**Transcript of Cyberseminar**

**Spotlight on Womens Health**

**Gender-based Analysis in Health Services Research:  Making the Research Actionable**

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

**September 23, 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, Chloe@rand.org.*

Moderator:     It looks like we are just at the top of the hour here. I would like to introduce our presenter for today. Chloe Bird is a senior sociologist at the Rand Corporation, where she has studies gender differences in physical and mental health, and social determinacies of health. She is a professor at the Pardee Rand Graduate School. I would like to turn things over to Chloe. 

Dr. Bird:     Hello. Thank you everyone, for joining on the call today. You have heard a little bit about me. I would like to ask a couple of questions about you. The first question is what your primary role is in the VA. The options are student trainee or fellow, clinician, researcher, manager or policy maker and the last option is other. You can indicate no vote. I am not sure how many people we have on the call. I do not know. We still have more people coming in. we have about 14% who are students or trainees. Forty-two percent are researchers. Seventeen percent are managers or policy makers. Another 14% are other. That is very good. Thank you. 

Now we are going to go on. I have one other question so I have a better handle on you as an audience. What best describes your research experience? You have not done research, you have collaborated on research, you have conducted research yourself, applied for research funding or led a funded research grant. There are still more answers coming in. it looks like there is about 18% on the line that have not done research. Another 29% have collaborated. Thirty-three percent have conducted research themselves. Eight percent have applied for funding and 12% have led a funded grant. Thank you. This is really helpful in getting a handle on who I am talking to and how I can try to convey this usefully to you. 

I will give you a brief outline of the talk. I want to start by talking about moving beyond sex and gender as a control variable in research. I will then discuss assessing gender differences in care and outcomes of care. Then I want to turn in another direction and talk about aging stakeholders and decision makers, how we make our work actionable. Lastly, I will talk about an approach I have been using. Some of my colleagues have been using it for some time now, to make research actionable. It is putting gender on the map. I will be talking a little bit about literally mapping your findings. 

On moving beyond gender as a control variable, I want to talk to the problem that is longstanding in understanding women’s health, health disparities or gender differences in health. Too often, research on health and healthcare adjust for gender, race, ethnicities, socioeconomic status and taking a very epidemiological approach to take out that variation. Instead of assessing for differences, simply control for it statistically. 

The problem with this approach is that we end up with average answers. This may not speak to the problems of any major group, particularly if the general problems are quite different for men and women, minorities and other disadvantaged groups, and so on. In particular, it may not speak to a group that is underrepresented and their needs. We need to systematically look at men’s and women’s health separately. Actionable research needs to make this distinction, and in many cases to look systematically at the health and wellbeing or healthcare needs and outcomes needs for sexual minorities. 

When we turn to assessing gender differences in care and outcomes, I am discussing taking a gender based analysis approach, where you systematically stratify your models or otherwise look at what is happening with men and women, whether and how they differ in their healthcare and their health needs, and their outcomes of care. Gender stratified analyses are often important to understanding whether you are seeing the same causal relationships or the same pathways. Here, I always tell my students that you can think of the stratified models as fully interactive. You can use a comparison of what you are getting in these models to a combined model to start to understand where there are statistically meaningful differences in the impact of a particular variable, a barrier to care or other patterns. Then you are able to report where there are gender differences that are significant and where there are not. This is very important, to understanding where we need to be looking and where we have a track record of finding differences. 

On the next part, as we start to do research looking at men’s and women’s health needs, you need to think about how to make your work accessible. Unfortunately, odds ratios, or data progession coefficients and the like only make your heart go pitty-pat if you personally do work that produces odds ratios. To the rest of the world, this does not speak to them. You need to actually speak to decision makers and address their questions in your work, as well as clinical questions or policy questions that you have in mind going in, in order to make the work accessible and meaningful to them and where it fits in their decision making processes. 

This also includes looking at things in the metrics that they use to assess impact. If you are looking at clinical work and you are looking at social patterns, you might look at the impact of socioeconomic status to the impact to using statins, in the case of some heart disease outcomes. Here, you start to make the work meaningful to people in terms of a metric they are familiar with and understand. That would not be the relative metric if you were trying to talk to people out in the community necessarily. You need your research to be specific and actionable, and to be relevant to local situations, local organizations and specific audiences. This is a requirement that takes us above and beyond what we usually need to do for academic work. 

