**Transcript of Cyberseminar**

**Spotlight on Women’s Health**

**Mapping Quality of Care: Assessing Gender Gaps in DM and CVD Care in a California Health Care Plan**

**Presenter: Chloe E. Bird, PhD**

**November 18, 2013**

*This is an unedited transcript of this session. As such, it may contain omissions or errors due to sound quality or misinterpretation. For clarification or verification of any points in the transcript, please refer to the audio version posted at www.hsrd.research.va.gov/cyberseminars/catalog-archive.cfm or contact Chloe E. Bird, PhD, at chloe@rand.org*

Moderator:We do have Dr. Chloe E. Bird presenting for us today. She is a senior sociologist at the RAND Corporation and a professor at Pardee RAND Graduate School. So we are very grateful to have her presenting to the VA field. And Chloe, I will turn it over to you now.

Chloe E. Bird, Ph.D.:Hi, thank you so much. The work I am presenting today is funded by the Barbara Streisand Women’s Heart Center at Cedar-Sinai’s Medical Center. And I just wanted to acknowledge that because I am not sure if the slide is included in this deck. I will see if we can add it for the recording before this is uploaded.

But I will be talking to you today about assessing gender gaps in quality of ambulatory care or cardiovascular disease and diabetes, major cardiovascular risk factor. And we are looking at one California health plan demonstrating the added value of mapping quality of care to understand and address disparities.

Moderator:Sorry for interrupting, can you double-check and make sure that your computer speakers are muted? I am hearing a little bit of an echo.

Chloe E. Bird, Ph.D.:Mine are muted.

Moderator:Okay.

Chloe E. Bird, Ph.D.:I did in fact have to mute them earlier.

Moderator:Okay, thank you.

Chloe E. Bird, Ph.D.:I do not hear it here.

Moderator:Oh, good.

Chloe E. Bird, Ph.D.:Okay, thank you. So I will start by reviewing briefly some of the gender differences in cardiovascular disease and diabetes. Talk a little bit about what mapping can provide, give you a bit of background on the study design for the work we are presenting today, and then talk about what the conventional analysis tells us, the kind of work we would normally do, specifically assessing gender disparity, what mapping adds, and then look at where we go from here. 

Since you now know a little bit about me, I would like to start first with finding out a little bit about you. The first poll question – that map has covered up my screen. What is your primary role in the VA? The first option is you are a student, trainee, or fellow, second a clinician, third, a researcher, fourth, a manager or policy maker, and fifth, other.

Moderator:Thank you. And it looks like our attendees responses are streaming in. So we will give people a few more seconds to get their responses in. Simply click the circle next to your answer choice and press submit. All right, it looks like the answers have stopped coming in. We have roughly nine percent each of student, trainee, or fellow and clinicians, thirty percent are researchers, and twenty-six percent are manager or policy makers, and twenty-six percent say other. Thank you.

Chloe E. Bird, Ph.D.:And one more question, which best describes your research experience? You have done no research. The second option that you have collaborated on research. Third, you have conducted research. I have conducted research myself. Fourth, have applied for research funding and just have led funded research grant.

Moderator:Great, it looks like we are pretty spread across the board on this one. Hovering right around 12 or 13 percent are people that have not done research. Forty percent have collaborated on research. Twenty-three percent have conducted research themselves. Four percent have applied for research funding. And about 19 percent have led a funded research grant. Thank you to our attendees for responding.

Chloe E. Bird, Ph.D.:Thank you. That is very helpful. So gender differences in the cardiovascular disease prevalence. As I assume you all know, heart disease is the leading cause of death in the United States. And when you look globally, it is also the leading cause of death. Many people assume that part of this, for women, is due to their high incidents of vascular disease. However, even when we divide the two, vascular disease is the third leading cause of death among women? The rate is lower among men. And heart disease remains number one. Taken together heart disease and stroke kill 8.6 million women annually worldwide accounting for 1 in 3 deaths. And what people are more familiar with is that because men have earlier onset of heart disease on average, their prevalence in age adjusted cardiovascular disease death rate is greater than women. However, even though women are slightly older at onset on average, women spend more years living with cardiovascular disease then do men. In addition, since 1984, more women than men have died of cardiovascular disease. 

