Envisioning a Conceptual Model of Sex and Gender Differences in Health and Disease
>> WEBINAR OPERATOR: Hello and thank you for joining the National Institute of Mental Health 2018 webinar series. This presentation is entitled Envisioning a Conceptual Model of Sex and Gender Differences in Health and Disease. Please note all lines are in a “listen-only” mode. If you'd like to ask a question during today's presentation, you may do so at any time through the Q&A pod located in the lower right hand corner of your screen. This call is being recorded. It is now my pleasure to turn the call over to Tamara Lewis. Please go ahead.
>> TAMARA LEWIS JOHNSON: Hi, this is Tamara Lewis Johnson. I am the Chief of the Women’s Mental Health Program at the National Institute of Mental Health. Today we are having, are hosting a talk that is in partnership with National Women's Health Week. So each year, millions of women take steps to improve their health, and at this 19th National Women’s Health Week kick off, which started on Mother's Day and runs through the 19th, Department of Health and Human Services Office of Women's Health is encouraging women to be as healthy as possible. And one of the things that we’re doing in conjunction with this is our talk today. So I'm going to I encourage you to go to that website, you can put it in the search engine if you'd like to find out about more activities related to National Women's Health Week.
Now I’m going to talk briefly about our speaker, Dr. Chloe Bird is a senior social scientist at the RAND Corporation, where she studies women’s health and determinants of gender differences in health and healthcare. She's also a member of the Pardee RAND graduate school faculty. Dr. Bird recently served as a senior advisor to the director of the NIH Office for Research on Women’s Health and as editor-in-chief of the journal Women's Health Issues, where she is now associate editor. Her recent work includes a study assessing and mapping gender disparities and quality of care for cardiovascular disease and diabetes among United States Department of Veteran Affairs patients in California and Texas, as well as NIH funded research on the impact of neighborhoods and behaviors on allostatic load, morbidity and mortality. In her book Gender and Health: The Effects of Constrained Choice and Social Policies, Dr. Bird and co author Patricia Rieker explore how policymakers and other stakeholders shape men and women's opportunities to pursue a healthy life. They emphasize the need for research that informs stakeholder decisions in order to improve women’s health and reduce disparities. Dr. Bird is working to build a donor funded women’s heart health research and policy center at RAND to improve women’s health by improving science and policy related to the health of women and by addressing deficits in women's health and healthcare. Dr. Bird received her Ph.D. in Sociology from the University of Illinois at Urbana-Champaign. She's a fellow of the American Association for the Advancement of Science and the American Association for Health Behavior. I'm delighted to introduce you to Dr. Bird.
>> CHLOE BIRD: Thank you, it's an honor and pleasure to be here and speak about the ways we can improve science on women's health. I'll be talking today about envisioning a conceptual model of sex and gender differences in health and disease.
Here we go. So... the NIMH mission is to seek fundamental knowledge about the nature and behavior of living systems and the application of that knowledge to enhance health, lengthen life, reduce illness and disability. That’s actually the NIH mission overall, not NIMH. I apologize. The NIMH mission statement says it's to transform the understanding of treatment of mental illness through basic and clinical research, paving the way for prevention, recovery and cure. How are we doing on assessing and addressing mental health and physical health on women? I would argue there are some gaps in where we've come thus far.
So today we'll be talking about what do we know about women's health. But the challenge is to what extent is the evidence base built on studies that excluded or underrepresented women? And especially those that exclude diverse populations of women, the relevance populations for specific disease and disorders. With that in mind, we need look at what are the gaps? Where does the evidence base end and assumption based agreement begin?
Third, what can we do as researchers to close the gap? How can we improve the science on women's mental and physical health? And in turn, the understanding of health and disease across the population?
So, our challenge is to consider the gaps and knowledge in science and a needs assessment and use these to improve care and outcomes.
Now I'm showing a population, an image of nine women and it's important to think about, ask yourself, does this represent diversity? Who is missing? Who is underrepresented? Were they included in the research? I want you to think about three points as we go through today. First, I argue that better population samples make better science. Asking better questions, doing better analysis makes better science. And then “half right” can't be the answer.
There's an uneven history in physical health research and in mental health research as well. And I’m going to use mental health as an example today. Many of you are probably more familiar in some ways with some of the gaps in physical health research. But in mental health, the animal studies long relied very, heavily on small samples of mice and rats. The joke when I was in graduate school was that even the mice and rats are predominantly white and male. And this is persistent with some change since the sexism behavioral variable policy.
Human studies also, overrepresented men for many years and weren’t acknowledged. And... if you look back at the literature, it's not immediately visible that they did so. They aren't defined as having studies of what we know in men. They often misconstrued women in the case of mental health and having poorer health overall than men. And this was based on the prevalence of anxiety and depression in females compared to males and those seeking treatment. So, the Catchment studies that were done long suggested that women simply had poor mental health. And it wasn't until Ron Kessler’s ground-breaking national population based studies there, that there was a shift in the understanding of the prevalence and distribution of mental health disorders. That there are disorders that are more common in women and others that are more common in men. And on average, in the U.S. and globally, men and women have similar, overall mental health, but with different conditions, and increasingly we're starting to understand somewhat different expressions of those conditions. We missed a lot of that, because of the way we ask the questions.