To make our findings actionable, you need to understand that the differences in men’s and women’s health can come from different sources. They could be social due to differences in their social and economic resources, access to care, barriers to care, caregiving burdens and the like. they can be biological, where men and women experience different patterns of cardiovascular disease, different symptoms of cardiovascular disease and quite different problems within their hearts, in terms of women having much more small vessel disease. If you were seeing difference in outcomes, it does not necessarily come from the same source. As a result, if you are not doing gender-stratified modeling and looking systematically at where there are differences and why there are differences, you may come up with an approach that fits one set of explanations and problems and improves care, but does not do so for everyone. 

Understanding when and where the disparities occur is critical to effective intervention. It is also important to understand, as I was just suggesting, that men and women might need different interventions. If you want to improve quality of care, which is one of the areas that I work on, if men and women are experiencing different barriers to quality of care, an approach may work very differentially and produce more gains with one group than the other group. We have often seen this in racial/ethnic disparities research, where if you are not strategic in understanding what you need to do to improve care of a disadvantaged minority, you may go in with an intervention and improve care for the advantaged group more than you do so for the minorities. 

While it is very good to have made some gains in care, you may have actually exacerbated the disparities by not being able to bring a lot a disadvantaged group as far. In some cases, the disadvantaged group is men and in some cases, the disadvantaged group is women. The point is the need to look systematically and understand what is going on. In addition, you need to understand that there could be differences instances or prevalence. If that is what the source of the problem is, then you want to be focusing on prevention, not necessarily on diagnosis and treatment. If the patterns and problems were arising later on, you would need to look to the different points. 

What we want to do is identify relevant action points and in doing so, to think more broadly about where we could intervene. If we want to address differences in health trajectories, then we need to understand what is going on in terms of the incidence, prevalence, patterns of care and the outcomes of care. If you are seeing differences, are they at the level of care delivery? Are we seeing differences in screening, diagnosis or treatment? Are men and women in this case, getting similar care but achieving different outcomes? That would lead you in a different direction again. It is part of making the research actionable. 

Another approach that we often look at is whether there are time trends for the diffusion curve, such as men initially getting a newer treatment. Are women getting similar care, but not similar outcomes? We have done some of this work in modeling racial and ethnic disparities as well, where in the case of new cardiovascular interventions; you will see an initial advantage to the white Anglo population. The African-Americans start to catch up. In the research we did here at Rand on this, at every point they were catching up there was some newer and more cutting edge intervention or approach that had come out. The disparity jumped to a different problem. You need to have a sense of the big picture of what is going on. 

Another point that I want to introduce is understanding constrained choice, both at the individual level and the level of the decision maker. You need to know whose behavior and what choices are actions you want to impact. Are you trying to change care delivery and affect clinicians, patients and decision makers? Is it those who affect the organization of care or the payments? Are you trying to affect individual patients and the impact on those decision makers and clinicians? It may be part of the process, but you need another approach to whether it is going to intervene more directly, potentially, as an individual. 

We need to take into account that individuals have agency, the ability to make choice and act. It varies across individuals for how many choices are available to them, and what their actions are likely to produce. Conversely, what are the costs, the opportunity costs or the financial costs, of making a particular choice? If you are talking about an individual who has very low paid work and works very long hours, the opportunity cost of adding exercise to their life to reduce their cardiovascular risk may come at a much higher price. It may be much harder to achieve than an individual with a higher socioeconomic status, with shorter work hours and potentially more options of where to exercise safely. We need to understand that there are constraints on individual behavior. 

To change behavior, you need to also consider what other levels of decision making impact individual options and the relative cost of those specific choices. Here, you might consider the availability, and my own work certainly goes beyond the clinical environment, of nutritious foods. What is available in the communities they live in? What is made available in the workplaces where they work? many of the decisions that impact how easy or difficult it is to engage in either positive health behavior, seeking care, complying with treatment and the like, and achieving the opportunity to have a healthy life, vary across individuals. As you understand what other decision makers play into the ease or difficulty of making those choices, you may identify other action points where you or your collaborators would be interested in either directing your research or disseminating your findings, and then going back and looking at how you articulate your findings for those specific audiences. 

