2008 State Snapshots

On July 9, 2009, Ernie Moy, Foster Gesten, and Keely Cofrin Allen presented a Web conference on the AHRQ 2008 State Snapshots Tool. This is the transcript of the event's presentation.

July 9, 2009
1:00-2:30 PM ET

Margie Shofer: Hello, everyone. I'm Margie Shofer with the Office of Communications and Knowledge Transfer at the Agency for Healthcare Research and Quality, also known as AHRQ. Thank you for joining us for this Web conference on the 2008 State Snapshots tool. The State Snapshots, which AHRQ has been releasing since 2005, are dashboards that offer visual presentations of health care quality measures. They provide users with a summary overview of States' performance on: types of care (preventive, acute, and chronic); settings of care (hospital, ambulatory, nursing home, and home health); and care by clinical areas (cancer, diabetes, heart disease, maternal and child health, and respiratory disease). Users can also drill down into the individual component measures of the summary to see how their State performance compares to all other States, best performing States, and selected States in their region.

This Web conference is part of a project to share AHRQ tools of interest to State policymakers. In addition to sharing our products we are also offering technical assistance in using the product so if after learning more about State Snapshots today you're interested in further assistance in using this tool, please let us know. I also want to mention we'll be holding another Web conference on another AHRQ tool, the Asthma Return-on-Investment Calculator, in the early fall. Please stay tuned for an announcement about that event later on this summer.

Today we'll spend some time demonstrating how to use the State Snapshots, primarily examining the latest features of the tool, which include revised graphics to display summary measures, the ability to calculate the potential return on investment of the asthma care quality programs for Medicaid, State employees, and the privately insured, as well as diabetes programs, and a link to examples of innovative practices to improve quality of care in the area of diabetes and asthma, drawn from AHRQ's Health Care Innovations Exchange.

We will also discuss the data underlying the tool. We will have some time for questions after this presentation before moving on to presentations highlighting two States, New York and Utah, which have used the tool in their quality improvement efforts. We will end with another question and answer period in which we will address as many of your questions as possible. We really would appreciate your active participation today since we want to make sure you're comfortable with using the Snapshots after this Web conference ends. That said, there are a large number of individuals on this call so if we don't get to your question please e-mail it to us. The e-mail address will be posted at the end of the Web conference along with the link to access the 2008 State Snapshots. We promise we'll respond to all questions we receive after the Web conference ends.

Now I'd like to introduce our three speakers. Today's tool demonstration will be given by Dr. Ernie Moy, who is a Medical Officer in the Center for Quality Improvement and Patient Safety at AHRQ. His work has included directing the development of the annual National Healthcare Disparities Report [NHDR] and the National Healthcare Quality Report [NHQR], and supporting AHRQ's excellence centers for the elimination of ethnic racial disparities and AHRQ's Patient Safety Organization program. Ernie's research interests include disparities and access and quality of care, particularly as they relate to academic medical centers, patient safety, and technology diffusion.

Our next speaker will be Dr. Foster Gesten, who is a Medical Director for the Office of Health Insurance Programs in the New York State Department of Health. Dr. Gesten provides clinical direction and leadership for a team of professionals engaged in quality oversight, performance measurement, and clinical improvement with health plans and public insurance programs in New York. Major initiatives include the development of State-wide public reporting systems for commercial, Medicaid, and child health managed care programs on quality, access, and satisfaction. His interests include population health, health services research, and quality improvement projects directed to prevention services and chronic care.

Our final speaker is Dr. Keely Cofrin Allen and she is the Director of the Office of Health Care Statistics at the Utah Department of Health. Her department is responsible for a variety of health care quality data such as in-patient hospital, emergency room, and out-patient surgery data, as well as HEDIS [Healthcare Effectiveness Data and Information Set] and CAHPS [Consumer Assessment of Healthcare Providers and Systems] data from Utah's HMOs [Health Maintenance Organizations] and PPOs [Prefered Provider Organizations]. The office publishes hospital comparison reports on six different health conditions and an annual HMO performance report on commercial Medicaid and CHIP [Children's Health Insurance Program] health plans and annual reports on hospital in-patient, emergency department, and ambulatory surgical visits. So without further ado I'm going to hand this over to Ernie.