Now diabetes is a major risk factor for cardiovascular disease. But it increases risk for women much more so than for men. It doubles men’s risk in working age population. It quadruples in women. In women, it basically eliminates the difference, the advantage that women may have premenopausally. And often we treat – we recognize this impact on risk and treat anyone with diabetes as having cardiovascular disease. And this is a shifting terrain as we increasingly try to come to ways to manage and mitigate risk in this secondary prevention, which is part of the area which we are focusing on today in terms of ambulatory care.

People more often think of and respond to breast disease as prevalent among women. But breast disease affects one in seven women in this country. Whereas one in three women have some form of cardiovascular disease. 

Now just to take a look for a minute at the numbers. Back in about 1984, men’s cardiovascular mortality began to decline as women are continued on erratically. Women’s did not start to decline until closer to 2000. And although it has declined and largely caught up, we seem to be at a plateau with a gap there that continues. 

Cardiovascular disease in women is also different than in men. Women do not tend to have the classic TV heart attacks. Their symptoms are often more subtle and less specific. And this is often used to explain differences in treatment. However, when we start to focus on the ambulatory care measures used here, we are actually looking among people who already have a diagnosis. So in many ways we are looking at the tip of the iceberg because women are somewhat less likely to come in because there are other explanations for their symptoms. And they are less likely to be recognized in an urgent care or emergency setting depending on the severity of their symptoms. Women can present, for example, with symptoms like throat pain or a sore back. 

Another common problem is that in the general public there is not a good recognition that chest pain that occurs on exertion and diminishes with rest is a particularly bad sign. People, in general, tend to think that it is possibly a good find that things get better when I just sit down and relax. So people may not be quick to respond. 

In addition, women, we have always thought about men being the worried well and not interested in coming in and being exposed as not having been sick as having actually had indigestion or something. But as we look increasingly at the difficulty of getting women in and treated, we see that women find it difficult to leave caregiving roles to come in and seek care. Even the most commonly reported response when an ambulance is called to take a woman in for chest pain and symptoms of a heart attack or myocardial infarction. They very often tell the EMTs I hate to bother you. It is not the framework of urgency and understanding that leads people to present sickly.

Interestingly and importantly, in addition, 64% of women who die suddenly from heart disease had no previous symptoms at all. So we are looking for where are there missed opportunities, where we can do a better job in identifying women with heart disease risk and getting them into care. And seeing that the care they get when they come in and seek care or have a diagnosis is of high quality and appropriate. 

Now there are a number of important consequences of differences in women’s cardiovascular disease. The tests aimed at identifying major coronary artery blockage are less informative on cardiac risk in women than in men. At least half of women myocardial infarctions are caused by coronary microvascular disease. This is disease is the small arteries in the heart. This can be narrowing or damage. But you would not pick it up by looking at whether they have major artery blockage. And so being told you do not have major arterial blockage in the heart is not as informative for women. And may inappropriately suggest to them or they may inappropriately frequently be told that this is a sign they do not have a serious heart disease. Smaller vessel disease makes a diagnosis challenging. And it poses problems for treatment because we cannot use the same approaches. Noel Bairey Merz is leading work in this area. And we have a long way to go just to understand why there are some of the same treatments that are ineffective, for example, in women.

Part of the reason we are in this situation is although we came in the 80’s to no longer excluding women from cardiovascular research and many other kinds of research in the ways they had been so frequently in the past. Very little has been done to increase their proportions in research such that we are able to do important sub group analysis and look at whether findings about cardiovascular disease actually applies similarly to women as to men. Whether or not women are getting the same outcomes of intervention, there is just less work on that. There is less funding for it in this country. It is becoming more common in other countries to require such analysis and to support it in grant funding. But it has not been a priority here. We do not have an institute for research on women’s health or on sex and gender as in other countries. So we have got these different gaps that come along. 

Moreover, 26 percent of women who are over age 45 die within a year of having a heart attack. And that is compared to 19 percent of men. Now often, the immediate reaction that I get when I talk about this is well the women are older. Well the women are in fact a little bit older on average. But they also have longer life expectancies prior to the event than do men. So it is not clear that as have often been attributed any differences in outcomes or simply women’s age and acceptable in some way.