Now I want everyone to keep in mind, no one actually plans to know less about the health of women. For experimental animal research, population samples were too complicated. The idea of experiments was to do a simple enough study that you could control everything. So they were traditionally done on very small samples with assumptions of generalizability, and assumptions in animals: “you're only studying sex in animals”. In animals, what you, what you saw was generalizable. And the conclusion was made that there were big differences in behavioral responses related to hormones, and that was probably the only difference going on between males and females. We now know that's not the case, but that was the concern at the time.
For experimental research in humans, again, population samples were too complicated, but there was a struggle over whether physicians or scientists could define affective medical practices. Who was going to decide what was going on and what should be done? That struggle was to debate in terms of how we would determine what care was good. And... the debate was initially two physicians having more to say, physicians having the authority. But the DES and thalidomide disasters really shifted that domain. Thalidomide in particular was much less prevalent as a problem in the U.S. than other countries because of dispute action by a woman at the FDA early in the regulatory period. It's a pretty striking story, if you get a chance to look at that sometime.
But that shifted from a focus on “we would just figure things out and going from there”, to “we must avoid the risk of birth defects at all costs.” We must avoid accidentally treating women who might be pregnant, and this morphed into not including women in research who were potentially pregnant, which became all women. There was very little research at the time on postmenopausal women, and there were exceptional studies done in nuns. But you couldn't say you were not at risk of being pregnant. So this shifted things, not out of a purported decision to only understand men's health, but a belief that it was going to protect babies, it was going to protect women, it was going to prevent birth defects and it meant men would carry on as the burden of research subjects. We understand it's more complicated than that it can’t work that way. We also know that excluding women from the research who might be pregnant means that rather than having small numbers of well informed women, consenting to participate in controlled studies, today many women and over time have lacked critical information on the effectiveness and side effects of treatment that apply to them, especially during pregnancy. Now the good news is that assessing gaps involving and improving research on mental and physical health and needs assessment and information will inform the understanding of health and mental illness in women and girls. It will improve analysis of mechanisms in women and girls. And the identification of barriers to diagnosis, treatment and positive outcomes in women and girls.
In essence, this is going to give us better knowledge of human behavior, health, disease and response to treatment. And improve the care of ultimately men as well as women. All of these things can be achieved through the same basic approach. And the intention here is to support the development of scientific knowledge and foster a critical understanding around multilevel approaches of research for women’s health across the life course, and to be able to improve mental and physical health for diverse populations.
In three steps. To improving the science, the practice and the policy that I want to emphasize today. The first is improving our conceptual models of sex and gender influences on health and disease. The second is to recognize what we do and don't know. The third is to ask better questions. Because half right can't be the answer.
So let's start with a conceptual model. What we have here is a Venn diagram. The circle to the left represents the influence of sex, biology and the right gender or social factors. When we study animals, it is simpler. The differences between males and females are sex differences and they are biological. When we study humans, there are biological sex differences, and there are gender differences which are associated with a social and environmental context in which we live. Both sex and gender influence health. In some cases, we know that differences in health and disease are biological in origin. Now these include cancers, such as sex specific organs or sex linked diseases such as some forms of hemophilia which is more common, but not exclusive, in males than females.
On the other end of the spectrum, there are likely health conditions or events that might be categorized as purely gender, meaning entirely socially created and not biological. But it's difficult to point to specific examples.
For example, we could discuss or debate whether some forms of violence against women are purely social phenomena. We're not going to do that today. And it would probably be a long discussion, at best.
In most cases, when we study human health, we are studying the impact of sex and gender, not sex or gender. Because humans live in a world with a myriad of life long gendered expectations and socializations which overlay any inherent biological differences. And these, in fact, have been shown to impact how perceive and interact with newborns or infants, based on their presumed sex.
In the simplest case illustrated here, both sex and gender are treated as dichotomous, so we are not even getting to the complexity that go beyond that. In reality, we recognize, there is more variation, all the way down to the genetic level, which determines sex in humans and other mammals, And individuals who are intersex, including those who are not XX or XY, as well as those who are transgender, have additional complexities to their health and medical care.
Similarly, in some societies, they recognize three or more genders and those categories and related life experiences likely shape complex health differences in ways that go beyond what we can get to. So we're saying there's something missing, even at that first cut.
So... the influence of sex and gender…here the I'm separating the two, and I'm narrating this in part, in case there's no one who thinks it's hard to make out due to your own vision capacity or the monitor you're looking at. But I'm showing here, how the impact of sex influences may affect the exposure and health impact of gender and vice versa.
So the differences are not only additive, they can interact in ways that amplify or confound health effects. When sex can contribute to exposure to and the impact of social factors and contexts. For example... women’s longevity exposes them to a greater risk of widowhood and poverty, both in old age and these in turn impact health. And on average is exacerbated by social forms. Whereby women typically marry men who are, on average, two years older. Accelerating the process, the inequity.
Similarly, gendered social exposures can influence and impact an exposure for biological factors on health. And here, I like to consider the case of workplace childcare, which could impact men’s and women's health. There are reasons we might expect stronger exposure and impact on women. For women, on site child care, especially infant care, can support extended breastfeeding, and in so doing, in turn, affect a woman's risk of breast cancer or exposure to oxytocin, a hormone that helps her connect with the baby and copes with all the stresses of new parenthood which has an impact on depression in other coping as a new parent.