Ernie Moy: Thank you, Margie. And I'm going to start the demo by shifting over to my desktop. Hopefully you all can see the main page for the National Healthcare Quality and Disparities Reports. I'm supposed to remind you that if you want the captioning panel, you should click on the icon all the way on the right—to the right of the question mark—to open that particular panel.

So, I start here—even though today's talk is about the State Snapshots—because I wanted to remind you and remind myself that State Snapshots are part of a panel of tools that we have at AHRQ, the core of which are these two reports: the National Healthcare Quality and Disparities Reports. These two annual reports to Congress on the status of quality and the status of disparities try to provide people with baselines, benchmarks, as well as trends in quality and disparities over time. The Reports themselves have a couple of parts so I wanted to highlight several things that do relate directly to the State Snapshots. They take all of the measures, roughly 250 measures we track in the two Reports, at the national level and for those measures that we have State data for—not all have State data but many of them do—we then focus in on the State aspects in the State Snapshots. All of the tables that go to support the State Snapshots are located in the two Reports, specifically in two places. In the data tables part of the Report Appendices you can find the series of tables that go along with each measure. A product that we call the NHQR/DRnet is a place where you can develop customized tools to look up each measure and pull up the information that you're interested in related to that particular measure, including all of the State data tables.

I'll now head into the 2008 State Snapshots. This is the main page where you'll arrive. Again, the purpose of the State Snapshots is to allow States to see how they are performing, but more importantly to see how they're performing relative to other States. Here we're looking at an individual State compared with other States. I'm going to give examples of this tool using our two guests, Utah and New York, and I'm going to start with Utah. I'm going to go to the State selection map and pick Utah. When you first come in, you come to this dashboard, but I'm actually going to skip past it and come back to it. You'll see on the left hand menu how everything is organized, and this is consistent with what Margie had mentioned earlier. I'm going to walk you quickly through the different parts of it. Later on if there's a desire to go back to something I think we can do that and answer specific questions. I'll go through it really quickly because there are lots of parts. I'm not going to go through all parts either; I'll just try to key in on the ones that are most important and newest.

What everybody typically wants to see first is overall quality. This summarizes overall quality of care across all of the different measures we track in a State. Again, the emphasis here is that we track many measures. As a consequence, we don't drill particularly deeply into anything but we have measures that cover everything from ambulatory care, hospitals, home health, nursing homes, and hospice—a very broad range of different kinds of settings. And we sum it up into this metric.

Here, you see Utah is doing about average; the dotted line indicates the baseline year, and the solid line is the most recent year. One feature built into this is, if you click on each of the meters and everything is shown in the same pattern, you see the underlying summary data table that goes to create the average you see. What we see for the State of Utah is that they have 112 measures which we have State information for and of those 112 measures, they're better than average for 32, average for 53, worse than average for 27, and missing information for 8. That's just a very overall high level distribution, and then immediately underneath it we have the measures, all of the measures categorized by where they're better, about the same, and what I'm mostly interested in, maybe you too, is where they're worse than the State average. I won't dwell on it but this where people might look for opportunities for improvement in their State.

I'll show some of the other features that Margie mentioned. This compares a State, Utah, with all of the other States, but maybe you don't want to know about how you compare to all of the other States because your part of the country is different. You can pick your census division and here this shifts it from Utah compared to all States to Utah compared to just the Mountain States, those parts of the census division that are in the mountain region.

The last part that Margie also mentioned is the ability to compare to the best performing States. If you click on this you see the overall performance again. Utah's overall performance on the meter was a 52 and you see the best performing States—this is available for all of the different subscales that we have as well. In addition to seeing how their State is doing, users can see how they stack up against the best performing States and then the 25th, 50th and 75th percentile. We particularly put the best performing States up there because I might want to contact a different State to see how these States were able to achieve their high performance levels.