Here, I want to focus on gaps and the quality of ambulatory care and whether these contribute to the differential outcomes we see including that when your survival rate. Now there have been varieties of studies of managed care and care within the VA that have demonstrated gender differences in quality of care for cardiovascular disease and diabetes. These large multi-state studies of gender differences failed to stimulate efforts to identify and address the gap. 

I will tell you a little bit more about a couple of the studies. But in general, we found average differences of five percent or more on many measures. We had a special issue of women’s health issues, a journal devoted to this topic. This was in 2007. We did a congressional briefing on this in 2008 with the American Heart Association. So as far as academic research goes, this followed the roots all the way up the chain of command. And these are in fact important demonstrable differences that we have to understand as tip of the iceberg. They are among the insured high quality health insurance that a regular provider who already have a diagnosis. And we are seeing these gaps that are consistent across different plans, across different states, and something needs to be done. But there has been very action that the VA has taken on looking systematically at men’s and women’s quality of care and at addressing the gaps in the quality of care and reducing differences in men and women’s care. And I would like to hold up the VA efforts as an example that this is a moveable target. This is worth studying and working to improve.

Here are a couple of findings from some of the papers that were in the women’s health issue that I mentioned. One plan looked across multiple states across multiple healthcare plans in one of the studies. They found that among plans that had a difference of five percent or more in the quality of care. And here we are looking at a set of measures that are all control measures. You tend to, if you see small gaps in screening, and then there are often larger gaps in treatment and even larger gaps in achieving intermediate outcomes, achieving control over the indicated measure. And what they showed was that in the vast majority of the cases where there was a gap of five percentage points or more that gap favored men.

In a study that I led in the same issue, we looked at the gap in these intermediate outcomes. And again, they were often as large as five percentage points or higher which on the one hand might not seem very large as an individual measure. But when you are talking about the population of the state or of the health plan, it does become quite large and meaningful.

So we see these differences. And we were pointing out that a 5 to 11 percentage point difference translates into rates of control that we are between 26 and 19 percent lower for women than for men. So I want to make it clear that gaps of this size matter. 

So our question is what mapping could provide. Well the statistical models that we typically use to assess quality of care only control for gender most of the time. In fact, it is in exceptional studies such as the ones that I mentioned that examine gender differences. And there again, our intention was to measure the average gap to demonstrate that this was a prevalent problem and a persistent one that occurred across different parts of the country. The problem is that these average answers have not engaged health plans or other stakeholders to take action aside from the exception I gave with the VA, which has taken this rather seriously.

Now, this work has been used effectively in bringing attention to racial ethnic disparities. But still, we have no requirements for routine assessment either by gender, by race ethnicity, otherwise to cut the data. And in the case of gender, we are talking about the majority of the population that we do not have a very good handle on here. So one intention is that mapping can identify whether the gender differences are uniform, whether they vary geographically, whether they are spots where women are facing above average disparities, and whether they are cold spots where women are receiving better quality of care. Another point we can look at is whether these differences diminish in particular geographic regions such that we could look at correlated factors. And see what it takes to make better care for women. And is it the same thing that it takes to make better care for men.

In the study presenting here, the data are from one of the large regional California managed care plans. We examined care for two populations of individuals, those who had documented ischemic vascular disease or had experienced a cardiovascular event in the prior year. So for that population, we have approximately a little over 30 thousand individuals. The second population is larger. Those are those who had a diagnosis of diabetes. So for that we have a little over 155 thousand. 

Now the point of looking at one large regional California plan was not to understand what is going on necessarily specifically within that plan. But our intention is to go on to do work where we pull data from multiple large plans such that we can speak the experience of the majority of plans across the state. And that we can talk about and assess whether patients in different plans are getting the same care or different care again leading to the idea that where there is variation we may have an opportunity to learn from those locations, those plans that are doing better by women and achieving better outcomes of all. And use that to improve care. So this is really a feasibility and desirability study.

Now the outcomes that we are looking at are heated measures, the kind I was presenting before. A number of cardiovascular disease and diabetes quality measures of part of the National Center for Quality Assurance Healthcare Effectiveness Data and Information Sets, the NCQA’s HEDIS measures. The measures report the percentage of eligible patients who receive indicated care after excluding those with contraindications. So we were talking about measures where there is agreement in medical practice that 100 percent of the people that are included for a given measure should be getting the indicated care. And the contraindications might include being on specific medications, having other specific diagnoses such that you would not expect to be able to lower someone’s blood pressure or such that it would not be necessary or appropriate to be doing a specific screening. But after taking everyone out of the denominator who does not belong, ideal care would mean 100 percent of the patients were receiving the indicated care.