As we turn to identifying decision makers, you need to take into account that change is much more likely to be achieved if you can identify and reach out to the relevant decision makers. I often talk to my students about this. if you wanted to increase active commuting, a simple approach to try to have people have more physical activity in their daily lives and reduce the variety of negative outcomes, including cardiovascular risk by having them bicycle to work, I could take a very simple approach of looking at interventions and programs that motivate an individual to bicycle to work. In fact, few changes are actually achieved only by those individuals, or by their clinicians recommending that change. In fact, what you need to do is identify the other levels of decision making that could help make it more feasible and more likely that people adopt and make the change, to active commuting. 

Here I am thinking of engaging stakeholders and decision makers. You want to be clear of what is relevant to them. If I wanted to increase active commuting and I wanted to work with the community at an administrative and leadership level, I would want them to think about what barriers they have control over that can help. Increasing bike lanes could make it easier to get people to engage in active commuting. You can make sure that the parking is not aligned such that doors open and bicyclists get hit with car doors, or having public education campaigns that help educate individuals about sharing the roads with bicycles and pedestrians, thereby reducing accidents. 

As you communicated with them on that kind of approach, you would also want to convey how it fits with other goals they already have about reducing traffic, about having other benefits that fit their needs. You would want to demonstrate how the problem operates locally, not just what you find in a national study on the impact of active commuting on cardiovascular risks in this case. You want to be able to bring it down to things that are within the purview of the decision makers, and ideally to data points that are relevant to them and speak to the problem. You would have a different conversation around active commuting in New England, where in the winter people are much less likely to be bicycling to work, than in the southern tier where it may be a year round possibility. Across this approach, you are looking at levers to address the problem and how to articulate it to the relevant decision makers. 

How do actionable and traditional academic research differ? We have all certainly read a lot of traditional academic or clinical research. Unlike what we often read, to be actionable, research also needs to speak to whoever would be making this decision. If it is not something that is fully handled in a doctor/patient encounter, or if it is something that would require a change in the way we allow for billing or tracking, even to organize care, then we need to move beyond just speaking to measuring this problem, it is this size and this is exactly what it looks like. That can be a very important academic or clinical contribution. It would not be actionable. 

Instead, we also need to identify action points. We identify the mix of issues that need to be addressed in order to act and intervene, and to understand what it will take to act and intervene locally, whatever that means, whether that means across half the country, one state, a couple of counties or just in your specific clinical center. You not only need to understand that three specific problems explain 80% of the variance, but do they explain enough about what goes on where you want to intervene, or if the one thing you were thinking of not focusing on is creating a bottleneck. You would have the same problem if you were trying to intervene in education. If you know the top three things that make for a very good school, it does not mean that going in and focusing only on those top three things would help you move the needle on how well they are achieving outcomes at that school. The same thing applies as we try to improve quality of healthcare. 

We also need to, when we want to make work actionable, move beyond just measuring the average, the national average of the overall difference in what men and women are achieving in terms of outcomes, or what their determinacies are. Think about what the differences are in the distribution. You might have relatively small differences in averages, but much more skewedness in one group, much more than the average would predict in terms of losers with very poor outcomes, the people who are really losing out on access or losing out in terms of achieving reasonable outcomes. They may be having very different side effects and problems, the people who are missing achieving quality of care, when one group may be missing it by a great deal. 

You need to think differently about generalizability. As I was saying, what matters is not just if your work speaks to all other settings, but how well it speaks to exactly where you want to intervene. You can think about this overlap as a kind of Venn diagram, where if what you want to do is making a change in one institution, what may be most important is not the generalizability of your findings to other institutions, but that you have gotten a very good snapshot or understanding of what is going on where you are studying the problem and where you are going to go from there. It may or may not be a primary goal or even a secondary goal in your work, to make the findings generalizable to other settings. It may be a secondary benefit entirely, to go on and discuss how this would be useful in some other circumstance or some other situation. 