Another feature that we've had for a number of years is the strongest and weakest measures. The strongest measures are measures where the State performs the best relative to other States. You see how Utah does, and the weakest measures are those measures where the State does the worst and you can see our assessment of areas where Utah is weakest relative to other States: Pap tests, fecal blood testing, quality of dialysis, diabetes, eye exams, and the nursing home measure, a broad spectrum of measures but at least three of them relate to primary care activities.

I'm going to walk you very quickly through the different categories that we have for Utah and for all States. One categorization is by type of care, so this would include preventive care, acute care, and chronic care. I'll click on preventive care and you can see Utah is above or about average. And if I click on the underlying data, we can see how they are doing compared to all of the other States. You can also see what their summary looks like for preventive services only, and again, about average looking. I'll scroll all the way down to the bottom and you see areas popping up. I think these are some areas that we saw earlier on the worst performing aspects of Utah, but again, you see some other ones popping up as worse than average as well. Again, a lot of preventive services: blood testing, cholesterol screening, prenatal care.

Under acute care, Utah is about average. Clicking to see areas of opportunities for improvement potentially, we see a number of heart attack related measures and a number of surgical antibiotic measures are down here at the worse than average range. For chronic care—this is chronic management of a disease—they're a little bit better. The measures are in the upper range of average, getting into the strong sector, but opportunities might exist here as well; they typically do. We see some of the things that we saw before; diabetes related services, and nursing home related activities where they are performing worse than the national or State average. That's one way of looking at a State to try to get some insight about where to target, in this case maybe preventive care as opposed to chronic care or within a particular type of care, specific types of conditions or measures that might benefit from some improvement.

Another way to look at it is by setting of care: hospital care, ambulatory care, nursing home care, and home health care. Hospital care is about average. Scrolling all the way to the bottom, we're seeing again those heart attack measures popping up, as well as the surgical antibiotic measures popping up. And there is a strong relationship, as you can imagine, between acute care and hospital care. For ambulatory care, we're seeing again the cancer screening measures and then a couple of diabetes measures, more screening measures, prenatal care, and suicide deaths. Again, the notion here is to give a State some insight into the areas where they might be able to see improvement.

Nursing home care is more of a problem. I think we saw that earlier when we saw a long list of the worse than average nursing home measures. And here we see them. For many of the measures of nursing home care, it's worse than average. And home health care is very strong. Every State has something they're really good at, and home health care measures for the State of Utah are no worse than average.

Another way that we have for people to examine their State is by clinical area. These are the main domains that we examine and allow examination: cancer, diabetes, heart disease, child health, and respiratory disease. I think we already saw a lot of cancer, diabetes and heart disease pop-up. Maybe I'll click here for maternal and child health—strong performance. A measure that's a problem: prenatal care. Everything else is average or above average. Respiratory disease—also strong performance. Hospitalizations for influenza are higher than the all-State average.

And now, we'll show you the way that you would probably do it. Instead of walking through each of these individual different panels, although I like to do that to just show you exactly what's in each panel, just go to the State Dashboard which is where you first come in, where you'd see this overall quality of care meter. Underneath it, you would see all these rows that show the performance along types of care and settings of care by clinical areas, these match up exactly with each of the meters I showed you earlier. Now, it's been flattened out into this spectrum instead and you see where Utah currently performs and you see where the baseline year is. This is a way to look across all of the different dimensions to see areas where there might be the most opportunities for improvement. You see preventive care and acute care are a little bit worse than the chronic care. You see nursing home care being the worst of the different settings of care, and you see maternal and child and respiratory disease measures being better than cancer, diabetes, and heart disease. This is kind of a similar picture but summarized all into one place. You can still click on these and get the underlying tables as well.

So that's Utah and I'm going to switch over to our other State, New York, because they have some interesting data related to some of our focal areas. Quickly, the Dashboard for New York looks like this: overall, average kind of quality, and you can see areas that they're doing the best and worst in, acute care measures, hospital measures typically as well as home health, respiratory disease, and diabetes seem to be potential problem areas but I wasn't going to focus on that.