Now, HEDIS measures are also designed such that they allow direct comparisons across populations. So we can look at the quality of care for a given measure in different parts of the country, different parts of the State of California, or compare the care given to two different demographic populations, men versus women, or specific minority groups among themselves or against an angle of majority or other groups.

For those statistical analyses I wanted to point out that we, in addition to having gender or sex measures, we have age and years. We have insurance type. We have got Medicare patients, commercial HMO, commercial PPO, and Medicaid in the population. And so in a few analyses, we would break out commercial HMO versus commercial PPO.

When we talk about regions, we are looking at the eight regions that are used in HEDIS reporting. And I will show you a map of California so you can see what that looks like. We also drilled down to look at counties within California. And I have got some slides from looking at zip codes. Also in the statistical models, we used region but we used obvious area income to take into account some socioeconomic differences across the state.

This is just a map of California showing the eight regions for those of you who are not familiar with California – California’s regions and there is no reason you would be. The population in California, might be helpful to know, is mainly congregated along the west coast, along the edge of California here. The big blue area – well I guess it is small relative to the other regions. But on mine, it is a very bright blue is Los Angeles. The smaller green area just below it is Orange County. And above LA, along the coast, is the central coast. So you have a lot of the population of the state along in here, but mainly to the coast on the edge and San Diego.

So what is conventional analysis? When we do the sort of work that we did with the prior articles or the way we look at things in terms of racial ethnic disparities. What we see in that bivariate models that among adults who had experienced a cardiovascular event or had ischemic heart disease, 77.7 percent of the men and 72.8 percent of the women had received an indicated LDL screening. So that is a gap of almost five percentage points. The gap was smaller among those with diabetes, 76.3 percent among men, and 74.2 percent among women receiving indicated screenings. And this may be because you do not have the same bias of later on of appropriateness and the need to aggressively pursue cardiovascular disease interventions in diabetes patients who tend to be a little bit younger. But we have not tried to tease all of that out here.

In addition, quality of care varied significantly across the regions to state, across the different business lines. In particular, an interesting finding is that we were able to see higher quality of care, ambulatory care among the HMO patients and among PPO patients.

This is just a quick map showing that California regions and healthcare varied across the state. And there is about a nine-percentage point difference in the quality across the state for this one measure. Now we looked at a variety of measures. But I am mainly going to stick with one measure throughout this for consideration. It intensifies a lot of the findings although the findings were a little bit stronger for cardiovascular disease. As a matter of fact, I think that this slide on the nine-percentage point variance may be cardiovascular disease.

Here, another point I wanted to make is that they are patterned by age. When we looked at the age distribution as a bivariate distribution in terms of quality, quality of care peaked around age 60 and fell off again. So we looked to see whether that pattern held when we look at it by gender. And we see it both for the cardiovascular population and for the diabetes population. In the cardiovascular disease population, I do not think it was significant. I do not have the other slide in front of me. I do not think it was significant in the youngest age group for the difference because there are fewer women in that age group. So we did not have quite as much data on the left hand side of the CVD figure. On the diabetes figure, that tiny difference in the middle age category is not significant. But what we are seeing is that the gender gap is larger where care is poorer. And it seems to get more similar in the middle age category. 

Now we are not clear because we are looking at commercial insurance whether the drop off between below age 64 and above age 64 mainly has to do with differences in the treatment in the Medicare versus working age population or if it is an attribute of the treatment of individual age. Is it an insurance issue? Is it a population and aging issue? We cannot separate that out very well here.

But even when we did multi-varied analyses where we adjusted for age, for insurance type, for region and area income, women were 80.5 percent as likely to men to receive indicated LDL screenings. That is for the cardiovascular disease populations. Among the women with diabetes, they were 94.3 percent as likely as men to receive the indicated screening. So we still have a gap even when we have taken into account things that you normally would not take into account in understanding a gap in care. But insurance type, variation across regions, age and so on did not explain the gender difference. In addition, the advantage we had seen before on HMO patients remained such that HMO patients were more likely than PPO patients to receive a screening in both the LDL and CVD populations.