Workplace child care will not have that same impact on men's health because there are biological differences in men and women. We have these confounding, what you see in, in adults, or in humans, maybe social in origin, it may be biological in origin, it may be impacted by an interactive effect.
So if we try to understand women's health and influence of sex and gender, we need a clear conceptual model that encompasses both types of exposures and impacts, and we need rigorous methods in research to overcome that. Because half right can't be the answer.
So when we look at the influences of sex and gender on health, we need to consider the levels at which they operate. On the left, I'm showing examples of internal factors, from the genome, to the individual. And... on the right, externally, from the individual, through the family, to the national or even global, social environmental contexts.
In short, both the internal biological factors and the external social factors shape our lives and shape our health.
Almost all aspects of health are influenced by exposures to internal and external factors, so... again, we have this feedback from one to the other, this interaction.
The exposures occur across the life course, and contribute to differences in health, such as the risk of type two diabetes in adolescents, and... which is higher among girls or the rates of autism, which like heart disease has been defined and understood as a disease of males, which has been thought to produce unrecognized expressions in females.
Let's think about what we know and what we don't know. Scientific advances arise from a clear understanding of an appreciation for the limits of our knowledge. In other words, knowing what we don't know is perhaps even more important to advancing science than knowing what we do know.
The question here, with this conceptual model in mind, how clear are we about the limits of our current knowledge on the health of women?
The evidence base is uneven. We have 100 + years of research, primarily on male subjects. And even male tissue samples. As well as male animals.
The reports of the research typically have ignored this critical limitation, even when we do a retrospective literature review. We typically failed to characterize the theories and findings as based on, and potentially only generalizable to males with few exceptions.
And they've assumed that methodological and statistical rigor is somehow sufficient to overcome these limitations, that it is sufficient and appropriate even as we add female samples to assume that what we know thus far is good enough.
The challenge is, having built this research on, on a sample that is fairly homogenous, there's a lot we don't know about women's health. And we need to be clear about the extent to which females were included in the research, to which the data was analyzed, if they were included, to, to whether differences were reported if it was in fact analyzed.
To do this, we need to begin to ask better questions. So part of what we know and what we don't know, involves recognizing where there are tested assumptions. Findings for men or male animals or tissue, can be, or have been generalized to females. And we can think about this evidence base as the foundation in the field. The question is, is the foundation we're working from solid? Are we aware of the gaps in the evidence base regarding the health of women? In some areas, the assumptions of generalizability of findings on men to women are so strongly held, so widely accepted, they're no longer questioned. And if you submit a proposal to study them, you'll get a comment back saying "well... this isn't very interesting" and “you'll only find what we already know”, which is an interesting presumption about science. In fact, that kind of response, which is not uncommon, forgets that these are testable hypotheses and we're interested in the empirical database.
So are we aware of the assumptions of generalizability? Now objective data on women's representation and research and the extent to which findings are consistent for women and men are valuable. In some instances, the exploration may begin with systematic reviews of the literature, re-analysis of data from existing studies which had the information on males and females, but didn't assess whether the findings held for women. This kind of exploratory work can provide the necessary preliminary studies, the necessary findings to allow us to protect studies, designed and powered to test for sex and gender influences and they evaluate why they might occur, the implications for research and for disease prevention and treatment. That kind of approach can help us ask better questions.
The next point is, how rigorous is our research? Are we building our science, our needs assessment, and evaluations on studies that are including population samples? We can't know what it is to be human or what health a disease looks like in a population if we consistently rely on the same subset to study the population. This is true in studies of physical health and true in studies of mental health. Mental health has been an easy target because of the number of studies that have been done on college students and we know, college students are a small, tiny subset of our population. Important, but tiny, and it may not tell us what things look like in the rest of the world. Have they tested and validated the assumptions we've made. Whether interventions are effective in women and girls as they are in women and boys. In addition are we required or are we expected to report differences and where there isn't a difference, to inform future research? That hasn't been a standard today. It's increasingly becoming a standard in Europe and I think it's time to catch up.
So... how rigorous is our research? Are we, in fact, ignoring sex and gender? Including women and simply adjusting for a dichotomous variable? And in fact, that's like saying, we know we're treating a population that on average has one testicle and one ovary and we'll control for that, and then ignoring that it's much more complicated than that. We need to explain, we need to examine beyond a narrow subset of women, which are included, but often too few to identify meaningful differences in incidence, risks, benefits or outcomes.
We also, this is a critical issue discussed in the women’s health initiative trials longitudinal follow up work, assuming generalizability beyond the data to women, especially to older women. We often miss the correct disease population. When we miss the relevant population, we miss a lot of what it takes to understand human health and to understand disease. Here I want to take this one example and go a little further.
Clinical trials are often held up as solving all our problems. They're the gold standard. And I’m arguing we need to think more about how we're doing them, and maybe that will be facilitated by all the new forms that we submit the more rigorized the standard information.