I was going to focus on those things that New York has really interesting information about. One of these is our focus on diabetes. Since we've had this now for a number of years, it's been refined a little bit. In the top part we try to show some information related to diabetes in the State of New York. And then down here we have a series of resources and links outside of this particular tool.

To give people a little bit of background about diabetes in New York, here is our prevalence map. It shows how New York is doing relative to the rest of the Nation in terms of overall prevalence of diabetes, and shows that diabetes is a concern in New York State. It's on the higher range in terms of prevalence. We can look at the quality of care for diabetes—this is the meter that focuses just on the diabetes measures, now differentiating process of care from outcomes of the care—so we can see for the process of care New York just does average on the different process of care that we can track. It is right in the middle. But in terms of outcomes of care you see New York State typically has higher rates of hospitalization for diabetes compared to the Northeast and to the Nation, and as a consequence of that, there are quality improvement opportunities.

For the diabetes example, we focus on potential cost savings related to State government employees, something that many States are interested in and so we produce an estimate of the proportion of expenditures on health care for State government employees that relate to diabetes care. There are opportunities for improvement for about 3 percent of those costs or this amount of money is at risk. Then from the diabetes calculator, we make projections about how much money could be saved per year with the implementation of a disease management program that achieves different levels of improvement. We have average results for an overall average improvement in the level of .5 percent, a reduction of .5 percent. These are the associated direct costs and productivity gains that could be achieved through that level of investment. If they could bring down their hemoglobin A1C on average by 1 percent then these are the corresponding amounts of money that could be saved each year. A caveat: many of these savings may not be realized for some time after implementing a disease management program, and so these are optimistic projections and longer term projections, not necessarily things that anyone would see right away.

Under the focus on diabetes, we have a section that looks at disparities in treatment. Instead of looking and sorting States by how well they perform on measures of diabetes quality, we're looking at the gap. The difference in this particular case is between residents with high income and low income. No actually, I take that back. This is actually based on self-reported income, so the difference is between people who report low income and high income. We see that New York is about average on this particular metric and you can look down and see what it actually looks like. For the proportion of diabetics in New York who get a hemoglobin A1C for the low income versus high income, you see a gap, but the gap is about comparable to what we see in other States, both in the Middle Atlantic and all States overall. And then we can see the similar contrast based upon ethnicity and it's about average for the Black/White difference and about average for the Hispanic/non-Hispanic White difference. Here you see the metrics, the actual measures: Hispanics and Blacks are less likely to receive hemoglobin A1C testing but the gap is about comparable to what's seen in other States as a whole.

I promised to go through these resources and innovations so I'll mention them. One is, now you've identified diabetes as a potential problem. What are things you can do? One thing you can do is look at our Resource Guide for State Action for Diabetes Quality Improvement. This is a resource guide and workbook that walks through what a State might want to do to improve quality, starting from making the case for improvement to actually developing an intervention. This is the diabetes cost calculator, which was the basis for making the estimates for the excess costs of diabetes and is a new link for the Diabetes Innovations Exchange. This links to AHRQ's Healthcare Innovations Exchange tool. This is a place where people have logged innovative and interesting stories about quality improvement, less successful attempts at quality improvement, and then tools that are useful for quality improvement purposes. This pulls up the ones that relate to diabetes, so if you want to know what to do next this might be one place to go.

Now, the focus on asthma. Asthma prevalence is another condition specific focus area that parallels the focus on diabetes. Again, New York is about average, a little bit on the high side in terms of asthma prevalence, and quality of care has very high rates of hospitalization for children, adults, and elderly adults. Here we have a calculator that allows people to calculate return-on-investment for improvements in asthma care. It's different from the one related to diabetes and I think it allows one to drill down a little bit more financially. It breaks the populations into children and adults and then separates them by insurance groups, into Medicaid, State employees, and private insurance. The State employee adults is similar to what I think we saw with diabetes but presented in quite a different fashion. Instead of giving you overall cost savings, this gives you approximate cost savings per person if the 253 adult State employees with asthma were able to be entered a disease management program that improves asthma management. These are the program costs over 3 years and these are the estimated savings in terms of direct costs and productivity gains. Here it gives you the return-on-investment, one for direct costs, just savings in terms of direct costs, and the other one incorporates the concept of improved productivity and also gives you estimates for the Medicaid population: 4,955 adult Medicaid individuals as well as privately insured individuals in the State.