So we pursued the insurance type piece a little bit further because we are interested in the potential to be able to intervene in improving quality of ambulatory care. And here we ran models separately for PPO and HMO populations. So women in the PPOs with CVD were 93 percent as likely as men to receive an LDL test. And they were equally likely as men to receive the test in the HMO population. Similarly, when we looked at the diabetes population, women in PPOs with diabetes were 97 percent as likely to receive an LDL test. But they were equally likely to receive on with men in the HMO. And this suggests that in addition to providing higher quality ambulatory care on average, manage care can effectively address aspects or some degree of the gender gap. 

Now the question is that is a long way. But what does mapping add to looking at the data? So here, we are looking in the diabetes population at the quality of care by gender. Now definitely the one before was cardiovascular disease. So in the diabetes population, we see a smaller variation in care. And we see it distributed across the state so that there are very small differences when slightly favoring women in San Diego. But then gaps get larger in other parts of the state. 

This is the kind of graph that we often use in some of our other types of work to show where you have many data points what is happening. So all of the dots above the line are, in this case, geographic regions where men’s care is higher on average in quality than women’s care. 

Another point that we are seeing that is interesting that we want to see how it plays out across multiple health plans is that it appears that when quality is very high, there is not a meaningful gender gap. The disparities are gone. But when quality is lower, one group falls behind. And interestingly in other work where we are looking at older adults, in the oldest adults, women come in for care more than men. And we see more dots at least in areas other than cardiovascular disease. We see more dots down here where women are getting screened at a higher rate than men. But when care is very high quality, the gaps go away. So this would suggest that dealing with the gender gaps could be helpful in improving quality of care.

Now here, we wanted to add more information to that same figure. And what we have added is the proportion of the number of members who are in a specific region who were eligible for the measures. So we are starting to use mapping to be able to convey where the diabetes patients are, where the affected individuals are. So the points fall the same as they did before. But you can see here that Los Angeles has the vast majority of the patients in the state. And this could also be useful either if you are interested in targeting across the state or you wanted to start with somewhere smaller but actionable. And then move into other areas. Versus if you wanted to be sure you were picking up enough areas to get a large enough share of the population to move the needle, for example, for the entire state. So this is one way to try to engage stakeholders to try to help people understand what the pattern of care in gaps looks like.

Now often, the stakeholders who would be moved by – whose curve view you are interested in being able to address your data to, they do not operate at the region level. So we can drill down to different areas. Here we are not working with based on the needs of a specific health plan. So we have a variety of different cuts on the data. Here we are looking by county at the LDL screening rate. And we have just divided the state into quintiles. I have not given the specific information on the eye for direction of the quintile. But the tail of men care exceeding women’s, the gaps is much bigger on men’s care exceeding women’s. And the areas where women’s care exceeds men’s, but you might notice from an earlier point I made is it mainly happening in these areas where there is much lower population. That is a big part of what is going on here. We have fewer people in the first place in these areas. So they are good spots. They are places where things are going well for women. It is not where a lot of the women are. And we have these areas where gaps are very strong favoring men’s quality of care. And we want to understand what is going on. But we can use this data both to address gaps that favor women and to address gaps that favor men and understand which group has fallen behind, what is going on, and why.

Now, we wanted to take this one level further. So here, we are looking. On the left, you see the northern California as in the prior figure. We added the names of the counties. I do not know if it is large enough that you can potentially see them. The white areas here are areas where we did not have enough men and women in the cell to qualify for a stable HEDIS measure. And when the IHA looks at these, they have a minimum of at least 30 men and at least 30 women who are eligible. This is distinct from the reporting that individual institutions do to NCQA. This is done through looking at administrative data or administrative data combined with data on labs and other reports such that you can tell whether control was achieved. So these would be called HEDIS like measures when you look at intermediate outcomes as opposed to just process measures.

