalence than other regression models. 

Diane Cowper Ripley:And Rob could probably answer that.

Eric Litt:Rob can definitely answer that. He is more of a statistician than I am. 

Molly:And I do believe Rob Penfold’s contact information is included in the handouts. And also I did want to mention Diane and Eric, if you would like to send me updated slides to post on our archive online with the correct percentages I am happy to switch those. 

Diane Cowper Ripley:Thank you very much. [crosstalk]

Molly:Not a problem. It is OK. Next question: this one is also to you, Eric. How was the effect of race on CHD mortality performed using point data?

Eric Litt:Well, it is point data because you are aggregating numbers up to the zip code level. So you are looking at the effect of race on a zip code level compared to the entire population. It is – the hot spots or the confidence interval of race of a specific location compared to rest of the model, compared to the rest of the United States. 

Molly:Excellent, thank you And we do have a couple questions that have come in that are very similar so I am just going to pull them all into one. It is a couple parts. Essentially can any VHA researcher download and use GIS programs and software? If so where can I get access to the software? And is there online training for GIS within the Veterans Healthcare Administration?

Diane Cowper Ripley:Who would you like to answer that?

Molly:Anyone who feels they know the correct answers. You can all have a shot at it. [laughter]

Mark Bauer:Somebody in south Georgia or north Florida. 

Diane Cowper Ripley: I can – Well, the VHA, sort of the gold standard for GIS software and what most of the VA GIS users use is ArcGIS. That can be purchased from Esri so if you have a grant you could put in that as a software expense on your grant. Other than that the users group has been pushing to get ArcGIS within VENCI [ph] so if you have a workspace within VINCI you can use that software. To answer the GIS training, I would strongly recommend that you go to the Esri, E-S-R-I, website. There are a number of training modules that you can access as well as some meetings for federal GIS users or national GIS users. There are meetings and within the meetings they have workshops that can train you on this particular software. I will warn you, however, that our GIS is not an easy plug and play. There is a fairly steep learning curve. But if you are interested in GIS it is a great program. 

Molly:Thank you for that response. Eric or Mark did you want to add anything to that? 

Mark Bauer:Not from here for me.

Eric Litt:No, I concur with everything Dr. Ripley said. It is a steep learning curve but if you have the patience to understand it then it is an invaluable tool. 

Molly:Excellent. Well we do have a large portion of our audience still with us and several pending questions so we will move right along. How can I calculate the distance between the patient address and the nearest hospital in GIS?

Eric Litt:One of the things, especially in research, if we do not like using the specific patient SSN level data just because of HIPAA. However, if you do have all the HIPAA waivers and you have gone through all the red tape and do all the things that you have to do there is a – first of all what you would have to do is geocode the patient’s location to the specific latitude and longitude of their address. And then there is a program within our GIS called network analyst and what you can do is you can plug in go from this latitude longitude to this other latitude longitude being the facility and there is actually a road network. So it is not an as the crow flies type of distance. You can choose to do that but what you can do is say find the shortest route from point A to point B. Tell me how long it should take in minutes and seconds and also what is the distance. 

Molly:Excellent, thank you. The next question we have are the state files the Office of Rural Health used for broadband coverage available? And if so, where? 

Diane Cowper Ripley:They are available, however, the National State File is not. So in order to create that National State File you have to go to the broadband map website and download each state at a time and then within GIS you need to merge those into your National State File. 

Molly:Thank you for that response. We do have another question but I believe it was just answered. If I have XY coordinates for two different points, latitude and longitude, how will I be able to calculate the distance between the two? But as I said, I do believe that we just covered that. So the next question: how was the travel time to the hospitals derived? Was it through reporting by patients or the ambulance systems?

Eric Litt:No, it was actually used by network analysts to – again what we do is we aggregate everything up to the zip code level. So we are looking at zip code centroids. And we go from the zip code centroid wherever that path may happen to be and it then goes to the hospital. But what we basically do is we start from the hospital and we go out 60 minutes in every single direction. And if the zip code falls within that 60 minute travel band they are covered. If they fall outside of that 60 minute travel time band they are not covered. 