But we need to think about what the studies collectively are telling us. Is the study that’s enrolled representative of those with the disease or condition and the wider population? And for many of the studies, I simply think of cardiovascular disease cause that’s an area I focus on much of the time, but it is not the only case. The age of onset is younger in men than in women. And if the studies were traditionally designed to understand a disease in men, then the cut point may make a lot of sense for understanding them. If you think of that as a Venn diagram between the population study and the population experience, you see that you end up with a pretty close overlap. When we go to add women, we try to follow those same criteria. The practice and clinical trials of studying younger patients with the disease, so it’s less complicated, we know what we're looking at. Studying healthier patients, for whom they don't have as many comorbidities, but we know what we're intervening on and how we're operating. These approaches may be appropriately held in the clinical trials when they focus only on males, but given very incomplete information, as we start to try to understand and generalize to women.
So, if you think again of that Venn diagram, if we drew it for women, it's going to be a much smaller subset of the actual disease population, that was included in the research. Not only are they different in age, they're different in comorbidities that may confound diagnosis, treatment, outcomes, and this turns out to be critical. If we go on a case by case basis, we'd look and say, “oh, okay, they're including women, and they're doing potentially some of the appropriate analysis, all consistent with sex as a biological variable standard.” But it doesn't necessarily mean at the end of the day, that we've done all of the work that closes the gap. We need to think of this in two approaches. One is what makes an individual study the best it can be? And to think, is it providing the same level of quality and evidence for using a treatment among women? Among a diverse population of women with the disease as it is in the intervention in men. Just doing business as usual and applying the scientific method as we have, may cause us inadvertently to make a series of missteps that give us narrower answers, that don't provide the same evidence base and quality we were seeking. So even for interventions that are found to work as well in women as men, it's a gap in the age of the population that's different, then we may have issues of polypharmacy. We may have more issues of depression or complications, that otherwise, could be avoided if we either think more about it going into it or recognize that just because we have findings from a clinical trial that included women, does not mean we have the same level of evidence for treating the actual population of women that it may have provided for treating the population for men.
Now that said, we may have been missing along the way. We may have been treating disproportionately, white men in studies and then generalizing to another population. That's another question to go back and look how representative is it? But it may have been good enough. So you know close enough in horse shoes, for men, taking the same steps and following all of the procedures that we learned as good science, we've undertaken good science, may not get as close to the goal as we go bedside to bedside to treating the populations of the world.
One element to think about all of the different dimensions of which we may unintentionally be extrapolating beyond the data because statistics alone will not solve the problem.
Now I want to introduce a few bold propositions. How do we begin to take this kind of conceptual approach and do better? And I've spent the last couple of years, actually discussing it with a lot of colleagues in the field, including other journal editors. And one possibility is for journals to begin to require electronic appendices reporting stratified analysis, and if there are limits on the capacity to report inside an article, whether findings held for both genders or both sexes then that could be explained in a discussion section added in an online report. Now in the days of paper journals, exclusively paper journals, this was a problem. But it's rarely a problem that the reviewers or journal editors ask someone to take out information about nonsignificant findings regarding sex differences. Although, they might ask people to not include underpowered information. If we don't start reporting that, if we don't start collecting that kind of information, we won't even have the signal of where there's a difference. Now this has been key to advancing research on the health in what we traditionally called minority populations, whites are about to be the minority population here. But to begin to report on and understand where there's a signal that might be a difference, even if a study was not powered to assess that. It is that kind of pieces of information that researchers can look across studies, across the existing database and be able to say there's some indication, we ought to look further.
So why wouldn't we report this? Because the average results don't mean one size fits all. One size doesn't fit all in underwear, it's unlikely that it fits all in health care. As we look forward, we're finding the growing number of areas all the way down to issue studies where there are differences and responses in male and female. So this would allow us to start to overcome one of these barriers, simply because half right can't be the answer. And it would inform the transparency of the work.
A second proposition that I would like to suggest is in order to close the gap, we need to overcome the assumption that we already know there aren't sex and gender differences. And it feels like we know that, because as many have been reported, but... in fact, we need to look at the questions. We need to do systematic reviews of the literature and the evidence base on the health of women across multiple domains. The gaps are not readily apparent, in part as I said they haven't been reported in the past and because it's really the tip of the iceberg, where we start to have large numbers of women included in studies that were not of diseases and disorders that predominantly occur in women. And even in those cases, we may have assumed we were understanding gender differences of what all of the mechanisms were in ways that overstepped the data.
But systematic reviews give us a possibility of adding value to the existing literature and of mining a database that, where we have invested heavily in large numbers of studies, that individually wouldn't necessarily speak to this, but simply reading the literature is not going be able to get us there. It takes a systematic review. I personally don't do the systematic reviews, but it's not just me speaking on behalf of my favorite possibility, but that are you looking for what are the most efficient, timely, cost effective approaches to establishing what do we know and how do we know it? And are there things we missed? Are there things the data could tell us today?
The third bold proposition is that we need to evaluate the generalizability of randomized control trial findings. And here I want to think of the lessons learned from the women's health initiative. The answer, two years in to the women's health initiative intervention, for anyone who is not familiar, this was a large-scale trial, designed to assess the benefits and risks of hormone replacement therapy, which was so standardly used at menopause that the colleagues who were principle investigators of this study actually got hate calls from other professors saying "how dare you randomize women off hormone replacement therapy? We know this is fantastic." The study, itself, was considered so obviously going show, that it was wonderful, that it was considered needless and in fact, it showed the opposite. It showed that there were heart attacks and deaths attributable to hormone replacement therapy. And yet many of those deaths included in older populations women who were 10 plus years out from menopause at the time of entry into the study.