But the amount of the state that is white gets larger when we drill down to look at specific counties because we have more counties that do not have enough individual to qualify. So the data – part of it is we want to avoid showing a lot of noise and the data gets a little bit thinner. But you see more places. And it depends on what stakeholders you are talking or what news media outlets or so on that you are working with whether zip code would in fact be the area that you would be interested in. Another possibility is that we look at some of the service areas based on the way that the Dartmouth Atlas frames service areas, primary care service areas. They are a little bit larger. So we might frame the data that way. Or you might use it to drill in and look what is happening across LA County, across a specific region. I believe I have an LA County map that will be coming up here. But the issue here is to be able to show that even within an area where care looks very good, there are spots where it is not necessarily. Or in areas where there seems to be care that is very poor for women, you can move in and see that there is variation within that. And we can try to start to understand what it looks like.

Now this odd shaped county is the blue LA County from the earlier figure. To the northwest there, you would have the central coast coming down. And then you are following the coastline. And then it would tend northeast and you would have Orange County below us to the southeast. And you see how quality of care varies around LA County considerably. 

One way we wanted to cut it whereas if we had data from multiple plans, we might say are two different plans treating patients the same way. Are men and women getting the same care in different plans? We are looking in one large health plan. We looked at the contrast within what is happening in those who have HMO insurance versus those who have PPO insurance. And although it is not universally true, it largely appears that that HMO advantage for women held across most of the state where care is better for women in the same geographic areas if you are in an HMO than a PPO although that is not uniformly true. And if you flip back and forth between looking at the two sides, you will see that there are some areas where there were enough HMO patients to populate and show zip codes and some areas where there were enough PPO, but not enough HMO. But again, this is to help understand how mapping starts to show differences in what is going on, differences in where you would intervene, or what you would be trying to understand.

So in general, mapping helps us move beyond the analyses of four where we were aimed at finding the average differences. And to start to look at what is going on locally with the intention being to show what the data looks like in an area that is meaningful to specific stakeholders in an area where specific decision makers can make the work actionable. Depending on what problem you are looking at in terms of quality of care, you might be interested in showing how it broke down by congressional district. You might be interested in showing inter work with the VA how it breaks down across divisions or we might look at service areas around specific VA Health Centers. There is a variety of ways to speak to the decision-making ability and curve view of specific stakeholders.

But the point of doing the mapping is not just the geography itself, but to make the patterns of predictors and disparities accessible and more actionable. In addition, we can illustrate whether specific relationships occur. Do the disparities map on to particular racial ethnics or socioeconomic neighborhood characteristics or to the availability of specific health services, which you see different gaps where, you have cardiovascular centers and higher quality cardiovascular care in those service areas? Or you could look at VAs that have made a high priority in addressing and improving quality of care for women and having women’s health centers and see whether those were in fact doing markedly better than others and what lessons you have learned from that.

We also want to use it to examine patterns of care and understand whether those are who are getting poorer care. You could map on the information of where people are treated and see whether they are getting their care the same place. Even if they are getting it at the same place, are they getting it with the same provider? Often when people read about or when the media coverage is working on health disparities by race ethnicity or by gender, there are attributions made to providers of treating patients differently when in fact a closer read makes it clear that there may be differences in terms of either the resources available at specific types of clinics in commercial healthcare. Obviously, if you are getting care at lower resource clinics, you might get lower quality of care or that you are being treated at the same place but not by the same providers. 

But in general, a relatively small proportion, if at all, is attributable to providers specifically or even symmetrized consciously treating patients differently. But that is a question you can look at in understanding whether you need to educate individual providers or systems or change where resources are to make the difference. It is important if you want to be able to intervene and improve care. And you know exactly what is going on, not have hunches, in other words. And this approach can be very useful in addressing that.

So the take home points are that gender differences in cardiovascular and diabetes ambulatory care are strictly small to moderate. And that these matter on a population level where even small average differences ignores large numbers of women not receiving indicated care. And it is not just that they may not be receiving the highest quality of care, but that they are receiving care that is poorer than a man being served in the same region or area so that we could narrow. We could improve care just by addressing that variate. Obviously, the end goal here is not just to resolve disparities and leave differences in care across the state or across the country. But it is to find ways to leverage and improve quality of care. But as we do so to assure that we are not actually making disparities worse that we are leveling the playing field and improving the quality of care for everybody.