You also want to drill down to make the findings actionable to your clients, the stakeholders or other decision makers you want to intervene. You really want to be able to understand what is specifically going on here, what the specific needs are and to make sure you are able to convey it in such a way that you understand what is impeding the good outcomes that you have in mind, or the higher utilization that is warranted. 

Audiences matter. Many research studies, academic, clinical and the like, share these objectives. Some give consideration to gender differences in health or health outcomes, but without being actionable. To sum up some of these points, we need to be effective for actionable gender research, to engage decision makers. That may be family members. That may be the community, as in your case, that the care is organized in. perhaps it is people who are stakeholders in women’s health and healthcare. It may be in serving the needs of a particular group of men. It could be the policy makers in a community that are involved. It could be the decision makers at other healthcare institutions where you know you have patients going out and seeking care, particularly in the case of women patients where their care may not be completely provided within the VA setting. You may be contracting at a specific VA for that care. Some people may be getting it elsewhere. There would be directly involved decision makers beyond your institution. 

You need to articulate the work to be able to translate it and speak to these different audiences at different levels of decision making. That may require multiple kinds of approaches, to what it is going to mean to disseminate your work or what parts of it you need to convey to specific audiences. You may not need to convey all aspects of it to patients, for example, other than saying that there is work going on at other levels. Similarly, you may not need to convey all of the details to another group at a higher level of decision making, other than that you are working on those approaches and this is what falls in their purview. You need to make sure that the work is relevant to these local situations and organizations, _____ [00:24:17] quality assurance. 

Before I go on, I have one more question to ask. Do you personally provide care to women VA patients? The first option is that you do not see patients. That looks to be a lot of you who are involved in the research side. You only see men at the VA. You see both women and men patients, which is going to be [inaud.]. Lastly, you see only women patients. It looks like the vast majority coming in are people who are full time research. It looks like about 75% or 76%. Two percent only see men. It is going up; almost 4% only see men. Nineteen percent see both. Five percent of people on here see only women patients. Thank you. That is very helpful. Let’s move on. 

I am going to talk a little bit about how I have been working with my colleagues here to make our work on gender differences in quality of care for cardiovascular disease accessible and actionable. We had previously done work looking within health plans across multiple states, establishing what the gender differences were in quality of care. Despite having excellent dissemination, doing the congressional briefings and getting a lot of acceptance, not much has changed. We started looking for what could make this work more actionable. We turned to mapping, and by this, I mean geographically mapping quality of care. Mapping disparities can reveal or confirm the location and geographic boundaries of hot spots, where there is a high prevalence of a specific health problem or low quality of care or outcomes. When we are looking at gender differences, we would be looking at where either men’s or women’s care differs more than what would be expected by chance, and or is markedly above or below average. You are looking for actual outliers in where the care is particularly better or worse. 

Some of the maps I am going to show you are from some of our racial/ethnic work. We are still putting together a lot of the work and finalizing our work on gender disparities in care. We can talk about that later. Here, we were looking at racial/ethnic disparities in diabetes, and who was not receiving a hemoglobin A1C. We have circled the clusters, the primary clusters and small areas, then the large clusters. In some of the kinds of work where we have done this mapping, cities that have been very interested and had an active engagement in addressing a problem have been inaccurate in the geography  of the hot spots, whether that was looking at the hot spots in asthma adverse events in Cleveland, or in another health problem in another state. Here, we are looking at Texas. 

Mapping is helpful because it can make the patterns of predictors and disparities accessible in a way that at statistical model does not. It can help you see patterns you might not have otherwise. It can help people understand what is going on and what it means. It can reveal whether or not specific relationships are or are not significant. We do both statistical modeling and the mapping of care. We look at whether disparities, in this case it was racial/ethnic or socioeconomic neighborhood characteristics, or the availability of healthcare services map on to that disparity. You can deal with some alternative hypotheses, if you will, that you are seeing this hot spot in care as we were for African-Americans for hemoglobin A1C. Does it follow a pattern of where there are high minority concentration neighborhoods or low income neighborhoods? Are you seeing the same pattern in Anglo neighborhoods that are also low income? It starts to tell you something about how you would intervene. 