So here's a little bit difference in the problem. I was saying generalizing beyond the data where you might have only treated one population in the intervention and there's a larger population that should be considered. Here, they skewed it the other way and included a lot of women that you wouldn't normally go out and intervene doing hormone replacement therapy. Because we did not at the time, do the kinds of bounded analysis and say okay, there were women dying. There were there are these strong effects, is there anybody it was working for? Is there anybody it wasn't working for? What are the next questions we ought to be asking? What should it look like? Instead it was taken as this was a definitive answer, close the book, end the randomization. Fortunately, the population followed over a long time.
We now know that at least with smaller doses, at least with some other approaches, such as using a patch, there are, there can be benefits of hormone replacement therapy, used right at menopause.
The point being, the one answer, was not definitive, we shouldn't treat it as definitive and we shouldn't generalize beyond what we looked at. In the same way, studies that either looked just at men or they looked at both men and women, we don't look further, we need to say what are the limitations on how it's working? And the kind of founded analysis that we'd do in economics, what happens when we shift these parameters a little bit? How certain do we have to be that this is right? Not based on a single significance test, but on how far off would we, could we be and this would still be a robust finding that what we say works, works or what we say doesn't work, clearly doesn't work.
Generalizing beyond a population study, beyond the gender, beyond the racial ethnic group, beyond the age group is unjustified. It can harm patients and it can miss opportunities to improve care and outcomes. So we can be doing better on this.
So what I want you to take away from this today is that we can improve policies that impact women's health and it requires us to do better with science. It requires us to study the population, we need, if we want to fulfill on the NIH mission, if we want to fulfill on the idea that we can provide fundamental knowledge about the nature and behavior of human systems and the application of that knowledge to enhance health, risks in life, reduced illness and disability, we have to study a full range of the population. And in many cases, historically, and even some of the studies recently, we've left out women who are the majority. We've left out diverse populations including minorities, including rural populations, including those with less education or lower income where it may be harder to treat. And we need to understand why and not just assume. The assumptions are not good science.
What we need to be doing is asking better questions and doing the analyses that make better science. Because half right can't be the answer. Thank you.
>> TAMARA LEWIS JOHNSON: So... thank you, Dr. Bird for a very thought provoking, informative presentation. The, you know... the floor is now open to those of you who are on the webinar, if you want to submit your questions please do so now and then I can share them with Dr. Bird.
But... let me ask, first... Dr. Bird, one of the major differences in sex and gender differences in mental health is the prevalence rate and the incidence rate of depression for women being higher than men. And then I'd like for you to maybe talk a little bit about that. I mean, some of the sex components of that situation and then, some of the gender, you know... factors that may affect depression. We know that adolescent girls are more likely to be depressed. We know that depression shows up during the perinatal period. We know another flash point over the life course is during the menopause transition and then, and later life, but we also know there are variations, you know...
>> CHLOE BIRD: Absolutely.
>> TAMARA LEWIS JOHNSON: Among subgroups. Not all women are experiencing this.
>> CHLOE BIRD: It's really interesting, we have a lot of data that suggests that women are more vulnerable at these periods of hormonal transition, of hormonal change, and... it is a shift in hormones can shift your entire experience of the world. Women talk about feeling emotionally hijacked. In a way that, that a toddler is, where an emotion runs away with them. Some women will say, they're actually wondering if they're having a reaction. "Am I really that upset about this? Am I really this sad about this?" But then getting pulled through and experiencing not remaining with that luxury of being an outsider looking in because we experience our emotions as real and impactful. As do men. We are certainly equally reliable as reporters.
Part of what ends up happening though, because of the differences in that hormonal transition in adolescence, we have more women who have experienced an initial episode of depression and once you've had an episode of depression, the chronicity is somewhat different in males and females. But the risk factors women may be exposed to more because women are in lower incomes, they're responsible for more care giving, they have more dependence specifically. But also, men and women have somewhat different social networks. And for men, a larger network, we know it's bad for anybody to be isolated. For men, a larger network seems, on average, just to get better and better, there just seem to be benefits. Although they may miss some of those strong ties that women make within their network. That may be something for women, they experience benefits in their network, but they experience responsibilities and trade-offs and emotional connection to people. So women are distressed by, or impacted by crimes that others in their network are exposed to, or losses that others in their networks are exposed to. So they both report more, having happened and they may feel them more or have more concerns for their families and for the world, out of that.
The classic story when I was in graduate school. One of our professors talks about having interviewed men and women about adverse life events in the past year. They were very perplexed. They were studying couples, a lot of times, the men wouldn't list many things and the women would say "when my husband's mother died, that was really awful." And just a different way of processing and taking responsibility for and managing the psychosocial well being of others may put women at risk even as they benefit.
One of the other things I wanted to talk about, we have do we have a question? Not where I can see one. Tell me if you see one. I was going to shift to an example I had mentioned earlier of systematic reviews. As you do a systematic review, the same issues of what are the questions you're asking? What are the population that's included show up there as do, in doing an individual study?