Again, just like for the diabetes, we have a link to a State-oriented resource guide and workbook that can walk you through how to potentially improve the quality of care in the State as well as the link over to the Innovations Exchange. This time links pull up interesting stories, those success stories related to asthma quality improvement, as well as tools that might be useful for asthma quality improvement.

I'm going to skip over these pieces and go to another new part of the State Snapshots: the focus on disparities. Just like I showed you earlier for diabetes, focus on a given measure is on looking at the gap between different populations as opposed to overall quality of care. This series—this focus on diabetes series—now looks at a number of potentially avoidable admissions and looks again at the gap between different populations as opposed to overall quality. This shows that looking just at the Black/White difference, New York tends to be a little bit worse. What does that mean? Well we can click on the meter and you can see that gap is defined as a relative rate, the Black rate divided by the White rate, and you see that for New York, Blacks do worse than Whites, that is Blacks have higher hospitalization than Whites for a number of the measures, only two or less. Moreover, if you compare the New York rate with the United States rate, the difference, the relative difference, is larger than in the United States overall. Divide the New York relative rate by the U.S. relative rate and when it is over one, New York has a larger relative disparity compared to the United States as a whole. You see it across many of these measures, it exceeds one overall. Our assessment is that the Black/White difference is larger in New York State overall than it is for the U.S.

We also have comparisons for other groups: Asians versus Whites, and here, New York does very well. Hispanics versus Whites: here we see that New York State again has some disparities issues. For differences based on income, in this particular case it's the income of the neighborhood in which patients live, we see that it's the biggest issue. There are very large differences between hospitalization rates in high income versus low income communities. In general, for most of these the rate exceeds the United States difference.

Another aspect I was going to show is this ranking table for 15 measures that all of the States supply information for. This is the State rank and you see a huge variation in the ranks—which is typical. What we typically see is across all measures, including the ones that we rank, is that there is no State that's best in everything. There is no State that's worst in everything. Every State has some things they're really good in and some things they're really bad in and this ranking table brings that to light.

The last part I'll mention about the State Snapshots is the last link, which has contextual factors. This is meant to show information not about quality of care but about some of the underlying issues that might drive quality of care in a given State. Here we see New York has relatively high levels of poverty, relatively high levels of Medicaid, and a slightly older population. These might be things that affect the assessments of quality of care. While we don't adjust for differences across States in their demographics, we try to provide some information about the importance for interpreting the context of their quality measurement.

The last thing I'm going to mention related to State resources is "other information." This is where you can access technical assistance and methods, guides for interpreting the results, as well as the mega tables. If you want to download all of the measures and all of the results for all of the States, go to this mega table and download everything. And with that, I am going to conclude this portion and return this control over to the boss, who would be Margie. I'm going to put up the question slide to open up for the first panel of questions.

Margie Shofer: Thanks, Ernie. Thanks for your presentation. As Ernie said we're going to break for questions so let me quickly remind you how to ask questions. As Zac mentioned, you can submit a question by typing your inquiry into the Q & A box located on the right hand tool bar on your screen beneath the participant list or ask a live question—asking live questions is great—by clicking on the "Raise Hand" button located at the bottom of the box containing the participants list. That will place you in the queue of questions and Zac will introduce you.

Question: One of the purposes of the State Snapshots is to compare States against each other, identify various strengths and weaknesses, and communicate with other States of interest. Does the Snapshots tool contain information on relevant contacts in each State?