Molly:Thank you for that clarification. Go ahead. 

Diane Cowper Ripley:One of the things you have to remember is that within the road network the travel time is weighted by population density and type of road. 

Eric Litt:Yes, it does not calculate rush hour or anything like that. 

Molly:Thank you both for those responses. We do have just two pending questions left. And the first one: does the GIS user group have a list serve that they post comments, community updates, or suggestions to? 

Diane Cowper Ripley:Yes, we have a Share Point and I have given the link to that Share Point in the slides so if you go out there you can take a look and see what we are doing. There is a place you can go to ask for help if you are using a GIS software and get some advice on your analysis. 

Molly:Thank you, that is very helpful to our audience. And the final question, suppose you do see geographic differences in patterns of care or behavior that you want to intervene around, how does that geographic information help you in planning an implementation strategy? 

Eric Litt:Well –

Diane Cowper Ripley:I would let Mark take that one. 

Eric Litt:All right. 

Mark Bauer:Either way is fine. Sure. As I mentioned at the beginning of the hour in the introduction, to do implementation strategy interventions, as people know, is extraordinarily expensive. One of the things we want to do is be able to segment the market or identify areas of high concentration or low concentration with behaviors we want to change. And we also want to know whatever we can about the characteristics of the people that are within those hot spots or cold spots. And so it is really a matter of targeting interventions and customizing as much as you can to the areas. The other piece of it, of course, if you think about Diane’s examples of broadband coverage, if you are trying to do for instance a telehealth or a web-based intervention there is no broadband there you are going to have an awfully difficult time doing it. So you have to be thinking – able to think about other avenues, other strategies. So it really is a matter of using large numbers to get a fine grade analysis to help you deploy your most expensive resources whether those are implementation strategies for an FTP or a policy roll out or they are allocation of resources like stroke centers. 

Molly: Thank you for that response. We do have one more question that just came in. are you all available for just another minute or two? 

Diane Cowper Riley:Sure. 

Eric Litt:Sure. 

Molly:Great. Would you be able to explain more about network analysis for calculating distance? I am trying to calculate the distance and I am kind of lost in the network analysis tab in the Arc toolbox. 

Eric Litt:One of the first things you have to do is make sure that you have – you have brought in your streets data. If you do not have your streets data you are not going to be able to do any type of analysis. But if you –

Molly:Thank you for that –

Eric Litt:I was just going to say if you want to email me, it is on the last – the second to last slide, Eric Litt, if you want to email me and then we can talk about that if you wish. 

Mark Bauer:And I can in closing speak for Rob who unfortunately could not be with us today although he is not – he is a WOC with VA Boston living in Seattle and is not intimately familiar with the VA databases he is very interested in being available to people for GIS type questions and potential collaborations. 

Molly:Thank you everyone for your responses. And at this time I would like to give each of you a chance to say any concluding comments to our audience. Mark, would you like to begin? 

Mark Bauer:Just say thank you for your participation, attention, and thank you in particular for what are really a very applied and because of that exciting questions that people actually incorporating some of these methods into their thinking and planning. And thank you Molly very much for your support for this seminar. 

Molly:Always happy to help. And Diane and Eric, would you like to say anything? 

Diane Cowper Ripley:Yes, I would just well, say ditto from what Mark said. Thank you very much for your attention and your questions. 

Eric Litt:I just hope that people – whenever they are putting together a research question to make sure that you think about things geographically because geography is definitely overlooked and it might answer a lot of the questions of why. 

Molly:Excellent advice. Well, I do also want to echo everyone’s comments and say thank you to our presenters for sharing your expertise and your time. And thank you to our attendees for joining us today. As most of you know this session has been recorded and will be posted in our cyberseminar archive catalogue within the next one to two days. Furthermore as you exit the session please do wait a moment there will be a survey that uploads onto your browser and we do appreciate you answering those few questions as it does help us to improve our program. So I want to thank everyone for joining us today and this does conclude our HSR&D cyberseminar. Have a wonderful day. 

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