So we have to look, not only at the same way we might have looked at problems in men, we're narrowly define this problem, but we need to step far enough back that we look and say the population of men with a given disease or with a given treatment. What does that population look like? And who do we include, depending on how we ask the question? And what does that population of women look like and who do we include in asking questions? It may be that the studies, systematically only looked at people with one problem, but... because women are more likely to have comorbidity, it goes back to your question about depression. If women with depression are systematically excluded from studies of other problems, we're going to misunderstand what happens when we intervene on the health of women. We're going to misunderstand what conditions look like in women and we now know that depression could be physical or social in origin, and can be part of the process that exacerbates the disease, or could be a signal about the extent of a disease. And of-course in part, it changes people's interest in engaging in the most basic self care, other things. Women usually are outstanding, compared to men. In terms of many times of self-care, centered around obesity risk, women have a higher rate of obesity.
But I think that some of the kinds of biases that happened in the questions asked and how we looked at things have happened because we just assumed women are good at something. We just assumed depression is a special subset, we'll get to it later. Well it’s later. It is time we start looking at in many of these diseases whether it’s depression and substance use, or depression and cardiovascular disease, or depression and some other kinds of pain disorders than women experience. We missed a lot of those problems, looking at things. But at the end of the day, it'll not be a magical solution to do systematic reviews, unless we bring the same rigor to looking at what's the question we're asking? And what are we not asking that may be relevant to provide [breaking up] key populations of women.
>> TAMARA LEWIS JOHNSON: Here's a question, it says could you say more about how depression can be either physical or social in origin?
>> CHLOE BIRD: Well... we in the simplest case, we see people who become acutely depressed in the face of huge financial losses. In the face of the loss of a spouse. We would say it is normal to be depressed after the loss of a spouse, in the face of bankruptcy, for example, or having a child who is gravely ill. You would say it's your life circumstances, it isn't innate, inside of you, independent of what's going on around you, that drove the episode or the experience. Now... the resources that you have, your social experiences and so on, may shape what that trajectory looks like, certainly, whether or not you have health care may shape what that response is, as well as the beliefs and kinds of supports that are available around you.
But... both things can play out. This is a great example for thinking about that Venn diagram again. I can't point to a case and say this person it was just social and this person it was just physical. I know when my great grandmother died, right afterwards, her daughter, my step grandmother, took to her bed. And I thought well perhaps even when your mother is about to turn 100, it’s always sudden, it’s always new, it’s always terrible. It turned out, in fact, that she had lung cancer and she died within a week. But it hadn't been obvious that there was an underlying physiological process going on that was making everything harder. It seemed obvious that everything was harder because she was dealing with her dying mother. We can misattribute and the same way we can make that distribution on an individual case, we can make that misdistribution about the way women are or the way circumstances are. But we end up defining as a research community and as a health and health care community, what is normal and looking at and developing systems to say how do we intervene and what do we do to intervene along the way. The other issue unrelated comes to mind for me goes back to differential presentation. I don't mean differential presentation as something biological or social but being the nonnormative group having a problem. And one example is the area of autism. Like cardiovascular disease, we developed our understanding of the disease by cases we identified among males, so we end up with a description of a disease that is really good at describing it among males and in the case of autism, partly had to do with young males being physically more aggressive. It's a bigger problem. It's a different problem. Not that it wasn't, it's not a problem in girls, but the girls weren't a problem in the classroom. Now that we're having more robust approaches to assessing "hey, this might be autism." To be fair, the autism spectrum may include a bunch of different things. We now understand it includes things that happen more commonly in females than we ever knew and they didn't get treated. That same problem happens in cardiovascular disease: we misattribute what's going on in women or we step over it. That's a huge problem in terms of, what do we do with information about women who, we see, high blood pressure, during pregnancy, we see, maybe even preeclampsia. We don't have a system for incorporating that into the risk calculators and even if we did, for most of those women, we're not talking about something that's going to happen in the next ten years, we're talking about something likely to happen in 20 years or so. Women have, because of the hormones, because of differences in their physiology and... very brief for anyone that hasn't thought about the physiology of cardiovascular systems in pregnancy, women have a much more flexible circulatory system such that we can carry a 20% higher blood volume during pregnancy. That becomes important so you can carry a fetus, but that greater flexibility means you're less likely to have high blood pressure and if you do, it doesn't do as much damage. Those differences subside at menopause, at which point, we have the same risks as men and we catch up.
>> TAMARA LEWIS JOHNSON: Here's another question coming in, could you tell us more about the Kessler study?
>> CHLOE BIRD: Ron Kessler led... It's not just one, there's a national comorbidity study, the national comorbidity follow up study, sub gently, these comorbidity studies have been carried out in countries all over the world. Very large number of countries.
The national comorbidity studies specifically assessed mental health conditions and looked at the population prevalence, very much so among men and women across a fair age distribution. To understand initial onset, to understand patterns of chronicity and specifically to understand comorbidity. Ultimately, this body of work led to a rethinking about what it means to have diagnosis that are entirely separate. And we now do many more diagnosis and by we, I don't mean me. I'm not a psychiatrist or psychologist, but now, dual diagnosis are common and we understand that rather than just chunk all these women with depression into depression and ignore those things, that women could have depression and ADHD or depression and alcohol disorder and so on and so forth. That bias, itself, may have kept us from including people in understanding what was going on in diseases, but it is fairly common. Are we still on?