Ernie Moy: At this time, we don't have that information included in the State Snapshots. One of the areas that we're trying to develop are linkages to other tools both within AHRQ and outside of AHRQ that would be potentially useful. Right now we're developing a linkage to a report card compendium, for instance, which is a number of local report cards which would then include linkages to local organizations that are doing a quality assessment. You saw in the areas of asthma and diabetes we have the linkage to innovations where people are doing interesting things related to a particular condition and those obviously would be resources. But we don't have a list of specific contacts for each State to go to at this time.

Margie Shofer: I'll just add that that would be a wonderful thing to do in theory, but technically it's probably pretty hard because the folks responsible for the different measures in States that actually have programs related to those measures are probably different people in each State. I'm sure we could also try to be creative and certainly if anybody on the phone is interested in being a contact for your State, that's great. We could always collect a list and as people come in and ask us for further contacts, we could share that. But we'll do a little bit more thinking on that too here at AHRQ.

Question: How is AHRQ assessing the quality of services and care received by children, adolescents, and adults identified through newborn screening as having a disease requiring long term care?

Ernie Moy: Well, the simple answer to that is that we're not. As I'd mentioned we track a huge number of measures across many types of settings and care, and that's just not one we've gotten to quite yet. But if the questioner has a specific measure or way of capturing that population we would be interested seeing if there is a way to include that population. I'm just not familiar with a data source or measure that's applicable specifically to that population.

Question: What software did you use to generate the dashboard?

Rosanna Coffey : All of what you see on the dashboard or the Web site is custom work that Thompson Reuters has done for AHRQ. We have worked with one other State interested in using this kind of display information—the Maine Quality Forum—and we built some different looking Web sites for them, mainly because they were trying to produce a different type of information. AHRQ is certainly willing to share any of the software that we developed for this if you wanted to take it and apply it directly in your case. If you're working at the State level and then trying to drill down to a hospital level, you probably need something closer to what we did for the Maine Quality Forum.

Question: There were some measures that did not have a baseline rate. What does that mean?

Ernie Moy: There are a couple of different reasons potentially for that. For some of the measures, we add-on new measures all of the time as they develop and for some measures there simply is only 1 year of data and so there's no baseline rate as a consequence for that particular measure. In addition there's some State variability in terms of the availability of data, so if a particular State did not have information for our baseline year, then again, that line would not be present.

Question (Libby Kessler): Hi, thank you. We're just wondering if you have county level data available as well.

Ernie Moy: We are very interested in county level data as a next step. Unfortunately, when we go from the national data we collect for the reports to the State level, we lose a bunch of measures, and as we go from the State level down to the county level, we lose even more measures; in fact we get attenuated very dramatically. While we do have county level information for a small sub-set of the measures that you see in the State Snapshots, it gets to be a pretty small number of measures. We are thinking about doing something allowing further drill down at that level, but data availability is a severe problem at this point.

Moderator: And we have another live Q & A request from Nancy Solis. Nancy, your line is open. Please proceed.

Question (Nancy Solis): Yes, we have a question about data sources. While this is the 2008 Snapshots, we're wondering if there is some data lag depending on the data source. Where would you find information about what years are being compared?

Ernie Moy: In each of the tables, if you go to underlying tables it should tell you what year is the most current data that we're reporting on as well as what year is the baseline data, so that's where you can get that information. There's a tremendous data lag in terms of what we're showing and the information for the 2008 State Snapshots is information that we received roughly a year to a year and a half ago. It just takes that long to process it all as well as to get all of the clearances that we need to go through. Before it got to us, of course it had to go to the individual data stewards who are actually collecting the data, so there's a long trail before it gets to us. At the State level you probably, in fact I'm sure, have access to more recent data than we do. The flip side of that is that typically, most of these quality problems and most of these disparities problems don't disappear overnight. I think that for a lot of the things that we're seeing—especially those that apply to multiple measures—the same basic pattern of a problem probably still persists. Most of these things do not change dramatically from year to year, especially in relative terms.

Moderator: And we have a live Q & A request from Maria Rossi. Your line is open. Please proceed.

Question (Maria Rossi): Hi. You have specific information on asthma and diabetes. I was wondering if in the future you'll have specific data information for Alzheimer's, obesity, and other diseases.