You could also look and see what is happening relative to individual care. Where you do have two different groups that are getting different care, are they getting care at different clinics, different hospitals or different VA settings? If they are getting care at the same place, are they being seen by the same providers? Are they being seen by different providers? If they are going to different clinics, it may be a problem of receiving your care at a lower resource institution. If they are going to the same institution but being seen by different providers, it may have to do with the organization of care and how the resources are being allocated within that system. Those approaches would be very different from going in with the assumption that providers are doing it wrong and intentionally or unintentionally, even unwittingly, treating people differently. This drilling down helps you to understand what you would do with the information and how you would frame it to decision makers. 

I want to consider a few other examples of actionable mapping. Mapping can make the patterns of predictors very accessible and actionable by drawing it to the attention of particular groups. You can map data in terms of how it varies across states. You could map it across congressional districts. Occasionally some groups have done that, and it can get a lot of attention. you know, if you... use it on work on parks and their availability. It is not what congressional representatives want in the news. It can look across counties to see how the patterns differ, and bring in the discussion of either public healthcare delivery or other aspects. Similarly, you could look across service areas, VISNs,  and in our commercial work with commercial plans, we are looking across different markets to see if there are other health plans in the same market that are not having the same type of disparity. That can start to raise the question of whether or not the disparity is due to a hard to treat population or something else about the way in which care is being organized or delivered. 

You can also look at examining patterns of care by delivery site, rather than patient care characteristics. Are some facilities doing better for women? If they are, are they facilities that have a women’s health clinic or are doing something differently in terms of how they are providing care to women patients? Are men and women getting care at the same or different facilities with the same or different providers? Do they have the same patient loads? Is that part of what is contributing to the difference? Are we seeing differences at the point of access, of diagnosis, of treatment or of outcomes? That is going to lead you in different directions in where you put your resources. 

This is a preliminary slide from our current work. I am doing a project funded by Barbara Streisand, using data. This is really a proof of concept. We are using data from one healthcare plan in California. We are looking at a number of scores, indicators of quality of care for cardiovascular disease, where people specifically have cardiovascular disease or the same indicators among those patients with diabetes. These are all HEDIS measures. Here, we are looking at LDL compliance. The way we are trying to make the work actionable is that we are mapping it across the state. This is called Red Light/Green Light mapping. The red is showing where it is poor and the green is showing where it is better. That makes it very intuitive for your audience. We have mapped male compliance on the left. This is the compliance in California for men. We have it for women. 

Lastly, we are looking at whether there is a gender disparity. When it is in one direction it is negative and when it is the other direction it is positive. A positive disparity would be here, when we are looking at the southern part of the state, where men had good care and women had better care. In the blue, women were doing better than men in that area were. That looks to be the kind valence across much of the state, although we are not seeing a difference around the Bay Area. Then it is going the other direction in Sacramento. That would be useful in order to know, both in terms of how we start to map and address care, how we start to look more at what is working in one place and what is different about another, but also when we disseminate these results through the media or to other stakeholders. There is an interest, because we have not just said there is an X-percentage point gender difference in California, but that where you live is related to the quality of care you are receiving. If there is interest in having media reporting or coverage, they have a local data point of what it looks like in the San Joaquin Valley for men’s care and women’s care, and the disparity in how that is different from Sacramento. It becomes more of interest. 

Of course, we drill down. I apologize because we have not gotten things very easy to look at. We are looking in the greater Sacramento area. It has the pattern of care for men and the pattern of care for women, and the disparity. Again, we see some areas that are blue, where women are actually doing better than men are. There are some areas that are red, where women are doing substantially worse. This is one piece of the puzzle, and we are starting to put it together because it is certainly not all. 

There are other things we can look at as we look at spatial data. One is that we can bring in and map, either in the background or in parallel, what another important variable is. When we were looking at race/ethnicity, particularly when we were looking at Latino/Anglo differences, it was important to look at what is happening in terms of the pattern of English proficiency. It seems that it is not primarily what is driving the care. Here, we were looking at clusters of low performance areas in this part of Missouri. That is part of Springfield. We were looking at what is happening for Hispanic diabetic patients. Is it mainly people who are living in areas that have populations with limited English ability? That can help you understand. 