>> TAMARA LEWIS JOHNSON: Yeah... I think we're still on. Could you talk a little bit more about interventions and the effects that they could have, differential effects of interventions on different groups? By that, I mean like, cognitive behavioral therapy or IPT and effects of this on men versus women or... you know... in terms of mental health and addressing mental illness.
>> CHLOE BIRD: I would like to. There are limits to whether they're studied and I can't go beyond the data. In a re-analysis that we did of depression care, and I don't think we ever published this part... so... it's again, a limit, at least what I know. Although... overall, we found that interventions were effective in men and women very similar. When you separate out the different treatment arms, there were differential effects and men benefitted from going into the cognitive behavior therapy. They didn't necessarily end up staying in it, but when they started with a cognitive behavioral therapy, they were more likely to end up not depressed at the end, because they'd go from that to medication. They started medication, they may or may not comply and go through it. It kind of went the opposite direction for women. And... so... it's possible, as we start to look at these paths through care, a one size fits all treatment regime may be entirely insufficient. And then there are other examples where based on the age of diagnosis, people who get a disease or disorder younger may be very different than people who get a disease or disorder later.
And here I think of examples we know from recent studies. In African Americans, you see earlier, more rapid onset for prostate cancer and breast cancer. The screening regimes that we undertake are insufficient, they don't catch early cases. We haven't started the screening by then. However, at the same time, if you have rapid acceleration of a cancer, screening doesn't have a long window if you're only screening ever so often, to catch a lot of cases while they're still inconsequential. So simply moving the start date back, even for minority populations wouldn't necessarily be enough. We need to think about, again, doing a Venn diagram, what do we know? What don't we know? What are the gaps in care? Are we treating this population well? Are the answers that are out there, do we have anecdotal or evidence based data? And if we only have assumptions, then we ought to be starting to study and assess, what's happening differently in this population? Are there things we need to do to give people access to care? Is it just a problem of access? There were many fights about this around the breast cancer literature. There still are concerns about bias in treatment. To what extent is it something more than that, where there may be a different, almost different disease. A different subset of disease going on. That could be entirely relevant if we're understanding you know, schizophrenia, which has different agencies of onset and women are more likely to be around the college age. How do you avoid getting dismissed? One of the problems that happens with women getting dismissed with diseases is to the extent that we offer alternative explanation about our stress level, about all the other things going on in our lives, that could have a differential diagnosis and we say, there are a lot of explanations why you aren't sleeping well or why you're depressed or having these could be cardiovascular symptoms that are not specific. We need to get better at what do we need to do to get the right diagnosis and to systematically look at whether a specific treatment works better in men than women. Or specific orders of treatment.
>> TAMARA LEWIS JOHNSON: So what different things would you like to see happen in the preclinical research or animal research that could help inform human studies? What sort of things should we be thinking about conceptually about sex differences as it relates to animal studies? Quite a bit of research here at NIMH is focused on animal study. And looking at sex differences. What sorts of things would you want researchers to thinks about as they are developing their study designs? Looking at sex specific effects, sex differential effects in animals.
>> CHLOE BIRD: I think that's a really exciting area, in part... I would encourage researchers who are doubtful about what to do and what they'll find to look at some of the literature that's finding incredible differences in females than males. I know in a systematic review that (inaudible) led, the alcohol behavioral responses in the I think it's rats. They have very different behaviors in terms of consuming in response to alcohol in females than males. This wasn't previously understood, because of the universal use of male animals. Males and females respond differently to the circumstance. And the assumption that a lot of this was driven by hormones. The first thing is to just be able to step back and say... what has only been studied in males? Let's look at it in females. And... then, to be able to start to see what are plausible explanations? And we may have very different issues in terms of what's a plausible explanation, in part because we know so much less about what things look like in females. Another piece is to not assume that this is just going to cost more, they know what they're going to see. You really need to be open to what, what do you see in your research and larger studies have, studies including both males and females have the possibility of advancing the science faster. Personally, I'm quite convinced that there are going to be many Nobel and other prizes won out of research looking at our theories and our ideas in female populations where we haven't before. In the research on medications that can slow aging, we found there are a number of medications that have a little bit of impact in rats. Doesn't mean you should run out and start taking anything, but they have phenomenal sex differences in the effect.
So... as that kind of finding comes out, as that gets reported in the alcohol intervention study and the study of animal aging and medical intervention that might slow cell aging in middle age rats, imagine such a thing. Some of you don't have to imagine but... as those findings come out, the question of whether to include females, the question of what's the value of this is immediately over. It's very interesting when I present on this to a general audience that includes some scientists, they'll often be a male researcher there that works in one of these areas that says that problem is completely gone. Everybody wants to study females because of what we find. And in fact, in some areas of research, there are researchers who completely flip and now they're exclusively studying females because there's so much difference they're finding out about the animal behavior, whether that's studying animals allowed or in more naturalistic settings.