Ernie Moy: That is one of the developmental pathways that we're interested in and we haven't chosen a next focus on condition, so if people have suggestions for that, that would be something to send into us. Basically, there are two lines of development that we're considering. One is focusing on an additional condition and the other is trying to allow drilling down to a finer geographic detail. That's something I would be interested in obtaining the listeners' feedback on. Would you look for finer geographic capability or would you look for more conditions to focus on in addition to the diabetes and asthma? Those are the two major developmental pathways that we have open to us.

Question: Are there performance measures for or related to behavioral health issues?

Ernie Moy: The answer is we do have some measures, although we really have to look hard for them at the national level, and when we drop down to the State level, unfortunately, we have very limited information available to us. I think we can track some mortality rates like suicide mortality, but that's not particularly satisfying for an assessment of behavioral health. It's primarily a data problem and as people know about good data that can provide measures of quality of behavioral health, we would love to hear about it at the State level. But we find the data in that particular area to be very limiting.

Question: Is there a list of the assumptions made or actual methodologies or formulas used for the Snapshots? I'm thinking this might be a Rosanna question.

Rosanna Coffey: Yes, there definitely is. It's on the Web site, and I don't know whether you can go back, Ernie, but it is at the very bottom of the left hand menu of the "other information" that he pointed out. There is a section there on methods, very detailed methods, that explains how the measures were selected for each type of care or clinical area, and then how the estimates were developed.

It's a very simple methodology. It really takes all of the measures in one area, looks at each one, determines whether or not a particular State is above, at, or below average based on that standard statistical test. For each measure, where a State is above average, you get a point, and for each measure that you're at average, you get a half a point, and for each measure that you're below average, you get 0. Those are summed up across the measures in a particular group and the scores come out to be between 0 and 100 and we translate that into 180 degrees for that fan-type meter or just the 100 degrees for the horizontal meter. It's divided into quintiles, then that score and the "very weak" is the lower 20 percent, the "very strong" is the upper 20 percent and so on.

Question: Where is baseline information from? Are all States using the same data or same year?

Ernie Moy: Yes. That should be typically true. There are maybe some subtle variations in that. For instance, all States don't necessarily do BRFSS [Behavioral Risk Factor Surveillance System] every year, so I think there are times when it's not exactly the same year, but typically they would be comparing the same baseline year.

Question: What is the data source for each State's information used in the reporting?

Ernie Moy: We use many different data sources, but if you look at the tabular information as opposed to the actual meters I think you should be able to identify what the specific data source was. It should be listed, but we use many different data sources.

Question: Can someone just compare some States only, for example run comparisons on two States only?

Ernie Moy: That's difficult to do inside the tool itself, let's put it that way. There's not a built-in capability to compare self-identified States. It's something that we've thought about doing. Of course the other option is simply to open two browser windows, which is something that I sometimes do to compare across two States.

Question: I see information for Medicaid, commercial, and State employees. Do you have Medicare data or are you planning to get Medicare data?

Ernie Moy: Rosanna, could you help me with that? That's specific to the asthma calculator.

Rosanna Coffey: That's correct. It was in the asthma calculator where we developed those particular populations. All of that information came out of the Thomson Reuters Market Scan data, where we have information for privately insured and for Medicaid, and it would be another developmental effort to put the Medicare population in there. I suspect we would want to look at the actual studies and how different the results were for just the Medicare population. We did look at some 52 studies in coming up with that calculator methodology, and in those 52 studies, we did look at the estimates differently by Medicaid, privately insured, and commercial—excuse me—and State employees. It would be an effort to go back and do it. It might be possible.

Ernie Moy: And isn't it right, Rosanna, that when we started to do the calculators we tried to put the focus on what States—as opposed to the Federal level—would be most interested in, and thought they would be most interested in State employees?

Rosanna Coffey: That's exactly right.

Proceed to Next Section

Page last reviewed July 2009
Internet Citation: 2008 State Snapshots. July 2009. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/cpi/centers/ockt/kt/webinars/snapshotstrans/stsnaptrans0709_pt1.html