So another point that we saw was some evidence that managed care can produce higher quality ambulatory care and reduce gender gaps. And this is interesting evidence, the point that we have been looking at that policy makers have long been interested in the extent of which managed care demonstrates the added value that has long been unseen to be a potential. And many would argue that both these findings and this express on the VA are indicative of the ways in which being able to assess the data, track it, give feedback, and make efforts to bring people in for indicated screenings, to have those screenings occur when people have come in for visits and so on that these get realized.

Last, but not least, maps of gender differences in quality of care can be used to inform local and regional stakeholders including women. And help to answer questions and precipitate efforts to improve women’s quality of care. So I am arguing -- I am presenting at least, that mapping gives us one way to provide additional insight, make our findings more accessible, but also gain a better understanding of aspects of the patterning of care and of disparities in care so that we can do more to improve the quality of care and address gender gaps.

I would be happy to take questions. And before I do so, I just want to thank again the Barbara Streisand Women’s Heart Center at Cedar-Sinai Medical Center for supporting this work. Thank you.

Moderator:Okay, thank you very much, Dr. Bird. So for attendees that joined us after the top of hour to submit a question or comment, just use the Q&A box located in the upper right hand corner of your screen. A couple of people have written in wondering where to get to the slides. You can receive those or you can access those in the reminder email you received a couple hours ago. There should be a direct hyperlink leading to those slides. 

And we do not have any pending questions at this time. But we will give people a few moments if they would like to write one in. Also, we did record today’s session. And you will receive a follow up email with the link leading to the recording. So feel free to pass it along to any colleagues that were not able to make it to the live session.

The first question we have, is mapping based on provider or patient zip code?

Chloe E. Bird, Ph.D.:This is mapping based on patient zip code. Earlier kinds of work often looked at where healthcare was delivered. And you end up with some very skewed results as people go into tertiary medical care centers to get their care based on their health condition and so on. But no, we are actually trying to map who the patients are and where they live.

Moderator:Thank you for that response. The next question, what methodological approach did you use to adjust the quality scores in order to do the mapping?

Chloe E. Bird, Ph.D.:To adjust the quality scores in order to do the mapping, what we did was we looked at the averages just at objective data looking at the averages in the quality reported by individuals within specific geographic areas. Normally, with HEDIS measures, because everyone in the denominator should be getting the indicated care, you do not then adjust for age versus economic status or so on. So we do not have any adjustments in those models. We do, however, in the data by not showing areas where there is too little data because it contributes to noise. And it could cause confidentiality issues if there were very, very small numbers of people who had had a cardiovascular event in a specific area.

Moderator:Thank you. The next question, are you able to get the zip code data from HEDIS?

Chloe E. Bird, Ph.D.:HEDIS does not provide the data. HEDIS is a way that data is collected and submitted to NCQA. We did not go to NCQA to get this data. We went to a healthcare plan. And they have the zip code data on their plan participants. When we want to look at intermediate outcomes, healthcare plans are limited in the kind of data that they have above and beyond anything that is billable. So for that kind of data, we likely will continue to work with provider groups where they have a combination of the billing data and so on. But again, they still retain zip code data. But the data that is obligatorily reported is by agreement. But it is reported to NCQA. It is on a small number of patient events. It has to be over several hundred. But it is a relatively small number. And they do not have to be chosen randomly. So they are not really suitable to our work. And they do not include data on the gender mix and so on. So it is not just zip code that would be necessarily there.

Moderator:Great, thank you for that reply. That is the last pending question we have at this time. Would you like to give any concluding comments?

Chloe E. Bird, Ph.D.:No, please feel free to email me if you are interested in pursuing or applying this work. We have some information about other work we have done on the RAND website under Acute Art or in general. But I really thank everybody for taking the time.

Moderator:Great, well we really appreciate you sharing your expertise with the VA field. And I want to thank our attendees for joining us. And we do have somebody asking which VA you represent, but you are actually part of the RAND Corporation.

Chloe E. Bird, Ph.D.:I am part of the RAND Corporation. I have done some work by contract with the VA. But I am a researcher at RAND.

Moderator:Great, thank you for clarifying that. All right, well once again, thank you everybody for joining us. And thanks to Dr. Bracciano for arranging for Dr. Bird to come present. And for our attendees, I am about to close out the meeting. And a survey will pop up on your screen. So please take just a moment to provide us some feedback as it your feedback that helps us decide which sessions to present. Thank you very much once again, Dr. Bird. And everybody have a wonderful day.

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