>> TAMARA LEWIS JOHNSON: This is my last question. And this is... you know, if there are any more questions from the audience, from those of you that are listen to the webinar, I'll take one more, I'll take one additional question if anyone wants to send anything. But one of the research priorities of the NIMH is suicide, research on suicide prevention. We know there are differences in suicide rate. We know that men, there's more suicide ideation among females and also... there are attempts that do not result in suicide, but... with men, they, you know... when there are attempts they are, it does result in death by suicide. Suicide is also one of those factors that could have both gender component, it has environmental component or stressors, it has sex biological, sex specific effects. So if you could talk a little bit about what sort of things that you would advise, when thinking about suicide prevention research for researchers as they go forward, knowing that this is a research priority of the institute.
>> CHLOE BIRD: There are so many things I'd like to see us know about suicide. Historically, the higher rate of, of completed suicides, among men, has to do with use of more lethal means and so one area I'm concerned about, that is around the opioid epidemic and how is that shifting what's happening. We talk about the opioid epidemic, the public conversation has been around the impact of, of the very frustrated unhappy white males. But when you look at the mortality rates, it's the highest among white females. And so that makes it a different pattern...the gun use, gun ownership and so on... and whether things get classified correctly. One interesting piece of research that was going on at NIMH was trying to begin to look at, in a way we never have... what happens to individuals who are seen in an emergency room or self inflicted harm or for a suicide attempt. What happens in follow up? That is not historically something we've had data on because we don't focus on diagnosis and treatment “who caused what?”. We don't do that linkage and follow up in terms of how people are alive a year later. So there are a lot of things we probably don't even know about male and female patterns to suicide. Whether there were the same kind of life endangering attempt and changes in what somebody was doing, or avoid getting care, so some of those kinds of issues.
Another area, this is one of those things, combining the social and the biological understanding. There's a lot of work my colleagues at RAND have done looking at suicide in the military. I've wondered, would we benefit on the prevention side from talking to members of the military who are exposed to combat? To the highly stressful situations about the ways in which your brain adapts to an environment, is a function of your brain working very well.
Now... your brain working very well may over adapt you to a situation that is not working very well when you come home. Coming home, hyper vigilant. Coming home highly reactive. Coming home with a limited capacity to sleep. It becomes dysfunctional in part because we changed settings you were in. But if we talked about what had happened as part of your brain works, it might engage somebody in being more open to what can be offered in cognitive behavioral therapy, what can be offered in terms of teaching meditation or other practices to reduce that over heightened regulation. There are many studies being done with military populations showing the benefits of teaching people these kinds of approaches. They're often done, not from a perspective of, you have PTSD, but from are you in this position and you're experiencing problems with you know... resolving conflict or with sleep. And it is often the success in dealing with sleep that has someone continuing to participate. This is also true of women and in the general population, with the success in dealing with sleep. Sleep is fantastically important to our health and well being and to our physical functioning.
In my own experience, I'm much nicer after I sleep well. If I'm not getting enough sleep, the world doesn't show up as objectively the same place. So I think there are a range of issues and a range of framing, again... going back to what the contextual model, not just of the questions we ask, but how do we explain to people the origins of mental health problems. And if the origins are understood by people in the military to be an indication of weakness, or that you always were weak or individually flawed, that is a barrier to treatment in care and it sounds a particular way when you have depression, anxiety or PTSD already. It's a barrier to your transitioning back to civilian life.
Another issue that I have that I'm concerned with around the military population, and this is relevant to others as well, but... is for the, the reserves. People who are in the, formally in the military and active duty, they may return to a base setting, they may return to a community or many people know how to connect to the groups and can be looking out on their behalf. When they return to the from the reserves, they were returning to civilian life, probably in a rural area, where there's not as intense a range of services and there's not access to a base and there are not access to a lot of other people there who can say you know... this is what it was like for me, this is what you can do, this is how you go and handle these things. Things that help a community embrace an individual and take them where the care is and help all those caregivers. Libby Dole, Elizabeth Dole, has funded lots of work around supporting family caregivers of military personnel. I think we need to pay attention to helping parents understand what is depression and what does suicidality look like in adolescents. What should we be looking for to distinguish patterns in students and in many cases, maybe not adolescence, college students, young adults, whether or not they're in college and other populations. If we had a frame that says this is one of the things that healthy brains do, and this what you do to go and deal with it when you've gotten regulated to something else, this is exactly what it will look like when you get sleep deprived for many months or many years, this is what we do in those circumstances. That would be helpful both for people who experience an objective stressor: loss of a spouse, gravely ill child, huge financial loss, and for those where that wasn't that kind of preceding event. But as long as we have stigma driving it, as long as we have a sense that this was something that was always inherently wrong with you, then it's something to hide because it's harder to seek care. So I think we can do better on those fronts as we look at how do we do prevention, how do we understand things, how do we get better data on areas we've never collected around suicide and what's happening and what’s missing.
>> TAMARA LEWIS JOHNSON: Great, thank you. That's a wonderful way to close our seminar, thank you so much, Dr. Bird for your presentation. And... your response to the questions and answers. Thank you very much for all of you that have participated and enjoy the rest of your day.
>> WEBINAR OPERATOR: And this does conclude today's program. You may disconnect and have a wonderful day.