It was particularly relevant because when we first came up with the fact that we were seeing a Latino/Anglo difference, the health plan said they had it handled and they would send everything out in Spanish. That would have been very helpful if we had known that it was in fact not the case, that the problem was that people were not getting their information mailed to them in Spanish. There were many other kinds of problems. Using spatial data, we can start to look at what the relationships are, and convey and address those alternative hypotheses. 

Another very important piece if you are mapping a problem, and I encourage you to do this for a variety of reasons, is to look at where the majority of the patients are who are affected. We see different patterns of care across California, but as I am sure you are all aware, California’s population is concentrated toward the coast. In much of the state, it is very thin, if not approaching zero, as you get inland into parts of the high desert. Another important piece as you decide how to intervene and where to intervene is where the locations are where the patients are who are not receiving care. Here we are doing a chart on diabetics and where the patients are that are unspecified, or we do not know if they have received an indicated test. You see that you are approaching 80% by the time you have six or seven counties. You have the vast majority, more than half of the patients, by the time you get to the first three counties. That is going to be important as you start to target where you should put your resources to start to make a difference, and then subsequently disseminate by understanding where people concentrate and where in the state the minorities are concentrated. 

I have shown you a variety of mapping tools that can help as one approach, as one example, to making your gender stratified research actionable, or your research on other kinds of health/gender disparities. The take home points I want to leave you with are that you need to systematically assess gender differences and establish their pattern and impact, including the social and economic costs. If you want to make the findings actionable and not just to demonstrate that you have measured them well and they exist, the measurement is definitely an important part of the process, but I decided a few years ago that I am not comfortable with it stopping there. I do not do this just to say there is a problem, but because I want to make it actionable so that we can address the problem. To do that, we need to identify action and intervention plans that can help address gaps. In health and healthcare, when we are looking at this setting, we want to look on the prevention side, on the treatment side and on outcomes of care. 

As you move toward outcomes, you get more and more pulled in to understanding people’s lives and people’s life experiences, and the resources they have or do not have, outside the clinical encounter or clinical setting. You may be able to do a good job on making testing available, and even some clinical treatments available. It is harder from the healthcare side to deal with prevention beforehand or some of the aspects of secondary prevention and the outcomes on the other side. That is where we need to do more interdisciplinary research. Finally, we need to target findings that are relevant to decision makers. Otherwise, we have bottlenecks that limit people’s abilities to change their behaviors or to have an opportunity to have a healthy life. I want to know if there are questions. 

Moderator:     At this point, we do not have any questions. I will bet our audience does have some. We will wait for a minute while you guys are typing in the very well thought out and well-crafted long questions that I am sure you all have. Unfortunately, we are not able to open up the phone lines. We will be taking questions in writing. Please use the Q&A screen located at the lower right hand corner of your screen to type those questions in. I do not know. Maybe you answered everyone’s questions in your presentation. 

Chloe:     It is possible. I would be very happy if people feel their questions are answered. You can certainly email me afterward if you have other questions. I am going to flip this over to Q-Dart. Q-Dart is a program that we have developed at Rand, of tools that bring together some of these different tools for mapping quality of care and addressing disparities. It is often useful, even if it is not part of what your approach is to disseminating and making your own work actionable, but it can be useful to use the mapping to give something back to the communities that you have been studying. It may be valuable to them to understand how specific health problems are perhaps more prevalent or unusual in the communities where they are, so they can take that to bring in other stakeholders to address problems in health and healthcare. 

Moderator:     Great. We have gotten a couple of questions or comments in here. I will start with the first one we received here. To do gender based hypotheses, you run the model for women, then for men. The problem in the VA is that 90% are men. 

Chloe:     You can certainly have gender hypotheses without doing that. Ultimately, to look at things statistically, you want to be able to, as much as you can, look at differences in the men’s and women’s care patterns. You can also bring in information from understanding stratified models from other populations. In some populations, it is not as skewed. We have a rapidly growing number of women who are receiving care in the VA. In addition, we often have enough women when we are looking across a VISN, rather than in one institution. Ultimately on HEDIS scores, we make and view the problem of whether there are enough women who had a cardiovascular event in the past year to get gender specific outcomes on every quality measure. It is a goal. It is not an all or nothing test. 

Moderator:     Great. Thank you. The next question I have is about what mapping software you recommend. 

Chloe:     You can look at the tools we have at Rand on Q-Dart. That will give some information. I do not think we have any general descriptions about how we go about doing this work. What we have tried to do is the geocoding and incorporating it into a set of tools. We have worked with many tools over the past decade, to try to find things that are inexpensive and accessible for people to use. Ideally, we can actually give back a geoclip file as one example, or a Q-Dart tool, where we have limited the amount of individual level data that would be identifiable. If we are pooling across plans, we have made it such that we cannot see exactly what care is like in other plans. 

Instead of us saying the three right slides that tell you the true story, we can give it back to them such that they can look at their alternative hypotheses and bring up an image at a time. These are the hot spots. Does it map onto SES? Does it map onto race/ethnicity? If we have data on where people are receiving care, are they getting it in the same place? I put those things back into the hands of some of the decisions makers so that they have more transparency in the findings. I hope that helps. 

Moderator:     Great. Thank you. The next question we have here is this. Dignity and respect for women veterans is an issue that has come up. Have you looked at ways to measure or evaluate this at the facility, as both perception and actual experience may impact level of comfort for female patients? 

Chloe:     That is a really good point. I have not directly looked at that. I know that there is a lot of work looking at the experience of women veterans in accessing care and treatment, and their concerns, as well as the difference between those who do and do not choose to come to the VA for care, or what their experiences are after first coming to the VA for care. Most often, people make a decision to change their care because either their health changed or their socioeconomic position and alternative resources for care changed, such that the VA becomes an attractive or essential place to look for care. 

I know that Donna Washington has done a lot of the work in this area, and is involved with it, as has Becky Yano. I have not looked specifically across life. It has been very important to understand and to get the word out to women veterans about what is being made available to help address those concerns. One of the concerns that female veterans often have is that they want to have a separate waiting room. They want to have a place where they can be with other women to receive their care. Some institutions are also able to provide a separate entrance, and so on. Depending on what the nature of their experience was with their male peers when they were serving, that might be critical to addressing their dignity, privacy and in protecting them from what they perceive as or explicitly are unpleasant encounters outside of the clinical practice, but inside the VA. 

Moderator:     Great. Thank you. The next question we have is this. I like that you stressed the point of integrating the decision makers into the process to affect change. At what point do you usually see the decision maker input becoming most apparent when data collection is in progress? 

Chloe:     In my case, the research does not all happen in one study. It happens over time. It is a process of evolving those relationships. The sooner you can have some representative of that perspective of those issues informing your work, the better. The sooner you can have some understanding, for example when going back to the bicycle example, I know something about what the community thinks about things because I have been involved in government in the community for a long time, around affordable housing and around other issues that come up. 

One of the biggest concerns that come to this council in an ongoing manner is traffic. If you can understand what the main concerns are that the decision makers you want to engage deal with all the time, you are in a better position to start to articulate how what you are working on either does not impede addressing what they want to address or could help with addressing and serving the needs they want to address. I try to engage at some level, some examples of decision makers or some places I can go and talk to people who think from that perspective, to tell me how they understand things. If I do not know at all how they think of the problem, then I may unwittingly have a briefing, pitch or explanation that I want to talk to them about and it will not speak to their listening and their concerns. That is a missed opportunity. 

Moderator:     Great. Thank you. This is the next question. We are getting many comments saying great presentation, thank you for your presentation. Our next question started with that. Great presentation. I think it is important to acknowledge that RX and treatment may result in different outcomes for men and women. I love the mapping does Rand Q-Dart have more information about mapping available? 

Chloe:     I know it has information about what the tools do. In all honesty, this is an example of not necessarily speaking to your listening. I did not go to it looking at it and thinking about what researchers would want to know about what is available with these tools. I have gone to it with an approach more to what the audiences we want to impact would want. That is what we have built it for, partly. I expect to come back and talk more about mapping at another time. I will be sure to have answers to some of these questions for you. 

Moderator:     Great. Thank you. The next question is here. How do we educate reviewers about the value of stratified approaches? Do you have any references showing the value of stratified analyses, since most reviewers want the averages? 

Chloe:     I think that what I do is the analyses most ways, given the data and the opportunity to tell the story. Usually when I am doing an article, I have run things many ways. I am presenting one set of tables and then talking about what is different and what is important to understand what is going on. Very often, because there is an interest in that one set of models, I go through and use the stratified analyses to determine where there are significant gender differences that I need to put in some interaction terms to account for what is going on. It is mainly a matter of making the case that one approach is telling you one thing and this is what is missing. This is what is not being conveyed. It is also working in commentary to put out women’s health issues. That might be something you could cite referencing this. I will look for some other examples. 

Moderator:     Great. Thank you. The next question is here. thank you for noting the importance of getting findings out to the community participants in research, and doing so in ways that are more accessible to a lay audience than official reports and journal articles tend to be. Could you talk about how some of these technologies have been received in public forums? 

Chloe:     I am not sure which technologies. Does she mean the mapping? We are just at the beginning of giving the mapping to general audiences. When we did it with a health literacy project in Missouri quite a few years ago, mapping quality of care and mapping how health literacy works, it was very well embraced. People can find where they are on a map. They can get a sense of whether or not there is something missing for them. It makes things much easier to read.

We do not think about how much knowledge and assumptions about statistics, numerology and so on go into being able to read the findings in our studies, not only the abstracts but also the reports that we have tried to disseminate through the media. They require a very high level of understanding of the relationships going on. I believe that if you can do a good job of writing for a general audience, it reduces the possibility that your press release, commentary or dissemination piece, whatever form it takes, is totally misconstrued by the media unintentionally, which is how these things usually happen. You have ability to be able to find ways to convey a lot of information in an easy graphic, which I find to be a very easy graphic, that has easy to read coloring and marking where we use the Stop Light coloring in many cases. It is prettier to do it other ways. You want the default that most people would go to in trying to read something to be very much along those lines. 

I am also looking at other ways to disseminate findings to broader audiences. I recently did a podcast of a forum that we held here at Rand on women’s health, on gender and cardiovascular disease and how there are differences in care. I disseminated that to a very broad audience. Now we are looking at doing some shorter TED talk YouTube format where it could also be video, to communicate some kinds of findings, to put them out there where they can be useful to other audiences. I would say that this is working, because at least with the podcast it has been picked up in a variety of places, by people who are teaching women’s health courses. They are using that with students to talk about what is happening in a specific problem area. It is not the same as reaching the general public, but it is reaching people who are more at the beginning of learning and understanding a problem. I do not just mean the Birch scholars or the medical fellows, but people who are taking undergraduate classes on women’s health. So far, it seems to be working remarkably well. 

Moderator:     Fantastic. Thank you. We are out of questions. I do want to let the audience know that we did have an audience member write in with some mapping sources here at the VA. After the session is concluded, I will put together the attendee list for today’s session and send those resources out to everyone who did attend today. You will have those available in your email shortly. As I said, that does conclude all of our questions for today. Chloe, did you have any final remarks you want to make before we close things up today? 

Chloe:     I just want to thank everybody for taking the time to think about this problem. If you have any additional feedback, please feel free to email me, Chloe@Rand.org. I appreciate it. 

Moderator:     We will put your email back up there so people can see it if they want to grab it. Chloe, I really want to thank you so much for preparing and presenting today. We really do appreciate it. We have gotten a lot of great feedback already here. I do want to ask our participants. I am going to put a feedback form up for you in just a moment. We would love to hear your feedback on today’s session. We will be putting that together. We look at it here internally and we will be passing that on to Dr. Bird. Anything you would like to share, we would love to hear. Thank you everyone for joining us for today’s Spotlight on Women’s Health seminar. We hope to see you at a future session. Thank you. 

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